Published on in Vol 12 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58073, first published .
Predictive Factors and the Predictive Scoring System for Falls in Acute Care Inpatients: Retrospective Cohort Study

Predictive Factors and the Predictive Scoring System for Falls in Acute Care Inpatients: Retrospective Cohort Study

Predictive Factors and the Predictive Scoring System for Falls in Acute Care Inpatients: Retrospective Cohort Study

1Department of Nursing, Shizuoka General Hospital, Shizuoka, Japan

2Graduate School of Public Health, Shizuoka Graduate University of Public Health, 4-27-2, Kita-ando, Aoi-ku, Shizuoka, Japan

3Research Support Center, Shizuoka General Hospital, Shizuoka, Japan

4Department of Biostatistics and Health Data Science, Graduate School of Medical Science, Nagoya City University, Nagoya, Japan

5Department of General Medicine, Saga University Hospital, Saga, Japan

6Department of Medical Safety, Shizuoka General Hospital, Shizuoka, Japan

Corresponding Author:

Eiji Nakatani, BSP, MS, PhD


Background: Falls in hospitalized patients are a serious problem, resulting in physical injury, secondary complications, impaired activities of daily living, prolonged hospital stays, and increased medical costs. Establishing a fall prediction scoring system to identify patients most likely to fall can help prevent falls among hospitalized patients.

Objectives: This study aimed to identify predictive factors of falls in acute care hospital patients, develop a scoring system, and evaluate its validity.

Methods: This single-center, retrospective cohort study involved patients aged 20 years or older admitted to Shizuoka General Hospital between April 2019 and September 2020. Demographic data, candidate predictors at admission, and fall occurrence reports were collected from medical records. The outcome was the time from admission to a fall requiring medical resources. Two-thirds of cases were randomly selected as the training set for analysis, and univariable and multivariable Cox regression analyses were used to identify factors affecting fall risk. We scored the fall risk based on the estimated hazard ratios (HRs) and constructed a fall prediction scoring system. The remaining one-third of cases was used as the test set to evaluate the predictive performance of the new scoring system.

Results: A total of 13,725 individuals were included. During the study period, 2.4% (326/13,725) of patients experienced a fall. In the training dataset (n=9150), Cox regression analysis identified sex (male: HR 1.60, 95% CI 1.21‐2.13), age (65 to <80 years: HR 2.26, 95% CI 1.48‐3.44; ≥80 years: HR 2.50, 95% CI 1.60‐3.92 vs 20-<65 years), BMI (18.5 to <25 kg/m²: HR 1.36, 95% CI 0.94‐1.97; <18.5 kg/m²: HR 1.57, 95% CI 1.01‐2.44 vs ≥25 kg/m²), independence degree of daily living for older adults with disabilities (bedriddenness rank A: HR 1.81, 95% CI 1.26‐2.60; rank B: HR 2.03, 95% CI 1.31‐3.14; rank C: HR 1.23, 95% CI 0.83‐1.83 vs rank J), department (internal medicine: HR 1.23, 95% CI 0.92‐1.64; emergency department: HR 1.81, 95% CI 1.26‐2.60 vs department of surgery), and history of falls within 1 year (yes: HR 1.66, 95% CI 1.21‐2.27) as predictors of falls. Using these factors, we developed a fall prediction scoring system categorizing patients into 3 risk groups: low risk (0-4 points), intermediate risk (5-9 points), and high risk (10-15 points). The c-index indicating predictive performance in the test set (n=4575) was 0.733 (95% CI 0.684‐0.782).

Conclusions: We developed a new fall prediction scoring system for patients admitted to acute care hospitals by identifying predictors of falls in Japan. This system may be useful for preventive interventions in patient populations with a high likelihood of falling in acute care settings.

JMIR Hum Factors 2025;12:e58073

doi:10.2196/58073

Keywords



In 2022, the proportion of the population aged 65 years or older in Japan reached a record high of 29% [Ministry of Health, Labour and Welfare. White paper on aging society [Article in Japanese]. Cabinet Office. 2023. URL: https://www8.cao.go.jp/kourei/whitepaper/w-2023/zenbun/pdf/1s1s_01.pdf [Accessed 2024-12-13] 1]. A 2017 survey by the Ministry of Health, Labour and Welfare of Japan revealed that patients aged 75 years or older accounted for 41.5% of all patients admitted to acute care hospitals [Status of inpatients by category of basic charge for hospitalization in general ward [Article in Japanese]. Ministry of Health, Labour and Welfare. 2017. URL: https://www.mhlw.go.jp/file/05-Shingikai-12404000-Hokenkyoku-Iryouka/0000155341.pdf [Accessed 2024-12-13] 2]. Given the trend in population aging, it is projected that the number of older adult patients with a high risk of falling will further escalate in the future. Falls are not limited to older adults, and falls in hospitalized patients can lead to severe physical injuries, secondary complications, a marked decline in activities of daily living (ADL), and even death in extreme cases [Dykes PC, Curtin-Bowen M, Lipsitz S, et al. Cost of inpatient falls and cost-benefit analysis of implementation of an evidence-based fall prevention program. JAMA Health Forum. Jan 6, 2023;4(1):e225125. [CrossRef] [Medline]3,Aihara H, Tago M, Oishi T, Katsuki NE, Yamashita SI. Visual impairment, partially dependent ADL and extremely old age could be predictors for severe fall injuries in acute care settings. Int J Gerontol. Sep 2018;12(3):175-179. [CrossRef]4]. Therefore, fall prevention has become an important issue to protect patients’ lives and quality of life [Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society. Summary of the updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc. Jan 2011;59(1):148-157. [CrossRef]5].

