Clinical risk score to predict in-hospital mortality in COVID-19 patients: a retrospective cohort study

Abstract

ObjectivesSeveral physiological abnormalities that develop during COVID-19 are associated with increased mortality. In the present study, we aimed to develop a clinical risk score to predict the in-hospital mortality in COVID-19 patients, based on a set of variables available soon after the hospitalisation triage.SettingRetrospective cohort study of 516 patients consecutively admitted for COVID-19 to two Italian tertiary hospitals located in Northern and Central Italy were collected from 22 February 2020 (date of first admission) to 10 April 2020.ParticipantsConsecutive patients≥18 years admitted for COVID-19.Main outcome measuresSimple clinical and laboratory findings readily available after triage were compared by patients’ survival status (‘dead’ vs ‘alive’), with the objective of identifying baseline variables associated with mortality. These were used to build a COVID-19 in-hospital mortality risk score (COVID-19MRS).ResultsMean age was 67±13 years (mean±SD), and 66.9% were male. Using Cox regression analysis, tertiles of increasing age (≥75, upper vs <62 years, lower: HR 7.92; p<0.001) and number of chronic diseases (≥4 vs 0–1: HR 2.09; p=0.007), respiratory rate (HR 1.04 per unit increase; p=0.001), PaO2/FiO2 (HR 0.995 per unit increase; p<0.001), serum creatinine (HR 1.34 per unit increase; p<0.001) and platelet count (HR 0.995 per unit increase; p=0.001) were predictors of mortality. All six predictors were used to build the COVID-19MRS (Area Under the Curve 0.90, 95% CI 0.87 to 0.93), which proved to be highly accurate in stratifying patients at low, intermediate and high risk of in-hospital death (p<0.001).ConclusionsThe COVID-19MRS is a rapid, operator-independent and inexpensive clinical tool that objectively predicts mortality in patients with COVID-19. The score could be helpful from triage to guide earlier assignment of COVID-19 patients to the most appropriate level of care.

The Many Faces of Covid-19 at a Glance: A University Hospital Multidisciplinary Account From Milan, Italy

“…Mortality in other Italian hospitals in northern Italy varied between 43.6% ( 74 ) and 23.2% ( 75 ). In other hospitals in the Lombardy area, mortality ranged from 14.4 to 36.7% ( 75 – 80 ). Including our data, during the first epidemic wave the overall average in-hospital mortality in Italy therefore ranged around 30%.…”

Predicting critical illness on initial diagnosis of COVID-19 based on easily-obtained clinical variables: Development and validation of the PRIORITY model

“…Our approach for selecting predictors was developed to meet the recommendation that new prediction models, rather than using purely data-driven selection, should build on previous literature and expert opinion. 14 An initial list of 29 candidate variables was selected based on review of the existing evidence, [5][6][7][8][9][10][11][12][13][14][15][16] clinical plausibility and relevance to clinical care. Demographic variables included age, sex, ethnicity (defined as Caucasian, Latino or others), history of smoking and previous medication as angiotensin converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARBs).…”

“…16 It was also one of the largest cohorts of all previous models published to date. [9][10][11][12][13][14][15][16] Our model excluded readmissions, a feature that focusses the analysis on the question of interest, i.e. the need of triage in patients at their first COVID-19 presentation.…”

“…To date, studies of COVID-19 prognostic factors [5][6][7][8][9][10][11][12][13][14][15] have focused on laboratory test results and in-hospital data obtained following admission. They have tended not to include clinical variables that could easily be obtained from history and examination carried out on initial assessment in an outpatient setting.…”

“…A critical appraisal of the COVID-19 models 14 has shown poor reporting and high risk of bias. Recently published well-developed models [9][10][11][12][13] deploy radiological examinations and laboratory measurements such as blood counts, creatinine, lactate dehydrogenase, direct bilirubin, urea and C-reactive protein levels. These tests are not available on initial assessment or in resource-limited settings.…”