Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians.

Importance: While abundant work has examined patient-level differences in antidepressant treatment outcomes, little is known about the extent of clinician-level differences. Understanding these differences may be important in the development of risk models, precision treatment strategies, and more efficient systems of care. Objective: To characterize differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics. Design, Setting, and Participants: This was a longitudinal cohort study using data derived from electronic health records at 2 large academic medical centers and 6 community hospitals, and their affiliated outpatient networks, in eastern Massachusetts. Participants were deidentified clinicians who billed at least 10 International Classification of Diseases, Ninth Revision (ICD-9) or Tenth Revision (ICD-10) diagnoses of major depressive disorder per year between 2008 and 2022. Data analysis occurred between September 2023 and January 2024. Main Outcomes and Measures: Heterogeneity of prescribing, defined as the number of distinct antidepressants accounting for 75% of prescriptions by a given clinician; proportion of patients who did not return for follow-up after an index prescription; and proportion of patients receiving stable, ongoing antidepressant treatment. Results: Among 11 934 clinicians treating major depressive disorder, unsupervised learning identified 10 distinct clusters on the basis of ICD codes, corresponding to outpatient psychiatry as well as oncology, obstetrics, and primary care. Between these clusters, substantial variability was identified in the proportion of selective serotonin reuptake inhibitors, selective norepinephrine reuptake inhibitors, and tricyclic antidepressants prescribed, as well as in the number of distinct antidepressants prescribed. Variability was also detected between clinician clusters in loss to follow-up and achievement of stable treatment, with the former ranging from 27% to 69% and the latter from 22% to 42%. Clinician clusters were significantly associated with treatment outcomes. Conclusions and Relevance: Groups of clinicians treating individuals diagnosed with major depressive disorder exhibit marked differences in prescribing patterns as well as longitudinal patient outcomes defined by electronic health records. Incorporating these group identifiers yielded similar prediction to more complex models incorporating individual codes, suggesting the importance of considering treatment context in efforts at risk stratification.

The effectiveness of the lorazepam challenge test in pediatric catatonia: A multisite retrospective cohort study.

OBJECTIVE: Catatonia is a neuropsychiatric disorder associated with changes in behavior and affect. In adults, catatonia can respond rapidly to treatment with benzodiazepines as part of the "lorazepam challenge test." The acute effectiveness of benzodiazepine treatment in pediatric catatonia, however, has received less study. This study reports catatonia severity as measured by the Bush Francis Catatonia Rating Scale (BFCRS) in pediatric patients before and after treatment with lorazepam. METHODS: Multicenter retrospective cohort study from 1/1/2018 to 6/1/2023 of patients aged 18 and younger with a clinical diagnosis of catatonia and assessment using the BFCRS before and after treatment with lorazepam. RESULTS: Among 54 patients, median age was 16, and 26 (48.1 %) were female. Neurodevelopmental disabilities were present in 24 (44.4 %) of patients. Prior to treatment, patients had a mean BFCRS score of 16.6 ± 6.1, which significantly reduced to 9.5 ± 5.3 following treatment with lorazepam (mean paired difference 7.1; t = 9.0, df = 53, p < 0.001), representing a large effect size (Hedges's g = 1.20; 95 % CI: 0.85 to 1.55). No significant association was found between lorazepam dose or route of administration and clinical response, nor were age, sex, study site, the presence of a neurodevelopmental disorder, the presence of hyperactive catatonic features, or the time between treatment and reassessment associated with post-treatment BFCRS. CONCLUSIONS: Lorazepam resulted in a rapid improvement in BFCRS score in pediatric patients, with a large effect size. Further research is needed into optimal dosing and route of administration of the lorazepam challenge test in pediatric patients.

Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.

