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; Xie, Yuanliang; Woodruff, Henry C.; Rao, Xiangang; Guiot, Julien; Frix, Anne Noëlle; Louis, Renaud; Moutschen, Michel; 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; Lovinfosse, Pierre; Wang, Xiang; Wu, Jianlin; Lambin, Philippe

doi:10.1183/13993003.01104-2020

Cited by 188 publications

(166 citation statements)

References 27 publications

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“…Cytokine storm syndrome (CSS) or hyperin ammation has been recognized in the pathogenesis of COVID-19. Several studies have shown that different in ammatory cytokines are correlated to the severity of the disease and survival of patients [15,16]. This study provided evidence that the kinetic variation of IL-6, IL-8, and IL-10 was closely associated with the prognosis in patients with severe COVID-19, independent of other risk factors including demographics, comorbidities, and common laboratory markers.…”

Section: Il-6 Il-8 and Il-10 Are Associated With The Survival Of Pasupporting

confidence: 57%

“…Additionally, high-ow oxygen and non-invasive ventilation (22.5%), tracheal intubation (20%), and extracorporeal membrane oxygenation (n = 2) was performed on the patients. The median period from the time of symptom onset to admission into ICU was 20 days (IQR [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], and the median time of ICU stay was 8.…”

Section: Demographic and Baseline Clinical Characteristics Of Patientmentioning

confidence: 99%

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Dynamic changes in serum IL-6, IL-8, and IL-10 are associated with the outcome of patients with severe COVID-19 in ICU

Liu

Cui

et al. 2020

Preprint

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Background: Biomarkers that would help prognosticate outcomes and guide treatment of patients with severe coronavirus disease 2019 (COVID-19) are currently required. We aimed to investigate whether the dynamic variation of cytokines was associated with the survival of patients admitted to an intensive care unit (ICU).Methods: A retrospective study was performed on 40 patients with COVID-19 admitted to an ICU in Wuhan, China. Demographic, clinical, and laboratory variables were collected, and serum cytokines were kinetically assessed. A multivariable- adjusted generalized linear regression model was used to evaluate the differences in serum cytokine levels between survivor and non-survivors.Results: Among the 40 patients included, a significant positive correlation was found between multiple cytokines. Serum levels of IL-6, IL-10, and tumor necrosis factor alpha in non-survivors were consistently elevated compared to that of the survivors. Kinetic variations of IL-6, IL-8, and IL-10 were associated with a fatal outcome in severe patients with COVID-19, independent of sex, age, absolute lymphocyte count, direct bilirubin, hypertension, chronic obstructive pulmonary disease, and cancer.Conclusion: Dynamic changes in serum IL-6, IL-8, and IL-10 levels were associated with survival in ICU and could serve as a predictive biomarker in patients with severe COVID-19 to determine therapeutic options.

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Section: Il-6 Il-8 and Il-10 Are Associated With The Survival Of Pasupporting

confidence: 57%

Section: Demographic and Baseline Clinical Characteristics Of Patientmentioning

confidence: 99%

Dynamic changes in serum IL-6, IL-8, and IL-10 are associated with the outcome of patients with severe COVID-19 in ICU

Liu

Cui

et al. 2020

Preprint

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“…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

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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.

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“…Another web application uses hospitalization data from China, Italy and Belgium to predict severity of illness. 24 With the support of a machine-learning model, this application assesses severity risk for COVID-19 patients at hospital admission. Clinical, laboratory, and radiological characteristics were the predictors.…”

Section: Discussionmentioning

confidence: 99%

Peer Review #1 of "Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool (v0.2)"

Jaffé¹

2020

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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

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 188 publications

References 27 publications

Dynamic changes in serum IL-6, IL-8, and IL-10 are associated with the outcome of patients with severe COVID-19 in ICU

Dynamic changes in serum IL-6, IL-8, and IL-10 are associated with the outcome of patients with severe COVID-19 in ICU

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

Peer Review #1 of "Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool (v0.2)"

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