
==== Front
PLoS One
PLoS One
plos
PLOS ONE
1932-6203
Public Library of Science San Francisco, CA USA

10.1371/journal.pone.0307849
PONE-D-24-02817
Research Article
Medicine and Health Sciences
Critical Care and Emergency Medicine
Respiratory Failure
Medicine and Health Sciences
Medical Conditions
Respiratory Disorders
Respiratory Failure
Medicine and Health Sciences
Pulmonology
Respiratory Disorders
Respiratory Failure
Medicine and Health Sciences
Health Care
Health Care Facilities
Hospitals
Medicine and Health Sciences
Epidemiology
Research and Analysis Methods
Spectrum Analysis Techniques
Infrared Spectroscopy
near-Infrared Spectroscopy
Medicine and Health Sciences
Pulmonology
Chronic Obstructive Pulmonary Disease
Medicine and Health Sciences
Surgical and Invasive Medical Procedures
Intubation
Physical Sciences
Chemistry
Chemical Elements
Oxygen
Biology and Life Sciences
Physiology
Physiological Parameters
Body Weight
Body Mass Index
Noninvasive vs invasive respiratory support for patients with acute hypoxemic respiratory failure
Noninvasive respiratory support for acute hypoxemic respiratory failure
https://orcid.org/0000-0002-5371-0845
Mosier Jarrod M. Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Supervision Visualization Writing – original draft Writing – review & editing 1 2 *
Subbian Vignesh Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Resources Supervision Writing – review & editing 3 4 5
Pungitore Sarah Data curation Formal analysis Investigation Writing – review & editing 6
Prabhudesai Devashri Data curation Formal analysis Visualization Writing – review & editing 5 7
https://orcid.org/0000-0002-4179-1449
Essay Patrick Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Writing – review & editing 3
Bedrick Edward J. Conceptualization Formal analysis Methodology Supervision Visualization Writing – review & editing 5 7
https://orcid.org/0000-0003-0775-684X
Stocking Jacqueline C. Conceptualization Formal analysis Visualization Writing – review & editing 8
Fisher Julia M. Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Supervision Validation Visualization Writing – original draft Writing – review & editing 4 5 7
1 Department of Emergency Medicine, The University of Arizona College of Medicine, Tucson, Arizona, United States of America
2 Division of Pulmonary, Allergy, Critical Care, and Sleep, Department of Medicine, The University of Arizona College of Medicine, Tucson, Arizona, United States of America
3 Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, Arizona, United States of America
4 Department of Biomedical Engineering, College of Engineering, The University of Arizona, Tucson, Arizona, United States of America
5 BIO5 Institute, The University of Arizona, Tucson, Arizona, United States of America
6 Program in Applied Mathematics, The University of Arizona, Tucson, Arizona, United States of America
7 Statistics Consulting Laboratory, The University of Arizona, Tucson, Arizona, United States of America
8 Pulmonary, Critical Care, and Sleep, Department of Medicine, UC Davis, Sacramento, California, United States of America
Grosek Stefan Editor
University Medical Centre Ljubljana (UMCL) / Faculty of Medicine, University Ljubljana (FM,UL), SLOVENIA
Competing Interests: Dr. Mosier’s competing interest statement has been amended to the following: JMM has received meeting travel support from Fisher & Paykel Healthcare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

* E-mail: jmosier@aemrc.arizona.edu
6 9 2024
2024
19 9 e030784916 2 2024
12 7 2024
© 2024 Mosier et al
2024
Mosier et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Background

Noninvasive respiratory support modalities are common alternatives to mechanical ventilation in acute hypoxemic respiratory failure. However, studies historically compare noninvasive respiratory support to conventional oxygen rather than mechanical ventilation. In this study, we compared outcomes in patients with acute hypoxemic respiratory failure treated initially with noninvasive respiratory support to patients treated initially with invasive mechanical ventilation.

Methods

This is a retrospective observational cohort study between January 1, 2018 and December 31, 2019 at a large healthcare network in the United States. We used a validated phenotyping algorithm to classify adult patients (≥18 years) with eligible International Classification of Diseases codes into two cohorts: those treated initially with noninvasive respiratory support or those treated invasive mechanical ventilation only. The primary outcome was time-to-in-hospital death analyzed using an inverse probability of treatment weighted Cox model adjusted for potential confounders. Secondary outcomes included time-to-hospital discharge alive. A secondary analysis was conducted to examine potential differences between noninvasive positive pressure ventilation and nasal high flow.

Results

During the study period, 3177 patients met inclusion criteria (40% invasive mechanical ventilation, 60% noninvasive respiratory support). Initial noninvasive respiratory support was not associated with a decreased hazard of in-hospital death (HR: 0.65, 95% CI: 0.35–1.2), but was associated with an increased hazard of discharge alive (HR: 2.26, 95% CI: 1.92–2.67). In-hospital death varied between the nasal high flow (HR 3.27, 95% CI: 1.43–7.45) and noninvasive positive pressure ventilation (HR 0.52, 95% CI 0.25–1.07), but both were associated with increased likelihood of discharge alive (nasal high flow HR 2.12, 95 CI: 1.25–3.57; noninvasive positive pressure ventilation HR 2.29, 95% CI: 1.92–2.74).

Conclusions

These data show that noninvasive respiratory support is not associated with reduced hazards of in-hospital death but is associated with hospital discharge alive.

http://dx.doi.org/10.13039/100000050 National Heart, Lung, and Blood Institute 5T32HL007955 https://orcid.org/0000-0002-4179-1449
Essay Patrick http://dx.doi.org/10.13039/100000001 National Science Foundation 1838745 Subbian Vignesh Emergency Medicine Foundation, Fisher & Paykel Healthcare E.20124 https://orcid.org/0000-0002-5371-0845
Mosier Jarrod M. http://dx.doi.org/10.13039/100006108 National Center for Advancing Translational Sciences UL1 TR001860 https://orcid.org/0000-0003-0775-684X
Stocking Jacqueline C. http://dx.doi.org/10.13039/100000050 National Heart, Lung, and Blood Institute K01HL168222 https://orcid.org/0000-0003-0775-684X
Stocking Jacqueline C. This work was supported by: E.20124, J.M.M, Emergency Medicine Foundation, Fisher & Paykel Healthcare Directed Grant. Authors supported: Mosier, Subbian, Fisher. https://www.emfoundation.org/grantee/past-grantees/Grantees-2020-2021 1838745, V.S, National Science Foundation. Authors supported: Subbian. https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838745 5T32HL007955, P.E, National Heart, Lung, and Blood Institute, Authors supported: Essay. https://reporter.nih.gov/project-details/8680297 UL1 TR001860, J.C.S, National Center for Advancing Translational Sciences. Authors supported: Stocking. https://reporter.nih.gov/search/pzhJ4ZAoSUelr6CoWSajpg/projects K01HL168222, J.C.S, National Heart, Lung, And Blood Institute. Authors supported: Stocking. https://reporter.nih.gov/search/z70wGdlt2ESfrAcjDewhGA/project-details/10643357 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study. Data AvailabilityData cannot be shared publicly per University of Arizona Health Sciences Research Administration and Banner Health Research Administration restrictions because of the data contain potentially identifying or sensitive patient information, and are owned by the Banner Health clinical data warehouse. Data are available for researchers who meet the criteria for access to confidential data and with institutional review board approval and a negotiated data use agreement with the University of Arizona. Please direct data use agreement requests to the University of Arizona Health Sciences Research Administration at UAHSContracts@email.arizona.edu.
Data Availability

Data cannot be shared publicly per University of Arizona Health Sciences Research Administration and Banner Health Research Administration restrictions because of the data contain potentially identifying or sensitive patient information, and are owned by the Banner Health clinical data warehouse. Data are available for researchers who meet the criteria for access to confidential data and with institutional review board approval and a negotiated data use agreement with the University of Arizona. Please direct data use agreement requests to the University of Arizona Health Sciences Research Administration at UAHSContracts@email.arizona.edu.
==== Body
pmcIntroduction

Noninvasive respiratory support strategies utilize an external interface (e.g., facemask, helmet, nasal cannula) to deliver either pressure-based support in the form of continuous or bilevel positive airway pressures; or flow-based support in the form of nasal high flow. Noninvasive modalities, particularly pressure-based support, are recommended for acute exacerbations of chronic obstructive pulmonary disease or acute cardiogenic pulmonary edema [1]. Noninvasive respiratory support modalities are also increasingly used for patients with acute de novo hypoxemic respiratory failure, despite unclear data on which strategies are superior and safer, or the impact on outcomes [2–5].

