Validation and Improvement of the Saga Fall Risk Model: A Multicenter Retrospective Observational Study

Masaki Tago,1 Risa Hirata,1 Naoko E Katsuki,1 Eiji Nakatani,2 Midori Tokushima,1 Tomoyo Nishi,1 Hitomi Shimada,3 Shizuka Yaita,1 Chihiro Saito,4 Kaori Amari,5 Kazuya Kurogi,6 Yoshimasa Oda,7 Kiyoshi Shikino,8,9 Maiko Ono,10 Mariko Yoshimura,11 Shun Yamashita,1 Yoshinori Tokushima,1 Hidetoshi Aihara,1 Motoshi Fujiwara,1 Shu-ichi Yamashita1 1Department of General Medicine, Saga University Hospital, Saga, Japan; 2Graduate School of Public Health, Shizuoka Graduate University of Public Health, Shizuoka, Japan; 3Shimada Hospital of Medical Corporation Chouseikai, Saga, Japan; 4Shizuoka General Hospital, Shizuoka, Japan; 5Department of Emergency Medicine, Saga-Ken Medical Centre Koseikan, Saga, Japan; 6Department of General Medicine, National Hospital Organization Ureshino Medical Center, Saga, Japan; 7Department of General Medicine, Yuai-Kai Foundation and Oda Hospital, Saga, Japan; 8Department of General Medicine, Chiba University Hospital, Chiba, Japan; 9Department of Community-Oriented Medical Education, Chiba University Graduate School of Medicine, Chiba, Japan; 10Department of General Medicine, Karatsu Municipal Hospital, Saga, Japan; 11Safety Management Section, Saga University Hospital, Saga, JapanCorrespondence: Masaki Tago, Department of General Medicine, Saga University Hospital, 5-1-1 Nabeshima, Saga, 849-8501, Japan, Tel +81 952 34 3238, Fax +81 952 34 2029, Email tagomas@cc.saga-u.ac.jpPurpose: We conducted a pilot study in an acute care hospital and developed the Saga Fall Risk Model 2 (SFRM2), a fall prediction model comprising eight items: Bedriddenness rank, age, sex, emergency admission, admission to the neurosurgery department, history of falls, independence of eating, and use of hypnotics. The external validation results from the two hospitals showed that the area under the curve (AUC) of SFRM2 may be lower in other facilities. This study aimed to validate the accuracy of SFRM2 using data from eight hospitals, including chronic care hospitals, and adjust the coefficients to improve the accuracy of SFRM2 and validate it.Patients and Methods: This study included all patients aged ≥ 20 years admitted to eight hospitals, including chronic care, acute care, and tertiary hospitals, from April 1, 2018, to March 31, 2021. In-hospital falls were used as the outcome, and the AUC and shrinkage coefficient of SFRM2 were calculated. Additionally, SFRM2.1, which was modified from the coefficients of SFRM2 using logistic regression with the eight items comprising SFRM2, was developed using two-thirds of the data randomly selected from the entire population, and its accuracy was validated using the remaining one-third portion of the data.Results: Of the 124,521 inpatients analyzed, 2,986 (2.4%) experienced falls during hospitalization. The median age of all inpatients was 71 years, and 53.2% were men. The AUC of SFRM2 was 0.687 (95% confidence interval [CI]:0.678– 0.697), and the shrinkage coefficient was 0.996. SFRM2.1 was created using 81,790 patients, and its accuracy was validated using the remaining 42,731 patients. The AUC of SFRM2.1 was 0.745 (95% CI: 0.731– 0.758).Conclusion: SFRM2 showed good accuracy in predicting falls even on validating in diverse populations with significantly different backgrounds. Furthermore, the accuracy can be improved by adjusting the coefficients while keeping the model’s parameters fixed.Keywords: accidental falls, inpatients, validation study, accident prevention