Objective: Postpartum depression (PPD) represents a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. Thus, we aimed to develop and estimate the performance of a generalizable risk stratification model for PPD in patients without a history of depression using information collected as part of routine clinical care. Methods: We performed a retrospective cohort study of all individuals who delivered between 2017 and 2022 in one of two large academic medical centers and six community hospitals. An elastic net model was constructed and externally validated to predict PPD using sociodemographic factors, medical history, and prenatal depression screening information, all of which was known before discharge from the delivery hospitalization. Results: The cohort included 29,168 individuals; 2,703 (9.3%) met at least one criterion for postpartum depression in the 6 months following delivery. In the external validation data, the model had good discrimination and remained well-calibrated: area under the receiver operating characteristic curve 0.721 (95% CI: 0.707-0.734), Brier calibration score 0.088 (95% CI: 0.084 - 0.092). At a specificity of 90%, the positive predictive value was 28.0% (95% CI: 26.0-30.1%), and the negative predictive value was 92.2% (95% CI: 91.8-92.7%). Conclusions: These findings demonstrate that a simple machine-learning model can be used to stratify the risk for PPD before delivery hospitalization discharge. This tool could help identify patients within a practice at the highest risk and facilitate individualized postpartum care planning regarding the prevention of, screening for, and management of PPD at the start of the postpartum period and potentially the onset of symptoms.

Dimensional Measures of Psychopathology in Children and Adolescents Using Large Language Models.

BACKGROUND: To enable greater use of National Institute of Mental Health Research Domain Criteria (RDoC) in real-world settings, we applied large language models (LLMs) to estimate dimensional psychopathology from narrative clinical notes. METHODS: We conducted a cohort study using health records from individuals age ≤18 years evaluated in the psychiatric emergency department of a large academic medical center between November 2008 and March 2015. Outcomes were hospital admission and length of emergency department stay. RDoC domains were estimated using a Health Insurance Portability and Accountability Act-compliant LLM (gpt-4-1106-preview) and compared with a previously validated token-based approach. RESULTS: The cohort included 3059 individuals (median age 16 years [interquartile range, 13-18]; 1580 [52%] female, 1479 [48%] male; 105 [3.4%] identified as Asian, 329 [11%] as Black, 288 [9.4%] as Hispanic, 474 [15%] as other race, and 1863 [61%] as White), of whom 1695 (55%) were admitted. Correlation between LLM-extracted RDoC scores and the token-based scores ranged from small to medium as assessed by Kendall's tau (0.14-0.22). In logistic regression models adjusting for sociodemographic and clinical features, admission likelihood was associated with greater scores on all domains, with the exception of the sensorimotor domain, which was inversely associated (p < .001 for all adjusted associations). Tests for bias suggested modest but statistically significant differences in positive valence scores by race (p < .05 for Asian, Black, and Hispanic individuals). CONCLUSIONS: An LLM extracted estimates of 6 RDoC domains in an explainable manner, which were associated with clinical outcomes. This approach can contribute to a new generation of prediction models or biological investigations based on dimensional psychopathology.

Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits.

OBJECTIVE: Treatment-resistant depression (TRD) occurs in roughly one-third of all individuals with major depressive disorder (MDD). Although research has suggested a significant common variant genetic component of liability to TRD, with heritability estimated at 8% when compared with non-treatment-resistant MDD, no replicated genetic loci have been identified, and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. The objective of this study was to perform a well-powered genetic study of TRD. METHODS: Using receipt of electroconvulsive therapy (ECT) as a surrogate for TRD, the authors applied standard machine learning methods to electronic health record data to derive predicted probabilities of receiving ECT. These probabilities were then applied as a quantitative trait in a genome-wide association study of 154,433 genotyped patients across four large biobanks. RESULTS: Heritability estimates ranged from 2% to 4.2%, and significant genetic overlap was observed with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits, and body mass index. Two genome-wide significant loci were identified, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications. CONCLUSIONS: This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.

Pediatric Physical Restraint Coding in US Hospitals: A 2019 Kids Inpatient Database Study.

