aceMachine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation
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 ma…
In 2530 paired urinalysis-culture records from three university hospitals, gradient-boosting models estimated culture positivity after urinalysis, with CatBoost achieving test-set AUC 0.858 and high specificity at the reported threshold.
Models were validated only at sample level without patient or centre grouping, and exploratory risk strata were not evaluated for clinical utility or safety, so they do not establish symptomatic UTI or safe antibiotic decisions.
Internal sample-level validation only without patient or centre grouping, culture positivity not equivalent to symptomatic UTI, and no evaluation of clinical outcomes or antibiotic decision safety; external validation required before clinical use.
Evidence
- Peer-reviewedBJUI Compass2026-09-08
How should this claim be treated?
Truvace Impact Record TRV-2026-1053, v1: “Machine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation.” Truvace, 2026-09-10. /record/TRV-2026-1053 (accessed at citation time). sha256 418e46de5c4d77a6…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1053 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.