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Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.
Kamran, Fahad; Tang, Shengpu; Otles, Erkin; McEvoy, Dustin S; Saleh, Sameh N; Gong, Jen; Li, Benjamin Y; Dutta, Sayon; Liu, Xinran; Medford, Richard J; Valley, Thomas S; West, Lauren R; Singh, Karandeep; Blumberg, Seth; Donnelly, John P; Shenoy, Erica S; Ayanian, John Z; Nallamothu, Brahmajee K; Sjoding, Michael W; Wiens, Jenna.
  • Kamran F; Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA.
  • Tang S; Joint first authors.
  • Otles E; Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA.
  • McEvoy DS; Joint first authors.
  • Saleh SN; Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, MI, USA.
  • Gong J; Medical Scientist Training Program, University of Michigan Medical School, Ann Arbor, MI, USA.
  • Li BY; Mass General Brigham Digital Health eCare, Somerville, MA, USA.
  • Dutta S; Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Liu X; Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Medford RJ; Center for Clinical Informatics and Improvement Research, University of California, San Francisco, CA, USA.
  • Valley TS; Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA.
  • West LR; Medical Scientist Training Program, University of Michigan Medical School, Ann Arbor, MI, USA.
  • Singh K; Mass General Brigham Digital Health eCare, Somerville, MA, USA.
  • Blumberg S; Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
  • Donnelly JP; Division of Hospital Medicine, University of California, San Francisco, San Francisco, CA, USA.
  • Shenoy ES; Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Ayanian JZ; Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Nallamothu BK; Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA.
  • Sjoding MW; Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
  • Wiens J; Infection Control Unit, Massachusetts General Hospital, Boston, MA, USA.
BMJ ; 376: e068576, 2022 02 17.
Article in English | MEDLINE | ID: covidwho-1691357
ABSTRACT

OBJECTIVE:

To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing.

DESIGN:

Retrospective cohort study.

SETTING:

One US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21.

PARTICIPANTS:

33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19. MAIN OUTCOME

MEASURES:

An ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early.

RESULTS:

9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge.

CONCLUSION:

A model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Risk Assessment / Machine Learning / Clinical Decision Rules / COVID-19 / Hospitalization Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Journal: BMJ Journal subject: Medicine Year: 2022 Document Type: Article Affiliation country: Bmj-2021-068576

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Risk Assessment / Machine Learning / Clinical Decision Rules / COVID-19 / Hospitalization Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Journal: BMJ Journal subject: Medicine Year: 2022 Document Type: Article Affiliation country: Bmj-2021-068576