
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13468-8
10.1016/j.heliyon.2024.e37437
e37437
Research Article
A nomogram to predict the risk of death during hospitalization in Chinese neonates with respiratory failure
Wang Bo a1
Wu Yue b1
Shao Jie c1
Cheng Rui chengrui350@163.com
b⁎
Yang Zuming 13915509208@163.com
d⁎⁎
Xu Yan xuyanxy2006@163.com
e⁎⁎⁎
Jiangsu Province Neonatal Respiratory Failure Collaborative GroupWang Bo f
Shu Guihua g
Li Hong h
Wang Huaiyan i
Cai Jinlan j
Shao Jie k
Zhou Jinjun l
Ye Li m
Yu Mengzhu n
Zhou Qin o
Cheng Rui p
Han Shuping k
Liu Songlin q
Chen Xiaoqing r
Wu Xinping s
Yin Xiaoping t
Gao Yan u
Wu Yue p
Xu Yan v
Bao Zhidan w
Li Zhenguang x
Pan Zhaojun y
Yang Zuming z
Li Zhengying aa
f Department of Neonatology, the Affiliated Suqian First People's Hospital of Nanjing Medical University, China
g Department of Neonatology, Subei People's Hospital of Jiangsu Province, China
h Department of Neonatology, Children's Hospital of Soochow University, China
i Department of Neonatology, Changzhou Maternal and Child Health Care Hospital, China
j Department of Neonatology, Yancheng Maternal and Child Health Care Hospital, China
k Department of Neonatology, Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital, China
l Department of Neonatology, Nantong Maternal and Child Health Care Hospital, China
m Department of Neonatology, Nanjing Drum Tower Hospital Group Suqian Hospital, China
n Department of Neonatology, Affiliated Hospital of Jiangsu University, China
o Department of Neonatology, Wuxi Maternal and Child Health Care Hospital, China
p Department of Neonatology, Children's Hospital of Nanjing Medical University, China
q Department of Neonatology, Xuzhou Maternal and Child Health Care Hospital, China
r Department of Neonatology, Jiangsu Province Hospital, China
s Department of Neonatology, Yangzhou Maternal and Child Care Service Centre, China
t Department of Neonatology, Jiangsu Taizhou People's Hospital, China
u Department of Neonatology, Lianyungang Maternal and Child Health Care Hospital, China
v Department of Neonatology, the Affiliated Hospital of Xuzhou Medical University, China
w Department of Neonatology, Jiangyin People's Hospital, China
x Department of Neonatology, Xuzhou Children's Hospital, China
y Department of Neonatology, Huai'an Maternal and Child Health Care Hospital, China
z Department of Neonatology, Suzhou Municipal Hospital, China
aa Department of Neonatology, Wuxi Children's Hospital, China

a Department of Neonatology, the Affiliated Suqian First People's Hospital of Nanjing Medical University, Suqian, 223800, China
b Department of Neonatology, Children's Hospital of Nanjing Medical University, Nanjing, 210008, China
c Department of Neonatology, Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital, Nanjing, 210004, China
d Department of Neonatology, Suzhou Municipal Hospital, Suzhou, 215002, China
e Department of Neonatology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, China
⁎ Corresponding author. chengrui350@163.com
⁎⁎ Corresponding author. 13915509208@163.com
⁎⁎⁎ Corresponding author. xuyanxy2006@163.com
1 These authors have contributed equally to this work.

06 9 2024
15 9 2024
06 9 2024
10 17 e3743726 2 2024
15 8 2024
3 9 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

Neonatal respiratory failure (NRF) is a critical condition with high morbidity and mortality rates. This study aimed to develop a nomogram prediction model to early predict the risk of death in Chinese neonates with NRF.

Methods

A retrospective analysis was conducted on NRF neonates from 21 tertiary neonatal intensive care units (NICUs) across 13 prefecture-level cities in Jiangsu Province, China, from March 2019 to March 2022. NRF neonates from one random NICU were selected as the external validation set, while those from the remaining 20 NICUs were divided into the training set and the internal validation set at a 7:3 ratio. Death was the primary outcome. LASSO regression and multivariate logistic regression were used to identify the predictive factors from the training set and then the nomogram was constructed.

