Center for Value-Based Care Research, Cleveland Clinic Community Care, Cleveland Clinic, Cleveland, Ohio.
Division of Infectious Diseases, University of Massachusetts Medical School - Baystate, Springfield, Massachusetts.
Infect Control Hosp Epidemiol. 2023 Jul;44(7):1143-1150. doi: 10.1017/ice.2022.229. Epub 2022 Sep 29.
To derive and validate a model for risk of resistance to first-line community-acquired pneumonia (CAP) therapy.
We developed a logistic regression prediction model from a large multihospital discharge database and validated it versus the Drug Resistance in Pneumonia (DRIP) score in a holdout sample and another hospital system outside that database. Resistance to first-line CAP therapy (quinolone or third generation cephalosporin plus macrolide) was based on blood or respiratory cultures.
This study was conducted using data from 177 Premier Healthcare database hospitals and 11 Cleveland Clinic hospitals.
Adults hospitalized for CAP.
Risk factors for resistant infection.
Among 138,762 eligible patients in the Premier database, 12,181 (8.8%) had positive cultures and 5,200 (3.8%) had organisms resistant to CAP therapy. Infection with a resistant organism in the previous year was the strongest predictor of resistance; markers of acute illness (eg, receipt of mechanical ventilation or vasopressors) and chronic illness (eg, pressure ulcer, paralysis) were also associated with resistant infections. Our model outperformed the DRIP score with a C-statistic of 0.71 versus 0.63 for the DRIP score ( < .001) in the Premier holdout sample, and 0.65 versus 0.58 ( < .001) in Cleveland Clinic hospitals. Clinicians at Premier facilities used broad-spectrum antibiotics for 20%-30% of patients. In discriminating between patients with and without resistant infections, physician judgment slightly outperformed the DRIP instrument but not our model.
Our model predicting infection with a resistant pathogen outperformed both the DRIP score and physician practice in an external validation set. Its integration into practice could reduce unnecessary use of broad-spectrum antibiotics.
建立并验证预测一线治疗社区获得性肺炎(CAP)耐药风险的模型。
我们从大型多医院出院数据库中开发了逻辑回归预测模型,并在保留样本和数据库外的另一家医院系统中对该模型进行了验证,并与 DRIP 评分进行了比较。一线 CAP 治疗耐药(喹诺酮类或第三代头孢菌素类加大环内酯类)基于血液或呼吸道培养。
这项研究使用了 177 家 Premier Healthcare 数据库医院和 11 家克利夫兰诊所医院的数据。
因 CAP 住院的成年人。
感染耐药的危险因素。
在 Premier 数据库中,138762 名符合条件的患者中,12181 名(8.8%)的培养结果呈阳性,5200 名(3.8%)的病原体对 CAP 治疗耐药。前一年感染耐药病原体是耐药的最强预测因素;急性疾病的标志物(如接受机械通气或血管加压药)和慢性疾病(如压疮、瘫痪)也与耐药感染有关。我们的模型在 Premier 保留样本中的 C 统计量为 0.71,优于 DRIP 评分的 0.63( <.001),在克利夫兰诊所医院中为 0.65 优于 0.58( <.001)。Premier 医疗机构的临床医生为 20%~30%的患者使用了广谱抗生素。在区分有和无耐药感染的患者时,医生的判断略优于 DRIP 工具,但不如我们的模型。
我们预测感染耐药病原体的模型在外部验证集中优于 DRIP 评分和医生实践。将其纳入实践可以减少不必要的广谱抗生素使用。