دورية أكاديمية
أعرض في EDS

A retrospective cohort study on the use of machine learning to predict stone-free status following percutaneous nephrolithotomy: An experience from Saudi Arabia.

التفاصيل البيبلوغرافية
العنوان: A retrospective cohort study on the use of machine learning to predict stone-free status following percutaneous nephrolithotomy: An experience from Saudi Arabia.
المؤلفون: Alghafees MA; College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia., Abdul Rab S; College of Medicine, Alfaisal University, Riyadh, Saudi Arabia., Aljurayyad AS; Department of Urology, King Saud University Medical City, Riyadh, Saudi Arabia., Alotaibi TS; Department of Urology, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia., Sabbah BN; College of Medicine, Alfaisal University, Riyadh, Saudi Arabia., Seyam RM; Department of Urology, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia., Aldosari LH; Department of Urology, King Fahad University Hospital, Al-Khobar, Saudi Arabia., Alomar MA; Department of Urology, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
المصدر: Annals of medicine and surgery (2012) [Ann Med Surg (Lond)] 2022 Nov 17; Vol. 84, pp. 104957. Date of Electronic Publication: 2022 Nov 17 (Print Publication: 2022).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Wolters Kluwer Country of Publication: England NLM ID: 101616869 Publication Model: eCollection Cited Medium: Print ISSN: 2049-0801 (Print) Linking ISSN: 20490801 NLM ISO Abbreviation: Ann Med Surg (Lond) Subsets: PubMed not MEDLINE
أسماء مطبوعة: Publication: 2023- : [London] : Wolters Kluwer
Original Publication: London : Surgical Associates Ltd., 2012-
مستخلص: Background: Machine learning techniques have been used extensively in the field of clinical medicine, especially when used for the construction of prediction models. The aim of the study was to use machine learning to predict the stone-free status after percutaneous nephrolithotomy (PCNL).
Materials and Methods: This is a retrospective cohort study of 137 patients. Data from adult patients who underwent PCNL at our institute were used for the purpose of this study. Three supervised machine learning algorithms were employed: Logistic Regression, XGBoost Regressor, and Random Forests. A set of variables comprising independent attributes including age, gender, body mass index (BMI), chronic kidney disease (CKD), hypertension (HTN), diabetes mellitus, gout, renal and stone factors (previous surgery, stone location, size, and staghorn status), and pre-operative surgical factors (infections, stent, hemoglobin, creatinine, and bacteriuria) were entered.
Results: 137 patients were identified. The majority were males (65.4%; n = 89), aged 50 years and above (41.9%; n = 57). The stone-free status (SFS) rate was 86% (n = 118). An inverse relation was detected between SFS, and CKD and HTN. The accuracies were 71.4%, 74.5% and 75% using Logistic Regression, XGBoost, and Random Forest algorithms, respectively. Stone size, pre-operative hemoglobin, pre-operative creatinine, and stone type were the most important factors in predicting the SFS following PCNL.
Conclusion: The Random Forest model showed the highest efficacy in predicting SFS. We developed an effective machine learning model to assist physicians and other healthcare professionals in selecting patients with renal stones who are most likely to have successful PCNL treatment based on their demographics and stone characteristics. Larger multicenter studies are needed to develop more powerful algorithms, such as deep learning and other AI subsets.
(© 2022 The Authors.)
References: BMC Urol. 2020 Jul 3;20(1):88. (PMID: 32620102)
J Endourol. 2011 Jan;25(1):11-7. (PMID: 21247286)
Front Mol Biosci. 2022 May 04;9:880291. (PMID: 35601833)
Urology. 2011 Aug;78(2):277-81. (PMID: 21333334)
Arab J Urol. 2012 Sep;10(3):317-23. (PMID: 26558043)
Med Sci Monit. 2016 Nov 14;22:4363-4368. (PMID: 27842051)
Arab J Urol. 2012 Sep;10(3):265-72. (PMID: 26558035)
PLoS One. 2013 Jun 18;8(6):e65888. (PMID: 23824752)
Urol Int. 2012;89(1):103-6. (PMID: 22626928)
F1000Res. 2016 Aug 30;5:2106. (PMID: 27703669)
Urology. 2013 Jun;81(6):1154-9. (PMID: 23540858)
Nat Rev Nephrol. 2020 Dec;16(12):736-746. (PMID: 32753740)
BMC Med Res Methodol. 2019 Mar 19;19(1):64. (PMID: 30890124)
Kidney Int. 2003 Jan;63(1):255-65. (PMID: 12472791)
Rev Urol. 2010 Spring;12(2-3):e86-96. (PMID: 20811557)
J Urol. 2013 Jul;190(1):149-56. (PMID: 23353048)
J Endourol. 2016 Jan;30(1):13-23. (PMID: 26414226)
Urology. 2012 Jun;79(6):1236-41. (PMID: 22465085)
Int J Surg. 2017 Oct;46:198-202. (PMID: 28890409)
J Endourol. 2020 Jun;34(6):692-699. (PMID: 31886708)
J Endourol. 2017 May;31(5):461-467. (PMID: 28287830)
Australas Phys Eng Sci Med. 2019 Sep;42(3):771-779. (PMID: 31332724)
J Family Med Prim Care. 2021 Feb;10(2):974-977. (PMID: 34041107)
J Endourol. 2021 Sep;35(9):1307-1313. (PMID: 33691473)
فهرسة مساهمة: Keywords: Artificial intelligence; Machine learning; Percutaneous nephrolithotomy; Renal stone; Stone-free status prediction; Urolithiasis
تواريخ الأحداث: Date Created: 20221220 Latest Revision: 20221222
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC9758327
DOI: 10.1016/j.amsu.2022.104957
PMID: 36536733
قاعدة البيانات: MEDLINE
الوصف
تدمد:2049-0801
DOI:10.1016/j.amsu.2022.104957