Hospital de Clínicas of Porto Alegre, Porto Alegre, Brazil.
Faculty of Medical Sciences, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
J Adv Nurs. 2019 Mar;75(3):563-572. doi: 10.1111/jan.13882. Epub 2018 Nov 19.
To develop and validate a predictive model for falls in hospitalized adult clinical and surgical patients, assessing intrinsic (i.e. patient-related) and extrinsic factors (i.e. care process-related).
To identify factors predictive of falls and enable appropriate management of fall risk it is necessary to understand patient and environmental factors, along with care delivery processes.
A matched case-control study.
This study was conducted in the medical and surgical wards of a Brazilian teaching hospital. The sample included 536 patients, with data collected in 2013-2014. Data analysis included descriptive statistics and conditional logistic regression. Cases of patients aged 18 years or older who fell while hospitalized were included. One patient who did not fall during hospitalization, matched by sex, ward and admission date, was selected as a control for each included case.
The SAK Fall Scale (Severo-Almeida-Kuchenbecker) was developed and validated. The scale includes seven variables: disorientation/confusion, frequent urination, walking limitations, lack of caregiver, postoperative status, previous falls and number of medications administered within 72 hr prior to the fall. This scale showed acceptable predictive accuracy.
The newly developed SAK Fall Scale includes five intrinsic and two extrinsic variables and differs from other predictive scales for falls. The findings of this study are broad and the scale, which is easy to apply, can be used worldwide by nurses in health services. In advanced practice, the testing of a new model for fall risk contributes to preventive interventions and thus has an impact on patient safety.
开发并验证一个针对住院成年临床和外科患者跌倒的预测模型，评估内在因素（即与患者相关的因素）和外在因素（即与护理过程相关的因素）。
为了识别跌倒的预测因素，并能够对跌倒风险进行适当管理，有必要了解患者和环境因素以及护理提供过程。
匹配病例对照研究。
本研究在巴西一所教学医院的内科和外科病房进行。样本包括 536 名患者，数据收集于 2013 年至 2014 年。数据分析包括描述性统计和条件逻辑回归。纳入了年龄在 18 岁及以上、住院期间跌倒的患者病例。每个纳入病例匹配一名性别、病房和入院日期相同、住院期间未跌倒的患者作为对照。
开发并验证了 SAK 跌倒量表（Severo-Almeida-Kuchenbecker）。该量表包括七个变量：定向障碍/意识模糊、频繁排尿、行走受限、缺乏照顾者、术后状态、既往跌倒和跌倒前 72 小时内给予的药物数量。该量表具有可接受的预测准确性。
新开发的 SAK 跌倒量表包含五个内在因素和两个外在因素，与其他跌倒预测量表不同。本研究的发现具有广泛的代表性，该量表易于应用，可被全球范围内的护士在医疗服务中使用。在高级实践中，对跌倒风险的新模型进行测试有助于预防干预，从而对患者安全产生影响。