Results

A total of 5387 neonates with NRF were included in the analysis. Among them, 3444 were in the training set, 1470 were in the internal validation set, and 473 were in the external validation set. The nomogram was constructed based on the eight predictors of the 1-min Apgar score, birth weight, gestational age, the relationship between birth weight and gestational age, mode of first respiratory support, inhaled nitric oxide, antenatal corticosteroids, and vasoactive drugs. The area under the curve of the nomogram in the training set, internal validation set, and external validation set was 0.763, 0.733, and 0.891, respectively. The P-values of the Hosmer-Lemeshow goodness of fit test were 0.638, 0.273, and 0.253, respectively. Brier scores were 0.066, 0.072, and 0.037, respectively. The decision curve analysis demonstrated a significant net benefit in all cases. These data indicate the good performance of the nomogram.

Conclusions

This nomogram can serve as a reference for clinicians to identify high-risk neonates early and reduce the incidence of neonatal mortality.

Keywords

Death
Neonate
Prediction nomogram
Respiratory failure
==== Body
pmc1 Introduction

Neonatal respiratory failure (NRF), a respiratory dysfunction caused by various etiologies, is a major critical condition in the neonatal intensive care unit (NICU) with high morbidity and mortality [1,2]. Large-scale epidemiologic surveys conducted in various regions of China between 2004 and 2015 revealed an incidence rate of NRF ranging from 13.2 % to 19.7 %, with a corresponding mortality rate of 15.5 %–32.1 % [[3], [4], [5], [6]].

With the advancement of medical technology, the establishment of universal health insurance coverage, and ongoing enhancements in perinatal care infrastructure for neonates in China, the mortality rate of NRF has exhibited a significant decline [6,7]. Nevertheless, NRF remains the primary cause of death among critically ill neonates [[8], [9], [10]]. In addition, compared to the 5 % mortality rate in developed countries [11], China still has ample space for improvement. Therefore, early identification of neonates at high risk for mortality and active intervention using advanced medical resources may be indispensable in reducing death from NRF.

In 2021, Hsu et al. developed a model for predicting the mortality rate of NRF using specific criteria suitable for their study population [12]. However, considering the differences in clinical practices and medical environments across regions, it is necessary to develop a model that is tailored to the unique medical environment of mainland China. Currently, there is no predictive model that can relatively accurately forecast the risk of NRF mortality in this region, taking into account local medical practices and patient population characteristics. Therefore, we have conducted this study.

This study aims to construct a nomogram prediction model for assessing the risk of death from NRF by utilizing clinical indicators of early hospitalization and diagnostic criteria in mainland China. Our findings may offer clinicians a reference for early identification of neonates at high risk for NRF, ultimately reducing the incidence of neonatal deaths.

2 Materials and methods

2.1 Study design and participants

This retrospective study analyzed neonates treated in 21 tertiary NICUs across 13 prefecture-level cities in Jiangsu Province, China, from March 2019 to March 2022. Inclusion criteria included neonates with a confirmed diagnosis of respiratory failure, while exclusion criteria comprised patients with incomplete data or those diagnosed with chromosomal or genetic abnormalities. Using a computer-based method, one of the 21 NRF datasets was randomly selected for the external validation set. The remaining 20 datasets were randomly assigned to training and internal validation sets at a 7:3 ratio. The occurrence of death during hospitalization was used as the outcome indicator.

The conduct and reporting of this study complied with the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklist [13]. This study was approved by the Ethics Committee of the Multicenter Clinical Research Collaborative Group on Neonatal Respiratory Failure in Jiangsu Province, China (Ethics No. 202004037-1). Due to the retrospective nature of this study with anonymized patient data, the ethics committee waived the requirement for informed consent.

2.2 Data collection and definitions

Data were retrieved from the electronic medical record systems of each participating institution, and a standardized data collection form was developed to gather the following information: (1) Maternal information: age, gestational diabetes, gestational hypertension, chorioamnionitis, placental abruption, and premature rupture of membranes (PROM); (2) Perinatal information: antenatal corticosteroid usage, methods of delivery, history of intrauterine distress, nature of amniotic fluid, 1-min Apgar score, 5-min Apgar score, neonatal resuscitation, oxygen administration, positive pressure ventilation, intubation, cardiac compressions, and pulmonary surfactant (PS) administration in the delivery room; and (3) Neonatal information: sex, gestational age, birth weight, birth weight to gestational age ratio (RBG), hypothermia, respiratory distress syndrome (RDS), meconium aspiration syndrome (MAS), persistent pulmonary hypertension of the newborn (PPHN), air leak, mode of first respiratory support, number of PS administrations, use of inhaled nitric oxide (INO), caffeine, vasoactive drugs, and mortality status.

