Machine learning based predictors for COVID-19 disease severity

Patel, Dhruv; Kher, Vikram; Desai, Bhushan; Lei, Xiaomeng; Cen, Steven; Nanda, Neha; Gholamrezanezhad, Ali; Duddalwar, Vinay; Varghese, Bino; Oberai, Assad A.

doi:10.1038/s41598-021-83967-7

Cited by 62 publications

(39 citation statements)

References 16 publications

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“…This study also showed significant increased leukocyte count and decreased lymphocyte count in non-survivors [ 26 ]. ML was successfully applied to determine COVID-19 severity by predicting the need for ICU (AUC = 0.80) and the need for mechanical ventilation (AUC = 0.82) [ 27 ]. Random forest analysis showed that PCT, DD, CRP, respiratory rate, SpO2, albumin, AST/SGOT, calcium, influenza-like symptoms, and ALT/SGPT are the most important variables to predict the need for ICU.…”

Section: Discussionmentioning

confidence: 99%

“…Random forest analysis showed that PCT, DD, CRP, respiratory rate, SpO2, albumin, AST/SGOT, calcium, influenza-like symptoms, and ALT/SGPT are the most important variables to predict the need for ICU. Also, CRP, DD, PCT, SpO2, respiratory rate, creatinine, total protein, albumin, calcium, and age were the most important variables to predict the need for mechanical ventilation [ 27 ]. In a similar study, SpO2/FiO2, CRP, estimated glomerular filtration rate (eGFR), age, Charlson score, lymphocyte count, and PCT were the most important variables for the prediction COVID severity [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

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Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

et al. 2021

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Background The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. Methods Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. Results SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. Conclusions An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

et al. 2021

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“…In terms of diagnostic capability with machine learning, some recent studies have also been performed, but with smaller datasets, lack of temporal validation and often without clinical comparison 24 – 26 . More recently several machine learning based approaches have been published demonstrating more broader applicability in COVID-19 related applications including triage assessment 27 , severity classifcaiton 28 , 29 , risk prognostication including mortality 30 as well as applying to multi-omics data 31 . For example, a similar approach was tried with similar findings also with an attempt for explanability similar to our study 32 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph

Tsougenis

et al. 2021

Sci Rep

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Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9–95.8%; Sensitivity: 55.5–77.8%; Specificity: 91.5–98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.

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“…During the COVID-19 pandemic, machine learning tools have been used for a variety of investigations, ranging from image analysis for diagnosis of SARS-CoV-2 pneumonia to predicting future pandemic waves [17]. These algorithms have the advantage of being able to combine a large amount of information, extracting the most predictive characteristics of the diagnosis associated with coronavirus disease [17,[21][22][23][25][26][27][28][29][30][31].…”

Section: Introduction 1contextmentioning

confidence: 99%

“…Therefore, during the COVID-19 pandemic, Machine Learning has been crucial in developing tools for a variety of investigations, ranging from image analysis for diagnosing SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) pneumonia, predicting future pandemic waves, detecting multiple predictive and prognostic factors associated with increased severity of infection, to building severity models that help health systems prioritise care [8,[21][22][23][24][25][26][27][28][29][30].…”

Section: Introduction 1contextmentioning

confidence: 99%

A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms

Aznar-Gimeno

Esteban

Labata-Lezaun

et al. 2021

IJERPH

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The purpose of the study was to build a predictive model for estimating the risk of ICU admission or mortality among patients hospitalized with COVID-19 and provide a user-friendly tool to assist clinicians in the decision-making process. The study cohort comprised 3623 patients with confirmed COVID-19 who were hospitalized in the SALUD hospital network of Aragon (Spain), which includes 23 hospitals, between February 2020 and January 2021, a period that includes several pandemic waves. Up to 165 variables were analysed, including demographics, comorbidity, chronic drugs, vital signs, and laboratory data. To build the predictive models, different techniques and machine learning (ML) algorithms were explored: multilayer perceptron, random forest, and extreme gradient boosting (XGBoost). A reduction dimensionality procedure was used to minimize the features to 20, ensuring feasible use of the tool in practice. Our model was validated both internally and externally. We also assessed its calibration and provide an analysis of the optimal cut-off points depending on the metric to be optimized. The best performing algorithm was XGBoost. The final model achieved good discrimination for the external validation set (AUC = 0.821, 95% CI 0.787–0.854) and accurate calibration (slope = 1, intercept = −0.12). A cut-off of 0.4 provides a sensitivity and specificity of 0.71 and 0.78, respectively. In conclusion, we built a risk prediction model from a large amount of data from several pandemic waves, which had good calibration and discrimination ability. We also created a user-friendly web application that can aid rapid decision-making in clinical practice.

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Machine learning based predictors for COVID-19 disease severity

Cited by 62 publications

References 16 publications

Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph

A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms

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