Improving metabolic monitoring in patients maintained on antipsychotics in Penang, Malaysia

Metabolic Monitoring for Adults Living with a Serious Mental Illness on a Second-Generation Antipsychotic Agent: A Scoping Review

Premature mortality in people living with a severe mental illness (SMI) is often attributed to multiple factors including the use of medicines such as antipsychotics. Second-generation antipsychotics (SGAs) are known to cause metabolic syndrome which can increase the risk of cardiovascular disease. Practice guidelines have recommended regular physical health monitoring, particularly of metabolic parameters, however, metabolic monitoring for people living with SMI using antipsychotics remains suboptimal. Therefore, highlighting the need for ongoing research. This scoping review aimed to provide an overview of current metabolic monitoring practices. We anticipate that this information will assist clinicians and policymakers and inform future research. The following databases were searched: MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCO), the Cochrane Database of Systemic Reviews (Wiley), APA PsycInfo (Ovid) and Scopus (Elsevier Science Publishers). The target group was adults (aged ≥ 18) diagnosed with SMI (including bipolar disorder, major depressive disorder and psychotic disorders) and taking SGAs. In total, 44 studies from 14 countries were retrieved. Our findings highlighted that most studies conducted in hospitals did not report on metabolic monitoring practices. Additionally, the roles and responsibilities of healthcare professionals in metabolic monitoring for SMI were infrequently described and parameters such as waist circumference and BMI were often poorly monitored. The scoping review highlights that no streamlined approach towards metabolic monitoring currently exists. There is a need to stipulate and define the roles and responsibilities of all health professionals involved in metabolic monitoring in SMI to optimise care for these individuals. Moreover, there is a need for ongoing research, particularly in the community setting, to promote increased accessibility to metabolic monitoring for SMI.

Impact of institutional quality improvement initiatives on metabolic monitoring in mental disorder in patients treated with antipsychotics: A meta-analysis of intervention studies

Background

Compliance with guidelines regarding monitoring of metabolic adverse effects induced by antipsychotics has been consistently low. We characterised and evaluated the quality of institutional quality improvement (QI) interventions designed to address disparities between guidelines and real-world practices. Furthermore, we assessed the impact of these interventions on the screening and management of metabolic risks for inpatients receiving treatment with antipsychotic medications.

Methods

We conducted a meta-analysis of institutional QI intervention studies aimed at improving antipsychotic-associated metabolic risk monitoring in hospitalised mental disease patients. Relevant studies were identified through searches conducted in the Embase and PubMed databases, as well as by reviewing previous reviews and meta-analyses. Quantitative analyses were performed, calculating odds ratios (ORs) and 95% confidence intervals (CIs) to assess the impact of QI programmes on guideline adherence in clinical practice.

Results

We identified 12 intervention studies (n = 10 128 and n = 2667 patients in the pre-and post-intervention groups, respectively) and included them in our meta-analysis. QI interventions demonstrated effectiveness in bridging the guideline-practice gap in monitoring antipsychotic-induced metabolic adverse effects, as supported by the ORs and 95% CIs for post-intervention monitoring of plasma glucose, lipids, and blood pressure (BP) vs the pre-intervention period being OR = 6.90 (95% CI = 1.51-31.48), OR = 5.39 (95% CI = 4.01-7.24), and OR = 4.81 (95% CI = 1.23-18.79), respectively. Only 33.3% (4/12) of studies reported screening rates for all four metabolic parameters (plasma glucose, lipids, weight/body mass index (BMI), and BP). The median rates for metabolic screening of plasma glucose, lipids, and BP increased from 51.0-80.0%, 28.7-66.7%, and 91.7-95.8%, respectively. Up to 66.7% (8/12) of intervention studies lacked follow-up measures to treat or manage identified risks in hospitalised psychiatric patients, such as patient referrals, prescription of medications, and switching of antipsychotics. The odds of monitoring weight/BMI and glucose were greatest when QI programmes involved the participation of multidisciplinary health care professionals and patients, yielding OR = 3.35 (95% CI = 2.45-4.59) and OR = 57.51 (95% CI = 24.11-137.21), respectively.

Conclusions

Institutional QI interventions were effective in enhancing monitoring practices in alignment with established guidelines for metabolic risk screening among hospitalised patients with mental disorders maintained on antipsychotic medications. Future institutional QI programmes should incorporate multidisciplinary strategies involving patient engagement and extend their focus beyond screening to incorporate follow-up risk management strategies once risks have been identified.

Registration

PROSPERO CRD42023452138.

Adherence to the monitoring of metabolic syndrome in patients receiving antipsychotics in outpatient clinics in Saudi Arabia

BACKGROUND

Monitoring protocols have been developed because patients taking atypical antipsychotics are more prone to developing metabolic syndrome, which leads to possible increased mortality and morbidity. The aim of this study was to assess the degree of adherence to the recommendations of metabolic syndrome monitoring.

