No abstract

No abstract

Background Although several short‐forms of the posttraumatic stress disorder (PTSD) Checklist (PCL) exist, all were developed using heuristic methods. This report presents the results of analyses designed to create an optimal short‐form PCL for DSM‐5 (PCL‐5) using both machine learning and conventional scale development methods. Methods The short‐form scales were developed using independent datasets collected by the Army Study to Assess Risk and Resilience among Service members. We began by using a training dataset (n = 8,917) to fit short‐form scales with between 1 and 8 items using different statistical methods (exploratory factor analysis, stepwise logistic regression, and a new machine learning method to find an optimal integer‐scored short‐form scale) to predict dichotomous PTSD diagnoses determined using the full PCL‐5. A smaller subset of best short‐form scales was then evaluated in an independent validation sample (n = 11,728) to select one optimal short‐form scale based on multiple operating characteristics (area under curve [AUC], calibration, sensitivity, specificity, net benefit). Results Inspection of AUCs in the training sample and replication in the validation sample led to a focus on 4‐item integer‐scored short‐form scales selected with stepwise regression. Brier scores in the validation sample showed that a number of these scales had comparable calibration (0.015–0.032) and AUC (0.984–0.994), but that one had consistently highest net benefit across a plausible range of decision thresholds. Conclusions The recommended 4‐item integer‐scored short‐form PCL‐5 generates diagnoses that closely parallel those of the full PCL‐5, making it well‐suited for screening.

Although suicide risk is often thought of as existing on a graded continuum, its latent structure (i.e., whether it is categorical or dimensional) has not been empirically determined. Knowledge about the latent structure of suicide risk holds implications for suicide risk assessments, targeted suicide interventions, and suicide research. Our objectives were to determine whether suicide risk can best be understood as a categorical (i.e., taxonic) or dimensional entity, and to validate the nature of any obtained taxon. We conducted taxometric analyses of cross-sectional, baseline data from 16 independent studies funded by the Military Suicide Research Consortium. Participants (N = 1,773) primarily consisted of military personnel, and most had a history of suicidal behavior. The Comparison Curve Fit Index values for MAMBAC (.85), MAXEIG (.77), and L-Mode (.62) all strongly supported categorical (i.e., taxonic) structure for suicide risk. Follow-up analyses comparing the taxon and complement groups revealed substantially larger effect sizes for the variables most conceptually similar to suicide risk compared with variables indicating general distress. Pending replication and establishment of the predictive validity of the taxon, our results suggest the need for a fundamental shift in suicide risk assessment, treatment, and research. Specifically, suicide risk assessments could be shortened without sacrificing validity, the most potent suicide interventions could be allocated to individuals in the high-risk group, and research should generally be conducted on individuals in the high-risk group. (PsycINFO Database Record

IMPORTANCE Several statistical models for predicting suicide risk have been developed, but how accurate such models must be to warrant implementation in clinical practice is not known. OBJECTIVE To identify threshold values of sensitivity, specificity, and positive predictive value that a suicide risk prediction method must attain to cost-effectively target a suicide risk reduction intervention to high-risk individuals. DESIGN, SETTING, AND PARTICIPANTSThis economic evaluation incorporated published data on suicide epidemiology, the health care and societal costs of suicide, and the costs and efficacy of suicide risk reduction interventions into a novel decision analytic model. The model projected suicide-related health economic outcomes over a lifetime horizon among a population of US adults with a primary care physician. Data analysis was performed from September 19, 2019, to July 5, 2020.INTERVENTIONS Two possible interventions were delivered to individuals at high predicted risk: active contact and follow-up (ACF; relative risk of suicide attempt, 0.83; annual health care cost, $96) and cognitive behavioral therapy (CBT; relative risk of suicide attempt, 0.47; annual health care cost, $1088). MAIN OUTCOMES AND MEASURESFatal and nonfatal suicide attempts, quality-adjusted life-years (QALYs), health care sector costs and societal costs (in 2016 US dollars), and incremental cost-effectiveness ratios (ICERs) (with ICERs Յ$150 000 per QALY designated cost-effective).RESULTS With a specificity of 95% and a sensitivity of 25%, primary care-based suicide risk prediction could reduce suicide death rates by 0.5 per 100 000 person-years (if used to target ACF) or 1.6 per 100 000 person-years (if used to target CBT) from a baseline of 15.3 per 100 000 person-years. To be cost-effective from a health care sector perspective at a specificity of 95%, a risk prediction method would need to have a sensitivity of 17.0% or greater (95% CI, 7.4%-37.3%) if used to target ACF and 35.7% or greater (95% CI, 23.1%-60.3%) if used to target CBT. To achieve cost-effectiveness, ACF required positive predictive values of 0.8% for predicting suicide attempt and 0.07% for predicting suicide death; CBT required values of 1.7% for suicide attempt and 0.2% for suicide death.CONCLUSIONS AND RELEVANCE These findings suggest that with sufficient accuracy, statistical suicide risk prediction models can provide good health economic value in the US. Several existing suicide risk prediction models exceed the accuracy thresholds identified in this analysis and thus may warrant pilot implementation in US health care systems.