In this study, NRF was defined by clinical and blood gas-confirmed hypoxemia in neonates within the first seven days of life, requiring invasive mechanical or noninvasive assisted ventilation for at least 24 h [[3], [4], [5], [6]]. Clinical indicators encompassed inspiratory retraction, grunting, central cyanosis, or a respiratory rate above 60 breaths per minute. Blood gas indicators included arterial oxygen partial pressure (PaO2) below 50 mmHg (corresponding to SpO2 under 0.80), arterial blood pH below 7.2, and/or PaCO2 above 60 mmHg. Noninvasive assisted ventilation methods encompassed continuous positive airway pressure, non-invasive positive pressure ventilation, bilevel positive airway pressure, high-flow nasal cannula, and non-invasive high-frequency oscillatory ventilation. Definitions of other variables are detailed in Supplementary Material 1.

2.3 Sample size

The sample size for developing the predictive model was determined according to the rule of having a minimum of 10 events per predictor variable [14,15]. Accordingly, the total number of deaths in the training set was at least 10 times greater than the number of predicted independent variables.

2.4 Statistical analysis

Quantitative data normality was assessed using histograms and the Kolmogorov-Smirnov test. Normally distributed data were reported as mean ± standard deviation, while skewed data were expressed as interquartile range. Categorical data were presented as frequency and percentage (%).

In the training set, the least absolute shrinkage and selection operator (LASSO) regression selected variables corresponding to the minimum value of λ as predictive factors. These variables underwent further screening and model construction through multivariate logistic regression analysis using forward stepwise, backward stepwise, and stepwise regression methods. The final nomogram prediction model was chosen based on the Akaike Information Criterion (AIC), selecting the model with the smallest AIC value.

The model performance was assessed and validated in the training set, internal validation set, and external validation set. The receiver operating characteristic (ROC) curve was plotted to evaluate the discriminative ability of the model. The area under the curve (AUC) was calculated, with an AUC>0.7 indicating good discriminative ability of the model [16]. The Youden index, positive predictive power, and negative predictive power were calculated to further evaluate the model's discriminative ability. The calibration of the prediction model was qualitatively evaluated by a calibration scatter plot, where closer proximity of predicted values to the diagonal line indicated better calibration. Additionally, the calibration of the prediction model was quantitatively evaluated by the Hosmer-Lemeshow goodness of fit test and the Brier score, with good calibration indicated by P > 0.05 and Brier score <0.25 [17]. The clinical relevance of the predictive model was evaluated by the decision curve analysis.

The statistical analysis was performed using SPSS version 26.0 (IBM, Armonk, NY, USA), R version 3.1.2 (R Foundation for Statistical Computing, Vienna, Austria), and Stata version 16.0 (Stata Corporation, College Station, TX). A significance level of P < 0.05 indicated a statistically significant difference.

3 Results

3.1 General characteristics

The study included a training set of 3444 individuals and an internal validation set of 1470 individuals. In the training set, there were 278 deaths, while in the internal validation set, there were 128 deaths. Additionally, an external validation set of 473 individuals was included, with 27 deaths recorded. Fig. 1 illustrates the screening of the study population and the development and validation of the nomogram prediction model. The demographic characteristics of the study population are presented in Table 1.Fig. 1 The flow chart depicting the screening of the study population and the development and validation of the nomogram prediction model.

Fig. 1

Table 1 Baseline characteristics of all patients in the training set and validation set.

