Predictive Factors of Efficacy Maintenance after Testosterone Treatment Cessation
Min Gu Park1, Jeong Kyun Yeo2, Sun Gu Park3
1Department of Urology, Men's Health Clinic, Seoul Paik Hospital, Seoul 04551, Korea. uromgpark@gmail.com.
Abstract:
There is no conclusive evidence as to whether patients with testosterone deficiency (TD) who benefit from testosterone treatment (TRT) must continue the treatment for the rest of their lives. In some patients, the effect of TRT does not maintained after stopping TRT and, some patients show no significant TD symptoms, with normal testosterone levels after TRT cessation. Therefore, we investigated the predictive factors of response maintenance after TRT cessation. A total of 151 men with TD who responded to TRT were followed up for six months after TRT discontinuation. Ninety-two patients (Group I) failed to show response maintenance; 59 patients (Group II) had a maintained response. The groups did not differ in baseline characteristics or the type of TRT (oral, gel, short/long-acting injectables). However, TRT duration was significantly longer (10.7 vs. 5.2 months), and peak total testosterone (TT) level was significantly higher (713.7 vs. 546.1 ng/dL), in Group II than in Group I. More patients regularly exercised in Group II than in Group I (45.8% vs. 9.8%, p < 0.001). A multivariate logistic regression analysis revealed that exercise (B = 2.325, odds ratio = 10.231, p < 0.001) and TRT duration (B = 0.153, Exp(B) = 1.166, p < 0.001) were independent predictive factors of response maintenance. In men with TD who respond to TRT, longer treatment periods can improve the response durability after TRT cessation, regardless of the type of TRT. Additionally, regular exercise can increase the probability of maintaining the response after TRT cessation.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
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