Susan M. Frayne, MD, MPH

To characterize warfarin eligibility and receipt among Veterans Health Administration (VHA) patients with and without mental health conditions (MHCs).

This observational study identified VHA atrial fibrillation (AF) patients with and without MHCs in 2004. We examined unadjusted MHC-related differences in warfarin eligibility and warfarin receipt among warfarin-eligible patients, using logistic regression for any MHC and for specific MHCs (adjusting for sociodemographic and clinical characteristics).

Of 125,670 patients with AF, most (96.8%) were warfarin-eligible based on a CHADS2 stroke risk score. High stroke risk and contraindications to anticoagulation were both more common in patients with MHC. Warfarin-eligible patients with MHC were less likely to receive warfarin than those without MHC (adjusted odds ratio [AOR], 0.90; 95% CI, 0.87-0.94). The association between MHC and warfarin receipt among warfarin-eligible patients varied by specific MHC. Patients with anxiety disorders (AOR, 0.86; 95% CI, 0.80-0.93), psychotic disorders (AOR, 0.77; 95% CI, 0.65-0.90), and alcohol use disorders (AOR 0.62, 95% CI 0.54-0.72) were less likely to receive warfarin than patients without these conditions, whereas patients with depressive disorders and posttraumatic stress disorder were no less likely to receive warfarin than patients without these conditions.

Compared with patients with AF without MHCs, those with MHCs are less likely to be eligible for warfarin receipt and, among those eligible, are less likely to receive such treatment. Although patients with AF with MHC need careful assessment of bleeding risk, this finding suggests potential missed opportunities for more intensive therapy among some individuals with MHCs.

Patients with atrial fibrillation and mental health conditions (MHCs) may not receive anticoagulation treatment, even when they exhibit indications for anticoagulation for stroke prevention. 

Atrial fibrillation patients with MHCs are a clinically complex group; stroke prevention decision making with this group of patients must be particularly deliberative. 

Clinicians should consider MHCs when developing an anticoagulation treatment plan, though presence of MHCs should not be considered an absolute contraindication to anticoagulation.

Atrial fibrillation and atrial flutter (AF, collectively) affect almost 7 million Americans

— including 6% of people aged over 65 years old

—and account for at least 15% of the 700,000 strokes per year in the United States.

 Anticoagulation with warfarin can decrease ischemic stroke risk in AF by more than 60%.

 However, warfarin has a narrow therapeutic window: subtherapeutic and supratherapeutic anticoagulation can cause ischemic and hemorrhagic stroke, respectively, making ongoing laboratory monitoring and dose titration necessary. Consequently, certain clinical scenarios preclude safe warfarin use, such as situations in which the risk of hemorrhage is high or the likelihood is low that the patient will be able to achieve and maintain good anticoagulation control.

 Healthcare providers may include patients with mental health conditions (MHCs) in the latter group due to concerns (founded or not) about effectiveness or safety of anticoagulation (eg, related to comorbidities, concomitant medications, or anticipated nonadherence). The Veterans Health Administration (VHA) has invested considerable resources in maintaining a robust anticoagulation care infrastructure, but does not provide explicit guidance on anticoagulation in the context of mental illness,

despite the high prevalence of MHCs among VHA patients.

 Other national guidelines have provided scant input on this issue,

 and little research characterizes warfarin receipt among AF patients with MHCs.

We therefore performed a descriptive study of national data to determine: 1) whether AF patients with versus without MHCs differ in eligibility for anticoagulation; and 2) whether, among AF patients eligible for warfarin, warfarin receipt differs for patients with versus without MHC. The study was approved by the Institutional Review Board at Stanford University, Stanford, California.

We created a database containing variables characterizing AF care for all VHA patients with prevalent AF, as of the first day of fiscal year 2004. This database was created through a series of processing steps, quality checks, merges, and variable creation from VHA’s centralized inpatient and outpatient treatment and pharmacy databases, linked to Medicare inpatient and outpatient treatment claims data. Medicare pharmacy records were not available.

 describes cohort construction. We used Veterans Affairs (VA) encounter data to identify all veterans using VHA outpatient care during 2004 who had AF as of the first day of 2004. AF was defined as the presence of at least 1 

International Classification of Diseases, Ninth Revision

) AF diagnosis code 427.31 or 427.32 in 2002, and at least 1 confirmatory AF diagnosis in 2003 in VHA or Medicare inpatient or outpatient encounter records (based on records involving a face-to-face visit with a clinician). We excluded patients institutionalized for the majority of 2004, and those who received some of their 2004 VHA care outside of the continental United States (who thus might have incomplete data capture for processes of care). We also excluded patients whose MHC status could not be determined (see description of MHC variable creation below) to create “cohort 1.”

To examine warfarin receipt among patients with and without MHCs, we created “cohort 2,” which was the subset of cohort 1 patients who were apparently eligible for warfarin based on the presence of diagnosed stroke risk factors and absence of diagnosed contraindications to anticoagulation. 

Mental Health Conditions (independent variables)

Starting with the Agency for Health Research and Quality’s Clinical Classifications Software (CCS),

 we conducted an expert panel process to make modifications to the 

 codes uniquely to 5 common specific MHCs (depressive disorders, posttraumatic stress disorder [PTSD], other anxiety disorders, psychotic disorders, and alcohol use disorders) and “other” psychiatric disorders. A patient was considered to be “MHC Yes” if he/she had at least 1 instance of an 

 code falling into an MHC condition category during 2002-2003, plus at least 1 confirmatory 

 code in 2004 (ie, during the period in which warfarin receipt was assessed), in a VHA or Medicare record associated with an outpatient face-to-face visit with a clinician or an inpatient record. A patient was considered to be “MHC No” if he/she had no instance of an MHC 

 code in the entire interval of 2002 to 2004. Patients for whom MHC status was uncertain (ie, those with an MHC diagnosis at baseline [2002-2003] or during the study period [2004], but not both) were excluded, allowing for direct comparisons between the 2 distinct groups of “MHC Yes” and “MHC No.”

