Your browser doesn't support javascript.
Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA.
Incerti, Devin; Rizzo, Shemra; Li, Xiao; Lindsay, Lisa; Yau, Vincent; Keebler, Dan; Chia, Jenny; Tsai, Larry.
  • Incerti D; Product Development, Genentech, South San Francisco, California, USA incerti.devin@gene.com.
  • Rizzo S; Product Development, Genentech, South San Francisco, California, USA.
  • Li X; Product Development, Genentech, South San Francisco, California, USA.
  • Lindsay L; Product Development, Genentech, South San Francisco, California, USA.
  • Yau V; Product Development, Genentech, South San Francisco, California, USA.
  • Keebler D; Product Development, Genentech, South San Francisco, California, USA.
  • Chia J; Product Development, Genentech, South San Francisco, California, USA.
  • Tsai L; Product Development, Genentech, South San Francisco, California, USA.
BMJ Open ; 11(4): e047121, 2021 04 07.
Article in English | MEDLINE | ID: covidwho-1172761
ABSTRACT

OBJECTIVES:

To develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19.

DESIGN:

Retrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores and calibration plots in the test set.

SETTING:

Optum de-identified COVID-19 Electronic Health Record dataset including over 700 hospitals and 7000 clinics in the USA.

PARTICIPANTS:

17 086 patients hospitalised with COVID-19 between 20 February 2020 and 5 June 2020. MAIN OUTCOME

MEASURE:

All-cause mortality while hospitalised.

RESULTS:

The full model that included information on demographics, comorbidities, laboratory results, and vital signs had good discrimination (C-index=0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were similar on the training and test sets, suggesting that there was little overfitting. Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index=0.79) was only slightly better than a model that only included age (C-index=0.76). Across the study period, predicted mortality was 1.3% for patients aged 18 years old, 8.9% for 55 years old and 28.7% for 85 years old. Predicted mortality across all ages declined over the study period from 22.4% by March to 14.0% by May.

CONCLUSION:

Age was the most important predictor of all-cause mortality, although vital signs and laboratory results added considerable prognostic information, with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase and white cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The full model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Country/Region as subject: North America Language: English Journal: BMJ Open Year: 2021 Document Type: Article Affiliation country: Bmjopen-2020-047121

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Country/Region as subject: North America Language: English Journal: BMJ Open Year: 2021 Document Type: Article Affiliation country: Bmjopen-2020-047121