Association of opioid or other substance use disorders with health care use among patients with suicidal symptoms.

INTRODUCTION: Prior literature establishes noteworthy relationships between suicidal symptoms and substance use disorders (SUDs), particularly opioid use disorder (OUD). However, engagement with health care services among this vulnerable population remains underinvestigated. This study sought to examine patterns of health care use, identify risk factors in seeking treatment, and assess associations between outpatient service use and emergency department (ED) visits. METHODS: Using electronic health records (EHRs) derived from five health systems across New York City, the study selected 7881 adults with suicidal symptoms (including suicidal ideation, suicide attempt, or self-harm) and SUDs between 2010 and 2019. To examine the association between SUDs (including OUD) and all-cause service use (outpatient, inpatient, and ED), we performed quasi-Poisson regressions adjusted for age, gender, and chronic disease burden, and we estimated the relative risks (RR) of associated factors. Next, the study evaluated cause-specific utilization within each resource category (SUD-related, suicide-related, and other-psychiatric) and compared them using Mann-Whitney U tests. Finally, we used adjusted quasi-Poisson regression models to analyze the association between outpatient and ED utilization among different risk groups. RESULTS: Among patients with suicidal symptoms and SUD diagnoses, relative to other SUDs, a diagnosis of OUD was associated with higher all-cause outpatient visits (RR: 1.22), ED visits (RR: 1.54), and inpatient hospitalizations (RR: 1.67) (ps < 0.001). Men had a lower risk of having outpatient visits (RR: 0.80) and inpatient hospitalizations (RR: 0.90), and older age protected against ED visits (RR range: 0.59-0.69) (ps < 0.001). OUD was associated with increased SUD-related encounters across all settings, and increased suicide-related ED visits and inpatient hospitalizations (p < 0.001). Individuals with more mental health outpatient visits were less likely to have suicide-related ED visits (RR: 0.86, p < 0.01), however this association was not found among younger and male patients with OUD. Although few OUD patients received medications for OUD (MOUD) treatment (9.9 %), methadone composed the majority of MOUD prescriptions (77.7 %), of which over 70 % were prescribed during an ED encounter. CONCLUSIONS: This study reinforces the importance of tailoring SUD and suicide risk interventions to different age groups and types of SUDs, and highlights missed opportunities for deploying screening and prevention resources among the male and OUD populations. Redressing underutilization of MOUD remains a priority to reduce acute health outcomes among younger patients with OUD.

Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records.

Objective: To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression. Method: This study examined 808 patients with depression at a New York City-based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either "Recovering" or "Worsening" (i.e., non-Recovering), measured by the slope of individual-level Patient Health Questionnaire-9 (PHQ-9) score trajectory spanning 6 months following treatment initiation. A patient was designated as "Recovering" if the slope is less than 0 and as "Worsening" if the slope was no less than 0. Multiple machine learning (ML) models including L2 norm regularized Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting Decision Tree (GBDT) were used to predict treatment outcome based on additional data from EHRs, including demographics and diagnoses. Shapley Additive Explanations were applied to identify the most important predictors. Results: The GBDT achieved the best results of predicting "Recovering" (AUC: 0.7654 ± 0.0227; precision: 0.6002 ± 0.0215; recall: 0.5131 ± 0.0336). When excluding patients with low PHQ-9 scores (<10) at baseline, the results of predicting "Recovering" (AUC: 0.7254 ± 0.0218; precision: 0.5392 ± 0.0437; recall: 0.4431 ± 0.0513) were obtained. Prior diagnosis of anxiety, psychotherapy, recurrent depression, and baseline depression symptom severity were strong predictors. Conclusions: The results demonstrate the potential utility of using ML in longitudinal EHRs to predict antidepressant treatment outcome. Our predictive tool holds the promise to accelerate personalized medical management in patients with psychiatric illnesses.

Longitudinal Trajectories of Symptom Change During Antidepressant Treatment Among Managed Care Patients with Co-Occurring Depression and Anxiety.

Depression and anxiety are highly correlated, yet little is known about the course of each condition when presenting concurrently. This study aimed to identify longitudinal patterns and changes in depression and anxiety symptoms during antidepressant treatment, and evaluate clinical factors associated with each response pattern. Self-report Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scores were used to track the courses of depression and anxiety respectively over a three-month window, and group-based trajectory modeling was used to derive subgroups of patients who have similar response patterns. Multinomial regression was used to associate various clinical variables with trajectory subgroup membership. Of the 577 included adults, 373 (64.6%) were women, and the mean age was 39.3 (SD: 12.9) years. Six depression and six anxiety trajectory subgroups were computationally derived; three depression subgroups demonstrated symptom improvement, and three exhibited nonresponse. Similar patterns were observed in the six anxiety subgroups. Factors associated with treatment nonresponse included higher pretreatment depression and anxiety severity and poorer sleep quality, while better overall health and younger age were associated with higher rates of remission. Synchronous and asynchronous paths to improvement were also observed between depression and anxiety. High baseline depression or anxiety severity alone may be an insufficient predictor of treatment nonresponse. These findings have the potential to motivate clinical strategies aimed at treating depression and anxiety simultaneously.

