Published November 6, 2025 | Version 1
Journal article Open

External Validation of the CHOKAI Score for the Prediction of Ureteral Stones

Description

Background: Accurate risk stratification for patients presenting with loin pain is essential to optimize diagnostic imaging and reduce unnecessary radiation exposure. The CHOKAI score is a recently developed clinical prediction tool designed to estimate the probability of ureteral stones without immediate computed tomography (CT) scanning. This study aimed to externally validate the CHOKAI score in an Omani population and compare its diagnostic performance with established scoring systems.

Methods: We conducted a multicenter prospective observational study from November 2020 to September 2021 across three tertiary hospitals in Oman. Adult patients presenting to the emergency department with acute flank, loin or lower abdominal pain were enrolled. Clinical data, urine analysis and point-of-care ultrasonography (POCUS) were collected to calculate the CHOKAI and STONE scores. All patients subsequently underwent CT or alternative imaging for confirmation. Receiver operating characteristic (ROC) analysis was performed to assess diagnostic accuracy, with the area under the curve (AUC), sensitivity, specificity and optimal cut-off points calculated.

Results: A total of 200 patients were included (mean age 39.4 ± 16.1 years), of whom 128 (64.3%) were diagnosed with ureteral stones. The CHOKAI score demonstrated an AUC of 0.831 (95% CI: 0.771–0.880). At the optimal cut-off of >5, sensitivity was 82.0% and specificity was 67.6%. Hydronephrosis was detected in 73.4% of patients on POCUS. While sensitivity was acceptable, specificity fell short of the desired ≥80% threshold.

Conclusion: The CHOKAI score showed moderate diagnostic performance in predicting ureteral stones in the studied population. Although it may assist in identifying high-risk patients, it should not replace confirmatory CT scanning. Integrating the CHOKAI score with clinician expertise and improved ultrasound training may enhance diagnostic accuracy and support more judicious use of imaging.

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