key: cord-0873763-pfyer9x5
authors: Aghajani, Mohammad Haji; Sistanizad, Mohammad; Pourhoseingholi, Asma; Asadpoordezaki, Ziba; Taherpour, Niloufar
title: Development of a scoring system for the prediction of in-hospital mortality among COVID-19 patients
date: 2021-10-06
journal: Clin Epidemiol Glob Health
DOI: 10.1016/j.cegh.2021.100871
sha: af64831bdf0c67797d961649cff104c0ca35cac8
doc_id: 873763
cord_uid: pfyer9x5

BACKGROUND: The aim of this study is to develop and validate a scoring system as a tool for predicting the in-hospital mortality in COVID-19 patients in early stage of disease. METHODS: This retrospective cohort study, conducted on 893 COVID-19 patients in Tehran from February 18 to July 20, 2020. Potential factors were chosen via stepwise selection and multivariable logistic regression model. Cross-validation method was employed to assess the predictive performance of the model as well as the scoring system such as discrimination, calibration, and validity indices. RESULTS: The COVID-19 patients’ median age was 63 yrs (54.98% male) and 233 (26.09%) patients expired during the study. The scoring system was developed based on 8 selected variables: age ≥55 yrs (OR = 5.67, 95% CI: 3.25–9.91), males (OR = 1.51, 95% CI: 1.007–2.29), ICU need (OR = 16.32, 95% CI 10.13–26.28), pulse rate >90 (OR = 1.89, 95% CI: 1.26–2.83), lymphocytes <17% (OR = 2.33, 95%CI: 1.54–3.50), RBC ≤4, 10 (6)/L (OR = 2.10, 95% CI: 1.35–3.26), LDH >700 U/L (OR = 1.68, 95%CI: 1.13–2.51) and troponin I level >0.03 ng/mL (OR = 1.75, 95%CI: 1.17–2.62). The AUC and the accuracy of scoring system after cross-validation were 79.4% and 79.89%, respectively. CONCLUSION: This study showed that developed scoring system has a good performance and can use to help physicians for identifying high-risk patients in early stage of disease.

Novel coronavirus (COVID-19) is the most recent emerging viral disease from the end of 2019 30 (1). Following the global outbreak of the virus, on January.20. 2020, the World Health 31 Organization (WHO) issued a statement declaring the new coronavirus to be the sixth leading 32 public health emergency worldwide, posing a threat not only to China but to all countries around 33 the globe (2). In addition, the extent of coronaviruses incidences has been constantly on the rise 34 which is an indication that the virus has been able to cross the continents very quickly and become prognosis of the disease. The indicated scoring system is expected to be able to distinguish patients 49 who may manifest severe symptoms of the disease, and therefore support patients' well-being by 50 prioritizing and identifying high-risk individuals (7). Thus, the one of the main task of clinical or 51 statistical tools is risk stratification. (8). Nowadays, exploration of the factors that predispose or 52 protect someone from mortality due to COVID-19 are valuable and has public health importance. 53 These factors would be knowing with the clinical scoring system. So, scoring systems play an 54 important role in clinical medicine and triage (8, 9) . Considering the high prevalence of this disease 55 as well as the significant percentage of COVID19 related fatalities in Iran, it is crucial to evaluate 56 the epidemiological characteristics of this disease and to further build a predictive model for the 57 intention of classification (triage) of patients. In addition, it is also essential to be aware of the risk 58 factors associated with the various outcomes of this emerging disease, such as premature death. 59 In other words, designing predictive models based on risk factors, not only creates more awareness 60 about the disease, but also can be used as an effective tool in the decision making of the hospitals' 61 staff and physicians in the early stage management of the disease. In this study, fever was considered as the measured body temperature in the axilla at 37.3 ° C or 101 higher (10). In the meantime, the Mean Arterial Blood Pressure (MAP) was calculated based on In this study, the normality of the data was based on the results attained from the Kolmogorov-118 Smirnov test. Furthermore, this study utilized median and interquartile range (IQR) to describe 119 quantitative data and also frequency and percentage to describe qualitative data. T-test or Mann-120 Whitney test was also applied to compare the mean the of variables being studied by separation of 121 living and deceased patients based on the normality of the quantitative data and the Chi-square test 122 or Fisher's exact test was used to compare the qualitative data between the two groups. The cut 123 off value for the quantitative variables was based on maximum optimal cut point value of 124 sensitivity and specificity. For the variables that were less than 15% missing multiple imputations 125 were performed using the "Amelia" package of R.3.6.2 software. It is also important to note that 126 the variables that were above 15% missing were omitted from the study. Moreover, in order to 127 initiate the modeling process and select the best variables to enter the multivariable model, the 128 stepwise selection method with two backward and forward approaches along with Akaike's 129 Information Criterion (AIC) was done using the "MASS" package. Using AIC has been 130 recommended in prediction models. The use of AIC has been found to provide more power for the 131 selection of predictors with relatively weak effects (12) . Univariate and multivariable logistic 132 regression models were also employed to evaluate the variables being studied and to further 133 construct a prediction model.

