Postcrisis Follow-Up and Linkage to Community Services.

During the postcrisis period, many individuals struggle to transition to available care, often falling through the cracks. This article discusses effective postcrisis approaches that provide rapid access to transitional team-based care using critical time intervention strategies. It also highlights the development of state, county, and funder models for "care-traffic control" to ensure swift linkage to follow-up services, along with new funding models that support intensive community crisis stabilization during the postcrisis period. Emerging crisis systems can leverage these emerging services and approaches to facilitate successful transitions for individuals in need.

Evaluating and mitigating unfairness in multimodal remote mental health assessments.

Research on automated mental health assessment tools has been growing in recent years, often aiming to address the subjectivity and bias that existed in the current clinical practice of the psychiatric evaluation process. Despite the substantial health and economic ramifications, the potential unfairness of those automated tools was understudied and required more attention. In this work, we systematically evaluated the fairness level in a multimodal remote mental health dataset and an assessment system, where we compared the fairness level in race, gender, education level, and age. Demographic parity ratio (DPR) and equalized odds ratio (EOR) of classifiers using different modalities were compared, along with the F1 scores in different demographic groups. Post-training classifier threshold optimization was employed to mitigate the unfairness. No statistically significant unfairness was found in the composition of the dataset. Varying degrees of unfairness were identified among modalities, with no single modality consistently demonstrating better fairness across all demographic variables. Post-training mitigation effectively improved both DPR and EOR metrics at the expense of a decrease in F1 scores. Addressing and mitigating unfairness in these automated tools are essential steps in fostering trust among clinicians, gaining deeper insights into their use cases, and facilitating their appropriate utilization.

Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns.

OBJECTIVE: Psychiatric evaluation suffers from subjectivity and bias, and is hard to scale due to intensive professional training requirements. In this work, we investigated whether behavioral and physiological signals, extracted from tele-video interviews, differ in individuals with psychiatric disorders. METHODS: Temporal variations in facial expression, vocal expression, linguistic expression, and cardiovascular modulation were extracted from simultaneously recorded audio and video of remote interviews. Averages, standard deviations, and Markovian process-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination. RESULTS: Statistically significant feature differences were found between psychiatric and control subjects. Correlations were found between features and self-rated depression and anxiety scores. Heart rate dynamics provided the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities provided AUROCs of 0.72-0.82. CONCLUSION: Multimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status. SIGNIFICANCE: The proposed multimodal approach has the potential to facilitate scalable, remote, and low-cost assessment for low-burden automated mental health services.

Using HIPAA (Health Insurance Portability and Accountability Act)-Compliant Transcription Services for Virtual Psychiatric Interviews: Pilot Comparison Study.

BACKGROUND: Automatic speech recognition (ASR) technology is increasingly being used for transcription in clinical contexts. Although there are numerous transcription services using ASR, few studies have compared the word error rate (WER) between different transcription services among different diagnostic groups in a mental health setting. There has also been little research into the types of words ASR transcriptions mistakenly generate or omit. OBJECTIVE: This study compared the WER of 3 ASR transcription services (Amazon Transcribe [Amazon.com, Inc], Zoom-Otter AI [Zoom Video Communications, Inc], and Whisper [OpenAI Inc]) in interviews across 2 different clinical categories (controls and participants experiencing a variety of mental health conditions). These ASR transcription services were also compared with a commercial human transcription service, Rev (Rev.Com, Inc). Words that were either included or excluded by the error in the transcripts were systematically analyzed by their Linguistic Inquiry and Word Count categories. METHODS: Participants completed a 1-time research psychiatric interview, which was recorded on a secure server. Transcriptions created by the research team were used as the gold standard from which WER was calculated. The interviewees were categorized into either the control group (n=18) or the mental health condition group (n=47) using the Mini-International Neuropsychiatric Interview. The total sample included 65 participants. Brunner-Munzel tests were used for comparing independent sets, such as the diagnostic groupings, and Wilcoxon signed rank tests were used for correlated samples when comparing the total sample between different transcription services. RESULTS: There were significant differences between each ASR transcription service's WER (P<.001). Amazon Transcribe's output exhibited significantly lower WERs compared with the Zoom-Otter AI's and Whisper's ASR. ASR performances did not significantly differ across the 2 different clinical categories within each service (P>.05). A comparison between the human transcription service output from Rev and the best-performing ASR (Amazon Transcribe) demonstrated a significant difference (P<.001), with Rev having a slightly lower median WER (7.6%, IQR 5.4%-11.35 vs 8.9%, IQR 6.9%-11.6%). Heat maps and spider plots were used to visualize the most common errors in Linguistic Inquiry and Word Count categories, which were found to be within 3 overarching categories: Conversation, Cognition, and Function. CONCLUSIONS: Overall, consistent with previous literature, our results suggest that the WER between manual and automated transcription services may be narrowing as ASR services advance. These advances, coupled with decreased cost and time in receiving transcriptions, may make ASR transcriptions a more viable option within health care settings. However, more research is required to determine if errors in specific types of words impact the analysis and usability of these transcriptions, particularly for specific applications and in a variety of populations in terms of clinical diagnosis, literacy level, accent, and cultural origin.