Separate dichotomous indicator variables were created for each of the 5 specific MHCs and for “other” MHCs for each patient. A patient could have more than 1 diagnosed MHC.

Eligibility for Anticoagulation (independent variable)

We examined patient characteristics suggesting greater eligibility for anticoagulation (ie, risk factors for stroke), and those suggesting less eligibility for anticoagulation (ie, risk factors for hemorrhage). To characterize stroke risk as of the start of the study period (ie, prior to 2004), we calculated the CHADS2 score, which gives 2 point each for being 75 years or older, having congestive heart failure, hypertension, or diabetes; and 2 points for prior stroke or transient ischemic attack.

 Based on available national guidelines during the study period, CHADS2 was recommended for stroke risk stratification, and warfarin was recommended for patients with AF who had a CHADS2 score of 2 or higher. Warfarin was recommended, but not required, in patients with a CHADS2 score of 1.

 Contraindications to anticoagulation included a history of intracranial hemorrhage, history of other hemorrhage, dementia, cirrhosis, seizure disorder, and end-stage renal disease.

 These conditions, and conditions included in the CHADS2 score, were identified based on 

 codes in 2001 to 2003 VA and Medicare encounter data.

VHA Decision Support System (DSS) pharmacy records contain a record of dispensed medications from all pharmacy orders entered by VHA clinicians. From the DSS data, we identified every outpatient warfarin prescription issued in the VHA in 2004 to patients in our warfarin-eligible analytic cohort (cohort 2). A patient was considered to have received warfarin if he/she received at least 2 VHA outpatient warfarin prescriptions in 2004, with at least 30 days between the start of one prescription and the start of another prescription. Although our focus was on warfarin receipt—rather than warfarin persistency or adherence, which would be better examined in a cohort of new warfarin users&mdash;we required 2 warfarin prescriptions to confirm that the patient was actively prescribed warfarin through the VHA, and that, for example, he/she had not merely been issued a single warfarin prescription as part of an emergency department visit. Prescriptions issued on the last day of an inpatient stay were counted as outpatient prescriptions because they represent discharge medications. Warfarin prescriptions issued by Medicare providers were not available to us.

Patient age, gender, race/ethnicity, and physical comorbidity index were derived from VHA and Medicare patient treatment databases. The Selim physical comorbidity index is a count of common nonpsychiatric medical conditions developed for VHA outpatient case-mix adjustment.

In cohort 1 (AF patients, n = 125,670) and then in cohort 2 (AF patients eligible for warfarin, n = 87,248), we first compared those with versus without an MHC on sociodemographic characteristics, health status, and eligibility for anticoagulation (stroke and hemorrhage risk factors), using χ

 tests for continuous variables. Next, in cohort 2 we descriptively examined the proportion of patients who received warfarin, first by MHC status, and then in the subgroups with the 5 most common MHCs or with “other” MHCs. Finally, we performed 4 logistic regression analyses on cohort 2 to calculate unadjusted and adjusted odds of warfarin receipt (overall and by CHADS2 score) for patients with versus without diagnosed MHCs (Model 1 unadjusted and adjusted), and for patients with versus without specific MHCs (Model 2 unadjusted and adjusted). Because a patient could have more than 1 specific MHC (for example, a patient might have a depressive disorder, PTSD, and alcohol use disorder), Model 2 included a binary indicator variable for each of the 5 most common MHCs, and a binary indicator variable for other MHCs in aggregate, in a single logistic regression model; each estimate represents the conditional effect of each specific MHC, controlling for the presence of the other specific MHCs. Multivariate logistic regression models controlled for age as a continuous variable, gender, and physical comorbidities; overall models additionally controlled for stroke risk (CHADS2). 

After applying cohort selection and exclusion criteria, we identified 125,670 AF patients (cohort 1), and among them, 87,248 (69%) were apparently eligible for warfarin (cohort 2). Among cohort 1 patients, 22,247 (18%) had an MHC. Compared with patients without an MHC, patients with an MHC were younger (18% versus 8%, respectively, were under age 65) and slightly less likely to be male or Caucasian (

). Among cohort 2 patients, 12,190 (14%) had any MHC—the most common specific MHCs were depressive disorders (7% of cohort 2 patients), PTSD (2%), other anxiety disorders (3%), psychotic disorders (1%), and alcohol use disorders (1%).

Eligibility for Anticoagulation Among All AF Patients

In cohort 1, indications for anticoagulation based on CHADS2 stroke risk index were greater in patients with an MHC. Nearly all AF patients (97% in both MHC and no MHC groups) were warfarin-eligible, based on a CHADS2 score of 1 or more (Table 1). A slightly greater proportion of patients with MHC than without MHC had high levels of stroke risk (ie, CHADS2 score ≥2). Regarding specific stroke risk factors in patients with versus without an MHC: respectively, 54% versus 48% had congestive heart failure; 91% versus 90% had hypertension; 41% versus 40% had diabetes; 39% versus 31%, had prior stroke or transient ischemic attack (Table 1).

However, contraindications to anticoagulation (ie, risk factors for hemorrhage) were also more common in patients with MHC than in those without MHC: respectively, 1.4% versus 0.6% had a history of intracranial hemorrhage; 27% versus 20% had a history of other hemorrhage; 17% versus 3% had a history of dementia; 2% versus 1% had a history of cirrhosis; and 0.6% versus 0.5% had a history of end-stage renal disease. (

Receipt of Warfarin Among AF Patients Eligible for Warfarin

Among warfarin-eligible patients with AF, 50.6% of those with MHC and 52.4% of those with no MHC received warfarin (

 additionally shows the rate of receipt of warfarin for patients with each of the 5 most common specific MHCs or with any other MHC. Although we did not specify a minimum prescription days’ supply, 99% of patients who received warfarin had at least 60 days of warfarin coverage in 2004. 