MATERIALS AND METHODS

This study was conducted in two large psychiatric facilities in the Eastern Province of Saudi Arabia. A retrospective analysis of the medical records of 350 patients taking antipsychotic medications was done, and an assessment was made of the frequency of metabolic monitoring at each of the intervals as suggested by the American Diabetes Association. Data was analyzed using SPSS; descriptive statistics. were computed and Chi-square test was used to determine statistical significance for association between categorical variable.

RESULTS

The mean age of the patients was 34.9 ± 18 years; 64.6% were males. Olanzapine was the most prescribed medication (43.7%, n = 153), followed by quetiapine (17.4%, n = 61). Only one-third of the patients (29.6%) completed all the baseline parameters. Documentation of baseline parameters was low for glucose level (38.9%), lipid panel (17.3%), weight (25.2%), and waist circumference (1.4%). Adherence to yearly monitoring was much lower than at baseline (mean percentage: 29.6% vs. 1.7%). Furthermore, 45% of the patients were classified as obese and 10% had metabolic comorbidity.

CONCLUSION

Individuals with mental illness who were taking antipsychotics did not undergo proper metabolic screening during antipsychotic treatment. Barriers to adherence to the monitoring guidelines should be examined and addressed. Giving assistance to practitioners to recall the required laboratory tests and vitals at certain intervals could help improve metabolic monitoring practices.

Guideline adherence for cardiometabolic monitoring of patients prescribed antipsychotic medications in primary care: a retrospective observational study

BACKGROUND

Despite their known effectiveness, antipsychotics possess significant cardiometabolic adverse event profiles. Guidelines emphasise routine monitoring, however, practices are known to be suboptimal.

AIM

To investigate the level of cardiometabolic monitoring among people prescribed antipsychotic therapy in primary care, and patient-related factors that may influence monitoring patterns.

METHOD

Data were collected for patients with mental disorders and prescribed antipsychotics at two general practices in England (February 2016-February 2021). The main outcome measures were the proportion of patients with evidence of monitoring for cardiometabolic parameters (body composition, anthropometrics, lipids, glucose outcomes). Regression analysis was used to explore factors predicting monitoring practices.

RESULTS

Data from 497 patients were included. The proportion of patients receiving cardiometabolic monitoring at least once yearly varied across different parameters. Patients were mostly monitored for BP (92.0%), body weight (BMI > 85.0%) and HDL (72.0%), but to a lesser extent for other lipid parameters (non-HDL < 2.0%) and blood glucose (< 2.0%). Ageing (OR:2.0-7.0, p < 0.001) and chronic conditions (e.g., CVD and Type 2 DM, p < 0.05) were associated with frequent cardiometabolic monitoring. Conversely, antipsychotics with high metabolic risks (olanzapine), patients prescribed antipsychotic polypharmacy (≥ 2 antipsychotics) and cardiometabolic dysregulations (e.g., dyslipidaemias) did not improve monitoring frequencies.

CONCLUSION

Cardiometabolic health monitoring was generally infrequent, irregular, and did not change in response to abnormal test results or antipsychotic treatment with high cardiometabolic risks, suggesting more efforts need to be made to ensure the guidelines for cardiometabolic monitoring are followed. Future studies should investigate practices by using a large UK primary care database.

Prevalence and Factors Associated with Metabolic Syndrome in Patients at a Psychosocial Care Center: A Cross-Sectional Study

BACKGROUND

Metabolic syndrome (MS) is associated with greater risk of morbimortality and it has high prevalence in people with mental illness.

OBJECTIVE

Estimate the prevalence of Metabolic Syndrome (MS) and its associated factors in the patients of a Psychosocial Care Center (CAPS in Brazilian Portuguese) in the city of Salvador, state of Bahia, Brazil.

METHOD

Cross-sectional study set at CAPS in the city of Salvador-Bahia between August 2019 and February 2020. MS was evaluated according to the National Cholesterol Education Program's Adult Treatment Panel III. In addition to descriptive statistics, gross and adjusted prevalence ratios were described.

RESULTS

MS was found in 100 (35.2%) individuals, 116 (40.9%) were obese and 165 (58.1%) had increased waist circumference. Polypharmacy was identified in 63 (22.3%) patients and 243 (85.9%) used antipsychotics. Under gross evaluation, women (PR = 1.88; 95%CI: 1.35-2.63) and those who used antidepressants (PR = 1.41; 95%CI: 1.05-1.88) showed an association with MS. After logistic regression, depression (PR = 1.86; 95%CI: 1.38-2.51), acanthosis (PR = 1.50; 95%CI: 1.18-1.90), use of antipsychotics (PR = 1.88; 95%CI: 1.13-2.75), and hypertriglyceridemic waist (PR = 3.33; 95%CI: 2.48-4.46) were associated with MS.

CONCLUSION

The prevalence of MS signals multimorbidity among individuals with mental disorders and suggests a need for clinical screening.

