Clozapine blood level assessment using a point-of-care device: feasibility and reliability.

Background: Therapeutic drug monitoring (TDM) is useful to assess clozapine adherence and optimize treatment. However, analysis of venous blood levels by liquid chromatography tandem mass spectrometry (LC-MS/MS) is often logistically complicated and process time is prolonged. Objective: To assess the feasibility and reliability of a new point-of-care device, (MyCare™ Insite), using capillary blood for clozapine therapeutic monitoring. Methods: Matched venous and capillary blood samples were collected from patients treated with clozapine on a stable dose. Samples were analyzed by LC-MS/MS and MyCare Insite Clozapine Test. Clozapine plasma levels were compared between methods using linear regression model. Both patients and treatment team completed questionnaires about the feasibility of blood sampling. Results: Of the total sample (44 patients, 61% males, mean age 43 ± 12 years), mean daily clozapine dose was 293 ± 134 mg/day. Linear regression model demonstrated high correlation with R 2 = 0.83 (p < 0.0001) and mean difference of 26 ± 162 ng/ml. More than 60% of the patients found the clozapine TDM to be important. Most of the participants (58%) favored the capillary sampling and 11% claimed that testing method would affect their adherence to TDM. Moreover, a larger portion (72%) strongly preferred to be tested at the office rather than at the lab. Conclusions: The point-of-care device offers an accessible and satisfactory measurement of clozapine blood levels. Both patients and healthcare providers reported preference for capillary sampling as well as for the in-office TDM procedure. The immediate results provided by the device can facilitate rapid and informed clinical decisions and therefore improve clozapine treatment outcomes.

Trends of new-onset psychosis or mania in psychiatric emergency departments during the COVID19 pandemic: a longitudinal comparative study.

COVID19 infection was associated with possible psychiatric manifestations, including psychosis and mania. In addition, psychiatric disorders might be triggered by severe psychological reactions to the pandemic or the measures taken to contain it. This study aimed to assess the trends of new-onset psychosis/mania during the pandemic timeline. Psychiatric emergency department records during January-July 2019 and 2020 of two regional mental health centers were manually examined. Cases of new-onset psychosis or mania were found in 326 out of 5161 records examined. The ratio of these cases increased by 45.5% in 2020 compared to 2019 (189 out of 2367, 137 out of 2479, respectively, p = 0.001). The peak increase was in April 2020 (9.4% vs. 4.7%, p = 0.015). There was no association between the rise of new-onset psychotic or manic episodes and national incidence of COVID19 cases, as observed during Israel 2nd wave. PCR tests were negative, except a single case. In this study, an increase in new-onset psychosis/mania was identified during the initial phase of the pandemic. Though causality could not be directly inferred, lack of infection symptoms, negative PCR testing and temporal distribution incongruent with COVID19 caseload did not support a direct effect of SARS-CoV-2. Alternative explanations are discussed, such as psychological reaction to stress and preventive measures, as well as case-shifting between different mental health settings.

Association between Adherence to SSRI Treatment and Mortality among Individuals with Metabolic Syndrome Components.

INTRODUCTION: Depression and anxiety have been associated with type 2 diabetes mellitus and metabolic syndrome, major causes of cardiovascular morbidity and mortality. The effect of antidepressants in this association is unknown. This study aimed to examine the association between adherence to selective serotonin receptor inhibitors (SSRIs) and all-cause mortality among individuals with metabolic syndrome components (hypertension, obesity, and diabetes mellitus). METHODS: Data on 201 777 patients who were prescribed SSRIs during the years 2008-2011 were analyzed retrospectively. Adherence was measured using prescription purchase records. The moderating effect of SSRI and statin adherence on the association between metabolic syndrome load and mortality hazard risk (HR) during the study period were analyzed. The Cox-proportional hazard model adjusted to background variables was used to this end. RESULTS: During the study period, the maximal metabolic load was associated with mortality HR=1.89 (95% CI: 1.79-2) compared to participants without metabolic risk factors. A slight reduction in mortality HR was demonstrated among those with low and moderate SSRI adherence rates. Adherence to statins was negatively associated with the risk of mortality across all levels of adherence. A significant association (r=0.214, p<0.01) was found between adherence to statins and adherence to SSRIs, with higher rates of adherence to statins across all metabolic load categories. DISCUSSION: While a high metabolic load is associated with a higher risk of mortality, adherence to SSRIs only partially moderated the risk of mortality, in contrast to the protective effect of statins. Adherence differences to statins and SSRIs among individuals prescribed both medications merit further investigation.

Metabolic predictors for mortality among patients treated with long-term clozapine - A longitudinal study.

Clozapine is the only antipsychotic compound indicated for refractory-schizophrenia. However, it is associated with emergent metabolic dysregulation and cardiovascular risk which may lead to mortality. In this study we aimed to explore predictors for mortality in a large cohort of schizophrenia patients treated with long-term clozapine, using the electronic medical records of the largest health care provider in Israel. Among 27,929 patients diagnosed with schizophrenia, 1817 were prescribed clozapine during the years 2012-2014. We compared patients who survived (n=1705) and patients who died (n=112) during the 3-year follow-up period. Socio-demographic background, cardiovascular morbidity, medication prescriptions and health-care utilization were compared between groups. Cox proportional hazard models were used to assess the association of variables with survival. Chronic hypertension was found to be the only metabolic factor associated with significant hazard ratio (HR) for mortality (HR: 1.55 95% CI: 1.03-2.34). Moreover, those who died had more prevalent ischemic heart disease (14% vs 3%, p<0.005) as well as more frequent hospitalizations (0.01±0.02 vs 0.11±0.18 average per month, p<0.005), for longer periods (2.22±9.87 vs 20.38±33.76 days per month, p<0.005). Among those who died, less patients received prescriptions of statins for hyperlipidemia (13.7% vs. 52.9% in survivors, p<0.005) and hypoglycemics for diabetes mellitus (16.3% vs. 67.1% in survivors, p<0.005). Inadequate treatment of metabolic syndrome, under chronic clozapine treatment, was found to be an independent predictor for mortality. Adequate rigorous regimen for diagnosis and treatment of metabolic risk factors, especially hyperlipidemia and diabetes mellitus, might lower complications rate and prolong life expectancy among this population.

Predicting Affect Classification in Mental Status Examination Using Machine Learning Face Action Recognition System: A Pilot Study in Schizophrenia Patients.

Classifying patients' affect is a pivotal part of the mental status examination. However, this common practice is often widely inconsistent between raters. Recent advances in the field of Facial Action Recognition (FAR) have enabled the development of tools that can act to identify facial expressions from videos. In this study, we aimed to explore the potential of using machine learning techniques on FAR features extracted from videotaped semi-structured psychiatric interviews of 25 male schizophrenia inpatients (mean age 41.2 years, STD = 11.4). Five senior psychiatrists rated patients' affect based on the videos. Then, a novel computer vision algorithm and a machine learning method were used to predict affect classification based on each psychiatrist affect rating. The algorithm is shown to have a significant predictive power for each of the human raters. We also found that the eyes facial area contributed the most to the psychiatrists' evaluation of the patients' affect. This study serves as a proof-of-concept for the potential of using the machine learning FAR system as a clinician-supporting tool, in an attempt to improve the consistency and reliability of mental status examination.