Posttraumatic Stress Disorder Is Associated With Elevated Risk of Incident Stroke and Transient Ischemic Attack in Women Veterans

BACKGROUND

Posttraumatic stress disorder (PTSD) has been associated with ischemic heart disease in women veterans, but evidence for associations with other cardiovascular disorders remains limited in this population. This retrospective longitudinal cohort study evaluated the association of PTSD with incident stroke/transient ischemic attack (TIA) in women veterans.

METHODS AND RESULTS

Veterans Health Administration electronic health records were used to identify women veterans aged ≥18 years engaged with Veterans Health Administration health care from January 1, 2000 to December 31, 2019. We identified women veterans with and without PTSD without a history of stroke or TIA at start of follow-up. Propensity score matching was used to match groups on age, race or ethnicity, traditional cardiovascular risk factors, female-specific risk factors, a range of mental and physical health conditions, and number of prior health care visits. PTSD, stroke, TIA, and risk factors used in propensity score matching were based on diagnostic codes. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs for associations of PTSD with an incident stroke/TIA composite. Subanalyses considered stroke and TIA separately, plus age- and race- or ethnicity-stratified analyses were carried out. The analytic sample included 208 092 women veterans (104 046 with and 104 046 without PTSD). PTSD was associated with a greater rate of developing stroke/TIA (HR, 1.33 [95% CI, 1.25-1.42], P<0.001). This elevated risk was especially pronounced in women <50 years old and in Hispanic/Latina women.

CONCLUSIONS

Findings indicate a strong association of PTSD with incident stroke/TIA in women veterans. Research is needed to determine whether addressing PTSD and its downstream consequences can offset this risk.

Machine learning-directed electrical impedance tomography to predict metabolically vulnerable plaques

The characterization of atherosclerotic plaques to predict their vulnerability to rupture remains a diagnostic challenge. Despite existing imaging modalities, none have proven their abilities to identify metabolically active oxidized low-density lipoprotein (oxLDL), a marker of plaque vulnerability. To this end, we developed a machine learning-directed electrochemical impedance spectroscopy (EIS) platform to analyze oxLDL-rich plaques, with immunohistology serving as the ground truth. We fabricated the EIS sensor by affixing a six-point microelectrode configuration onto a silicone balloon catheter and electroplating the surface with platinum black (PtB) to improve the charge transfer efficiency at the electrochemical interface. To demonstrate clinical translation, we deployed the EIS sensor to the coronary arteries of an explanted human heart from a patient undergoing heart transplant and interrogated the atherosclerotic lesions to reconstruct the 3D EIS profiles of oxLDL-rich atherosclerotic plaques in both right coronary and left descending coronary arteries. To establish effective generalization of our methods, we repeated the reconstruction and training process on the common carotid arteries of an unembalmed human cadaver specimen. Our findings indicated that our DenseNet model achieves the most reliable predictions for metabolically vulnerable plaque, yielding an accuracy of 92.59% after 100 epochs of training.