Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19.
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ABSTRACT:
SUBMITTER: Subudhi S
PROVIDER: S-EPMC8140139 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters. Results Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered models. Conclusions Our study provides a useful and reliable tool, through a machine learning approach, for predicting ICU mortality in COVID-19 patients. In all the estimated models, age was the variable showing the most important impact on mortality. Supplementary Information The online version contains supplementary material available at 10.1186/s44158-021-00002-x.
| S-EPMC8413709 | biostudies-literature
Project description:Drug-induced liver injury (DILI) is one the most unpredictable adverse reactions to xenobiotics in humans and the leading cause of postmarketing withdrawals of approved drugs. To date, these drugs have been collated by the FDA to form the DILIRank database, which classifies DILI severity and potential. These classifications have been used by various research groups in generating computational predictions for this type of liver injury. Recently, groups from Pfizer and AstraZeneca have collated DILI in vitro data and physicochemical properties for compounds that can be used along with data from the FDA to build machine learning models for DILI. In this study, we have used these data sets, as well as the Biopharmaceutics Drug Disposition Classification System data set, to generate Bayesian machine learning models with our in-house software, Assay Central. The performance of all machine learning models was assessed through both the internal 5-fold cross-validation metrics and prediction accuracy of an external test set of compounds with known hepatotoxicity. The best-performing Bayesian model was based on the DILI-concern category from the DILIRank database with an ROC of 0.814, a sensitivity of 0.741, a specificity of 0.755, and an accuracy of 0.746. A comparison of alternative machine learning algorithms, such as k-nearest neighbors, support vector classification, AdaBoosted decision trees, and deep learning methods, produced similar statistics to those generated with the Bayesian algorithm in Assay Central. This study demonstrates machine learning models grouped in a tool called MegaTox that can be used to predict early-stage clinical compounds, as well as recent FDA-approved drugs, to identify potential DILI.
| S-EPMC7702310 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2023-06-14 | GSE220078 | GEO
Project description:In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired-8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient's initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. 'Time to event' using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired-8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52-3.53], Male Gender (HR 1.72, 95% CI 1.06-2.85), Respiratory Distress (HR 1.79, 95% CI 1.32-2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83-1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72-5.38), Coronary Artery Disease (HR 1.56, 95% CI - 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03-2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87-4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23-2.32), INR (HR 1.71, 95% CI 1.31-2.13), LDH (HR 4.02, 95% CI 2.66-6.07) and Ferritin (HR 2.48, 95% CI 1.32-4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on 'time to event' at the admission, accurately predicting patient outcomes.
| S-EPMC8211710 | biostudies-literature
Project description: Not available
| S-EPMC8764887 | biostudies-literature
Project description:BackgroundThis study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients.MethodsThis retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC).ResultsThe top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760-0.785]) and 0.844 (95% CI [0.839-0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726-0.729]) and 0.848 (95% CI [0.847-0.849]), respectively.ConclusionsDeep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.
| S-EPMC7651477 | biostudies-literature
Project description:The present work aims to identify the predictors of COVID-19 in-hospital mortality testing a set of Machine Learning Techniques (MLTs), comparing their ability to predict the outcome of interest. The model with the best performance will be used to identify in-hospital mortality predictors and to build an in-hospital mortality prediction tool. The study involved patients with COVID-19, proved by PCR test, admitted to the "Ospedali Riuniti Padova Sud" COVID-19 referral center in the Veneto region, Italy. The algorithms considered were the Recursive Partition Tree (RPART), the Support Vector Machine (SVM), the Gradient Boosting Machine (GBM), and Random Forest. The resampled performances were reported for each MLT, considering the sensitivity, specificity, and the Receiving Operative Characteristic (ROC) curve measures. The study enrolled 341 patients. The median age was 74 years, and the male gender was the most prevalent. The Random Forest algorithm outperformed the other MLTs in predicting in-hospital mortality, with a ROC of 0.84 (95% C.I. 0.78-0.9). Age, together with vital signs (oxygen saturation and the quick SOFA) and lab parameters (creatinine, AST, lymphocytes, platelets, and hemoglobin), were found to be the strongest predictors of in-hospital mortality. The present work provides insights for the prediction of in-hospital mortality of COVID-19 patients using a machine-learning algorithm.
