Antipsychotics, a mainstay of treatment for persons with schizophrenia and other psychotic disorders, are also used for other psychiatric and nonpsychiatric conditions (1). Second-generation antipsychotic medications are associated with increased risk of metabolic side effects such as weight gain, diabetes, and hyperlipidemia. These side effects are particularly problematic because individuals with serious mental illness have a higher risk of diabetes and cardiovascular disease and therefore represent a vulnerable population (2–6).
In 2003, the U.S. Food and Drug Administration (FDA) required that antipsychotic product labeling include a warning about hyperglycemia and diabetes and recommended monitoring fasting blood glucose of patients with diabetes, diabetes risk factors, or symptoms of hyperglycemia. In 2004, the American Diabetes Association and a panel of other biomedical professional associations, including the American Psychiatric Association (2), and the Mount Sinai Summit panel (4) published expert consensus recommendations for metabolic monitoring of patients prescribed antipsychotic medications. These groups recommended that providers consider the relative risk of antipsychotic agents and monitor weight, glucose or glycosylated hemoglobin, and lipids when a patient begins a new antipsychotic medication and continue to monitor these metabolic parameters and appropriately assess and treat any abnormalities (2,4).
Despite the expert recommendations and the FDA warning, the rates of metabolic monitoring have remained low among Medicaid enrollees (7–10), veterans with schizophrenia-related diagnoses who switched to a second-generation antipsychotic (11), and privately insured patients (12–14). A recent review of 48 studies of monitoring practices, which spanned five countries, ncluding the United States, concluded that monitoring for metabolic effects of antipsychotics is too low (15).
Most studies of Veterans Affairs (VA) patients that have described monitoring for metabolic side effects from antipsychotics concerned patients with schizophrenia or schizoaffective disorder (11,16–18), although over half of VA patients prescribed antipsychotics do not have diagnoses of schizophrenia, schizoaffective disorder, bipolar disorder, or other psychotic disorder (1). Most published studies reported monitoring either lipids alone or glucose and lipids (7–14,16), and only two studies examined weight monitoring (17,18). In addition, many of these studies reported baseline or follow-up monitoring over long intervals (six months before starting a new antipsychotic or in a 12-month follow-up period after starting a new medication) (8,9,11,14,18) instead of monitoring around the time of the medication change and at specific time points thereafter.
This study aimed to fill the gaps in the existing literature by examining recent practices for outpatient monitoring of metabolic side effects (weight gain, hyperglycemia, and hyperlipidemia) when a new antipsychotic medication was prescribed (baseline) for any reason and at three-month follow-up as recommended by the consensus guidelines (2). Using VA data extracted from national databases and a regional data warehouse, we also examined the association of patient demographic and clinical characteristics with the frequency of monitoring side effects.
Study design and data source
This study was a retrospective cohort analysis that included patients prescribed antipsychotics in 32 VA medical centers within Veterans Integrated Service Networks (VISNs) 18–22. Data on service utilization, diagnosis, prescribed medications, and laboratory tests were extracted from Veterans Health Administration Medical SAS Data Sets, and vital signs were extracted from the Region 1 Data Warehouse.
This project was approved by the Central Arkansas Veterans Healthcare System Institutional Review Board and Research and Development Committee.
Patients were included if they had a new antipsychotic prescription (the index medication) prescribed between April 1, 2008, and March 31, 2009, with at least a 60-day supply in the subsequent 90 days. The index medication was defined as a prescription of a given antipsychotic that had not been prescribed in the previous 180 days. Patients were included if the index prescription was a “new antipsychotic start” (that is, for a patient without a prescription for any antipsychotic agent in the previous 180 days), a switch to a different antipsychotic agent, or an addition of a different antipsychotic medication to ongoing antipsychotic treatment. If more than one new prescription was identified within the study period, we selected the most recent occurrence.
The second inclusion criterion was that the medication regimen was stable during the six months before the index prescription so that this new antipsychotic medication likely represented a new treatment episode. To focus on outpatient monitoring practices, we excluded patients who had a hospital stay or who received extended care (such as in a nursing home or residential program) from 30 days before through 120 days after the index prescription date. In analyses not reported here, hospital or extended-care stays were strongly associated with completion of metabolic monitoring. The final sample included 12,009 veterans.
