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

IF 2.6 Q2 HEALTH CARE SCIENCES & SERVICES
JMIR Human Factors Pub Date : 2025-01-13 DOI:10.2196/58073
Chihiro Saito, Eiji Nakatani, Hatoko Sasaki, Naoko E Katsuki, Masaki Tago, Kiyoshi Harada
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引用次数: 0

Abstract

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.

急症住院病人跌倒的预测因素和预测评分系统:回顾性队列研究。
背景:住院患者跌倒是一个严重的问题,会导致身体损伤、继发性并发症、日常生活活动受损、住院时间延长和医疗费用增加。建立跌倒预测评分系统,识别最有可能跌倒的患者,有助于预防住院患者跌倒。目的:本研究旨在找出急症病人跌倒的预测因素,建立评分系统,并评估其效度。方法:这项单中心、回顾性队列研究纳入了2019年4月至2020年9月在静冈县总医院住院的年龄在20岁及以上的患者。从医疗记录中收集人口统计数据、入院时的候选预测因子和跌倒发生率报告。结果是从入院到需要医疗资源的跌倒时间。随机选取三分之二的病例作为训练集进行分析,采用单变量和多变量Cox回归分析确定影响跌倒风险的因素。基于预估风险比(hr)对跌倒风险进行评分,构建跌倒预测评分系统。其余三分之一的病例被用作测试集,以评估新的评分系统的预测性能。结果:共纳入13725人。在研究期间,2.4%(326/ 13725)的患者经历了跌倒。在训练数据集中(n=9150), Cox回归分析确定了性别(男性:HR 1.60, 95% CI 1.21-2.13)、年龄(65)。结论:我们通过识别日本急症医院住院患者的跌倒预测因素,开发了一种新的跌倒预测评分系统。该系统可用于在急性护理环境中对极有可能跌倒的患者群体进行预防性干预。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR Human Factors
JMIR Human Factors Medicine-Health Informatics
CiteScore
3.40
自引率
3.70%
发文量
123
审稿时长
12 weeks
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