Development and validation of a prognostic model based on comorbidities to predict COVID-19 severity: a population-based study

Abstract

Background: The prognosis of patients with COVID-19 infection is uncertain. We derived and validated a new risk model for predicting progression to disease severity, hospitalization, admission to intensive care unit (ICU) and mortality in patients with COVID-19 infection (Gal-COVID-19 scores).

Methods: This is a retrospective cohort study of patients with COVID-19 infection confirmed by reverse transcription polymerase chain reaction (RT-PCR) in Galicia, Spain. Data were extracted from electronic health records of patients, including age, sex and comorbidities according to International Classification of Primary Care codes (ICPC-2). Logistic regression models were used to estimate the probability of disease severity. Calibration and discrimination were evaluated to assess model performance.

Results: The incidence of infection was 0.39% (10 454 patients). A total of 2492 patients (23.8%) required hospitalization, 284 (2.7%) were admitted to the ICU and 544 (5.2%) died. The variables included in the models to predict severity included age, gender and chronic comorbidities such as cardiovascular disease, diabetes, obesity, hypertension, chronic obstructive pulmonary disease, asthma, liver disease, chronic kidney disease and haematological cancer. The models demonstrated a fair-good fit for predicting hospitalization {AUC [area under the receiver operating characteristics (ROC) curve] 0.77 [95% confidence interval (CI) 0.76, 0.78]}, admission to ICU [AUC 0.83 (95%CI 0.81, 0.85)] and death [AUC 0.89 (95%CI 0.88, 0.90)].

Conclusions: The Gal-COVID-19 scores provide risk estimates for predicting severity in COVID-19 patients. The ability to predict disease severity may help clinicians prioritize high-risk patients and facilitate the decision making of health authorities.

Keywords: COVID-19; admission to ICU; death; hospitalization; prediction model; severity.

Figure 1

Age and gender distributions of patients with SARS-CoV-2 infection (crude cases and incidence).

Figure 2

Distributions of patients with SARS-CoV-2 infection by municipality. Cases (left) and incidence (right). Low incidence is shown in green whereas high incidence is displayed in red.

Figure 3

Forest plots showing the adjusted ORs for hospitalization, ICU admission and death. Upper panel derivation cohort. Lower panel validation cohort. The ORs come from logistic models using age, gender and comorbidities as predictors.

Figure 4

Calibration plots of the final models for predicting hospitalization, ICU admission and death in the derivation cohort. The dotted line shows the actual relation between observed outcomes and predicted risks; the solid line shows the smoothed relation. Ideally, these lines equal the dashed diagonal line that represents perfect calibration.

Figure 5

Density distributions of risk hospitalization (top), admission to ICU (middle) and death (bottom) in the entire cohort.