
==== Front
Crit Care Explor
Crit Care Explor
CC9
Critical Care Explorations
2639-8028
Lippincott Williams & Wilkins Hagerstown, MD

39254650
CCE-D-23-00496
00005
10.1097/CCE.0000000000001154
3
Observational Study
Effects of Post-Hospital Arrival Factors on Out-of-Hospital Cardiac Arrest Outcomes During the COVID-19 Pandemic
https://orcid.org/0000-0003-3630-6135
Kawai Yasuyuki MD
Yamamoto Koji MD k.yamamoto@naramed-u.ac.jp

Miyazaki Keita MD keitanara1082@naramed-u.ac.jp

Asai Hideki MD, PhD asaih@naramed-u.ac.jp

Fukushima Hidetada MD, PhD hidetada@naramed-u.ac.jp

All authors: Department of Emergency and Critical Care Medicine, Nara Medical University, Nara, Japan.
For information regarding this article, E-mail: k6k6k@naramed-u.ac.jp
10 9 2024
9 2024
6 9 e1154Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

IMPORTANCE:

The relationship between post-hospital arrival factors and out-of-hospital cardiac arrest (OHCA) outcomes remains unclear.

OBJECTIVES:

This study assessed the impact of post-hospital arrival factors on OHCA outcomes during the COVID-19 pandemic using a prediction model.

DESIGN, SETTING, AND PARTICIPANTS:

In this cohort study, data from the All-Japan Utstein Registry, a nationwide population-based database, between 2015 and 2021 were used. A total of 541,781 patients older than 18 years old who experienced OHCA of cardiac origin were included.

MAIN OUTCOMES AND MEASURES:

The primary exposure was trends in COVID-19 cases. The study compared the predicted proportion of favorable neurologic outcomes 1 month after resuscitation with the actual outcomes. Neurologic outcomes were categorized based on the Cerebral Performance Category score (1, good cerebral function; 2, moderate cerebral function).

RESULTS:

The prediction model, which had an area under the curve of 0.96, closely matched actual outcomes in 2019. However, a significant discrepancy emerged after the pandemic began in 2020, where outcomes continued to deteriorate as the virus spread, exacerbated by both pre- and post-hospital arrival factors.

CONCLUSIONS AND RELEVANCE:

Post-hospital arrival factors were as important as pre-hospital factors in adversely affecting the prognosis of patients following OHCA during the COVID-19 pandemic. The results suggest that the overall response of the healthcare system needs to be improved during infectious disease outbreaks to improve outcomes.

COVID-19 pandemic
disease prognosis
out-of-hospital cardiac arrest
post-hospital arrival factors
prediction models
OPEN-ACCESSTRUE
SDCT
==== Body
pmcKEY POINTS

Question: How did pre- and post-hospital arrival factors affect out-of-hospital cardiac arrest (OHCA) outcomes during the COVID-19 pandemic?

Findings: This nationwide cohort study of 541,781 OHCA cases in Japan revealed that pre- and post-hospital arrival factors during the pandemic were associated with worse neurologic outcomes following OHCA. A cumulative effect of COVID-19 on neurologic outcomes following OHCA was also noted.

Meaning: Hospital responses and healthcare delivery during infectious disease epidemics need to be further optimized.

The COVID-19 pandemic significantly impacted emergency medical care systems worldwide, affecting the entire “chain of survival” for out-of-hospital cardiac arrest (OHCA) from rapid pre-hospital interventions to subsequent treatment in medical facilities (1–7). Worse outcomes have been reported during the pandemic due to pre-hospital factors such as decreased bystander cardiopulmonary resuscitation (CPR) (8, 9) and delayed arrival of emergency medical services (EMS) (8, 10–13). The pandemic continued to affect patients even after they arrived at the hospital, with infection risk and lack of resources adversely affecting the care of OHCA patients (14, 15). However, previous studies on the impact of pandemics on OHCA outcomes have focused primarily on pre-hospital arrival factors (11, 16–19); the relationship between post-hospital arrival factors and OHCA outcomes remains unclear (20). An analysis of factors that had the greatest impact on outcomes throughout the year found that EMS factors were significant (21) but did not address the impact of the dynamically fluctuating pandemic (22).