BACKGROUND: Reduction of physical restraint utilization is a goal of high-quality hospital care, but there is little nationally-representative data about physical restraint utilization in hospitalized children in the United States. This study reports the rate of physical restraint coding among hospitalizations for patients aged 1 to 18 years old in the United States and explores associated demographic and diagnostic factors. METHODS: The Kids' Inpatient Database, an all-payors database of community hospital discharges in the United States, was queried for hospitalizations with a diagnosis of physical restraint status in 2019. Logistic regression using patient sociodemographic characteristics was used to characterize factors associated with physical restraint coding. RESULTS: A coded diagnosis of physical restraint status was present for 8893 (95% confidence interval [CI]: 8227-9560) hospitalizations among individuals aged 1 to 18 years old, or 0.63% of hospitalizations. Diagnoses associated with physical restraint varied by age, with mental health diagnoses overall the most frequent in an adjusted model, male sex (adjusted odds ratio [aOR] 1.56; 95% CI: 1.47-1.65), Black race (aOR 1.43; 95% CI: 1.33-1.55), a primary mental health or substance diagnosis (aOR 7.13; 95% CI: 6.42-7.90), Medicare or Medicaid insurance (aOR 1.33; 95% CI: 1.24-1.43), and more severe illness (aOR 2.83; 95% CI: 2.73-2.94) were associated with higher odds of a hospitalization involving a physical restraint code. CONCLUSIONS: Physical restraint coding varied by age, sex, race, region, and disease severity. These results highlight potential disparities in physical restraint utilization, which may have consequences for equity.

Feasibility, Acceptability, and Preliminary Efficacy of a Smartphone App-Led Cognitive Behavioral Therapy for Depression Under Therapist Supervision: Open Trial.

BACKGROUND: Major depressive disorder affects approximately 1 in 5 adults during their lifetime and is the leading cause of disability worldwide. Yet, a minority receive adequate treatment due to person-level (eg, geographical distance to providers) and systems-level (eg, shortage of trained providers) barriers. Digital tools could improve this treatment gap by reducing the time and frequency of therapy sessions needed for effective treatment through the provision of flexible, automated support. OBJECTIVE: This study aimed to examine the feasibility, acceptability, and preliminary clinical effect of Mindset for Depression, a deployment-ready 8-week smartphone-based cognitive behavioral therapy (CBT) supported by brief teletherapy appointments with a therapist. METHODS: This 8-week, single-arm open trial tested the Mindset for Depression app when combined with 8 brief (16-25 minutes) video conferencing visits with a licensed doctoral-level CBT therapist (n=28 participants). The app offers flexible, accessible psychoeducation, CBT skills practice, and support to patients as well as clinician guidance to promote sustained engagement, monitor safety, and tailor treatment to individual patient needs. To increase accessibility and thus generalizability, all study procedures were conducted remotely. Feasibility and acceptability were assessed via attrition, patient expectations and feedback, and treatment utilization. The primary clinical outcome measure was the clinician-rated Hamilton Depression Rating Scale, administered at pretreatment, midpoint, and posttreatment. Secondary measures of functional impairment and quality of life as well as maintenance of gains (3-month follow-up) were also collected. RESULTS: Treatment credibility (week 4), expectancy (week 4), and satisfaction (week 8) were moderate to high, and attrition was low (n=2, 7%). Participants self-reported using the app or practicing (either on or off the app) the CBT skills taught in the app for a median of 50 (IQR 30-60; week 4) or 60 (IQR 30-90; week 8) minutes per week; participants accessed the app on an average 36.8 (SD 10.0) days and completed a median of 7 of 8 (IQR 6-8) steps by the week 8 assessment. The app was rated positively across domains of engagement, functionality, aesthetics, and information. Participants' depression severity scores decreased from an average Hamilton Depression Rating Scale score indicating moderate depression (mean 19.1, SD 5.0) at baseline to a week 8 mean score indicating mild depression (mean 10.8, SD 6.1; d=1.47; P<.001). Improvement was also observed for functional impairment and quality of life. Gains were maintained at 3-month follow-up. CONCLUSIONS: The results show that Mindset for Depression is a feasible and acceptable treatment option for individuals with major depressive disorder. This smartphone-led treatment holds promise to be an efficacious, scalable, and cost-effective treatment option. The next steps include testing Mindset for Depression in a fully powered randomized controlled trial and real-world clinical settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT05386329; https://clinicaltrials.gov/study/NCT05386329?term=NCT05386329.

