School of Nursing and Rehabilitation, Shandong University, Jinan, China.
Department of Infection Prevention and Control, Qilu Hospital of Shandong University, Jinan, China.
J Clin Nurs. 2023 Apr;32(7-8):1466-1475. doi: 10.1111/jocn.16489. Epub 2022 Aug 21.
To construct a predictive nomogram of the risk of nosocomial infections among patients after cardiac valve replacement surgery.
Nosocomial infections are a standout challenge that worsens the prognosis of patients after valve replacement surgery. However, studies on the nomogram of nosocomial infections in these patients have remained scarce.
A retrospective cohort study.
Patients (n = 720) following valve replacement surgery from 2018 to 2019 were selected. LASSO regression and multivariate logistic regression were utilised to ascertain predictors of nosocomial infections. The predictive performance of the nomogram was appraised by calibration and discrimination. Decision and impact curves were used to assess the clinical utility. Internal validation was implemented via 1000 bootstrap samples to mitigate overfitting. TRIPOD guidelines were used in this study.
One hundred and fifty one patients (20.97%) experienced nosocomial infections following valve replacement surgery. Heart failure, preoperative anaemia, valve material, American Society of Anesthesiologists score ≥ IV, prolonged duration of surgery, duration of mechanical ventilation ≥ 24 h and indwelling nasogastric tube were predictors of nosocomial infections. Using these variables, we developed a predictive nomogram of the occurrence of nosocomial infections and the internal validation results demonstrated good discrimination and calibration of the nomogram. The clinical decision and impact curve revealed significant clinical utility.
The present study constructed a nomogram for predicting the risk of nosocomial infections in patients following cardiac valve replacement surgery. This nomogram may strengthen the effective screening of patients at high risk of nosocomial infections.
This risk warning tool can assist clinical staff in making decisions and providing individualised infection control measures for patients, which has a significant reference value for clinical practice.
The data for this study were obtained from the hospital database, and the entire process of the study did not involve patient participation.
构建心脏瓣膜置换术后患者医院感染风险的预测列线图。
医院感染是心脏瓣膜置换术后患者预后恶化的突出挑战。然而，关于这些患者医院感染列线图的研究仍然很少。
回顾性队列研究。
选择 2018 年至 2019 年接受瓣膜置换术的患者（n=720）。利用 LASSO 回归和多变量逻辑回归确定医院感染的预测因素。通过校准和区分评估列线图的预测性能。决策和影响曲线用于评估临床实用性。通过 1000 个 bootstrap 样本进行内部验证以减轻过度拟合。本研究遵循 TRIPOD 指南。
151 例（20.97%）患者在瓣膜置换术后发生医院感染。心力衰竭、术前贫血、瓣膜材料、美国麻醉医师协会评分≥IV、手术时间延长、机械通气时间≥24 小时和留置鼻胃管是医院感染的预测因素。使用这些变量，我们开发了一种预测心脏瓣膜置换术后患者医院感染发生的列线图，内部验证结果表明该列线图具有良好的区分度和校准度。临床决策和影响曲线显示出显著的临床实用性。
本研究构建了预测心脏瓣膜置换术后患者医院感染风险的列线图。该列线图可加强对医院感染高危患者的有效筛查。
该风险预警工具可以帮助临床医生为患者做出决策并提供个体化的感染控制措施，对临床实践具有重要的参考价值。
本研究的数据来自医院数据库，研究的整个过程不涉及患者参与。