Interventions for fall prevention must be strategically targeted to populations with a high risk of falling during hospitalization. Furthermore, previous studies have emphasized the importance of patient exercise therapy [Cuevas-Trisan R. Balance problems and fall risks in the elderly. Clin Geriatr Med. May 2019;35(2):173-183. [CrossRef] [Medline]6,Canning CG, Sherrington C, Lord SR, et al. Exercise for falls prevention in Parkinson disease: a randomized controlled trial. Neurology (ECronicon). Jan 20, 2015;84(3):304-312. [CrossRef] [Medline]7] and education for both patients and health care providers in fall prevention [Hill AM, Francis-Coad J, Haines TP, et al. “My independent streak may get in the way”: how older adults respond to falls prevention education in hospital. BMJ Open. Jul 26, 2016;6(7):e012363. [CrossRef] [Medline]8-Heng H, Jazayeri D, Shaw L, Kiegaldie D, Hill AM, Morris ME. Hospital falls prevention with patient education: a scoping review. BMC Geriatr. Apr 15, 2020;20(1):140. [CrossRef] [Medline]12]. Educating patients about the risks of falls and strategies to mitigate these risks is crucial in reducing the incidence of falls in hospitalized patients. To effectively conduct patient education, it is imperative to construct a fall prediction model for the accurate identification of these high-risk patients. Currently, fall prevention measures in hospitals include fall prediction models using information from electronic health record (EHR) systems [Kawazoe Y, Shimamoto K, Shibata D, Shinohara E, Kawaguchi H, Yamamoto T. Impact of a clinical text-based fall prediction model on preventing extended hospital stays for elderly inpatients: model development and performance evaluation. JMIR Med Inform. Jul 27, 2022;10(7):e37913. [CrossRef] [Medline]13-Yaita S, Tago M, Katsuki NE, et al. A simple and accurate model for predicting fall injuries in hospitalized patients: insights from a retrospective observational study in Japan. Med Sci Monit. Aug 14, 2023;29:e941252. [CrossRef] [Medline]22], as well as predictive models that analyze patient information from EHRs and nursing records using artificial intelligence [Cho I, Jin IS, Park H, Dykes PC. Clinical impact of an analytic tool for predicting the fall risk in inpatients: controlled interrupted time series. JMIR Med Inform. Nov 25, 2021;9(11):e26456. [CrossRef] [Medline]15,Nakatani H, Nakao M, Uchiyama H, Toyoshiba H, Ochiai C. Predicting inpatient falls using natural language processing of nursing records obtained from Japanese electronic medical records: case-control study. JMIR Med Inform. Apr 22, 2020;8(4):e16970. [CrossRef] [Medline]23]. Here, we present several fall prediction models that can be used with EHRs [Oliver D, Britton M, Seed P, Martin FC, Hopper AH. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies. BMJ. Oct 25, 1997;315(7115):1049-1053. [CrossRef] [Medline]17-Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20]. The STRATIFY scale [Oliver D, Britton M, Seed P, Martin FC, Hopper AH. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies. BMJ. Oct 25, 1997;315(7115):1049-1053. [CrossRef] [Medline]17] uses a history of falls, visual impairment, mental status, frequency of elimination, and ability to transfer and move as factors in a prediction model. The Morse Fall Scale [Morse JM, Morse RM, Tylko SJ. Development of a scale to identify the fall-prone patient. Can J Aging. 1989;8(4):366-377. [CrossRef]18] includes 6 items related to a history of falls, comorbidities, use of walking aids, intravenous fluids, ability to walk and move, and mental status. The Medication Fall Risk Score and Evaluation Tool [Beasley B, Patatanian E. Development and implementation of a pharmacy fall prevention program. Hosp Pharm. Dec 2009;44(12):1095-1102. [CrossRef]19] assesses the medication-related fall risk. This tool considers a patient’s use of medications as predictors, classified according to the associated risk levels. Tago et al [Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20] reported 8 predictors of falls in people with disabilities in Japan: age, sex, emergency hospitalization, admission to neurosurgery, use of sleeping pills, history of falls, independence in eating, and level of independence in daily living.

Although fall prediction tools are widely used in Japanese hospitals, existing models vary in predictors and are often difficult to apply due to differences in facility and patient characteristics. Many hospitals also rely on tools that lack a strong evidence base [Soyano A, Suzuki M, Harada A, et al. JSFP Research Report〉fall risk assessment tool usage and issues: results of a fall risk assessment tool usage and issues: results of a. Jpn J Fall Prev. 2018;5:41-49. [CrossRef]24,Higaonna M. The predictive validity of a modified Japanese Nursing Association fall risk assessment tool: a retrospective cohort study. Int J Nurs Stud. Sep 2015;52(9):1484-1494. [CrossRef] [Medline]25] and are not well integrated with EHR systems. In our clinical environment, we found it challenging to implement existing models due to their reliance on numerous variables that are either difficult to extract from the EHR or unavailable. Therefore, we aimed to develop a streamlined fall risk assessment tool that considers facility-specific factors and can be seamlessly integrated with EHR systems, enabling real-time insights and more efficient fall prevention strategies.

The purpose of this study was to identify predictive factors for falls in patients admitted to an acute care hospital as well as to develop a scoring system using these factors and evaluate its validity.


Participants and Study Design

We conducted a retrospective cohort study of patients aged ≥20 years admitted to Shizuoka General Hospital between April 2019 and September 2020. Inpatients excluded from the study included those not covered by the Diagnosis Procedure Combination system, such as dental and oral surgery patients and obstetrics and gynecology inpatients during pregnancy, childbirth, and postpartum. In addition, inpatients lacking data on known risk factors such as the degree of independence in daily living for the older adults with disabilities, bedriddenness rank (BR), emergency admission, dietary independence, mobility, and history of falls in the past year were also excluded, as illustrated in Figure 1.

Figure 1. Flowchart illustrating the study population.

