Development and validation of a prediction model for functional decline in older medical inpatients. (July 2018) - Takada, Toshihiko; Fukuma, Shingo; Yamamoto, Yosuke; Tsugihashi, Yukio; Nagano, Hiroyuki; Hayashi, Michio; Miyashita, Jun; Azuma, Teruhisa; Fukuhara, Shunichi
Highlights: A model to assess the risk of functional decline in older medical inpatients. The model demonstrated better diagnostic performance than existing models. Further clinical impact studies would be expected to quantify the model. Abstract: Objective: To prevent functional decline in older inpatients, identification of high-risk patients is crucial. The aim of this study was to develop and validate a prediction model to assess the risk of functional decline in older medical inpatients. Methods: In this retrospective cohort study, patients ≥65 years admitted acutely to medical wards were included. The healthcare database of 246 acute care hospitals (n = 229, 913) was used for derivation, and two acute care hospitals (n = 1767 and 5443, respectively) were used for validation. Data were collected using a national administrative claims and discharge database. Functional decline was defined as a decline of the Katz score at discharge compared with on admission. Results: About 6% of patients in the derivation cohort and 9% and 2% in each validation cohort developed functional decline. A model with 7 items, age, body mass index, living in a nursing home, ambulance use, need for assistance in walking, dementia, and bedsore, was developed. On internal validation, it demonstrated a c-statistic of 0.77 (95% confidence interval (CI) = 0.767–0.771) and good fit on the calibration plot. On external validation, the c-statistics were 0.79 (95% CI = 0.77–0.81) and 0.75 (95% CI = 0.73–0.77) for each cohort, respectively. Calibration plots showed good fit in one cohort and overestimation in the other one. Conclusions: A prediction model for functional decline in older medical inpatients was derived and validated. It is expected that use of the model would lead to early identification of high-risk patients and introducing early intervention.