Effect of Clinical Decision Support on Cardiovascular Risk Among Adults With Bipolar Disorder, Schizoaffective Disorder, or Schizophrenia: A Cluster Randomized Clinical Trial

Comorbid physical health outcomes in patients with bipolar disorder: An umbrella review of systematic reviews and meta-analyses

BACKGROUND

Although several meta-analyses have examined the association between bipolar disorder (BD) and its comorbid health outcomes, this evidence has not been comprehensively assembled.

OBJECTIVE

We aimed to systematically review existing meta-analyses based on multiple physical outcomes and validate the evidence level by examining the existing certainty of evidence.

METHODS

We systematically searched databases, including PubMed/MEDLINE, Embase, Google Scholar, and CINAHL, for articles published up to July 2023. We included meta-analyses of cohort, case-control, and/or cross-sectional studies investigating any comorbid health outcomes in patients with BD. We conducted quality assessments of the included meta-analysis using AMSTAR2. The credibility of findings was categorized into five levels of class and quality of evidence (CE), including convincing, highly suggestive, suggestive, weak, or not significant.

RESULTS

We analyzed 12 meta-analyses, including 145 original articles, covering 14 unique health outcomes with over 60 million participants across 29 countries and five continents. Among 14 health outcomes, BD was significantly associated with eight comorbid health outcomes, including dementia (equivalent odds ratio [eOR], 2.96 [95 % confidence intervals {CI}, 1.69-5.17]; CE=suggestive), Parkinson's disease (3.35 [1.72-6.53]; CE=suggestive), asthma (1.86 [1.42-2.42]; CE=weak), toxoplasmosis (1.69 [1.21-2.37]; CE=weak), hypertension (1.28 [1.02-1.60]; CE=convincing), breast cancer (1.33 [1.15-1.55]; CE=weak), obesity (1.64 [1.30-1.99]; CE=suggestive), and type 2 diabetes mellitus (1.98 [1.55-2.52]; CE=weak).

CONCLUSION

Individuals with BD are predisposed to numerous comorbid physical conditions, though these links are supported by various evidence levels and necessitate further studies. It is imperative that physicians be aware of these potential comorbidities in patients with BD and take proactive measures to manage them.

A CKD Clinical Decision Support System: A Cluster Randomized Clinical Trial in Primary Care Clinics

Rationale & Objective

The study aimed to develop, implement, and evaluate a clinical decision support (CDS) system for chronic kidney disease (CKD) in a primary care setting, with the goal of improving CKD care in adults.

Study Design

This was a cluster randomized trial.

Setting & Participants

A total of 32 Midwestern primary care clinics were randomly assigned to either receive usual care or CKD-CDS intervention. Between April 2019 and March 2020, we enrolled 6,420 patients aged 18-75 years with laboratory-defined glomerular filtration rate categories of CKD Stage G3 and G4, and 1 or more of 6 CKD care gaps: absence of a CKD diagnosis, suboptimal blood pressure or glycated hemoglobin levels, indication for angiotensin-converting enzyme inhibitor or angiotensin receptor blocker but not prescribed, a nonsteroidal anti-inflammatory agent on the active medication list, or indication for a nephrology referral.

Intervention

The CKD-CDS provided personalized suggestions for CKD care improvement opportunities directed to both patients and clinicians at primary care encounters.

Outcomes

We assessed the proportion of patients meeting each of 6 CKD-CDS quality metrics representing care gap resolution after 18 months.

Results

The adjusted proportions of patients meeting quality metrics in CKD-CDS versus usual care were as follows: CKD diagnosis documented (26.6% vs 21.8%; risk ratio [RR], 1.17; 95% CI, 0.91-1.51); angiotensin-converting enzyme inhibitor or angiotensin receptor blocker prescribed (15.9% vs 16.1%; RR, 0.95; 95% CI, 0.76-1.18); blood pressure control (20.4% vs 20.2%; RR, 0.98; 95% CI, 0.84-1.15); glycated hemoglobin level control (21.4% vs 22.1%; RR, 1.00; 95% CI, 0.80-1.24); nonsteroidal anti-inflammatory agent not on the active medication list (51.5% vs 50.4%; RR, 1.03; 95% CI, 0.90-1.17); and referral or visit to a nephrologist (38.7% vs 36.1%; RR, 1.02; 95% CI, 0.79-1.32).