Variables at Hospitalization as Candidate Predictive Factors

All data used in this study were extracted from patients’ medical records as of February 3, 2022. Preadmission medical history variables included dementia [Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society. Summary of the updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc. Jan 2011;59(1):148-157. [CrossRef]5,Cuevas-Trisan R. Balance problems and fall risks in the elderly. Clin Geriatr Med. May 2019;35(2):173-183. [CrossRef] [Medline]6], Parkinson disease [Canning CG, Sherrington C, Lord SR, et al. Exercise for falls prevention in Parkinson disease: a randomized controlled trial. Neurology (ECronicon). Jan 20, 2015;84(3):304-312. [CrossRef] [Medline]7,Okuma Y. Freezing of gait and falls in Parkinson’s disease. J Parkinsons Dis. 2014;4(2):255-260. [CrossRef] [Medline]26], stroke [Cuevas-Trisan R. Balance problems and fall risks in the elderly. Clin Geriatr Med. May 2019;35(2):173-183. [CrossRef] [Medline]6], visual impairment (with or with no diagnosis of glaucoma or cataracts) [Aihara H, Tago M, Oishi T, Katsuki NE, Yamashita SI. Visual impairment, partially dependent ADL and extremely old age could be predictors for severe fall injuries in acute care settings. Int J Gerontol. Sep 2018;12(3):175-179. [CrossRef]4,Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society. Summary of the updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc. Jan 2011;59(1):148-157. [CrossRef]5], history of falls [Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society. Summary of the updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc. Jan 2011;59(1):148-157. [CrossRef]5,Jung H, Yoo S, Kim S, et al. Patient-level fall risk prediction using the observational medical outcomes partnership’s common data model: pilot feasibility study. JMIR Med Inform. Mar 11, 2022;10(3):e35104. [CrossRef] [Medline]16,Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Hodgson G, Pace A, Carfagnini Q, et al. Risky business: factors that increase risk of falls among older adult in-patients. Gerontol Geriatr Med. 2023;9:23337214231189930. [CrossRef] [Medline]27], and use of sleep medications [Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Hendrich AL, Bender PS, Nyhuis A. Validation of the Hendrich II Fall Risk Model: a large concurrent case/control study of hospitalized patients. Appl Nurs Res. Feb 2003;16(1):9-21. [CrossRef] [Medline]28-Noh HM, Song HJ, Park YS, Han J, Roh YK. Fall predictors beyond fall risk assessment tool items for acute hospitalized older adults: a matched case-control study. Sci Rep. Jan 15, 2021;11(1):1503. [CrossRef] [Medline]30]. The following variables at admission were also collected: age [Cuevas-Trisan R. Balance problems and fall risks in the elderly. Clin Geriatr Med. May 2019;35(2):173-183. [CrossRef] [Medline]6,Jung H, Yoo S, Kim S, et al. Patient-level fall risk prediction using the observational medical outcomes partnership’s common data model: pilot feasibility study. JMIR Med Inform. Mar 11, 2022;10(3):e35104. [CrossRef] [Medline]16,Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Hodgson G, Pace A, Carfagnini Q, et al. Risky business: factors that increase risk of falls among older adult in-patients. Gerontol Geriatr Med. 2023;9:23337214231189930. [CrossRef] [Medline]27], sex [Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Hodgson G, Pace A, Carfagnini Q, et al. Risky business: factors that increase risk of falls among older adult in-patients. Gerontol Geriatr Med. 2023;9:23337214231189930. [CrossRef] [Medline]27,Hendrich AL, Bender PS, Nyhuis A. Validation of the Hendrich II Fall Risk Model: a large concurrent case/control study of hospitalized patients. Appl Nurs Res. Feb 2003;16(1):9-21. [CrossRef] [Medline]28], BMI [Nakamura K, Kitamura K, Watanabe Y, et al. Body mass index and risk of recurrent falls in community-dwelling Japanese aged 40-74 years: the Murakami cohort study. Geriatr Gerontol Int. Jun 2021;21(6):498-505. [CrossRef] [Medline]31-Ogliari G, Ryg J, Andersen-Ranberg K, Scheel-Hincke LL, Masud T. Association between body mass index and falls in community-dwelling men and women: a prospective, multinational study in the Survey of Health, Ageing and Retirement in Europe (SHARE). Eur Geriatr Med. Aug 2021;12(4):837-849. [CrossRef] [Medline]35], date of admission [Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20], disease name at admission, department [Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20], mode of admission, ambulance transport, consciousness disorders [Jung H, Yoo S, Kim S, et al. Patient-level fall risk prediction using the observational medical outcomes partnership’s common data model: pilot feasibility study. JMIR Med Inform. Mar 11, 2022;10(3):e35104. [CrossRef] [Medline]16,Nakatani H, Nakao M, Uchiyama H, Toyoshiba H, Ochiai C. Predicting inpatient falls using natural language processing of nursing records obtained from Japanese electronic medical records: case-control study. JMIR Med Inform. Apr 22, 2020;8(4):e16970. [CrossRef] [Medline]23,Hendrich AL, Bender PS, Nyhuis A. Validation of the Hendrich II Fall Risk Model: a large concurrent case/control study of hospitalized patients. Appl Nurs Res. Feb 2003;16(1):9-21. [CrossRef] [Medline]28], requirement for nursing care, good sleep condition, use of sleeping medication [Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Hendrich AL, Bender PS, Nyhuis A. Validation of the Hendrich II Fall Risk Model: a large concurrent case/control study of hospitalized patients. Appl Nurs Res. Feb 2003;16(1):9-21. [CrossRef] [Medline]28,Noh HM, Song HJ, Park YS, Han J, Roh YK. Fall predictors beyond fall risk assessment tool items for acute hospitalized older adults: a matched case-control study. Sci Rep. Jan 15, 2021;11(1):1503. [CrossRef] [Medline]30], status of medication management, BR [Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Tago M, Katsuki NE, Yaita S, et al. High inter-rater reliability of Japanese bedriddenness ranks and cognitive function scores: a hospital-based prospective observational study. BMC Geriatr. Mar 9, 2021;21(1):168. [CrossRef] [Medline]36], Cognitive Function Scores [Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society. Summary of the updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc. Jan 2011;59(1):148-157. [CrossRef]5,Cuevas-Trisan R. Balance problems and fall risks in the elderly. Clin Geriatr Med. May 2019;35(2):173-183. [CrossRef] [Medline]6], ADL at admission (eating, transferring, dressing, toilet transfer or use, bathing, level walking, stair use, changing clothes, defecation management, and urinary management) [Oliver D, Britton M, Seed P, Martin FC, Hopper AH. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies. BMJ. Oct 25, 1997;315(7115):1049-1053. [CrossRef] [Medline]17,Morse JM, Morse RM, Tylko SJ. Development of a scale to identify the fall-prone patient. Can J Aging. 1989;8(4):366-377. [CrossRef]18,Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20], fall assessment end points at admission (history of falls or falls within 1 year, inability to stand without holding on [Hendrich AL, Bender PS, Nyhuis A. Validation of the Hendrich II Fall Risk Model: a large concurrent case/control study of hospitalized patients. Appl Nurs Res. Feb 2003;16(1):9-21. [CrossRef] [Medline]28], impaired judgment and comprehension, toilet assistance, and use of portable toilet), and presence of physical restraint screening at admission.

The BR [Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Tago M, Katsuki NE, Yaita S, et al. High inter-rater reliability of Japanese bedriddenness ranks and cognitive function scores: a hospital-based prospective observational study. BMC Geriatr. Mar 9, 2021;21(1):168. [CrossRef] [Medline]36] is an official assessment tool in Japan’s long-term care insurance system [Ozawa MN, Nakayama S. Long-term care insurance in Japan. J Aging Soc Policy. 2005;17(3):61-84. [CrossRef] [Medline]37]. The BR is an assessment of the degree to which a person’s daily life is restricted; this degree is mainly assessed in terms of mobility in daily life, such as whether the person is independent, in a wheelchair, or bed bound. The Ministry of Health, Labour and Welfare ranks the degree of BR based on evaluations by nurses and other health care professionals according to the daily care they provide during hospitalization, as well as on reports from family members. The procedure for assessing BR and its reliability have been reported [Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Tago M, Katsuki NE, Yaita S, et al. High inter-rater reliability of Japanese bedriddenness ranks and cognitive function scores: a hospital-based prospective observational study. BMC Geriatr. Mar 9, 2021;21(1):168. [CrossRef] [Medline]36].

Falls During Hospitalization as an Outcome

The primary end point was the time from the date of admission to a fall at incident level [Council for Medical Safety Management of National University Hospitals influence classification [Article in Japanese]. Japan National University Hospital Alliance on Patient Safety. URL: https://square.umin.ac.jp/anzenhc/information/pdf/23_business_text2022.pdf [Accessed 2024-12-13] 38] 2 or higher (hereafter referred to as “fall”), which requires medical resources. For patients who died during hospitalization and those who did not have a fall, the date of death or date of discharge, respectively, was used as the censoring date. The classification of incident levels is shown in Table S1 in

Multimedia Appendix 1

Supplementary tables and figure.

DOCX File, 151 KBMultimedia Appendix 1.

Statistical Analysis

Demographic data and potential predictors at the time of hospital admission were summarized as follows. Continuous variables are described using mean (SD) or median (range), considering the distribution type. Categorical variables are summarized as frequency (%). For comparisons between the groups with and with no fall occurrence, t tests were used for continuous variables and chi-squared tests for categorical variables. The Kaplan-Meier method was applied to estimate the fall rate.

