Dataset Information

A novel post-percutaneous nephrolithotomy sepsis prediction model using machine learning.

ABSTRACT:

Objectives

To establish a predictive model for sepsis after percutaneous nephrolithotomy (PCNL) using machine learning to identify high-risk patients and enable early diagnosis and intervention by urologists.

Methods

A retrospective study including 694 patients who underwent PCNL was performed. A predictive model for sepsis using machine learning was constructed based on 22 preoperative and intraoperative parameters.

Results

Sepsis occurred in 45 of 694 patients, including 16 males (35.6%) and 29 females (64.4%). Data were randomly segregated into an 80% training set and a 20% validation set via 100-fold Monte Carlo cross-validation. The variables included in this study were highly independent. The model achieved good predictive power for postoperative sepsis (AUC = 0.89, 87.8% sensitivity, 86.9% specificity, and 87.4% accuracy). The top 10 variables that contributed to the model prediction were preoperative midstream urine bacterial culture, sex, days of preoperative antibiotic use, urinary nitrite, preoperative blood white blood cell (WBC), renal pyogenesis, staghorn stones, history of ipsilateral urologic surgery, cumulative stone diameters, and renal anatomic malformation.

Conclusion

Our predictive model is suitable for sepsis estimation after PCNL and could effectively reduce the incidence of sepsis through early intervention.

SUBMITTER: Shen R

PROVIDER: S-EPMC10837989 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

A novel post-percutaneous nephrolithotomy sepsis prediction model using machine learning.

Shen Rong R Ming Shaoxiong S Qian Wei W Zhang Shuwei S Peng Yonghan Y Gao Xiaofeng X

BMC urology 20240202 1

<h4>Objectives</h4>To establish a predictive model for sepsis after percutaneous nephrolithotomy (PCNL) using machine learning to identify high-risk patients and enable early diagnosis and intervention by urologists.<h4>Methods</h4>A retrospective study including 694 patients who underwent PCNL was performed. A predictive model for sepsis using machine learning was constructed based on 22 preoperative and intraoperative parameters.<h4>Results</h4>Sepsis occurred in 45 of 694 patients, including ...[more]

PMID: 38308308

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Project description:BackgroundThe postoperative sepsis is a latent fatal complication for both flexible ureteroscopy (fURS) and percutaneous nephrolithotomy (PNL). An effective predictive model constructed by readily available clinical markers is urgently needed to reduce postoperative adverse events caused by infection. This study aims to determine the pre-operative predictors of sepsis in patients with unilateral, solitary, and proximal ureteral stones after fURS and PNL.MethodsWe retrospectively enrolled 910 patients with solitary proximal ureteral stone with stone size 10-20 mm who underwent fURS or PNL from Tongji Hospital's database, including 412 fURS cases and 498 PNL cases. We used the least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis to identify the risk factors for sepsis. Finally, a nomogram was assembled utilizing these risk factors.ResultsIn this study, 49 patients (5.4%) developed sepsis after fURS or PNL surgery. Lasso regression showed postoperative sepsis was associated with gender (female), pre-operative fever, serum albumin (<35 g/L), positive urine culture, serum WBC (≥10,000 cells/ml), serum neutrophil, positive urine nitrite and operation type (fURS). The multivariate logistic analysis indicated that positive urine culture (odds ratio [OR] = 5.9092, 95% CI [2.6425-13.2140], p < 0.0001) and fURS (OR = 1.9348, 95% CI [1.0219-3.6631], p = 0.0427) were independent risk factors of sepsis and albumin ≥ 35g/L (OR = 0.4321, 95% CI [0.2054-0.9089], p = 0.0270) was independent protective factor of sepsis. A nomogram was constructed and exhibited favorable discrimination (area under receiver operating characteristic curve was 0.78), calibration [Hosmer-Lemeshow (HL) test p = 0.904], and net benefits displayed by decision curve analysis (DCA).ConclusionsPatients who underwent fURS compared to PNL or have certain pre-operative characteristics, such as albumin <35 g/L and positive urine culture, are more likely to develop postoperative sepsis. Cautious preoperative evaluation and appropriate operation type are crucial to reducing serious infectious events after surgery, especially for patients with solitary, unilateral, and proximal ureteral stones sized 10-20 mm.

Project description:OBJECTIVES:Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. DESIGN:Observational cohort study. SETTING:Academic medical center from January 2013 to December 2015. PATIENTS:Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. CONCLUSIONS:Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed sepsis prediction model.

Dataset Information

A novel post-percutaneous nephrolithotomy sepsis prediction model using machine learning.

Objectives

Methods

Results

Conclusion

Publications

A novel post-percutaneous nephrolithotomy sepsis prediction model using machine learning.

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