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns.

Objective: The current clinical practice of psychiatric evaluation suffers from subjectivity and bias, and requires highly skilled professionals that are often unavailable or unaffordable. Objective digital biomarkers have shown the potential to address these issues. In this work, we investigated whether behavioral and physiological signals, extracted from remote interviews, provided complimentary information for assessing psychiatric disorders. Methods: Time series of multimodal features were derived from four conceptual modes: facial expression, vocal expression, linguistic expression, and cardiovascular modulation. The features were extracted from simultaneously recorded audio and video of remote interviews using task-specific and foundation models. Averages, standard deviations, and hidden Markov model-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination. Results: Statistically significant feature differences were found between controls and subjects with mental health conditions. Correlations were found between features and self-rated depression and anxiety scores. Visual heart rate dynamics achieved the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities achieved AUROCs of 0.72-0.82. Features from task-specific models outperformed features from foundation models. Conclusion: Multimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status. Significance: The proposed multimodal approach has the potential to facilitate objective, remote, and low-cost assessment for low-burden automated mental health services.

An international research agenda for clozapine-resistant schizophrenia.

Treatment-resistant symptoms occur in about a third of patients with schizophrenia and are associated with a substantial reduction in their quality of life. The development of new treatment options for clozapine-resistant schizophrenia constitutes a crucial, unmet need in psychiatry. Additionally, an overview of past and possible future research avenues to optimise the early detection, diagnosis, and management of clozapine-resistant schizophrenia is unavailable. In this Health Policy, we discuss the ongoing challenges associated with clozapine-resistant schizophrenia faced by patients and health-care providers worldwide to improve the understanding of this condition. We then revisit several clozapine guidelines, the diagnostic tests and treatment options for clozapine-resistant schizophrenia, and currently applied research approaches in clozapine-resistant schizophrenia. We also suggest methodologies and targets for future research, divided into innovative nosology-oriented field trials (eg, examining dimensional symptom staging), translational approaches (eg, genetics), epidemiological research (eg, real-world studies), and interventional studies (eg, non-traditional trial designs incorporating lived experiences and caregivers' perspectives). Finally, we note that low-income and middle-income countries are under-represented in studies on clozapine-resistant schizophrenia and propose an agenda to guide multinational research on the cause and treatment of clozapine-resistant schizophrenia. We hope that this research agenda will empower better global representation of patients living with clozapine-resistant schizophrenia and ultimately improve their functional outcomes and quality of life.

Escaping the Long Shadow Cast by Agranulocytosis: Reflections on Clozapine Pharmacovigilance Focused on the United Kingdom.

