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首页> 外文期刊>Infectious Diseases and Therapy >Development of a Risk-Scoring Tool to Determine Appropriate Level of Care in Acute Bacterial Skin and Skin Structure Infections in an Acute Healthcare Setting
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Development of a Risk-Scoring Tool to Determine Appropriate Level of Care in Acute Bacterial Skin and Skin Structure Infections in an Acute Healthcare Setting

机译：开发一种风险评估工具，以确定急性医疗保健环境中急性细菌皮肤和皮肤结构感染的适当护理水平

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IntroductionAcute bacterial skin and skin structure infections (ABSSSIs) represent a large burden to the US healthcare system. There is little evidence-based guidance regarding the appropriate level of care for ABSSSIs. This study aimed to develop a prediction model and risk-scoring tool to determine appropriate levels of care. MethodsThis was a single-center observational cohort study of adult patients treated for ABSSSIs from 2012 to 2015 at the Detroit Medical Center. The predictive model used to create a novel risk-scoring tool was derived using multinomial regression analysis. The overall accuracy of this tool was compared to the Clinical Resource Efficacy Support Team (CREST) Classification and Standardized Early Warning Score (SEWS) using area-under-the- receiver-operator-curve (AUROC) analysis and Z-statistic. ResultsFinal patient disposition was 230 (45.5%) home from the emergency department (ED), 65 (12.8%) observation unit (OU), and 211 (41.7%) initial inpatient. IV antibiotic therapy was used in 358 (70.8%) patients. CREST and SEWS were not accurate in the determination of ED versus OU disposition [AUROC CREST 0.0.682 (95% CI 0.640–0.724), AUROC SEWS 0.686 (95% CI 0.641–0.731)], but performed better in determining ED/OU versus inpatient [AUROC CREST?=?0.678 (95% CI 0.630–0.725), AUROC SEWS 0.693 (95% CI 0.645–0.740)]. These scores were also not accurate in determining IV versus PO antibiotic therapy [AUROC CREST?=?0.586 (95% CI 0.530–0.624), AUROC SEWS?=?0.630 (95% CI 0.576–0.684)]. A risk-scoring tool ranging from 0 to 10 points was derived incorporating WBC, temperature, site of infection, and past medical history of diabetes, liver disease, PVD, AKI, and/or CKD. The AUROC of the new model was 0.675 (95% CI 0.611–0.739) ED versus OU, 0.789 (95% CI 0.748–0.829) ED/OU versus inpatient, and 0.742 (95% CI 0.694–0.789) IV versus oral antibiotics. The new score had a significantly higher AUROC compared to both the CREST and SEWS for determining ED/OU versus inpatient ( p ConclusionPrediction models based on patient risk may be useful for determining appropriate level of care during for ABSSSIs. While the prediction model demonstrated moderate to high levels of correlation with patient level of care, further validation of a prospective cohort of patients is warranted.
机译：简介急性细菌性皮肤和皮肤结构感染（ABSSSI）对美国医疗保健系统构成了沉重负担。关于ABSSSI的适当护理水平，几乎没有循证指南。这项研究旨在开发一种预测模型和风险评分工具，以确定适当的护理水平。方法：这是2012年至2015年在底特律医学中心对接受ABSSSI治疗的成年患者的单中心观察性队列研究。使用多项式回归分析推导了用于创建新型风险评分工具的预测模型。使用接收者-操作者曲线下方面积（AUROC）分析和Z统计量，将该工具的整体准确性与临床资源功效支持团队（CREST）分类和标准化早期预警得分（SEWS）进行了比较。结果最终患者的处置为急诊科（ED）的230例（45.5％），观察单元（OU）的65例（12.8％），初次住院的211例（41.7％）。 358名患者（70.8％）使用了静脉抗生素治疗。 CREST和SEWS在确定ED与OU的处置中不准确[AUROC CREST 0.0.682（95％CI 0.640-0.724），AUROC SEWS 0.686（95％CI 0.641-0.731）]，但在确定ED / OU方面表现更好与住院患者相比[AUROC CREST？=？0.678（95％CI 0.630–0.725），AUROC SEWS 0.693（95％CI 0.645–0.740）]。这些分数在确定静脉输注与口服抗生素治疗之间的比较上也不准确[AUROCCREST≥0.586（95％CI 0.530–0.624），AUROCSEWS≥0.630（95％CI 0.576–0.684）]。得出的风险评分工具范围为0到10分，其中包括WBC，温度，感染部位以及糖尿病，肝脏疾病，PVD，AKI和/或CKD的既往病史。新模型的AUROC为ED vs OU为0.675（95％CI 0.611-0.739）ED / OU，住院患者为0.789（95％CI 0.748-0.829）ED / OU，口服抗生素为0.742（95％CI 0.694-0.789）。新的评分与CREST和SEWS相比，在确定ED / OU与住院患者方面具有更高的AUROC（p结论基于患者风险的预测模型可能有助于确定ABSSSI期间的适当护理水平。与患者护理水平的高度相关性，有必要进一步验证患者的预期队列。

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