Overall, noninvasive strategies likely reduce the need for intubation and consequently lower mortality compared to standard oxygen [2, 6–8]. However, intubation after failed noninvasive respiratory support is associated with prolonged intensive care unit (ICU) stays and excess mortality [9–16]. The main theory is that nonintubated patients with acute hypoxemic respiratory failure may produce injurious transpulmonary pressures that are inhomogeneously amplified [17] and accelerate lung injury (i.e., patient self-inflicted lung injury). Noninvasive modalities are typically compared to conventional oxygen with the primary outcome of intubation, either alone or in combination with mortality. The benefits of noninvasive respiratory support (improved respiratory mechanics, reduced work of breathing, and improved gas exchange), however, render noninvasive strategies a more appropriate comparison to mechanical ventilation. Data comparing noninvasive respiratory support to invasive mechanical ventilation after conventional oxygen, however, are lacking. The goal of this study was to explore that comparison by investigating the outcomes in patients with acute hypoxemic respiratory failure treated with initial noninvasive respiratory support compared to invasive mechanical ventilation.

Methods

Study design, setting, and participants

This retrospective cohort study used de-identified structured clinical data from the Banner Health Network clinical data warehouse. Banner Health spans 26 hospitals across six states in the western United States and uses the Cerner Millennium (Oracle Health, formerly Cerner Corporation, North Kansas City, MO, USA) electronic health record system. Data for all adult patients (≥18 years) admitted to the hospital between January 1, 2018 and December 31, 2019 were extracted on September 16, 2020. Patients were included if they had an admission diagnosis consistent with the pertinent International Classification of Diseases (version 10) subcodes for acute hypoxemic respiratory failure (J.96): J96.00, J96.01, J96.02, J96.20, J96.21, J96.22, J96.90, J96.91, and J96.92. Patients were excluded if they had a first treatment location other than emergency department, intensive care unit, stepdown unit, or medical/surgical unit. This work adheres to the STROBE reporting guidelines, guidelines from journal editors [18], and was approved by the University of Arizona (#1907780973) and Banner Health Institutional Review Boards (#483-20-0018).

Cohort assignment

We used a validated phenotyping algorithm to classify eligible cases by the sequence of respiratory support [19–21] into two cohorts: those treated initially with noninvasive respiratory support and those treated initially with invasive mechanical ventilation. All patients in both cohorts were included in the analysis, as there is variation in the determination of failure and physiologic thresholds that prompt intubation [22]. Patients on conventional oxygen only were excluded. Secondary analyses were conducted separating noninvasive respiratory support into noninvasive positive pressure ventilation (either continuous or bilevel positive airway pressure) and nasal high flow. Noninvasive positive pressure ventilation, in any form, in the Banner Health System is provided using a noninvasive ventilator, and nasal high flow is delivered by either the Vapotherm (Vapotherm, Exeter, New Hampshire) system or the OptiFlow (Fisher & Paykel, Auckland, New Zealand) with or without the AirVo™ 2 system.

A subset of patients received noninvasive positive pressure ventilation, nasal high flow, and invasive mechanical ventilation. These patients were manually assigned to the appropriate cohort based on treatment start times and included for analyses comparing noninvasive support to mechanical ventilation but were excluded from analyses separating noninvasive support into noninvasive positive pressure ventilation and nasal high flow.

We estimated the propensity for invasive mechanical ventilation or noninvasive respiratory support (noninvasive positive pressure ventilation or nasal high flow separately in secondary analyses) by using generalized boosted models and used inverse probability of treatment weighting in the models to account for non-random treatment assignment [23], mirroring our previous comparisons in patients with COVID-19 associated respiratory failure [20]. The variables for propensity score estimation included age, body mass index, sex, ethnicity (non-Hispanic, Hispanic), race (white, other), respiratory rate and SpO2/FiO2 ratio immediately prior to first treatment, comorbidities (diabetes, chronic kidney disease, heart failure, hypertension, chronic obstructive pulmonary disease, neoplasm/immunosuppression, chronic liver disease, obesity), diagnoses of influenza or sepsis, vasopressor infusion before first treatment, first treatment location (emergency department, intensive care unit, stepdown, medical/surgical unit), hospital, time period of hospital admission (time period 1 [January 1—June 30, 2018], time period 2 [July 1—December 31, 2018], time period 3 [January 1—June 30, 2019], and time period 4 [July 1—December 31, 2019]), and hours from hospital admission to first treatment, transformed via the Box-Cox method with negatives [24]. Hospitals with <30 observations were grouped together for ease of modeling and to preserve de-identification. These variables were additionally included in later modeling to further improve balance between treatment groups.

Outcomes and data analysis

The primary outcome was time-to-in-hospital death, defined as time from initiation of respiratory support to death with hospital discharge considered a competing event. It was modeled using a cause-specific Cox model with the first treatment (noninvasive respiratory support versus invasive mechanical ventilation) as the key predictor. A secondary outcome of time-to-hospital discharge alive was also evaluated using the same method. Each outcome was also evaluated with secondary analyses separating noninvasive respiratory support into noninvasive positive pressure ventilation and nasal high flow and a sensitivity analysis that removed patients with evidence of all three treatments. We assessed the proportional hazard assumption in the Cox models by including an interaction of time with first treatment and reported the model with the interaction if it was statistically significant at α = 0.05. Then, due to limitations of hazard ratios in the presence of competing events, we estimated cumulative incidence curves associated with each treatment using the Cox model estimates [25]. We explored cumulative incidence curves associated with the comorbidities of heart failure and chronic obstructive pulmonary disease and diagnoses of influenza or sepsis both individually and in combination. We set the remaining covariate values to their sample median (continuous covariates) or most frequent value (categorical covariates) [26, 27]. The unweighted outcomes of mortality, intubation rate, days to intubation, and duration of mechanical ventilation were assessed using Fisher’s Exact and Kruskal-Wallis rank sum tests where appropriate. We also conducted a subgroup analysis on the primary outcome after excluding patients with a comorbidity of chronic obstructive pulmonary disease or a body mass index >35. Finally, E values for the hazard ratios and hazard ratio confidence limits closest to one are shown in order to estimate how much residual confounding would need to be present to have the true hazard ratio (or limit) be one; the higher the E value, the larger the residual confounding would need to be [28].

Electronic health record data requires accounting for varying levels of missingness among variables [29–31]. Missing data were handled by using multiple imputation by chained equations [32, 33]. For each analysis, we created 50 imputed data sets using all variables in the propensity score, the Nelson-Aalen estimate of the cumulative hazard rate function of available time-to-event data, the time-to-event, and event (i.e., in-hospital death, hospital discharge alive). Body mass index, SpO2/FiO2, and respiratory rate were imputed via predictive mean matching. Sex, ethnicity, race, and comorbidities were imputed with logistic regression. All variables used for propensity score estimation and the outcome variables were used to model any variables with missing data with the exception that raw time-to-event was not used to predict other variables in the multiple imputation by chained equations algorithm. Instead, temporal information was used for predicting missing values via the Nelson-Aalen estimate. We estimated propensity scores for each imputed data set separately. For the Cox models, the propensity scores from a specific imputed data set were used for inverse probability of treatment weighting for that data set [32, 33], and results were combined using Rubin’s Rules. All data preprocessing and statistical analyses were done using R version 4.1.0 [34] with the following packages: twang [35], survival [36, 37], survminer [38], mice [32], xtable [39], and tidyverse [40]. Further detailed descriptions of data preprocessing can be found in our previous work [20].

Results

There were 3177 patients who met the inclusion criteria. Of these, 1266 (40%) were intubated initially while 1911 (60%) were initially treated with noninvasive respiratory support, Fig 1 and Table 1. There are important differences between cohorts. Patients intubated initially were more commonly male (55% vs. 49%) and disproportionately at large hospitals (57% vs. 44%). They were also of higher acuity based on median APACHE score (68 vs. 49), although only patients admitted to an ICU were given an APACHE score in the electronic health record. Those intubated initially were less likely to have comorbid heart failure (36% vs. 47%) or chronic obstructive pulmonary disease (68% vs. 85%), more likely to be septic (36% vs. 19%), and had more severe hypoxemia based on SpO2/FiO2 on treatment assignment (medians 130 vs. 261, difference of means 90.52, 95% CI: 84.04–97.01) despite clinically similar worst PaO2/FiO2 in the first 24 hours (medians 124 vs. 142, difference of means 4.59, 95% CI: -4.39–13.58). Of the 1850 (97%) patients where the sequence of noninvasive respiratory support could be reliably classified, most patients (96%) were treated with noninvasive positive pressure ventilation (S1 Table: Demographics). All-cause in-hospital mortality was 13%, higher for patients intubated first than for those treated with noninvasive respiratory support first (18% vs 9%), S2 Table: Unmatched Outcomes. Mortality was significantly higher for noninvasive respiratory support patients who required intubation compared to those who did not (22% vs. 6%).

