Department of Pharmacy, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Division of Infectious Diseases and Geographic Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Microbiol Spectr. 2022 Jun 29;10(3):e0042422. doi: 10.1128/spectrum.00424-22. Epub 2022 May 23.
Given the focus of existing clinical prediction scores on identifying drug-resistant pathogens as a whole, the application to individual pathogens and other institutions may yield weaker performance. This study aimed to develop a locally derived clinical prediction model for Pseudomonas-mediated pneumonia. This retrospective study included patients ≥18 years of age who were admitted to an academic medical center between 1 July 2010 and 31 July 2020 with a CDC National Healthcare Safety Network confirmed pneumonia diagnosis and were receiving antimicrobials during the index encounter, with a positive respiratory culture. Cystic fibrosis patients were excluded. Logistic regression analysis identified risk factors associated with the isolation of Pseudomonas aeruginosa from respiratory cultures within the derivation cohort ( = 186), which were weighted to generate a prediction score that was applied to the derivation and internal validation ( = 95) cohorts. A total of 281 patients met the inclusion criteria. Five predictor variables were identified, namely, tracheostomy status (4 points), chronic obstructive pulmonary disease (5 points), enteral nutrition (9 points), chronic steroid use (11 points), and Pseudomonas aeruginosa isolation from any culture in the prior 6 months (14 points). At a score of >11, the prediction score demonstrated a sensitivity of 52.4% (95% confidence interval [CI], 36.4 to 68.0%) and a specificity of 84.9% (95% CI, 72.4 to 93.35%) in the validation cohort. Score accuracy was 70.5% (95% CI, 60.3 to 79.4%), and the area under the receiver operating characteristic curve (AUROC) was 0.77 (95% CI, 0.68 to 0.87) in the validation cohort. A prediction score for identifying Pseudomonas aeruginosa in pneumonia was derived, which may have the potential to decrease the use of broad-spectrum antibiotics. Validation with larger and external cohorts is necessary. In this study, we aimed to develop a locally derived clinical prediction model for Pseudomonas-mediated pneumonia. Utilizing a locally validated prediction score may help direct therapeutic management and be generalizable to other clinical settings and similar populations for the selection of appropriate antimicrobial coverage when data are lacking. Our study highlights a unique patient population, including immunocompromised, structural lung disease, and transplant patients. Five predictor variables were identified, namely, tracheostomy status, chronic obstructive pulmonary disease, enteral nutrition, chronic steroid use, and Pseudomonas aeruginosa isolation from any culture in the prior 6 months. A prediction score for identifying Pseudomonas aeruginosa in pneumonia was derived, which may have the potential to decrease the use of broad-spectrum antibiotics, although validation with larger and external cohorts is necessary.
鉴于现有临床预测评分主要关注识别耐药病原体，因此应用于个别病原体和其他机构可能会导致性能下降。本研究旨在开发一种用于假单胞菌介导肺炎的本地衍生临床预测模型。这项回顾性研究纳入了 2010 年 7 月 1 日至 2020 年 7 月 31 日期间在学术医疗中心住院的年龄≥18 岁的患者，这些患者符合美国疾病控制与预防中心国家医疗保健安全网络确认的肺炎诊断标准，且在就诊时正在接受抗生素治疗，并伴有呼吸道培养阳性。排除囊性纤维化患者。逻辑回归分析确定了与衍生队列中呼吸道培养分离出铜绿假单胞菌相关的风险因素（n=186），这些因素经过加权以生成预测评分，然后应用于衍生和内部验证队列（n=95）。共有 281 名患者符合纳入标准。确定了五个预测变量，即气管造口术状态（4 分）、慢性阻塞性肺疾病（5 分）、肠内营养（9 分）、慢性类固醇使用（11 分）和 6 个月内任何培养物中分离出铜绿假单胞菌（14 分）。在验证队列中，评分>11 分的预测评分的敏感性为 52.4%（95%置信区间[CI]，36.4%至 68.0%），特异性为 84.9%（95%CI，72.4%至 93.35%）。评分准确性为 70.5%（95%CI，60.3%至 79.4%），验证队列的受试者工作特征曲线下面积（AUROC）为 0.77（95%CI，0.68 至 0.87）。本研究旨在开发一种用于假单胞菌介导肺炎的本地衍生临床预测模型。使用本地验证的预测评分可能有助于指导治疗管理，并可推广到其他临床环境和类似人群，以便在缺乏数据时选择适当的抗菌药物覆盖范围。我们的研究强调了一个独特的患者群体，包括免疫功能低下、结构性肺病和移植患者。确定了五个预测变量，即气管造口术状态、慢性阻塞性肺疾病、肠内营养、慢性类固醇使用以及 6 个月内任何培养物中分离出铜绿假单胞菌。开发了一种用于识别肺炎中铜绿假单胞菌的预测评分，该评分可能具有减少广谱抗生素使用的潜力，尽管需要更大的外部队列进行验证。