We explored predictors of falls and constructed a predictive model using two-thirds of the total cases, randomly selected as the training group, with the remaining one-third serving as the validation group for the scoring system. In the training group, predictive factors were identified using the Cox proportional hazards model, and we calculated hazard ratios (HRs), 95% confidence intervals (CIs), and P values. Predictive candidates that were significant (P<.05) in the comparison of backgrounds among patients with and with no falls, along with known predictors, were included in a multivariable model. Factors with P value of <.2 in this model were identified as predictors of inhospital falls. Independence between explanatory variables was confirmed using an absolute value of Spearman rank correlation coefficient of >0.4. Among 2 correlated variables, 1 was chosen based on ease of collection or clinical significance; this variable was then included in the multivariable model. According to the identified predictive factors, a score was created for each HR, and these scores were summed. We used a method called “conditional inference tree analysis” to categorize patients into 3 groups based on their risk of falling. This approach works by first dividing the data into 2 groups based on their overall scores. Then, a statistical test is performed to see whether these 2 groups are significantly different, and the variable that shows the strongest difference (the one with the lowest P value) is used to split the groups. This process is repeated within each subgroup until no further meaningful divisions can be made or the smallest group size allowed is reached. The predictive performance of this score and the fall risk groups in the validation group were evaluated using the c-index.

The significance level of the 2-tailed test was set at .05. Missing values were not imputed in the analyses. All analyses were performed using R (version 4.1.1; The R Foundation for Statistical Computing), EZR (version 1.54; Saitama Medical Center, Jichi Medical University) [Kanda Y. Investigation of the freely available easy-to-use software “EZR” for medical statistics. Bone Marrow Transplant. Mar 2013;48(3):452-458. [CrossRef] [Medline]39], and IBM SPSS (version 28; IBM Corp).

Ethical Considerations

This study conformed to the Ethical Principles for Medical Research Involving Human Subjects issued by the Ministry of Health, Labour and Welfare and the Ministry of Education, Culture, Sports, Science and Technology of Japan. Following these guidelines, the Shizuoka General Hospital research ethical committee determined that individual patient informed consent was not required because we analyzed existing information in this study, and patients were given the right to refuse participation via disclosure. After obtaining committee approval (SGHIRB #2020075; January 15, 2022) and publishing the disclosure document on Shizuoka General Hospital’s website, the information for each individual was anonymized, and the analysis was conducted.


Patient Background and Falls at Incident Level 2 or Higher on Admission

From April 1, 2019, to September 30, 2020, a total of 24,932 inpatients aged 20 years or older were admitted to Shizuoka General Hospital. We excluded 3.3% (829/24,932) of patients not covered by the Diagnosis Procedure Combination, 29.2% (7288/24,932) with duplicate admissions, and 12.4% (3090/24,932) with missing known risk factors of falls. Consequently, 55% (13,725/24,932) of patients were included in the analysis (Figure 1).

During the observation period, defined as the length of hospital stay (median [range]: 13 [1-271] days), 3.6% (489/13,725) of patients experienced falls across all incident levels, of which 2.4% (326/13,725) falls were classified as incident level 2 or higher. For this study, we used fall data meticulously managed by the hospital’s medical safety department, ensuring accuracy and reliability. The details are shown in Table S2 in

Multimedia Appendix 1

Supplementary tables and figure.

DOCX File, 151 KBMultimedia Appendix 1. The median age (range) of the patients included in the analysis was 66 (20-103) years, with 52.1% (7150/13,725) male patients, and the median BMI (range) was 22.8 (9.6‐58.1) kg/m2. Table S3 in

Multimedia Appendix 1

Supplementary tables and figure.

DOCX File, 151 KB
Multimedia Appendix 1
shows the results of the comparison between the backgrounds of patients in the training and test datasets. In a univariable analysis of the training dataset (n=9150; two-thirds of the study population), we compared patient backgrounds according to the presence of falls (Table 1).

Table 1. Patient background on admissiona.
VariableWith no fall (n=8934)With fall (n=216)P value
Age<.001
Age (years), mean (SD)65.9 (17)75.0 (11.5)
Age<.001
20 to <65 years, n (%)3347 (37.5)28 (13)
65 to <80 years, n (%)3673 (41.1)105 (48.6)
≥80 years, n (%)1914 (21.4)83 (38.4)
Sex, n (%)<.001
Female4287 (48)75 (34.7)
Male4647 (52)141 (65.3)
BMI<.001
BMI, mean (SD)22.9 (4.2)21.1 (3.9)
BMI<.001
<18.5 kg/m2, n (%)1142 (12.8)48 (22.2)
18.5 to <25 kg/m2, n (%)5363 (60)132 (61.1)
≥25 kg/m2, n (%)2403 (26.9)36 (16.7)
Missing, n (%)26 (0.3)0 (0)
Dementia, n (%).927
No8697 (97.3)211 (97.7)
Yes237 (2.7)5 (2.3)
Parkinson disease, n (%).592
No8861 (99.2)213 (98.6)
Yes73 (0.8)3 (1.4)
Stroke, n (%).003
No8121 (90.9)183 (84.7)
Yes813 (9.1)33 (15.3)
Visual impairment, n (%).778
No7991 (89.4)195 (90.3)
Yes943 (10.6)21 (9.7)
Cognitive function score, n (%)<.001
No7855 (87.9)179 (82.9)
Yes710 (7.9)37 (17.1)
Missing369 (4.1)0 (0)
Ambulance transport, n (%)<.001
No7432 (83.2)156 (72.2)
Yes1502 (16.8)60 (27.8)
Emergency admission, n (%)<.001
Scheduled hospitalization5528 (61.9)74 (34.3)
Emergency hospitalization3406 (38.1)142 (65.7)
Department, n (%).007
Internal medicine4496 (50.3)118 (54.6)
Department of surgery4025 (45.1)80 (37)
Emergency department413 (4.6)18 (8.3)
Consciousness disorders, n (%)<.001
No8022 (89.8)167 (77.3)
Yes912 (10.2)49 (22.7)
Bedriddenness rank, n (%)<.001
Rank J5500 (61.6)65 (30.1)
Rank A1568 (17.6)58 (26.9)
Rank B526 (5.9)34 (15.7)
Rank C1340 (15)59 (27.3)
Eating, n (%)<.001
Independent7067 (79.1)107 (49.5)
Requiring assistance1867 (20.9)109 (50.5)
Transferring, n (%)<.001
Independent6579 (73.6)89 (41.2)
Requiring assistance2355 (26.4)127 (58.8)
Dressing, n (%)<.001
Independent7065 (79.1)112 (51.9)
Requiring assistance1853 (20.7)104 (48.1)
Missing16 (0.2)0 (0)
Toilet transfer or use, n (%)<.001
Independent6820 (76.3)94 (43.5)
Requiring assistance2099 (23.5)121 (56)
Missing15 (0.2)1 (0.5)
Bathing, n (%)<.001
Independent6673 (74.7)95 (44)
Requiring assistance2042 (22.9)114 (52.8)
Missing219 (2.5)7 (3.2)
Level walking, n (%)<.001
Independent6707 (75.1)93 (43.1)
Requiring assistance2107 (23.6)122 (56.5)
Missing120 (1.3)1 (0.5)
Stair use, n (%)<.001
Independent6541 (73.2)86 (39.8)
Requiring assistance1986 (22.2)111 (51.4)
Missing407 (4.6)19 (8.8)
Changing clothes, n (%)<.001
Independent6799 (76.1)97 (44.9)
Requiring assistance2116 (23.7)119 (55.1)
Missing19 (0.2)0 (0)
Defecation management, n (%)<.001
Independent7254 (81.2)127 (58.8)
Requiring assistance1618 (18.1)87 (40.3)
Missing62 (0.7)2 (0.9)
Urination management, n (%)<.001
Independent7243 (81.1)126 (58.3)
Requiring assistance1625 (18.2)88 (40.7)
Missing66 (0.7)2 (0.9)
History of falls within 1 year, n (%)<.001
No7830 (87.6)153 (70.8)
Yes1104 (12.4)63 (29.2)
Inability to stand without holding, n (%)<.001
No5318 (59.5)53 (24.5)
Yes3486 (39)159 (73.6)
Missing130 (1.5)4 (1.9)
Impaired judgment and comprehension, n (%)<.001
No7592 (85)157 (72.7)
Yes1116 (12.5)53 (24.5)
Missing226 (2.5)6 (2.8)
Toileting assistance, n (%)<.001
No6694 (74.9)106 (49.1)
Yes1795 (20.1)99 (45.8)
Missing445 (5)11 (5.1)

aBetween-group comparisons were made using t tests and χ2 tests for continuous and categorical variables, respectively. The P value was calculated using the Wald test. Bedriddenness rank J=independent or autonomous, rank A=housebound, rank B=chair, and rank C=bedridden.