In this study, we aimed to provide a descriptive analysis of the impact of COVID-19 on favorable neurologic outcomes in patients following OHCA, focusing on post-hospital arrival factors within the constraints of available data. Additionally, we aimed to generate hypotheses for future research that can explore these relationships more deeply, including potential pre-hospital variables and their effects on outcomes.

MATERIALS AND METHODS

Study Design, Population, and Data Collection

Japan has a population of 130 million, among whom 28.9% are older than 65 years old (23). National EMS respond to all emergency calls and annually transport approximately 125,000 patients following OHCA to hospitals (24). In this retrospective study, we examined the pre-hospital records of patients who following OHCA received CPR by EMS and were transported to hospitals across Japan between 2015 and 2021. Data were systematically collected on a monthly basis by the Fire and Disaster Management Agency under the Ministry of Internal Affairs and Communications and followed the Utstein style (24). We obtained data on COVID-19 numbers in Japan from the official Ministry of Health, Labour and Welfare website (25).

Measures were implemented to reduce heterogeneity in patient conditions by excluding individuals younger than 18 years old and those with noncardiogenic cardiac arrest. Additionally, cases were excluded if the calculated activity time was less than 0 minute or greater than 24 hours because of the high likelihood of input error.

This study was approved by the Ethics Committee of Nara Medical University on July 4, 2023 (approval number: 3586). The requirement for informed consent was waived because the activity records used in this study were anonymized. This study was conducted following the tenets of the Declaration of Helsinki and adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.

Overview of the COVID-19 Pandemic in Japan

The trends of infection spread and domestic policy were retrieved from the Fire Disaster Management Agency (26). The first COVID-19 case in Japan was confirmed in January 2020, and within the same month, there was a severe shortage of masks and hygiene products (27). Fire department guidelines were issued in response to the novel COVID infection in February (28). The initial outbreak (first wave) was concentrated in urban areas, leading the government to declare the first state of emergency in April, calling for voluntary isolation (29). In July, a second wave of infections emerged, leading to an increase in instances where hospital admissions became difficult. The third wave in December exacerbated medical constraints in urban areas. Infections peaked in January 2021, particularly in urban areas, leading to frequent instances wherein ambulances could not find hospitals to admit patients. New preventive measures were introduced in April (30), although infections increased again in May owing to variant strains. The highest infection numbers were recorded in August, when the healthcare system was overwhelmed by a surge of critically ill patients and a critical shortage of resources.

The EMS System in Japan

In Japan, an ambulance is dispatched to all calls of suspected cardiac arrest. EMS activities are guided by the Resuscitation Guideline standards of the Japanese Resuscitation Council (31). In certain regions or scenarios, physicians may join the EMS team or provide immediate medical intervention at the scene using vehicles, including rapid response cars or a medical helicopter (32). Unless there is clear evidence that the patient is deceased, hospital transport is the standard protocol.

Data Preprocessing

The following factors were extracted as study predictors: prefecture number (a unique identifier for each administrative division in Japan, akin to states in the United States), age, sex, year and month of onset, presence and type of witnesses and bystanders, presence of bystander chest compressions, presence of bystander rescue breaths, bystander automated external defibrillator shock use, presence of emergency lifesaving technicians in the EMS team, presence of a physician on board, time from EMS call to their arrival time, initial cardiac rhythm, defibrillation performed or not, time from EMS arrival to the first defibrillation, number of defibrillations, epinephrine administered or not, time from EMS arrival to first epinephrine administration, number of epinephrine administrations, whether the airway was secured with equipment, time from EMS arrival to hospital arrival, and whether there was spontaneous circulation return during transport from EMS arrival to hospital arrival.

Categorical data were coded using the one-hot coding approach. This method involves converting categorical variables into a form that can be processed by machine learning algorithms to improve their prediction capabilities. For each category, one-hot coding creates a new column (or variable) where 1 indicates the presence of the category and 0 indicates its absence. This process transforms categorical data into a binary matrix, thereby improving the model’s ability to understand and use the data effectively by simplifying it (33). Additionally, we assigned a “missing” code for missing EMS activity data due to the inability to ensure randomness (34, 35). However, bystander CPR was categorized as “not performed” according to Japanese data collection rules, where an empty field indicates that no CPR was performed. If neither defibrillation nor epinephrine administration occurred, a new code for “no intervention” was assigned. For sensitivity analysis, time intervals were coded as categorical data in minutes, including time to the first defibrillation, first epinephrine administration, and hospital arrival. Continuous variables were standardized. To ensure sufficient number of cases in the training data, the time from EMS contact to hospital arrival was coded as one category for greater than or equal to 101 minutes, which constituted less than 0.2% of the total number of cases. These steps yielded 248 different characteristics (Table 1).

