迷你经皮肾镜取石术与标准经皮肾镜取石术：手术成功率比较的围手术期决策支持系统

IF 2.8 3区医学 Q1 Pharmacology, Toxicology and Pharmaceutics

Therapeutics and Clinical Risk Management Pub Date : 2023-12-29 DOI:10.2147/tcrm.s444519

Kerem Gencer

{"title":"迷你经皮肾镜取石术与标准经皮肾镜取石术：手术成功率比较的围手术期决策支持系统","authors":"Kerem Gencer","doi":"10.2147/tcrm.s444519","DOIUrl":null,"url":null,"abstract":"Purpose: This study aimed to rank the features that are important in terms of safety and effectiveness in choosing the surgical method and providing appropriate care to the patient by using the variables examined before and after the surgery to evaluate the success of mini percutaneous nephrolithotomy and standard percutaneous nephrolithotomy surgeries. Patients and Methods: The features evaluated before and after surgery were ranked according to their importance in the features considered, using Multivariate Adaptive Regression Splines (MARS), LASSO, Ridge, Elastic_net, and Random Forest algorithms as variable selection techniques. There are 278 samples in the relevant data set. Results: Type of surgery (100%), intercostal access (97.75%), kidney opening procedure (94.25%), postoperative creatinine (59.22%), hydronephrosis (52.23%), the number of entries (41.61%), and pre- and post-operative hemoglobin difference (45.13%) were determined as the most critical variables. The MARS algorithm showed the most successful performance, with the lowest mean absolute error (MAE) value of 0.3622, the lowest root mean square error (RMSE) value of 0.3960, and the highest R2 value of 0.3405. Conclusion: Clinical decision support systems can be helpful in eliminating errors and reducing costs. It can also improve the quality of healthcare and aid in the early diagnosis of diseases. Computer-aided decision-making systems can be developed using the results of such products. These systems can provide doctors with better information about their patient’s treatment options and improve decision-making. It can contribute to patients being better informed about the surgery results and taking an active role. In conclusion, this study provides essential information that should be included in the surgical decision-making process for patients using medications and with a history of percutaneous nephrolithotomy. Keywords: digital decision in healthcare, percutaneous nephrolithotomy, surgery success, machine learning, MARS ","PeriodicalId":22977,"journal":{"name":"Therapeutics and Clinical Risk Management","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mini Percutaneous Nephrolithotomy vs Standard Percutaneous Nephrolithotomy: A Perioperative Decision Support System for Surgical Success Comparison\",\"authors\":\"Kerem Gencer\",\"doi\":\"10.2147/tcrm.s444519\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose: This study aimed to rank the features that are important in terms of safety and effectiveness in choosing the surgical method and providing appropriate care to the patient by using the variables examined before and after the surgery to evaluate the success of mini percutaneous nephrolithotomy and standard percutaneous nephrolithotomy surgeries. Patients and Methods: The features evaluated before and after surgery were ranked according to their importance in the features considered, using Multivariate Adaptive Regression Splines (MARS), LASSO, Ridge, Elastic_net, and Random Forest algorithms as variable selection techniques. There are 278 samples in the relevant data set. Results: Type of surgery (100%), intercostal access (97.75%), kidney opening procedure (94.25%), postoperative creatinine (59.22%), hydronephrosis (52.23%), the number of entries (41.61%), and pre- and post-operative hemoglobin difference (45.13%) were determined as the most critical variables. The MARS algorithm showed the most successful performance, with the lowest mean absolute error (MAE) value of 0.3622, the lowest root mean square error (RMSE) value of 0.3960, and the highest R2 value of 0.3405. Conclusion: Clinical decision support systems can be helpful in eliminating errors and reducing costs. It can also improve the quality of healthcare and aid in the early diagnosis of diseases. Computer-aided decision-making systems can be developed using the results of such products. These systems can provide doctors with better information about their patient’s treatment options and improve decision-making. It can contribute to patients being better informed about the surgery results and taking an active role. In conclusion, this study provides essential information that should be included in the surgical decision-making process for patients using medications and with a history of percutaneous nephrolithotomy. Keywords: digital decision in healthcare, percutaneous nephrolithotomy, surgery success, machine learning, MARS \",\"PeriodicalId\":22977,\"journal\":{\"name\":\"Therapeutics and Clinical Risk Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2023-12-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Therapeutics and Clinical Risk Management\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/tcrm.s444519\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Pharmacology, Toxicology and Pharmaceutics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Therapeutics and Clinical Risk Management","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/tcrm.s444519","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Pharmacology, Toxicology and Pharmaceutics","Score":null,"Total":0}