Predictors of Falls

In the training dataset, univariable Cox regression analysis compared patient backgrounds based on the presence or absence of falls and identified factors affecting the time from admission to the date of fall (Table 2, left side). Correlations between explanatory variables were checked using Spearman correlation coefficients, and independent explanatory variables were entered into a multivariable regression model. The variables were narrowed down by applying a high absolute value of the Spearman correlation coefficient (>0.4) (Table S4 in

Multimedia Appendix 1

Supplementary tables and figure.

DOCX File, 151 KBMultimedia Appendix 1). Among the 21 variables that were significant in the univariable analysis, 5 variables (age, sex, BMI, department, and history of falls within 1 year) were not correlated with each other. Sixteen variables (including 12 ADL-related variables, emergency admission, and consciousness disorders) had a correlation coefficient of 0.4 or higher. From the correlated variables, BR was ultimately chosen. This decision was influenced by the fact that 12 other ADL variables showed correlation, and BR could potentially explain physical severity, including factors such as emergency transport and impaired consciousness. The variables were chosen based on ease of collection or clinical significance.

Table 2. Univariable and multivariable Cox regression analysis results for fall rates in the training dataseta.
Variable (reference) and categoryTraining dataset (n=9150)
Univariable modelMultivariable model
HRb95% CIP valueHR95% CIP value
Age
1 year1.031.02‐1.04<.001c
Age (20 to <65 years)
65 to <80 years2.711.79‐4.12<.0012.261.48‐3.44<.001
≥80 years3.532.29‐5.43<.0012.501.60‐3.92
Sex (female)
Male1.601.21‐2.12.0011.601.21‐2.13.001
BMI
1 kg/m20.920.89‐0.96<.001
BMI (≥25 kg/m2)
18.5 to <25 kg/m21.571.09‐2.27.0171.360.94‐1.97.127
≤18.5 kg/m22.011.30‐3.10.0021.571.01‐2.44
Ambulance transport (no)
Yes1.771.33‐2.36<.001
Emergency admission (scheduled hospitalization)
Emergency hospitalization1.220.90‐1.64.203
Stroke (no)
Yes1.180.81‐1.72.376
Bedriddenness rank (rank J)
Rank A2.271.59‐3.23<.0011.811.26‐2.60.001
Rank B2.931.93‐4.45<.0012.031.31‐3.14
Rank C1.941.35‐2.77<.0011.230.83‐1.83
Cognitive function score (no)
Yes1.400.98‐2.01.063
Department (department of surgery)
Internal medicine1.361.02‐1.81.0341.230.92‐1.64.119
Emergency department2.161.29‐3.60.0031.811.26‐2.60
Consciousness disorders (no)
Yes1.471.06‐2.03.020
Eating (independent)
Requiring assistance2.051.56‐2.69<.001
Transferring (independent)
Requiring assistance2.121.60‐2.80<.001
Dressing (independent)
Requiring assistance1.881.43‐2.48<.001
Toilet transfer or use (independent)
Requiring assistance2.221.68‐2.93<.001
Bathing (independent)
Requiring assistance2.101.59‐2.78<.001
Level walking (independent)
Requiring assistance2.181.65‐2.88<.001
Stair use (independent)
Requiring assistance2.171.63‐2.90<.001
Changing clothes (independent)
Requiring assistance2.101.60‐2.77<.001
Defecation management (independent)
Requiring assistance1.631.23‐2.15.001
Urination management (independent)
Requiring assistance1.641.24‐2.17.001
History of falls within 1 year (no)
Yes2.011.50‐2.70<.0011.661.21‐2.27.002
Inability to stand without holding (no)
Yes2.511.83‐3.45<.001
Impaired judgment and comprehension (no)
Yes1.401.03‐1.92.035
Toileting assistance (no)
Yes2.101.59‐2.77<.001

aBetween-group comparisons were made using t tests and χ2 tests for continuous and categorical variables, respectively. The P value was calculated using the Wald test. Bedriddenness rank J= independent or autonomous, rank A=housebound, rank B=chair, and rank C= bedridden.

bHR: hazard ratio.

cNot applicable.

In multivariable analysis, age (65 to <80 years: HR 2.26, 95% CI 1.48‐3.44; ≥80 years: HR 2.50, 95% CI 1.60‐3.92 vs 20 to <65 years), sex (male: HR 1.60, 95% CI 1.21‐2.13), BMI (18.5 to <25 kg/m²: HR 1.36, 95% CI 0.94‐1.97; <18.5 kg/m²: HR 1.57, 95% CI 1.01‐2.44 vs ≥25 kg/m²), BR (rank A: HR 1.81, 95% CI 1.26‐2.60; rank B: HR 2.03, 95% CI 1.31‐3.14; rank C: HR 1.23, 95% CI 0.83‐1.83 vs rank J), emergency department (internal medicine: HR 1.23, 95% CI 0.92‐1.64; emergency department: HR 1.81, 95% CI 1.26‐2.60 vs department of surgery), and history of falls within 1 year (yes: HR 1.66, 95% CI 1.21‐2.27) are shown as predictors of inhospital falls (Table 2, right side).

Construction of a Fall Scoring System

Based on the results of multivariable analysis using the training set, we weighted the scores based on each HR (Table 2, right side) and formed 3 fall-risk groups (low risk: 0-4 points, moderate risk: 5-9 points, and high risk: 10-15 points) using conditional inference tree analysis (Figure S1 in

Multimedia Appendix 1

Supplementary tables and figure.

DOCX File, 151 KBMultimedia Appendix 1). The new fall prediction scoring system built on this basis is shown in Figure 2. As a predictor of the performance of these 3 classifications of fall prediction, the c-index in the validation set (n=4561) was 0.733 (95% CI 0.684‐0.782). The cumulative fall incidences in training and test datasets are shown, with Kaplan-Meier curves presented for the training dataset (Figure 3A) and the test dataset (Figure 3B).

Figure 2. Fall prediction scoring system to be implemented at the time of admission.
Figure 3. Fall risk classification-specific cumulative fall incidence. Cumulative fall incidences classified by fall risk scoring are shown for the training dataset (A) and test dataset (B).

