Clinical implementation of risk calculator models in the clinical high-risk for psychosis (CHR-P) population has been hindered by heterogeneous risk distributions across study cohorts which could be attributed to pre-ascertainment illness progression. To examine this, we tested whether the duration of attenuated psychotic symptom (APS) worsening prior to baseline moderated performance of the North American prodrome longitudinal study 2 (NAPLS2) risk calculator. We also examined whether rates of cortical thinning, another marker of illness progression, bolstered clinical prediction models.

Participants from both the NAPLS2 and NAPLS3 samples were classified as either ‘long’ or ‘short’ symptom duration based on time since APS increase prior to baseline. The NAPLS2 risk calculator model was applied to each of these groups. In a subset of NAPLS3 participants who completed follow-up magnetic resonance imaging scans, change in cortical thickness was combined with the individual risk score to predict conversion to psychosis.

The risk calculator models achieved similar performance across the combined NAPLS2/NAPLS3 sample [area under the curve (AUC) = 0.69], the long duration group (AUC = 0.71), and the short duration group (AUC = 0.71). The shorter duration group was younger and had higher baseline APS than the longer duration group. The addition of cortical thinning improved the prediction of conversion significantly for the short duration group (AUC = 0.84), with a moderate improvement in prediction for the longer duration group (AUC = 0.78).

These results suggest that early illness progression differs among CHR-P patients, is detectable with both clinical and neuroimaging measures, and could play an essential role in the prediction of clinical outcomes.

Assessment of risks of illnesses has been an important part of medicine for decades. We now have hundreds of ‘risk calculators’ for illnesses, including brain disorders, and these calculators are continually improving as more diverse measures are collected on larger samples.

We first replicated an existing psychosis risk calculator and then used our own sample to develop a similar calculator for use in recruiting ‘psychosis risk’ enriched community samples. We assessed 632 participants age 8–21 (52% female; 48% Black) from a community sample with longitudinal data on neurocognitive, clinical, medical, and environmental variables. We used this information to predict psychosis spectrum (PS) status in the future. We selected variables based on lasso, random forest, and statistical inference relief; and predicted future PS using ridge regression, random forest, and support vector machines.

Cross-validated prediction diagnostics were obtained by building and testing models in randomly selected sub-samples of the data, resulting in a distribution of the diagnostics; we report the mean. The strongest predictors of later PS status were the Children's Global Assessment Scale; delusions of predicting the future or having one's thoughts/actions controlled; and the percent married in one's neighborhood. Random forest followed by ridge regression was most accurate, with a cross-validated area under the curve (AUC) of 0.67. Adjustment of the model including only six variables reached an AUC of 0.70.

Results support the potential application of risk calculators for screening and identification of at-risk community youth in prospective investigations of developmental trajectories of the PS.

Few of the previous studies of clinical high risk of psychosis (CHR) have explored whether outcomes other than conversion, such as poor functioning or treatment responses, are better predicted when using risk calculators. To answer this question, we compared the predictive accuracy between the outcome of conversion and poor functioning by using the NAPLS-2 risk calculator.

Three hundred CHR individuals were identified using the Chinese version of the Structured Interview for Prodromal Symptoms. Of these, 228 (76.0%) completed neurocognitive assessments at baseline and 199 (66.3%) had at least a 1-year follow-up assessment. The latter group was used in the NAPLS-2 risk calculator.

We divided the sample into two broad categories based on different outcome definitions, conversion (n = 46) v. non-conversion (n = 153) or recovery (n = 138) v. poor functioning (n = 61). Interestingly, the NAPLS-2 risk calculator showed moderate discrimination of subsequent conversion to psychosis in this sample with an area under the receiver operating characteristic curve (AUC) of 0.631 (p = 0.007). However, for discriminating poor functioning, the AUC of the model increased to 0.754 (p < 0.001).

Our results suggest that the current risk calculator was a better fit for predicting a poor functional outcome and treatment response than it was in the prediction of conversion to psychosis.

Appropriate selection of tongue cancer patients considering surgery is critical in ensuring optimal outcomes. The American College of Surgeons' National Surgical Quality Improvement Program (‘ACS-NSQIP’) risk calculator was developed to assess patients' 30-day post-operative risk, providing surgeons with information to guide decision making.

A retrospective review of 30-day actual mortality and morbidity of tongue cancer patients was undertaken to investigate the validity of this tool for South Australian patients treated from 2005 to 2015.

One hundred and twenty patients had undergone glossectomy. Predicted length of stay using the risk calculator was significantly different from actual length of stay. Predicted mortality and other complications were found to be similar to actual outcomes.

The American College of Surgeons' National Surgical Quality Improvement Program risk calculator was found to be effective in predicting post-operative complication rates in South Australian tongue cancer patients. However, significant discrepancies in predicted and actual length of stay may limit its use in this population.