Identifying data-driven subtypes of major depressive disorder with electronic health records.

BACKGROUND: Efforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration. METHODS: We utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups. RESULTS: Among 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %). LIMITATIONS: Electronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions. CONCLUSION: These data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.

Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning.

OBJECTIVE: To develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovarian cancer patients at risk of early mortality more accurately than the Charlson Comorbidity Index (CCI) for use in health services research. METHODS: We utilized SEER-Medicare data to identify patients with stage IIIC and IV ovarian cancer, diagnosed in 2010-2015. We employed partial least squares regression, a supervised machine learning algorithm, to develop the MCI by extracting latent factors that optimally captured the variation in health insurance claims made in the year preceding cancer diagnosis, and 1-year mortality. We assessed the discrimination and calibration of the MCI for 1-year mortality and compared its performance to the commonly-used CCI. Finally, we evaluated the MCI's ability to reduce confounding in the association of neoadjuvant chemotherapy (NACT) and all-cause mortality. RESULTS: We included 4723 patients in the development cohort and 933 in the validation cohort. The MCI demonstrated good discrimination for 1-year mortality (c-index: 0.75, 95% CI: 0.72-0.79), while the CCI had poor discrimination (c-index: 0.59, 95% CI: 0.56-0.63). Calibration plots showed better agreement between predicted and observed 1-year mortality risk for the MCI compared with CCI. When comparing all-cause mortality between NACT with primary cytoreductive surgery, NACT was associated with a higher hazard of death (HR: 1.13, 95% CI: 1.04-1.23) after controlling for tumor characteristics, demographic factors, and the CCI. However, when controlling for the MCI instead of the CCI, there was no longer a significant difference (HR: 1.05, 95% CI: 0.96-1.14). CONCLUSIONS: The MCI outperformed the conventional CCI in predicting 1-year mortality, and reducing confounding due to differences in baseline health status in comparative effectiveness analysis of NACT versus primary surgery.

Performance of a Maternal Risk Stratification System for Predicting Low Apgar Scores.

OBJECTIVE: Maternal risk stratification systems are increasingly employed in predicting and preventing obstetric complications. These systems focus primarily on maternal morbidity, and few tools exist to stratify neonatal risk. We sought to determine if a maternal risk stratification score was associated with neonatal morbidity. STUDY DESIGN: Retrospective cohort study of patients with liveborn infants born at ≥24 weeks at four hospitals in one health system between January 1, 2020, and December 31, 2020. The Expanded Obstetric Comorbidity Score (EOCS) is used as the maternal risk score. The primary neonatal outcome was 5-minute Apgar <7. Logistic regression models determined associations between EOCS and neonatal morbidity. Secondary analyses were performed, including stratifying outcomes by gestational age and limiting analysis to "low-risk" term singletons. Model discrimination assessed using the area under the receiver operating characteristic curves (AUC) and calibration via calibration plots. RESULTS: A total of 14,497 maternal-neonatal pairs were included; 236 (1.6%) had 5-minute Apgar <7; EOCS was higher in 5-minute Apgar <7 group (median 41 vs. 11, p < 0.001). AUC for EOCS in predicting Apgar <7 was 0.72 (95% Confidence Interval (CI) 0.68, 0.75), demonstrating relatively good discrimination. Calibration plot revealed that those in the highest EOCS decile had higher risk of neonatal morbidity (7.6 vs. 1.7%, p < 0.001). When stratified by gestational age, discrimination weakened with advancing gestational age: AUC 0.70 for <28 weeks, 0.63 for 28 to 31 weeks, 0.64 for 32 to 36 weeks, and 0.61 for ≥37 weeks. When limited to term low-risk singletons, EOCS had lower discrimination for predicting neonatal morbidity and was not well calibrated. CONCLUSION: A maternal morbidity risk stratification system does not perform well in most patients giving birth, at low risk for neonatal complications. The findings suggest that the association between EOCS and 5-minute Apgar <7 likely reflects a relationship with prematurity. This study cautions against intentional or unintentional extrapolation of maternal morbidity risk for neonatal risk, especially for term deliveries. KEY POINTS: · EOCS had moderate discrimination for Apgar <7.. · Predictive performance declined when limited to low-risk term singletons.. · Relationship between EOCS and Apgar <7 was likely driven by prematurity..