Warfarin-eligible patients with an MHC were less likely to receive warfarin than those without an MHC (Model 1: unadjusted OR, 0.93, 95% CI, 0.90-0.97; adjusted OR, 0.90, 95% CI, 0.87-0.94). In the adjusted model, differences persisted in CHADS2-stratified analyses (

We also analyzed warfarin receipt for patients with specific MHCs, which is important because clinical presentation differs by MHC. Patients with some types of MHC might have particular difficulty understanding or adhering to a complex medication regimen, or their provider's perception might be that they would. The Model 2 estimates presented in Table 2 represent the marginal differences in warfarin receipt associated with any one specific MHC, controlling for the presence of the other specific MHCs.

The association between MHC and warfarin receipt varied substantially by specific MHC type (Table 2, Model 2). After adjustment, patients with anxiety disorders (OR, 0.86; 95% CI, 0.80-0.93), psychotic disorders (OR, 0.77, 95% CI 0.65-0.90), and alcohol use disorders (OR, 0.62; 95% CI, 0.54-0.72) were less likely to have received warfarin than were patients with absence of the specific condition. In CHADS2-stratified analyses, adjusted odds of warfarin receipt was lower for patients with alcohol use disorders at a CHADS2 score of 2-3 (OR, 0.53; 95% CI, 0.43-0.64) than at a CHADS2 score of 1 (OR, 0.70; 95% CI, 0.55-0.90).

Main findings (adjusted ORs for Model 1 and Model 2) remained robust across a series of sensitivity analyses: 1) we included the MHC-indeterminate patients as a third level of exposure; 2) we removed patients who died in 2004 or who developed a new contraindication to warfarin in 2004; and 3) we added clinic site (as a fixed effect) to the models.

Our study provides clinically relevant insights into the connection between mental illness and stroke prevention care in VHA. We found that AF patients with an MHC have an excess burden of both stroke risk factors and contraindications to anticoagulation. Among those apparently eligible for warfarin, patients with an MHC were less likely to receive warfarin, although this association varied by specific MHC type.

Our findings suggest that special clinical decision-making challenges may arise in the management of AF patients with an MHC. On the one hand, patients with an MHC have excess burden of stroke risk factors: therefore, many of them have strong indications for anticoagulation for stroke prevention. On the other hand, AF patients with MHC also have an excess burden of risk factors for hemorrhage, and thus, may have disproportionate rates of contraindications to anticoagulation. In such a setting, assessment of risk versus benefit of warfarin receipt must be individualized, and shared decision-making approaches

Our findings in a national sample of more than 12,000 warfarin-eligible patients with AF with an MHC are consistent with, and expand upon, a prior single-site chart-review analysis, which found lower odds of warfarin receipt in 66 warfarin-eligible AF patients with an MHC.

  The lower frequency of warfarin receipt seen for patients with an MHC in our study is also in line with studies showing that patients with an MHC may be less likely to receive pharmacotherapy for various medical conditions,

 although such MHC-related disparities are not universally seen.

We also found that the odds of warfarin receipt in eligible patients with AF varied by MHC type. This finding is not surprising, given the heterogeneity of the clinical presentation of different MHCs. Patients with alcohol use disorders were less likely to receive warfarin—perhaps related to concerns about drug-drug interactions between warfarin and alcohol, concerns about falls risk, or concerns about adherence

; clinicians’ expectations about whether a patient will adhere to treatment can directly influence their treatment decisions.

 Interestingly, this effect was even stronger at a higher level of stroke risk (CHADS2 score of 2-3) than at a lower level of stroke risk (CHADS2 score of 1). Although our study did not directly examine MHC severity, patients with psychotic disorders also were less likely to receive warfarin, which could reflect clinical impressions that their thought processes were too disorganized to support faithful use of a complicated medication regimen, and the fact that medication nonadherence in patients with psychotic disorders is known to be high.

In contrast, patients with depression and PTSD were no less likely than patients without these conditions to receive warfarin, suggesting that clinicians respond to different mental health conditions differently. Future studies using qualitative methods or examining potential mediators at a more granular level may provide greater insight into drivers of medical decision making, especially now that new alternatives to warfarin (eg, dabigatran, rivaroxaban, apixaban) with decreased intracranial hemorrhage risk and less onerous laboratory monitoring requirements are available.

Our results need to be interpreted in the context of several limitations. First, there is risk of misclassification of MHC status. MHCs often go undetected in a primary care setting; therefore, some patients in our “No MHC” group may have had an undetected MHC. If anything, this would be expected to bias our findings toward the null, given that patients with an undetected MHC probably would be less likely to receive warfarin. Furthermore, our decision to exclude patients with an indeterminate MHC status (MHC diagnoses present at baseline or during the study period, but not both) should have helped to minimize misclassification. There is also the potential for uncaptured processes of care (warfarin receipt). This study was conducted before Medicare Part D (prescription) data were available; thus, any prescriptions issued through Medicare were unavailable. Since VHA patients with an MHC tend to be less likely to receive care outside the VHA, it is possible that rates of warfarin receipt in the “No MHC” group were disproportionately underestimated, again tending to bias our warfarin-receipt findings toward the null.

Additionally, although warfarin remains the mainstay of AF stroke prevention treatment in VHA, AF stroke prevention care has evolved since 2004 when this study was conducted. However, the focus of this study was not on absolute rates of anticoagulation treatment, but rather on MHC-related differences in anticoagulation treatment; such effects would not be expected to have changed substantially over this time period. Furthermore, a recent longitudinal study demonstrated that the rate of warfarin receipt in VA patients with AF remained fairly stable from 2004 (the year of the results presented here) through 2008 (the last year of data examined in that study).

 Finally, this study examined warfarin receipt as a process of care, and thus, we did not examine patient outcomes. Finally, results apply to atrial fibrillation patients who use VHA services, who are primarily men, and cannot necessarily be generalized to women, veterans who do not use VHA, or to nonveterans.

In a large national cohort of patients with AF, those with MHC were more likely than those without MHC to have contraindications to anticoagulation, suggesting that cautious assessment of bleeding risk factors prior to initiation of warfarin may be particularly salient for patients with MHC. Several recent studies suggest that outcomes of anticoagulation therapy in the presence of MHC may be somewhat worse, but that presence of MHC does not consistently preclude safe treatment with warfarin.