Recommended baseline monitoring of weight, plasma glucose or hemoglobin A1c, and serum low-density lipoprotein (LDL) cholesterol at the time of a new antipsychotic prescription was defined as monitoring of each parameter within 30 days before or after the index prescription date. Based on the published recommendations (2), follow-up monitoring involved recording these parameters between 60 and 120 days after the index date. Because VA data often do not indicate whether the sample was fasting or random, any glucose or LDL test was included. Although the consensus statements specifically recommend fasting laboratory tests, most studies of routine monitoring practices examined whether any glucose or lipid tests were obtained (15).
We characterized the sample by age on the index date, gender, race-ethnicity, marital status, preexisting medical comorbidities (diabetes, dyslipidemia, obesity, hypertension , and heart disease [19,20]), and psychiatric diagnosis. Patients were assigned to mental health diagnostic categories on the basis of the recorded diagnostic codes for schizophrenia, bipolar disorder, other psychotic disorders, or other (nonpsychotic) mental disorders at any time between 180 days before through 120 days after the index date (Table 1). Patients with two or more different psychotic disorder diagnoses were categorized according to the most frequently recorded diagnosis. If ties occurred, diagnoses were assigned with the following hierarchy: schizophrenia outranked bipolar disorder, which outranked other psychotic disorders (21). The propensity of the index antipsychotic medication to cause metabolic side effects was classified as high (clozapine and olanzapine), medium (chlorpromazine, loxapine, perphenazine, paliperidone, quetiapine, risperidone, thioridazine, thiothixene, and trifluoperazine) or low (aripiprazole, fluphenazine, haloperidol, mesoridazine, molindone, pimozide, and ziprasidone) risk based on the consensus statement from biomedical associations (2), comprehensive review articles (22,23), and results of the CATIE (Clinical Antipsychotic Trials of Intervention Effectiveness) study (24).
Table 1Psychiatric diagnosis groups assigned to a cohort of 12,009 veterans prescribed antipsychotics
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|Bipolar disorder||296.0x, 296.1x, 296.4x–296.8x|
|Other psychotic disorders||293.81, 293.82, 297.0x–297.3x, 297.8x, 297.9x, 298.0x–298.4x, 298.8x–298.9x|
|Other (nonpsychotic) disorders||290.xx–316.xx excluding ICD-9-CM codes for psychotic disorders specified above|
|No psychiatric diagnosis||All others|
Frequency of monitoring for weight, glucose or hemoglobin A1c, and LDL was examined at baseline and follow-up. Baseline and follow-up monitoring rates were compared by using McNemar’s test.
Repeated-measures analysis using the generalized estimating equation for binary variables examined the association of patient characteristics with the likelihood of receiving recommended baseline and follow-up monitoring for each metabolic side-effect parameter. The model was adjusted for the patient demographic and clinical characteristics listed above as well as for interactions of these variables with time. For this analysis, preexisting medical comorbidities were defined as time-dependent variables to account for newly developed metabolic conditions between the index prescription and start of the follow-up period. Adjusted odds ratios (AORs) and 95% confidence intervals were reported. All analyses were conducted with SAS version 9.1, and p values less than .05 were considered statistically significant.
Demographic and clinical characteristics are reported in Table 2. Frequency of monitoring was greater at baseline than at follow-up for each metabolic parameter (Figure 1): 66.6% versus 49.5% for weight, 45.8% versus 27.1% for glucose or hemoglobin A1c, and 32.1% versus 16.2% for LDL (p<.001 for each comparison). The most frequently monitored metabolic parameter was weight at both baseline and follow-up.
Table 2Baseline characteristics of 12,009 veterans prescribed antipsychotics
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|Age on index prescription date (mean±SD)||55.7±14.7|
| Not married||7,324||60.1|
|Preexisting comorbidity diagnosis|
| Diabetes (ICD-9 code 250.xx or medication for diabetes)||2,205||18.4|
| Dyslipidemia (ICD-9 code 272.xx or medication for lowering lipids)||5,431||45.2|
| Obesity (ICD-9 code 278.0x or medication for obesity)||4,722||39.3|
| Heart diseaseb||1086||9.0|
|Psychiatric diagnosis group (hierarchically defined)c|
| Bipolar disorder||698||5.8|
| Other psychotic disorders||1,151||9.6|
| Other (nonpsychotic) mental disorders||7,971||66.4|
| No psychiatric diagnoses||263||2.2|
|Metabolic properties of index antipsychotic agent|
| Highest risk||792||6.6|
| Medium risk||7,408||61.7|
| Lowest risk||3,809||31.7|
Figure 1Monitoring for metabolic side effects at baseline and 90-day follow-upa
aChanges in monitoring rates of all metabolic parameters from baseline to follow-up are statistically significant by McNemar’s test (p<.001). N=12,009 veterans
Table 3 reports AORs for the association of demographic and clinical variables with baseline and follow-up monitoring. Although some of these AORs were statistically significant, diagnoses of diabetes, dyslipidemia, obesity, and hypertension were the most consistently significant predictors of increased metabolic monitoring. In addition, monitoring did not significantly differ between patients with schizophrenia and patients with nonpsychotic mental disorders. However, patients without psychiatric diagnoses were significantly less likely than patients with schizophrenia to have each of the metabolic parameters measured at both time points. Compared with veterans prescribed an antipsychotic with a low risk of metabolic abnormalities, veterans prescribed a high-risk antipsychotic were significantly more likely to be weighed at baseline. Veterans prescribed medium-risk antipsychotics were significantly more likely to have glucose and hemoglobin A1c as well as LDL tested at baseline but were less likely to be weighed at follow-up.