Residual symptoms following electroconvulsive therapy: A retrospective cohort study.

BACKGROUND: Residual depressive symptoms following treatment are a burden for patients and are associated with increased risk of relapse. While this phenomenon has been explored following pharmacotherapy, there is little research into residual symptoms following electroconvulsive therapy (ECT). This study quantifies the frequency and type of residual symptoms following ECT treatment. METHODS: This study used retrospective data from patients receiving ECT as part of routine clinical care. Depressive symptomatology was assessed using the Quick Inventory of Depressive Symptomatology - Self-Report 16 item scale (QIDS), which includes 9 symptom domains graded from 0 to 3. We quantified the frequency of mild or greater (QIDS≥1) and moderate or greater (QIDS ≥ 2) residual symptoms following treatment among patients responding to ECT (QIDS decrease ≥50 % from baseline) and non-responders (QIDS decrease <50 %). RESULTS: Among 1799 patients, 1015 (56.4 %) responded to ECT and 784 (43.6 %) did not. Among responders, 99.5 % had at least one residual symptom of mild severity or greater (median = 5, IQR = 3-6) and 83.3 % had at least one residual symptom of moderate severity or greater (median = 1, IQR = 1-2). Among non-responders, 100 % had residual symptoms of mild severity or greater (median = 8, IQR = 7-9), and 99.2 % had a residual symptom of moderate severity or greater (median = 4, IQR = 3-5). The most common residual symptoms among both responders and non-responders were sleep disturbances (93.1 % and 98.7 %, respectively) and sadness (68.9 % and 96.4 %, respectively). LIMITATIONS: Retrospective data from a single freestanding psychiatric hospital. CONCLUSION: Among patients with depression receiving ECT, there were high rates of residual symptoms even among patients responding to treatment.

Dimensional clinical phenotyping using post-mortem brain donor medical records: post-mortem RDoC profiling is associated with Alzheimer's disease neuropathology.

Introduction: Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to post-mortem work, as assessments of phenotypic concepts need to rely on existing records. Methods: We adapted well-validated methodologies to compute National Institute of Mental Health Research Domain Criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether cognitive domain scores were associated with Alzheimer's disease neuropathological measures. Results: Our results confirm an association of EHR-derived cognitive scores with neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß = 0.38, P = 0.0004), parietal (ß = 0.35, P = 0.0008), temporal (ß = 0.37, P = 0.0004) and occipital (ß = 0.37, P = 0.0003) lobes. Discussion: This proof-of-concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from post-mortem EHR. The associations may accelerate post-mortem brain research beyond classical case-control designs.

Changes in self-reported suicidal ideation during treatment with electroconvulsive therapy: A retrospective cohort study.

