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

Abstract 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.

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Improving Outcome Predictions for Patients Receiving Mechanical Circulatory Support by Optimizing Imputation of Missing Values

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.120.007071 ◽

2021 ◽

Author(s):

Byron C. Jaeger ◽

Ryan Cantor ◽

Venkata Sthanam ◽

Rongbing Xie ◽

James K. Kirklin ◽

...

Keyword(s):

Multiple Imputation ◽

Risk Prediction ◽

Random Forests ◽

Missing Values ◽

Prediction Models ◽

Model Performance ◽

Circulatory Support ◽

Risk Prediction Models ◽

Prognostic Accuracy ◽

The Mean

Background: Risk prediction models play an important role in clinical decision making. When developing risk prediction models, practitioners often impute missing values to the mean. We evaluated the impact of applying other strategies to impute missing values on the prognostic accuracy of downstream risk prediction models, that is, models fitted to the imputed data. A secondary objective was to compare the accuracy of imputation methods based on artificially induced missing values. To complete these objectives, we used data from the Interagency Registry for Mechanically Assisted Circulatory Support. Methods: We applied 12 imputation strategies in combination with 2 different modeling strategies for mortality and transplant risk prediction following surgery to receive mechanical circulatory support. Model performance was evaluated using Monte-Carlo cross-validation and measured based on outcomes 6 months following surgery using the scaled Brier score, concordance index, and calibration error. We used Bayesian hierarchical models to compare model performance. Results: Multiple imputation with random forests emerged as a robust strategy to impute missing values, increasing model concordance by 0.0030 (25th–75th percentile: 0.0008–0.0052) compared with imputation to the mean for mortality risk prediction using a downstream proportional hazards model. The posterior probability that single and multiple imputation using random forests would improve concordance versus mean imputation was 0.464 and >0.999, respectively. Conclusions: Selecting an optimal strategy to impute missing values such as random forests and applying multiple imputation can improve the prognostic accuracy of downstream risk prediction models.

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