Serious Mental Illness

Annotated Abstracts of Journal Articles
2015, 1st Quarter

Serious Mental Illness

Annotations by Lydia Chwastiak, MD, MPH, FAPM and Oliver Freudenreich, MD, FAPM
March 2015

  1. Cardiovascular risk prediction models for people with severe mental illness: results from the prediction and management of cardiovascular risk in people with severe mental illnesses (PRIMROSE) research program
  2. High rates of obstructive sleep apnea symptoms among patients with schizophrenia

Also of interest:

  • Hampton LM, Daubresse M, Chang HY, Alexander GC, Budnitz DS: Emergency department visits by children and adolescents for antipsychotic drug adverse events
    JAMA Psychiatry 2015; 72(3):292-4This research letter used the National Electronic Injury Surveillance System-Cooperative Adverse Drug Event Surveillance system to estimate the number of ED visits by children caused by antipsychotic medications. From 2009-2011, there were an estimated 1350 ED visits in children 10 years and younger and 2674 ED visits by 11-14 year olds due to antipsychotic medication adverse events. Antipsychotic medications were implicated in ER visits at almost 4-fold higher rate than stimulants. Atypical antipsychotic medications were implicated in 97.6% of all ED visits due to antipsychotic medications.


PUBLICATION #1 — Serious Mental Illness
Cardiovascular risk prediction models for people with severe mental illness: results from the prediction and management of cardiovascular risk in people with severe mental illnesses (PRIMROSE) research program

Osborn DP, Hardoon S, Omar R, Holt R, King M, Larsen J, et al
JAMA Psychiatry 2015; 72(2):143-51

ANNOTATION (Chwastiak & Freudenreich)

The Finding: These investigators developed two CVD risk-prediction models (PRIMROSE BMI and PRIMROSE lipid) specifically for patients with SMI based on prospective data from a large cohort with SMI in the UK. Both models performed better than a standard model currently used in the general population (Cox Framingham) in predicting >20% risk of CVD event in 10 years among both men and women.

Strength and Weaknesses: These risk-prediction models were based on prospective data from a large primary care cohort (n=38,824 adults) with SMI (schizophrenia, schizoaffective disorder, bipolar disorder, or other nonorganic psychoses) in the UK with median follow-up of 5.6 years. This dataset was particularly suitable for the development of these models because 98% of UK population is registered with a general practitioner.
Limitations include that routine clinical data may be less complete than data obtained in research studies (e.g., levels of detected diabetes may be under-estimated if patients were not screened). CVD events were obtained from primary care data, not review of patients’ entire medical records (some events may have been missed).

Relevance: Current models to estimate 10-year risk of incident CVD events (such as the Cox Framingham) may not accurately determine the excess CVD risk experienced by persons with SMI. This study develops and validates two models to specifically estimate CVD risk among this health disparities population. Further research is needed to determine the clinical utility and cost-effectiveness of these risk models.


Importance: People with severe mental illness (SMI), including schizophrenia and bipolar disorder, have excess rates of cardiovascular disease (CVD). Risk prediction models validated for the general population may not accurately estimate cardiovascular risk in this group.

Objective: To develop and validate a risk model exclusive to predicting CVD events in people with SMI incorporating established cardiovascular risk factors and additional variables.

Design, Setting, and Participants: We used anonymous/de-identified data collected between January 1, 1995, and December 31, 2010, from the Health Improvement Network (THIN) to conduct a primary care, prospective cohort and risk score development study in the United Kingdom. Participants included 38,824 people with a diagnosis of SMI (schizophrenia, bipolar disorder, or other nonorganic psychosis) aged 30 to 90 years. During a median follow-up of 5.6 years, 2324 CVD events (6.0%) occurred.

Main Outcomes and Measures: Ten-year risk of the first cardiovascular event (myocardial infarction, angina pectoris, cerebrovascular accidents, or major coronary surgery). Predictors included age, sex, height, weight, systolic blood pressure, diabetes mellitus, smoking, body mass index (BMI), lipid profile, social deprivation, SMI diagnosis, prescriptions for antidepressants and antipsychotics, and reports of heavy alcohol use.

