Metabolic side effects of risperidone in children and adolescents with early-onset schizophrenia

Risperidone and Cardiometabolic Risk in Children and Adolescents: Clinical and Instrumental Issues

PURPOSE/BACKGROUND

Although second-generation antipsychotics are used to treat and manage symptoms for several psychiatric disorders, data about their adverse effects in developmental age are limited. The aim of this prospective observational study was to verify the cardiovascular and metabolic risk in a sample of antipsychotic-naive children/adolescent patients starting risperidone therapy.

METHODS

Twenty-two patients, younger than 18 years, were recruited. The assessment included anthropometric data, cardiovascular parameters, blood tests, and ultrasonographic abdominal study.

RESULTS

After an average follow-up period of 7.6 months, statistically significant increases in mean values of waist circumference, body mass index (BMI), BMI percentile, BMI z score, total cholesterol, and prolactin were found. Other cardiometabolic parameters showed an upward trend in time. Subjects in pubertal/postpubertal stage and female patients were more susceptible to developing cardiometabolic changes. Moreover, significant correlations between changes in anthropometric and several metabolic parameters were found. A tendency to change in constitution of the liver parenchyma and distribution of the abdominal fat mass with ultrasonographic abdominal study was also evident.

CONCLUSIONS

In our sample, several metabolic parameters showed a sensitivity to risperidone treatment. Because most of these parameters are age dependent, metabolic syndrome criteria used for adults were inappropriate in children and adolescents. Periodic clinical and instrumental evaluations and guidelines for monitoring of any metabolic, laboratory, and instrumental complications are necessary in the perspective of even long-time second-generation antipsychotics treatment in children and adolescents.

Missing laboratory results data in electronic health databases: implications for monitoring diabetes risk

Aim: Laboratory test (lab) results may be useful to detect incident diabetes in electronic health record and claims-based studies. Research design & methods: Using the Mini-Sentinel distributed database, we assessed the value of lab results added to diagnosis codes and dispensing claims to identify incident diabetes. Results: Inclusion of lab results increased the number of diabetes outcomes identified by 21%. In settings where capture of lab results was relatively complete, the absence of lab results was associated with implausibly low rates of the outcome. Conclusion: Lab results can increase sensitivity of algorithms for detecting diabetes, and missing lab results are associated with much lower rates of diabetes ascertainment regardless of algorithm. Patterns of missing lab results may identify ascertainment bias.

Methods for using clinical laboratory test results as baseline confounders in multi-site observational database studies when missing data are expected

PURPOSE

Our purpose was to quantify missing baseline laboratory results, assess predictors of missingness, and examine performance of missing data methods.

METHODS

Using the Mini-Sentinel Distributed Database from three sites, we selected three exposure-outcome scenarios with laboratory results as baseline confounders. We compared hazard ratios (HRs) or risk differences (RDs) and 95% confidence intervals (CIs) from models that omitted laboratory results, included only available results (complete cases), and included results after applying missing data methods (multiple imputation [MI] regression, MI predictive mean matching [PMM] indicator).

RESULTS

Scenario 1 considered glucose among second-generation antipsychotic users and diabetes. Across sites, glucose was available for 27.7-58.9%. Results differed between complete case and missing data models (e.g., olanzapine: HR 0.92 [CI 0.73, 1.12] vs 1.02 [0.90, 1.16]). Across-site models employing different MI approaches provided similar HR and CI; site-specific models provided differing estimates. Scenario 2 evaluated creatinine among individuals starting high versus low dose lisinopril and hyperkalemia. Creatinine availability: 44.5-79.0%. Results differed between complete case and missing data models (e.g., HR 0.84 [CI 0.77, 0.92] vs. 0.88 [0.83, 0.94]). HR and CI were identical across MI methods. Scenario 3 examined international normalized ratio (INR) among warfarin users starting interacting versus noninteracting antimicrobials and bleeding. INR availability: 20.0-92.9%. Results differed between ignoring INR versus including INR using missing data methods (e.g., RD 0.05 [CI -0.03, 0.13] vs 0.09 [0.00, 0.18]). Indicator and PMM methods gave similar estimates.

CONCLUSION

Multi-site studies must consider site variability in missing data. Different missing data methods performed similarly. Copyright © 2016 John Wiley & Sons, Ltd.

Adherence to guidelines for glucose assessment in starting second-generation antipsychotics

OBJECTIVES

In 2003, the US Food and Drug Administration issued warnings about hyperglycemia and diabetes with second-generation antipsychotics (SGAs); guidelines have recommended metabolic screening since 2004. However, little is known of contemporary practices of glucose screening among youth initiating SGAs. Our objective was to evaluate baseline glucose assessment among youth in the Mini-Sentinel Distributed Database starting an SGA.

METHODS

The cohort included youth ages 2 through 18 newly initiating SGAs January 1, 2006, through December 31, 2011, across 10 sites. Baseline glucose was defined as fasting/random glucose or hemoglobin A1c (GLU) measurement occurring relative to first SGA dispensing. Differences in GLU assessment were evaluated with χ(2) tests and logistic regression.

RESULTS

The cohort included 16,304 youth; 60% boys; mean age 12.8 years. Risperidone was most commonly started (43%). Eleven percent (n = 1858) had GLU assessed between 90 days before and 3 days after first dispensing. Assessment varied across SGAs (olanzapine highest), sites (integrated health care systems higher), ages (16-18 highest), years (2007 highest), and gender (female higher; all P < .001). GLU assessment among those starting olanzapine was more likely than among those starting quetiapine (odds ratio [OR]: 1.72 [95% confidence interval (CI): 1.37-2.18]), aripiprazole (OR: 1.49 [95% CI: 1.18-1.87]), or risperidone (OR: 1.61 [95% CI: 1.28-2.03]).

CONCLUSIONS

Few children and adolescents starting SGA have baseline glucose assessed. This is concerning because those at high diabetes risk may not be identified. Further, lack of screening impedes determining the contribution of SGAs to hyperglycemia development.