Shimada Hitomi, Hirata Risa, Katsuki Naoko E, Nakatani Eiji, Shikino Kiyoshi, Ono Maiko, Tokushima Midori, Nishi Tomoyo, Yaita Shizuka, Saito Chihiro, Amari Kaori, Kurogi Kazuya, Oda Yoshimasa, Yoshimura Mariko, Yamashita Shun, Tokushima Yoshinori, Aihara Hidetoshi, Fujiwara Motoshi, Tago Masaki
Department of General Medicine, Saga University Hospital, Saga, Japan.
Shimada Hospital of Medical Corporation Chouseikai, Saga, Japan.
Int J Med Sci. 2025 Jun 9;22(12):2877-2883. doi: 10.7150/ijms.106600. eCollection 2025.
The Saga Falls Risk Model 2 (SFRM2) is a simplified fall prediction model that we recently developed. It uses eight items that are easy to assess at the time of admission to an acute care hospital. However, patients in long-term care hospitals have poor activities of daily living and a high risk of falls compared to those in acute care hospitals. Although effective fall predictive models exist for long-term care hospitals, their accuracy remains suboptimal. This study aimed to validate the SFRM2 for predicting falls in long-term care hospital patients. This multicenter retrospective observational study was conducted in three long-term care hospitals in Japan from April 2018 to March 2021. All inpatients aged ≥20 years were included. The eight items of the SFRM2 (age, sex, emergency admission, department of admission, hypnotic medication use, history of falls, eating independence, and Bedriddenness rank) and in-hospital falls were collected from medical records. The accuracy of SFRM2 was assessed by calculating the area under the curve (AUC) and shrinkage coefficient, as well as the sensitivity, specificity, positive predictive value, and negative predictive value. Among the 1182 patients (median age: 86 years, 538 males) included in the analysis, 140 (11.8%) experienced in-hospital falls. The fall incidence rate was 4.4 per 1000 patient-days. SFRM2 exhibited an AUC of 0.889 (95% confidence interval: 0.861-0.916), consistent with the actual incidence of falls, with a shrinkage coefficient of 0.975. The cutoff score for SFRM2 on the Youden index was -2.14, with a sensitivity of 77.9%, specificity of 84.7%, positive predictive value of 40.6%, and negative predictive value of 96.6%. SFRM2 showed good discriminative ability in external validation at long-term care hospitals. Its applicability in this setting may be advantageous due to the relatively stable condition of older inpatients compared to those in acute care hospitals.
佐贺瀑布风险模型2（SFRM2）是我们最近开发的一种简化的跌倒预测模型。它使用八项在急性护理医院入院时易于评估的指标。然而，与急性护理医院的患者相比，长期护理医院的患者日常生活活动能力较差且跌倒风险较高。尽管存在适用于长期护理医院的有效跌倒预测模型，但其准确性仍不尽人意。本研究旨在验证SFRM2在预测长期护理医院患者跌倒方面的有效性。 这项多中心回顾性观察研究于2018年4月至2021年3月在日本的三家长期护理医院进行。纳入了所有年龄≥20岁的住院患者。从病历中收集SFRM2的八项指标（年龄、性别、急诊入院、入院科室、催眠药物使用、跌倒史、进食独立性和卧床等级）以及院内跌倒情况。通过计算曲线下面积（AUC）、收缩系数以及灵敏度、特异度、阳性预测值和阴性预测值来评估SFRM2的准确性。 在纳入分析的1182例患者（中位年龄：86岁，男性538例）中，140例（11.8%）发生了院内跌倒。跌倒发生率为每1000患者日4.4次。SFRM2的AUC为0.889（95%置信区间：0.861 - 0.916），与实际跌倒发生率一致，收缩系数为0.975。SFRM2在约登指数上的截断分数为 - 2.14，灵敏度为77.9%，特异度为84.7%，阳性预测值为40.6%，阴性预测值为96.6%。 SFRM2在长期护理医院的外部验证中显示出良好的判别能力。由于与急性护理医院的老年住院患者相比，长期护理医院的老年住院患者病情相对稳定，因此SFRM2在这种情况下的适用性可能具有优势。
