Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19

McRae, Michael; Simmons, Glennon W.; Christodoulides, Nicolaos; Lu, Zhibing; Kang, Stella K.; Fenyö, David; Alcorn, Timothy M.; Dapkins, Isaac; Sharif, Iman; Vurmaz, Deniz; Modak, Sayli S.; Rajsri, Kritika Srinivasan; Warhadpande, Shruti; Shrivastav, Ravi; McDevitt, John T.

doi:10.1101/2020.04.16.20068411

Cited by 27 publications

(28 citation statements)

References 29 publications

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“…Previous studies have reported that heart injury was common in patients with pneumonia ( Marrie & Shariatzadeh, 2007 ). It was reported that elevated concentrations of Myo in venous blood could predict the severity of COVID-19 ( McRae et al, 2020 ). Myo is a significant myocardial marker that is used in the clinical detection of patients with severe pneumonia.…”

Section: Discussionmentioning

confidence: 99%

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

Wang

Yu²,

Hua

et al. 2020

PeerJ

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Background The outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients. Methods The clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients’ clinical prognoses. Results Seven of the 126 patients were excluded for losing endpoints, 103 of the remaining 119 patients were discharged (alive) and 16 died in the hospital. A synthetic minority over-sampling technique (SMOTE) was used to correct the imbalanced distribution of clinical patients. Recursive feature elimination (RFE) was used to select the optimal subset for analysis. Eleven clinical parameters, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK were chosen with AUC approximately 0.9905. The RF algorithm was built to predict the prognoses of COVID-19 patients based on the best subset, and the area under the ROC curve (AUC) of the test data was 100%. Moreover, two optimal clinical risk predictors, lactate dehydrogenase (LDH) and Myoglobin (Myo), were selected based on the Gini index. The univariable logistic analysis revealed a substantial increase in the risk for in-hospital mortality when Myo was higher than 80 ng/ml (OR = 7.54, 95% CI [3.42–16.63]) and LDH was higher than 500 U/L (OR = 4.90, 95% CI [2.13–11.25]). Conclusion We applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk factors for the prognoses of COVID-19 patients in the early stage of the disease.

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Section: Discussionmentioning

confidence: 99%

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

Wang

Yu²,

Hua

et al. 2020

PeerJ

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“…En relación con el péptido natriurético B (PNB) y la creatina quinasa isoforma cardiaca (CKMB), estos tienen un inferior valor pronóstico que las troponinas en la patología cardiaca asociada a pacientes con SARS-CoV-2 (3,7,12,23,(24)(25)(26)(27)(28)(29).…”

Section: Marcadores Cardiovascularesunclassified

“…En cuanto a los marcadores de coagulación, todos los estudios publicados demuestran un incremento en los valores de dímero D (>0,5mg/l) (1,3,6,9,(12)(13)(14)19,25,(27)(28)(29)(30)(31)(32)(33)(34)(35), mientras que solo algunos revelan prolongación en el tiempo de protrombina (PT > 12 s) (1,3,8,14,19,25,29) y niveles decrecientes de fibrinógeno (23). En todos los casos, los niveles anormales de estos marcadores, en el ingreso de los pacientes, se asociaron con lesión cardíaca aguda, lo que implica que se correlacionan con la lesión miocárdica y la gravedad de la infección.…”

Section: Marcadores Cardiovascularesunclassified

“…La nefropatía es una complicación importante de COVID-19 y un factor de riesgo significativo de muerte (10). Aunque las concentraciones de creatinina y alfa-hidroxibutirato deshidrogenasa son más altas en pacientes con infección severa (>90 μmol/L), la diferencia no es estadísticamente significativa al compararlas con pacientes con sintomatología leve (10,11,27,29). Sin embargo, la relación establecida entre los incrementos en creatinina (>99 μmol/L) y las concentraciones de proteinuria y hematuria en pacientes hospitalizados pone de manifiesto una insuficiencia renal con probabilidad más alta de ingreso a UCI y mayor riesgo de deterioro de los pacientes (10).…”

