Identifying and Predicting Risk for Hospital Admission among Patients with Parkinsonism.

Abstract

Background: Patients with parkinsonism are more likely than age-matched controls to be admitted to hospital. It may be possible to reduce the cost and negative impact by identifying patients at highest risk and intervening to reduce hospital-related costs. Predictive models have been developed in nonparkinsonism populations.

Objectives: The aims were to (1) describe the reasons for admission, (2) describe the rates of hospital admission/emergency department attendance over time, and (3) use routine data to risk stratify unplanned hospital attendance in people with parkinsonism.

Methods: This retrospective cohort study used Clinical Practice Research Datalink GOLD, a large UK primary care database, linked to hospital admission and emergency department attendance data. The primary diagnoses for nonelective admissions were categorized, and the frequencies were compared between parkinsonism cases and matched controls. Multilevel logistic and negative binomial regression models were used to estimate the risk of any and multiple admissions, respectively, for patients with parkinsonism.

Results: There were 9189 patients with parkinsonism and 45,390 controls. The odds of emergency admission more than doubled from 2010 to 2019 (odds ratio [OR] 2.33; 95% confidence interval [CI] 1.96, 2.76; P-value for trend <0.001). Pneumonia was the most common reason for admission among cases, followed by urinary tract infection. Increasing age, multimorbidity, parkinsonism duration, deprivation, and care home residence increased the odds of admission. Rural location was associated with reduced OR for admission (OR 0.79; 95% CI 0.70, 0.89).

Conclusions: Our risk stratification tool may enable empirical targeting of interventions to reduce admission risk for parkinsonism patients.