In addition, to assess the overall performance of model the AIC criterion, Nagelkerke R 2 , Brier Table 1 , after comparing demographic and 159 clinical factors, it was observed that the median age of COVID-19 patients who expired in the 160 hospital (median = 73, IQR = (61-83)) was significantly greater than those discharged (Median = 161 59 (47 -71)) (P <0.001). Moreover, upon the arrival at the hospital the patients who expired in 162 the later stages of the disease had exposed significantly higher pulse rates (median = 90 (80-100)) 163 as well as higher respiratory rates (median = 20 (18-24)) than those discharged. In addition, SPO2 164 level (median = 88 (82-91)) measured in patients that died later was significantly lower than in 165 those who were ultimately discharged (P <0.001). This study also determined a significant link 166 between some other factors such as sex, myalgia, headache, history of cardiovascular disease, 167 hypertension, and CNS with mortality (P <0.001). Also, mortality among hospitalized patients (Table 1) 173 174

The evaluation results of effective factors in predicting in-hospital death among COVID-19 177 patients by means of univariate and multivariable logistic regression model are reported in Table2.

Furthermore, in this particular study after selecting the variables using the stepwise approach, it 179 was observed that among the selected variables of univariate analysis, increased age, male sex, (Table 2) . The assessment results of performance and internal validity of the model are reported in Table 3 . After developing the final model, the significant variables were contemplated based on its adjusted 211 effect and according to the results of the multivariable model, and lastly after making the score, its 212 performance was examined (Table 4) . 213 Subsequent to measuring the internal validity of the score, it was also determined that at the cut- In this study, another predictor of in-hospital death was the patient referrals who exhibited severe 257 manifestations of the disease and therefore required admittance into ICU or mechanical ventilation 258 assistance. Moreover, this group of patients was at higher risk than others. Furthermore, severe 259 cases often displayed symptoms such as severe hypoxia and severe pulmonary involvement at the 

Based on the prognostic factors of the scoring system in this study, patients who complained of 264 heart palpitations had a worse prognosis than others. In addition, it is essential to note that 265 conditions such as fever, pain, lack of appetite, and insufficient fluid intake can similarly cause an 266 increased pulse rate which might also be detected among COVID-19 patients (21) and therefore it 267 must be carefully taken into account by physicians. Nevertheless, people with severe pulmonary 268 involvement may directly or indirectly develop myocarditis and heart failure, which ultimately 269 may become the grounds for dangerous tachycardia or arrhythmia (22). Thus, it is crucial to 270 monitor the heart rhythm accurately and on a regular basis in order to avoid the potential serious 271 consequences among COVID-19 cases as much as possible.

In this study, the drop in red blood cell count to below normal standards (≤ 4, 10 6 /L) was identified 273 as a prognostic biomarker for COVID-19 disease. Also, red blood cells are responsible for 274 delivering oxygen to various tissues in the body, and their deficiency causes hypoxia and a wide 275 range of other difficulties. Anemia may also be due to a patient's specific clinical condition, such 276 as endocrine disease, or it might be age and/or sex related. Therefore, anemia due to any reason 277 should be treated in COVID-19 patients because this disorder will lead to weakened immune Length of hospital stat (days) 6 (4 -10) 6 (4 -10) 6 (3 -10) 0.573 J o u r n a l P r e -p r o o f Figure . 1. Receiver-operating characteristic curve (ROC curve) of scoring system in train (A) and test data (B) using cross-validation. The AUC of scoring system in train data was 82.6 % and sensitivity, specificity and accuracy (optimal cut point: 0.65) were 72.34 %, 86.12 % and 82.49%, respectively. After performing the internal validation, observed that AUC was 79.4% and validity indices such as sensitivity, specificity and accuracy in optimal cut point of 0.50 were 64.44 %, 85.07 % and 79.89 %, respectively.

Calibration plot of scoring system in train (A) and test data (B) using cross-validation. Calibration plot showed agreement of the predicted probability using model with the observed rate of mortality among hospitalized COVID-19 patients (P_value > 0.05) Figure. 3. Nomogram for the estimation of probability of in-hospital mortality among hospitalized COVID-19 patients based on significant predictors of prediction scoring model. Each predictor with a given value can be found to the points axis. The sum of these points can be referred to in the total Points axis. Total point for each patient corresponds to a predicted probability (probability axis).

Scoring systems for predicting mortality 365 for severe patients with COVID-19

Mean Arterial Pressure

Treasure Island (FL): StatPearls Publishing

Clinical prediction models

A Novel Scoring System for Prediction 372 of Disease Severity in COVID-19. Front Cell Infect Microbiol

A predictive model and scoring system 375 combining clinical and CT characteristics for the diagnosis of COVID-19

Novel risk scoring system for predicting 379 acute respiratory distress syndrome among hospitalized patients with coronavirus disease

Scoring systems for predicting mortality 382 for severe patients with COVID-19

Development of a Novel Scoring System for Comprehensive COVID-19 Patient 385 Evaluation: An Evidence-Based Systematic Review

A Novel Coronavirus from Patients 388 with Pneumonia in China

Sex and gender differences in the outcome of patients with COVID-391 19

Clinical 393 characteristics and outcomes of critically ill patients with COVID-19 admitted to an intensive 394 care unit in London: A prospective observational cohort study

The relationship between body temperature, heart rate and respiratory 398 rate in children

Epub 402 ahead of print