10.1371/journal.pone.0307849.g001 Fig 1 STROBE diagram of included subjects.

There were 89,002 total visits during the study period. Of those, most (85,825) failed to meet exclusion criteria. *The subjects that were excluded because they were not classified by the algorithm but had an eligible diagnostic code on admission likely represent those only requiring conventional oxygen. Repeat admissions and interhospital transfers were excluded due to confounding with the outcome.

10.1371/journal.pone.0307849.t001 Table 1 Demographics.

Measure	Invasive Mechanical Ventilation	Noninvasive Respiratory Support	Total	
N (%)	1266 (40%)	1911 (60%)	3177	
Female Sex	574 (45%)	970 (51%)	1544 (49%)	
Age, median (IQR)	61 (48–72)	68 (58–77)	66 (54–75)	
BMI, median (IQR)	28 (23–35)	29 (23–37)	28 (23–36)	
Ethnicity, n(%)a				
Not Hispanic or Latino	1031 (82%)	1645 (86%)	2676 (85%)	
Hispanic or Latino	225 (18%)	261 (14%)	486 (15%)	
Race, n (%)a				
White	1078 (82%)	1690 (89%)	2768 (88%)	
Black or African American	76 (6%)	105 (6%)	181 (6%)	
Asian/Native Hawaiian/Pacific Islander	16 (1%)	18 (1%)	34 (1%)	
American Indian or Alaska Native	62 (5%)	39 (2%)	101 (3%)	
Other	25 (2%)	53 (3%)	78 (2%)	
Hospital Size, n(%)				
small	39 (4%)	158 (9%)	197 (7%)	
medium	428 (39%)	815 (47%)	1243 (44%)	
large	631 (57%)	779 (44%)	1410 (49%)	
APACHE IVa median (IQR)	68 (50–87)	49 (38–63)	57 (43–76)	
Vital Signs on Treatment Assignment median (IQR)				
Heart rate	93 (79–112)	88 (75–104)	90 (76–107)	
Systolic blood pressure	120 (103–140)	132 (116–149)	127 (111–146)	
Diastolic blood pressure	68 (57–82)	73 (64–83)	71 (61–83)	
SpO2	98 (96–100)	96 (93–98)	97 (94–99)	
FiO2b	75 (50–100)	36 (30–50)	45 (33–80)	
SpO2:FiO2	130 (99–200)	261 (186–323)	200 (116–297)	
Temperature (°C)	37 (36.6–37)	36.8 (36.5–37)	36.9 (36.5–37)	
Respiratory Rate	19 (16–23)	20 (18–25)	20 (18–24)	
Comorbidities n(%)				
Diabetes	468 (40%)	756 (40%)	1224 (40%)	
Chronic Kidney Disease	261 (22%)	527 (28%)	788 (26%)	
Heart Failure	418 (36%)	903 (47%)	1321 (43%)	
Hypertension	839 (72%)	1427 (75%)	2266 (74%)	
Chronic Liver Disease	242 (21%)	157 (8%)	399 (13%)	
Neoplasm or Immunosuppression	175 (15%)	293 (15%)	468 (15%)	
COPD	799 (68%)	1613 (85%)	2412 (78%)	
Obesity	186 (16%)	350 (18%)	536 (17%)	
Acute Influenza Diagnosis	2 (0%)	4 (0%)	6 (0%)	
Acute sepsis diagnosis	419 (36%)	354 (19%)	773 (25%)	
Labs on Admission median (IQR)				
PaO2 (mmHg) (Worst Value)	79 (65–107)	75 (63–102)	77 (63–104)	
PaO2:FiO2 (Worst Value)	124 (79–220)	142 (84–218)	133 (81–218)	
White Blood Cell Count (K/uL)	8 (2.45–14)	7.7 (2–13)	7.9 (2–13.5)	
Lactate (mmol/L)	1.9 (1.2–3.7)	1.5 (1–2.3)	1.7 (1.1–2.8)	
pH	7.31 (7.19–7.39)	7.33 (7.26–7.41)	7.33 (7.24–7.4)	
PaCO2 (mmHg)	46 (38–64)	53 (40–70)	50 (38–68)	
HCO3 (mmol/L)	24 (20–28)	27 (23–32)	26 (22–30)	
BNP (pg/mL)	1490 (359–5781)	1316 (327–5381)	1369 (334–5476)	
Creatinine (mg/dL)	1.0 (0.76–1.58)	0.95 (0.73–1.36)	0.97 (0.74–1.43)	
Time from hospital admission to treatment (h)	0.6 (0–7)	27 (0–89)	6 (0–61)	
Treatment Assignment Location n (%)a				
Emergency Department	666 (53%)	523 (27%)	1189 (37%)	
ICU	505 (40%)	644 (34%)	1149 (36%)	
Non-ICU ward	16 (1%)	272 (14%)	288 (9%)	
Stepdown	79 (6%)	472 (25%)	551 (17%)	
a Data are presented as percent of available.

b FiO2 determined by documented FiO2, if documented, or by FiO2 = 100(0.21 + oxygen flow [L/min-1] x 0.03 if a flow rate was documented.

Hazard ratios are shown in Table 2. Initial noninvasive respiratory support was not associated with a decreased hazard of in-hospital death (HR: 0.65, 95% CI: 0.35–1.2); with no significant interaction of treatment and time and a reasonable protection from unmeasured confounders with an E-value of 2.04. Initial noninvasive respiratory support was, however, associated with an increased hazard of discharge alive (HR: 2.26, 95% CI: 1.92–2.67) that decreased over time (interaction between time and noninvasive respiratory support HR: 0.97, 95% CI: 0.95–0.98) and an even stronger E-value (2.90), Fig 2. The subgroup analysis excluding patients with comorbid chronic obstructive pulmonary disease or a body mass index >35 showed no statistically significant association between initial respiratory support modality and time to in-hospital death, but the hazard ratio increased for noninvasive respiratory support compared to mechanical ventilation (1.71, 95% CI: 0.85–3.45), with an E-value of 2.25. Sensitivity analyses excluding the patients without clear treatment sequence shows noninvasive respiratory support was associated with a reduced hazard of in-hospital death (HR: 0.53, 95% CI: 0.28–0.99, E-value 2.49) and an increased hazard of hospital discharge alive (HR: 2.34, 95% CI: 1.97–2.77, E-value 2.98) that again decreased over time (interaction between time and noninvasive respiratory support HR: 0.97, 95% CI: 0.95–0.98). Representative cumulative incidence curves show a consistent trend of slightly reduced probability of in-hospital death and increased probability of discharge alive for noninvasive respiratory support across a range of comorbidities (chronic obstructive pulmonary disease, congestive heart failure) and diagnoses (influenza, sepsis), except for the subgroup analysis S1–S5 Figs. The cumulative incidence curves in the subgroup analysis suggest an increased probability of in-hospital death with noninvasive respiratory support.

10.1371/journal.pone.0307849.g002 Fig 2 Top: Model-estimated cumulative incidence curves for noninvasive respiratory support (NIRS) vs invasive mechanical ventilation (IMV) showing the probabilities for hospital discharge alive (left) and in-hospital death (right). Bottom: Estimated time-varying hospital discharge alive hazard ratios for NIRS versus IMV with pointwise 95% confidence intervals. The following values were used for covariates: male, not Hispanic or Latino, white, one of the large hospitals (hospital A), hospital admission to the emergency department between January 1, 2018 and June 30, 2018, no vasopressor infusion before treatment, no diabetes, no chronic kidney disease, no heart failure, yes hypertension, no chronic obstructive pulmonary disease, no neoplasm/immunosuppression, no chronic liver disease, no obesity, no influenza, yes sepsis, and continuous covariates set at their median values (age = 66 years, SpO2/FiO2 = 200, respiratory rate = 20 breaths/min, BMI = 28.44, transformed hours from hospital admission to first treatment = 1.77). Each imputed data set generates a pair of curves (one for IMV, one for NIRS).

10.1371/journal.pone.0307849.t002 Table 2 Cox model results.

