Metabolic monitoring in adults living with a serious mental illness using antipsychotics: a scoping review protocol.
Bibliographic record
Abstract
Mental illness, which affects one in five Australians every year, is one of the leading causes of disability worldwide. In particular, people living with serious mental illness (SMI), including schizophrenia, major depressive disorders and bipolar disorder, have symptoms that can often be debilitating and restricting, resulting in significant impairment to daily functioning. Antipsychotics are used for a variety of indications, including hallucinations, delusions or abnormal behaviour/thought, and are often the mainstay treatment for people living with SMI. The prescribing of second-generation antipsychotics (SGAs) has increased significantly over the years in countries like Australia. SGAs, also known as atypical antipsychotics, are preferred over first-generation antipsychotics (FGAs) as they have a significantly lower risk of extrapyramidal side effects.6 However, SGAs are associated with a significantly higher risk of metabolic disturbances. Antipsychotics can affect cardiometabolic health either directly, through increasing appetite and food intake, and/or indirectly by causing sedation leading to a sedentary lifestyle and weight gain. Common adverse effects such as weight gain can contribute to negative sequalae such as chronic metabolic complications, characterized as metabolic syndrome (MetSyn). MetSyn includes the elevation of cardiometabolic parameters such as glucose and lipid blood levels, central obesity, and blood pressure. Therefore, whilst the use of antipsychotics in managing SMI supported the shift towards a community based model of care, antipsychotic use should be accompanied by regular metabolic monitoring in order to ensure safe and effective therapy. In a population where MetSyn is already prevalent, the additional risk of metabolic disorders caused by SGA use is of significant concern. In recognition of this, many management guidelines (for example, the American Diabetes Association– American Psychiatric Association (ADA-APA) consensus guideline on Antipsychotic Drugs and Obesity and Diabetes (USA) and the Royal Australian and New Zealand College of Psychiatrists Guidelines for the Management of Schizophrenia and Related Disorders (Australia and New Zealand)) exist. Despite this, concerns have been raised over the apparent disparity between guidelines and existing practice. Reports indicate persistently low metabolic monitoring rates (MMR) and poor referrals rates to other healthcare professionals for medical or lifestyle intervention. For example, the ADA-APA recommends the routine monitoring of parameters such as weight, waist circumference, blood pressure, plasma glucose and lipid profile. However, it has been noted that all metabolic parameters are rarely measured in practice. A multi-country (UK, Canada, Spain, the USA and Australia) systematic review and meta-analysis of 218 940 patients (inpatient and community dwelling) found that only blood pressure and triglycerides were routinely monitored for at least 50% of the cohort, with weight (47.9%), glucose (44.3%) and cholesterol (41.5%) being measured in fewer than half of the individuals. Similarly, suboptimal monitoring rates have been identified in an Australian inpatient ward, where parameters such as height and weight were measured in less than half (46%) of the patients and lipid monitoring measured in 23% of patients. Therefore there is an urgent need to optimize MMRs in practice to ensure that all individuals at risk are screened at adequate intervals and appropriate management strategies are promptly implemented. Research efforts have explored the role of allied health professionals such as nurses and pharmacists in the management of cardiometabolic risk, metabolic syndrome and related diseases in people living with SMI. Interventions focused on MMRs have generated relatively positive results. Future research initiatives should be conceptualized in the context of current practice. Current literature focuses on quantifying and improving MMRs but lacks details on practice implementation. For example, a review by Mitchell et al. quantified and compared the rates of metabolic monitoring pre- and post-guideline implementation, but did not describe the current processes of metabolic monitoring. Questions remain about the health professionals. involved in metabolic monitoring, where metabolic monitoring commonly occurs (e.g primary vs tertiary settings) and how (e.g procedures and systems) this monitoring is integrated and actioned in practice. Addressing these questions will inform future implementation of more targeted and streamlined interventional approaches. We aim to conduct a scoping review that will provide an overview of current metabolic monitoring practices. This review will use studies’ descriptions of pre-interventional (i.e without the influence of study interventions) monitoring procedure as proxy measures of monitoring practices. The scoping review will summarise and map existing metabolic monitoring practices, highlight current gaps in practice and facilitate the direction of future research initiatives. This information will assist clinicians and policy makers or administrators when reviewing their current metabolic monitoring practices and facilitate the implementation of future research initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".