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STone Episode Prediction: Development and validation of the prediction nomogram for urolithiasis
Author(s) -
Okita Kazutaka,
Hatakeyama Shingo,
Imai Atsushi,
Tanaka Toshikazu,
Hamano Itsuto,
Okamoto Teppei,
Tobisawa Yuki,
Yoneyama Tohru,
Yamamoto Hayato,
Yoneyama Takahiro,
Hashimoto Yasuhiro,
Nakaji Shigeyuki,
Suzuki Tadashi,
Ohyama Chikara
Publication year - 2020
Publication title -
international journal of urology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.172
H-Index - 67
eISSN - 1442-2042
pISSN - 0919-8172
DOI - 10.1111/iju.14203
Subject(s) - nomogram , medicine , logistic regression , receiver operating characteristic , area under the curve , gold standard (test) , diabetes mellitus , urology , endocrinology
Objectives To develop and validate a nomogram predicting the occurrence of a stone episode, given the lack of such predicting risk tools for urolithiasis. Methods We retrospectively analyzed 1305 patients with urolithiasis and 2800 community‐dwelling individuals who underwent a comprehensive health survey. The STone Episode Prediction nomogram was created based on data from the medical records of 600 patients with urolithiasis and 1300 controls, and was validated using a different population of 705 patients with urolithiasis and 1500 controls. Logistic regression analysis was used to construct a model to predict the potential candidate for a stone episode. The predictive ability of the model was evaluated using the results of the area under the receiver operating characteristics curve (area under the curve). Results Age, sex, diabetes mellitus, renal function, serum albumin, and serum uric acid were found to be significantly associated with urolithiasis in the training set and were included in the STone Episode Prediction nomogram. The optimal cut‐off value for the probability of a stone episode using the nomogram was >28% with a sensitivity of 79%, a specificity of 76%, and area under the curve of 0.860. In the validation test, area under the curve for the detection of urolithiasis was 0.815 with a sensitivity of 81% and specificity of 63%. Conclusions Herein, we developed and validated the STone Episode Prediction nomogram that can predict a potential candidate for an episode of urolithiasis. This nomogram might be beneficial for the first step in stone screening in individuals with lifestyle‐related diseases.