Outcome	First Treatment	Hazard Ratio	95% CI	P-Value	E-Value	CL E-Value	
Time to In-Hospital Death	Primary analysis	NIRS	0.65	0.35–1.2	0.167	2.04	1.00	
Sensitivity analysis	NIRS	0.53	0.28, 0.99	0.047	2.49	1.08	
Subgroup analysis*	NIRS	1.71	0.85, 3.45	0.136	2.25	1.00	
Secondary analysis	NHF	3.27	1.43, 7.45	0.005	3.91	1.89	
NIPPV	0.52	0.25, 1.07	0.076	2.53	1.00	
Time to Hospital Discharge Alive	Primary analysis	NIRS	2.26	1.92, 2.67	<0.001	2.90	2.51	
Time x NIRS	0.97	0.95, 0.98	<0.001	1.18	1.13	
Sensitivity analysis	NIRS	2.34	1.97, 2.77	<0.001	2.98	2.57	
Time x NIRS	0.97	0.95, 0.98	<0.001	1.18	1.14	
Secondary analysis	NHF	2.12	1.25, 3.57	0.005	2.74	1.62	
NIPPV	2.29	1.92, 2.74	<0.001	2.94	2.51	
Time x NHF	0.99	0.93, 1.05	0.795	1.08	1.00	
Time x NIPPV	0.96	0.95, 0.98	<0.001	1.19	1.14	
NIRS = Noninvasive respiratory support

NHF = nasal high flow

NIPPV = noninvasive positive pressure ventilation

* excluding patients with comorbid chronic obstructive pulmonary disease or a body mass index >35. Note that for this analysis, we had to remove influenza from the propensity score calculation and regroup some hospitals due to the reduced sample size.

Additional predictors include age, BMI, gender, ethnicity, white race, respiratory rate (breaths/min), the ratio of SPO2 to FIO2, diabetes, CKD, hypertension, heart failure, COPD, neoplasm or immunosuppression, chronic liver disease, obesity, influenza, sepsis, vasopressors before treatment, transformed first treatment start (in days after hospital admission), first treatment location type (levels ED, ICU, Stepdown, or Med/Surg), hospital (with a grouped level for hospitals with very few patients in the data set), and time period.

The probability of discharge alive is greater for NIRS than for IMV, and the probability of in-hospital death is lower. The hazard ratio of discharge alive for NIRS vs. IMV starts out statistically significantly positive shortly before 20 days after treatment initiation and eventually switches direction around 40 days.

In the secondary analyses separating noninvasive respiratory support modalities, the hazard for in-hospital death ranged from increased with nasal high flow (HR 3.27, 95% CI: 1.43–7.45, E-value 3.91) to non-significantly decreased with noninvasive positive pressure ventilation (HR 0.52, 95% CI 0.25–1.07, E-value 2.53), neither with an interaction with time, Fig 3. Both modalities were associated with increased hazard of discharge alive (nasal high flow HR 2.12, 95 CI: 1.25–3.57, E-value 2.74; noninvasive positive pressure ventilation HR 2.29, 95% CI: 1.92–2.74, E-value 2.94), both with strong E-values, but only noninvasive positive pressure ventilation showed an interaction with time (interaction between time and nasal high flow HR 0.99, 95% CI: 0.93–1.05; interaction between time and noninvasive positive pressure ventilation HR 0.96, 95% CI: 0.95–0.98). Representative cumulative incidence curves are shown in S6 and S7 Figs.

10.1371/journal.pone.0307849.g003 Fig 3 Top: Model-estimated cumulative incidence curves for noninvasive positive pressure ventilation (NIPPV), nasal high flow (NHF), and invasive mechanical ventilation (IMV) showing the probabilities for hospital discharge alive (left) and in-hospital death (right). Bottom: Estimated time-varying hospital discharge alive hazard ratios for NHF versus IMV (left) and NIPPV versus IMV (right) with pointwise 95% confidence intervals.

The following values were used for covariates: male, not Hispanic or Latino, white, one of the large hospitals (hospital A), hospital admission to the emergency department between January 1, 2018 and June 30, 2018, no vasopressor infusion before treatment, no diabetes, no chronic kidney disease, no heart failure, yes hypertension, no chronic obstructive pulmonary disease, no neoplasm/immunosuppression, no chronic liver disease, no obesity, no influenza, yes sepsis, and continuous covariates set at their median values (age = 66 years, SpO2/FiO2 = 200, respiratory rate = 20 breaths/min, BMI = 28.44, transformed hours from hospital admission to first treatment = 1.70). Each imputed data set generates a triple of curves (one for IMV, NHF, and NIPPV).

Patients initially treated with either non-invasive modality had a higher probability of hospital discharge alive until roughly 15 days after treatment initiation. After that, the probability of discharge alive remained higher for NIPPV compared to IMV but reversed direction for NHF and IMV. Nasal high flow had a slightly higher probability of in-hospital death than IMV, and NIPPV had a slightly lower probability of in-hospital death than IMV. The hospital discharge alive hazard ratio of NHF to IMV was positive but decreasing until around day 15, at which point there was no further clear difference between those two treatments. The same pattern held for the hospital discharge alive hazard ratio of NIPPV to IMV except that it eventually reversed direction, resulting in the hazard of hospital discharge alive being greater for IMV than NIPPV starting at roughly 35 days after treatment initiation.

Discussion

Noninvasive respiratory support strategies are increasingly used as alternative initial strategies to early intubation and mechanical ventilation for patients with acute hypoxemic respiratory failure. Thus, the goal of this study was to compare the outcomes between those two approaches. Our results show that initial noninvasive respiratory support modalities in patients with acute hypoxemic respiratory failure were most likely not associated with a reduced hazard of in-hospital death (no association in the primary analyses, weakly significant association in the sensitivity analysis), yet were associated with an increased probability of hospital discharge alive when compared to initial invasive mechanical ventilation. The existing literature comparing noninvasive strategies to conventional oxygen suggests that noninvasive respiratory support is probably associated with reduced mortality, reduced intubation, and shorter hospitals stays, but to varying degrees among the different noninvasive modalities [2, 4, 5, 41, 42]. Our results expand upon this knowledge by comparing outcomes between noninvasive strategies and invasive mechanical ventilation in non-COVID-19 acute hypoxemic respiratory failure. Despite patients who were intubated first generally being more severely ill and more likely to be septic, there was no increase in hazard of our primary outcome (in-hospital death). However, noninvasive respiratory support was associated with an increased likelihood of hospital discharge alive, which waned over time for nasal high flow. Further study may show that failure of a noninvasive respiratory support modality may be associated with increased mortality beyond the progression of disease, thus having an outsized influence on the overall association with mortality [43]. This hypothesis is supported by the observed 3.5-fold increase in unweighted mortality for patients that failed a noninvasive strategy compared to those that did not, and is also suggested by the results of our subgroup analysis.

These data suggest that noninvasive respiratory support modalities can be effective alternatives to mechanical ventilation for the initial treatment of some patients with acute hypoxemic respiratory failure. Successful noninvasive support increases the likelihood of earlier hospital discharge, but an unsuccessful trial may carry outsized consequences for mortality. These results add to findings from the Lung Safe study, that showed noninvasive positive pressure ventilation was used in 15% of patients with acute respiratory distress syndrome, but both failure and mortality increased as the severity of disease worsened [15]. While spontaneous breathing can have some advantages in acute hypoxemic respiratory failure patients, patient self-inflicted lung injury is the likely reason for worse outcomes in patients that fail a noninvasive strategy [44, 45]. Taking these data and existing literature together, balancing the double-edged sword with noninvasive respiratory support involves early application of noninvasive respiratory support with close monitoring of the patient’s work of breathing and avoiding delayed intubation in patients where noninvasive modalities fail to sufficiently reduce the work of breathing [46].

