Hospital treatment costs and length of stay associated with hypertension and multimorbidity after hemorrhagic stroke

Background: Previous studies have identified various treatment and patient characteristics that may be associated with higher hospital cost after spontaneous intracerebral hemorrhage (ICH); a devastating type of stroke. Patient morbidity is perhaps the least understood of these cost-driving factors. We describe how hypertension and other patient morbidities affect length of stay, and hospital treatment costs after ICH using primary and simulated data. We also describe the relationship between cost and length of stay within these patients. Methods: We used a cohort design; evaluating 987 consecutive ICH patients across one decade in a Canadian center. Economic, treatment, and patient data were obtained from clinical and administrative sources. Multimorbidity was defined as the presence of one or more diagnoses at hospital admission in addition to a primary diagnosis of ICH. Results: Hypertension was the most frequent (67%) morbidity within these patients, as well as the strongest predictor of longer stay (adjusted RR for >7days: 1.31, 95% CI: 1.07-1.60), and was significantly associated with higher cost per visit when accounting for other morbidities (adjusted cost increase for hypertension $8123.51, 95% CI: $4088.47 to $12,856.72 USD). A Monte Carlo simulation drawing one million samples of patients estimated for a generation (100years) assuming 0.94% population growth per year, and a hospitalization rate of 12 per 100,000 inhabitants, supported these findings (p=0.516 for the difference in unadjusted cost: simulated vs primary). Using a restricted cubic spline, we observed that the rate of change in overall cost for all patients was greatest for the first 3weeks (p<0.001) compared to subsequent weeks. Conclusion: Patient multimorbidity, specifically hypertension, is a strong predictor of longer stay and cost after ICH. The non-linear relationship between cost and time should also be considered when forecasting healthcare spending in these patients.