| S-EPMC8147079 | biostudies-literature
Project description:BackgroundSeveral studies have recently addressed factors associated with severe Coronavirus disease 2019 (COVID-19); however, some medications and comorbidities have yet to be evaluated in a large matched cohort. We therefore explored the role of relevant comorbidities and medications in relation to the risk of intensive care unit (ICU) admission and mortality.MethodsAll ICU COVID-19 patients in Sweden until 27 May 2020 were matched to population controls on age and gender to assess the risk of ICU admission. Cases were identified, comorbidities and medications were retrieved from high-quality registries. Three conditional logistic regression models were used for risk of ICU admission and three Cox proportional hazards models for risk of ICU mortality, one with comorbidities, one with medications and finally with both models combined, respectively.ResultsWe included 1981 patients and 7924 controls. Hypertension, type 2 diabetes mellitus, chronic renal failure, asthma, obesity, being a solid organ transplant recipient and immunosuppressant medications were independent risk factors of ICU admission and oral anticoagulants were protective. Stroke, asthma, chronic obstructive pulmonary disease and treatment with renin-angiotensin-aldosterone inhibitors (RAASi) were independent risk factors of ICU mortality in the pre-specified primary analyses; treatment with statins was protective. However, after adjusting for the use of continuous renal replacement therapy, RAASi were no longer an independent risk factor.ConclusionIn our cohort oral anticoagulants were protective of ICU admission and statins was protective of ICU death. Several comorbidities and ongoing RAASi treatment were independent risk factors of ICU admission and ICU mortality.
| S-EPMC8013403 | biostudies-literature
Project description:Individuals infected with SARS-CoV-2 vary greatly in their symptomatology and disease progression, likely as a result of numerous genetic, biological and environmental factors and their complex interactions. Meanwhile, the potential roles of microRNAs (miRNAs) in SARS-CoV-2 infection have not been fully described. MiRNAs have emerged as key post-transcriptional regulators of gene expression, and their dysregulation can be indicative of aberrant immune function. In this study, we characterize the potential roles of mIRNAs in early COVID-19 disease progression. We studied a diverse cohort of 259 patients admitted to hospitals in Abu Dhabi, United Arab Emirates to understand the clinical and biological factors associated with ICU admission during COVID-19 treatment, integrating electronic health records (EHR), global miRNA and RNA expression, and genotyping data. Using EHR, we identified 26 factors correlated with ICU admission, including 8 blood phenotypes such as neutrophil-to-lymphocyte ratio, Interleukin-6, and C-reactive protein levels. Using genome-wide miRNA expression data for a subset of 96 individuals from Southeast Asia and the Middle East and North Africa, we identified 27 miRNAs significantly associated with ICU admission (p < 0.01), and 97 miRNAs associated with at least one of the 8 blood phenotypes. [cross-cor] Integrating expression data for 632 miRNAs and genotyping data for ~260,000 SNPs, we identified 168 significant cis-expression quantitative trait loci (cis-eQTLs), of which 59 were associated with either ICU admission or one of the 8 blood phentoypes. Overall, our findings characterize the miRNA architecture of blood phenotypes during the early stages of COVID-19 infection, identify miRNAs associated with ICU admission and therefore COVID-19 disease severity, and suggest a potential genetic control of miRNA expression during early COVID-19 disease progression.
2023-06-14 | GSE220077 | GEO
Project description:Individuals infected with SARS-CoV-2 vary greatly in their symptomatology and disease progression, likely as a result of numerous genetic, biological and environmental factors and their complex interactions. Meanwhile, the potential roles of microRNAs (miRNAs) in SARS-CoV-2 infection have not been fully described. MiRNAs have emerged as key post-transcriptional regulators of gene expression, and their dysregulation can be indicative of aberrant immune function. In this study, we characterize the potential roles of mIRNAs in early COVID-19 disease progression. We studied a diverse cohort of 259 patients admitted to hospitals in Abu Dhabi, United Arab Emirates to understand the clinical and biological factors associated with ICU admission during COVID-19 treatment, integrating electronic health records (EHR), global miRNA and RNA expression, and genotyping data. Using EHR, we identified 26 factors correlated with ICU admission, including 8 blood phenotypes such as neutrophil-to-lymphocyte ratio, Interleukin-6, and C-reactive protein levels. Using genome-wide miRNA expression data for a subset of 96 individuals from Southeast Asia and the Middle East and North Africa, we identified 27 miRNAs significantly associated with ICU admission (p < 0.01), and 97 miRNAs associated with at least one of the 8 blood phenotypes. [cross-cor] Integrating expression data for 632 miRNAs and genotyping data for ~260,000 SNPs, we identified 168 significant cis-expression quantitative trait loci (cis-eQTLs), of which 59 were associated with either ICU admission or one of the 8 blood phentoypes. Overall, our findings characterize the miRNA architecture of blood phenotypes during the early stages of COVID-19 infection, identify miRNAs associated with ICU admission and therefore COVID-19 disease severity, and suggest a potential genetic control of miRNA expression during early COVID-19 disease progression.
2023-06-14 | GSE220076 | GEO