Hyun Sookyung, Moffatt-Bruce Susan, Cooper Cheryl, Hixon Brenda, Kaewprag Pacharmon
College of Nursing, Pusan National University, Yangsan-si, Republic of Korea.
Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States.
JMIR Med Inform. 2019 Jul 18;7(3):e13785. doi: 10.2196/13785.
A pressure ulcer is injury to the skin or underlying tissue, caused by pressure, friction, and moisture. Hospital-acquired pressure ulcers (HAPUs) may not only result in additional length of hospital stay and associated care costs but also lead to undesirable patient outcomes. Intensive care unit (ICU) patients show higher risk for HAPU development than general patients. We hypothesize that the care team's decisions relative to HAPU risk assessment and prevention may be better supported by a data-driven, ICU-specific prediction model.
The aim of this study was to determine whether multiple logistic regression with ICU-specific predictor variables was suitable for ICU HAPU prediction and to compare the performance of the model with the Braden scale on this specific population.
We conducted a retrospective cohort study by using the data retrieved from the enterprise data warehouse of an academic medical center. Bivariate analyses were performed to compare the HAPU and non-HAPU groups. Multiple logistic regression was used to develop a prediction model with significant predictor variables from the bivariate analyses. Sensitivity, specificity, positive predictive values, negative predictive values, area under the receiver operating characteristic curve (AUC), and Youden index were used to compare with the Braden scale.
The total number of patient encounters studied was 12,654. The number of patients who developed an HAPU during their ICU stay was 735 (5.81% of the incidence rate). Age, gender, weight, diabetes, vasopressor, isolation, endotracheal tube, ventilator episode, Braden score, and ventilator days were significantly associated with HAPU. The overall accuracy of the model was 91.7%, and the AUC was .737. The sensitivity, specificity, positive predictive value, negative predictive value, and Youden index were .650, .693, .211, 956, and .342, respectively. Male patients were 1.5 times more, patients with diabetes were 1.5 times more, and patients under isolation were 3.1 times more likely to have an HAPU than female patients, patients without diabetes, and patients not under isolation, respectively.
Using an extremely large, electronic health record-derived dataset enabled us to compare characteristics of patients who develop an HAPU during their ICU stay with those who did not, and it also enabled us to develop a prediction model from the empirical data. The model showed acceptable performance compared with the Braden scale. The model may assist with clinicians' decision on risk assessment, in addition to the Braden scale, as it is not difficult to interpret and apply to clinical practice. This approach may support avoidable reductions in HAPU incidence in intensive care.
压疮是由压力、摩擦和潮湿引起的皮肤或皮下组织损伤。医院获得性压疮（HAPU）不仅可能导致住院时间延长和相关护理费用增加，还可能导致不良的患者预后。重症监护病房（ICU）患者发生HAPU的风险高于普通患者。我们假设，一个数据驱动的、针对ICU的预测模型可能会更好地支持护理团队关于HAPU风险评估和预防的决策。
本研究的目的是确定具有ICU特定预测变量的多元逻辑回归是否适用于ICU中HAPU的预测，并将该模型在此特定人群中的表现与Braden量表进行比较。
我们通过使用从一家学术医疗中心的企业数据仓库中检索到的数据进行了一项回顾性队列研究。进行双变量分析以比较发生HAPU的组和未发生HAPU的组。使用多元逻辑回归从双变量分析中具有显著预测变量的因素来建立预测模型。使用敏感性、特异性、阳性预测值、阴性预测值、受试者工作特征曲线下面积（AUC）和尤登指数与Braden量表进行比较。
研究的患者就诊总数为12654例。在ICU住院期间发生HAPU的患者有735例（发病率为5.81%）。年龄、性别、体重、糖尿病、血管活性药物使用、隔离、气管插管、机械通气次数、Braden评分和机械通气天数与HAPU显著相关。该模型的总体准确率为91.7%，AUC为0.737。敏感性、特异性、阳性预测值、阴性预测值和尤登指数分别为0.650、0.693、0.211、0.956和0.342。男性患者发生HAPU的可能性分别是女性患者的1.5倍，糖尿病患者是无糖尿病患者的1.5倍，隔离患者是未隔离患者的3.1倍。
使用一个极其庞大的源自电子健康记录的数据集使我们能够比较在ICU住院期间发生HAPU的患者与未发生HAPU的患者的特征，并且还使我们能够从实证数据中开发出一个预测模型。与Braden量表相比，该模型表现出可接受的性能。该模型除了Braden量表外，还可以辅助临床医生进行风险评估决策，因为它不难解释且可应用于临床实践。这种方法可能有助于避免重症监护中HAPU发生率的不必要降低。