PURPOSE/BACKGROUND: A recent article in this journal presented a US perspective regarding the modernization of clozapine prescription and proposed an escape from the long shadow cast by agranulocytosis. METHODS: Here, an international group of collaborators discusses a point of view complementary to the US view by focusing on worldwide outcomes of clozapine usage that may be uneven in terms of frequency of clozapine adverse drug reactions. FINDINGS/RESULTS: Studies from the Scandinavian national registries (Finland and Denmark) did not find increased mortality in clozapine patients or any clear evidence of the alleged toxicity of clozapine. Data on clozapine-associated fatal outcomes were obtained from 2 recently published pharmacovigilance studies and from the UK pharmacovigilance database. A pharmacovigilance study focused on physician reports to assess worldwide lethality of drugs from 2010 to 2019 found 968 clozapine-associated fatal outcomes in the United Kingdom. Moreover, the United Kingdom accounted for 55% (968 of 1761) of worldwide and 90% (968 of 1073) of European fatal clozapine-associated outcomes. In a pharmacovigilance study from the UK database (from 2008 to 2017), clozapine was associated with 383 fatal outcomes/year including all reports from physicians and nonphysicians. From 2018 to 2021, UK clozapine-associated fatal outcomes increased to 440/year. IMPLICATIONS/CONCLUSIONS: The interpretation of fatal outcomes in each country using pharmacovigilance databases is limited and only allows gross comparisons; even with those limitations, the UK data seem concerning. Pneumonia and myocarditis may be more important than agranulocytosis in explaining the uneven distribution of fatal outcomes in clozapine patients across countries.

The Protective Effect of Clozapine on Suicide: A Population Mortality Study of Statewide Autopsy Records in Maryland.

Objective: Clozapine is the most efficacious antipsychotic medication, but it is underutilized and its mechanism of action is still poorly understood. One aspect of its unique efficacy that requires further study is its effect on suicidality. A randomized controlled trial, the InterSePT study, yielded evidence that clozapine reduces suicidality more than olanzapine, after which it became the only medication indicated for recurrent suicidal behavior in schizophrenia and schizoaffective disorder. We present here the first study of population mortality data to investigate the effect of clozapine on suicide.Methods: We reviewed statewide autopsy records of Maryland's Office of the Chief Medical Examiner, which performs uniquely comprehensive death investigations that include full toxicologic panels with postmortem blood levels of antipsychotics. Our study compared clozapine- and olanzapine-positive decedents across demographic, clinical, and manner-of-death outcomes using contingency table analysis and logistic regression.Results: Of 53,144 decedents from 2003 to 2021, 621 had clozapine or olanzapine detected on autopsy, with the two groups showing no demographic differences. Decedents with clozapine were significantly less likely to have died by suicide than by accident compared to those with olanzapine (odds ratio = 0.47; 95% CI, 0.26-0.84; P = .011).Conclusions: Our study thus adds more naturalistic evidence to the growing literature on the beneficial effect of clozapine on suicidality. Our findings also highlight the utility of statewide autopsy records, an untapped resource for investigating the potential protective effect of psychiatric medications on suicide at a population level.

Feasibility of an Open Dialogue-Inspired Approach for Young Adults with Psychosis in a Public Hospital System.

The objective was to determine the feasibility of an Open Dialogue-inspired approach in a metropolitan, public hospital setting with predominately African American participants. Participants were ages 18-35, experienced psychosis within the past month, and involved at least one support person in their care. We evaluated domains of feasibility including implementation, adaptation, practicality, acceptability, and limited-efficacy. An organizational change model (Addressing Problems Through Organizational Change) facilitated implementation. Clinicians received three trainings and ongoing supervision. Network meetings were successfully implemented with good self-reported fidelity to principles of dialogic practice. Some adaptations (less frequent meetings and no home visits) were necessary. A subset of individuals completed research assessments over 12 months. Qualitative interviews with participants suggested the intervention was acceptable. Symptom and functional outcomes were preliminary but trended toward improvement. Implementation was feasible with relatively brief training, organizational change processes, and context-specific adaptations. Lessons learned can assist in planning a larger research study.

Mortality among psychiatric inpatients in China: A national survey.

BACKGROUND: Patients with mental disorders are at increased risk of premature mortality. Psychiatric inpatients are a particularly vulnerable population, yet data on the mortality rate and causes of death among psychiatric inpatients in a national sample are scarce. METHODS: We analyzed data collected from patients who died during psychiatric hospitalization in 2019 and 2020 from 41 psychiatric hospitals in China. RESULTS: In total, 719 inpatients died over the study period. There were more deaths in 2019 (N = 409, 56.9%) compared to 2020 (N = 310, 43.1%). The mean age was 73.3 ± 16.5 years old, with males significantly younger than females (71.5 ± 16.9 vs. 75.9 ± 15.6, p < 0.001). Sudden death accounted for 11.5% of all deaths. The cause was unknown for 31.2% of cases. Among those with known causes of death, respiratory disorders were most common in patients with psychotic disorders (41.9%) and mood disorders (29.8%). Suicide accounted for 17.0% of deaths in patients with mood disorders. CONCLUSION: Patients who died during psychiatric hospitalization were overall older (>70 years), and more than one in ten died due to sudden death. While respiratory disorders accounted for the largest proportion of known causes, the causes were unknown in nearly one-third. Death due to suicide, a preventable cause, remained common among patients with mood disorders. Evidence-based interventions should be implemented.