Limitations

We encountered an overall reduction in expected primary care encounters and obstacles to point-of-care CKD-CDS utilization because of the coronavirus disease 2019 pandemic.

Conclusions

The CKD-CDS intervention did not lead to a significant improvement in CKD quality metrics. The challenges to CDS use during the coronavirus disease 2019 pandemic likely influenced these results.

Funding

National Institute of Diabetes and Digestive and Kidney Diseases (R18DK118463).

Trial Registration

clinicaltrials.gov Identifier: NCT03890588.

Psychotropic Drug-Related Weight Gain and Its Treatment

Psychotropic drug-related weight gain (PDWG) is a common occurrence and is highly associated with non-initiation, discontinuation, and dissatisfaction with psychiatric drugs. Moreover, PDWG intersects with the elevated risk for obesity and associated morbidity that has been amply reported in the psychiatric population. Evidence indicates that differential liability for PDWG exists for antipsychotics, antidepressants, and anticonvulsants. During the past two decades, agents within these classes have become available with significantly lower or no liability for PDWG and as such should be prioritized. Although lithium is associated with weight gain, the overall extent of weight gain is significantly lower than previously estimated. The benefit of lifestyle and behavioral modification for obesity and/or PDWG in psychiatric populations is established, with effectiveness similar to that in the general population. Metformin is the most studied pharmacological treatment in the prevention and treatment of PDWG, and promising data are emerging for glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., liraglutide, exenatide, semaglutide). Most pharmacologic antidotes for PDWG are supported with low-confidence data (e.g., topiramate, histamine-2 receptor antagonists). Future vistas for pharmacologic treatment for PDWG include large, adequately controlled studies with GLP-1 receptor agonists and possibly GLP-1/glucose-dependent insulinotropic polypeptide co-agonists (e.g., tirzepatide) as well as specific dietary modifications.

30-year Cardiovascular Disease Risk for Young Adults with Serious Mental Illness

Objective

To estimate 30-year CVD risk and modifiable risk factors in young adults with serious mental illness (SMI) versus those without, and assess variations in CVD risk by race, ethnicity, and sex.

Method

In this cross-sectional study, we estimated and compared the Framingham 30-year CVD risk score and individual modifiable CVD risk factors in young adult (20-39 years) primary care patients with and without SMI at two US healthcare systems (January 2016-Septemeber 2018). Interaction terms assessed whether the SMI-risk association differed across demographic groups.

Results

Covariate-adjusted 30-year CVD risk was significantly higher for those with (n=4228) versus those without (n=155,363) SMI (RR 1.28, 95% CI [1.26, 1.30]). Patients with SMI had higher rates of hypertension (OR 2.02 [1.7, 2.39]), diabetes (OR 3.14 [2.59, 3.82]), obesity (OR 1.93 [1.8, 2.07]), and smoking (OR 4.94 [4.6, 5.36]). The increased 30-year CVD risk associated with SMI varied significantly by race and sex: there was an 8% higher risk in Black compared to White patients (RR 1.08, [1.04, 1.12]) and a 9% lower risk in men compared to women (RR 0.91 [0.88, 0.94]).

Conclusions

Young adults with SMI are at increased 30-year risk of CVD, and further disparities exist for Black individuals and women.

Differential Effects of an Intervention to Reduce Cardiovascular Risk for Patients With Bipolar Disorder, Schizoaffective Disorder, or Schizophrenia: A Randomized Clinical Trial

Objective: To measure the impact of a clinical decision support (CDS) tool on total modifiable cardiovascular risk at 12 months separately for outpatients with 3 subtypes of serious mental illness (SMI) identified via ICD-9 and ICD-10 codes: bipolar disorder, schizoaffective disorder, and schizophrenia.