Section: Marcadores De Daño Renalunclassified

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Valor pronóstico de los marcadores bioquímicos en pacientes con COVID-19

Suarez¹,

Urrego²,

Rojas³

et al. 2020

nova

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El SARS-CoV-2 es un virus de la familia Coronaviridae, subfamilia coronavirus (CoV) y género β. Este se ha convertido en una amenaza inminente para toda la humanidad por ser el agente causal de la pandemia COVID-19, la cual llevó, por un lado, a la declaratoria de emergencia sanitaria a nivel mundial por parte de la Organización Mundial de la Salud (OMS) y, por otro, a instituir estrictas medidas de control para prevenir su contagio por parte de muchos gobiernos. En cuanto a la fisiopatología presentada en esta entidad, aunque las lesiones pulmonares han sido consideradas como las principales consecuencias de esta infección, a medida que avanza el conocimiento sobre el virus se han identificado también lesiones a nivel cardiaco, hepático y renal, que potencian la severidad de la infección y generan un mayor deterioro de los pacientes, su ingreso a las Unidades de Cuidados Intensivos y un mayor riesgo de mortalidad. Con base en esto, diversas investigaciones se han encaminado a determinar aquellos hallazgos clínicos y paraclínicos que puedan ser relevantes frente al pronóstico de los pacientes. Por lo anterior, la presente revisión aborda literatura disponible sobre los principales biomarcadores bioquímicos reportados por su asociación a daños cardiaco, hepático y renal, los cuales presentan mayor significancia para evaluar el curso, severidad, manejo y pronóstico de la infección, y cuya alteración conlleva finalmente a un mayor riesgo de mortalidad en pacientes hospitalizados que presentan COVID-19.

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“…However, these tools have either not yet been externally validated in peer-reviewed publications or were not developed specifically for COVID-19 patient populations. Recently, we developed an integrated point-of-care COVID-19 Severity Score and CDSS that combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality with excellent diagnostic accuracy [ 20 ]. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China.…”

Section: Introductionmentioning

confidence: 99%

Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation

McRae¹,

Dapkins²,

Sharif³

et al. 2020

J Med Internet Res

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Background The coronavirus disease (COVID-19) pandemic has resulted in significant morbidity and mortality; large numbers of patients require intensive care, which is placing strain on health care systems worldwide. There is an urgent need for a COVID-19 disease severity assessment that can assist in patient triage and resource allocation for patients at risk for severe disease. Objective The goal of this study was to develop, validate, and scale a clinical decision support system and mobile app to assist in COVID-19 severity assessment, management, and care. Methods Model training data from 701 patients with COVID-19 were collected across practices within the Family Health Centers network at New York University Langone Health. A two-tiered model was developed. Tier 1 uses easily available, nonlaboratory data to help determine whether biomarker-based testing and/or hospitalization is necessary. Tier 2 predicts the probability of mortality using biomarker measurements (C-reactive protein, procalcitonin, D-dimer) and age. Both the Tier 1 and Tier 2 models were validated using two external datasets from hospitals in Wuhan, China, comprising 160 and 375 patients, respectively. Results All biomarkers were measured at significantly higher levels in patients who died vs those who were not hospitalized or discharged (P<.001). The Tier 1 and Tier 2 internal validations had areas under the curve (AUCs) of 0.79 (95% CI 0.74-0.84) and 0.95 (95% CI 0.92-0.98), respectively. The Tier 1 and Tier 2 external validations had AUCs of 0.79 (95% CI 0.74-0.84) and 0.97 (95% CI 0.95-0.99), respectively. Conclusions Our results demonstrate the validity of the clinical decision support system and mobile app, which are now ready to assist health care providers in making evidence-based decisions when managing COVID-19 patient care. The deployment of these new capabilities has potential for immediate impact in community clinics and sites, where application of these tools could lead to improvements in patient outcomes and cost containment.

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Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19

Cited by 27 publications

References 29 publications

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

Valor pronóstico de los marcadores bioquímicos en pacientes con COVID-19

Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation

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