Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data

Abstract

Background: Older adults have worse outcomes following hospitalization with COVID-19, but within this group there is substantial variation. Although frailty and comorbidity are key determinants of mortality, it is less clear which specific manifestations of frailty and comorbidity are associated with the worst outcomes.

Objective: We aimed to identify the key comorbidities and domains of frailty that were associated with in-hospital mortality in older patients with COVID-19 using models developed for machine learning algorithms.

Methods: This was a retrospective study that used the Hospital Episode Statistics administrative data set from March 1, 2020, to February 28, 2021, for hospitalized patients in England aged 65 years or older. The data set was split into separate training (70%), test (15%), and validation (15%) data sets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domains of frailty were identified using the Global Frailty Scale (GFS). Comorbidity was assessed using the Charlson Comorbidity Index (CCI). Additional features employed in the random forest algorithms included age, sex, deprivation, ethnicity, discharge month and year, geographical region, hospital trust, disease severity, and International Statistical Classification of Disease, 10th Edition codes recorded during the admission. Features were selected, preprocessed, and input into a series of random forest classification algorithms developed to identify factors strongly associated with in-hospital mortality. Two models were developed; the first model included the demographic, hospital-related, and disease-related items described above, as well as individual GFS domains and CCI items. The second model was similar to the first but replaced the GFS domains and CCI items with the HFRS as a global measure of frailty. Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve and measures of model accuracy.

Results: In total, 215,831 patients were included. The model using the individual GFS domains and CCI items had an AUROC curve for in-hospital mortality of 90% and a predictive accuracy of 83%. The model using the HFRS had similar performance (AUROC curve 90%, predictive accuracy 82%). The most important frailty items in the GFS were dementia/delirium, falls/fractures, and pressure ulcers/weight loss. The most important comorbidity items in the CCI were cancer, heart failure, and renal disease.

Conclusions: The physical manifestations of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who need additional support during hospitalization with COVID-19.

Keywords: COVID-19; SARS-CoV-2; age; cancer; comorbidity; coronavirus; death; descriptive statistics; disease; elder; ethnicity; frailty; geriatric; heart; heart failure; hospital; hospital admission; hospitalisation; hospitalization; machine learning; model; mortality; older adult; patient; renal disease; sex; support; weight; weight loss.

Figure 1

Shapley value dot summary plot for model 1. Each dot in the plot represents a patient. The x-axis indicates whether there is a positive or negative correlation between the value of the feature and its contribution to the model prediction of a patient dying. The color of the dot represents the size of the feature relative to the range of values that feature can take, with red representing large feature values and blue low feature values. The horizontal axis represents the association of the feature value with the outcome. A positive SHAP value means the feature is associated with mortality. A negative SHAP value means the feature contributes to the patient surviving to discharge. The features are ranked by the mean of the absolute value of the SHAP values. CCI: Charlson Comorbidity Index; GFS: Global Frailty Scale; ICD-10: International Statistical Classification of Disease, 10th Edition; IMD: index of multiple deprivation; SHAP: Shapley additive explanation.

Figure 2

Plot of the predicted probability of death as a function of the length of stay.

Figure 3

SHAP value dot summary plot for model 2. Each dot in the plot represents a patient. The x-axis indicates whether there is a positive or negative correlation between the value of the feature and its contribution to the model prediction of a patient dying. The color of the dot represents the size of the feature relative to the range of values that feature can take, with red representing large feature values and blue low feature values. The horizontal axis represents the association of the feature value with the outcome. A positive SHAP value means the feature is associated with mortality. A negative SHAP value means the feature contributes to the patient surviving to discharge. The features are ranked by the mean of the absolute value of the SHAP values. HFRS: Hospital Frailty Risk Score; ICD-10: International Statistical Classification of Disease, 10th Edition; IMD: index of multiple deprivation; SHAP: Shapley additive explanation.