Table 1Variables	Training set (n = 3444)	Internal validation set (n = 1470)	External validation set (n = 473)	p Value	
Maternal information	
Age(weeks), mean(SD)	30.3 ± 4.9	30.2 ± 5.0	30.5 ± 4.6	0.645	
Gestational diabetes, n(%)	611(17.7)	208(14.1)	58(12.3)	0.063	
Gestational hypertension, n(%)	499(14.5)	212(14.4)	32(6.8)	0.04	
Chorioamnionitis, n(%)	135(3.9)	57(3.9)	6(1.3)	0.069	
Placental abruption, n(%)	133(3.9)	59(4.0)	9(1.9)	0.107	
PROM, n(%)	328(9.5)	151(10.3)	61(12.9)	0.543	
Perinatal information	
Delivery method				0.703	
Natural delivery, n(%)	1140(33.1)	384(32.9)	164(34.7)		
Assisted delivery, n(%)	35(1.0)	10(0.7)	4(0.8)		
Elective cesarean section, n(%)	1476(42.9)	619(42.1)	245(51.8)		
Emergency cesarean section, n(%)	793(23)	357(24.3)	60(12.7)		
Intrauterine distress	331(9.6)	164(11.2)	43(9.1)	0.088	
Amniotic fluid				0.252	
Amniotic fluid III-degree turbid, or bloody amniotic fluid, n(%)	291(8.4)	135(9.2)	59(12.5)		
1-min Apgar score				0.394	
<8, n(%)	1096(31.8)	498(33.9)	155(32.8)		
5-min Apgar score				0.041	
<8, n(%)	513(14.9)	237(16.1)	41(8.7)		
Delivery room resuscitation, n(%)	1950(56.6)	830(56.5)	250(52.9)	0.356	
Oxygen administration, n(%)a	984(28.6)	425(28.9)	170(35.9)	0.051	
Positive pressure ventilation, n(%)a	670(19.5)	297(20.2)	105(22.2)	0.702	
Tracheal intubation, n(%)a	269(7.8)	145(9.9)	44(9.3)	0.165	
Cardiac compression, n(%)a	140(4.1)	84(5.7)	6(1.3)	<0.001	
PS, n(%)a	39(1.1)	9(0.6)	0(0)	0.976	
Antenatal corticosteroids, n(%)c	417(28.4)	976(28.3)	184(38.9)	0.054	
Neonatal information	
Gender				0.988	
Male, n(%)	2083(60.5)	869(59.1)	272(57.5)		
Female, n(%)	1361(39.5)	601(40.9)	201(42.5)		
Gestational age (weeks), mean(SD)	33.3 ± 3.7	33.3 ± 3.6	33.4 ± 3.5	0.52	
Birth weight (g), mean (SD)	2086 ± 837	2090 ± 841	2012 ± 734	0.383	
RBG				0.089	
SGA, n (%)	193(5.6)	67(4.6)	76(16.1)		
hypothermia, n (%)	114(3.3)	51(3.5)	15(3.2)	0.73	
RDS, n (%)	1867(54.2)	815(55.4)	158(33.4)	0.035	
MAS, n (%)	54(1.6)	16(1.1)	11(2.3)	0.05	
PPHN, n (%)	59(1.7)	22(1.5)	3(0.6)	0.506	
Air leak, n (%)	56(1.6)	19(1.3)	1(0.2)	0.426	
Non-invasive respiratory support, n(%)b	1769(51.4)	762(51.8)	269(56.9)	0.229	
INO, n (%)	103(3.0)	30(2.0)	11(2.3)	0.52	
PS usage times (≥2), n (%)	225(6.5)	96(6.5)	13(2.7)	0.03	
Caffeine use, n (%)	1288(37.4)	559(38)	160(33.8)	0.043	
Vasoactive drug use, n (%)	937(27.2)	420(28.6)	119(25.2)	0.054	
Mortality, n (%)	278(8.1)	128(8.7)	27(6.0)	0.082	
Note: SD, standard deviation; PROM, premature rupture of membrane. SGA, small for gestational age. RBG, the relationship between birth weight and gestational age. RDS, neonatal respiratory distress syndrome. MAS, meconium aspiration syndrome. PPHN, persistent pulmonary hypertension of the newborn. INO, inhaled nitric oxide. PS, pulmonary surfactant.

a Measures were taken during resuscitation in the delivery room.

b Respiratory support mode was adopted for the first time.

c Full course of treatment.

3.2 Development of the nomogram prediction model

Lasso regression analyzed all variables, with each curve in Fig. 2A showing the coefficient change trajectory for each predictor variable. Fig. 2B highlights the minimum λ value with a vertical line, identifying 27 predictor variables with non-zero coefficients. These variables served as independent variables in constructing a multivariate logistic regression prediction model. Forward stepwise, backward stepwise, and stepwise regression methods were applied separately for variable screening and model construction. AIC values were calculated for each model, revealing that models built with backward and stepwise regression were identical and had the lowest AIC scores. Ultimately, a nomogram prediction model was developed using the eight variables with the lowest AIC values: 1-min Apgar score, birth weight, gestational age, RBG, mode of initial respiratory support, INO, antenatal corticosteroids, and vasoactive drugs. Table 2 presents the results of the multivariate logistic regression analysis. As the final model included eight predictors, the number of deaths in the training set should not be less than 80. In this study, the number of deaths in the training set was 278, meeting the sample size requirement.Fig. 2 Screening of variables based on LASSO regression.

(A) The variation characteristics of the coefficient of variables; (B) The results of the LASSO regression cross-validation screening variable.