Methods: This cluster-randomized pragmatic clinical trial was active from March 2016 to September 2018; data were analyzed from April 2021 to September 2022. Clinicians and patients from 78 primary care clinics participated. All 8,922 adult patients aged 18-75 years with diagnosed SMI, at least 1 cardiovascular risk factor not at goal, and an index and follow-up visit during the study period were included. The CDS tool provided a summary of modifiable cardiovascular risk and personalized treatment recommendations.

Results: Intervention patients had 4% relative reduction in total modifiable cardiovascular risk at 12 months compared to controls (relative risk ratio = 0.96; 95% CI, 0.94 to 0.98), with similar intervention benefits for all 3 SMI subtypes. At index, 10-year cardiovascular risk was higher for patients with schizophrenia (mean [SD]  = 11.3% [9.2%]) than for patients with bipolar disorder (8.5% [8.9%]) or schizoaffective disorder (9.4% [8.1%]), while 30-year cardiovascular risk was highest for patients with schizoaffective disorder (44% with 2 or more major cardiovascular risk factors, compared to 40% for patients with schizophrenia and 37% for patients with bipolar disorder). Smoking was highly prevalent (47%), and mean (SD) BMI was 32.7 (7.9).

Conclusions: This CDS intervention produced a clinically and statistically significant 4% relative reduction in total modifiable cardiovascular risk for intervention patients versus controls at 12 months, an effect observed across all 3 SMI subtypes and attributable to the aggregate impact of small changes in multiple cardiovascular risk factors.

Trial Registration: ClinicalTrials.gov Identifier: NCT02451670.

Early identification of chronic kidney disease: it is time to enhance patient and population-based informatics tools for general practitioners

Chronic kidney disease (CKD) is a global public health issue that can lead to several complications such as, kidney failure, cerebro/cardiovascular disease, and death.There is a well-documented "awareness gap" among general practitioners (GPs) to recognize CKD. As shown by estimates stemming from the Health Search Database (HSD) of the Italian College of General Practitioners and Primary Care (SIMG), no substantial changes were observed in terms of the incident rate of CKD over the last 10 years. Namely, 10.3 to 9.5 per 1,000 new cases of CKD were estimated in 2012 and 2021, respectively. Thus, strategies to reduce under-recognized cases are needed. Early identification of CKD might improve patient's quality of life and clinical outcomes. In this context, patient- and population-based informatic tools may support both opportunistic and systematic screening of patients at greater risk of CKD. As such, the new effective pharmacotherapies for CKD would be proficiently administered. To this aim, these two complimentary tools have been developed and will be further implemented by GPs.The effectiveness of these instruments in identifying the condition at an early stage and reducing the burden of CKD on the national health system needs to be verified according to the new regulations on medical device (MDR: (EU) 2017/745).

Adoption of shared decision-making and clinical decision support for reducing cardiovascular disease risk in community health centers

Objective

Electronic health record (EHR)-based shared decision-making (SDM) and clinical decision support (CDS) systems can improve cardiovascular disease (CVD) care quality and risk factor management. Use of the CV Wizard system showed a beneficial effect on high-risk community health center (CHC) patients' CVD risk within an effectiveness trial, but system adoption was low overall. We assessed which multi-level characteristics were associated with system use.

Materials and Methods

Analyses included 80 195 encounters with 17 931 patients with high CVD risk and/or uncontrolled risk factors at 42 clinics in September 2018-March 2020. Data came from the CV Wizard repository and EHR data, and a survey of 44 clinic providers. Adjusted, mixed-effects multivariate Poisson regression analyses assessed factors associated with system use. We included clinic- and provider-level clustering as random effects to account for nested data.

Results

Likelihood of system use was significantly higher in encounters with patients with higher CVD risk and at longer encounters, and lower when providers were >10 minutes behind schedule, among other factors. Survey participants reported generally high satisfaction with the system but were less likely to use it when there were time constraints or when rooming staff did not print the system output for the provider.

Discussion

CHC providers prioritize using this system for patients with the greatest CVD risk, when time permits, and when rooming staff make the information readily available. CHCs' financial constraints create substantial challenges to addressing barriers to improved system use, with health equity implications.

Conclusion

Research is needed on improving SDM and CDS adoption in CHCs.

Trial Registration

ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.

