Cesare Manuele, D'Agostino Fabio, Hill-Rodriguez Deborah, Sarik Danielle Altares, Cocchieri Antonello
A. Gemelli IRCCS University Hospital Foundation, Section of Hygiene, Department of Health Science and Public Health, Catholic University of the Sacred Heart, 00168 Rome, Italy.
Department of Medicine, Saint Camillus International University of Health Sciences, 00131 Rome, Italy.
Healthcare (Basel). 2025 Jul 19;13(14):1748. doi: 10.3390/healthcare13141748.
: Falls in hospitalized pediatric patients are frequent and can lead to serious complications and increased healthcare costs. Nurses typically assess fall risk using structured tools such as the Humpty Dumpty Fall Scale (HDFS), alongside nursing diagnoses such as Fall risk ND, which are based on clinical reasoning. However, the degree of alignment between the HDFS and the nursing reasoning-based diagnostic approach in assessing fall risk remains unclear. This study aims to assess the alignment between the HDFS and Fall risk ND in identifying fall risk among hospitalized pediatric patients. : A retrospective observational study was conducted in a tertiary pediatric hospital in Italy, including all pediatric patients admitted in 2022. Fall risk was assessed within 24 h from hospital admission using two approaches, the HDFS (risk identified with the standard cutoff, score ≥ 12) and Fall risk ND, based on the nurse's clinical reasoning and recorded through the PAI clinical nursing information system. Discriminative performance was analyzed using receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. A confusion matrix evaluated classification performance at the cutoff (≥12). : Among 2086 inpatients, 80.9% had a recorded Fall risk ND. Of the 1853 patients assessed with the HDFS, 52.7% were classified as at risk (HDFS score ≥ 12). The HDFS showed low discriminative ability in detecting patients with a Fall risk ND (AUC = 0.568; 95% CI: 0.535-0.602). The PPV was high (85.1%), meaning that most patients identified as at risk by the HDFS were also judged to be at risk by nurses through Fall risk ND. However, the NPV was low (20.1%), indicating that many patients with low HDFS scores were still diagnosed with Fall risk ND by nurses. : The HDFS shows limited ability to discriminate pediatric patients with Fall risk ND, capturing a risk profile that does not fully align with nursing clinical reasoning. This suggests that standardized tools and clinical reasoning address distinct yet complementary dimensions of fall risk assessment. Integrating the HDFS into a structured nursing diagnostic process-guided by clinical expertise and supported by continuous education-can strengthen the effectiveness of fall prevention strategies and enhance patient safety in pediatric settings.
住院儿科患者跌倒情况频繁发生，可能导致严重并发症并增加医疗成本。护士通常使用结构化工具（如矮胖子跌倒量表（HDFS））评估跌倒风险，同时结合基于临床推理的护理诊断，如跌倒风险护理诊断（Fall risk ND）。然而，在评估跌倒风险时，HDFS与基于护理推理的诊断方法之间的一致性程度尚不清楚。本研究旨在评估HDFS与Fall risk ND在识别住院儿科患者跌倒风险方面的一致性。
在意大利一家三级儿科医院进行了一项回顾性观察研究，纳入了2022年收治的所有儿科患者。在入院后24小时内，使用两种方法评估跌倒风险，即HDFS（采用标准临界值识别风险，分数≥12）和基于护士临床推理并通过PAI临床护理信息系统记录的Fall risk ND。使用受试者工作特征曲线分析来分析判别性能。计算曲线下面积（AUC）、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。使用混淆矩阵评估临界值（≥12）时的分类性能。
在2086名住院患者中，80.9%记录了Fall risk ND。在使用HDFS评估的1853名患者中，52.7%被分类为有风险（HDFS分数≥12）。HDFS在检测有Fall risk ND的患者方面显示出较低的判别能力（AUC = 0.568；95%置信区间：0.535 - 0.602）。PPV较高（85.1%），这意味着大多数被HDFS识别为有风险的患者也被护士通过Fall risk ND判断为有风险。然而，NPV较低（20.1%），表明许多HDFS分数低的患者仍被护士诊断为有Fall risk ND。
HDFS在区分有Fall risk ND的儿科患者方面能力有限，所捕捉到的风险概况与护理临床推理并不完全一致。这表明标准化工具和临床推理处理的是跌倒风险评估中不同但互补的维度。将HDFS整合到由临床专业知识指导并辅以持续教育的结构化护理诊断过程中，可以加强跌倒预防策略的有效性，并提高儿科环境中的患者安全性。
