Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy.
Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
Infection. 2022 Oct;50(5):1243-1253. doi: 10.1007/s15010-022-01801-2. Epub 2022 Apr 29.
The aim of our study was to build a predictive model able to stratify the risk of bacterial co-infection at hospitalization in patients with COVID-19.
Multicenter observational study of adult patients hospitalized from February to December 2020 with confirmed COVID-19 diagnosis. Endpoint was microbiologically documented bacterial co-infection diagnosed within 72 h from hospitalization. The cohort was randomly split into derivation and validation cohort. To investigate risk factors for co-infection univariable and multivariable logistic regression analyses were performed. Predictive risk score was obtained assigning a point value corresponding to β-coefficients to the variables in the multivariable model. ROC analysis in the validation cohort was used to estimate prediction accuracy.
Overall, 1733 patients were analyzed: 61.4% males, median age 69 years (IQR 57-80), median Charlson 3 (IQR 2-6). Co-infection was diagnosed in 110 (6.3%) patients. Empirical antibiotics were started in 64.2 and 59.5% of patients with and without co-infection (p = 0.35). At multivariable analysis in the derivation cohort: WBC ≥ 7.7/mm, PCT ≥ 0.2 ng/mL, and Charlson index ≥ 5 were risk factors for bacterial co-infection. A point was assigned to each variable obtaining a predictive score ranging from 0 to 5. In the validation cohort, ROC analysis showed AUC of 0.83 (95%CI 0.75-0.90). The optimal cut-point was ≥2 with sensitivity 70.0%, specificity 75.9%, positive predictive value 16.0% and negative predictive value 97.5%. According to individual risk score, patients were classified at low (point 0), intermediate (point 1), and high risk (point ≥ 2). CURB-65 ≥ 2 was further proposed to identify patients at intermediate risk who would benefit from early antibiotic coverage.
Our score may be useful in stratifying bacterial co-infection risk in COVID-19 hospitalized patients, optimizing diagnostic testing and antibiotic use.
本研究旨在建立一个预测模型，以分层 COVID-19 住院患者发生细菌合并感染的风险。
这是一项多中心观察性研究，纳入 2020 年 2 月至 12 月间住院的确诊 COVID-19 成年患者。终点是在住院后 72 小时内经微生物学确诊的细菌合并感染。该队列被随机分为推导队列和验证队列。采用单变量和多变量逻辑回归分析探讨合并感染的危险因素。多变量模型中，将β系数对应的分值赋值给变量，以获得预测风险评分。在验证队列中进行 ROC 分析以评估预测准确性。
共分析了 1733 例患者：男性占 61.4%，中位年龄 69 岁（IQR 57-80），中位 Charlson 评分为 3 分（IQR 2-6）。110 例（6.3%）患者诊断为合并感染。合并感染组和无合并感染组分别有 64.2%和 59.5%的患者接受了经验性抗生素治疗（p=0.35）。在推导队列的多变量分析中：WBC≥7.7/mm3、PCT≥0.2ng/ml 和 Charlson 指数≥5 是细菌合并感染的危险因素。每个变量赋值一个分值，得到一个 0-5 分的预测评分。在验证队列中，ROC 分析显示 AUC 为 0.83（95%CI 0.75-0.90）。最佳截断值为≥2，其敏感性为 70.0%，特异性为 75.9%，阳性预测值为 16.0%，阴性预测值为 97.5%。根据个体风险评分，患者分为低危（0 分）、中危（1 分）和高危（≥2 分）。还提出 CURB-65≥2 用于识别中危患者，这些患者可从早期抗生素覆盖中获益。
我们的评分可能有助于分层 COVID-19 住院患者发生细菌合并感染的风险，优化诊断检测和抗生素使用。