TABLE 1. Overview of Data Preprocessing

Variables	Processing	Detail	No. of Generated Factors	
Prefecture number (continuous)	Normalized	(1–47)	1	
Age, yr (continuous)	Normalized	(18–111)	1	
Sex (male/female)	Binary value	0 or 1	1	
Onset month (continuous)	Normalized	(1–12)	1	
Witnessed out-of-hospital cardiac arrest (yes/no)	Binary value	0 or 1	1	
Type of witness (continuous)	Normalized	(0–8)	1	
Bystander presence (yes/no)	Binary value	0 or 1	1	
Bystander-chest compression (yes/no)	Binary value	0 or 1	1	
Bystander-rescue breathing (yes/no)	Binary value	0 or 1	1	
Bystander-defibrillation (yes/no)	Binary value	0 or 1	1	
EMS with an emergency life-saving technician (yes/no)	Binary value	0 or 1	1	
EMS with an onboard physician (yes/no)	Binary value	0 or 1	1	
Time from emergency call to EMS arrival, min (continuous)	Continuous value	(0–56)	1	
Initial cardiac rhythm (continuous)	Normalized	(1–5)	1	
Defibrillation attempted (yes/no)	Binary value	0 or 1	1	
Time from contact to first defibrillation, min (continuous)	One-hot encoding, no defibrillation was newly coded	(0–60 and 61+, non: new coded)	63	
Number of EMS defibrillation shocks delivered (continuous)	Normalized	(0–10)	1	
Epinephrine administered (yes/no)	Binary value	0 or 1	1	
Time from contact to first epinephrine, min (continuous)	One-hot encoding, no administration was newly coded	(0–60 and 61+, non: new coded)	63	
Number of prehospital epinephrine doses (continuous)	Normalized	(0–5)	1	
Advanced airway management (yes/no)	Binary value	0 or 1	1	
Time from EMS arrival to hospital arrival, min (continuous)	Continuous value	(0–100 and 101 or more)	102	
Return of spontaneous circulation during transport (yes/no)	Binary value	0 or 1	1	
Cerebral Performance Category 1 or 2 (yes/no)	Binary value	0 or 1		
Total	248	
EMS = emergency medical services.

For the prediction model, the primary variable of interest was the favorable neurologic outcome number observed 1 month after the event, as obtained from the same dataset. Favorable neurologic outcome determination was based on the Cerebral Performance Category (CPC) score (36), with scores of one and two considered favorable neurologic outcomes, while those of three to five were considered unfavorable.

Predictive Model Development, Training, and Evaluation

We developed a predictive model to estimate neurologic outcomes 1 month after cardiac arrest using a dataset from 2015 to 2018. We allocated 20% of the data, stratified by neurologic outcomes, as the internal test dataset. Data from 2019 to 2021 served as the external test dataset. The remaining 80% of the data were used for training, with five-fold cross-validation (Fig. 1).

Figure 1. Overview of the data splitting, stratified cross-validation methods, and the neural network-based machine learning model. The model was developed using a stratified cross-validation method with Cerebral Performance Category (CPC) 1/2. The machine learning model consisted of a five-layer neural network.

Our model was constructed using a neural network of fully connected layers with a sigmoid function as the activation mechanism. We incorporated batch normalization in each layer and introduced dropout elements to mitigate overfitting. Neurons were initialized according to the Glorot normal distribution, and biases were excluded. The Adam optimizer facilitated training, running 30 epochs with a batch size of 1024.

Considering that CPC 1/2 is typically a minority class in OHCA outcomes, we introduced class weights to address this imbalance and improve model performance in predicting minority classes (37). Based on Bayesian optimization, the Optuna framework (38) facilitated the hyperparameter tuning process over 100 trials.

We used the optimized hyperparameters for training and observed a decrease in the binary cross-entropy loss function until the 100th epoch, after which it began to increase. This increase marked the point at which training was stopped. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and accuracy metrics. A comparative visual assessment of predicted and actual monthly results was performed using internal test data.