 In that context, our finding that warfarin-eligible patients with AF with an MHC were less likely than other AF patients to receive warfarin, raises concerns about potential missed opportunities for more intensive therapy among some patients with an MHC. This supports the recommendation that patients and their healthcare providers weigh the pros and cons of anticoagulation with warfarin versus other management approaches so as to maximize delivery of equitable, high-quality, patient-centered care.

The authors gratefully acknowledge Vu Nguyen and Stephanie Le for their contributions to earlier phases of this project. Support for VA/CMS data was provided by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development, VA Information Resource Center (Project Numbers SDR 02-237 and 98-004).

 VA HSR&D Center for Innovation to Implementation (SKS, MPT, CSP, RHM, PH, VYC, SAF, CTT, SMF), and Health Economics Resource Center (SKS, CSP), and Women’s Health Section, Medical Service (SMF), VA Palo Alto Health Care System, Palo Alto, CA; Stanford University School of Medicine (MPT, CSP, RHM, PH, SMF), Stanford, CA; Department of Veterans Affairs Center for Healthcare Organization and Implementation Research, Edith Nourse Rogers Memorial Veterans Hospital (DB), Bedford, MA; Department of Health Policy and Management, Boston University School of Public Health (DB), Boston, MA; Division of Research, Kaiser Permanente of Northern California (ASG), Oakland, CA; Departments of Epidemiology, Biostatistics, and Medicine, University of California at San Francisco (ASG), San Francisco, CA.

 This work was supported by VA HSR&D IIR 04-248, CDA09-027, and RCS 90-001. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the US government.

 Dr Go has previously received a grant from the FDA. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (SKS, SMF, SAF, MPT, PH, DB, CSP, RHM); acquisition of data (SMF, SAF, CSP); analysis and interpretation of data (SKS, SAF, SMF, MPT, PH, DB, ASG, CSP, RHM); drafting of the manuscript (SKS, DB); critical revision of the manuscript for important intellectual content (SKS, SMF, MPT, PH, DB, VYC, ASG, CSP, RHM); statistical analysis (SKS, SAF, VYC, CSP); obtaining funding (SMF, MPT, CSP); administrative, technical, or logistic support (VYC, RHM, CTT); and supervision (MPT, SMF).

1. Miyasaka Y, Barnes ME, Gersh BJ, et al. Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. 

2. Feinberg WM, Blackshear JL, Laupacis A, Kronmal R, Hart RG. Prevalence, age distribution, and gender of patients with atrial fibrillation: analysis and implications. 

Articles

Atrial fibrillation patients with mental health conditions are less likely to be eligible for warfarin receipt, and those who are eligible receive warfarin at lower rates.

Anticoagulation in Atrial Fibrillation: Impact of Mental Illness

 To determine whether atrial fibrillation (AF) patients with mental health conditions (MHCs) were less likely than AF patients without MHCs to be prescribed warfarin and, if receiving warfarin, to maintain an International Normalized Ratio (INR) within the therapeutic range.

 Detailed chart review of AF patients using a Veterans Health Administration (VHA) facility in 2003.

 For a random sample of 296 AF patients, records identified clinician-diagnosed MHCs (independent variable) and AF-related care in 2003 (dependent variables), receipt of warfarin, INR values below/above key thresholds, and time spent within the therapeutic range (2.0-3.0) or highly out of range. Differences between the MHC and comparison groups were examined using X

 tests and logistic regression controlling for age and comorbidity.

 Among warfarin-eligible AF patients (n = 246), 48.5% of those with MHCs versus 28.9% of those without MHCs were 

 = .004). Among those receiving warfarin and monitored in VHA, highly supratherapeutic INRs were more common in the MHC group; for example, 27.3% versus 1.6% had any INR >5.0 (

 <.001). Differences persisted after adjusting for age and comorbidity.

 MHC patients with AF were less likely than those without MHC to have adequate management of their AF care. Interventions directed at AF patients with MHC may help to optimize their outcomes.

	Mental illness decreased likelihood of warfarin receipt among patients without recorded contraindications, but clinicians did not list mental illness as a rationale for withholding warfarin.

		Among warfarin-treated patients, those with mental illness were more likely to have some International Normalized Ratio (INR) values substantially above the therapeutic range, but some with mental illness consistently maintained therapeutic INRs.

		Identifying subgroups with mental illness who can/cannot safely take warfarin would inform interventions and guidelines.

The Veterans Health Administration (VHA) National Mental Health Strategic Plan has recently called attention to optimizing treatment of coexisting medical conditions in patients with mental illness. Research is beginning to amass suggesting that patients with mental health conditions (MHCs), representing nearly one third of Americans,

 may receive less intensive care for a range of conditions from diabetes to coronary artery disease to chronic pain.

 However, the influence of mental illness on the management of atrial fibrillation (AF) is not yet well understood. This is important because AF is highly prevalent, especially among older adults, and is associated with increased stroke risk.

 An oral anticoagulant, warfarin, reduces AF-related stroke risk from approximately 5% per year to 1% to 2% per year,

 but also has downsides: it increases major bleeding risk, it typically involves a complex and rigorous dosing schedule, and it requires frequent blood monitoring of coagulation status (International Normalized Ratio [INR]). Given warfarin’s narrow therapeutic window, clinicians may have concerns about whether patients with MHC can safely take warfarin as prescribed and follow through with necessary monitoring tests.

Unfortunately, major AF practice guidelines do not currently address whether mental illness should influence anticoagulant therapy decision making.

 This is concerning, since there are potential adverse consequences of prescribing warfarin for a patient who cannot use it safely: excess major bleeding if overdosed or thromboembolic events if underdosed. Likewise, there are potential adverse consequences of not prescribing warfarin for a patient who could take it safely, such as unnecessarily increased stroke risk.

Therefore, a detailed medical chart review methodology was used at 1 VHA facility to determine:

• Whether VHA AF patients with MHCs were less likely than those without MHCs to receive warfarin, even in the absence of contraindications.

