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Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients.
Castro, Victor M; Hart, Kamber L; Sacks, Chana A; Murphy, Shawn N; Perlis, Roy H; McCoy, Thomas H.
  • Castro VM; Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Research Information Science and Computing, Mass General Brigham, 399 Revolution Drive, Somerville, MA 02145, USA.
  • Hart KL; Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA.
  • Sacks CA; Department of Medicine, Massachusetts General Hospital, 100 Cambridge Street, Boston, MA 02114, USA.
  • Murphy SN; Research Information Science and Computing, Mass General Brigham, 399 Revolution Drive, Somerville, MA 02145, USA; Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
  • Perlis RH; Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA.
  • McCoy TH; Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA. Electronic address: thmccoy@partners.org.
Gen Hosp Psychiatry ; 74: 9-17, 2022.
Article in English | MEDLINE | ID: covidwho-1568701
ABSTRACT

OBJECTIVE:

To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19).

METHOD:

Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission.

RESULTS:

Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73-0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave.

CONCLUSION:

This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Delirium / COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Gen Hosp Psychiatry Year: 2022 Document Type: Article Affiliation country: J.genhosppsych.2021.10.005

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Delirium / COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Gen Hosp Psychiatry Year: 2022 Document Type: Article Affiliation country: J.genhosppsych.2021.10.005