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吻合口漏在结肠切除术后感染时增加:机器学习增强的倾向评分修正分析 46735 例患者。

Anastomotic Leak is Increased With Infection After Colectomy: Machine Learning-Augmented Propensity Score Modified Analysis of 46 735 Patients.

机构信息

Department of Colorectal Surgery, 5786Ochsner Clinic, LA, USA.

Georgia Colon & Rectal Surgical Associates, 1366Northside Hospital, GA, USA.

出版信息

Am Surg. 2022 Jan;88(1):74-82. doi: 10.1177/0003134820973720. Epub 2020 Dec 24.

DOI:10.1177/0003134820973720
PMID:33356437
Abstract

BACKGROUND

infection (CDI) is now the most common cause of healthcare-associated infections, with increasing prevalence, severity, and mortality of nosocomial and community-acquired CDI which makes up approximately one third of all CDI. There are also increased rates of asymptomatic colonization particularly in high-risk patients. is a known collagenase-producing bacteria which may contribute to anastomotic leak (AL).

METHODS

Machine learning-augmented multivariable regression and propensity score (PS)-modified analysis was performed in this nationally representative case-control study of CDI and anastomotic leak, mortality, and length of stay for colectomy patients using the ACS-NSQIP database.

RESULTS

Among 46 735 colectomy patients meeting study criteria, mean age was 61.7 years (SD 14.38), 52.2% were woman, 72.5% were Caucasian, 1.5% developed CDI, 3.1% developed anastomotic leak, and 1.6% died. In machine learning (backward propagation neural network)-augmented multivariable regression, CDI significantly increases anastomotic leak (OR 2.39, 95% CI 1.70-3.36; < .001), which is similar to the neural network results. Having CDI increased the independent likelihood of anastomotic leak by 3.8% to 6.8% overall, and in dose-dependent fashion with increasing ASA class to 4.3%, 5.7%, 7.6%, and 10.0%, respectively, for ASA class I to IV. In doubly robust augmented inverse probability weighted PS analysis, CDI significantly increases the likelihood of AL by 4.58% (95% CI 2.10-7.06; < .001).

CONCLUSIONS

This is the first known nationally representative study on CDI and AL, mortality, and length of stay among colectomy patients. Using advanced machine learning and PS analysis, we provide evidence that suggests CDI increases AL in a dose-dependent manner with increasing ASA Class.

摘要

背景

感染(CDI)现在是最常见的医疗保健相关感染的原因,医院获得性和社区获得性 CDI 的患病率、严重程度和死亡率都在增加,约占所有 CDI 的三分之一。无症状定植的发生率也在增加,尤其是在高危患者中。是一种已知的胶原酶产生菌,可能导致吻合口漏(AL)。

方法

在这项针对 CDI 和吻合口漏、死亡率以及结直肠切除术患者住院时间的全国代表性病例对照研究中,我们使用 ACS-NSQIP 数据库,对 CDI 和吻合口漏、死亡率以及住院时间进行了机器学习增强的多变量回归和倾向评分(PS)修正分析。

结果

在符合研究标准的 46735 例结直肠切除术患者中,平均年龄为 61.7 岁(标准差 14.38),52.2%为女性,72.5%为白种人,1.5%发生 CDI,3.1%发生吻合口漏,1.6%死亡。在机器学习(反向传播神经网络)增强的多变量回归中,CDI 显著增加吻合口漏的风险(OR 2.39,95%CI 1.70-3.36;<0.001),这与神经网络的结果相似。总的来说,患有 CDI 会使吻合口漏的独立可能性增加 3.8%至 6.8%,并且随着 ASA 分级的增加呈剂量依赖性,分别为 ASA 分级 I 至 IV 的 4.3%、5.7%、7.6%和 10.0%。在双重稳健增强逆概率加权 PS 分析中,CDI 显著增加 AL 的可能性 4.58%(95%CI 2.10-7.06;<0.001)。

结论

这是第一项关于 CDI 和 AL、死亡率以及结直肠切除术患者住院时间的全国代表性研究。使用先进的机器学习和 PS 分析,我们提供的证据表明,CDI 以剂量依赖性方式增加 ASA 分级,从而增加 AL 的发生。

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