RESUMEN
Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (TRD). Leveraging electronic medical records (EMR), this retrospective cohort study applied supervised machine learning (ML) to sociodemographic, clinical, and treatment-related data to predict depressive symptom response (>50% reduction on PHQ-9) and remission (PHQ-9 < 5) following rTMS in 232 patients with TRD (mean age: 54.5, 63.4% women) treated at the University of California, San Diego Interventional Psychiatry Program between 2017 and 2023. ML models were internally validated using nested cross-validation and Shapley values were calculated to quantify contributions of each feature to response prediction. The best-fit models proved reasonably accurate at discriminating treatment responders (Area under the curve (AUC): 0.689 [0.638, 0.740], p < 0.01) and remitters (AUC 0.745 [0.692, 0.797], p < 0.01), though only the response model was well-calibrated. Both models were associated with significant net benefits, indicating their potential utility for clinical decision-making. Shapley values revealed that patients with comorbid anxiety, obesity, concurrent psychiatric medication use, and more chronic TRD were less likely to respond or remit following rTMS. Patients with trauma and former tobacco users were more likely to respond. Furthermore, delivery of intermittent theta burst stimulation and more rTMS sessions were associated with superior outcomes. These findings highlight the potential of ML-guided techniques to guide clinical decision-making for rTMS treatment in patients with TRD to optimize therapeutic outcomes.
RESUMEN
Objectives: Adherence to lifestyle modification (diet, exercise, and alcohol cessation) for fatty liver disease (FLD) management remains challenging. The study examined stigma, barriers, and factors associated with motivation to adhere to lifestyle modification in a diverse and vulnerable population with FLD. Methods: From 2/19/2020 to 2/28/2022, 249 FLD patients within San Francisco safety-net hepatology clinics were surveyed along with clinical data taken from medical records. Multivariable modeling assessed factors associated with motivation to adhere to lifestyle modification in a cross-sectional study. Results: Median age was 53 years, 59% female, 59% Hispanic, 25% Asian/Pacific Islander, 9% White, and 2% Black, 79% were non-English speakers, 64% had ≤ high school education, and 82% reported <$30,000 annual income. Common comorbidities included hyperlipidemia (47%), hypertension (42%), diabetes (39%), and heavy alcohol use (22%). Majority (78%) reported experiencing stigma, 41% reported extreme motivation, and 58% reported ≥ two barriers. When controlling for age, sex, Hispanic ethnicity, alcohol consumption, BMI, >high school (coef 1.41, 95% CI 0.34-2.48), stigma (coef 0.34, 95% CI 0.07-0.62), and depression (coef -1.52, 95% CI -2.79 to -0.26) were associated with motivation. Conclusions: Stigma is commonly reported among FLD patients. Interventions to enhance patient education and mental health support are critical to FLD management, especially in vulnerable populations.
RESUMEN
Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major depressive disorder (MDD), most studies to date have administered continuous or open-loop stimulation with promising but mixed results. One factor contributing to these mixed results may stem from when the stimulation is applied. Stimulation administration specific to high-symptom states in a personalized and responsive manner may be more effective at reducing symptoms compared to continuous stimulation and may avoid diminished therapeutic effects related to habituation. Additionally, a lower total duration of stimulation per day is advantageous for reducing device energy consumption. This protocol describes an experimental workflow using a chronically implanted neurostimulation device to achieve closed-loop stimulation for individuals with treatment-refractory MDD. This paradigm hinges on determining a patient-specific neural biomarker that is related to states of high symptoms and programming the device detectors, such that stimulation is triggered by this read-out of symptom state. The described procedures include how to obtain neural recordings concurrent with patient symptom reports, how to use these data in a state-space model approach to differentiate low- and high-symptom states and corresponding neural features, and how to subsequently program and tune the device to deliver closed-loop stimulation therapy.