OBJECTIVE: Suicidal ideation (SI) is common in patients with depressive symptoms, who are the most common recipients of electroconvulsive therapy (ECT). We sought to quantify changes in self-reported SI occurring during treatment with ECT, and to identify factors associated with persistence of SI in patients beginning treatment with SI. METHOD: Retrospective, single-center cohort study of patients receiving ECT and who self-reported symptoms using Quick Inventory of Depressive Symptomatology (QIDS) prior to ECT and after treatment #5 or #10. Changes in QIDS-reported SI over the course of ECT were calculated, and logistic regression models were performed to assess factors associated with reporting SI at the end of treatment. RESULTS: 2554 provided baseline and follow-up SI scores, of whom, 1931 (75.6%) endorsed SI at baseline. There was a reduction in SI with ECT treatment (McNemar's test; df = 1, Χ2 = 803.7; p < 0.001), and in adjusted models 64.0% of individuals with baseline SI reported resolution of SI with ECT treatment, while 3.3% without baseline SI reported SI at the end of treatment. Higher baseline SI severity and outpatient treatment were associated with a higher odds of persistent SI among individuals beginning treatment with SI. CONCLUSION: Electroconvulsive therapy treatment was associated with reductions in self-reported SI. These results support the use of ECT in the treatment of patients with SI, but further research is needed to determine the effects of ECT on suicidal behavior.

Declining to Provide or Continue Requested Life-Sustaining Treatment: Experience With a Hospital Resolving Conflict Policy.

In 2015, the major critical care societies issued guidelines outlining a procedural approach to resolving intractable conflict between healthcare professionals and surrogates over life-sustaining treatments (LST). We report our experience with a resolving conflict procedure. This was a retrospective, single-centre cohort study of ethics consultations involving intractable conflict over LST. The resolving conflict process was initiated eleven times for ten patients over 2,015 ethics consultations from 2000 to 2020. In all cases, the ethics committee recommended withdrawal of the contested LST. In seven cases, the patient died or was transferred or a legal injunction was obtained before completion of the process. In the four cases in which LST was withdrawn, the time from ethics consultation to withdrawal of LST was 24.8 ± 12.2 days. Healthcare provider and surrogate were often distressed during the process, sometimes resulting in escalation of conflict and legal action. In some cases, however, surrogates appeared relieved that they did not have to make the final decision regarding LST. Challenges regarding implementation included the time needed for process completion and limited usefulness in emergent situations. Although it is feasible to implement a due process approach to conflict over LST, there are factors that limit the procedure's usefulness.

Dimensional clinical phenotyping using post-mortem brain donor medical records: Association with neuropathology.

INTRODUCTION: Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to postmortem work, as assessment of newly developed phenotypic concepts needs to rely on existing records. METHODS: We adapted well-validated methodologies to compute NIMH research domain criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether RDoC cognitive domain scores were associated with hallmark Alzheimer's disease (AD) neuropathological measures. RESULTS: Our results confirm an association of EHR-derived cognitive scores with hallmark neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß=0.38, p=0.0004), parietal (ß=0.35, p=0.0008), temporal (ß=0.37, p=0. 0004) and occipital (ß=0.37, p=0.0003) lobes. DISCUSSION: This proof of concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from postmortem EHR.

Changes in Inpatient Electroconvulsive Therapy Utilization Between 2019 and 2020: A National Inpatient Sample Analysis.

OBJECTIVES: Electroconvulsive therapy (ECT) is an essential procedure for a range of psychiatric conditions. Multiple single-center studies have documented reduction in ECT administration in 2020 because of the coronavirus disease 2019 pandemic, but there have been little nationally representative data from the United States. The aim of this study was to examine the demographics of patients receiving ECT in 2019 and 2020 and to characterize temporal and regional variations in ECT utilization. METHODS: The 2019 and 2020 National Inpatient Sample, an administrative database of inpatient hospitalizations in the United States, was queried for hospitalizations involving the delivery of ECT based on procedural codes. Overall number of ECT procedures was calculated based on the overall number of ECT procedural claims. RESULTS: In the 2019 NIS, 14,230 inpatient hospitalizations (95% confidence interval, 12,936-15,524) involved the use of ECT, with a cumulative 52,450 inpatient ECT procedures administered. In 2020, the number of inpatient hospitalizations with ECT decreased to 12,055 (95% confidence interval, 10,878-13,232), with a 10.0% reduction in overall procedures to 47,180. Whereas January and February ECT hospitalizations were comparable in both years, ECT hospitalizations decreased by more than 25% in March through May 2020 relative to 2019 volume. There was regional variability in the change in ECT utilization between 2019 and 2020. CONCLUSIONS: Electroconvulsive therapy use among general hospital inpatients declined between 2019 and 2020, with regional variability in the magnitude of change. Further study is warranted into the root causes and optimal responses to these changes.

