Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study

Wu, Guangyao; Yang, Peng; Woodruff, Henry C.; Rao, Xiangang; Guiot, Julien; Frix, Anne‐Noëlle; Moutschen, Michel; Louis, Renaud; Li, Jiawei; Li, Jing; Yan, Changqing; Du, Dan; Zhao, Shengchao; Ding, Yi; Liu, Bin; Sun, Wenwu; Albarello, Fabrizio; D’Abramo, Alessandra; Schininà, Vincenzo; Nicastri, Emanuele; Occhipinti, Mariaelena; Barisione, Giovanni; Barisione, Emanuela; Halilaj, Iva; Xie, Yuanliang; Wang, Xiang; Lovinfosse, Pierre; Wu, Jianlin; Lambin, Philippe

doi:10.1101/2020.05.01.20053413

Cited by 33 publications

(52 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several clinical predictive models have recently been proposed for COVID-19, for example, for predicting potential COVID-19 diagnoses using data from emergency care admission exams [ 60 ] and chest imaging data [ 61 - 66 ], for predicting COVID-19–related mortality from clinical risk factors [ 67 , 68 ], for predicting which patients will develop acute respiratory distress syndrome from patients’ clinical characteristics [ 69 ], for predicting critical illness in patients with COVID-19 [ 70 , 71 ], and for predicting progression risk in patients with COVID-19 pneumonia [ 72 ]. Siordia [ 73 ] presented a review of epidemiology and clinical features associated with COVID-19, and Wynants et al [ 74 ] performed a critical review that assessed limitations and risk of bias in diagnostic and prognostic models for COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Clinical Predictive Models for COVID-19: Systematic Study

Schwab¹,

Schütte²,

Dietz³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

Background COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU. Objective The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care. Methods Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations. Results Our experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00). Conclusions Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.

show abstract

Section: Discussionmentioning

confidence: 99%

Clinical Predictive Models for COVID-19: Systematic Study

Schwab¹,

Schütte²,

Dietz³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…LASSO regularized logistic regression was the best performer with an AUC of 0.8409. Another web application uses hospitalization data from China, Italy and Belgium to predict severity of illness ( Wu et al, 2020 ). With the support of a machine-learning model, this application assesses severity risk for CoVID-19 patients at hospital admission.…”

Section: Discussionmentioning

confidence: 99%

Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool

Das

Mishra²,

Gopalan

2020

PeerJ

View full text Add to dashboard Cite

Background The recent pandemic of CoVID-19 has emerged as a threat to global health security. There are very few prognostic models on CoVID-19 using machine learning. Objectives To predict mortality among confirmed CoVID-19 patients in South Korea using machine learning and deploy the best performing algorithm as an open-source online prediction tool for decision-making. Materials and Methods Mortality for confirmed CoVID-19 patients (n = 3,524) between January 20, 2020 and May 30, 2020 was predicted using five machine learning algorithms (logistic regression, support vector machine, K nearest neighbor, random forest and gradient boosting). The performance of the algorithms was compared, and the best performing algorithm was deployed as an online prediction tool. Results The logistic regression algorithm was the best performer in terms of discrimination (area under ROC curve = 0.830), calibration (Matthews Correlation Coefficient = 0.433; Brier Score = 0.036) and. The best performing algorithm (logistic regression) was deployed as the online CoVID-19 Community Mortality Risk Prediction tool named CoCoMoRP (https://ashis-das.shinyapps.io/CoCoMoRP/). Conclusions We describe the development and deployment of an open-source machine learning tool to predict mortality risk among CoVID-19 confirmed patients using publicly available surveillance data. This tool can be utilized by potential stakeholders such as health providers and policymakers to triage patients at the community level in addition to other approaches.

show abstract

“…Unfortunately, data access limitations 10 , particularly, to the datasets from relevant COVID-19 cohorts may in some cases hinder progress, specially, in time-sensitive scenarios such as a global pandemic. Nevertheless, methods for predictive models based on both statistical and machine learning approaches have been explored, to address different predictive tasks associated with COVID-19, e.g., infection rates and mortality forecasting, outbreak detection, (rapid) diagnosis from medical images or molecular markers, and prediction of outcomes (prognosis) [11][12][13][14][15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Predicting Hospital Utilization and Inpatient Mortality of Patients Tested for COVID-19

Davis

Gao

Nichols

et al. 2020

Preprint

View full text Add to dashboard Cite

Using structured elements from Electronic Health Records (EHR), we seek to: i) build predictive models to stratify patients tested for COVID-19 by their likelihood for hospitalization, ICU admission, mechanical ventilation and inpatient mortality, and ii) identify the most important EHR-based features driving the predictions. We leveraged EHR data from the Duke University Health System tested for COVID-19 or hospitalized between March 11, 2020 and August 24, 2020, to build models to predict hospital admissions within 4 weeks. Models were also created for ICU admissions, need for mechanical ventilation and mortality following admission. Models were developed on a cohort of 86,355 patients with 112,392 outpatient COVID-19 tests or any-cause hospital admissions between March 11, 2020 and June 4, 2020. The four models considered resulted in AUROC=0.838 (CI: 0.832-0.844) and AP=0.272 (CI: 0.260-0.287) for hospital admissions, AUROC=0.847 (CI: 0.839-855) and AP=0.585 (CI: 0.565-0.603) for ICU admissions, AUROC=0.858 (CI: 0.846-0.871) and AP=0.434 (CI: 0.403-0.467) for mechanical ventilation, and AUROC=0.0.856 (CI: 0.842-0.872) and AP=0.243 (CI: 0.205-0.282) for inpatient mortality. Patient history abstracted from the EHR has the potential for being used to stratify patients tested for COVID-19 in terms of utilization and mortality. The dominant EHR features for hospital admissions and inpatient outcomes are different. For the former, age, social indicators and previous utilization are the most important predictive features. For the latter, age and physiological summaries (pulse and blood pressure) are the main drivers.

show abstract

Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study

Cited by 33 publications

References 23 publications

Clinical Predictive Models for COVID-19: Systematic Study

Clinical Predictive Models for COVID-19: Systematic Study

Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool

Predicting Hospital Utilization and Inpatient Mortality of Patients Tested for COVID-19

Contact Info

Product

Resources

About