Development and Validation of a Dynamic Risk Prediction Model to Forecast Psychosis Onset in Patients at Clinical High Risk

The prediction of outcomes in patients at Clinical High Risk for Psychosis (CHR-P) almost exclusively relies on static data obtained at a single snapshot in time (ie, baseline data). Although the CHR-P symptoms are intrinsically evolving over time, available prediction models cannot be dynamically updated to reflect these changes. Hence, the aim of this study was to develop and internally validate a dynamic risk prediction model (joint model) and to implement this model in a user-friendly online risk calculator. Furthermore, we aimed to explore the prognostic performance of extended dynamic risk prediction models and to compare static with dynamic prediction. One hundred ninety-six CHR-P patients were recruited as part of the “Basel Früherkennung von Psychosen” (FePsy) study. Psychopathology and transition to psychosis was assessed at regular intervals for up to 5 years using the Brief Psychiatric Rating Scale-Expanded (BPRS-E). Various specifications of joint models were compared with regard to their cross-validated prognostic performance. We developed and internally validated a joint model that predicts psychosis onset from BPRS-E disorganization and years of education at baseline and BPRS-E positive symptoms during the follow-up with good prognostic performance. The model was implemented as online risk calculator (http://www.fepsy.ch/DPRP/). The use of extended joint models slightly increased the prognostic accuracy compared to basic joint models, and dynamic models showed a higher prognostic accuracy than static models. Our results confirm that extended joint modeling could improve the prediction of psychosis in CHR-P patients. We implemented the first online risk calculator that can dynamically update psychosis risk prediction.

When can active surveillance be less active? Prediction of long-term nonreclassification for men with low-risk prostate cancer.

140 Background: Active surveillance is endorsed as the preferred management strategy for most men with low-risk prostate cancer. However, nearly all active surveillance protocols entail prostate specific antigen (PSA) testing every 3-6 months, and prostate biopsies every 1-2 years. For many men with indolent tumors, this regimen is overly intense, and exposes men to the discomfort, risks, and costs of repeated biopsies. We aimed to determine if some men can be safely selected for a less intense surveillance regimen by predicting the probability of non-reclassification over the next 4 years of surveillance. Methods: Data were collected from men in the multicenter Canary Prostate Active Surveillance Study (PASS), in which PSAs are collected q3 months and biopsies performed 12 months of diagnosis and then every 2 years. For inclusion in this study, men had to have undergone ≤ 1 follow up biopsy, and have Gleason grade group 1 at diagnosis. Reclassification was defined as increase in Gleason grade group on subsequent biopsy; those without reclassification were censored at last study contact, treatment or 2 years after last biopsy. A dynamic risk prediction model based on a Cox regression with robust variance estimates was used to construct and test a model predicting non-reclassification. Results: Of 1082 men included, 362 (33%) reclassified and the remaining were censored. The final regression model included percent of biopsy cores involved, prior biopsy history, time since diagnosis, BMI, prostate size, diagnostic PSA, and PSAk (a measure of PSA kinetics). This dynamic risk prediction model was assessed at a measurement time of 1 year after diagnosis, predicting risk of reclassification at 4 years. Men at lowest and highest deciles of this model-based risk faced 6% (95%CI 0-12%) and 73% (55-84%) risks of reclassification within 5 years. For at least 10% of the men in the cohort, the negative predictive value (NPV) for reclassification was 95% or higher. Conclusions: A substantial proportion of men with low-risk prostate cancer can safely be followed with a de-intensified active surveillance protocol, which would improve both the tolerability and cost-effectiveness of this management strategy.