Principal Results

This study was a retrospective cohort investigation conducted at an institution specialized in acute inpatient care, aimed at identifying the risk factors for falls using the time from admission to fall as the outcome variable. Fall risk factors included age, sex, BMI, BR, emergency department, and history of falls within 1 year. Specifically, the study found that older patients (aged 80 years and older) had a higher risk of falls, with men being more at risk than women. Patients with a BMI of <18.5 and those admitted through the emergency department had an increased risk. In addition, those with a history of falls within the past year were particularly vulnerable. We constructed a new predictive scoring system for falls by weighting scores based on each HR according to the results of multivariable analysis and using statistical methods to classify fall risk groups into 3 categories (low risk: 0-4 points, moderate risk: 5-9 points, and high risk: 10-15 points). The present fall prediction scoring system could facilitate preventive interventions for high-risk patients, potentially reducing the likelihood of falls among the most susceptible patient populations and improving patient safety and care in the hospital environment.

Comparison With Prior Work

In terms of age-specific fall incidence, it was evident that a higher proportion of falls occurred among people aged 65 years and older. This result is in alignment with previous reports [Cuevas-Trisan R. Balance problems and fall risks in the elderly. Clin Geriatr Med. May 2019;35(2):173-183. [CrossRef] [Medline]6,Jung H, Yoo S, Kim S, et al. Patient-level fall risk prediction using the observational medical outcomes partnership’s common data model: pilot feasibility study. JMIR Med Inform. Mar 11, 2022;10(3):e35104. [CrossRef] [Medline]16,Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Yaita S, Tago M, Katsuki NE, et al. A simple and accurate model for predicting fall injuries in hospitalized patients: insights from a retrospective observational study in Japan. Med Sci Monit. Aug 14, 2023;29:e941252. [CrossRef] [Medline]22,Hodgson G, Pace A, Carfagnini Q, et al. Risky business: factors that increase risk of falls among older adult in-patients. Gerontol Geriatr Med. 2023;9:23337214231189930. [CrossRef] [Medline]27,Hirata R, Tago M, Katsuki NE, et al. History of falls and bedriddenness ranks are useful predictive factors for in-hospital falls: a single-center retrospective observational study using the Saga Fall Risk Model. Int J Gen Med. 2022;15:8121-8131. [CrossRef] [Medline]40] identifying advanced age as a predictive factor for falls. Consistent with past studies [Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society. Summary of the updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc. Jan 2011;59(1):148-157. [CrossRef]5,Jung H, Yoo S, Kim S, et al. Patient-level fall risk prediction using the observational medical outcomes partnership’s common data model: pilot feasibility study. JMIR Med Inform. Mar 11, 2022;10(3):e35104. [CrossRef] [Medline]16,Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Yaita S, Tago M, Katsuki NE, et al. A simple and accurate model for predicting fall injuries in hospitalized patients: insights from a retrospective observational study in Japan. Med Sci Monit. Aug 14, 2023;29:e941252. [CrossRef] [Medline]22,Hirata R, Tago M, Katsuki NE, et al. History of falls and bedriddenness ranks are useful predictive factors for in-hospital falls: a single-center retrospective observational study using the Saga Fall Risk Model. Int J Gen Med. 2022;15:8121-8131. [CrossRef] [Medline]40], a history of falls was determined to be a predictive factor.

Previous reports on predictors of falls have shown that sex can be a predictor for both men and women. In this study, being male was identified as a risk factor. In addition, men have been found to experience multiple falls more frequently [Hodgson G, Pace A, Carfagnini Q, et al. Risky business: factors that increase risk of falls among older adult in-patients. Gerontol Geriatr Med. 2023;9:23337214231189930. [CrossRef] [Medline]27]. Past reports indicating that being male increases the risk of falls [Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Hendrich AL, Bender PS, Nyhuis A. Validation of the Hendrich II Fall Risk Model: a large concurrent case/control study of hospitalized patients. Appl Nurs Res. Feb 2003;16(1):9-21. [CrossRef] [Medline]28] have focused on hospitalized patients whereas those suggesting an increased risk for women [Masumoto T, Yamada Y, Yamada M, et al. Fall risk factors and sex differences among community-dwelling elderly individuals in Japan. A Kameoka study. Nihon Koshu Eisei Zasshi. 2015;62(8):390-401. [CrossRef] [Medline]41,Bally ELS, Ye L, van Grieken A, et al. Factors associated with falls among hospitalized and community-dwelling older adults: the APPCARE study. Front Public Health. 2023;11:1180914. [CrossRef] [Medline]42] have focused on community-dwelling individuals. These differences may be owing to the different characteristics of the study populations, that is, relatively healthy community residents and patients in health care facilities. This may be related to the fact that hospitalized patients tend to have reduced physical activity, which increases the risk of falls. However, the relationship between sex differences and fall risk factors in the hospitalized population remains unclear, and further research is needed to elucidate these aspects.

A systematic review targeting community-dwelling older adults showed that a low BMI (<17 kg/m2) is associated with a greater risk of falls, when using 23.5 kg/m2 as the baseline [Trevisan C, Crippa A, Ek S, et al. Nutritional status, body mass index, and the risk of falls in community-dwelling older adults: a systematic review and meta-analysis. J Am Med Dir Assoc. May 2019;20(5):569-582. [CrossRef] [Medline]34]. In addition, some reports indicate that both high (25‐35 kg/m2 or above) and low (below 18.5 kg/m2) BMI values are associated with increased fall risks [Nakamura K, Kitamura K, Watanabe Y, et al. Body mass index and risk of recurrent falls in community-dwelling Japanese aged 40-74 years: the Murakami cohort study. Geriatr Gerontol Int. Jun 2021;21(6):498-505. [CrossRef] [Medline]31-Yi SW, Kim YM, Won YJ, Kim SK, Kim SH. Association between body mass index and the risk of falls: a nationwide population-based study. Osteoporos Int. Jun 2021;32(6):1071-1078. [CrossRef] [Medline]33,Ogliari G, Ryg J, Andersen-Ranberg K, Scheel-Hincke LL, Masud T. Association between body mass index and falls in community-dwelling men and women: a prospective, multinational study in the Survey of Health, Ageing and Retirement in Europe (SHARE). Eur Geriatr Med. Aug 2021;12(4):837-849. [CrossRef] [Medline]35]. These findings suggest that extreme BMI values influence the risk of falling. However, in our study, we identified low BMI (below 18.5 kg/m2) and normal BMI (18.5‐25 kg/m2) as risk factors for falls, using high BMI (25 kg/m2 or above) as the reference category. This divergence in results might be attributed to the fact that in our Japanese study population, few patients met the international high BMI criterion (>30 kg/m2). In the study, because only 5.7% (6/741) of people in this training category fell, BMI >30 kg/m2 was not able to be included as the category for a categorical variable BMI.