Our secondary analyses also suggested differences between noninvasive positive pressure ventilation and nasal high flow. There are four possible explanations for these findings. The first is that noninvasive positive pressure may be the better noninvasive strategy. The second possibility is that the patients treated with nasal high flow may not have been similar to the patients treated with noninvasive positive pressure ventilation, and that our efforts to account for treatment confounding were not fully successful. Patients treated with nasal high flow in our dataset had a higher median APACHE score on admission (56 vs. 48, difference of means 11.77, 95% CI: 6.36, 17.18) a lower median SpO2/FiO2 on treatment assignment (136 vs 274, difference of means -105, 95% CI: -126 - -85) and lower median worst PaO2/FiO2 (76 vs 150, difference of means -76, 95% CI: -106 - -45), more commonly had neoplasm or immunosuppression (26% vs 15%) and were more commonly septic (34% vs 17%). The third possibility is that patients may not have been treated similarly. Patients initiated on noninvasive positive pressure in a non-intensive care unit were more commonly transferred to the intensive care unit by 12 hours than patients started on nasal high flow (32% vs 12%). Lastly, there may have been imbalanced, imprecise, or incorrect delivery of one modality compared to the other. The median flow rate for nasal high flow was 40lpm (95% CI: 35 - 50lpm). While gas exchange can improve at lower flow rates, higher flow rates are required for the work of breathing benefits related to changes in resting lung volume and strain [47]. Monitoring likely differed between intensive care (e.g., work of breathing changes, signs of fatigue) and non-intensive care units (e.g., oxygen saturation). Additionally, managing failure could have differed as failing nasal high flow could have resulted in more crossover to noninvasive positive pressure ventilation than intubation compared to failing noninvasive positive pressure ventilation.

There are important limitations to our results. Our data were limited to pre-COVID-19 data. As such, the use of noninvasive respiratory support modalities has likely evolved as is evident by the relatively low number of patients treated with nasal high flow across the entire health network. Second, we used admission diagnostic codes to select patients treated at the time for acute hypoxemic respiratory failure without the selection bias of using discharge diagnostic codes. These results are contingent upon accurate coding of admission diagnoses and important patient groups may have been excluded. Since these data are non-randomized observational data, non-protocolized clinical care may have contributed unmeasured confounding differences in the selection for and management of each modality. We attempted to control for confounding by inverse probability for treatment assignment weighting and further adjusting for potential confounders in the Cox models. Furthermore, our E-values are relatively strong, so any unmeasured confounding would have needed to have a substantial effect to alter the results. Another limitation is that results are based on the first assigned therapy, and symptom onset time is not available in our dataset. Thus, crossover (and imbalanced crossover), and symptom duration could confound the findings. Lastly, goals of care and end-of-life issues are not readily extractable from structured electronic health record data. There is an important difference between a patient who is a do-not-intubate on admission treated with rescue noninvasive respiratory support and a similar patient who worsened during noninvasive respiratory support and chose to become a do-not-intubate. However, both patients would have been included in our dataset and could contribute some confounding in the results.

Despite these limitations, our results provide an overview of outcomes between respiratory support modalities that were pragmatically applied across a large healthcare network. These results highlight important knowledge gaps needing further study, including: 1. the risks of failing noninvasive respiratory support, mechanisms of those risks, thresholds of, monitoring for, and management of failure, 2. reproducible phenotypes likely to do well or not do well with noninvasive respiratory support modality, 3. optimal noninvasive support modality by phenotype, 4. optimal noninvasive respiratory support delivery by modality and, 5. optimal hospital location and minimal monitoring capabilities for patients with acute hypoxemic respiratory failure requiring noninvasive respiratory support.

Our data across a large and diverse healthcare network show that initial treatment with noninvasive respiratory support is not associated with a reduced hazard of in-hospital death compared to invasive mechanical ventilation for patients admitted with acute hypoxemic respiratory failure. However, noninvasive respiratory support is associated with a higher likelihood of earlier hospital discharge. Lastly, our data suggest potential differences between noninvasive respiratory support modalities that require further exploration.

Supporting information

S1 Table Demographics of patients without clear sequence of support.

(DOCX)

S2 Table Unmatched outcomes.

(DOCX)

S1 Fig Representative in-hospital death-model cumulative incidence curves.

(DOCX)

S2 Fig Representative in-hospital death-model cumulative incidence curves excluding patients without clear sequence of noninvasive respiratory support.

(DOCX)

S3 Fig Representative in-hospital death-model cumulative incidence curves of a sub-group excluding patients with a comorbidity of COPD or body mass index >35.

(DOCX)

S4 Fig Representative hospital discharge alive model-estimated cumulative incidence curves.

(DOCX)

S5 Fig Representative hospital discharge alive model-estimated cumulative incidence curves excluding patients without clear sequence representative hospital discharge alive model-estimated cumulative incidence curves excluding patients without clear sequence of noninvasive respiratory support.

(DOCX)

S6 Fig Representative in-hospital death model-estimated cumulative incidence curves excluding patients without clear sequence of noninvasive respiratory support.

(DOCX)

S7 Fig Representative hospital discharge alive model-estimated cumulative incidence curves excluding patients without clear sequence of noninvasive respiratory support.

(DOCX)

The authors would like to thank Don Saner and Mario Arteaga from the Banner Health Network Clinical Data Warehouse for their support during this project.

10.1371/journal.pone.0307849.r001
Decision Letter 0
Grosek Stefan Academic Editor
© 2024 Stefan Grosek
2024
Stefan Grosek
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
24 Mar 2024

PONE-D-24-02817Noninvasive vs invasive respiratory support for patients with acute hypoxemic respiratory failurePLOS ONE

Dear Dr. Mosier,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by May 08 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Stefan Grosek, Ph.D., M.D.,

Academic Editor

PLOS ONE

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at 

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for stating in your Funding Statement: 

This work was supported by an Emergency Medicine Foundation grant sponsored by Fisher & Paykel, and in part by the National Science Foundation under grant #1838745 and the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number 5T32HL007955. Neither funding agency or sponsor was involved in the design or conduct of the study or interpretation and presentation of the results. 

Please provide an amended statement that declares all the funding or sources of support (whether external or internal to your organization) received during this study, as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now.  Please also include the statement “There was no additional external funding received for this study.” in your updated Funding Statement. 

Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf.

3. Thank you for stating the following in the Competing Interests section: 

JMM has received travel support from Fisher & Paykel.

We note that you received funding from a commercial source: Fisher & Paykel.

Please provide an amended Competing Interests Statement that explicitly states this commercial funder, along with any other relevant declarations relating to employment, consultancy, patents, products in development, marketed products, etc. 

Within this Competing Interests Statement, please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests).  If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared. 

Please include your amended Competing Interests Statement within your cover letter. We will change the online submission form on your behalf.

Additional Editor Comments:

Dear Authors

This retrospective study on patients with acute hypoxemic respiratroy support who were treated as first with invasive or noninvasive ventilation answered to may interesting questions It is well designed and written and brings some interesting answers to yours questions. Both reviewers are of the same opinion as meed but found somme issues to be discussed or elaborated.

I'm looking forward hearing from you soon.

Kind regards.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Mosier and co-authors explored the outcomes of patients affected by acute hypoxemic respiratory failure treated with NIRS (noninvasive respiratory supports) compared to patients initially treated with IMV (invasive mechanical ventilation). They found that initial treatment with NIRS is not associated with a reduced in-hospital death when compared to IMV, but NIRS treatment is associated with a higher likelihood of earlier hospital discharge.

The authors should be commended for the work they have performed. However, I believe that there is a major issue to resolve:

It is not clear if cases analyzed are “de novo acute hypoxemic respiratory failure”, exacerbations or a mix of cases. In the introduction section authors correctly referred to the use of NIRS in acute exacerbation of COPD or acute cardiogenic pulmonary oedema, and in “de novo” hypoxemic respiratory failure (lines 94-99, page 3). However, having a look at data presented in table 1, it comes out that the median pH of the included population is 7.31 (IMV) and 7.33 (NIV). This potentially correlates with the level of CO2 found (46 vs 53). Taking into account that bicarbonate levels were pretty normal or even higher in the noninvasive group, this means that most of the patients were affected already by a chronic respiratory condition. Indeed, 78% and 16% in IMV and 85% and 18% in noninvasive group were respectively COPD or obese. Surprisingly, only 0% was affected by acute respiratory conditions as influenza, and no ARDS patients were enlisted in the retrieved data.

In conclusion, it seems that there is no distinction between de novo and acute exacerbations of chronic condition, and that the majority of this cohort of patients analyzed is affected by exacerbation of chronic condition. I believe that this distinction and a subanalysis of specific populations should also be considered (obesity, COPD, etc).