引用次数: 0

摘要

目的：本研究旨在通过手术前后检查的变量来评估迷你经皮肾镜取石术和标准经皮肾镜取石术手术的成功率，从而对选择手术方法和为患者提供适当护理的安全性和有效性方面的重要特征进行排序：使用多变量自适应回归样条（MARS）、LASSO、Ridge、Elastic_net 和随机森林算法作为变量选择技术，根据其在所考虑的特征中的重要性对手术前后评估的特征进行排序。相关数据集中有 278 个样本：手术类型（100%）、肋间入路（97.75%）、肾脏开放手术（94.25%）、术后肌酐（59.22%）、肾积水（52.23%）、输入次数（41.61%）和术前术后血红蛋白差（45.13%）被确定为最关键的变量。MARS 算法的表现最为成功，平均绝对误差（MAE）值最低，为 0.3622，均方根误差（RMSE）值最低，为 0.3960，R2 值最高，为 0.3405：临床决策支持系统有助于消除错误和降低成本。结论：临床决策支持系统有助于消除错误和降低成本，还能提高医疗质量，帮助早期诊断疾病。可以利用这类产品的结果开发计算机辅助决策系统。这些系统可以为医生提供有关病人治疗方案的更好信息，并改进决策。它还能帮助病人更好地了解手术结果，并发挥积极作用。总之，这项研究为使用药物和有经皮肾镜取石术病史的患者提供了手术决策过程中应包含的基本信息。关键词：医疗保健中的数字决策；经皮肾镜取石术；手术成功率；机器学习；MARS

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Mini Percutaneous Nephrolithotomy vs Standard Percutaneous Nephrolithotomy: A Perioperative Decision Support System for Surgical Success Comparison

Purpose: This study aimed to rank the features that are important in terms of safety and effectiveness in choosing the surgical method and providing appropriate care to the patient by using the variables examined before and after the surgery to evaluate the success of mini percutaneous nephrolithotomy and standard percutaneous nephrolithotomy surgeries.
Patients and Methods: The features evaluated before and after surgery were ranked according to their importance in the features considered, using Multivariate Adaptive Regression Splines (MARS), LASSO, Ridge, Elastic_net, and Random Forest algorithms as variable selection techniques. There are 278 samples in the relevant data set.
Results: Type of surgery (100%), intercostal access (97.75%), kidney opening procedure (94.25%), postoperative creatinine (59.22%), hydronephrosis (52.23%), the number of entries (41.61%), and pre- and post-operative hemoglobin difference (45.13%) were determined as the most critical variables. The MARS algorithm showed the most successful performance, with the lowest mean absolute error (MAE) value of 0.3622, the lowest root mean square error (RMSE) value of 0.3960, and the highest R² value of 0.3405.
Conclusion: Clinical decision support systems can be helpful in eliminating errors and reducing costs. It can also improve the quality of healthcare and aid in the early diagnosis of diseases. Computer-aided decision-making systems can be developed using the results of such products. These systems can provide doctors with better information about their patient’s treatment options and improve decision-making. It can contribute to patients being better informed about the surgery results and taking an active role. In conclusion, this study provides essential information that should be included in the surgical decision-making process for patients using medications and with a history of percutaneous nephrolithotomy.

Keywords: digital decision in healthcare, percutaneous nephrolithotomy, surgery success, machine learning, MARS

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Therapeutics and Clinical Risk Management HEALTH CARE SCIENCES & SERVICES-

CiteScore

5.30

自引率

3.60%

发文量

139

审稿时长

16 weeks

期刊介绍： Therapeutics and Clinical Risk Management is an international, peer-reviewed journal of clinical therapeutics and risk management, focusing on concise rapid reporting of clinical studies in all therapeutic areas, outcomes, safety, and programs for the effective, safe, and sustained use of medicines, therapeutic and surgical interventions in all clinical areas. The journal welcomes submissions covering original research, clinical and epidemiological studies, reviews, guidelines, expert opinion and commentary. The journal will consider case reports but only if they make a valuable and original contribution to the literature. As of 18th March 2019, Therapeutics and Clinical Risk Management will no longer consider meta-analyses for publication. The journal does not accept study protocols, animal-based or cell line-based studies.