Prediction Model of Acute Respiratory Distress Syndrome for Hospitalized Patients with Covid-19 Pneumonia

Research Article

Austin J Pulm Respir Med. 2021; 8(1): 1070.

Prediction Model of Acute Respiratory Distress Syndrome for Hospitalized Patients with Covid-19 Pneumonia

Zhang J1#, Liu X2#, Yang J2#, Wang F1, Yang C1, Jiang X1, Su L1* and Peng Z1*

¹Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, China

²Department of Respiratory and Critical Care Medicine, Wuhan Fourth Hospital, China

#Contributed equally to this study

*Corresponding author: Zhiyong Peng, Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Hubei 430000, China. Email: pengzy5@ hotmail.com

Lianjiu Su, Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Hubei 430000, China. Email: sulianjiu@whu.edu.cn

Received: April 03, 2021; Accepted: April 22, 2021; Published: April 29, 2021

Abstract

Background: COVID-19 pneumonia has become a worldwide epidemic. Acute Respiratory Distress Syndrome (ARDS) is a major cause of mortality. Early recognition the risk of ARDS of COVID-19 patients is vital.

Methods: Descriptive study from Zhongnan Hospital of Wuhan University and Wuhan Fourth Hospital. 394 consecutive hospitalized patients with confirmed COVID-19 infection from January 1 to March 15, 2020.

Results: We developed a risk prediction model of ARDS for COVID-19 among 394 enrolled patients. The variables included in the model were sex, age, diabetes mellitus, neutrophil and lymphocyte counts, serum urea levels, and pulmonary lesion range. The model performed well in predicting ARDS occurrence with excellent discrimination (C-stat=0.81) and appropriate calibration. The predictive value of our model was better than that of the Lung Injury Prediction Score (LIPS) in the discovery set [AUC: 0.77 (0.71, 0.82) vs 0.68 (0.61, 0.75), P=0.02].

Conclusions: Our prediction model provides clinicians and researchers a simple tool to screen for COVID-19 patients at high risk of ARDS. Potential clinical benefits of using this model deserve assessment.

Keywords: Pneumonia; COVID-19; Early prediction; Model; ARDS

Introduction

In December 2019, a cluster of acute respiratory illnesses, now known as COVID-19, occurred in Wuhan, Hubei Province, China [1]. In recent days, infections have rapidly spread from Wuhan to other areas and in more than 190 countries around the world [2,3]. Several studies have reported the epidemiological and clinical characteristics of COVID-19 with a mortality of 4.3% in Wuhan [3- 5]. Acute Respiratory Distress Syndrome (ARDS) is a primary cause of death in many COVID-19 patients. It was reported that more than 60% of COVID-19 patients in intensive care units developed ARDS and most of the patients eventually died of severe ARDS [6]. Our previous study has confirmed that the median time from the first symptom to ARDS is about eight days [6]. Thus, it is essential to recognize the ARDS risk factors early and prevent its development or progression in COVID-19 patients. Previous studies showed that the Lung Injury Prediction Score (LIPS) could predict ARDS early, which included four indicators, such as susceptibility factors, high-risk surgery or trauma, interventions to mitigate the risk [7]. However, this scoring model might not fit in COVID-19 patients due to different study cohort with different etiology and pathogenesis [8]. A proprietary prediction model is urgently needed to identify the risk of ARDS in COVID-19 patients, which may be provide the chance to implement effective preventive strategies to improve patients’ clinical outcome. The aim of the current study was to develop and validate a model to predict the risk of ARDS in COVID-19 patients.

Methods

Study Population

Retrospective data from two centers, i.e., Zhongnan Hospital of Wuhan University and Wuhan Fourth Hospital, Wuhan city in China between January 1 to March 15, 2020, were utilized. We included all consecutive patients with COVID-19 who were diagnosed according to World Health Organization interim guidance in this study [4]. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) was used as a gold standard to diagnose COVID-19 in multiple and different clinical specimens when necessary. We excluded patients who presented ARDS on admission. The ethical committees of Wuhan University and Wuhan Forth hospital approved the project (No. 2020020 and No.202002, respectively), and we obtained oral consent from patients or patients’ relatives.

Data collection

Epidemiological, clinical, laboratory, and radiological characteristics and treatment and outcomes data were abstracted using electronic medical records. The information recorded included demographics, comorbidities, laboratory findings (blood routine, liver function, renal function,) and LIPS calculated on the first admission day, imaging data (chest CT scans, and X-rays) during the first two admission days. The investigators followed the same protocols and definitions to review and analyze the collected data and were blinded to the patients’ ARDS status. A consensus resolved any disagreements between the investigators.

Outcome variables

The primary outcome was the development of ARDS during the hospitalization. ARDS diagnosis was according to the Berlin definition established in 2012 [9], including the development of bilateral pulmonary infiltrates on chest radiography, PaO2/FiO2 ratio <300, and the absence of left atrial hypertension as the primary explanation for pulmonary edema. The diagnosis of ARDS was made by consensus among two physicians.

Sample size estimation

The available sample size was evaluated to ensure there would be an adequate number of ARDS cases to support logistic regression modeling. The previous study suggested that the ARDS incidence in this cohort would be 19.6% [6]. Using the standard rule of at least ten events for each covariate, our study sample was believed to be sufficiently large.

Statistical analysis, model establishment, and validation

Continuous characteristics were reported by median (interquartile range), categorical variables by number (%). Differences between the two groups were compared with the t-test, chi-square test, or Mann Whitney U test accordingly. Missing data occurred only in liver and kidney function tests, accounting for <2% due to the absence of the test results. We used the multiple imputation method to impute the missing data. Categorical variables were transformed into dummy variables for analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) method, suitable for the reduction in high dimensional data, was used to regularize a logistic regression model to build the prediction model [10]. For model training and validation, the entire cohort was randomly divided into discovery and validation sets using a 7:3 ratio. Optimal parameter (lambda) giving the most regularized model was tuned through 10-fold cross-validation using the R package “glmnet” (version 2.0-16) [11,12]. Receiver Operating Characteristic (ROC) curves were generated to evaluate the LASSO-regularized logistic regression model in the discovery set and validation set. Features selecting through the LASSO were incorporated into a multivariable logistic regression model to build a nomogram. The nomogram was internally validated through 1000 bootstrap resamples. Calibration analysis [13] were performed to evaluate the calibration. The corresponding nomogram and calibration curve were drawn using the R package “rms” (version 5.1- 3). Analyses were performed using SPSS 22.0 software and R version 3.5.2. Any difference at P-value<0.05 was considered statistically significant.

Results

Patient characteristics

Of the 394 patients with COVID-19 infection included in our study, 117 (29.7%) patients developed ARDS, among them, 22(18.8%) patients died. The median time from admission to the ARDS diagnosis was 4 (2-6) days. The median age of those with and without ARDS was statistically different, i.e., 61 years old for ASRDS group and 52 years old non-ARDS patients (p<0.05). Patients with hypertension, diabetes mellitus, cerebrovascular disease were more likely to develop ARDS (p<0.05). Lymphocyte and platelet counts, and albumin in the ARDS group were significantly lower than that non-ARDS group. Neutrophil count, alanine aminotransferase, and aspartate aminotransferase in the ARDS group were substantially higher than those in the non-ARDS group. The range of lung lesions and their bilateral distribution was significantly higher in the ARDS group. The median length of hospital stay in the ARDS group was significantly higher than that in the non-ARDS group (Table 1).