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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.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.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.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.

COVID-19 is associated with a range of neuropsychiatric manifestations. While case reports and case series have reported catatonia in the setting of COVID-19 infection, its rate has been poorly characterized.

Neuropsychiatric symptoms may persist following acute COVID-19 illness, but the extent to which these symptoms are specific to COVID-19 has not been established. We utilized electronic health records across 6 hospitals in Massachusetts to characterize cohorts of individuals discharged following admission for COVID-19 between March 2020 and May 2021, and compared them to individuals hospitalized for other indications during this period. Natural language processing was applied to narrative clinical notes to identify neuropsychiatric symptom domains up to 150 days following hospitalization, in addition to those reflected in diagnostic codes as measured in prior studies. Among 6619 individuals hospitalized for COVID-19 drawn from a total of 42,961 hospital discharges, the most commonly-documented symptom domains between 31 and 90 days after initial positive test were fatigue (13.4%), mood and anxiety symptoms (11.2%), and impaired cognition (8.0%). In regression models adjusted for sociodemographic features and hospital course, none of these were significantly more common among COVID-19 patients; indeed, mood and anxiety symptoms were less frequent (adjusted OR 0.72 95% CI 0.64–0.92). Between 91 and 150 days after positivity, most commonly-detected symptoms were fatigue (10.9%), mood and anxiety symptoms (8.2%), and sleep disruption (6.8%), with impaired cognition in 5.8%. Frequency was again similar among non-COVID-19 post-hospital patients, with mood and anxiety symptoms less common (aOR 0.63, 95% CI 0.52–0.75). Propensity-score matched analyses yielded similar results. Overall, neuropsychiatric symptoms were common up to 150 days after initial hospitalization, but occurred at generally similar rates among individuals hospitalized for other indications during the same period. Post-acute sequelae of COVID-19 may benefit from standard if less-specific treatments developed for rehabilitation after hospitalization.

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Multidimensional transdiagnostic phenotyping systems are increasingly important to neuropsychiatric phenotyping, particularly in translational research settings. The relationship the National Institute of Mental Health's Research Domain Criteria multidimensional approach to psychopathology and nonpsychiatric diagnoses has not been studied at scale but is relevant to those caring for neuropsychiatric illness in medical and surgical settings.

Angiotensin-converting enzyme inhibitors (ACEI) and angiotensin-receptor blockers (ARB) are the most commonly prescribed anti-hypertensive medications in the United States, yet whether ACEI or ARB use is associated with a greater risk of hyperkalemia remains uncertain. Using real-world evidence from electronic health records, our study demonstrates that treatment with ACEI is associated with both a higher incidence and greater degree of hyperkalemia than treatment with ARB in adjusted models, especially in patients with chronic kidney disease. Providers should therefore consider this possible difference in hyperkalemia risk when choosing between ACEI and ARB therapy.

We aimed to develop and validate classification models able to identify individuals at high risk for transition from a diagnosis of depressive disorder to one of bipolar disorder. This retrospective health records cohort study applied outpatient clinical data from psychiatry and nonpsychiatry practice networks affiliated with two large academic medical centers between March 2008 and December 2017. Participants included 67,807 individuals with a diagnosis of major depressive disorder or depressive disorder not otherwise specified and no prior diagnosis of bipolar disorder, who received at least one of the nine antidepressant medications. The main outcome was at least one diagnostic code reflective of a bipolar disorder diagnosis within 3 months of index antidepressant prescription. Logistic regression and random forests using diagnostic and procedure codes as well as sociodemographic features were used to predict this outcome, with discrimination and calibration assessed in a held-out test set and then a second academic medical center. Among 67,807 individuals who received at least one antidepressant medication, 925 (1.36%) subsequently received a diagnosis of bipolar disorder within 3 months. Models incorporating coded diagnoses and procedures yielded a mean area under the receiver operating characteristic curve of 0.76 (ranging from 0.73 to 0.80). Standard supervised machine learning methods enabled development of discriminative and transferable models to predict transition to bipolar disorder. With further validation, these scores may enable physicians to more precisely calibrate follow-up intensity for high-risk patients after antidepressant initiation.

