突尼斯中心使用“ACS NSQIP®风险计算器”预测结直肠手术术后并发症的背景验证

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2022-11-10 eCollection Date: 2022-01-01 DOI:10.1177/11769351221135153
Mehdi Ben Abdelkrim, Mohamed Amine Elghali, Amany Moussa, Ahmed Ben Abdelaziz
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引用次数: 0

摘要

背景:预测手术并发症个体风险的模型有利于手术决策和术后管理程序的性质。目的:验证“ACS NSQIP®风险计算器”对2015 - 2019年结直肠癌手术术后并发症的预测效果。方法:回顾性应用“ACS NSQIP®”对Farhat Hached医院外科2015 - 2019年5年期结直肠癌手术患者进行预后验证研究。三个层次的调整。区分和校准主要通过ROC曲线(AUC大于或等于0.8)进行。结果:本研究纳入129例患者,性别比1.22,中位年龄62岁。最常见的手术方式是低位结肠切除术并结直肠吻合术。37例(28.7%)患者出现至少一种术后并发症。死亡率预测值和截断点值(AUC = 0.858;CI95% [0.570 - -0.960];cut -off points = 1.8%),心脏并发症(AUC = 0.824;CI95% [0.658 - -0.990];截点= 1.8%),血栓栓塞并发症(AUC = 0.802;CI95% [0.617 - -0.987];截断点= 3.1%),肾功能不全(AUC = 0.802;95% [0.623-0.981];截止点= 1.2%)根据计算器等级1调整。结论:本研究将突尼斯Sousse Farhat Hached大学普外科结直肠手术术后并发症的预测纳入背景,为提高手术护理的质量和安全性提供了可能。建议应用突尼斯微型计算器,并在为所有操作程序开发通用计算器之后进行验证的推广。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Contextual Validation of the Prediction of Postoperative Complications of Colorectal Surgery by the "<i>ACS NSQIP</i> <sup>®</sup> <i>Risk Calculator</i>" in a Tunisian Center.

Contextual Validation of the Prediction of Postoperative Complications of Colorectal Surgery by the "<i>ACS NSQIP</i> <sup>®</sup> <i>Risk Calculator</i>" in a Tunisian Center.

Contextual Validation of the Prediction of Postoperative Complications of Colorectal Surgery by the "<i>ACS NSQIP</i> <sup>®</sup> <i>Risk Calculator</i>" in a Tunisian Center.

Contextual Validation of the Prediction of Postoperative Complications of Colorectal Surgery by the "ACS NSQIP ® Risk Calculator" in a Tunisian Center.

Context: Models for predicting individual risks of surgical complications are advantageous for operative decision making and the nature of postoperative management procedures.

Objective: Validate the "ACS NSQIP® Risk Calculator" in the prediction of postoperative complications during colorectal cancer surgery, operated during the years 2015 to 2019.

Methods: this is a prognostic validation study of the "ACS NSQIP®" applied retrospectively to patients operated on for colorectal cancer in the surgical department of Farhat Hached hospital, during the 2015 and 2019 5-year term. Three levels of adjustment. Discrimination and calibration were carried out mainly by ROC curves (AUC ⩾ 0.8).

Results: In this study, 129 patients were included with a sex ratio of 1.22 and a median age of 62 years. The most common operative procedure was low segmental colectomy with colorectal anastomosis. Thirty-seven patients (28.7%) had at least one postoperative complication. The prediction and cuts-off points values of mortality (AUC = 0.858; CI95% [0.570-0.960]; Cuts-off points = 1.8%), cardiac complications (AUC = 0.824; CI95% [0.658-0.990]; Cuts-off points = 1.8%), thromboembolic complications (AUC = 0.802; CI95% [0.617-0.987]; Cuts-off point = 3.1%), and renal insufficiency (AUC = 0.802; CI95% [ 0.623-0.981]; Cuts-off point = 1.2%) were adjusted according to level 1 of the calculator.

Conclusion: This work contextualized the prediction of postoperative complications in colorectal surgery in the university general surgery department of Farhat Hached in Sousse (Tunisia), making it possible to improve the quality and safety of surgical care. The application of the Tunisian mini calculator is recommended as well as the generalization of validation following the development of a generic calculator for all operating procedures.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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