Using the finalized model, we predicted the number of cases with a favorable neurologic outcome from 2019 to 2021 based on monthly EMS activity records pre- and post-pandemic, which served as external test data. We compared these predictions with actual observed data and the nationwide COVID-19 case trends in Japan.

Statistical Analyses

Patient characteristics are presented as median (interquartile range) and count (percentage) for continuous and categorical variables, respectively. Statistical significance was set at p value of less than 0.05. For the 2019–2021 data, the chi-square and Kruskal-Wallis tests were used for categorical and continuous data, respectively. The Mann-Whitney U test was used to compare data between years. Post hoc tests following the chi-square and Mann-Whitney U tests were performed using the Bonferroni correction. Model accuracy is expressed as the mean ± sd of the five-model metrics obtained using cross-validation methods.

During the COVID-19 pandemic, patient numbers and infection status rapidly changed (25, 26). Additionally, the number of OHCA cases and outcomes often fluctuated annually (39). This study aimed to carefully analyze these complex dynamics. Specifically, we visually compared the actual number of favorable neurologic outcomes in OHCA patients with the average predicted outcomes and their 95% CIs. Using the Shapiro-Wilk test, the normality of the five predicted prognostic numbers obtained by the cross-validation method was evaluated.

Sensitivity Analysis

In the sensitivity analysis, we aimed to evaluate the robustness of our predictive model against changes in pre-hospital arrival data. We examined the variability by combining two items related to EMS activities. We used a predictive model to examine the prognosis prediction variability for the combination of “time from contact to first defibrillation” and “time from EMS arrival to hospital arrival,” as well as “time from contact to first epinephrine administration” and “time from EMS arrival to hospital arrival.” This variation was visually assessed using a heat map. Our previous study has more details on this method (40). We used the 2019 annual data to determine how accurately the model predicted the actual results. The training was conducted using Python, Version 3.8.5 (Python Software Foundation, Beaverton, OR).

RESULTS

Patient Characteristics

The data of 883,054 patients who experienced OHCA, for whom EMS performed resuscitation, were analyzed. A total of 541,781 patients met the inclusion criteria (Fig. 2). Of the cases excluded for noncardiac reasons, 116,653 were classified as “other” with no specific cause identified. This category may include deaths potentially related to COVID-19. Table 2 summarizes the background details of eligible patients (overall mean age, 81 yr; 57.4% male). Despite statistical differences attributed to the large sample size, no clinically relevant changes were observed in the frequency of witnessed OHCA, bystander involvement, or initial rhythm before and after the COVID-19 pandemic. However, significant decreases were observed in bystander rescue breaths and instrumented airway management by paramedics. Furthermore, there was a decrease in spontaneous circulation return during transport and favorable neurologic outcome frequency.

