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心血管手术后感染的诊断(DICS):前瞻性观察研究中开发和验证预测模型的研究方案。

Diagnosis of infection after cardiovascular surgery (DICS): a study protocol for developing and validating a prediction model in prospective observational study.

机构信息

Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Graduate School, Nanjing, China.

Statistical Genetics, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia.

出版信息

BMJ Open. 2021 Sep 21;11(9):e048310. doi: 10.1136/bmjopen-2020-048310.

DOI:10.1136/bmjopen-2020-048310
PMID:34548352
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8458369/
Abstract

INTRODUCTION

Postoperative infection (PI) is one of the main severe complications after cardiovascular surgery. Therefore, antibiotics are routinely used during the first 48 hours after cardiovascular surgery. However, there is no effective method for early diagnosis of infection after cardiovascular surgery, particularly, to determine whether postoperative patients need to prolong the use of antibiotics after the first 48 hours. In this study, we aim to develop and validate a diagnostic model to help identify whether a patient has been infected after surgery and guide the appropriate use of antibiotics.

METHODS AND ANALYSIS

In this prospective study, we will develop and validate a diagnostic model to determine whether the patient has a bacterial infection within 48 hours after cardiovascular surgery. Baseline data will be collected through the electronic medical record system. A total of 2700 participants will be recruited (n=2000 for development, n=700 for validation). The primary outcome of the study is the newly PI during the first 48 hours after cardiovascular surgery. Logistic regression penalised with elastic net regularisation will be used for model development and bootstrap and k-fold cross-validation aggregation will be performed for internal validation. The derived model will be also externally validated in patients who are continuously included in another time period (N=700). We will evaluate the calibration and differentiation performance of the model by Hosmer-Lemeshow good of fit test and the area under the curve, respectively. We will report sensitivity, specificity, positive predictive value and negative predictive value in the validation data-set, with a target of 80% sensitivity.

ETHICS AND DISSEMINATION

Ethical approval was obtained from Medical Ethics Committee of Affiliated Nanjing Drum Tower Hospital, Nanjing University Medical College (2020-249-01).

TRIAL REGISTRATION NUMBER

Chinese Clinical Trial Register (www.chictr.org.cn, ChiCTR2000038762); Pre-results.

摘要

简介

术后感染(PI)是心血管手术后的主要严重并发症之一。因此,心血管手术后的头 48 小时内常规使用抗生素。然而,目前尚无有效的方法用于早期诊断心血管手术后的感染,特别是确定术后患者是否需要在头 48 小时后延长抗生素的使用。在本研究中,我们旨在开发和验证一种诊断模型,以帮助确定患者手术后是否感染,并指导抗生素的合理使用。

方法和分析

本前瞻性研究旨在开发和验证一种诊断模型,以确定心血管手术后 48 小时内患者是否发生细菌感染。通过电子病历系统收集基线数据。共招募 2700 名参与者(n=2000 用于开发,n=700 用于验证)。研究的主要结局是心血管手术后头 48 小时内新发生的 PI。使用具有弹性网正则化的逻辑回归进行模型开发,并进行自举和 k 折交叉验证聚合进行内部验证。在另一个时间段连续纳入的患者中(n=700),还将对该模型进行外部验证。我们将分别通过 Hosmer-Lemeshow 拟合优度检验和曲线下面积评估模型的校准和区分性能。我们将在验证数据集中报告敏感性、特异性、阳性预测值和阴性预测值,目标是敏感性达到 80%。

伦理和传播

南京鼓楼医院附属南京医科大学医学伦理委员会(2020-249-01)已批准本研究。

临床试验注册

中国临床试验注册中心(www.chictr.org.cn,ChiCTR2000038762);预注册结果。

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本文引用的文献

1
Pulmonary infection after cardiopulmonary bypass surgery in children: a risk estimation model in China.小儿体外循环心脏手术后肺部感染:中国的风险评估模型。
J Cardiothorac Surg. 2021 Apr 7;16(1):71. doi: 10.1186/s13019-021-01450-w.
2
Mortality due to hospital-acquired infection after cardiac surgery.心脏手术后医院获得性感染导致的死亡率。
J Thorac Cardiovasc Surg. 2022 Jun;163(6):2131-2140.e3. doi: 10.1016/j.jtcvs.2020.08.094. Epub 2020 Sep 2.
3
Calculating the sample size required for developing a clinical prediction model.计算开发临床预测模型所需的样本量。
BMJ. 2020 Mar 18;368:m441. doi: 10.1136/bmj.m441.
4
The efficacy and safety of prophylactic corticosteroids for the prevention of adverse outcomes in patients undergoing heart surgery using cardiopulmonary bypass: a systematic review and meta-analysis of randomized controlled trials.预防性使用皮质类固醇对接受体外循环心脏手术患者预防不良结局的疗效和安全性:一项随机对照试验的系统评价和荟萃分析
Eur J Cardiothorac Surg. 2020 Apr 1;57(4):620-627. doi: 10.1093/ejcts/ezz325.
5
Morbidity and Mortality of Nosocomial Infection after Cardiovascular Surgery: A Report of 1606 Cases.心血管手术后医院感染的发病率和死亡率:1606 例报告。
Curr Med Sci. 2018 Apr;38(2):329-335. doi: 10.1007/s11596-018-1883-4. Epub 2018 Apr 30.
6
Risk Factors and Predictive Model Development of Thirty-Day Post-Operative Surgical Site Infection in the Veterans Administration Surgical Population.退伍军人管理局外科手术人群术后30天手术部位感染的危险因素及预测模型构建
Surg Infect (Larchmt). 2018 Apr;19(3):278-285. doi: 10.1089/sur.2017.283. Epub 2018 Feb 1.
7
Centers for Disease Control and Prevention Guideline for the Prevention of Surgical Site Infection, 2017.美国疾病预防控制中心 2017 年《手术部位感染预防指南》。
JAMA Surg. 2017 Aug 1;152(8):784-791. doi: 10.1001/jamasurg.2017.0904.
8
Postoperative Infection in Developing World Congenital Heart Surgery Programs: Data From the International Quality Improvement Collaborative.发展中国家先天性心脏病手术项目中的术后感染:来自国际质量改进协作组的数据。
Circ Cardiovasc Qual Outcomes. 2017 Apr;10(4). doi: 10.1161/CIRCOUTCOMES.116.002935.
9
Pneumonia after cardiac surgery: Experience of the National Institutes of Health/Canadian Institutes of Health Research Cardiothoracic Surgical Trials Network.心脏手术后肺炎:美国国立卫生研究院/加拿大卫生研究院心胸外科临床试验网络的经验。
J Thorac Cardiovasc Surg. 2017 Jun;153(6):1384-1391.e3. doi: 10.1016/j.jtcvs.2016.12.055. Epub 2017 Feb 9.
10
Healthcare-Associated Infections in Cardiac Surgery Patients With Prolonged Intensive Care Unit Stay.在重症监护病房长期住院的心脏手术患者中的医疗相关感染
Ann Thorac Surg. 2017 Apr;103(4):1165-1170. doi: 10.1016/j.athoracsur.2016.12.041. Epub 2017 Mar 6.