Assessing cardiometabolic parameter monitoring in inpatients taking a second-generation antipsychotic: The CAMI-SGA study - a cross-sectional study

OBJECTIVES

This study aims to determine the proportion of initial cardiometabolic assessment and its predicting factors in adults with schizophrenia, bipolar disorder or other related diagnoses for whom a second-generation antipsychotic was prescribed in the hospital setting.

DESIGN

Cross-sectional study.

SETTING

The psychiatry unit of a Canadian tertiary care teaching hospital in Montreal, Canada.

PARTICIPANTS

402 patients with aforementioned disorders who initiated, restarted or switched to one of the following antipsychotics: clozapine, olanzapine, risperidone, paliperidone or quetiapine, between 2013 and 2016.

PRIMARY OUTCOME MEASURES

We assessed the proportion of cardiometabolic parameters monitored.

SECONDARY OUTCOME MEASURES

We identified predictors that influence the monitoring of cardiometabolic parameters and we assessed the proportion of adequate interventions following the screening of uncontrolled blood pressure and fasting glucose or glycated haemoglobin (HbA1c) results.

RESULTS

Only 37.3% of patients received monitoring for at least three cardiometabolic parameters. Blood pressure was assessed in 99.8% of patients; lipid profile in 24.4%; fasting glucose or HbA1c in 33.3% and weight or body mass index in 97.8% of patients while waist circumference was assessed in 4.5% of patients. For patients with abnormal blood pressure and glycaemic values, 42.3% and 41.2% subsequent interventions were done, respectively. The study highlighted the psychiatric diagnosis (substance induced disorder OR 0.06 95% CI 0.00 to 0.44), the presence of a court-ordered treatment (OR 0.79 95% CI 0.35 to 1.79) and the treating psychiatrist (up to OR 34.0 95% CI 16.2 to 140.7) as predictors of cardiometabolic monitoring.

CONCLUSIONS

This study reports suboptimal baseline cardiometabolic monitoring of patients taking an antipsychotic in a Canadian hospital. Optimising collaboration within a multidisciplinary team may increase cardiometabolic monitoring.

Identification of risk factors and metabolic monitoring practices in patients on antipsychotic drugs in South India

The objectives of this study were to identify the risk factors for metabolic syndrome in patients on antipsychotics and to compare the frequency of metabolic monitoring with evidence-based guidelines. We conducted a retrospective cohort study in a tertiary care health institution of South India. The study included patients with schizophrenia, bipolar disorder, and schizoaffective disorders prescribed with antipsychotic drugs. Data was collected from the medical records department. American Diabetic Association/American Psychiatric Association (ADA/APA) guidelines were used as a reference standard to assess the monitoring for metabolic parameters. Diagnosis of metabolic syndrome was done according to the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) guidelines. Risk factors for metabolic syndrome and frequency of metabolic monitoring were analyzed. A total of 668 patients were included for clinical audit. About 16.5 % of the patients were diagnosed with metabolic syndrome. Age >50 years (Odds Ratio (OR) 2.00; p value <0.001) and duration of antipsychotic treatment>5 years (OR 1.55; p value< 0.05) were recognized as the independent risk factors for metabolic syndrome using multiple logistic regression. Blood pressure (BP) and fasting blood sugar (FBS) levels were documented in 99.7 % and 47 % of cases at baseline respectively, however, subsequent annual data on BP and FBS monitoring was reduced to 72.7 % and 46 % respectively. Weight was documented in 60 % of the cases at baseline, whereas the subsequent data on four times the annual assessment of weight was reduced to 9.8 %. The extent of documentation of metabolic monitoring parameters was inadequate.

Interventions to Improve Metabolic Risk Screening Among Adult Patients Taking Antipsychotic Medication: A Systematic Review

OBJECTIVE

Antipsychotic use is associated with elevated cardiometabolic risk. Guidelines for metabolic risk screening of individuals taking antipsychotics have been issued, but with little uptake into clinical practice. This review systematically assessed interventions that address this guideline-to-practice gap and described their quality, improvement strategies, and effect on screening rates.

METHODS

Studies of interventions that addressed metabolic risk screening of adult patients taking antipsychotics, published from inception to July 2018, were selected from MEDLINE, Embase, PsycINFO, CINAHL, and Cochrane Reviews databases. Information was extracted on study characteristics; improvement strategies at the provider, patient, and system levels; and screening rates in the intervention and comparison groups.

RESULTS

The review included 30 complex interventions that used between one and nine unique improvement strategies. Social influence to shift provider and health organization culture to encourage metabolic risk screening was a common strategy, as were clinical prompts and monitoring tools to capture provider attention. Most studies were deemed at high risk of bias. Relative to comparison groups, the interventions were associated with an increase in median screening rates for glucose (28% to 65%), lipids (22% to 61%), weight (19% to 67%), and blood pressure (22% to 80%).

CONCLUSIONS

This knowledge synthesis points to shortcomings of current interventions to improve antipsychotic metabolic risk screening, both in quality and in outcomes. Findings may be used to inform the design of future programs. Additional interventions are needed to address the current guideline-to-practice gap, in which approximately one-third of patients are unscreened for metabolic risk.