Yap Tracey L, Horn Susan D, Sharkey Phoebe D, Brooks Katie R, Kennerly Susan
School of Nursing, Duke University, Durham, NC, United States.
School of Medicine, University of Utah, Salt Lake City, UT, United States.
JMIR Aging. 2023 Feb 9;6:e43130. doi: 10.2196/43130.
An assessment tool is needed to measure the clinical severity of nursing home residents to improve the prediction of outcomes and provide guidance in treatment planning.
This study aims to describe the development of the Nursing Home Severity Index, a clinical severity measure targeted for nursing home residents with the potential to be individually tailored to different outcomes, such as pressure injury.
A retrospective nonexperimental design was used to develop and validate the Nursing Home Severity Index using secondary data from 9 nursing homes participating in the 12-month preintervention period of the Turn Everyone and Move for Ulcer Prevention (TEAM-UP) pragmatic clinical trial. Expert opinion and clinical literature were used to identify indicators, which were grouped into severity dimensions. Index performance and validation to predict risk of pressure injury were accomplished using secondary data from nursing home electronic health records, Minimum Data Sets, and Risk Management Systems. Logistic regression models including a resident's Worst-Braden score with/without severity dimensions generated propensity scores. Goodness of fit for overall models was assessed using C statistic; the significance of improvement of fit after adding severity components to the model was determined using the likelihood ratio chi-square test. The significance of each component was assessed with odds ratios. Validation based on randomly selected 65% training and 35% validation data sets was used to confirm the reliability of the severity measure. Finally, the discriminating ability of models was evaluated using propensity stratification to evaluate which model best discriminated between residents with/without pressure injury.
Data from 1015 residents without pressure injuries on admission were used for the Nursing Home Severity Index-Pressure Injury and included laboratory, weights/vitals/pain, underweight, and locomotion severity dimensions. Logistic regression C statistic measuring predictive accuracy increased by 19.3% (from 0.627 to 0.748; P<.001) when adding four severity dimensions to Worst-Braden scores. Significantly higher odds of developing pressure injuries were associated with increasing dimension scores. The use of the three highest propensity deciles predicting the greatest risk of pressure injury improved predictive accuracy by detecting 21 more residents who developed pressure injury (n=58, 65.2% vs n=37, 42.0%) when both severity dimensions and Worst-Braden score were included in prediction modeling.
The clinical Nursing Home Severity Index-Pressure Injury was successfully developed and tested using the outcome of pressure injury. Overall predictive capacity was enhanced when using severity dimensions in combination with Worst-Braden scores. This index has the potential to significantly impact the quality of care decisions aimed at improving individual pressure injury prevention plans.
ClinicalTrials.gov NCT02996331; http://clinicaltrials.gov/ct2/show/NCT02996331.
需要一种评估工具来衡量疗养院居民的临床严重程度，以改善对结果的预测并为治疗计划提供指导。
本研究旨在描述疗养院严重程度指数的开发，这是一种针对疗养院居民的临床严重程度测量工具，有可能针对不同的结果进行个性化定制，如压疮。
采用回顾性非实验设计，利用参与“预防压疮全员行动”（TEAM-UP）实用临床试验12个月干预前期的9家疗养院的二手数据，开发并验证疗养院严重程度指数。利用专家意见和临床文献确定指标，并将其分组为严重程度维度。使用疗养院电子健康记录、最小数据集和风险管理系统的二手数据，完成指数性能评估及预测压疮风险的验证。包括居民最差布拉德评分（有/无严重程度维度）的逻辑回归模型生成倾向得分。使用C统计量评估整体模型的拟合优度；使用似然比卡方检验确定在模型中添加严重程度成分后拟合改善的显著性。用比值比评估每个成分的显著性。基于随机选择的65%训练数据集和35%验证数据集进行验证，以确认严重程度测量的可靠性。最后，使用倾向分层评估模型的辨别能力，以评估哪种模型能最佳地区分有/无压疮的居民。
来自1015名入院时无压疮居民的数据用于疗养院严重程度指数 - 压疮，包括实验室检查、体重/生命体征/疼痛、体重过轻和运动严重程度维度。当在最差布拉德评分中添加四个严重程度维度时，测量预测准确性的逻辑回归C统计量提高了19.3%（从0.627提高到0.748；P<0.001）。压疮发生几率显著增加与维度得分增加相关。在预测模型中同时纳入严重程度维度和最差布拉德评分时，使用预测压疮风险最高的三个最高倾向十分位数，通过检测出另外21名发生压疮的居民，提高了预测准确性（n = 58，65.2%对n = 37，42.0%）。
成功开发并使用压疮结果对临床疗养院严重程度指数 - 压疮进行了测试。将严重程度维度与最差布拉德评分结合使用时，整体预测能力得到增强。该指数有可能显著影响旨在改善个体压疮预防计划的护理决策质量。
ClinicalTrials.gov NCT02996331；http://clinicaltrials.gov/ct2/show/NCT02996331