• Whether, among those treated with warfarin and monitored in VHA, those with MHCs were more likely than those without MHCs to have INRs falling outside the standard therapeutic range.

Patients were drawn from a national database of AF/atrial flutter cases developed for another ongoing study, where AF was identified by the presence of

 International Classification of Diseases, Ninth Revision (ICD-9)

 code 427.3x in fiscal year 2001-2002 VHA or Medicare outpatient or inpatient administrative data. Patients with AF/atrial flutter were included regardless of whether or not the arrhythmia was thought to be paroxysmal, and regardless of duration of the diagnosis (except for transient AF; see below). From the 2490 members of this database who received more than 50% of their VHA outpatient care in both 2002 and 2003 at a suburban, West Coast VHA facility, a random sample of 450 was selected for detailed chart review of electronic medical records. After refining the standardized abstraction protocol on 50 cases not included in the study, a trained senior medical student identified information on variables of interest and recorded them on a standardized abstraction form. Ongoing quality control occurred at weekly meetings of the research team to ensure consistency in coding.

 Administrative data supplemented chart data for some variables.

Excluded were 6 patients with inaccurate chart record identifiers, 33 patients who died prior to the end of 2003, 17 patients with transient AF (occurring only during an acute hospitalization, or within 30 days of cardiac surgery or cardiac catheterization), and 98 with no VHA chart evidence of AF based on data from available electrocardiograms, patient problem list, or outpatient provider diagnoses from progress notes. (Among this latter subgroup, 75 were identified as having AF by Medicare

 codes only; likely their AF was managed by a non-VHA provider, and thus the diagnosis did not appear in VHA files.) A total of 296 AF cases (full cohort) were thus included in the study. Of these, 51 patients had evidence of 1 or more potential contraindications to warfarin: history of nonadherence, hemorrhage, falls, dementia, advanced liver disease, or malignancy.

 Among the remaining 246 patients, all of whom were apparently eligible to receive warfarin (warfarin-eligible subcohort), 161 (65.4%) received warfarin within or outside of VHA. Among them, 84 received their INR monitoring in the VHA Anticoagulation Clinic and had reliable INR data available, such that warfarin effect could be assessed; they constituted the warfarin-treated-VHA-monitored subcohort. This study was approved by the Institutional Review Board at Stanford University, Stanford, California.

The primary independent variable was MHC. MHC was defined as (1) 1 or more provider diagnosis of substance use disorder, depressive disorder, anxiety disorder, psychotic disorder, dysfunctional personality disorder, or bipolar disorder (in progress notes or problem lists) during 2001-2003; or (2) receipt of psychopharmacologic agents including antidepressants, antipsychotics, or mood stabilizers (based on the VHA medication log or based on a progress note referring to a non-VHA prescription) during 2001-2003.

Dependent variables included (1) receipt of warfarin in 2003 (yes/no), based upon the chart medication log or a progress note referring to a warfarin prescription by a non-VHA provider; (2) presence of at least 1 subtherapeutic INR in 2003 (<1.5, <2.0) based on laboratory result file data; and (3) presence of at least 1 supratherapeutic INR in 2003 (>3.0, >3.5, >4.0, >4.5, >5.0). A standard procedure was used to calculate time in therapeutic range (ie, the percentage of days in 2003 during which the INR fell within the 2.0 to 3.0 range, linearly interpolating INR values for days on which an actual value was not available).

 Time highly out of therapeutic range was calculated as percentage of days in 2003 during which the INR was less than 1.5 or greater than 4.0.

Other variables included known stroke risk factors for AF patients

: age 75 years or older, congestive heart failure, hypertension, diabetes, and prior nonhemorrhagic stroke or transient ischemic attack, based on 2001-2002 VHA outpatient or inpatient administrative data. Comorbidity was identified from the medical component of the Comorbidity Index (a count of medical conditions common in veterans and used for ambulatory care case-mix adjustment), using VHA administrative data.

 A count of primary care visits in 2003 came from clinic type codes in VHA administrative files. Patients were considered to have received only VHA care if they lacked any forms of outside insurance, based on the medical record, and if there was no mention of receipt of outside medical care or medications, based on primary care and cardiology clinic progress notes.

Among the full cohort, the warfarin-eligible subcohort, and the warfarin-treated-VHA-monitored subcohort, the following characteristics were examined for patients with and without MHC: demographics, stroke risk factors, Comorbidity Index, and utilization.

Among warfarin-eligible patients, the next analyses compared the proportions of those with and without MHC who were receiving warfarin, using a X

 test. In this group, multivariable logistic regression was performed to examine receipt of warfarin as a function of the presence of MHC, first unadjusted, then controlling for age, and then controlling for age and Comorbidity Index.

Next, among warfarin-treated-VHA-monitored patients, proportions were sequentially compared for those with and without MHC who had at least 1 low INR (<2.0, <1.5) and for those with and without MHC who had at least 1 high INR (>3.0, >3.5, >4.0, >4.5, >5.0), using a X

 test. Fisher’s exact test was used to calculate 

 values in instances of small cell size. Then logistic regression was used to examine high or low INR as a function of MHC, first unadjusted, then controlling for age, and then controlling for age and Comorbidity Index.

Among warfarin-treated-VHA-monitored patients, patients with and without MHC were compared with respect to mean time in therapeutic range and to mean time highly out of therapeutic range, using 

Sensitivity analyses tested the stability of findings. Because of a concern that some VHA patients may receive their AF care from non-VHA providers, sensitivity analysis 1 examined the effect of restricting analyses to patients who used VHA as their exclusive source of care (ie, for whom all AF care could be reliably captured). Because of the possibility that some patients with a single recording of an AF diagnosis could have been miscoded as being an AF case, or could have had transient AF (despite efforts to exclude transient AF cases), sensitivity analysis 2 examined the effect of restricting analyses to patients who had at least 2 instances of an AF diagnosis from electrocardiograms or primary care/cardiology clinic notes.

