Kaier Klaus, Heidenreich Adrian, Jäckel Markus, Oettinger Vera, Maier Alexander, Hilgendorf Ingo, Breitbart Philipp, Hartikainen Tau, Keller Till, Westermann Dirk, von Zur Mühlen Constantin
Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Hugstetter Str. 49, Freiburg, 79106, Germany.
Centre for Big Data Analysis in Cardiology (CeBAC), Department of Cardiology and Angiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
BMC Geriatr. 2024 Jun 13;24(1):517. doi: 10.1186/s12877-024-05107-w.
In the hospital setting, frailty is a significant risk factor, but difficult to measure in clinical practice. We propose a reweighting of an existing diagnoses-based frailty score using routine data from a tertiary care teaching hospital in southern Germany.
The dataset includes patient characteristics such as sex, age, primary and secondary diagnoses and in-hospital mortality. Based on this information, we recalculate the existing Hospital Frailty Risk Score. The cohort includes patients aged ≥ 75 and was divided into a development cohort (admission year 2011 to 2013, N = 30,525) and a validation cohort (2014, N = 11,202). A limited external validation is also conducted in a second validation cohort containing inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251). In the development cohort, LASSO regression analysis was used to select the most relevant variables and to generate a reweighted Frailty Score for the German setting. Discrimination is assessed using the area under the receiver operating characteristic curve (AUC). Visualization of calibration curves and decision curve analysis were carried out. Applicability of the reweighted Frailty Score in a non-elderly population was assessed using logistic regression models.
Reweighting of the Frailty Score included only 53 out of the 109 frailty-related diagnoses and resulted in substantially better discrimination than the initial weighting of the score (AUC = 0.89 vs. AUC = 0.80, p < 0.001 in the validation cohort). Calibration curves show a good agreement between score-based predictions and actual observed mortality. Additional external validation using inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251) confirms the results regarding discrimination and calibration and underlines the geographic and temporal validity of the reweighted Frailty Score. Decision curve analysis indicates that the clinical usefulness of the reweighted score as a general decision support tool is superior to the initial version of the score. Assessment of the applicability of the reweighted Frailty Score in a non-elderly population (N = 198,819) shows that discrimination is superior to the initial version of the score (AUC = 0.92 vs. AUC = 0.87, p < 0.001). In addition, we observe a fairly age-stable influence of the reweighted Frailty Score on in-hospital mortality, which does not differ substantially for women and men.
Our data indicate that the reweighted Frailty Score is superior to the original Frailty Score for identification of older, frail patients at risk for in-hospital mortality. Hence, we recommend using the reweighted Frailty Score in the German in-hospital setting.
在医院环境中，虚弱是一个重要的风险因素，但在临床实践中难以测量。我们建议使用德国南部一家三级护理教学医院的常规数据对现有的基于诊断的虚弱评分进行重新加权。
数据集包括患者特征，如性别、年龄、主要和次要诊断以及住院死亡率。基于这些信息，我们重新计算现有的医院虚弱风险评分。该队列包括年龄≥75岁的患者，并分为一个开发队列（2011年至2013年入院，N = 30525）和一个验证队列（2014年，N = 11202）。还在第二个验证队列中进行了有限的外部验证，该队列包含2022年德国各地年龄≥75岁的住院病例（N = 491251）。在开发队列中，使用LASSO回归分析来选择最相关的变量，并生成针对德国情况的重新加权虚弱评分。使用受试者操作特征曲线（AUC）下的面积评估辨别力。进行校准曲线的可视化和决策曲线分析。使用逻辑回归模型评估重新加权虚弱评分在非老年人群中的适用性。
虚弱评分的重新加权仅包括109个与虚弱相关诊断中的53个，并且在辨别力方面比评分的初始加权有显著改善（验证队列中AUC = 0.89 vs. AUC = 0.80，p < 0.001）。校准曲线显示基于评分的预测与实际观察到的死亡率之间有良好的一致性。使用2022年德国各地年龄≥75岁的住院病例（N = 491251）进行的额外外部验证证实了关于辨别力和校准的结果，并强调了重新加权虚弱评分的地理和时间有效性。决策曲线分析表明，重新加权评分作为一般决策支持工具的临床实用性优于评分的初始版本。对重新加权虚弱评分在非老年人群（N = 198819）中的适用性评估表明，辨别力优于评分的初始版本（AUC = 0.92 vs. AUC = 0.87，p < 0.001）。此外，我们观察到重新加权虚弱评分对住院死亡率的影响在年龄上相当稳定，男女之间没有实质性差异。
我们的数据表明，重新加权的虚弱评分在识别有住院死亡风险的老年虚弱患者方面优于原始虚弱评分。因此，我们建议在德国医院环境中使用重新加权的虚弱评分。
