Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study
Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study
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DOI：
10.2196/13785
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发表时间：
2019-07-01
影响因子：
3.2
通讯作者：
Kaewprag, Pacharmon
中科院分区：
文献类型：
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作者：
Hyun, Sookyung;Moffatt-Bruce, Susan;Kaewprag, Pacharmon
背景：压力性溃疡是由压力、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）不仅可能导致额外的住院时间和相关的护理费用，还可能导致不良的患者结局。重症监护室（ICU）患者发生HAPU的风险高于普通患者。我们假设，护理团队的决定相对于HAPU的风险评估和预防可能会更好地支持由数据驱动，ICU特定的预测model.Objective：本研究的目的是确定是否与ICU特定的预测变量的多元逻辑回归是适合ICU HAPU的预测，并比较该模型的性能与Braden量表在这个特定的人群。我们使用从学术医疗中心的企业数据仓库检索的数据进行了回顾性队列研究。进行双变量分析以比较HAPU和非HAPU组。多元逻辑回归用于开发具有来自双变量分析的显著预测变量的预测模型。灵敏度，特异性，阳性预测值，阴性预测值，受试者工作特征曲线（AUC）下的面积，和Youden指数被用来比较与Braden scale.Results：研究的患者接触的总数为12654。在ICU住院期间发生HAPU的患者人数为735例（发生率为5.81%）。年龄、性别、体重、糖尿病、血管加压药、隔离、气管插管、呼吸机发作、Braden评分和呼吸机天数与HAPU显著相关。该模型的总体准确度为91.7%，AUC为0.737。敏感性、特异性、阳性预测值、阴性预测值和约登指数分别为0.650、0.693、0.211、956和0.342。男性患者、糖尿病患者和隔离患者发生HAPU的可能性分别是女性患者、非糖尿病患者和非隔离患者的1.5倍、1.5倍和3.1倍。使用一个非常大的，电子健康记录衍生的数据集使我们能够比较在ICU停留期间发生HAPU的患者与未发生HAPU的患者的特征，这也使我们能够根据经验数据建立一个预测模型。与Braden量表相比，该模型表现出可接受的性能。除了Braden量表外，该模型还可以帮助临床医生做出风险评估的决定，因为它不难解释并应用于临床实践。这种方法可以支持重症监护中HAPU发生率的可避免的降低。
Background: 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.Objective: 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.Methods: 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.Results: 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.Conclusions: 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.