The Modernization of Clozapine: A Recapitulation of the Past in the United States and the View Forward.

PURPOSE: Although clozapine was Food and Drug Administration (FDA) approved more than 3 decades ago, major barriers and gaps in knowledge continue to prevent its effective and safe use. We review modern-day problems encountered with clozapine in the United States (US). METHODS: Information surrounding current administrative, clinical, research, and technological gaps or barriers related to clozapine use in the US was reviewed. FINDINGS: The history of how clozapine became FDA approved likely contributes to gaps in knowledge. The frequency of safety warnings added to the FDA prescribing information may add to fears about clozapine, as evidence by numerous published survey studies. The clozapine Risk Evaluation and Mitigation Strategy (REMS) program has been modified several times in the last decade, causing access and safety issues for patients, which are discussed. Evidence may suggest that the FDA REMS requirements for hematologic monitoring are too cumbersome, and there may be ability to safely loosen requirements. The COVID-19 pandemic brought forth the ability for extended interval monitoring but also greater awareness of the clozapine-inflammation interaction. Newer guidelines published describe considerations in personalizing clozapine titration based on principles of ethnopsychopharmacology. Emerging technologies to support the use of clozapine are not widely available. IMPLICATIONS: Clozapine is a unique life-saving drug but it is underused in the US, despite its established efficacy. The 2021 REMS changes led to significant difficulties for providers and patients. We highlight the importance of the clozapine-inflammation interaction, therapeutic drug monitoring, and the ability for individual care based on patient-specific factors. There is an urgent need for advancing technology used for clozapine monitoring, evaluating barriers created by REMS, and establishing consistent practices throughout the US.

Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study.

BACKGROUND: Current standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient's outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinician's interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared, and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders. OBJECTIVE: We present a research protocol that utilizes facial expression, eye gaze, voice and speech, locomotor, heart rate, and electroencephalography monitoring to assess schizophrenia symptoms and to distinguish patients with schizophrenia from those with other psychiatric disorders and control subjects. METHODS: We plan to recruit three outpatient groups: (1) 50 patients with schizophrenia, (2) 50 patients with unipolar major depressive disorder, and (3) 50 individuals with no psychiatric history. Using an internally developed semistructured interview, psychometrically validated clinical outcome measures, and a multimodal sensing system utilizing video, acoustic, actigraphic, heart rate, and electroencephalographic sensors, we aim to evaluate the system's capacity in classifying subjects (schizophrenia, depression, or control), to evaluate the system's sensitivity to within-group symptom severity, and to determine if such a system can further classify variations in disorder subtypes. RESULTS: Data collection began in July 2020 and is expected to continue through December 2022. CONCLUSIONS: If successful, this study will help advance current progress in developing state-of-the-art technology to aid clinical psychiatric assessment and treatment. If our findings suggest that these technologies are capable of resolving diagnoses and symptoms to the level of current psychometric testing and clinician judgment, we would be among the first to develop a system that can eventually be used by clinicians to more objectively diagnose and assess schizophrenia and depression with the possibility of less risk of bias. Such a tool has the potential to improve accessibility to care; to aid clinicians in objectively evaluating diagnoses, severity of symptoms, and treatment efficacy through time; and to reduce treatment-related morbidity. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36417.

Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review.

