Establishment and Validation of a Nomogram to Predict Hospital-Acquired Infection in Elderly Patients After Cardiac Surgery

Abstract

Yuchen Gao,1,* Chunrong Wang,1,* Yuefu Wang,2 Jun Li,1 Jianhui Wang,1 Sudena Wang,1 Yu Tian,1 Jia Liu,1 Xiaolin Diao,3 Wei Zhao3 1Department of Anesthesiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 2Department of Anesthesiology and Surgical Intensive Care Unit, Beijing Shijitan Hospital, Capital Medical University, Beijing, People’s Republic of China; 3Department of Information Center, Skate Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuefu WangDepartment of Anesthesiology and Surgical Intensive Care Unit, Beijing Shijitan Hospital, Capital Medical University, 10 Tieyi Road, Haidian District, Beijing, People’s Republic of China, Email [email protected]: Hospital-acquired infection (HAI) after cardiac surgery is a common clinical concern associated with adverse prognosis and mortality. The objective of this study is to determine the prevalence of HAI and its associated risk factors in elderly patients following cardiac surgery and to build a nomogram as a predictive model.Methods: We developed and internally validated a predictive model from a retrospective cohort of 6405 patients aged ≥ 70 years, who were admitted to our hospital and underwent cardiac surgery. The primary outcome was HAI. Multivariable logistic regression analysis was used to identify independent factors significantly associated with HAI. The performance of the established nomogram was assessed by calibration, discrimination, and clinical utility. Internal validation was achieved by bootstrap sampling with 1000 repetitions to reduce the overfit bias.Results: Independent factors derived from the multivariable analysis to predict HAI were smoking, myocardial infarction, cardiopulmonary bypass use, intraoperative erythrocytes transfusion, extended preoperative hospitalization days and prolonged duration of mechanical ventilation postoperatively. The derivation model showed good discrimination, with a C-index of 0.706 [95% confidence interval 0.671– 0.740], and good calibration [Hosmer–Lemeshow test P = 0.139]. Internal validation also maintained optimal discrimination and calibration. The decision curve analysis revealed that the nomogram was clinically useful.Conclusions: We developed a predictive nomogram for postoperative HAIs based on routinely available data. This predictive tool may enable clinicians to achieve better perioperative management for elderly patients undergoing cardiac surgery but still requires further external validation.Keywords: prediction, model, older patients, nosocomial infection

Keywords

• prediction
• model
• older patients
• nosocomial infection