Beyond one-cutoff-fits-all: determining cutoff values for the PTSD checklist for DSM-5 (PCL-5).

Abstract

Background: There is no universally optimal cutoff score for identifying probable PTSD, which makes reliable PTSD diagnosis challenging not only across different populations but also in different settings. Reliable outcomes require tailoring cutoff scores to the population, intended use (clinical, research, or prevalence estimation), and appropriate statistical methods to ensure their validity.Objective: While previously little emphasis has been placed on thorough methodological evaluation and purpose-driven cutoff selection, this work addresses these gaps by evaluating optimal PCL-5 cutoff scores for clinical use, prevalence estimation, and research in a German-speaking clinical sample.Methods: Previously published data from 443 trauma-exposed individuals in Germany were re-analyzed for this purpose. PTSD was assessed using the PCL-5 and with CAPS-5 clinical interview. Optimal cutoffs were identified using ROC analysis, applying standard estimation methods and prioritising diagnostic utility based on specific objectives.Results: After evaluating various cutoff points for different purposes, we identified the following as most suitable for this sample: a cutoff of 34 for clinical use (sensitivity: 0.892, specificity: 0.645, PPV: 0.824, NPV: 0.763); 38 for prevalence estimation (sensitivity: 0.840, specificity: 0.703, PPV: 0.840, NPV: 0.703); and 42 or 43 for identifying clear-cut cases in research or resource-limited settings (sensitivity: 0.774-0.760, specificity: 0.742-0.761, PPV: 0.848-0.855, NPV: 0.639-0.631). The originally intended cutoffs of 31-33 yielded acceptable to excellent diagnostic utility parameters but were not identified as optimal for any specific purpose.Conclusion: This study highlights the variability in optimal PCL-5 cutoffs, linking selection to specific clinical or research aims. It provides validated cutoffs for PTSD prevalence in a German clinical sample, with limitations regarding generalizability to lower-prevalence populations. Future research should refine cutoffs for diverse populations and improve diagnostic precision.