Prediction of left ventricular ejection fraction changes in heart failure patients using machine learning and electronic health records: a multi-site study.

Left ventricular ejection fraction (EF) is a key measure in the diagnosis and treatment of heart failure (HF) and many patients experience changes in EF overtime. Large-scale analysis of longitudinal changes in EF using electronic health records (EHRs) is limited. In a multi-site retrospective study using EHR data from three academic medical centers, we investigated longitudinal changes in EF measurements in patients diagnosed with HF. We observed significant variations in baseline characteristics and longitudinal EF change behavior of the HF cohorts from a previous study that is based on HF registry data. Data gathered from this longitudinal study were used to develop multiple machine learning models to predict changes in ejection fraction measurements in HF patients. Across all three sites, we observed higher performance in predicting EF increase over a 1-year duration, with similarly higher performance predicting an EF increase of 30% from baseline compared to lower percentage increases. In predicting EF decrease we found moderate to high performance with low confidence for various models. Among various machine learning models, XGBoost was the best performing model for predicting EF changes. Across the three sites, the XGBoost model had an F1-score of 87.2, 89.9, and 88.6 and AUC of 0.83, 0.87, and 0.90 in predicting a 30% increase in EF, and had an F1-score of 95.0, 90.6, 90.1 and AUC of 0.54, 0.56, 0.68 in predicting a 30% decrease in EF. Among features that contribute to predicting EF changes, baseline ejection fraction measurement, age, gender, and heart diseases were found to be statistically significant.

Comorbidity and healthcare utilization in patients with treatment resistant depression: A large-scale retrospective cohort analysis using electronic health records.

BACKGROUND: Medical comorbidity and healthcare utilization in patients with treatment resistant depression (TRD) is usually reported in convenience samples, making estimates unreliable. There is only limited large-scale clinical research on comorbidities and healthcare utilization in TRD patients. METHODS: Electronic Health Record data from over 3.3 million patients from the INSIGHT Clinical Research Network in New York City was used to define TRD as initiation of a third antidepressant regimen in a 12-month period among patients diagnosed with major depressive disorder (MDD). Age and sex matched TRD and non-TRD MDD patients were compared for anxiety disorder, 27 comorbid medical conditions, and healthcare utilization. RESULTS: Out of 30,218 individuals diagnosed with MDD, 15.2 % of patients met the criteria for TRD (n = 4605). Compared to MDD patients without TRD, the TRD patients had higher rates of anxiety disorder and physical comorbidities. They also had higher odds of ischemic heart disease (OR = 1.38), stroke/transient ischemic attack (OR = 1.57), chronic kidney diseases (OR = 1.53), arthritis (OR = 1.52), hip/pelvic fractures (OR = 2.14), and cancers (OR = 1.41). As compared to non-TRD MDD, TRD patients had higher rates of emergency room visits, and inpatient stays. In relation to patients without MDD, both TRD and non-TRD MDD patients had significantly higher levels of anxiety disorder and physical comorbidities. LIMITATIONS: The INSIGHT-CRN data lack information on depression severity and medication adherence. CONCLUSIONS: TRD patients compared to non-TRD MDD patients have a substantially higher prevalence of various psychiatric and medical comorbidities and higher health care utilization. These findings highlight the challenges of developing interventions and care coordination strategies to meet the complex clinical needs of TRD patients.

Extracting social determinants of health from electronic health records using natural language processing: a systematic review.

OBJECTIVE: Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. MATERIALS AND METHODS: A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. RESULTS: Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). CONCLUSION: NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

Association of co-occurring opioid or other substance use disorders with increased healthcare utilization in patients with depression.

Substance use disorders (SUDs) commonly co-occur with mental illness. However, the ongoing addiction crisis raises the question of how opioid use disorder (OUD) impacts healthcare utilization relative to other SUDs. This study examines the utilization patterns of patients with major depressive disorder (MDD) and: (1) co-occurring OUD (MDD-OUD); (2) a co-occurring SUD other than OUD (MDD-NOUD); and (3) no co-occurring SUD (MDD-NSUD). We analyzed electronic health records (EHRs) derived from multiple health systems across the New York City (NYC) metropolitan area between January 2008 and December 2017. 11,275 patients aged ≥18 years with a gap of 30-180 days between 2 consecutive MDD diagnoses and an antidepressant prescribed 0-180 days after any MDD diagnosis were selected, and prevalence of any SUD was 24%. Individuals were stratified into comparison groups and matched on age, gender, and select underlying comorbidities. Prevalence rates and encounter frequencies were measured and compared across outpatient, inpatient, and emergency department (ED) settings. Our key findings showed that relative to other co-occurring SUDs, OUD was associated with larger increases in the rates and odds of using substance-use-related services in all settings, as well as services that integrate mental health and substance abuse treatments in inpatient and ED settings. OUD was also associated with larger increases in total encounters across all settings. These findings and our proposed policy recommendations could inform efforts towards targeted OUD interventions, particularly for individuals with underlying mental illness whose treatment and recovery are often more challenging.

Subphenotyping depression using machine learning and electronic health records.

OBJECTIVE: To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications. MATERIALS AND METHODS: Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics. RESULTS: Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype. CONCLUSION: Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.