Reviewer #2: This is an interesting retrospective comparison of in-hospital mortality and the discharge alive of patients with acute respiratory failure treated initially either with noninvasive respiratory support or with invasive mechanical ventilation and deserves to be published. Noninvasive respiratory was associated with more discharges alive, but no reduction in in-hospital mortality at the expense of the high-flow nasal oxygen (HFNO) therapy group. Due to the retrospective nature of the study, treatment with NHFO was problematic and inadequate in relation to the need for respiratory support. Thus, the results do not reflect the actual effectiveness of NHO therapy. It is true, however, that the data comes from real life, even from the pre-Covid-19 period. In addition to the benefits of HFNO in preventing intubation, prior studies have already demonstated the deleterous effect with inadequate use of HFNO. Meanwhile, the knowledge and the way of using NHFO has improved. The authors have already discussed in details the limitations of the study. One of the important issues is the imbalance of the groups. HFNO was the ceiling treatment for terminal-stage diseased patients irrespective of the severity of respiratory failure. Thus, the inclusion criteria for NIPPV group and HFNO group differ significantly as does the outcome.

The authors should address the perspectives of the study in discussion: When is it safe to start HFNO in acute respiratory failure? When to escalate from HFNO to NIPPV or to MV? Is a stepwise approach the right one?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

10.1371/journal.pone.0307849.r002
Author response to Decision Letter 0
Submission Version1
27 Apr 2024

PONE-D-24-02817

Noninvasive vs invasive respiratory support for patients with acute hypoxemic respiratory failure

PLOS ONE

Dear Dr. Grosek and editorial board,

Thank you for the opportunity to revise our manuscript. We thank the reviewers for the excellent comments and think they have strengthened our paper significantly. Below is a point-by-point response for your consideration. We hope you find these revisions to be acceptable for publication in PLOS ONE and we look forward to your decisions.

On behalf of all authors,

Jarrod Mosier, MD

Professor and Vice Chair of Emergency Medicine

Professor of Medicine

Department of Emergency Medicine

Department of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep

University of Arizona College of Medicine-Tucson

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdfand

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: The manuscript has been edited to meet the additional formatting requirements.

2. Thank you for stating in your Funding Statement:

This work was supported by an Emergency Medicine Foundation grant sponsored by Fisher & Paykel, and in part by the National Science Foundation under grant #1838745 and the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number 5T32HL007955. Neither funding agency or sponsor was involved in the design or conduct of the study or interpretation and presentation of the results.

Please provide an amended statement that declares all the funding or sources of support (whether external or internal to your organization) received during this study, as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now. Please also include the statement “There was no additional external funding received for this study.” in your updated Funding Statement.

Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf.

Response: Our funding statement has been updated in the cover letter to the following:

This work was supported by:

E.20124, J.M.M, Emergency Medicine Foundation, Fisher & Paykel Healthcare Directed Grant. Authors supported: Mosier, Subbian, Fisher. https://www.emfoundation.org/grantee/past-grantees/Grantees-2020-2021

1838745, V.S, National Science Foundation. Authors supported: Subbian. https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838745

5T32HL007955, P.E, National Heart, Lung, and Blood Institute, Authors supported: Essay. https://reporter.nih.gov/project-details/8680297

UL1 TR001860, J.C.S, National Center for Advancing Translational Sciences. Authors supported: Stocking. https://reporter.nih.gov/search/pzhJ4ZAoSUelr6CoWSajpg/projects

K01HL168222, J.C.S, National Heart, Lung, And Blood Institute. Authors supported: Stocking. https://reporter.nih.gov/search/z70wGdlt2ESfrAcjDewhGA/project-details/10643357

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

There was no additional external funding received for this study.

3. Thank you for stating the following in the Competing Interests section:

JMM has received travel support from Fisher & Paykel.

We note that you received funding from a commercial source: Fisher & Paykel.

Please provide an amended Competing Interests Statement that explicitly states this commercial funder, along with any other relevant declarations relating to employment, consultancy, patents, products in development, marketed products, etc.

Within this Competing Interests Statement, please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests). If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include your amended Competing Interests Statement within your cover letter. We will change the online submission form on your behalf.

Response: Dr. Mosier’s competing interest statement has been amended to the following:

JMM has received meeting travel support from Fisher & Paykel Healthcare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Additional Editor Comments:

Dear Authors

This retrospective study on patients with acute hypoxemic respiratroy support who were treated as first with invasive or noninvasive ventilation answered to may interesting questions It is well designed and written and brings some interesting answers to yours questions. Both reviewers are of the same opinion as meed but found somme issues to be discussed or elaborated.

I'm looking forward hearing from you soon.

Kind regards.

Response: Thank you for the kind feedback. We hope you find the revision acceptable.

Reviewers' comments:

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Mosier and co-authors explored the outcomes of patients affected by acute hypoxemic respiratory failure treated with NIRS (noninvasive respiratory supports) compared to patients initially treated with IMV (invasive mechanical ventilation). They found that initial treatment with NIRS is not associated with a reduced in-hospital death when compared to IMV, but NIRS treatment is associated with a higher likelihood of earlier hospital discharge.

The authors should be commended for the work they have performed. However, I believe that there is a major issue to resolve:

It is not clear if cases analyzed are “de novo acute hypoxemic respiratory failure”, exacerbations or a mix of cases. In the introduction section authors correctly referred to the use of NIRS in acute exacerbation of COPD or acute cardiogenic pulmonary oedema, and in “de novo” hypoxemic respiratory failure (lines 94-99, page 3). However, having a look at data presented in table 1, it comes out that the median pH of the included population is 7.31 (IMV) and 7.33 (NIV). This potentially correlates with the level of CO2 found (46 vs 53). Taking into account that bicarbonate levels were pretty normal or even higher in the noninvasive group, this means that most of the patients were affected already by a chronic respiratory condition. Indeed, 78% and 16% in IMV and 85% and 18% in noninvasive group were respectively COPD or obese. Surprisingly, only 0% was affected by acute respiratory conditions as influenza, and no ARDS patients were enlisted in the retrieved data.

In conclusion, it seems that there is no distinction between de novo and acute exacerbations of chronic condition, and that the majority of this cohort of patients analyzed is affected by exacerbation of chronic condition. I believe that this distinction and a subanalysis of specific populations should also be considered (obesity, COPD, etc).

Response: We thank the reviewer for the thoughtful comments. We narrowed our inclusion criteria based on admission codes that are consistent with acute hypoxemic respiratory failure (AHRF). The reason we chose admission diagnosis rather than discharge or final diagnosis is to better characterize outcomes based on the diagnoses that the clinicians thought they were treating at the time rather than a biased look at a narrower slice of those patients. It does appear that we were successful in narrowing the included population to AHRF based on the median and ranges of both PaO2 and PaO2/FiO2 ratio. We agree that the slightly elevated median PaCO2 correlates well with the slightly acidemic median pH, we disagree with the reviewer’s conclusion that most of the NIRS cohort has a chronic condition for a few reasons. Firstly, as the reviewer points out, the serum bicarbonate is generally normal and based on the H-H equation, using the median numbers, they both calculate as acute respiratory acidoses. Second, the relatively normal serum bicarb and H-H results argue against a chronic condition as the primary cause. Third, there is significant overlap in the IQRs, and the minor differences are accounted for by the use of inverse probability of treatment weighting, thus making broad comparisons about the minor differences in median values is unwarranted. Lastly, given the severity of hypoxemia based on the PaO2 and PF ratio, the degree of mild hypercapnia (based on median values) is certainly within the range of an acute dead space commonly seen in AHRF patients.

There is a significant number of patients in each cohort, as the reviewer points out, that had comorbid obesity and/or COPD. But these diagnoses are comorbidities and not the primary cause of respiratory failure. This is consistent with data from other studies by Dr. Mosier and collaborators. In a study recently accepted by Critical Care Explorations, a retrospective study in ED patients at the University of Michigan that were treated with noninvasive respiratory support for acute hypoxemic respiratory failure (https://www.medrxiv.org/content/10.1101/2023.09.26.23296167v1) showed that 2/3rds of patients had comorbid COPD and half of patients had a CO2 above expected based on the Winter’s formula, consistent with acute dead space. In the other study, a pilot study conducted at the University of Arizona in preparation for a clinical trial, half of patients with acute hypoxemic respiratory failure had comorbid COPD, and clinicians commonly misattributed (about ½ the time) the admission diagnoses to decompensated COPD or heart failure rather than pneumonia. Thus, extrapolating those findings to this study, since we used admission diagnoses, it may be more likely that we excluded patients with AHRF as the primary cause rather than included patients with decompensated chronic disease as the primary cause.

Regarding the influenza and ARDS question, we agree these numbers are surprisingly low but we think they are misleading. The reason they may be misleading is that we used admission diagnosis, and influenza results may have been pending at the time the admission diagnosis was entered. Similarly with ARDS, an ARDS diagnosis is commonly a discharge diagnosis, uncommonly an admission diagnosis, and during the timeframe of this study was an impossible diagnosis for any patient in the noninvasive cohort.