Fig. 2

Table 2 Multivariate logistic regression analysis of the predictive factors.

Table 2Variables	β value	SE value	Wald value	P value	OR value	95%CI	
1-min Apgar score (4–7)	1.409	0.200	49.661	<0.001	4.090	2.764–6.052	
Birth weight (1000 g–1500 g)	2.737	0.774	12.490	<0.001	15.433	3.383–70.401	
Non-invasive respiratorya	−0.599	0.151	15.646	<0.001	0.550	0.409–0.739	
INO	1.105	0.262	17.802	<0.001	3.020	1.807–5.046	
Gestational age (≥37 weeks)	−0.821	0.265	9.592	0.002	0.440	0.262–0.740	
Antenatal corticosteroidsb	−0.425	0.174	5.929	0.015	0.654	0.465–0.920	
Vasoactive drugs	0.332	0.144	5.293	0.021	1.394	1.050–1.849	
RBG (AGA)	−0.771	0.449	2.948	0.086	0.463	0.192–1.115	
Note: RBG, the relationship between birth weight and gestational age. AGA, appropriate for gestational age.

a Respiratory support mode was adopted for the first time.

b Full course of treatment.

In the nomogram prediction model (Fig. 3), the predictive variables for each case were assigned scores, which were then summed to obtain a total score. This total score was used to derive the probability of death during hospitalization for a case of NRF.Fig. 3 A nomogram model for predicting the occurrence of death during hospitalization among neonates with respiratory failure.

(RBG, the relationship between birth weight and gestational age. INO, inhaled nitric oxide.).

Fig. 3

3.3 Model evaluation and validation

ROC curve analysis demonstrated that the AUC was 0.763 (95 % CI 0.733–0.792) for the training set, 0.733 (95 % CI 0.731–0.814) for the internal validation set, and 0.891 (95 % CI 0.815–0.967) for the external validation set (Fig. 4). All AUC values exceeded 0.7, indicating a discriminative ability of the model. The positive predictive power values for the training, internal validation, and external validation sets were 0.725, 0.706, and 0.883, respectively. The negative predictive power values for these sets were 0.763, 0.828, and 0.778, respectively. The cutoff values of the ROC curve, calculated using Youden's Index, were 0.065, 0.059, and 0.104, respectively, for these sets.Fig. 4 Validation of the prediction model using the receiver operating characteristic (ROC) curve.

(A) Training set; (B) Internal validation set; (C) External validation set. (AUC, the area under the ROC curve.)

Fig. 4

In terms of model calibration, the predicted values for the training set, internal validation set, and external validation set were all close to the diagonal (Fig. 5). The p-values obtained from the Hosmer-Lemeshow goodness of fit test were 0.638, 0.273, and 0.253 for the respective sets, all of which exceeded 0.05. Similarly, the Brier scores for these sets were 0.066, 0.072, and 0.037, all falling below 0.25. These findings collectively suggest that the model demonstrates strong agreement between the actual and predicted incidence rates.Fig. 5 The calibration scatter plot of the predictive model.

(A) Training set; (B) Internal validation set; (C) External validation set.

Fig. 5

In terms of clinical relevance, decision curve analysis demonstrated significant net benefits for the training set, internal validation set, and external validation set. The net benefits for different threshold probabilities are presented in Fig. 6.Fig. 6 Decision curve analysis of the predictive model.

(A) Training set; (B) Internal validation set; (C) External validation set.

Fig. 6

4 Discussion

This study is the first to develop and validate a nomogram prediction model using multicenter, large-sample data and diagnostic criteria in mainland China. The model aims to predict the risk of death in neonates with NRF and comprises eight predictive factors: 1-min Apgar score, birth weight, gestational age, RBG, mode of first respiratory support, INO, antenatal corticosteroids, and vasoactive drugs. These clinical indicators are readily obtainable in the early stages of hospitalization, enabling clinical physicians to identify high-risk neonates and allocate necessary medical resources for proactive intervention, potentially reducing the NRF mortality rate.

The timeframe of this study was set from March 2019 to March 2022, coinciding with the global pandemic of COVID-19. During this period, a series of strict isolation policies and hospital infection control measures were implemented in Mainland China. Specifically, we conducted a meticulous assessment of the risk of COVID-19 infection for all neonates admitted to the hospital. For those infants who were assessed as high-risk or suspected of infection, we implemented single-room isolation treatment and promptly transferred them to designated infectious disease hospitals for specialized treatment upon confirmation of the diagnosis. Given that the 21 medical institutions participating in this study are not designated hospitals for the treatment of COVID-19, and coupled with the strict enforcement of the aforementioned prevention and control measures, the possibility of COVID-19 infection in our study population was essentially excluded.