Objective: This descriptive study queries the attitudes of psychiatry residents regarding provision of practice habit data to trainees by residency programs, as required by the Accreditation Council for Graduate Medical Education (ACGME). Identifying trainee perspectives may assist program directors in tailoring practice habit data reporting to better engage residents and to increase resident-reported adherence to the ACGME requirement. Methods: Residents at a large, hospital-based adult psychiatry training program completed an anonymous survey of attitudes regarding practice habit data, including perceptions of the residency program’s current reporting, preferences toward mechanisms of delivering this data, and perceived relative utility of five hypothetical domains of practice habit data. Results: Of 61 eligible residents, 52 (85%) completed surveys. Only 29 (56%) recalled receiving prior-year individual practice habit data, and only 10 (19%) recalled receiving team-based data. Seventy-five percent desired more practice habit data. Out of five hypothetical thematic domains for practice habit reporting, residents preferred patient-oriented domains as opposed to process-oriented domains. Resident concerns about dissemination of these data included confidentiality, effect on evaluations, and difficulty translating data to changes in clinical practice. Conclusions: Residents generally desire increased dissemination of practice habit data that focuses on patient-oriented measures such as adherence to disease-specific guidelines and is both individual and team-based. Residency programs may benefit their trainees and improve resident-reported adherence to the ACGME requirement both by taking resident preferences into account and by addressing concerns about confidentiality when providing practice habit data.

The opioid epidemic has resulted in an increased number of patients with opioid use disorder (OUD) hospitalized for serious medical conditions. The intersection between hospital ethics consultations and the opioid crisis has not received significant attention.

Delirium is an acute confusional state, associated with morbidity and mortality in diverse medically-ill populations. Delirium is recognized, through both professional competencies and instructional materials, as a core topic in consultation psychiatry.

Delirium is an acute confusional state, associated with morbidity and mortality in diverse medically ill populations. Delirium is preventable and treatable when diagnosed but the diagnosis is often missed. This important and difficult diagnosis is an attractive candidate for computer-aided decision support if it can be reliably identified at scale.

Delirium is an acute neuropsychiatric syndrome that portends poor prognosis and represents a significant burden to the health care system. Although detection allows for efficacious treatment, the diagnosis is frequently overlooked. This underdiagnosis makes delirium an appealing target for translational predictive algorithmic modeling; however, such approaches require accurate identification in clinical training datasets.

Although lithium preparations remain first-line treatment for bipolar disorder, risk for development of renal insufficiency may discourage their use. Estimating such risk could allow more informed decisions and facilitate development of prevention strategies. We utilized electronic health records from a large New England health-care system between 2006 and 2013 to identify patients aged 18 years or older with a lithium prescription. Renal insufficiency was identified using the presence of renal failure by ICD9 code or laboratory-confirmed glomerular filtration rate below 60 ml/min. Logistic regression was used to build a predictive model in a random two-thirds of the cohort, which was tested in the remaining one-third. Risks associated with aspects of pharmacotherapy were also examined in the full cohort. We identified 1445 adult lithium-treated patients with renal insufficiency, matched by risk set sampling 1 : 3 with 4306 lithium-exposed patients without renal insufficiency. In regression models, features associated with risk included older age, female sex, history of smoking, history of hypertension, overall burden of medical comorbidity, and diagnosis of schizophrenia or schizoaffective disorder (p<0.01 for all contrasts). The model yielded an area under the ROC curve exceeding 0.81 in an independent testing set, with 74% of renal insufficiency cases among the top two risk quintiles. Use of lithium more than once daily, lithium levels greater than 0.6 mEq/l, and use of first-generation antipsychotics were independently associated with risk. These results suggest the possibility of stratifying risk for renal failure among lithium-treated patients. Once-daily lithium dosing and maintaining lower lithium levels where possible may represent strategies for reducing risk.

The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts.

Electronic health records (EHRs) contain important temporal information about the progression of disease and treatment outcomes. This paper proposes a transitive sequencing approach for constructing temporal representations from EHR observations for downstream machine learning. Using clinical data from a cohort of patients with congestive heart failure, we mined temporal representations by transitive sequencing of EHR medication and diagnosis records for classification and prediction tasks. We compared the classification and prediction performances of the transitive sequential representations (bag-of-sequences approach) with the conventional approach of using aggregated vectors of EHR data (aggregated vector representation) across different classifiers. We found that the transitive sequential representations are better phenotype “differentiators” and predictors than the “atemporal” EHR records. Our results also demonstrated that data representations obtained from transitive sequencing of EHR observations can present novel insights about the progression of the disease that are difficult to discern when clinical data are treated independently of the patient's history.