Among the full cohort (n = 296), 29.7% had at least 1 diagnosed MHC: substance abuse 7.8%, depression 18.9%, anxiety 15.2%, psychosis 3.0%, dysfunctional personality 1.0%, and mania 0.7%. Patients with MHC tended to be younger than those without MHC, yet tended to have greater medical comorbidity, including stroke risk factors (

). Among the 17.2% of patients with a listed contraindication to warfarin, the most common contraindications cited were dementia (7.8%), history of falls (2.7%), and history of hemorrhage (3.7%). Of note, no records mentioned MHC as a contraindication to warfarin.

In the warfarin-eligible subcohort, 48.5% of those with MHC versus 28.9% of those without MHC were not treated with warfarin (

); even after adjustment for age and comorbidity, those with MHC were significantly less likely to receive warfarin (

). The frequency of not being treated with warfarin varied by specific MHC, as follows: substance abuse 66.7%, depression 50.0%, anxiety 47.4%, psychosis 25.0%, and personality disorder 50.0%. Note that among the warfarin-eligible subcohort (n = 246), 19.7% of patients with MHC and 15.6% of patients without MHC had a CHADS2 (congestive heart failure, hypertension, age 75 years or older, diabetes mellitus, previous stroke or transient ischemic attack) score below 2, a level at which alternatives to warfarin may be appropriate.

Among the warfarin-treated-VHA-monitored subcohort (n = 84), patients with MHC were more likely than patients without MHC to have high INR values, both before and after adjustment for age. This would put them at increased risk for hemorrhage. There was a trend toward the same effect after additionally adjusting for Comorbidity Index. Differences between patients with versus without MHC were even more pronounced at higher INR thresholds. There was also a nonsignificant trend toward higher rates of subtherapeutic INR (<1.5) among patients with MHC.

Among the warfarin-treated-VHA-monitored subcohort, mean time spent in the 2.0 to 3.0 therapeutic range was lower for the MHC group than for the group without MHC: 56.8 (standard deviation [SD] 16.9) versus 65.9 (SD 18.2) percent of days (

 = .04). Mean time spent highly out of range was higher for the MHC group than for the group without MHC: 9.0 (SD 9.8) versus 3.4 (SD 7.1) percent of days (

In sensitivity analysis 1 (restricting to patients who received all care in VHA), the magnitude and direction of unadjusted differences were similar to those in the main analyses. The same was true in sensitivity analysis 2, restricting to patients with 2 or more AF diagnoses (

Warfarin-eligible AF patients with MHC were substantially less likely than those without MHC to receive warfarin, even after controlling for age and comorbidity. This is of potential concern, since stroke risk factors were greater in AF patients with MHC than in those without MHC. When prescribed warfarin, patients with MHC were substantially more likely than those without MHC to have INRs in the highly supratherapeutic range, with 27% having at least 1 INR value above 5.0. There was also a trend for them to have greater risk of having INRs in the highly subtherapeutic range. As a result, these patients could have increased risk of either thromboembolism (in the setting of low INR) or hemorrhage (in the setting of high INR).

The differences in warfarin receipt observed here are consistent with emerging literature documenting lower-intensity care provided to patients with MHC for a range of medical conditions,

 and could reflect several potential mechanisms. Patient preferences could be important,

but have not been well characterized with respect to MHC. Mental illness can influence physicians’ perceptions of patients,

 and physician expectations of patients—founded or not&mdash;may affect management.

 Alternatively, physicians’ threshold for concluding that falls, dementia, or nonadherence (known to affect prescribing behavior

) should be counted as a warfarin contraindication may be lower for patients with MHCs. Mental illness may also affect communication between physician and patient, or may cause patients to be labeled as “difficult.”

 While sample sizes were small for specific MHCs, this study did find preliminary evidence that warfarin receipt varied by specific MHC, and that rates were particularly low for patients with substance use disorders, suggesting that risky behaviors could factor into clinical decision making. Other possibilities are that providers may simply assume that patients will be nonadherent,

 or may worry about drug interactions with psychiatric medications, some of which can increase bleeding risk.

 Patient and provider-level contributors to this effect merit more inquiry.

While these findings reflect care provided at a single site and thus will require replication, they raise several issues of serious clinical relevance. Findings shed light on actual physician behavior: in the absence of explicit national guidelines to shape their decisions, clinicians are prescribing warfarin substantially less often to AF patients with MHC than to AF patients without MHC. This suggests that the presence of MHC may be influencing their decisions, even though no physicians in this study listed the presence of MHC as the rationale for withholding warfarin. But is this concern warranted? In one sense, the answer is yes: patients with MHC were more likely to have supratherapeutic INR values, which may explain why another study (which focused on warfarin-treated Medicaid patients) found higher rates of bleeds in those with MHC than in those without MHC.

 In another sense, the answer is no: nearly 20% of patients with MHC were able to maintain an INR level below 3.0 throughout the full year of surveillance. Thus, for at least a subset of AF patients with MHC, it may be possible to administer warfarin safely. Perhaps, for example, different types of MHC have different effects upon patients’ ability to take warfarin safely. More work with larger samples is needed to help clinicians distinguish which patients with MHC are likely to have adverse effects, and which are likely to do well on warfarin.

Some have suggested that perhaps newer agents like dabigatran that do not require monitoring

 may be better options for patients with MHC. This may be true: perhaps the simpler dosing regimen that dabigatran provides would facilitate adherence, which can be an issue for patients with MHC.

 The reverse may also be true: perhaps patients with MHC do well in anticoagulation clinics that provide close, mandatory follow-up and that allow clinicians to monitor them. In contrast, medications like dabigatran, which lack an easy laboratory test for monitoring and currently lack a reversal agent for use if taken in overdose or with bleeding, may not offer this advantage. Further research will be needed to understand the pros and cons of dabigatran in patients with MHC.