The Apgar score is affected by various factors, and thus the prognosis of a neonate cannot be determined solely based on a high or low score [18]. Although lower Apgar scores are associated with higher rates of neonatal mortality and morbidity, and the 5-min Apgar score is more strongly correlated with death than the 1-min Apgar score, the Apgar score cannot reliably predict individual patient outcomes [[19], [20], [21]]. Our prediction model included the 1-min Apgar score as a predictor, which is not contradictory. This is because the prediction model assesses the comprehensive predictive capability of all predictive factors combined, rather than the predictive effect of individual predictive factors.

Previous studies have demonstrated an inverse relationship between neonatal mortality and birth weight as well as gestational age, i.e. lower birth weight and younger gestational age correlates with higher neonatal mortality rates [20,22,23]. The same trend was observed in the present study. In our prediction model, birth weight and gestational age gradually decreased while the corresponding scores gradually increased, and the highest scores were associated with weights less than 1000 g. Thus, these factors have a greater impact on NRF mortality.

There is also a correlation between RBG and neonatal mortality. RBG categorized neonates as small for gestational age (SGA), appropriate for gestational age (AGA), or large for gestational age (LGA). Flamant et al. demonstrated that the relative risk of neonatal mortality was 2–4 times higher in SGA infants than in AGA infants irrespective of the gestational age [24]. Moreover, Chen et al. reported that among 123383 live births, the infant mortality rate was 11 per 1000 for SGA infants and 2.7 per 1000 for LGA infants [25]. Additionally, neonatal mortality was higher in LGA infants compared to AGA infants at the same gestational age [26,27]. A retrospective study of preterm infants from the Canadian Neonatal Network and Canadian Neonatal Follow-Up Network databases indicated that the risk of death in LGA infants was 1.6 times higher than in AGA infants [28]. Here, we observed a consistent trend in our NRF cohort. The scores corresponding to AGA, LGA, and SGA showed a gradual increase in our established prediction model.

Invasive respiratory support, INO, and vasoactive drugs play a beneficial role in managing critically ill neonates. Nonetheless, our prediction model indicated higher scores for these factors compared to noninvasive respiratory support, absence of INO use, and absence of vasoactive drug use. However, it is important to note that this does not imply that the use of invasive ventilatory support, INO, and vasoactive drugs is a risk factor for death in NRF and should therefore be minimized or avoided. Rather, it suggests that neonates who are hospitalized early with invasive respiratory support, INO, and vasoactive drugs are typically in a more critical condition, and consequently, are at a higher risk of death.

The prenatal application of glucocorticoid therapy to pregnant women has improved the survival of preterm infants by mimicking physiologic stress levels [29]. In a retrospective analysis of a national cohort study in Canada, involving 43456 preterm infants born at less than 34 weeks of gestational age, it was found that prenatal glucocorticoids significantly reduced the risk of preterm infant mortality [30]. Similarly, a retrospective study conducted at 87 tertiary care centers of the Japanese Neonatal Network, which included 11607 preterm infants, showed that prenatal use of glucocorticoids significantly enhanced the survival rate of preterm infants [31]. Consistently, our study in the NRF cohort also demonstrated that prenatal use of hormones reduced the risk of NRF deaths.

This study faces several limitations. Firstly, as a multicenter, retrospective study, internal bias is inherent. Secondly, data collection relied on various electronic medical record systems, leading to difficulties due to system disparities. Some systems could not retrieve laboratory data of discharged patients or ventilator settings, resulting in missing important variables for model construction. Future studies should address these data collection challenges and evaluate more potential indicators, including laboratory test results and ventilator settings, to develop a more accurate prediction model and reduce neonatal mortality. Thirdly, the exclusion of patients with chromosomal and genetic abnormalities is notable, as routine screening for these conditions was not conducted by the collaborating team, potentially overlooking some patients. Finally, data from Jiangsu Province, China—a region with ample healthcare resources and economic prosperity—were used, which may limit the model's applicability to other regions.

In conclusion, we have successfully developed a nomogram model for predicting the risk of death in neonates with NRF. This nomogram provides clinicians with a simple and intuitive tool for early detection and identification of high-risk neonates, which could be crucial in reducing the high mortality associated with NRF.

