Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population – a machine learning approach

Abstract

Background The proportion of patients with post-traumatic stress disorder (PTSD) that remain undiagnosed may be substantial. Without an accurate diagnosis, these patients may lack PTSD-targeted treatments and experience adverse health outcomes. This study used a machine learning approach to identify and describe civilian patients likely to have undiagnosed PTSD in the US commercial population. Methods The IBM® MarketScan® Commercial Subset (10/01/2015–12/31/2018) was used. A random forest machine learning model was developed and trained to differentiate between patients with and without PTSD using non–trauma-based features. The model was applied to patients for whom PTSD status could not be confirmed to identify individuals likely and unlikely to have undiagnosed PTSD. Patient characteristics, symptoms and complications potentially related to PTSD, treatments received, healthcare costs, and healthcare resource utilization were described separately for patients with PTSD (Actual Positive PTSD cohort), patients likely to have PTSD (Likely PTSD cohort), and patients without PTSD (Without PTSD cohort). Results A total of 44,342 patients were classified in the Actual Positive PTSD cohort, 5683 in the Likely PTSD cohort, and 2,074,471 in the Without PTSD cohort. While several symptoms/comorbidities were similar between the Actual Positive and Likely PTSD cohorts, others, including depression and anxiety disorders, suicidal thoughts/actions, and substance use, were more common in the Likely PTSD cohort, suggesting that certain symptoms may be exacerbated among those without a formal diagnosis. Mean per-patient-per-6-month healthcare costs were similar between the Actual Positive and Likely PTSD cohorts ($11,156 and $11,723) and were higher than those of the Without PTSD cohort ($3616); however, cost drivers differed between cohorts, with the Likely PTSD cohort experiencing more inpatient admissions and less outpatient visits than the Actual Positive PTSD cohort. Conclusions These findings suggest that the lack of a PTSD diagnosis and targeted management of PTSD may result in a greater burden among undiagnosed patients and highlights the need for increased awareness of PTSD in clinical practice and among the civilian population.

Systematic review of machine learning in PTSD studies for automated diagnosis evaluation

“…Gagnon-Sanschagrin et al used an RF classifier with data on antiadrenergic medication use, bipolar disorder diagnosis, musculoskeletal and connective tissue diseases, substance use/abuse, and physiological symptoms or reactions to identify individuals with undiagnosed PTSD. An AUC of 0.75 was reported in this study 63 .…”

“…The Supplementary Table 1 summarizes data extracted from these 41 studies, including 21 neuroimaging research studies 23 – 43 . Six of the studies used clinical interviews 44 – 49 , eight studies used data extracted from self-reported questionnaires or online surveys 50 – 57 , and six used blood markers, facial features, social media, GPS, or EMR to diagnose PTSD 58 – 63 . ML models were used in all studies to diagnose PTSD in diverse sample sets, including data collected from the general population as a heterogeneous group, patients who witnessed traumatic incidents, online databases, veterans, firefighters, and healthcare providers.…”

Risk and early signs of PTSD in people indirectly exposed to October 7 events

“…The significance of these findings is amplified by the fact that a substantial number of individuals with PTSD remain undiagnosed, despite the availability of early and personalized treatments [34][35][36][37][38] . The absence of proper diagnosis and subsequent treatments can lead to more severe long-term health outcomes, including persistent depressive symptoms, increased suicide attempts, higher morbidity, and premature death [38][39][40] .…”

“…Further, our findings suggest that the extent of these physiological reactions, as detected objectively via smartwatches and subjectively by the individual, provides early signs from the impact phase. The significance of these findings is amplified by the fact that a substantial number of individuals with PTSD remain undiagnosed, despite the availability of early and personalized treatments 34–38 . The absence of proper diagnosis and subsequent treatments can lead to more severe long-term health outcomes, including persistent depressive symptoms, increased suicide attempts, higher morbidity, and premature death 38–40 .…”

“…The significance of these findings is amplified by the fact that a substantial number of individuals with PTSD remain undiagnosed, despite the availability of early and personalized treatments [34][35][36][37][38] . The absence of proper diagnosis and subsequent treatments can lead to more severe long-term health outcomes, including persistent depressive symptoms, increased suicide attempts, higher morbidity, and premature death [38][39][40] . The ability to monitor and analyze biometric data from smartwatches opens new avenues for identifying early signs of PTSD at the population level, particularly in cases of indirect exposure to trauma through mass media.…”