BACKGROUND: Schizophrenia is a severe psychiatric disorder that causes significant social and functional impairment. Currently, the diagnosis of schizophrenia is based on information gleaned from the patient's self-report, what the clinician observes directly, and what the clinician gathers from collateral informants, but these elements are prone to subjectivity. Utilizing computer vision to measure facial expressions is a promising approach to adding more objectivity in the evaluation and diagnosis of schizophrenia. METHOD: We conducted a systematic review using PubMed and Google Scholar. Relevant publications published before (including) December 2021 were identified and evaluated for inclusion. The objective was to conduct a systematic review of computer vision for facial behavior analysis in schizophrenia studies, the clinical findings, and the corresponding data processing and machine learning methods. RESULTS: Seventeen studies published between 2007 to 2021 were included, with an increasing trend in the number of publications over time. Only 14 articles used interviews to collect data, of which different combinations of passive to evoked, unstructured to structured interviews were used. Various types of hardware were adopted and different types of visual data were collected. Commercial, open-access, and in-house developed models were used to recognize facial behaviors, where frame-level and subject-level features were extracted. Statistical tests and evaluation metrics varied across studies. The number of subjects ranged from 2-120, with an average of 38. Overall, facial behaviors appear to have a role in estimating diagnosis of schizophrenia and psychotic symptoms. When studies were evaluated with a quality assessment checklist, most had a low reporting quality. CONCLUSION: Despite the rapid development of computer vision techniques, there are relatively few studies that have applied this technology to schizophrenia research. There was considerable variation in the clinical paradigm and analytic techniques used. Further research is needed to identify and develop standardized practices, which will help to promote further advances in the field.

Four cases of myocarditis in US hospitals possibly associated with clozapine poor metabolism and a comparison with prior published cases.

Objectives: Clozapine-induced myocarditis may be a hypersensitivity reaction due to titration that was too rapid for a patient's clozapine metabolism. Obesity, infections, and inhibitors (e.g., valproate) may lead to clozapine poor metabolizer (PM) status. The hypothesis that 4 patients with clozapine-induced myocarditis from two United States hospitals were clozapine PMs was tested by studying their minimum therapeutic clozapine doses and titrations. Methods: Using methodology from a prior myocarditis case series of 9 Turkish patients, we studied: 1) the concentration-to-dose (C/D) ratio; 2) minimum therapeutic dose required to reach 350 ng/ml (a marker for PM status); and 3) titration speed. Results: All 4 patients were possible clozapine PMs (their respective minimum therapeutic doses were: 134, 84, 119 and 107 mg/day). The identified possible contributors to clozapine PM status were: 1) valproate in Cases 1, 2 and 4; 2) obesity and a urinary tract infection in Case 2; and 3) obesity and very rapid titration in Case 4. Case 3, who was given a normal US titration, appeared to be a genetic clozapine PM. He developed clozapineinduced drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome after rechallenge using 12.5 mg/day > 3 months later. The results were similar to 9 Turkish cases, all of which were PMs (6 on valproate, 4 with obesity, 1 with infection and 1 possibly genetic). Conclusions: Future studies using clozapine levels and considering the role of clozapine PM status should explore whether or not all cases of clozapine-induced myocarditis could be explained by lack of individualized titration. (Neuropsychopharmacol Hung 2022; 24(1): 29-41).

Should we routinely add CRP to clozapine titrations? - Learning from three cases.

Objectives: An international guideline recently provided certain personalized schedules for titrating clozapine in adult inpatients by considering: 1) DNA ancestry group, 2) sexsmoking subgroup, and 3) presence/absence of clozapine poor metabolizer (PM) status. Measuring CRP levels at baseline and during the first 4 weeks is recommended. Titrations too fast for the metabolism of specific patients can lead to clozapine-induced inflammations and CRP elevations. Methods: Three published cases are reinterpreted. Better outcomes might have been obtained by using the guideline. Results: Case 1 was a Chinese male non-smoker, a clozapine PM due to an underlying inflammation. Case 2 was a Turkish female non-smoker who developed clozapine-induced myocarditis in the context of 4 risk factors (undiagnosed infl ammation, obesity, valproate and olanzapine co-prescription). Case 3 was a United States patient of European ancestry with no known risk factors who developed myocarditis after a routine titration and had an unsuccessful rechallenge with 12.5 mg/day. Application of the international clozapine titration guideline may have prevented: 1) Case 1 by recommending against clozapine titration for a patient with an abnormal CRP level, 2) Case 2 by considering 4 risk factors and using a slow titration for clozapine PMs, and 3) Case 3 by using CRP elevations for early identification of a possible genetic PM. Conclusions: When baseline or prior CRPs are normal and then become abnormal during a clozapine titration, this indicates: 1) clozapine-induced inflammation associated with too-rapid titration for that specific patient, and/or 2) co-occurrence of an infection. Prospective studies need to verify this hypothesis.