Glucose dysregulation among veterans living with schizophrenia-related disorders after switching second-generation antipsychotics.

OBJECTIVES: To compare (1) blood glucose and glycosylated hemoglobin (A1C) laboratory results and (2) longitudinal trends in blood glucose levels among veterans switched from one second-generation antipsychotic (SGA) to another. DESIGN: Retrospective, naturalistic, nonequivalent control group. SETTING: United States between April 1, 2003, and September 30, 2003. PATIENTS: 1,776 U.S. Veterans Health System beneficiaries living with schizophrenia-related disorders switching (1) from olanzapine to another SGA, (2) to olanzapine from another SGA, and (3) among nonolanzapine SGAs. INTERVENTION: Data were retrieved from the laboratory results (LAR) database for a maximum of 180 days before and 365 days after the index date. MAIN OUTCOME MEASURES: Mean blood glucose, A1C, and change in blood glucose. RESULTS: Blood glucose (36.0 mg/dL, paired t test109 = -4.87, P < 0.001) and A1C (1.0%, paired t143 = -4.84, P < 0.001) declined among veterans switched from olanzapine who were taking a blood glucose-lowering agent before the switch but was unchanged for those who were not. Adjusting for age, gender, and race, addition of the switch-type variables improved prediction of blood glucose change (F-ratio = 3.76, P = 0.03). Linear mixed-effects models confirmed that blood glucose levels declined for veterans switched from olanzapine with glucose dysregulation before the switch (Est(beta2 - beta1) = -34.5 mg/dL, t424 = -5.05, P < 0.001). CONCLUSION: Blood glucose and A1C were significantly improved among veterans switched from olanzapine with evidence of glucose dysregulation before the switch. They were stable among those without evidence of preexisting glucose dysregulation. Therapeutic switches from one SGA to another should be monitored as a risk factor for changes in glucose regulation.

Metabolic monitoring in veterans with schizophrenia-related disorders and treated with second-generation antipsychotics: findings from a Veterans Affairs-based population.

OBJECTIVES: To describe the proportions of veterans living with schizophrenia-related disorders monitored for dyslipidemia and hyperglycemia and to investigate whether the likelihood of metabolic dysregulation monitoring was influenced by veterans' sociodemographic characteristics, preswitch pharmacologic treatment, and monitoring before the switch from one second-generation antipsychotic (SGA) to another. DESIGN: Retrospective, observational, descriptive study. SETTING: Veterans Affairs (VA) Healthcare System between October 1, 2001, and December 31, 2003. PATIENTS: 1,826 veterans with schizophrenia-related disorders. INTERVENTION: Veterans who were dispensed two or more prescriptions for one of five SGAs (i.e., clozapine, olanzapine, quetiapine, risperidone, and ziprasidone) on the VA Healthcare System formulary were identified. Of these veterans, a subset that was switched from one SGA to another was identified. From this subset, veterans were identified who were on the first SGA continuously for at least 90 days before the index date and the new SGA for 180 or more days after. Finally, among these veterans, ICD-9 codes were used to identify veterans with a schizophrenia or schizoaffective disorder diagnosis (ICD-9 code 295.xx or 296). MAIN OUTCOME MEASURES: Proportions of veterans with lipid (i.e., low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides) and blood glucose (i.e., fasting blood glucose [FBG], glycosylated hemoglobin [A1C]) laboratory results. RESULTS: Nearly 39% of the veterans had at least one of three lipid fractions monitored 6 months or less before their SGA switch and 59% during the 12 months after. The corresponding proportions of veterans monitored were 57% and 80% for FBG and 19% and 31% for A1C. Pharmacologic agent for metabolic dysregulation, monitoring during the 6 months before the switch, and age 50 years or older were significant predictors of monitoring after the SGA switch for all three laboratory parameters. CONCLUSION: These findings serve as a benchmark for lipid and blood glucose monitoring among patients who switch SGA therapy. Veterans' metabolic dysregulation was more likely to be monitored after SGA switch for those receiving pharmacologic treatment for metabolic dysregulation, monitored before the switch, and aged 50 years or older. Implementation of monitoring guidelines in daily practice is emphasized to ensure that individuals living with schizophrenia-related disorders and taking SGAs achieve optimal physical health.

Weight and blood pressure changes after switching second-generation antipsychotics in a population of veterans with schizophrenia-related disorders.