The use of electroconvulsive therapy for children and adolescents in general hospitals: A 2019 kids' inpatient database analysis.

OBJECTIVE: Electroconvulsive therapy (ECT) devices are classified as class II (moderate risk) for the treatment of depressive disorders and catatonia in patients aged 13 and older, but it is unknown how often the treatment is utilized by child and adolescent patients. The aim of this study was to examine the demographics of child and adolescent hospitalizations involving ECT, the medical and psychiatric comorbidities of these hospitalizations, and the overall number of treatments administered per hospitalization. METHOD: The 2019 Kids' Inpatient Database, a national sample of pediatric discharges from 3998 acute care hospitals, was analyzed for hospitalizations involving patients aged 19 and younger receiving ECT based on inpatient procedural codes. RESULTS: 315 (95% confidence interval 275 to 354) discharges among child and adolescent patients, or 0.03% of youth hospitalizations, involved the administration of ECT in the KID in 2019. Hospitalizations in the Northeast, those involving patients residing in ZIP codes in the top income quartile, and those for commercially insured patients had higher odds of ECT administration. Primary discharge diagnoses among ECT recipients were major depressive disorder (143; 46.4%), schizophrenia and other psychotic disorders (71; 23.1%) and bipolar disorder (59; 19.2%). In total 153 (48.6%) of ECT recipients had a coded diagnosis of suicidal ideation. Hospitalizations involved a median of 2 (IQR 1 to 5) ECT treatments before discharge. CONCLUSIONS: ECT is rarely utilized in the inpatient treatment of child and adolescent patients, but is most often administered to patients with mood and psychotic disorders. Commercial insurance and higher income were associated with higher odds of ECT administration, suggesting that access to care may be limited.

Coding for Physical Restraint Status Among Hospitalized Patients: a 2019 National Inpatient Sample Analysis.

BACKGROUND: The reduction of physical restraint utilization in the hospital setting is a key goal of high-quality care, but little is known about the rate of restraint use in general hospitals in the USA. OBJECTIVE: This study reports the rate of physical restraint coding among acute care hospital discharges in the USA and explores associated demographic and diagnostic factors. DESIGN: The National Inpatient Sample, a de-identified all-payors database of acute care hospital discharges in the USA, was queried for patients aged 18 and older with a diagnosis code for physical restraint status in 2019. PARTICIPANTS: Hospitalized patients aged 18 and older. MAIN MEASURES: Demographics, discharge diagnoses, in-hospital mortality, length of stay, total hospital charges. KEY RESULTS: In total, 220,470 (95% CI: 208,114 to 232,826) hospitalizations, or 0.7% of overall hospitalizations, included a discharge code for physical restraint status. There was a 700-fold difference in coding for restraint utilization based on diagnosis, with 7.4% of patients with encephalitis receiving restraint diagnosis codes compared to < 0.01% of patients with uncomplicated diabetes. In an adjusted model, male sex was associated with an odds ratio of 1.4 (95% CI: 1.4 to 1.5) for restraint utilization coding, and Black race was associated with an odds ratio of 1.3 (95% CI: 1.2 to 1.4) relative to white race. CONCLUSIONS: In the general hospital setting, there is variability in physical restraint coding by sex, race, and clinical diagnosis. More research is needed into the appropriate utilization of restraints in the hospital setting and possible inequities in restraint utilization.