Table 3Adjusted odds ratios of monitoring for three metabolic parameters when prescribed an antipsychotic (baseline) and at 90-day follow-up
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|Weight||Glucose||Low-density lipoprotein cholesterol|
|Characteristic||AOR||95% CI||pa||AOR||95% CI||pa||AOR||95% CI||pa|
|Follow-up versus baseline||.47||.39–.56||<.001||.29||.24–.36||<.001||.28||.22–.35||<.001|
|Age (reference: <45)|
|Female (reference: male)||.004||.157||.033|
|Race (reference: white)|
| Race missing||.151||.731||.958|
| Other race||.756||.735||.615|
|Married (reference: unmarried)||.353||.223||.088|
| Diabetes (reference: none)||.952||.011||.241|
| Dyslipidemia (reference: none)||.847||.039||.003|
| Obesity (reference: none)||.807||<.001||.001|
| Hypertension (reference: none)||.941||.302||.402|
| Heart disease (reference: none)||.838||.790||.571|
|Psychiatric diagnosis group (reference: schizophrenia)|
| Bipolar disorder||.785||.280||.866|
| Other psychotic disorders||.480||.530||.649|
| Nonpsychotic mental disorders||.473||.510||.277|
| No psychiatric conditions||<.001||.028||.057|
|Metabolic risk category of index antipsychotic (reference: low risk)|
| High risk||.053||.530||.718|
| Medium risk||.006||.040||.030|
This study was one of the largest and most comprehensive examinations of monitoring for metabolic side effects of antipsychotics among VA outpatients beginning new antipsychotic treatment episodes. A unique aspect of this study is that we included all patients with a new antipsychotic prescription, regardless of diagnosis, and examined both baseline and 90-day follow-up monitoring. Most other VA studies have focused on patients with psychotic disorders (11,16–18,25). With the exception of two previous VA studies (17,18), no U.S. studies have examined monitoring practices for weight (7–12,14,16), and previous studies used a much wider window to assess follow-up monitoring (10,16), whereas we focused on the first follow-up time point (90 days) recommended by the consensus panel (2). In their recent review of metabolic monitoring practices, Mitchell and colleagues (15) did not report specific baseline and follow-up monitoring rates after a new antipsychotic prescription.
Monitoring rates for the metabolic parameters at the time of a new antipsychotic prescription were suboptimal (just 45.8% for glucose and hemoglobin A1c, for example), although with the same baseline window, rates for glucose and lipid monitoring were higher than those observed in earlier studies of largely nonelderly Medicaid enrollees (7) or privately insured adults (14). The frequencies of baseline monitoring for the three parameters in our VA sample were also higher than those reported by Mitchell and colleagues (15).
Previous VA studies that used a wider time frame than we used in this study reported higher frequency of baseline monitoring (11,18). In 2001–2003, Hsu and colleagues (11) found baseline monitoring rates for fasting blood glucose of 57.2% among patients with schizophrenia or schizoaffective disorders in the six months prior to a switch in second-generation antipsychotic. Using VISN 16 data from 2003 to 2005, Shi and colleagues (18) found that 75.8% of patients with schizophrenia had any monitoring of blood glucose or lipids during the 180 days before a new second-generation antipsychotic prescription. In a sensitivity analysis, applying this same six-month window to our sample, we found that 82.7% of patients received monitoring of glucose or hemoglobin A1c. Our results could reflect improved monitoring over time or geographic variation in monitoring practices. The publication of monitoring recommendations and subsequent quality improvement efforts within the VA could have resulted in increased metabolic monitoring rates, as suggested by Mitchell and colleagues’ review (15). Two other VA studies of monitoring for metabolic side effects (16,17) did not focus on new antipsychotic treatment episodes and therefore could not be directly compared.