The BR degree, which is strongly correlated with ADL, was identified as a risk factor for inhospital falls. This finding aligns with several fall prediction models [Oliver D, Britton M, Seed P, Martin FC, Hopper AH. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies. BMJ. Oct 25, 1997;315(7115):1049-1053. [CrossRef] [Medline]17,Morse JM, Morse RM, Tylko SJ. Development of a scale to identify the fall-prone patient. Can J Aging. 1989;8(4):366-377. [CrossRef]18,Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20], demonstrating the significant role of a decline in ADL, as it is known that exercise therapy is effective in fall prevention [Cuevas-Trisan R. Balance problems and fall risks in the elderly. Clin Geriatr Med. May 2019;35(2):173-183. [CrossRef] [Medline]6,Canning CG, Sherrington C, Lord SR, et al. Exercise for falls prevention in Parkinson disease: a randomized controlled trial. Neurology (ECronicon). Jan 20, 2015;84(3):304-312. [CrossRef] [Medline]7]. This underscores that impaired ADL is a crucial factor in determining the outcomes of patients who experience falls. In addition, because the overall assessment of ADL is predominantly based on mobility, a substantial correlation [Tsuboi A, Murakami T, Kurumadani H, et al. Relationship between criteria for evaluating the degree of independence of disabled elderly persons in performing activities of daily living. J Jpn Occup Ther Assoc. 2002;21:255-262. [CrossRef]43] between ADL and BR has been observed. In our study, of the 15 variables that showed a correlation with BR, 12 were related to components of ADL.

Being admitted to the emergency department (as an inpatient department) was identified as a risk factor for inhospital falls. The emergency department inpatient population comprises patients who have been transported to the emergency department or who otherwise came voluntarily to the emergency department. This population typically has more severe illness, which may explain why emergency department admission is a risk factor for inhospital falls. Moreover, a history of falls within the past 1 year was identified as a risk factor for inhospital falls. This result was similar to previous reports that identified a fall history as a risk factor for subsequent falls [Oliver D, Britton M, Seed P, Martin FC, Hopper AH. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies. BMJ. Oct 25, 1997;315(7115):1049-1053. [CrossRef] [Medline]17,Morse JM, Morse RM, Tylko SJ. Development of a scale to identify the fall-prone patient. Can J Aging. 1989;8(4):366-377. [CrossRef]18,Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20].

Falls are internationally recognized as a serious health issue [Dykes PC, Curtin-Bowen M, Lipsitz S, et al. Cost of inpatient falls and cost-benefit analysis of implementation of an evidence-based fall prevention program. JAMA Health Forum. Jan 6, 2023;4(1):e225125. [CrossRef] [Medline]3,Noh HM, Song HJ, Park YS, Han J, Roh YK. Fall predictors beyond fall risk assessment tool items for acute hospitalized older adults: a matched case-control study. Sci Rep. Jan 15, 2021;11(1):1503. [CrossRef] [Medline]30], and various efforts to prevent falls are undertaken worldwide. Particularly in Japan, where rapid aging is prevalent, fall prevention has become an increasingly critical issue. Education for patients and staff is considered a fundamental approach to addressing this problem [Hill AM, Francis-Coad J, Haines TP, et al. “My independent streak may get in the way”: how older adults respond to falls prevention education in hospital. BMJ Open. Jul 26, 2016;6(7):e012363. [CrossRef] [Medline]8,Morris ME, Webster K, Jones C, et al. Interventions to reduce falls in hospitals: a systematic review and meta-analysis. Age Ageing. May 1, 2022;51(5):afac077. [CrossRef] [Medline]10-Heng H, Jazayeri D, Shaw L, Kiegaldie D, Hill AM, Morris ME. Hospital falls prevention with patient education: a scoping review. BMC Geriatr. Apr 15, 2020;20(1):140. [CrossRef] [Medline]12,Haines TP, Hill AM, Hill KD, et al. Patient education to prevent falls among older hospital inpatients: a randomized controlled trial. Arch Intern Med. Mar 28, 2011;171(6):516-524. [CrossRef] [Medline]44], and the introduction and enhancement of educational programs in Japanese hospitals are desirable [Morris ME, Webster K, Jones C, et al. Interventions to reduce falls in hospitals: a systematic review and meta-analysis. Age Ageing. May 1, 2022;51(5):afac077. [CrossRef] [Medline]10]. To achieve this, it is essential to identify high-priority patients among inpatients and implement fall prevention measures as a high-risk approach. Multifactorial interventions to comprehensively assess and address multiple fall risk factors have proven effective [Morris ME, Webster K, Jones C, et al. Interventions to reduce falls in hospitals: a systematic review and meta-analysis. Age Ageing. May 1, 2022;51(5):afac077. [CrossRef] [Medline]10]. However, some tools for evaluating fall risk have been criticized for their time-consuming nature and limited effectiveness [Montero-Odasso M, van der Velde N, Martin FC, et al. World guidelines for falls prevention and management for older adults: a global initiative. Age Ageing. Sep 2, 2022;51(9):afac205. [CrossRef] [Medline]45-Morris ME, Haines T, Hill AM, et al. Divesting from a scored hospital Fall Risk Assessment Tool (FRAT): a cluster randomized non-inferiority trial. J Am Geriatr Soc. Sep 2021;69(9):2598-2604. [CrossRef] [Medline]47], necessitating judicious selection and effective use. Recent research supports these multifactorial interventions and highlights the importance of tailored educational programs and effective risk assessment tools. Guidelines from the United Kingdom’s National Institute for Health and Care Excellence offer specific approaches for fall prevention [Swift CG, Iliffe S. Assessment and prevention of falls in older people—concise guidance. Clin Med (Northfield). Dec 2014;14(6):658-662. [CrossRef]48], which could serve as valuable references for fall prevention strategies in Japan. In the approach to fall prevention in Japanese hospitals, international insights and guidelines should be considered while also tailoring unique approaches according to facility characteristics, health care delivery systems, and patient backgrounds.

Statistical Validation and Clinical Application of Risk Categorization

In our research, we used the conditional inference tree method for statistical analysis to categorize patients into low-, middle-, and high-risk groups, as detailed in Table S4 in

Multimedia Appendix 1

Supplementary tables and figure.

DOCX File, 151 KBMultimedia Appendix 1. This methodological choice ensured that our risk stratifications were based on solid data analysis, avoiding arbitrary determinations. In addition, this scoring approach permits adjustment of cutoff values based on each health care facility’s resources and circumstances, enhancing its applicability in diverse clinical settings. Our aim is not to change the patient’s fall risk level based on resources but to adjust the intensity of preventive interventions according to available resources. The fall risk classification is based solely on clinical characteristics, while resource availability guides the prioritization and distribution of these interventions.

For patients identified as high risk for falls according to our predictive model, it is important to recognize that identifying these individuals is only the first step; providing effective interventions is a separate and critical challenge. Based on prior research demonstrating their effectiveness, we recommend several interventions tailored to implement fall prevention measures as a high priority. Here, we outline interventions such as increased monitoring, personalized environmental adjustments, nonslip footwear, assistive devices such as walkers or canes, one-on-one support, and immediate assistance. In addition, patient education and rehabilitation are crucial components. Educating both patients and health care providers about fall risks and preventive strategies, combined with physical therapy to enhance strength and balance, can significantly reduce the risk of falls [Cuevas-Trisan R. Balance problems and fall risks in the elderly. Clin Geriatr Med. May 2019;35(2):173-183. [CrossRef] [Medline]6-Heng H, Jazayeri D, Shaw L, Kiegaldie D, Hill AM, Morris ME. Hospital falls prevention with patient education: a scoping review. BMC Geriatr. Apr 15, 2020;20(1):140. [CrossRef] [Medline]12]. However, it may be necessary to adjust these cutoffs based on sensitivity and specificity considerations to enhance the accuracy of patient risk identification, aligning more closely with practical prediction practices in health care.

