Blood Pressure and Heart Rate Changes During Clozapine Treatment.

People with schizophrenia are 3-4 times more likely to die from cardiovascular disease than the general population. Clozapine (CLZ) is the gold standard of treatment for refractory schizophrenia. It has been associated with tachycardia and recent evidence shows individuals prescribed CLZ may develop blood pressure (BP) elevation and hypertension. The purpose of this study was to examine the effects of CLZ on BP and heart rate (HR). This was a retrospective chart review of patients 18-75 years old with a DSM IV diagnosis of Schizophrenia or Schizoaffective disorder. Primary outcomes were systolic blood pressure (SBP), diastolic blood pressure (DBP), and HR measured 12 weeks before and 24 weeks during CLZ treatment. Eighteen patient records were included in this study. The mean stabilized CLZ dose was 441.7 ± 171.8 mg/day. DBP (t = 1.02, df = 79.5, = 2.00, 0.049) and HR (t = 1.32, df = 355  = -4.61, < 0.0001) were significantly higher after CLZ initiation. A trend was noted for increase in SBP (p = 0.071). 22 % of patients met criteria for hypertension before CLZ and 67 % during CLZ treatment (Chi Square = 6.25, df = 1, p = 0.0124). No significant changes in weight or renal function occured during CLZ treatment. No patients had evidence of cardiomyopathy. The data suggest CLZ may be associated with a rise in BP and HR. The results of this study support previous literature that found an increase in SBP/DBP regardless of CLZ dose, occurring early in treatment. Due to high risk of cardiovascular morbidity and mortality, more work is needed to determine risk factors and understand the mechanism of action that may cause this side effect.

Comparing and validating medication complexity from insurance claims against electronic health records.

BACKGROUND: Patient effort to comply with complex medication instructions is known to be related to nonadherence and subsequent medical complications or health care costs. A widely used Medication Regimen Complexity Index (MRCI) has been used with electronic health records (EHRs) to identify patients who could benefit from pharmacist intervention. A similar claims-derived measure may be better suited for clinical decision support, since claims offer a more complete view of patient care and health utilization. OBJECTIVE: To define and validate a novel insurance claims-based medication complexity score (MCS) patterned after the widely used MRCI, derived from EHRs. METHODS: Insurance claims and EHR data were provided by HealthPartners (N = 54,988) (Bloomington, Minnesota) and The Johns Hopkins Health System (N = 28,589) (Baltimore, Maryland) for years 2013 and 2017, respectively. Yearly measures of medication complexity were developed for each patient and evaluated with one another using rank correlation within different clinical subgroupings. Indicators for the presence of individually complex prescriptions were also developed and assessed using exact agreement. Complexity measures were then correlated with select covariates to further validate the concordance between MCS and MRCI with respect to clinical metrics. These included demographic, comorbidity, and health care utilization markers. Prescribed medications in each system's EHR were coded using the previously validated MRCI weighting rules. Insurance claims for retail pharmacy medications were coded using our novel MCS, which closely followed MRCI scoring rules. RESULTS: EHR-based MRCI and claims-based MCS were significantly correlated with one another for most clinical subgroupings. Likewise, both measures were correlated with several covariates, including count of active medications and chronic conditions. The MCS was, in most cases, more associated with key health covariates than was MRCI, although both were consistently significant. We found that the highest correlation between MCS and MRCI is obtained with patients who have similar counts of pharmacy records between EHRs and claims (HealthPartners: P = 0.796; Johns Hopkins Health System: P = 0.779). CONCLUSIONS: The findings suggest good correspondence between MCS and MRCI and that claims data represent a useful resource for assessing medication complexity. Claims data also have major practical advantages, such as interoperability across health care systems, although they lack the detailed clinical context of EHRs. DISCLOSURES: The Johns Hopkins University holds the copyright to the Adjusted Clinical Groups (ACG) system and receives royalties from the global distribution of the ACG system. This revenue supports a portion of the authors' salary. No additional or external funding supported this work. The authors have no conflict of interest to disclose.

Reliable clinical serum analysis with reusable electrochemical sensor: Toward point-of-care measurement of the antipsychotic medication clozapine.

Clozapine is one of the most promising medications for managing schizophrenia but it is under-utilized because of the challenges of maintaining serum levels in a safe therapeutic range (1-3µM). Timely measurement of serum clozapine levels has been identified as a barrier to the broader use of clozapine, which is however challenging due to the complexity of serum samples. We demonstrate a robust and reusable electrochemical sensor with graphene-chitosan composite for rapidly measuring serum levels of clozapine. Our electrochemical measurements in clinical serum from clozapine-treated and clozapine-untreated schizophrenia groups are well correlated to centralized laboratory analysis for the readily detected uric acid and for the clozapine which is present at 100-fold lower concentration. The benefits of our electrochemical measurement approach for serum clozapine monitoring are: (i) rapid measurement (≈20min) without serum pretreatment; (ii) appropriate selectivity and sensitivity (limit of detection 0.7µM); (iii) reusability of an electrode over several weeks; and (iv) rapid reliability testing to detect common error-causing problems. This simple and rapid electrochemical approach for serum clozapine measurements should provide clinicians with the timely point-of-care information required to adjust dosages and personalize the management of schizophrenia.