The consensus panel recommended monitoring for metabolic abnormalities three months after a new antipsychotic prescription (2). However, nearly all previous studies allowed for a wide observation period (12 months after an index prescription, for example) (11), and others did not distinguish baseline from follow-up monitoring (8,9). Thus, compared with most published studies, our study more closely examined adherence to the consensus panel’s recommendations for follow-up monitoring. Without describing the time frames for baseline and the follow-up monitoring, Mitchell and colleagues (15) reported no difference in glucose monitoring frequency before and after initiation of treatment with antipsychotics (35.3% versus 33.2%, respectively).
The only previous study that used a similar follow-up monitoring window (84±40 days) involved privately insured adult patients who were started on a second-generation antipsychotic and used it consistently for six months between 2004 and 2006 (12). The follow-up monitoring rate for glucose was lower than that observed in our study (17.9% versus 27.1%), even though our observation window was narrower. The difference could be attributed to the older age of patients in our study, changes in practice over time (given our more recent data), or differences in care delivery systems, such as greater integration of primary care and mental health care in VA versus non-VA settings.
Although follow-up monitoring frequency was lower than baseline monitoring, the fact that those who had preexisting metabolic conditions that require monitoring indeed received greater monitoring is encouraging.
Mental diagnosis and monitoring
In this study the monitoring rates did not statistically differ between patients with schizophrenia and patients with other psychiatric diagnoses. However, the finding that patients without any psychiatric diagnosis (only 2.2% of the total sample) were significantly less likely to be monitored at both time points warrants further investigation. Moreover, we found that a majority of patients receiving a new antipsychotic medication did not have psychotic disorders for which antipsychotic use has been FDA approved. Only one-third had a diagnosis of schizophrenia, bipolar disorder, or another psychotic disorder. This finding is consistent with other studies reporting the high prevalence of off-label antipsychotic use (10,26). Our inclusion criteria required ongoing antipsychotic treatment after the index prescription (at least 60 days) to exclude patients who may have been receiving temporary treatment of insomnia or transient behavioral disturbances. Further research is needed to characterize the nature of and reasons for off-label antipsychotic prescribing. If such off-label antipsychotic prescribing is not well justified clinically, patients should not be exposed to the risk of these medications.
Metabolic comorbidities and monitoring
As reported in previous studies (18,27), our study showed that patients with preexisting comorbidities, such as diabetes, dyslipidemia, obesity, or hypertension, were significantly more likely to receive metabolic monitoring at both baseline and follow-up. These patients likely had more contact with primary care providers and required more frequent assessment of metabolic parameters than other patients, and this assessment may not have been obtained specifically to assess for antipsychotic side effects. However, the vast majority of our sample (85%) had an outpatient visit during the follow-up period, suggesting that increased contact alone does not ensure completion of metabolic monitoring.
Antipsychotic agents and metabolic monitoring
The study revealed minor increases in likelihood of baseline monitoring for patients taking antipsychotics with high and medium propensity to cause metabolic side effects. However, the pattern disappeared at follow-up. Although the consensus panel (2) recommended that these risks be considered when prescribing antipsychotics, it also recommended monitoring of metabolic parameters when any second-generation antipsychotic is prescribed. Also, we cannot determine whether providers ordered monitoring tests because of the new antipsychotic prescription or whether the tests were obtained for some other reason. Thus it is not surprising that we did not observe consistent differential monitoring rates for higher-risk agents. Only 6.6% of our sample received antipsychotics with a high risk of metabolic side effects. This finding is consistent with reports of decreased prescribing of the highest-risk agents, such as olanzapine, after the FDA warning concerning risk of diabetes for persons taking second-generation antipsychotics, as well as publication of consensus recommendations on monitoring (7).
Barriers to metabolic monitoring
Further research is needed to characterize barriers to metabolic monitoring both at the time a new antipsychotic is prescribed and thereafter. Evidence from both U.S. and European studies suggests that providers are aware of monitoring recommendations (15,28,29). Low baseline monitoring may result partly from prescription of antipsychotics in an emergent outpatient clinical setting where the focus is to ensure the safety of the patient and others rather than monitoring for metabolic abnormalities. Providers may rely on previous laboratory values rather than obtain new metabolic testing. Moreover, patients with agitation or psychosis may refuse laboratory testing (30).