This study was limited by possible underascertainment of medical care received (ie, non-VHA care), although the stability of findings in sensitivity analyses limited to patients who use VHA as their exclusive source of care was reassuring. This study did not distinguish between AF and atrial flutter, nor between continuous and paroxysmal AF. Also, it is unknown how many patients had ablation or surgical procedures for their AF; however, at the time, within the facility studied, ablation and appendage removal were not being offered and surgical procedures were very rare and limited. Contraindications to anticoagulation may also be underdocumented in medical records,

 although from a medicolegal standpoint, clinicians have an incentive to document why they are withholding warfarin. Results may not be generalizable to women or other geographic or healthcare settings.

This study also has several strengths. The mixed-modality approach made it possible to take advantage of information available in administrative data, such as all INR values across an entire year of surveillance. Supplementing this source with medical record review data facilitated identification of factors difficult to elicit from administrative records, such as clinicians’ decision-making logic (eg, the reasons they did not start warfarin) and information about receipt of care outside of VHA (eg, evidence of receipt of care in general and receipt of warfarin in particular from non-VHA sources).

Atrial fibrillation patients with mental illness are less likely to receive warfarin anticoagulation; those who do receive warfarin have excess risk of over-anticoagulation.

Mental Illness and Warfarin Use in Atrial Fibrillation

To examine whether veterans who received chronic opioid therapy had worse diabetes performance measures than patients who did not receive opioids.

We identified all patients with diabetes mellitus receiving care in US Department of Veterans Affairs facilities during 2004. Cases received at least 6 prescriptions for chronic opioids during 2004, while controls were randomly selected from among patients with diabetes who received no opioids. We compared process measures (glycosylated hemoglobin and low-density lipoprotein cholesterol levels tested and an eye examination performed) and outcome measures (glycosylated hemoglobin level ≤9.0% and low-density lipoprotein cholesterol level ≤130 mg/dL) between groups.

Cases (n = 47,756) had slightly worse diabetes performance measures than controls (n = 220,912) after adjustment for covariates. For example, 86.4% of cases and 89.0% of controls had a glycosylated hemoglobin test during fiscal year 2004 (adjusted odds ratio, 0.69; 

 <.001). Among cases, receipt of higher-dose opioids was associated with additional decrement in diabetes performance measures, with a dose-response relationship.

Chronic opioid therapy among patients within the Veterans Affairs system is associated with slightly worse diabetes performance measures compared with patients who do not receive opioids. However, patients receiving higher dosages of opioids had additional decrements in diabetes performance measures; these patients may be appropriate targets for interventions to improve their care for pain and diabetes.

Within the Veterans Affairs system, patients who received opioids for chronic pain had slightly worse diabetes performance measures than patients who did not receive opioids.

Comparisons included measurement of glycemic and lipemic control, achievement of moderate or better glycemic and lipemic control, and a yearly eye examination.

Among the group receiving opioids, the receipt of higher daily opioid doses predicted worse results for all 5 diabetes performance measures. A dose-response relationship was observed, lending additional credibility to this finding.

Understanding the role of clinical complexity as a determinant of quality of care is a major research goal.

 the effect of clinical complexity on quality of care has varied depending on the diagnoses, the clinician and patient, and the clinical environment. Seeking to harmonize these mixed results into a unifying theory, Piette and Kerr

 proposed that symptomatic conditions may have a greater effect on quality of care than asymptomatic conditions and that conditions with dissimilar management goals (“discordant conditions”) may have a greater effect than those with similar goals (“concordant conditions”).

By this reasoning, chronic pain could have a considerable adverse effect on quality of care for unrelated conditions. Pain is highly symptomatic, and pain management is discordant with the management of other conditions.

 While the use of opioids to treat chronic noncancer pain is increasingly accepted,

 opioid therapy may present additional challenges due to the potential for abuse, dependence, and diversion and due to conflicts over appropriate dosages.

 However, opioid therapy could also facilitate care for unrelated conditions. Patients receiving opioids may visit the clinic more often, allowing more opportunities for medical management.

 Adequate treatment of pain may improve the patient’s functional status and quality of life,

 allowing greater focus on self-care activities.

Diabetes mellitus, a common, costly, and highly morbid condition,

 is a good condition in which to examine this possibility. Adequate management of diabetes requires collaboration among clinicians and the patient within a system of care,

 and explicit guidelines and diabetes performance targets exist with which to examine the adequacy of diabetes care.

 showed that among patients with diabetes, chronic pain is a barrier to the completion of self-care activities such as taking medications, exercising, and pursuing a prudent diet. However, the effect of pain on process and outcome measures of diabetes care is unknown. In addition, no study has specifically examined the effect of opioid therapy on the quality of care for unrelated chronic conditions, but there is reason to believe that opioid therapy may impart more complexity and challenge than pain alone.

To clarify whether the net effect of opioid therapy is to promote or impede care for diabetes, we analyzed a large database of patients with diabetes in the US Department of Veterans Affairs (VA) and identified those receiving chronic opioid therapy. We compared patients receiving chronic opioids versus patients not receiving opioids regarding selected diabetes performance measures. We hypothesized that the distractions and concerns associated with chronic opioid therapy, as well as perhaps other characteristics of patients with chronic pain, would be reflected in worse diabetes performance measures. We also hypothesized that among those receiving opioids there would be a dose-response relationship between higher opioid dosages and decrements in diabetes performance measures.

We identified subjects from the Diabetes Epidemiology Cohort, which comprises all patients with diabetes seen in the VA.

 The Diabetes Epidemiology Cohort links administrative, laboratory, and pharmacy data from the VA with Medicare claims, providing a rich data set for analysis.

 We first looked at all veterans treated for diabetes during fiscal year (FY) 2004 whose diabetes had been diagnosed before the start of FY 2002. Based on earlier work,

 we defined patients as having diabetes if they had at least 2 

International Classification of Diseases, Ninth Revision, Clinical Modification

) codes for diabetes or any prescriptions for antiglycemic medications within a 2-year period.

 diagnosis of malignant neoplasm (other than basal or squamous carcinoma of the skin) within 2 years of study inception. The management of cancer-related pain is qualitatively different; moreover, diabetes performance measures may not apply to patients with active malignant neoplasms. We also excluded all patients receiving methadone hydrochloride or buprenorphine hydrochloride—naloxone hydrochloride for treatment of opioid dependence. Finally, we excluded patients who had fewer than 2 VA primary care visits in FY 2004, as a large portion of their diabetes care may not appear in our database. 

