{"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":"<strong>Purpose:</strong> 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.<br/><strong>Patients and Methods:</strong> 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.<br/><strong>Results:</strong> 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 R<sup>2</sup> value of 0.3405.<br/><strong>Conclusion:</strong> 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.<br/><br/><strong>Keywords:</strong> digital decision in healthcare, percutaneous nephrolithotomy, surgery success, machine learning, MARS<br/>","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}
引用次数: 0
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
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.