https://orcid.org/0000-0003-1092-1834
![Sample our Medicine, Dentistry, Nursing & Allied Health journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5944/TOC-Subject-Sample-2021-Medicine-Dentistry-Nursing-Allied-Health.jpg"})
Abstract
Purpose
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.
Abbreviations
ADL, activity of daily living; AUC, area under the receiver operating characteristic curve; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value; SFRM2, Saga Fall Risk Model 2.
Data Sharing Statement
The datasets generated and analyzed during the current study are available in the UMIN-ICDR repository, https://center6.umin.ac.jp/cgi-bin/icdr_e/ctr_view.cgi?recptno=R000050831.
Ethics Approval and Informed Consent
This study conforms to Ethical Guidelines for Medical and Health Research Involving Human Subjects issued by the Japanese Ministry of Health, Labour and Welfare and the Ministry of Education, Culture, Sports, Science, and Technology. This study was approved by the Ethics Committee of Saga University Hospital (approval ID: 2021-07-R-07). The study was registered at the University Hospital Medical Information Network (UMIN) at www.umin.ac.jp (UMIN ID: UMIN000045420). We obtained consent from all patients using the hospital’s comprehensive agreement method, and anonymity of patients was protected. We disclosed research information on the hospital’s website and allowed patients to opt out of participation.
Acknowledgments
We thank Miho Hayashida and Naoko Otsubo from Saga University Hospital; Kenta Yamaguchi, Yuka Hisamoto, Yasuhiro Chibu, and Toshinobu Eguchi from Yuai-Kai Foundation and Oda Hospital; Tomokazu Ichibakase from National Hospital Organization Ureshino Medical Center; Yoshihiko Nakashima and Kaori Hamai from Karatsu Municipal Hospital; and Yuriko Takao, Mika Tokushima, Yoshiro Nakayama, and Dr. Kozo Naito from Saga-Ken Medical Centre Koseikan for assistance with data acquisition.
Author Contributions
All authors made a significant contribution to the work reported with respect to the conception, study design, execution, acquisition of data, analysis, and interpretation. All authors took part in drafting, revising, or critically reviewing the article and gave their final approval of the version submitted for publication. All the authors have agreed on the journal for submission and agree to be accountable for all aspects of the work.
Disclosure
Masaki Tago is supported by grants from the Japan Society for the Promotion of Science, JSPS KAKENHI (Grant Number JP18K17322 and JP21H03166). Naoko E. Katsuki is supported by grants from the Japan Society for the Promotion of Science, JSPS KAKENHI (Grant Number JP23K16257). The authors report no other conflicts of interest in this work.