An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study

Abstract

Background Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. Objective The purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics—baseline demographics, comorbidities, and symptoms. Methods A retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25, 2020, to June 3, 2020, were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20, 2020, were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21, 2020. The machine learning model with the best discrimination performance was selected and compared against the CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65 years of age or older) score using the area under the receiver operating characteristic curve (AUC). Results A total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUC of the XGBoost model was 0.897 (95% CI 0.877-0.917) for the derivation group and 0.885 (95% CI 0.855-0.915) for the validation group. Both the AUCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825-0.847) and 0.843 (95% CI 0.829-0.857), respectively. Conclusions We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.

Trends in US Pediatric Hospital Admissions in 2020 Compared With the Decade Before the COVID-19 Pandemic

“…As complexity in medical information has increased, there has been progressive interest in the application of machine learning to facilitate clinical decision-making. Such algorithms have been studied in a diverse array of applications, including sepsis prediction, 52 COVID-19 prognosis, 53 population health, 54 and cyberbullying. 55 In the present study, before the COVID-19 pandemic, 6 , 7 ensemble time-series forecasting models accurately predicted 70 of 72 monthly pediatric admission rates (97.2%) between July 2019 and December 2019.…”

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review

“…of CPP* AI methods Predictors Val. methods Performance (AUC, Accuracy (Acc%), Sensitivity (SEN%), Specificity (SPE%), PPV/NPV (%), (95% CI)) Risk of Bias**: Participants/Predictors/Outcome/Analysis/Overall Muhammad et al [9] , South Korea, Recovery prediction, disease progression unclear DT, SVM, NB, LR, RF, K-NN unclear 5-FCV Acc 99.85 (Decision Tree) H U U U H Cheng et al [78] , United States, Severity Assessment, (risk prioritization tool that predicts ICU transfer within 24 h) 1987 RF respiratory failure, shock, inflammation, renal failure TTS, 10-FCV AUC 79.9 (95% CI: 75.2–84.6), Acc 76.2 (95% CI: 74.6–77.7), SEN 72.8 (95% CI: 63.2–81.1), SPE 76.3% (95% CI: 74.7–77.9) H H H H H Kim et al [79] , South Korea, ICU need prediction 4787 55 ML models developed, (XGBoost model revealed the highest discrimination perf.) age, sex, smoking history, body temperature, underlying comorbidities, activities of daily living (ADL), symptoms TTS AUC 0.897, (95% CI 0.877–0.917) H U H U H Yadaw et al [101] , United States, Mortality prediction 4802 ML, RF, LR, SVM, XGBoost age, minimum oxygen saturation over the course of their medical encounter, type of patient encounter (inpatient vs outpatient and telehealth visits) TTS AUC 91 L H H H H Klann et al [102] , USA, France, Italy, Germany, Singapore, Severity assessment 4227 ML PaCO2, PaO2, ARDS, sedatives, d-dimer, immature granulocytes, albumin, chlorhexidine, glycopyrrolate, palliative care encounter 5-FCV, TTS AUC 0.956 (95% CI: 0.952, 0.959) U U U H H Navlakha et al [103] , United States, Severity assessm...…”

“…In the participants domain, 30 studies had high risk of bias and 21 unclear. Sources of bias in the participants domain varied from small or incomplete datasets to exclusion criteria indicating the need of further data collection to test the generalizability of the developed AI models to other patient populations [2] , [7] , [15] , [16] , [17] , [35] , [37] , [43] , [48] , [57] , [59] , [65] , [69] , [71] , [72] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] , [81] , [82] , [83] , [84] , [85] , [86] , [87] , [88] , [89] , [90] , [91] . Thirty studies had high RoB in the “predictors” domain related to different ways of definitions and assessment for all participants or predictors availability.…”

Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study

“…These 8 variables have been reported as important predictor variables in the medical literature [23][24][25][26][27][28]. Specifically, age, shortness of breath, body temperature, lymphocytes, and hemoglobin have been reported as variables for predicting admission to the intensive care unit [23,25], critical illness [24,29], or severe disease [26,27,30]. In particular, Wu et al [26] reported that the severe group had a significantly lower platelet and higher WBC counts than the nonsevere group.…”

“…Our results indicate that age, body temperature, SOB, lymphopenia, a low hematocrit, low hemoglobin, a low platelet count, and a high WBC count were risk factors positively associated with the maximum clinical severity of COVID-19. These 8 variables have been reported as important predictor variables in the medical literature [23][24][25][26][27][28]. Specifically, age, shortness of breath, body temperature, lymphocytes, and hemoglobin have been reported as variables for predicting admission to the intensive care unit [23,25], critical illness [24,29], or severe disease [26,27,30].…”