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The authors developed a practical and clinically useful model to predict the risk of psychosis that utilizes clinical characteristics empirically demonstrated to be strong predictors of conversion to psychosis in clinical high-risk (CHR) individuals. The model is based upon the Structured Interview for Psychosis Risk Syndromes (SIPS) and accompanying clinical interview, and yields scores indicating one's risk of conversion.
Baseline data, including demographic and clinical characteristics measured by the SIPS, were obtained on 199 CHR individuals seeking evaluation in the early detection and intervention for mental disorders program at the New York State Psychiatric Institute at Columbia University Medical Center. Each patient was followed for up to 2 years or until they developed a syndromal DSM-4 disorder. A LASSO logistic fitting procedure was used to construct a model for conversion specifically to a psychotic disorder.
At 2 years, 64 patients (32.2%) converted to a psychotic disorder. The top five variables with relatively large standardized effect sizes included SIPS subscales of visual perceptual abnormalities, dysphoric mood, unusual thought content, disorganized communication, and violent ideation. The concordance index (c-index) was 0.73, indicating a moderately strong ability to discriminate between converters and non-converters.
The prediction model performed well in classifying converters and non-converters and revealed SIPS measures that are relatively strong predictors of conversion, comparable with the risk calculator published by NAPLS (c-index = 0.71), but requiring only a structured clinical interview. Future work will seek to externally validate the model and enhance its performance with the incorporation of relevant biomarkers.
Hypoplastic left heart syndrome is the most expensive birth defect managed in the United States, with a 5-year survival rate below 70%. Increasing evidence suggests that hospital volumes are inversely associated with mortality for infants with single ventricles undergoing stage 1 surgical palliation. Our aim was to examine the relative effects of surgeon and institutional volumes on outcomes and resource utilisation for these children.
A retrospective study was conducted using the Pediatric Health Information System database to examine the effects of the number of procedures performed per surgeon and per centre on mortality, costs, and post-operative length of stay for infants undergoing Risk Adjustment for Congenital Heart Surgery risk category six operations at tertiary-care paediatric hospitals, from 1 January, 2004 to 31 December, 2013. Multivariable modelling was used, adjusting for patient and institutional characteristics. Gaussian kernel densities were constructed to show the relative distributions of the effects of individual institutions and surgeons, before and after adjusting for the number of cases performed.
A total of 2880 infants from 35 institutions met the inclusion criteria. Mortality was 15.0%. Median post-operative length of stay was 24 days (IQR 14–41). Median standardized inpatient hospital costs were $156,000 (IQR $108,000–$248,000) in 2013 dollars. In the multivariable analyses, higher institutional volume was inversely associated with mortality (p=0.001), post-operative length of stay (p=0.004), and costs (p=0.001). Surgeon volume was associated with none of the measured outcomes. Neither institutional nor surgeon volumes explained much of the wide variation in outcomes and resource utilization observed between institutions and between surgeons.
Increased institutional – but not surgeon – volumes are associated with reduced mortality, post-operative length of stay, and costs for infants undergoing stage 1 palliation.
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