Other factors may include mental health providers’ lack of comfort with medical issues, lack of time and resources to address them during a clinical encounter (31), or difficulty getting patients to return for follow-up monitoring. Further, clinicians may not monitor for metabolic side effects when they prescribe low doses of antipsychotics for off-label uses such as insomnia or as an adjunct to other psychotropic medications for conditions such as dementia. When antipsychotics are prescribed, psychiatrists may expect primary care physicians to monitor metabolic values for patients, whereas primary care physicians may expect psychiatrists to conduct this monitoring (31). Recent reviews have emphasized the need for collaboration between primary care and mental health clinicians to improve metabolic monitoring (15,31).
The proportion of females in our sample was 10.3%, which is greater than the proportion of females in the veteran population (8.3%) (32). Reasons for this difference are not clear, although others have observed a similar proportion in studies of patients with serious mental illness (25).
As with any analysis using administrative databases, in this study we could not determine why many patients did not have metabolic monitoring performed when they initiated new antipsychotic treatment, nor could we determine whether the monitoring that was done was driven by providers’ concern about metabolic side effects or by assessment of other physical conditions. For instance, weight was monitored more frequently than glucose or LDL at both baseline and follow-up, presumably because vital signs are regularly obtained during clinic visits, whereas laboratory tests require a specific order from the clinician or a patient’s trip to a laboratory. However, in an integrated health care system such as the VA, which uses electronic medical records, results of laboratory tests are available to all providers. Thus metabolic testing done for other reasons could still be useful to assess metabolic side effects.
Similar to most previous studies, our study included any glucose or LDL tests rather than selecting only fasting tests as recommended by the consensus panel, which may overestimate the extent of monitoring of fasting glucose. However, the VA databases do not consistently indicate whether such tests have been obtained in the fasting state. Copeland and colleagues (27) have described a method of identifying “proxy” fasting glucose values in VA administrative data by using glucose tests that occur on the same day as a lipid assay. Applying this method to our study would result in an underestimate of the extent of glucose monitoring because in clinical settings fasting glucose tests are sometimes ordered without lipid assays. However, even with this inclusive measurement approach, we found that most patients did not have their glucose monitored.
For follow-up monitoring, we used a mean±SD 90±30-day period after the index prescription for monitoring metabolic side effects per the consensus panel recommendations. However, it is possible that a provider considered tests done outside of this time frame. Our study excluded patients who had inpatient or extended-care stays from baseline to follow-up and required use of the index antipsychotic agents for at least 60 days. Therefore, the results of this study are applicable only to outpatient populations with more than transient antipsychotic treatment.
Although we did not examine whether patients receiving polypharmacy with antipsychotics were more likely to have monitoring performed, this represents an important clinical question for future research.
Because a large proportion of our sample had missing values identifying race (19%), a well-known problem of VA data (33), we grouped all patients with missing race information into a separate category to maintain generalizability of our estimates. Because the reasons for missing data on race are often unknown, AOR estimates for this group cannot be interpreted. All other AORs of race variables are interpretable only among those with reported race information.
Despite these limitations, our findings describe recent monitoring practices in a large number of VA facilities, indicate that quality improvement efforts are needed to improve side-effect monitoring, and will inform further research regarding monitoring of metabolic side effects from antipsychotic medications.
The study results support conclusions from previous reports (12,15,17) that quality improvement efforts are needed to increase monitoring for the metabolic side effects of antipsychotics. Such efforts should not be limited to patients receiving care in specialty mental health clinics. Instead, they should be applicable to patients prescribed ongoing antipsychotic treatment regardless of diagnosis and clinical setting. It is noteworthy that the VA has undertaken national dissemination efforts and regional quality improvement activities since 2009 to improve metabolic monitoring.
There are several barriers to monitoring antipsychotic-associated metabolic side effects (31,34,35). Efficient tools and strategies are needed to help providers monitor patients receiving antipsychotic agents regardless of the clinic setting or the indication for antipsychotic treatment. Such efforts may lead to improved management of metabolic side effects and better overall quality and safety of care for veterans receiving antipsychotic treatment.
This research was supported by the U.S. Department of Veterans Affairs (VA), Office of Research and Development, Health Services Research and Development Service; VA Center for Mental Healthcare and Outcomes Research; and the Mental Health Quality Enhancement Research Initiative. The contents do not represent the views of the VA or the U.S. government.
Dr. Li received funding from Novartis Pharmaceuticals Corporation. The other authors report no competing interests.