Use of Temporally Validated Machine Learning Models To Predict Outcomes of Percutaneous Nephrolithotomy Using Data from the British Association of Urological Surgeons Percutaneous Nephrolithotomy Audit

医学 经皮肾镜取石术 逻辑回归 接收机工作特性 经皮 外科 内科学
作者
Robert Geraghty,Anshul Thakur,Sarah Howles,William Finch,Sarah Fowler,Alistair Rogers,Seshadri Sriprasad,Daron Smith,Andrew Dickinson,Zara Gall,Bhaskar K. Somani
出处
期刊:European urology focus [Elsevier BV]
被引量:1
标识
DOI:10.1016/j.euf.2024.01.011
摘要

Machine learning (ML) is a subset of artificial intelligence that uses data to build algorithms to predict specific outcomes. Few ML studies have examined percutaneous nephrolithotomy (PCNL) outcomes. Our objective was to build, streamline, temporally validate, and use ML models for prediction of PCNL outcomes (intensive care admission, postoperative infection, transfusion, adjuvant treatment, postoperative complications, visceral injury, and stone-free status at follow-up) using a comprehensive national database (British Association of Urological Surgeons PCNL).This was an ML study using data from a prospective national database. Extreme gradient boosting (XGB), deep neural network (DNN), and logistic regression (LR) models were built for each outcome of interest using complete cases only, imputed, and oversampled and imputed/oversampled data sets. All validation was performed with complete cases only. Temporal validation was performed with 2019 data only. A second round used a composite of the most important 11 variables in each model to build the final model for inclusion in the shiny application. We report statistics for prognostic accuracy.The database contains 12 810 patients. The final variables included were age, Charlson comorbidity index, preoperative haemoglobin, Guy's stone score, stone location, size of outer sheath, preoperative midstream urine result, primary puncture site, preoperative dimercapto-succinic acid scan, stone size, and image guidance (https://endourology.shinyapps.io/PCNL_Demographics/). The areas under the receiver operating characteristic curve was >0.6 in all cases.This is the largest ML study on PCNL outcomes to date. The models are temporally valid and therefore can be implemented in clinical practice for patient-specific risk profiling. Further work will be conducted to externally validate the models.We applied artificial intelligence to data for patients who underwent a keyhole surgery to remove kidney stones and developed a model to predict outcomes for this procedure. Doctors could use this tool to advise patients about their risk of complications and the outcomes they can expect after this surgery.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
hbutsj完成签到,获得积分10
1秒前
KK完成签到,获得积分10
3秒前
careyzhou发布了新的文献求助10
4秒前
中原第一深情完成签到,获得积分10
4秒前
小洪俊熙发布了新的文献求助10
5秒前
北望完成签到,获得积分20
5秒前
Lee完成签到 ,获得积分10
6秒前
科研狗完成签到 ,获得积分10
7秒前
量子星尘发布了新的文献求助10
9秒前
9秒前
romeo发布了新的文献求助10
12秒前
妖孽宇完成签到,获得积分10
14秒前
简简单单完成签到,获得积分10
14秒前
550190946发布了新的文献求助10
14秒前
16秒前
111完成签到,获得积分10
16秒前
zhubin完成签到 ,获得积分10
16秒前
18秒前
田南松发布了新的文献求助10
21秒前
搬砖美少女完成签到,获得积分10
21秒前
nn发布了新的文献求助10
22秒前
7ohnny完成签到,获得积分10
23秒前
apckkk完成签到 ,获得积分10
25秒前
深情安青应助550190946采纳,获得10
26秒前
27秒前
28秒前
jbq完成签到 ,获得积分20
28秒前
YM完成签到,获得积分10
30秒前
生动柔发布了新的文献求助10
30秒前
大旭完成签到 ,获得积分10
31秒前
Fn完成签到 ,获得积分10
33秒前
zero完成签到,获得积分10
35秒前
瘦瘦谷兰完成签到,获得积分10
35秒前
zcz完成签到 ,获得积分10
36秒前
白嘉乐完成签到,获得积分10
37秒前
考研小白完成签到,获得积分10
37秒前
高妍纯完成签到 ,获得积分10
39秒前
41秒前
风中的丝袜完成签到,获得积分10
41秒前
赵赵完成签到,获得积分10
44秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038201
求助须知:如何正确求助?哪些是违规求助? 3575940
关于积分的说明 11373987
捐赠科研通 3305747
什么是DOI,文献DOI怎么找? 1819274
邀请新用户注册赠送积分活动 892662
科研通“疑难数据库(出版商)”最低求助积分说明 815022