OBJECTIVES: To describe (1) the association between systolic blood pressure (SBP) and diastolic blood pressure (DBP) changes and weight change and (2) weight, SBP, and DBP changes attributable to the medication following a switch from one second-generation antipsychotic (SGA) to another. DESIGN: Retrospective, naturalistic, nonequivalent control group study. SETTING: United States between April 1, 2002, and September 30, 2002. PATIENTS: 1,425 U.S. Veterans Healthcare System enrollees with diagnoses of schizophrenia or schizoaffective disorders. INTERVENTION: Analysis of data from the Veterans Integrated System Technology Architecture. MAIN OUTCOME MEASURES: Veterans' weight, SBP, and DBP. RESULTS: Weight change and change in SBP (r = 0.19) and DBP (r = 0.15) were significant (P < 0.001), even after adjusting for obesity status (body mass index <30 or > or = 30 kg/m2). Veterans who were switched from olanzapine to another SGA lost weight (P < 0.001), whereas those switched from another SGA to olanzapine gained weight (P < 0.05). Weight change remained significant after controlling for preswitch obesity status (P < 0.001). Blood pressure was not associated with switch type after adjusting for preswitch obesity status. CONCLUSION: Monitoring and aggressively treating weight change is an evidence-based and relatively inexpensive strategy that primary care practitioners and psychiatrists can use in working in tandem toward reducing the already greater cardiovascular risk of patients with schizophrenia.

Effect on lipid profiles of switching from olanzapine to another second-generation antipsychotic agent in veterans with schizophrenia.

OBJECTIVE: To compare (1) mean low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride differences after switching from olanzapine to quetiapine, risperidone, or ziprasidone and (2) the mean lipid change between switch patterns. DESIGN: Retrospective, naturalistic, nonequivalent control group design. SETTING: United States between April 1, 2002, and September 30, 2002. PATIENTS: 1,826 U.S. Veterans Healthcare System enrollees with diagnoses of schizophrenia or schizoaffective disorders and receiving a second-generation antipsychotic (SGA) medication. INTERVENTIONS: Analysis of data from the Veterans Information Systems and Technology Architecture. MAIN OUTCOME MEASURES: Differences in LDL-C, HDL-C, and triglycerides and mean differences between switch patterns. Predictors were the type of switch (e.g., olanzapine to quetiapine) and switch patterns (e.g., olanzapine to quetiapine versus olanzapine to risperidone). Data were analyzed using Pearson's X2 and multivariate analysis of covariance with planned comparisons. RESULTS: After adjusting for age, gender, and race/ethnicity, LDL-C decreased significantly among patients switched from olanzapine to ziprasidone (-16.9 mg/dL, P <0.01) and olanzapine to quetiapine (-7.6 mg/dL, P = 0.04) and trended upward in patients switched from olanzapine to risperidone (+6.6 mg/dL, P = 0.12). Triglyceride levels decreased among those switched from olanzapine to ziprasidone (-62.9 mg/dL, P <0.01) and olanzapine to risperidone (-48.5 mg/dL, P <0.01) but not among veterans switched from olanzapine to quetiapine (+7.8 mg/dL, P = 0.54). HDL-C levels did not change significantly when veterans were switched from olanzapine to quetiapine, risperidone, or ziprasidone. CONCLUSION: Switching SGAs can increase or decrease cardiovascular risk depending on the clinician's follow-on SGA choice. LDL-C and triglyceride levels decreased significantly among veterans switched from olanzapine to ziprasidone. Switching to quetiapine was associated with a reduction in LDL-C, while switching to risperidone resulted in lower triglyceride levels. Clinicians should use these results when building a patient care plan that includes switching of SGAs.

Increased cardiovascular risk with second-generation antipsychotic agent switches.

OBJECTIVE: To model the risk of long-term, adverse cardiovascular events after switching from one second-generation antipsychotic medication (SGA) to another in patients with schizophrenia or schizoaffective disorder. DATA SOURCES: PubMed from 1985 to 2004 using the search terms atypical antipsychotics, obesity, weight, diabetes mellitus, dyslipidemia, hypercholesterolemia, lipids, second generation antipsychotics, antipsychotic agents, schizophrenia, metabolic syndrome, cardiovascular disease, and cardiovascular risk factors. STUDY SELECTION: By the authors. DATA EXTRACTION: By the authors. DATA SYNTHESIS: The selection of an SGA for an individual patient should be primarily based upon its therapeutic effectiveness. However, when two medications are clinically equivalent with respect to treatment outcomes, other important consequences of the medication choice should be considered. Depending upon the type of SGA switch, the risk of an adverse cardiovascular event may be lower, as when olanzapine is switched to risperidone, or may increase by as much as 33%, as when risperidone is switched to olanzapine or clozapine. CONCLUSION: Cardiovascular risk likely differs depending upon SGA choice, but limited data make it difficult to predict the metabolic changes associated with switching. Prospective controlled studies are needed to describe the cardiovascular consequences of switching among the antipsychotic agents so that evidence-based strategies can be developed for selection of the optimal SGA.