TY - JOUR
T1 - Novel algorithm for management of acute epididymitis
AU - Hongo, Hiroshi
AU - Kikuchi, Eiji
AU - Matsumoto, Kazuhiro
AU - Yazawa, Satoshi
AU - Kanao, Kent
AU - Kosaka, Takeo
AU - Mizuno, Ryuichi
AU - Miyajima, Akira
AU - Saito, Shiro
AU - Oya, Mototsugu
N1 - Publisher Copyright:
© 2016 The Japanese Urological Association
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Objectives: To identify predictive factors for the severity of epididymitis and to develop an algorithm guiding decisions on how to manage patients with this disease. Methods: A retrospective study was carried out on 160 epididymitis patients at Keio University Hospital. We classified cases into severe and non-severe groups, and compared clinical findings at the first visit. Based on statistical analyses, we developed an algorithm for predicting severe cases. We validated the algorithm by applying it to an external cohort of 96 patients at Tokyo Medical Center. The efficacy of the algorithm was investigated by a decision curve analysis. Results: A total of 19 patients (11.9%) had severe epididymitis. Patient characteristics including older age, previous history of diabetes mellitus and fever, as well as laboratory data including a higher white blood cell count, C-reactive protein level and blood urea nitrogen level were independently associated with severity. A predictive algorithm was created with the ability to classify epididymitis cases into three risk groups. In the Keio University Hospital cohort, 100%, 23.5%, and 3.4% of cases in the high-, intermediate-, and low-risk groups, respectively, became severe. The specificity of the algorithm for predicting severe epididymitis proved to be 100% in the Keio University Hospital cohort and 98.8% in the Tokyo Medical Center cohort. The decision curve analysis also showed the high efficacy of the algorithm. Conclusions: This algorithm might aid in decision-making for the clinical management of acute epididymitis.
AB - Objectives: To identify predictive factors for the severity of epididymitis and to develop an algorithm guiding decisions on how to manage patients with this disease. Methods: A retrospective study was carried out on 160 epididymitis patients at Keio University Hospital. We classified cases into severe and non-severe groups, and compared clinical findings at the first visit. Based on statistical analyses, we developed an algorithm for predicting severe cases. We validated the algorithm by applying it to an external cohort of 96 patients at Tokyo Medical Center. The efficacy of the algorithm was investigated by a decision curve analysis. Results: A total of 19 patients (11.9%) had severe epididymitis. Patient characteristics including older age, previous history of diabetes mellitus and fever, as well as laboratory data including a higher white blood cell count, C-reactive protein level and blood urea nitrogen level were independently associated with severity. A predictive algorithm was created with the ability to classify epididymitis cases into three risk groups. In the Keio University Hospital cohort, 100%, 23.5%, and 3.4% of cases in the high-, intermediate-, and low-risk groups, respectively, became severe. The specificity of the algorithm for predicting severe epididymitis proved to be 100% in the Keio University Hospital cohort and 98.8% in the Tokyo Medical Center cohort. The decision curve analysis also showed the high efficacy of the algorithm. Conclusions: This algorithm might aid in decision-making for the clinical management of acute epididymitis.
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U2 - 10.1111/iju.13236
DO - 10.1111/iju.13236
M3 - Article
C2 - 27714879
AN - SCOPUS:84995676817
SN - 0919-8172
VL - 24
SP - 82
EP - 87
JO - International Journal of Urology
JF - International Journal of Urology
IS - 1
ER -