Predicting mortality after hospitalisation for COPD using electronic health records

Background: Few prognostic models exist for patients hospitalised with chronic obstructive pulmonary disease (COPD); most are based on small cohorts enroled by specialists in academic centres. Electronic health records (EHRs) provide an opportunity to develop more representative models, although they may not record some variables used in existing models.Materials and methods: for this retrospective cohort study, using EHRs, we identified 17,973 patients with an unplanned hospitalisation for COPD (in any diagnostic position) in the Glasgow area between 2011 and 2017. Patients with known lung cancer were excluded. EHR were linked to prior admissions, community prescribing and laboratory data. A pragmatic, parsimonious multivariable model was developed to predict 90-day mortality. Results: we identified 12 variables strongly related to prognosis, including age, sex, length of index hospitalisation stay, prior diagnosis of cancer (excluding lung cancer) or dementia, prescription of oxygen or digoxin, neutrophil/lymphocyte ratio and serum chloride, urea, creatinine and albumin. The model achieved excellent calibration with reasonable discrimination (area under the curve: 0.806; 95% CI: 0.792-0.820). A risk-score was developed and an electronic risk-calculator is provided.Conclusions: a small number of variables, including prescriptions and laboratory data obtained from routine EHRs predict 90-day mortality after a hospitalisation for COPD. The risk-calculator provided might prove useful for service-evaluation and audit, to guide clinical management and to risk-stratify and select patients to be invited to participate in clinical research.

Automated summary

Using electronic health records, a pragmatic and parsimonious multivariable model was developed to predict 90-day mortality in patients hospitalized with chronic obstructive pulmonary disease (COPD). The model, based on a small number of variables including age, sex, length of hospital stay, prior diagnosis, prescription, and laboratory data, showed excellent calibration and reasonable discrimination. The risk-calculator can be useful for service-evaluation, clinical management, risk-stratification, and patient selection for clinical research.