Results: We developed 2 CVD risk prediction models for people with SMI: the PRIMROSE BMI model and the PRIMROSE lipid model. These models mutually excluded lipids and BMI. In terms of discrimination, from cross-validations for men, the PRIMROSE lipid model D statistic was 1.92 (95% CI, 1.80-2.03) and C statistic was 0.80 (95% CI, 0.76-0.83) compared with 1.74 (95% CI, 1.63-1.86) and 0.78 (95% CI, 0.75-0.82) for published Cox Framingham risk scores. The corresponding results in women were 1.87 (95% CI, 1.76-1.98) and 0.79 (95% CI, 0.76-0.82) for the PRIMROSE lipid model and 1.58 (95% CI, 1.48-1.68) and 0.77 (95% CI, 0.73-0.81) for the Cox Framingham model. Discrimination statistics for the PRIMROSE BMI model were comparable to those for the PRIMROSE lipid model. Calibration plots suggested that both PRIMROSE models were superior to the Cox Framingham models.

Conclusions and Relevance: The PRIMROSE BMI and lipid CVD risk prediction models performed better in SMI compared with models that include only established CVD risk factors. Further work on the clinical effectiveness and cost-effectiveness of the PRIMROSE models is needed to ascertain the best thresholds for offering CVD interventions.

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PUBLICATION #2 — Serious Mental Illness
High rates of obstructive sleep apnea symptoms among patients with schizophrenia
Annamalai A, Palmese LB, Chwastiak LA, Srihari VH, Tek C
Psychosomatics 2015; 56(1):59-66

ANNOTATION (Chwastiak & Freudenreich)

The Finding: This cross-sectional study of 175 outpatients with schizophrenia found that more than half were at high risk for OSA. The STOP Questionnaire, validated in surgical and primary care populations, may be useful as a clinical screen for OSA among patients with SMI.

Strength and Weaknesses: This study included 175 patients with schizophrenia and may be the first to report rates of OSA symptoms in this population, increasing awareness of an under-treated condition. Limitations of the study are that the sample was small, and the study used a modified STOP questionnaire (the study was not specifically designed to test the clinical utility of STOP). Definitive diagnoses of OSA (by polysmonography) were not available, so specificity and sensitivity of STOP questionnaire could not be determined.

Relevance: Obstructive sleep apnea may represent a target for cardiovascular risk reduction among patients with SMI. Obesity is a strong risk factor for OSA, and is highly prevalent among patients with SMI—and OSA is associated with cardiovascular morbidity and mortality. Identification and treatment of OSA may improve neurocognitive functioning and quality of life and decrease depression—as well as decrease cardiovascular risk—among these patients.


Background: Patients with schizophrenia have high rates of obesity and cardiovascular morbidity, which are strongly associated with obstructive sleep apnea (OSA). The prevalence and risk factors for OSA are not well studied in patients with schizophrenia.

Objective: The purpose of this study was to evaluate the frequency of OSA symptoms in a sample of outpatients with schizophrenia.

Methods: This cross-sectional study was a secondary analysis of data generated from an insomnia study that evaluated 175 outpatients with schizophrenia or schizoaffective disorder in a single, large urban community mental health center. Results of scales evaluating insomnia were used to complete the STOP questionnaire, which is a screening tool for OSA validated in surgical populations. Appropriate statistical analysis was done to compare participants across groups.

Results: Patients were classified into high risk for OSA (STOP ≥ 2) (57.7%), and low risk for OSA (STOP score < 2) (42.3%). We also identified patients with a known diagnosis of OSA (14.9%). Patients with diagnosed OSA had significantly higher STOP scores (mean 2.7 vs. 1.6 [t = 6.3; p < 0.001]). Only 23.8% of patients in the high-risk group were diagnosed with OSA. Body mass index was significantly higher in the diagnosed group (F[2,169] = 25; p < 0.001) as was diabetes (χ2 [2, N = 175] = 35, p < 0.001).

Conclusion: A large number of outpatients with severe mental illness are at high risk for OSA. The STOP questionnaire is easy to use and appears to have a very high clinical utility to detect OSA. Based on our findings, further studies are warranted to validate the tool in patients with severe mental illness.

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