TABLE 2. Baseline Characteristics

Patient Characteristic	Categories	Total n = 541,781	Training Data (2015–2018), n = 303,603	Test Data (2019, n = 78,189	Test Data (2020), n = 78,748	Test Data (2021), n = 81,241	
Patient age, yr		81 (71–88)	81 (70–87)	81 (71–88)	81 (71–88)	81 (72–88)	
Male patients		311,251 (57.4)	173,327 (57.1)	44,833 (57.3)	45,849 (58.2)	47,242 (58.2)	
Witnessed OHCA		218,627 (40.4)	123,027 (40.5)	31,294 (40.0)	31,711 (40.3)	32,595 (40.1)	
Type of OHCA witness	Family	108,432 (20.0)	61,066 (20.1)	15,115 (19.3)	15,846 (20.1)	16,405 (20.2)	
	Friends	7,554 (1.4)	4,752 (1.6)	1,051 (0.3)	897 (1.1)	854 (1.1)	
	Colleagues	7,170 (1.3)	4,025 (1.3)	1,073 (1.4)	1,041 (1.3)	1,031 (1.3)	
	Passengers	7,495 (1.4)	4,183 (1.4)	1,138 (1.5)	1,084 (1.4)	1,090 (1.3)	
	Others	47,648 (8.8)	26,981 (8.9)	6,968 (8.9)	6,721 (8.5)	6,978 (8.6)	
	Firefighters	933 (0.2)	521 (0.2)	161 (0.2)	141 (0.2)	110 (0.1)	
	Basic EMS personnel	17,734 (3.3)	9,823 (3.2)	2,517 (3.2)	2,647 (3.4)	2,747 (3.4)	
	Emergency life-saving technician	23,150 (4.3)	13,165 (4.3)	3,271 (4.2)	3,334 (4.2)	3,380 (4.2)	
	Unknown	321,665 (59.4)	179,087 (59.0)	46,895 (60.0)	47,037 (59.7)	48,646 (59.9)	
Presence of a bystander		277,369 (51.2)	153,866 (50.7)	40,366 (51.6)	41,281 (52.4)	41,856 (51.5)	
Bystander-initiated chest compressions		273,638 (50.5)	152,267 (50.2)	39,286 (50.2)	40,289 (51.2)	41,796 (51.4)	
Bystander-provided rescue breaths		30,494 (5.6)	18,604 (6.1)	5,114 (6.5)	4,495 (5.7)	2,281 (2.8)	
Bystander-initiated defibrillation		11,383 (2.1)	6,513 (2.1)	1,858 (2.4)	1,520 (1.9)	1,492 (1.8)	
EMS with an emergency life-saving technician		536,659 (99.1)	299,421 (98.6)	77,785 (99.5)	78,452 (99.6)	81,001 (99.7)	
EMS with an onboard physician		17,257 (3.2)	9,495 (3.1)	2,587 (3.3)	2,630 (3.3)	2,545 (3.1)	
Initial cardiac rhythm	Ventricular fibrillation	47,658 (8.8)	27,769 (9.1)	6,683 (8.5)	6,663 (8.5)	6,543 (8.1)	
	Pulseless ventricular tachycardia	1,463 (0.3)	928 (0.3)	173 (0.2)	194 (0.2)	168 (0.2)	
	Pulseless electrical activity	115,723 (21.4)	63,991 (21.1)	16,834 (21.5)	17,088 (21.7)	17,810 (21.9)	
	Asystole	353,521 (65.3)	197,658 (65.1)	50,941 (65.2)	51,464 (65.4)	53,458 (65.8)	
	Other	23,416 (4.3)	13,257 (4.4)	3,558 (4.6)	3,339 (4.2)	3,262 (4.0)	
Defibrillation attempted		70,021 (12.9)	40,155 (13.2)	9,977 (12.8)	9,936 (12.6)	9,953 (12.3)	
Time from contact to first defibrillation, min		2 (1–8)	2 (1–8)	2 (1–8)	2 (1–8)	2 (1–9)	
Number of defibrillation attempts by EMS		2 (1–3)	2 (1–3)	2 (1–3)	2 (1–3)	2 (1–3)	
Epinephrine administered		131,679 (24.3)	64,618 (21.3)	21,153 (27.1)	22,191 (28.2)	23,717 (29.2)	
Time from contact to first epinephrine administration, min		14 (10–19)	14 (10–19)	14 (10–18)	14 (10–19)	14 (10–19)	
Number of times epinephrine was administered by EMS		2 (1–3)	2 (1–3)	2 (1–3)	2 (1–3)	2 (1–3)	
Advanced airway management		437,999 (80.8)	261,287 (86.1)	67,447 (86.3)	68,912 (87.5)	40,353 (49.7)	
Time from emergency call to EMS arrival, min		9 (7–11)	9 (7–11)	9 (7–11)	9 (7–11)	9 (8–11)	
Time from EMS arrival to hospital arrival, min		23 (18–30)	23 (18–30)	23 (18–29)	23 (18–30)	24 (18–31)	
Return of spontaneous circulation		54,708 (10.1)	30,758 (10.1)	8,478 (10.8)	7,790 (9.9)	7,682 (9.5)	
Survival with a favorable neurologic outcome at 30 days		23,669 (4.4)	13,863 (4.6)	3,609 (4.6)	3,146 (4.0)	3,051 (3.8)	
EMS = emergency medical services, OHCA = out-of-hospital cardiac arrest.

Data are reported as n (%) or median (interquartile range).

Figure 2. Flowchart of patient selection. EMS = emergency medical services, OHCA = out-of-hospital cardiac arrest.

Evaluation of the Accuracy of the Predictive Model for OHCA

The AUROCs for the training, evaluation, and test data were 0.99, 0.96, and 0.96, respectively. Test data analysis (2015–2018) revealed that monthly predictions and the actual favorable neurologic outcome proportions tended to increase and decrease together, displaying a synchronized fluctuation pattern. The model evaluation metrics are summarized in eFigure 1 (http://links.lww.com/CCX/B399).

