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

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Abstract

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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Most cited references 19

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Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Fei Zhou, Ting Yu, Ronghui Du … (2020)

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

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Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

Nanshan Chen, Min Zhou, Xuan Dong … (2022)

Summary Background In December, 2019, a pneumonia associated with the 2019 novel coronavirus (2019-nCoV) emerged in Wuhan, China. We aimed to further clarify the epidemiological and clinical characteristics of 2019-nCoV pneumonia. Methods In this retrospective, single-centre study, we included all confirmed cases of 2019-nCoV in Wuhan Jinyintan Hospital from Jan 1 to Jan 20, 2020. Cases were confirmed by real-time RT-PCR and were analysed for epidemiological, demographic, clinical, and radiological features and laboratory data. Outcomes were followed up until Jan 25, 2020. Findings Of the 99 patients with 2019-nCoV pneumonia, 49 (49%) had a history of exposure to the Huanan seafood market. The average age of the patients was 55·5 years (SD 13·1), including 67 men and 32 women. 2019-nCoV was detected in all patients by real-time RT-PCR. 50 (51%) patients had chronic diseases. Patients had clinical manifestations of fever (82 [83%] patients), cough (81 [82%] patients), shortness of breath (31 [31%] patients), muscle ache (11 [11%] patients), confusion (nine [9%] patients), headache (eight [8%] patients), sore throat (five [5%] patients), rhinorrhoea (four [4%] patients), chest pain (two [2%] patients), diarrhoea (two [2%] patients), and nausea and vomiting (one [1%] patient). According to imaging examination, 74 (75%) patients showed bilateral pneumonia, 14 (14%) patients showed multiple mottling and ground-glass opacity, and one (1%) patient had pneumothorax. 17 (17%) patients developed acute respiratory distress syndrome and, among them, 11 (11%) patients worsened in a short period of time and died of multiple organ failure. Interpretation The 2019-nCoV infection was of clustering onset, is more likely to affect older males with comorbidities, and can result in severe and even fatal respiratory diseases such as acute respiratory distress syndrome. In general, characteristics of patients who died were in line with the MuLBSTA score, an early warning model for predicting mortality in viral pneumonia. Further investigation is needed to explore the applicability of the MuLBSTA score in predicting the risk of mortality in 2019-nCoV infection. Funding National Key R&D Program of China.

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Cardiovascular Implications of Fatal Outcomes of Patients With Coronavirus Disease 2019 (COVID-19)

Tao Guo, Yongzhen Fan, Ming Chen … (2020)

This case series study evaluates the association of underlying cardiovascular disease and myocardial injury on fatal outcomes in patients with coronavirus disease 2019 (COVID-19).

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Author and article information

Contributors

John T McDevitt:

ORCID: https://orcid.org/0000-0001-8789-9351

Department of Biomaterials, Bioengineering InstituteNew York University College of Dentistry433 First AvenueRoom 820New York, NY, 10010United States1 (212) 998-9204mcdevitt@nyu.edu

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J. Med. Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: August 2020

Publication date (Electronic): 24 August 2020

Publication date PMC-release: 24 August 2020

Volume: 22

Issue: 8

Electronic Location Identifier: e22033

Affiliations

[1 ] Department of Biomaterials, Bioengineering Institute New York University College of Dentistry New York, NY United States

[2 ] Department of Population Health and Internal Medicine Family Health Centers at NYU Langone New York University School of Medicine New York, NY United States

[3 ] Departments of Pediatrics and Population Health Family Health Centers at NYU Langone New York University School of Medicine New York, NY United States

[4 ] Family Health Centers at NYU Langone New York, NY United States

[5 ] Department of Biochemistry and Molecular Pharmacology New York University School of Medicine New York, NY United States

[6 ] Department of Medicine New York University School of Medicine New York, NY United States

[7 ] Department of Radiology New York University School of Medicine New York, NY United States

[8 ] Department of Population Health New York University School of Medicine New York, NY United States

[9 ] Department of Chemical and Biomolecular Engineering NYU Tandon School of Engineering New York University New York, NY United States

[10 ] Latham BioPharm Group Cambridge, MA United States

[11 ] OraLiva Naples, FL United States

Author notes

Corresponding Author: John T McDevitt mcdevitt@ 123456nyu.edu

Author information

Michael P McRae https://orcid.org/0000-0002-2126-9442

Isaac P Dapkins https://orcid.org/0000-0001-5257-8064

Iman Sharif https://orcid.org/0000-0002-8641-4938

Judd Anderman https://orcid.org/0000-0003-0682-9445

David Fenyo https://orcid.org/0000-0001-5049-3825

Odai Sinokrot https://orcid.org/0000-0003-1846-2687

Stella K Kang https://orcid.org/0000-0003-2402-3787

Nicolaos J Christodoulides https://orcid.org/0000-0002-6494-5586

Deniz Vurmaz https://orcid.org/0000-0002-0591-6371

Glennon W Simmons https://orcid.org/0000-0003-4560-2809

Timothy M Alcorn https://orcid.org/0000-0001-5255-4302

Marco J Daoura https://orcid.org/0000-0003-2475-2592

Stu Gisburne https://orcid.org/0000-0002-2981-6439

David Zar https://orcid.org/0000-0002-2853-0076

John T McDevitt https://orcid.org/0000-0001-8789-9351

Article

Publisher ID: v22i8e22033

DOI: 10.2196/22033

PMC ID: 7446714

PubMed ID: 32750010

SO-VID: a59a5cd8-2ce5-4eb5-88e2-55e9f49debbd

Copyright © ©Michael P McRae, Isaac P Dapkins, Iman Sharif, Judd Anderman, David Fenyo, Odai Sinokrot, Stella K Kang, Nicolaos J Christodoulides, Deniz Vurmaz, Glennon W Simmons, Timothy M. Alcorn, Marco J Daoura, Stu Gisburne, David Zar, John T McDevitt. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.08.2020.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 1 July 2020

Date revision requested : 22 July 2020

Date revision received : 22 July 2020

Date accepted : 23 July 2020

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