BJUI compass · 2026
Machine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation.
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Wasfy Isaac Samir, Ahmad Jamal, Elsegeay Hany, Elebiary Mohamed F, Haty Ahmed, El-Dydamony Eman M, Soliman Ahmed Mohamed, Alrefaey Ahmed, Abozied Hesham, Algammal Mohamed, Shouman Hossam A, Elzamek Maha M, Saleh Ahmed Abdel Galil, Shafi Ahmed Fetyan Abdelazim, Mohamed Osama Mostafa Abdalla
Аннотация
OBJECTIVES: This study aim to develop, compare and internally validate machine-learning models for predicting urine-culture positivity in patients who had both urinalysis and culture ordered and to explore descriptive probability strata. Post hoc secondary analyses examined age subgroups, the incremental contribution of text-derived features, simpler comparators and calibration.
PATIENTS AND METHODS: Urine culture results are typically unavailable for 24-72 h, creating uncertainty during initial assessment, and machine-learning models may help estimate the probability of culture positivity from routinely collected data. This retrospective study included 2530 urine-sample records originating from three university hospitals. Eligibility was based on paired urinalysis and urine culture records rather than symptom-based diagnostic criteria for urinary tract infection (UTI). Thirteen supervised algorithms were evaluated using a stratified 75:25 sample-record split. This constituted internal validation; records were not grouped by patient or centre because stable cross-centre patient, centre and collection-date identifiers were unavailable.
RESULTS: Several gradient-boosting algorithms showed similar discrimination. CatBoost had the numerically highest test-set AUC of 0.858 (95% CI 0.829-0.892), but its AUC did not differ significantly from gradient boosting or XGBoost. At the reported operating threshold, sensitivity was 0.587 (95% CI 0.513-0.662), specificity 0.930 (0.903-0.951), PPV 0.766 (0.693-0.833) and NPV 0.851 (0.821-0.882). Exploratory probability strata separated records with different observed rates of culture positivity, but their clinical utility and safety were not evaluated.
CONCLUSION: Machine-learning models discriminated between culture-positive and culture-negative sample records in a sample-level internal validation. Culture positivity is not synonymous with symptomatic or clinically significant UTI. The findings support further evaluation as diagnostic risk-estimation tools after urinalysis results are available. They do not establish clinical UTI or the safety or effectiveness of starting, withholding or delaying antibiotics. Patient-grouped external validation with clinical outcomes is required before clinical use.
PATIENTS AND METHODS: Urine culture results are typically unavailable for 24-72 h, creating uncertainty during initial assessment, and machine-learning models may help estimate the probability of culture positivity from routinely collected data. This retrospective study included 2530 urine-sample records originating from three university hospitals. Eligibility was based on paired urinalysis and urine culture records rather than symptom-based diagnostic criteria for urinary tract infection (UTI). Thirteen supervised algorithms were evaluated using a stratified 75:25 sample-record split. This constituted internal validation; records were not grouped by patient or centre because stable cross-centre patient, centre and collection-date identifiers were unavailable.
RESULTS: Several gradient-boosting algorithms showed similar discrimination. CatBoost had the numerically highest test-set AUC of 0.858 (95% CI 0.829-0.892), but its AUC did not differ significantly from gradient boosting or XGBoost. At the reported operating threshold, sensitivity was 0.587 (95% CI 0.513-0.662), specificity 0.930 (0.903-0.951), PPV 0.766 (0.693-0.833) and NPV 0.851 (0.821-0.882). Exploratory probability strata separated records with different observed rates of culture positivity, but their clinical utility and safety were not evaluated.
CONCLUSION: Machine-learning models discriminated between culture-positive and culture-negative sample records in a sample-level internal validation. Culture positivity is not synonymous with symptomatic or clinically significant UTI. The findings support further evaluation as diagnostic risk-estimation tools after urinalysis results are available. They do not establish clinical UTI or the safety or effectiveness of starting, withholding or delaying antibiotics. Patient-grouped external validation with clinical outcomes is required before clinical use.