Association of the COVID-19 Pandemic With OHCA Outcomes

Using this model, we predicted the monthly rate of favorable neurologic outcomes from January 2019 to December 2021. Figure 3 shows the predicted and actual value trends with changes in the number of patients with COVID-19. In 2019, before the COVID-19 pandemic, intrayear variations in actual prognosis were observed, and the predicted prognosis followed a similar fluctuating pattern. The differences between the two were not significant, with variations less than or equal to 0.5%. During the COVID-19 pandemic, the 2020 and 2021 predicted outcomes demonstrated intrayear variability similar to that in 2019, although they were approximately 1% lower than the 2019 values. During notable COVID-19 spread in April 2020 and August 2020, the actual outcomes were lower than predicted. Furthermore, in December 2020, the peak of the third COVID-19 wave, the actual and predicted outcomes reached the lowest levels observed throughout the period. In 2021, with a further increase in COVID-19 cases, the actual May to October outcomes were approximately 1% lower than the predicted values.

Figure 3. Rate of favorable neurologic outcomes with respect to COVID-19 cases in Japan by month. The predicted and actual outcome trends and the number of patients with COVID-19 in Japan are described. The solid blue line represents the average predicted outcomes, and the dashed orange line represents the actual outcomes. The gray shaded area describes the 95% CI of the prediction obtained from the five-fold cross-validation method. The green histogram indicates the number of patients with COVID-19. Before the 2019 pandemic, year-to-year variations in actual outcomes were observed, whereas the predicted outcomes were largely consistent, with a difference of less than 0.5%. During the pandemic, the predicted outcomes in 2020 and 2021 were approximately 1% lower than those in 2019. The actual outcomes were lower than predicted, particularly in 2020, when COVID-19 transmission was more widespread. In December 2020, during the peak of the third wave, both outcomes were at their lowest. Owing to a COVID-19 case surge in December 2020, the actual outcomes from May to October were 1% lower than predicted.

Sensitivity Analysis

eFigure 2 (http://links.lww.com/CCX/B399) shows the results of the robustness assessment for pre-hospital arrival data when the factors were increased and decreased. With simultaneous and consecutive increases or decreases in the two factors, time prolongation was associated with a worse outcome. Conversely, time reduction resulted in improved outcomes. This underscores the critical role of intervention timing in the prognosis of OHCA, reinforcing the necessity of rapid medical response in pre-hospital settings. Additionally, our model revealed a pattern before the COVID-19 pandemic in 2019, wherein the predicted and actual favorable outcome rates demonstrated consistent increases or decreases throughout the year.

DISCUSSION

We elucidated the influence of post-hospital arrival on favorable neurologic outcomes following OHCA during the COVID-19 pandemic. Our data demonstrated a pronounced trend shift. Before the pandemic in 2019, the predicted and actual outcomes were closely aligned. However, during the pandemic in 2020, there was a marked decline in both metrics. This downward trajectory intensified in 2021 as the actual outcomes continued to decrease beyond the predicted declines, coinciding with significant increases in COVID-19 cases. This is the first study to describe the relationship between dynamic COVID-19 infection rate changes and the associated decrease in favorable neurologic outcomes following OHCA due to factors that emerge after hospital arrival. Importantly, although our results suggest a negative impact of post-admission factors on OHCA outcomes during the pandemic, these post-hospital arrival factors were not explicitly examined.

In this study, in the context of the dynamically changing number of patients with COVID-19, we found that post-hospital arrival factors had an additive prognostic impact in addition to pre-hospital factors. This report is the first to specifically address post-hospital arrival factors.

Our analysis confirmed that from the beginning of the infection spread, favorable neurologic outcomes following OHCA decreased by approximately 1% compared with those in the previous year. However, the predicted and actual difference in favorable neurologic outcomes was only 0.5%, suggesting that post-hospital arrival factors had a limited impact. However, as the number of patients with COVID-19 increased from June 2021 to September 2021, the predicted and actual favorable neurologic outcomes decreased by an additional 1%. This may be attributable to medical resource depletion and the medical facility disruption caused by the rapid increase in infected patients. Notably, the predicted and actual numbers of favorable neurologic outcomes were significantly worse by December 2020 due to the greater deterioration caused by pre-hospital factors and smaller impact of subsequent post-hospital arrival factors on improving outcomes. These results suggest that our approach may be useful in assessing the dynamic impact of the pandemic, evaluating the influence of medical care that has been impacted due to the pandemic on favorable neurologic outcomes following OHCA, and improving future preventive measures and responses.