Funding

This study was supported by the 10.13039/501100007289 Nanjing Medical University Special Disease Cohort Foundation (grant number NMUC2020043 ) and the 10.13039/501100001809 National Natural Science Foundation of China (No. 82171705 ). The study's funders had no role in designing the research, collecting data, analyzing information, interpreting the data, or writing the report.

Data availability statement

The data supporting this article will be made available upon reasonable request to the corresponding author.

Ethical standards

This study was approved by the Ethics Committee of the Multicenter Clinical Research Collaborative Group on Neonatal Respiratory Failure in Jiangsu Province, China (Ethics No. 202004037-1). As this was a retrospective study, and patients were anonymized during data collection, informed consent was waived by the ethics committee.

CRediT authorship contribution statement

Bo Wang: Writing – original draft, Visualization, Validation, Formal analysis, Data curation. Yue Wu: Writing – review & editing, Visualization, Validation, Formal analysis, Data curation. Jie Shao: Writing – review & editing, Visualization, Validation, Formal analysis, Data curation. Rui Cheng: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. Zuming Yang: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Investigation, Data curation, Conceptualization. Yan Xu: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Investigation, Data curation, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Rui Cheng reports financial support was provided by 10.13039/501100007289 Nanjing Medical University Special Disease Cohort Foundation. Rui Cheng reports financial support was provided by 10.13039/501100001809 National Natural Science Foundation of China . If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Acknowledgements

We thank Bullet Edits Limited for English language editing and proofreading.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e37437.
==== Refs
References