Taken together, the findings of mild respiratory acidoses and a high incidence of comorbid disease is not surprising in patients with acute hypoxemic respiratory failure. However, the reviewer offers a good suggestion about a subgroup analysis. We have performed a secondary subgroup analysis on the primary outcome only, excluding anyone with a comorbidity for COPD or a BMI >35. We found that, while the hazard ratio changed directions, it was still non-significant after excluding these populations and there was no interaction with time. (Figures shown in attached word document of response letter)

In these cumulative incidence curve estimations, every instance suggested that the probability of death was higher with NIRS than with mechanical ventilation. These findings are merely hypothesis generating, but suggestive of a signal towards potential harm in a more heterogeneous patient population.

(see figures in attached word document of response letter)

We have revised the manuscript to include some of this information while being mindful of word count.

Reviewer #2: This is an interesting retrospective comparison of in-hospital mortality and the discharge alive of patients with acute respiratory failure treated initially either with noninvasive respiratory support or with invasive mechanical ventilation and deserves to be published. Noninvasive respiratory was associated with more discharges alive, but no reduction in in-hospital mortality at the expense of the high-flow nasal oxygen (HFNO) therapy group. Due to the retrospective nature of the study, treatment with NHFO was problematic and inadequate in relation to the need for respiratory support. Thus, the results do not reflect the actual effectiveness of NHO therapy. It is true, however, that the data comes from real life, even from the pre-Covid-19 period. In addition to the benefits of HFNO in preventing intubation, prior studies have already demonstated the deleterous effect with inadequate use of HFNO. Meanwhile, the knowledge and the way of using NHFO has improved. The authors have already discussed in details the limitations of the study. One of the important issues is the imbalance of the groups. HFNO was the ceiling treatment for terminal-stage diseased patients irrespective of the severity of respiratory failure. Thus, the inclusion criteria for NIPPV group and HFNO group differ significantly as does the outcome.

The authors should address the perspectives of the study in discussion: When is it safe to start HFNO in acute respiratory failure? When to escalate from HFNO to NIPPV or to MV? Is a stepwise approach the right one?

Response:

We thank the reviewer for the comments and suggestion. We are unsure what is meant by the following: “One of the important issues is the imbalance of the groups. HFNO was the ceiling treatment for terminal-stage diseased patients irrespective of the severity of respiratory failure. Thus, the inclusion criteria for NIPPV group and HFNO group differ significantly as does the outcome.”

What we interpret this to mean is that nasal high flow is the maximal therapy in end-stage disease patients with do-not-intubate orders, but there is no indication that is any more true in these data than NIPPV being the maximal therapy. We have elaborated the difficulties regarding end-of-life care in the limitations already. Any further elaboration between NIPPV and nasal high flow, which is only a secondary analysis in this study, would be purely speculation given the relatively low numbers of nasal high flow patients after restricting by admission diagnosis codes, and lack of time series information regarding outcomes with crossovers. We made a general comment in this revision in an attempt to address the reviewer’s request with a reference to a recent article by Dr. Mosier that directly addresses this concern, but we feel going any further would be inappropriate in this manuscript.

Attachment Submitted filename: PLOSone R1 response to reviewers.docx

10.1371/journal.pone.0307849.r003
Decision Letter 1
Grosek Stefan Academic Editor
© 2024 Stefan Grosek
2024
Stefan Grosek
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
12 Jul 2024

Noninvasive vs invasive respiratory support for patients with acute hypoxemic respiratory failure

PONE-D-24-02817R1

Dear Dr. Mosier,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Stefan Grosek, Ph.D., M.D.,

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: All the questions were adequately answered. There are no concerns regarding publication ethics and research ethics

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Andreja Sinkovič

**********

10.1371/journal.pone.0307849.r004
Acceptance letter
Grosek Stefan Academic Editor
© 2024 Stefan Grosek
2024
Stefan Grosek
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
25 Jul 2024

PONE-D-24-02817R1

PLOS ONE

Dear Dr. Mosier,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Professor Stefan Grosek