This study was approved by the Institutional Review Board of Bedford VA Medical Center.

Independent Variable: Chronic Opioid Therapy

Our independent variable was the prescription of chronic opioids. We considered the following “major” opioids: codeine, fentanyl citrate, hydrocodone, hydromorphone hydrochloride, methadone, morphine sulfate, and oxycodone; all are Schedule II or III controlled substances according to the US Drug Enforcement Administration.

 Any formulation suitable for outpatient administration was considered, including tablets, patches, elixirs, and sprinkles. We also included formulations that combine opioids with other drugs such as acetaminophen. Buprenorphine, butorphanol, nalbuphine hydrochloride, pentazocine, and propoxyphene, which are less potent, were considered “minor” opioids.

Patients who received at least 6 prescriptions for major opioids during FY 2004, with or without additional minor opioids, constituted the chronic opioid group (cases). This cutoff of 6 prescriptions was chosen to distinguish treatment for chronic pain from treatment for acute pain and is consistent with previous definitions of chronic pain.

 Patients who received any major or minor opioids during FY 2004 but did not meet criteria for the case group were excluded from the study. We randomly selected controls from among the remaining patients, who had received no opioids during FY 2004, to achieve a control group approximately 4 times as numerous as the case group.

Dependent Variables: Diabetes Performance Measures

Our 3 process measures, which could be completed at any time during FY 2004, were testing of glycosylated hemoglobin (A1C) level, testing of low-density lipoprotein cholesterol (LDL-C) level, and a dilated eye examination. Our 2 outcome measures were at least 1 A1C level of 9.0% or less and at least 1 LDL-C level of 130 mg/dL or less during FY 2004 (to convert A1C level to proportion of total hemoglobin, multiply by 0.01; to convert cholesterol level to millimoles per liter, multiply by 0.0259). If no test results were available among VA data, patients were considered to have levels above these thresholds. These diabetes performance measures are based on VA clinical practice guidelines for diabetes and reflect a minimal standard of care.

 We also examined lower targets for glycemic and lipemic control (ie, A1C level ≤8.0% and LDL-C level ≤100 mg/dL).

Age was divided into the following 4 categories: 54 years or younger, 55 to 64 years, 65 to 74 years, and 75 years or older. Race/ethnicity was categorized into the following 4 groups: white non-Hispanic, black non-Hispanic, all others, and missing. The VA priority status, which characterizes the degree of entitlement to VA care, was defined as follows: poverty, full disability, partial disability, or none of the above.

More or less intensive management of diabetes may be indicated depending on life expectancy and comorbidities.

 We focused on the following complications of diabetes by identifying conditions with at least 1 

 code during FYs 1997 through 2004: cellulitis, gangrene/ulcer, other diabetic infections, congestive heart failure, other heart diseases, cerebrovascular disease, peripheral vascular disease, renal disease, usaffect diabetes care.

 code—based definitions, we identified the following mental health conditions: major depression, bipolar disorder, anxiety disorders, posttraumatic stress disorder, and schizophrenia. We also recorded the number of VA primary care visits; more visits might allow more opportunities to complete diabetes performance measures. We also examined pain diagnoses, dividing them into the following 4 broad categories: neuropathic pain, musculoskeletal pain, chronic headache, and psychogenic pain. Using 

 codes, we categorized patients according to whether or not they had any diagnoses in each category (vs none).

We hypothesized that patients receiving higher daily doses of opioids might be at risk for additional decrements in diabetes performance measures, as the receipt of higher dosages suggests difficulties in pain management and possibly physiologic tolerance and an increased risk of dependence.

 to convert all opioid dosages to oral morphine equivalents. We calculated a mean daily dose of opioid therapy in FY 2004 for each patient in the study and categorized patients into quartiles based on their daily opioid doses.

Finally, we assigned each patient to 1 VA medical center so that we could control for site of care. Our assignment was based on the site the patient visited most often for diabetes care during FY 2004. If 2 sites were visited equally, we selected the site visited closest to the end of the year.

We began our analysis with bivariate comparisons of demographics, comorbidities, and healthcare utilization between cases and controls. Using 

tests, we then performed unadjusted comparisons of the proportions fulfilling each of the 5 diabetes performance measures. We performed adjusted analyses using generalized estimating equations to account for the clustering of outcomes by site of care, while adjusting for other covariates (sex, age, race/ethnicity, VA priority status, pain diagnoses, diabetic complications [including neuropathic pain], mental health and diabetic eye disease. Mental health conditions may also conditions, and the number of VA primary care visits during the study). We did not adjust for eye disease when studying the eye examination process measure.

To investigate the possible effect of missing data on our results, we repeated key analyses among subsets of patients who were likely to have complete data. We restricted process measures to patients 65 years or older, who would presumably use Medicare when not using the VA and thus would have complete data for process measures. We restricted outcome measures to patients who had an A1C or LDL-C test within the VA at least once during the study (ie, those for whom laboratory values were available).

Finally, we added the mean daily opioid dose to our models and examined its ability to risk stratify the cases regarding diabetes performance measures. Our analyses were conducted using SAS version 9.1 (SAS Inc, Cary, NC).

 shows the exclusions that led to our case and control groups. We compared summary statistics between cases and controls (

). Cases were younger (eg, 60.3% were <65 years vs 35.8% of controls). Despite their younger age, they were more likely to have at least 1 diabetic complication (70.9% vs 60.5%) and to have each particular diabetic complication. They were also more than twice as likely to have at least 1 mental health condition (36.8% vs 15.9%) and to have each specific mental health condition. They had more VA primary care visits than controls (eg, 41.1% vs 18.0% had ≥6 primary care visits). Despite he differences in age and VA primary care utilization, the 2 groups used Medicare at similar rates.

Within the Veterans Affairs system, diabetes performance measures were similar in patients who received chronic opioid therapy and in those who did not.

Susan M. Frayne, MD, MPH

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