1 Angus D.C. Linde-Zwirble W.T. Clermont G. Epidemiology of neonatal respiratory failure in the United States: projections from California and New York Am. J. Respir. Crit. Care Med. 164 7 2001 1154 1160 10.1164/ajrccm.164.7.2012126 11673202
2 Guimarães H. Kaam A.V. Rocha G. Neonatal lung disease and respiratory failure Crit. Care Res. Pract. 2013 2013 238909 10.1155/2013/238909
3 Qian L. Liu C. Zhuang W. Neonatal respiratory failure: a 12-month clinical epidemiologic study from 2004 to 2005 in China, Pediatric 121 5 2008 e1115 e1124 10.1542/peds.2006-2426
4 Wang H. Gao X. Liu C. Morbidity and mortality of neonatal respiratory failure in China: surfactant treatment in very immature infants Pediatrics 129 3 2012 e731 e740 10.1542/peds.2011-0725 22331337
5 Zhang L. Qiu Y. Yi B. Mortality of neonatal respiratory failure from Chinese northwest NICU network J. Matern. Fetal Neonatal Med. 30 17 2017 2105 2111 10.1080/14767058.2016.1238894 27651118
6 Ding S. Xu Y. Wang H. Outcome of neonatal hypoxemic respiratory failure: a livebirth population-based retrospective survey BMC Pediatr. 22 1 2022 552 10.1186/s12887-022-03603-9 36115974
7 Wang H. Dong Y. Sun B. Admission volume is associated with mortality of neonatal respiratory failure in emerging neonatal intensive care units, J. Matern. Fetal Neonatal Med. 32 13 2019 2233 2240 10.1080/14767058.2018.1430133
8 Muhe L.M. McClure E.M. Nigussie A.K. Major causes of death in preterm infants in selected hospitals in Ethiopia (SIP): a prospective, cross-sectional, observational study Lancet Glob. Heal. 7 8 2019 e1130 e1138 10.1016/S2214-109X(19)30220-7
9 Ellsbury D.L. Clark R. Ursprung R. A multifaceted approach to improving outcomes in the NICU: the pediatrix 100,000 babies campaign Pediatrics 137 4 2016 e20150389 10.1542/peds.2015-0389
10 Cao Y. Jiang S. Sun J. Assessment of neonatal intensive care unit practices, morbidity, and mortality among very preterm infants in China JAMA Netw. Open 4 8 2021 e2118904 10.1001/jamanetworkopen.2021.18904
11 Clark R.H. The epidemiology of respiratory failure in neonates born at an estimated gestational age of 34 weeks or more J. Perinatol. 25 4 2005 251 257 10.1038/sj.jp.7211242 15605071
12 Hsu J. Yang C. Lin C. Machine learning algorithms to predict mortality of neonates on mechanical intubation for respiratory failure Biomedicines 9 10 2021 1377 10.3390/biomedicines9101377 34680497
13 Moons K.G.M. Altman D.G. Reitsma J.B. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration Ann. Intern. Med. 162 1 2015 W1 W73 10.7326/M14-0698 25560730
14 Wang M. Hao M. Liu N. Nomogram for predicting the risk of preterm birth in women undergoing in vitro fertilization cycles BMC Pregnancy Childbirth 23 1 2023 324 10.1186/s12884-023-05646-x 37149590
15 Feleke S.F. Mulu B. Azmeraw M. Clinical prediction model development and validation for the detection of newborn sepsis, diagnostic research protocol Int. J. Gen. Med. 15 2022 8025 8031 10.2147/IJGM.S388120 36348975
16 Wu J. Zhang H.B. Li L. A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: a population-based analysis Cancer Commun. 40 7 2020 301 312 10.1002/cac2.12067
17 Aubert C.E. Rodondi N. Netzer S. Predictors of 1-year drug-related admissions in older multimorbid hospitalized adults J. Am. Geriatr. Soc. 70 5 2022 1510 1516 10.1111/jgs.17667 35064571
18 American Academy of Pediatrics Committee on fetus and newborn, American College of obstetricians and gynecologists, committee on obstetric practice, the apgar score Adv. Neonatal Care 6 4 2006 220 223 10.1016/j.adnc.2006.04.008 16890134
19 Razaz N. Cnattingius S. Joseph K.S. Association between Apgar scores of 7 to 9 and neonatal mortality and morbidity: population based cohort study of term infants in Sweden BMJ 365 2019 l1656 10.1136/bmj.l1656 31064770
20 Cnattingius S. Johansson S. Razaz N. Apgar score and risk of neonatal death among preterm infants N. Engl. J. Med. 383 1 2020 49 57 10.1056/NEJMoa1915075 32609981
21 American Academy of Pediatrics Committee on Fetus and Newborn American College of obstetricians and gynecologists committee on obstetric practice, the apgar score Pediatrics 136 4 2015 819 822 10.1542/peds.2015-2651 26416932
22 Watkins W.J. Kotecha S.J. Kotecha S. All-cause mortality of low birthweight infants in infancy, childhood, and adolescence: population study of England and wales PLoS Med. 13 5 2016 e1002018 10.1371/journal.pmed.1002018
23 Markestad T. Kaaresen P.I. Rønnestad A. Early death, morbidity, and need of treatment among extremely premature infants Pediatrics 115 5 2005 1289 1298 10.1542/peds.2004-1482 15867037
24 Flamant C. Gascoin G. Short-term outcome and small for gestational age newborn management J. Gynecol. Obstet. Biol. Reprod. 42 8 2013 985 995 10.1016/j.jgyn.2013.09.020
25 Chen H.Y. Chauhan S.P. Ward T.C.S. Aberrant fetal growth and early, late, and postneonatal mortality: an analysis of Milwaukee births, 1996-2007 Am. J. Obstet. Gynecol. 204 3 2011 10.1016/j.ajog.2010.11.040 261.e1-10
26 Boulet S.L. Alexander G.R. Salihu H.M. Macrosomic births in the United States: determinants, outcomes, and proposed grades of risk Am. J. Obstet. Gynecol. 188 5 2003 1372 1378 10.1067/mob.2003.302 12748514
27 Lackman F. Capewell V. Richardson B. The risks of spontaneous preterm delivery and perinatal mortality in relation to size at birth according to fetal versus neonatal growth standards Am. J. Obstet. Gynecol. 184 5 2001 946 953 10.1067/mob.2001.111719 11303203
28 Rustogi D. Synnes A. Alshaikh B. Neurodevelopmental outcomes of singleton large for gestational age infants <29 weeks' gestation: a retrospective cohort study J. Perinatol. 41 6 2021 1313 1321 10.1038/s41372-021-01080-z 34035448
29 Jue Z.Z. Song J. Zhou Z.Y. Establishment of a predictive nomogram model for predicting the death of very preterm infants during hospitalization Zhong Guo Dang Dai Er Ke Za Zhi 24 6 2022 654 661 10.7499/j.issn.1008-8830.2202027
30 Melamed N. Murphy K. Barrett J. Benefit of antenatal corticosteroids by year of birth among preterm infants in Canada during 2003-2017: a population-based cohort study BJOG 128 3 2021 521 531 10.1111/1471-0528.16511 32936996
31 Mori R. Kusuda S. Fujimura M. Antenatal corticosteroids promote survival of extremely preterm infants born at 22 to 23 weeks of gestation J. Pediatr. 159 1 2011 110 114.e1 10.1016/j.jpeds.2010.12.039 21334006