Establishing a Care Continuum for Cardiometabolic Conditions for Patients with Serious Mental Illness

PURPOSE OF REVIEW

Addressing cardiometabolic risk factors in persons with serious mental illness requires early screening and proactive medical management in both medical and mental health settings.

RECENT FINDINGS

Cardiovascular disease remains the leading cause of death for persons with serious mental illness (SMI), such as schizophrenia or bipolar disorder, much of which is driven by a high prevalence of metabolic syndrome, diabetes, and tobacco use. We summarize barriers and recent approaches to screening and treatment for metabolic cardiovascular risk factors within physical health and specialty mental health settings. Incorporating system-based and provider-level support within physical health and psychiatric clinical settings should contribute to improvement for screening, diagnosis, and treatment for cardiometabolic conditions for patients with SMI. Targeted education for clinicians and leveraging multi-disciplinary teams are important first steps to recognize and treat populations with SMI at risk of CVD.

Bolstering the Business Case for Adoption of Shared Decision-Making Systems in Primary Care: Randomized Controlled Trial

BACKGROUND

Limited budgets may often constrain the ability of health care delivery systems to adopt shared decision-making (SDM) systems designed to improve clinical encounters with patients and quality of care.

OBJECTIVE

This study aimed to assess the impact of an SDM system shown to improve diabetes and cardiovascular patient outcomes on factors affecting revenue generation in primary care clinics.

METHODS

As part of a large multisite clinic randomized controlled trial (RCT), we explored the differences in 1 care system between clinics randomized to use an SDM intervention (n=8) versus control clinics (n=9) regarding the (1) likelihood of diagnostic coding for cardiometabolic conditions using the 10th Revision of the International Classification of Diseases (ICD-10) and (2) current procedural terminology (CPT) billing codes.

RESULTS

At all 24,138 encounters with care gaps targeted by the SDM system, the proportion assigned high-complexity CPT codes for level of service 5 was significantly higher at the intervention clinics (6.1%) compared to that in the control clinics (2.9%), with P<.001 and adjusted odds ratio (OR) 1.64 (95% CI 1.02-2.61). This was consistently observed across the following specific care gaps: diabetes with glycated hemoglobin A_1c (HbA_1c)>8% (n=8463), 7.2% vs 3.4%, P<.001, and adjusted OR 1.93 (95% CI 1.01-3.67); blood pressure above goal (n=8515), 6.5% vs 3.7%, P<.001, and adjusted OR 1.42 (95% CI 0.72-2.79); suboptimal statin management (n=17,765), 5.8% vs 3%, P<.001, and adjusted OR 1.41 (95% CI 0.76-2.61); tobacco dependency (n=7449), 7.5% vs. 3.4%, P<.001, and adjusted OR 2.14 (95% CI 1.31-3.51); BMI >30 kg/m² (n=19,838), 6.2% vs 2.9%, P<.001, and adjusted OR 1.45 (95% CI 0.75-2.8). Compared to control clinics, intervention clinics assigned ICD-10 diagnosis codes more often for observed cardiometabolic conditions with care gaps, although the difference did not reach statistical significance.

CONCLUSIONS

In this randomized study, use of a clinically effective SDM system at encounters with care gaps significantly increased the proportion of encounters assigned high-complexity (level 5) CPT codes, and it was associated with a nonsignificant increase in assigning ICD-10 codes for observed cardiometabolic conditions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02451670; https://clinicaltrials.gov/ct2/show/NCT02451670.

Severe Mental Illness and Cardiovascular Disease: JACC State-of-the-Art Review

People with severe mental illness, consisting of schizophrenia, bipolar disorder, and major depression, have a high burden of modifiable cardiovascular risk behaviors and conditions and have a cardiovascular mortality rate twice that of the general population. People with acute and chronic cardiovascular disease are at a higher risk of developing mental health symptoms and disease. There is emerging evidence for shared etiological factors between severe mental illness and cardiovascular disease that includes biological, genetic, and behavioral mechanisms. This state-of-the art review will describe the relationship between severe mental illness and cardiovascular disease, explore the factors that lead to poor cardiovascular outcomes in people with severe mental illness, propose strategies to improve the cardiovascular health of people with severe mental illness, and present areas for future research focus.