Factors associated with early and late response to electroconvulsive therapy.

OBJECTIVE: Electroconvulsive therapy (ECT) is an effective treatment for severe depressive symptoms, yet more research is needed to examine predictors of treatment response, and factors associated with response in patients not initially improving with treatment. This study reports factors associated with time to response (early vs. late) to ECT in a real-world setting. METHODS: This was a retrospective, single-center cohort study of patients endorsing moderate to severe depressive symptoms using the Quick Inventory of Depressive Symptomatology (QIDS; QIDS>10). Response was defined as 50% or greater decrease in QIDS score from baseline. We used logistic regression to predict response at treatment #5 (early response) as well as after treatment #5 (late response) and followed patients through ECT discontinuation or through treatment #20. RESULTS: Of the 1699 patients included in this study, 555 patients (32.7%) responded to ECT treatment at treatment #5 and 397 (23.4%) responded after treatment #5. Among patients who did not respond by treatment #5, those who switched to brief pulse width ECT from ultrabrief pulse ECT had increased odds of response after treatment #5 compared with patients only receiving ultrabrief pulse (aOR = 1.55, 95% CI: 1.16-2.07). Additionally, patients with less improvement in QIDS from baseline to treatment #5 had decreased odds of response after treatment #5 (aOR = 0.97, 95% CI = 0.97-0.98). CONCLUSION: Among depressed patients treated with ECT, response occurred in 56.0% of patients by treatment #20. Patient receiving ultrabrief pulse ECT at baseline and who did not respond by treatment #5 had greater odds of subsequent response if switched to brief pulse ECT than if continued with ultrabrief pulse.

The Application of a Standard Risk Threshold for the Stratification of Maternal Morbidity among Population Subgroups.

OBJECTIVE: The aim of this study was to determine if a universally applied risk score threshold for severe maternal morbidity (SMM) resulted in different performance characteristics among subgroups of the population. STUDY DESIGN: This is a retrospective cohort study of deliveries that occurred between July 1, 2016, and June 30, 2020, in a single health system. We examined the performance of a validated comorbidity score to stratify SMM risk in our cohort. We considered the risk score that was associated with the highest decile of predicted risk as a "screen positive" for morbidity. We then used this same threshold to calculate the sensitivity and positive predictive value (PPV) of this "highest risk" designation among subgroups of the overall cohort based on the following characteristics: age, race/ethnicity, parity, gestational age, and planned mode of delivery. RESULTS: In the overall cohort of 53,982 women, the C-statistic was 0.755 (95% confidence interval [CI], 0.741-0.769) and calibration plot demonstrated that the risk score was well calibrated. The model performed less well in the following groups: non-White or Hispanic (C-statistic, 0.734; 95% CI, 0.712-0.755), nulliparas (C-statistic, 0.735; 95% CI, 0.716-0.754), term deliveries (C-statistic, 0.712; 95% CI, 0.694-0.729), and planned vaginal delivery (C-statistic, 0.728; 95% CI, 0.709-0.747). There were differences in the PPVs by gestational age (7.8% term and 29.7% preterm) and by planned mode of delivery (8.7% vaginal and 17.7% cesarean delivery). Sensitivities were lower in women who were <35 years (36.6%), non-White or Hispanic (40.7%), nulliparous (38.9%), and those having a planned vaginal delivery (40.9%) than their counterparts. CONCLUSION: The performance of a risk score for SMM can vary by population subgroups when using standard thresholds derived from the overall cohort. If applied without such considerations, such thresholds may be less likely to identify certain subgroups of the population that may be at increased risk of SMM. KEY POINTS: · Predictive risk models are helpful at condensing complex information into an interpretable output.. · Model performance may vary among different population subgroups.. · Prediction models should be examined for their potential to exacerbate underlying disparities..