When considering improvement efforts, it is important to focus on specific factors while considering the possibility that they may interact with other variables to influence outcomes. Therefore, when interpreting factors contributing to worsening outcomes, it is important to consider that medical facility factors may interact with pre-hospital factors. For example, as available medical facilities decrease due to bed shortages caused by the pandemic, more phone calls would be required to select a hospital, resulting in a longer stay on site. Furthermore, the time required to transport patients to distant locations increased. Additionally, the time spent in the field was extended due to screening for fevers and gathering information before arrival at the medical facility because of enhanced medical facility protocols. This increased transport time is often attributed to pre-hospital factors but may also be influenced by post-hospital arrival factors. Identifying these interrelated influences is critical for improving emergency response and patient outcomes.

A major strength of this study is that it captured and continuously analyzed the relationship between post-hospital arrival factors and OHCA outcomes during the pandemic. Specifically, as the number of infected patients increased relative to the pre-pandemic levels, we confirmed that outcomes worsened not only due to pre-hospital arrival factors previously reported but also due to additional deterioration caused by post-hospital arrival factors. This longitudinal approach can provide valuable information for improving emergency medical responses during future pandemics and public health crises.

This study has some limitations. First, we used Utstein-style EMS activity records, which limited our ability to assess the impact of resuscitation quality and other potentially influential pre-hospital variables (e.g., vaccination status, myocarditis after COVID-19 or vaccination, and changes in healthcare delivery during the pandemic) because they were not measured. The pandemic-induced strain on healthcare systems and its impact on individuals with preexisting conditions likely influenced the occurrence rate and outcomes of OHCA, affecting both pre-hospital care and overall patient recovery. Future studies should incorporate these nuanced details to improve analytical precision and clarify the factors influencing the outcomes of OHCA.

Second, the data analyzed in this study did not include information on the impact of the pandemic on hospital capacity or hospital interventions. Therefore, we believe it is necessary to establish a standardized database, such as the Utstein form, to identify specific factors that influence outcomes after arrival at the hospital, including intervention and treatment details within medical institutions.

Third, we hypothesized that COVID-19 and OHCA outcomes would be influenced only by pre- and post-hospital arrival factors. This hypothesis is based on previous studies reporting that COVID-19 was not directly responsible for most OHCA cases and decreased survival during the pandemic (11). However, we did not assess the direct impact of COVID-19 on OHCA outcomes because the registry did not include specific information on the COVID-19 status of individual OHCA patients and it was outside the scope of the study. In the future, studies should investigate how COVID-19 both directly and indirectly influences OHCA outcomes.

Fourth, this study was based on Japanese national data; thus, the generalizability of the results to other regions would require verification by the use of infection data and datasets unique to those regions.

Finally, we were unable to use data beyond 2022 due to data availability constraints within our study period. In the future, it is important to assess the extended normalization process of post-pandemic data from 2022 onward, which may improve the robustness and applicability of our predictive model.

CONCLUSIONS

In this study, we identified an association between post-hospital arrival factors and OHCA outcomes during the COVID-19 pandemic. Specifically, during the COVID-19 pandemic, post-hospital arrival factors were associated with worse OHCA outcomes, suggesting the need for further research to optimize hospital responses and healthcare delivery during infectious disease epidemics. Additionally, in-depth studies are needed to identify critical post-hospital arrival factors that affect OHCA outcomes, aiming to improve patient care and crisis preparedness.

ACKNOWLEDGMENTS

We thank Editage (https://www.editage.jp/) for English language editing.

Supplementary Material

The authors have disclosed that they do not have any potential conflicts of interest.

Dr. Kawai had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr. Kawai was involved in conceptualization, formal analysis, investigation, methodology, software, validation, visualization, writing the original draft, and reviewing and editing the writing. Drs. Yamamoto and Miyazaki were involved in data curation, formal analysis, validation, and reviewing and editing the writing. Dr. Asai was involved in investigation, validation, and reviewing and editing the writing. Dr. Fukushima was involved in supervision and reviewing and editing the writing.

The data used in this study were obtained from publicly available sources and, therefore, do not need to be shared separately.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccejournal).
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