Academic Editor

PLOS ONE
==== Refs
References

1 Rochwerg B , Brochard L , Elliott MW , Hess D , Hill NS , Nava S , et al . Official ERS/ATS clinical practice guidelines: noninvasive ventilation for acute respiratory failure. The European respiratory journal: official journal of the European Society for Clinical Respiratory Physiology. 2017;50 (2 ). doi: 10.1183/13993003.02426-2016 28860265
2 Ferreyro BL , Angriman F , Munshi L , Del Sorbo L , Ferguson ND , Rochwerg B , et al . Association of Noninvasive Oxygenation Strategies With All-Cause Mortality in Adults With Acute Hypoxemic Respiratory Failure: A Systematic Review and Meta-analysis. JAMA: the journal of the American Medical Association. 2020;324 (1 ):57–67. doi: 10.1001/jama.2020.9524 32496521
3 Rochwerg B , Einav S , Chaudhuri D , Mancebo J , Mauri T , Helviz Y , et al . The role for high flow nasal cannula as a respiratory support strategy in adults: a clinical practice guideline. Intensive Care Med. 2020;46 (12 ):2226–37. doi: 10.1007/s00134-020-06312-y 33201321
4 Pitre T , Zeraatkar D , Kachkovski GV , Leung G , Shligold E , Dowhanik S , et al . Noninvasive Oxygenation Strategies in Adult Patients With Acute Hypoxemic Respiratory Failure: A Systematic Review and Network Meta-Analysis. Chest. 2023. doi: 10.1016/j.chest.2023.04.022 37085046
5 Chaudhuri D , Trivedi V , Lewis K , Rochwerg B . High-Flow Nasal Cannula Compared With Noninvasive Positive Pressure Ventilation in Acute Hypoxic Respiratory Failure: A Systematic Review and Meta-Analysis. Crit Care Explor. 2023;5 (4 ):e0892. doi: 10.1097/CCE.0000000000000892 37007904
6 Ni YN , Luo J , Yu H , Liu D , Ni Z , Cheng J , et al . Can High-flow Nasal Cannula Reduce the Rate of Endotracheal Intubation in Adult Patients With Acute Respiratory Failure Compared With Conventional Oxygen Therapy and Noninvasive Positive Pressure Ventilation?: A Systematic Review and Meta-analysis. Chest. 2017;151 (4 ):764–75.28089816
7 Shen Y , Zhang W . High-flow nasal cannula versus noninvasive positive pressure ventilation in acute respiratory failure: interaction between PaO2/FiO2 and tidal volume. Crit Care. 2017;21 (1 ):285. doi: 10.1186/s13054-017-1861-4 29166943
8 Zhao H , Wang H , Sun F , Lyu S , An Y . High-flow nasal cannula oxygen therapy is superior to conventional oxygen therapy but not to noninvasive mechanical ventilation on intubation rate: a systematic review and meta-analysis. Crit Care. 2017;21 (1 ):184. doi: 10.1186/s13054-017-1760-8 28701227
9 Antonelli M , Conti G , Moro ML , Esquinas A , Gonzalez-Diaz G , Confalonieri M , et al . Predictors of failure of noninvasive positive pressure ventilation in patients with acute hypoxemic respiratory failure: a multi-center study. Intensive Care Med. 2001;27 (11 ):1718–28. doi: 10.1007/s00134-001-1114-4 11810114
10 Carrillo A , Gonzalez-Diaz G , Ferrer M , Martinez-Quintana ME , Lopez-Martinez A , Llamas N , et al . Non-invasive ventilation in community-acquired pneumonia and severe acute respiratory failure. Intensive Care Med. 2012;38 (3 ):458–66. doi: 10.1007/s00134-012-2475-6 22318634
11 Demoule A , Girou E , Richard JC , Taille S , Brochard L . Benefits and risks of success or failure of noninvasive ventilation. Intensive Care Med. 2006;32 (11 ):1756–65. doi: 10.1007/s00134-006-0324-1 17019559
12 Chandra D , Stamm JA , Taylor B , Ramos RM , Satterwhite L , Krishnan JA , et al . Outcomes of noninvasive ventilation for acute exacerbations of chronic obstructive pulmonary disease in the United States, 1998–2008. American Journal of Respiratory and Critical Care Medicine. 2012;185 (2 ):152–9. doi: 10.1164/rccm.201106-1094OC 22016446
13 Walkey AJ , Wiener RS . Use of noninvasive ventilation in patients with acute respiratory failure, 2000–2009: a population-based study. Annals of the American Thoracic Society. 2013;10 (1 ):10–7. doi: 10.1513/AnnalsATS.201206-034OC 23509327
14 Bellani G , Laffey JG , Pham T , Fan E , Brochard L , Esteban A , et al . Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA: the journal of the American Medical Association. 2016;315 (8 ):788–800. doi: 10.1001/jama.2016.0291 26903337
15 Bellani G , Laffey JG , Pham T , Madotto F , Fan E , Brochard L , et al . Noninvasive Ventilation of Patients with Acute Respiratory Distress Syndrome. Insights from the LUNG SAFE Study. Am J Respir Crit Care Med. 2017;195 (1 ):67–77. doi: 10.1164/rccm.201606-1306OC 27753501
16 Thille AW , Contou D , Fragnoli C , Cordoba-Izquierdo A , Boissier F , Brun-Buisson C . Non-invasive ventilation for acute hypoxemic respiratory failure: intubation rate and risk factors. Crit Care. 2013;17 (6 ):R269. doi: 10.1186/cc13103 24215648
17 Brochard L , Slutsky A , Pesenti A . Mechanical Ventilation to Minimize Progression of Lung Injury in Acute Respiratory Failure. Am J Respir Crit Care Med. 2017;195 (4 ):438–42. doi: 10.1164/rccm.201605-1081CP 27626833
18 Lederer DJ , Bell SC , Branson RD , Chalmers JD , Marshall R , Maslove DM , et al . Control of Confounding and Reporting of Results in Causal Inference Studies. Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals. Annals of the American Thoracic Society. 2019;16 (1 ):22–8. doi: 10.1513/AnnalsATS.201808-564PS 30230362
19 Essay P , Mosier J , Subbian V . Rule-Based Cohort Definitions for Acute Respiratory Failure: Electronic Phenotyping Algorithm. JMIR Med Inform. 2020;8 (4 ):e18402. doi: 10.2196/18402 32293579
20 Fisher J , Subbian V , Essay P , Pungitore S , Bedrick E , Mosier J . Acute Respiratory Failure from Early Pandemic COVID-19: Noninvasive Respiratory Support vs Mechanical Ventilation. CHEST Critical Care. 2023. doi: 10.1016/j.chstcc.2023.100030 38645483
21 Essay P , Fisher JM , Mosier JM , Subbian V . Validation of an Electronic Phenotyping Algorithm for Patients With Acute Respiratory Failure. Crit Care Explor. 2022;4 (3 ):e0645. doi: 10.1097/CCE.0000000000000645 35261979
22 Yarnell CJ , Johnson A , Dam T , Jonkman A , Liu K , Wunsch H , et al . Do Thresholds for Invasive Ventilation in Hypoxemic Respiratory Failure Exist? A Cohort Study. Am J Respir Crit Care Med. 2023;207 (3 ):271–82. doi: 10.1164/rccm.202206-1092OC 36150166
23 McCaffrey DF , Griffin BA , Almirall D , Slaughter ME , Ramchand R , Burgette LF . A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Statistics in medicine. 2013;32 (19 ):3388–414. doi: 10.1002/sim.5753 23508673
24 Hawkins D , Weisberg S . Combining the Box-Cox Power and Generalized Log Transformations to Accommodate Nonpositive Responses in Linear and Mixed-Effects Linear Models. South African Statist J. 2017;5 :317–28.
25 Putter H , Fiocco M , Geskus RB . Tutorial in biostatistics: competing risks and multi-state models. Statistics in medicine. 2007;26 (11 ):2389–430. doi: 10.1002/sim.2712 17031868
26 Bhattacharjee S , Patanwala AE , Lo-Ciganic WH , Malone DC , Lee JK , Knapp SM , et al . Alzheimer’s disease medication and risk of all-cause mortality and all-cause hospitalization: A retrospective cohort study. Alzheimers Dement (N Y). 2019;5 :294–302. doi: 10.1016/j.trci.2019.05.005 31338414
27 Eekhout I , van de Wiel MA , Heymans MW . Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis. BMC Med Res Methodol. 2017;17 (1 ):129. doi: 10.1186/s12874-017-0404-7 28830466
28 VanderWeele TJ , Ding P . Sensitivity Analysis in Observational Research: Introducing the E-Value. Ann Intern Med. 2017;167 (4 ):268–74. doi: 10.7326/M16-2607 28693043
29 Haneuse S , Daniels M . A General Framework for Considering Selection Bias in EHR-Based Studies: What Data Are Observed and Why? EGEMS (Wash DC). 2016;4 (1 ):1203. doi: 10.13063/2327-9214.1203 27668265
30 Botsis T , Hartvigsen G , Chen F , Weng C . Secondary Use of EHR: Data Quality Issues and Informatics Opportunities. Summit Transl Bioinform. 2010;2010 :1–5. 21347133
31 van der Lei J . Use and abuse of computer-stored medical records. Methods Inf Med. 1991;30 (2 ):79–80. 1857252
32 van Buuren S , Groothuis-Oudshoorn K . mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software. 2011;45 (3 ):1–67.
33 White IR , Royston P , Wood AM . Multiple imputation using chained equations: Issues and guidance for practice. Statistics in medicine. 2011;30 (4 ):377–99. doi: 10.1002/sim.4067 21225900
34 Team RC. R: A language and environment for statistical computing.: R Foundation for Statistical Computing. Vienna, Austria; 2020.
35 Cefalu M, Ridgeway G, McCaffrey D, A. M. twang: Toolkit for Weighting and Analysis of Nonequivalent Groups. R Package version 2.5 2021. https://CRAN.R-project.org/package=twang.
36 T T. A Package for Survival Analysis in R. R package version 3.1–12. 2020.
37 Therneau T , Grambsch P . Modeling Survival Data: Extending the Cox Model. 1 ed. New York: Springer; 2000. 350 p.
38 Kassambara A, Kosinski M, Biecek P. survminer: Drawing Survival Curves using ‘ggplot2.’ R package version 0.4.9 2021. https://CRAN.R-project.org/package=survminer.
39 Dahl DB , Scott D , Roosen C , Magnusson A , Swinton J . xtable: Export Tables to LaTeX or HTML. R package version 1.8–4. 2019.
40 Wickham H , Averick M , Bryan J , Chang W , al. e . Welcome to the tidyverse. Journal of Open Source Software. 2019;4 (43 ):1686.
41 Rochwerg B , Granton D , Wang DX , Helviz Y , Einav S , Frat JP , et al . High flow nasal cannula compared with conventional oxygen therapy for acute hypoxemic respiratory failure: a systematic review and meta-analysis. Intensive Care Med. 2019;45 (5 ):563–72. doi: 10.1007/s00134-019-05590-5 30888444
42 Ni YN , Luo J , Yu H , Liu D , Liang BM , Yao R , et al . Can high-flow nasal cannula reduce the rate of reintubation in adult patients after extubation? A meta-analysis. BMC pulmonary medicine. 2017;17 (1 ):142. doi: 10.1186/s12890-017-0491-6 29149868
43 Essay P , Mosier JM , Nayebi A , Fisher JM , Subbian V . Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning. Respiratory care. 2022. doi: 10.4187/respcare.10382 36543341
44 Grieco DL , Menga LS , Raggi V , Bongiovanni F , Anzellotti GM , Tanzarella ES , et al . Physiological Comparison of High-Flow Nasal Cannula and Helmet Noninvasive Ventilation in Acute Hypoxemic Respiratory Failure. Am J Respir Crit Care Med. 2020;201 (3 ):303–12. doi: 10.1164/rccm.201904-0841OC 31687831
45 Tonelli R , Fantini R , Tabbi L , Castaniere I , Pisani L , Pellegrino MR , et al . Early Inspiratory Effort Assessment by Esophageal Manometry Predicts Noninvasive Ventilation Outcome in De Novo Respiratory Failure. A Pilot Study. Am J Respir Crit Care Med. 2020;202 (4 ):558–67. doi: 10.1164/rccm.201912-2512OC 32325004
46 Mosier JM , Tidswell M , Wang HE . Noninvasive respiratory support in the emergency department: Controversies and state-of-the-art recommendations. J Am Coll Emerg Physicians Open. 2024;5 (2 ):e13118. doi: 10.1002/emp2.13118 38464331
47 Vieira F , Bezerra FS , Coudroy R , Schreiber A , Telias I , Dubo S , et al . High Flow Nasal Cannula compared to Continuous Positive Airway Pressure: a bench and physiological study. Journal of applied physiology. 2022. doi: 10.1152/japplphysiol.00416.2021 35511720
