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ORIGINAL ARTICLE
Year : 2020  |  Volume : 33  |  Issue : 2  |  Page : 528-533

A simplified model of Clinical Research Office of Endourological Society nomogram to predict percutaneous nephrolithotomy outcomes


1 Department of Urology, Faculty of Medicine, Menoufia University, Menoufia, Egypt
2 Department of Urology, Alex, Police Hospital, Ministry of Interior, Alexandria, Egypt

Correspondence Address:
Ahmed Hamady Mostafa
Semoha, Alexandria
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/mmj.mmj_254_19

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Objective The aim was to predict stone-free status calculated by the Clinical Research Office of Endourological Society (CROES) nomogram and to test the accuracy of our regression model to predict outcomes of percutaneous nephrolithotomy (PCNL). Patients and methods From July 2018 to May 2019, data of 100 patients who underwent PCNL procedure at Urology Department of the Menoufia University were collected, and postoperative results were compared with the preoperative predicted stone-free status. The CROES nomogram was applied to the data of all cases using its scale to calculate the total score and percent of stone-free status. The authors used binary logistic regression to test whether the six factors in the study can predict the PCNL outcome. We compared the calculated probabilities of stone free by the regression model with the traditional method using the six parameters on the scale of nomogram. Results A total of 100 patients were included in the study. Mean patients' age was 41 ± 9.6 years, and mean stone burden was 564.59 ± 533.869 mm2. Postoperative treatment success rate was 62%. CROES score was found to be an independent predictor of treatment success. The estimated area under the curve was 0.96, and the model provided good calibration. The accuracy of the fitted logistic model was 78% when using it as a single method when compared with the probabilities of CROES nomogram. Conclusion CROES nomogram is an efficient tool to predict outcomes of PCNL. The model has noticeable accuracy in predicting PCNL outcomes using the most influent variables in the CROES nomogram.


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