External validation of the Prostate Cancer Prevention Trial and the European Randomized Study of Screening for Prostate Cancer risk calculators in a Chinese cohort

Several prediction models have been developed to estimate the outcomes of prostate biopsies. Most of these tools were designed for use with Western populations and have not been validated across different ethnic groups. Therefore, we evaluated the predictive value of the Prostate Cancer Prevention Trial （PCPT） and the European Randomized Study of Screening for Prostate Cancer （ERSPC） risk calculators in a Chinese cohort. Clinicopathological information was obtained from 495 Chinese men who had undergone extended prostate biopsies between January 2009 and March 2011. The estimated probabilities of prostate cancer and high-grade disease （Gleason 〉6） were calculated using the PCPT and ERSPC risk calculators. Overall measures, discrimination, calibration and clinical usefulness were assessed for the model evaluation. Of these patients, 28.7% were diagnosed with prostate cancer and 19.4% had high-grade disease. Compared to the PCPT model and the prostate-specific antigen （PSA） threshold of 4 ng m1-1, the ERSPC risk calculator exhibited better discriminative ability for predicting positive biopsies and high-grade disease （the area under the curve was 0.831 and 0.852, respectively, P〈O.01 for both）. Decision curve analysis also suggested the favourable clinical utility of the ERSPC calculator in the validation dataset. Both prediction models demonstrated miscalibration： the risk of prostate cancer and high-grade disease was overestimated by approximately 20% for a wide range of predicted probabilities. In conclusion, the ERSPC risk calculator outperformed both the PCPT model and the PSA threshold of 4 ng ml- z in predicting prostate cancer and high-grade disease in Chinese patients. However, the prediction tools derived from Western men significantly overestimated the probability of prostate cancer and high-grade disease compared to the outcomes of biopsies in a Chinese cohort.

Comments on the release Laboratory Manual for the Human Semen of the 5th edition of the WHO Examination and Processing of

The authors of the World Health Organization Semen Analysis Manual are to be congratulated on producing a new edition; it is an essential tool to disseminate good practice in andrology. However, the tests described have poor prognostic power to predict a man＇s fertility and show little about the underlying causes of sub-fertility. This commentary urges a revival of research into the diagnosis of male fertility. It suggests that fertility should be regarded as a continuum and that the artificial binary division between fertile and infertile should be abandoned. Models to predict a sub-fertile couple＇s chance of conception in a year should be developed on the basis of prospective data. These models would allow for sophisticated decision making about management. The future lies in the identification of tests to detect underlying pathologies open to specific treatment. Leads such as oxidative stress, defects in the intracellular regulation and the developing field ofproteomics should be explored.

A Predictive Model for Estimation Risk of Proliferative Lupus Nephritis

Background： Lupus nephritis （LN） is classified by renal biopsy into proliferative and nonproliferative forms, with distinct prognoses, but renal biopsy is not available for every LN patient. The present study aimed to establish an alternate tool by building a predictive model to evaluate the probability of proliferative LN. Methods： In this retrospective cohort with biopsy-proven LN, 382 patients in development cohort, 193 in internal validation cohort, and 164 newly diagnosed patients in external validation cohort were selected. Logistic regression model was established, and the concordance statistics （C-statistics）, Akaike information criterion （AIC）, integrated discrimination improvement, Hosmer-Lemeshow test, and net reclassification improvement were calculated to evaluate the performance and validation of models. Results： The prevalence of proliferative LN was 77.7% in the whole cohort. A model, including age, gender, systolic blood pressure, hemoglobin, proteinuria, hematuria, and serum C3, performed well on good-of-fit and discrimination in the development chohort to predict the risk of proliferative LN （291 for AIC and 0.84 for C-statistics）. In the internal and external validation cohorts, this model showed good capability for discrimination and calibration （0.84 and 0.82 for C-statistics, and 0.99 and 0.75 for P values, respectively）. Conclusion： This study developed and validated a model including demographic and clinical indices to evaluate the probability of presenting proliferative LN to guide therapeutic decisions and outcomes.