How Specialist Aftercare Impacts Long-Term Readmission Risks in Elderly Patients With Metabolic, Cardiac, and Chronic Obstructive Pulmonary Diseases: Cohort Study Using Administrative Data.

Background: The health state of elderly patients is typically characterized by multiple co-occurring diseases requiring the involvement of several types of health care providers.
Objective: We aimed to quantify the benefit for multimorbid patients from seeking specialist care in terms of long-term readmission risks.
Methods: From an administrative database, we identified 225,238 elderly patients with 97 different diagnosis (ICD-10 codes) from hospital stays and contact with 13 medical specialties. For each diagnosis associated with the first hospital stay, we used multiple logistic regression analysis to quantify the sex-specific and age-adjusted long-term all-cause readmission risk (hospitalizations occurring between 3 months and 3 years after the first admission) and how specialist contact impacts these risks.
Results: Men have a higher readmission risk than women (mean difference over all first diagnoses 1.9%, P<.001), but similar reduction in readmission risk after receiving specialist care. Specialist care can reduce readmission risk by almost 50%. We found the greatest reductions in risk when the first hospital stay was associated with diagnoses corresponding to complex chronic diseases such as acute myocardial infarction (57.6% reduction in readmission risk, SE 7.6% for men [m]; 55.9% reduction, SE 9.8% for women [w]), diabetic and other retinopathies (m: 62.3%, SE 8.0; w: 60.1%, SE 8.4%), chronic obstructive pulmonary disease (m: 63.9%, SE 7.8%; w: 58.1%, SE 7.5%), disorders of lipoprotein metabolism (m: 64.7%, SE 3.7%; w: 63.8%, SE 4.0%), and chronic ischemic heart diseases (m: 63.6%, SE 3.1%; w: 65.4%, SE 3.0%).
Conclusions: Specialist care can greatly reduce long-term readmission risk for patients with chronic and multimorbid diseases. Further research is needed to identify the specific reasons for these findings and to understand the detected sex-specific differences.
(©Michaela Kaleta, Thomas Niederkrotenthaler, Alexandra Kautzky-Willer, Peter Klimek. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 16.09.2020.)