A machine learning model for predicting surgical intervention in renal colic due to ureteral stone(s) < 5 mm

Abstract

AbstractA 75–89% expulsion rate is reported for ureteric stones ≤ 5 mm. We explored which parameters predict justified surgical intervention in cases of pain caused by < 5 mm ureteral stones. We retrospectively reviewed all patients with renal colic caused by ureteral stone < 5 mm admitted to our urology department between 2016 and 2021. Data on age, sex, body mass index, the presence of associated hydronephrosis/stranding on images, ureteral side, stone location, medical history, serum blood count, creatinine, C-reactive protein, and vital signs were obtained upon admission. XGboost (XG), a machine learning model has been implemented to predict the need for intervention. A total of 471 patients (median age 49, 83% males) were reviewed. 74% of the stones were located in the distal ureter. 160 (34%) patients who sustained persistent pain underwent surgical intervention. The operated patients had proximal stone location (56% vs. 10%, p < 0.001) larger stones (4 mm vs. 3 mm, p < 0.001), longer length of stay (3.5 vs. 3 days, p < 0.001) and more emergency-room (ER) visits prior to index admission (2 vs. 1, p = 0.007) compared to those who had no surgical intervention. The model accuracy was 0.8. Larger stone size and proximal location were the most important features in predicting the need for intervention. Altogether with pulse and ER visits, they contributed 73% of the final prediction for each patient. Although a high expulsion rate is expected for ureteral stones < 5 mm, some may be painful and drawn out in spontaneous passage. Decision-making for surgical intervention can be facilitated by the use of the present prediction model.