The fall prediction tool we developed integrates seamlessly with a hospital’s EHR system. Upon a patient’s admission, it automatically retrieves EHR data and calculates a fall risk score based on various predictors. This integration allows all health care providers, including emergency and trauma physicians, to easily access the patient’s fall risk assessment. Emergency or trauma physicians can use this tool to quickly identify patients at high risk for falls and implement appropriate interventions. This proactive approach can significantly reduce the incidence of falls and enhance patient safety in fast-paced and high-stress environments such as emergency departments and trauma centers.

Limitations

This study has several limitations. First, owing to the identification of inhospital fall predictors based on the characteristics of the facility and patient population in an acute care hospital, generalizing the findings to other all hospitals may be challenging. While the generalizability of our model is not fully guaranteed due to the lack of external validation, hospitals with similar facilities and patient characteristics might find the identified predictors and scoring system developed in this study applicable to their setting. Second, our study did not examine psychiatric symptoms, including delirium, as a potential predictive factor [Wong CL, Holroyd-Leduc J, Simel DL, Straus SE. Does this patient have delirium?: value of bedside instruments. JAMA. Aug 18, 2010;304(7):779-786. [CrossRef] [Medline]49,Inouye SK, Westendorp RGJ, Saczynski JS. Delirium in elderly people. Lancet. Mar 2014;383(9920):911-922. [CrossRef]50]. Because previous studies have shown that psychiatric symptoms can be a risk factor for falls [Oliver D, Britton M, Seed P, Martin FC, Hopper AH. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies. BMJ. Oct 25, 1997;315(7115):1049-1053. [CrossRef] [Medline]17,Morse JM, Morse RM, Tylko SJ. Development of a scale to identify the fall-prone patient. Can J Aging. 1989;8(4):366-377. [CrossRef]18,Hendrich AL, Bender PS, Nyhuis A. Validation of the Hendrich II Fall Risk Model: a large concurrent case/control study of hospitalized patients. Appl Nurs Res. Feb 2003;16(1):9-21. [CrossRef] [Medline]28], it can be necessary to reconstruct the prediction model by including additional predictors of falls, including psychiatric symptoms such as delirium, in future studies. Third, we did not examine the association between medications and falls. Previous studies have shown that taking sleeping pills [Beasley B, Patatanian E. Development and implementation of a pharmacy fall prevention program. Hosp Pharm. Dec 2009;44(12):1095-1102. [CrossRef]19,Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: a retrospective observational study of 7,858 patients in acute care setting. PLoS One. 2020;15(7):e0236130. [CrossRef] [Medline]20,Hendrich AL, Bender PS, Nyhuis A. Validation of the Hendrich II Fall Risk Model: a large concurrent case/control study of hospitalized patients. Appl Nurs Res. Feb 2003;16(1):9-21. [CrossRef] [Medline]28-Noh HM, Song HJ, Park YS, Han J, Roh YK. Fall predictors beyond fall risk assessment tool items for acute hospitalized older adults: a matched case-control study. Sci Rep. Jan 15, 2021;11(1):1503. [CrossRef] [Medline]30,Herzig SJ, Rothberg MB, Moss CR, et al. Risk of in-hospital falls among medications commonly used for insomnia in hospitalized patients. Sleep. Sep 13, 2021;44(9):zsab064. [CrossRef] [Medline]51] and the number of medications [Bally ELS, Ye L, van Grieken A, et al. Factors associated with falls among hospitalized and community-dwelling older adults: the APPCARE study. Front Public Health. 2023;11:1180914. [CrossRef] [Medline]42,Kojima T, Akishita M, Nakamura T, et al. Association of polypharmacy with fall risk among geriatric outpatients. Geriatr Gerontol Int. Oct 2011;11(4):438-444. [CrossRef] [Medline]52-Dhalwani NN, Fahami R, Sathanapally H, Seidu S, Davies MJ, Khunti K. Association between polypharmacy and falls in older adults: a longitudinal study from England. BMJ Open. Oct 16, 2017;7(10):e016358. [CrossRef] [Medline]54] are risk factors for falls. At the time of our study, limitations in our EHR system made it difficult to collect accurate medication data. This prevented us from including medication type and number as predictors. Since then, our EHR system has been upgraded, and we can now reliably obtain medication data. We plan to incorporate these variables in future studies to enhance the accuracy of our fall prediction model. Fourth, we could not compare the results of our model with those of existing fall prediction models. However, we believe that our newly developed fall prediction model is highly useful in that it is easy to apply in many Japanese hospitals with acute care settings, similar to that in this study.

To further enhance the practical application of our fall prediction scoring system, we plan to integrate it into our hospital’s EHRs to gather real-world evidence. This will allow us to evaluate its usability, accuracy, and impact on reducing inhospital falls. Future research will include multisite longitudinal studies to validate the tool’s effectiveness across different health care settings.

Conclusions

We successfully identified predictors of falls within a patient population admitted to an acute care hospital and developed a novel prediction model in Japan. This model could serve as an effective tool to guide preventive interventions for both individual hospitalized patients and high-risk populations in many hospitals with acute care settings.

Acknowledgments

We thank Motoe Yumioka, Satomi Unno, Kazuyo Okushio, Sumiko Sano, Hideko Mochizuki, and Kazue Sano for their guidance in conducting this study. We thank Ms Etsuko Nagahashi and Mr Hiroki Ueda of the Information Support Office of Shizuoka General Hospital for assistance with data acquisition. We thank Katherine Thieltges and Analisa Avila, MPH, ELS, from Edanz for editing a draft of the manuscript. This study was supported by a grant-in-aid for Medical Research Promotion from the Shizuoka Prefectural Hospital Organization and a research instruction budget from the Shizuoka Graduate University of Public Health.

Data Availability

The datasets generated and analyzed during this study are not publicly available since the authors failed to obtain informed consent regarding the disclosure of raw patient data at the start of the study, which means the data in the manuscript, supporting information files, or public repositories cannot be freely accessed. However, the datasets are available from the Clinical Trial Management Office at Shizuoka General Hospital, located at 4-27-1 Kita-ando, Aoi-ku, Shizuoka, Japan 420-8527 (chiken-sougou@shizuoka-pho.jp), on reasonable request.

Authors' Contributions

CS and EN contributed to study concept and design and data analysis; CS and KH contributed to data acquisition; CS, EN, HS, NEK, MT, and KH did data interpretation; and CS contributed to first draft of the manuscript. All authors read drafts of the manuscript and approved the final version.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Supplementary tables and figure.

DOCX File, 151 KB

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ADL: activities of daily living
BR: bedriddenness rank
EHR: electronic health record
HR: hazard ratio


Edited by Pedro Santana-Mancilla; submitted 13.04.24; peer-reviewed by Susan Haag, Chiedozie Udeh, Lasse Lehtonen; final revised version received 24.09.24; accepted 30.09.24; published 13.01.25.

Copyright

© Chihiro Saito, Eiji Nakatani, Hatoko Sasaki, Naoko E Katsuki, Masaki Tago, Kiyoshi Harada. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 13.1.2025.

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