The findings showed considerable variation in cardiometabolic monitoring frequencies in patients with mental disorders prescribed antipsychotics. Regarding the monitoring frequency level, out of the seven parameters that were covered in this study (BMI, WC, BP, FBG, HbA1c, HDL and non-HDL), BP and BMI were mostly recorded. However, standard monitoring was only achieved at 20.0% for BMI, BP, and plasma lipids indicating that routine monitoring of antipsychotics for metabolic parameters in primary care was not conducted according to NICE guidelines [6].

Previous investigations found similar results. In a UK study, the authors found that physical assessments for BMI and BP were mostly recorded. The study also found that biochemical measures were obtained for half of the patients (BG was recorded for < 60.0% and lipid profiles were recorded for 3.5–23.0%) [14]. Similarly, a baseline audit aiming to assess the frequency of metabolic monitoring in outpatients with schizophrenia receiving antipsychotics in Malaysia revealed an extremely low initial level of metabolic monitoring (< 20.0%), which was significantly raised following intervention [15]. Another study assessing metabolic monitoring provided further evidence of inconsistent frequency of metabolic rate monitoring in psychiatric patients in South India. Initially, 99.7% had their BP, while 47.0% had their FBS levels recorded. Over time, monitoring rates decreased for BP but remained the same for FBS [16].

The regression model suggests that among the ten patient factors assessed for association with cardiometabolic monitoring, seven variables best predicted monitoring frequency. These factors included age, pre-existing metabolic conditions, CVD family history, risk of CVD and diabetes, and corresponding treatments. Frequent cardiometabolic monitoring related to age has been generally observed in related investigations [17, 18]. Yet, evidence addressing factors affecting cardiometabolic monitoring performance in primary mental health services is limited.

While our study sample consisted of few prediabetic patients, the number of BG records was generally low. Even among obese patients (BMI ≥ 25 kg/m2) without diabetes, BG monitoring was not adequately performed. Our findings contrast with previous evidence highlighting a potential association between conditions including diabetes and dyslipidaemia with cardiometabolic monitoring patterns in patients with mental disorders, suggesting that such comorbidities make patients more prone to monitoring. For example, data from 2010 viewed factors including pre-existing dyslipidaemia and diabetes as significant predictors for recording all metabolic syndrome diagnostic components among antipsychotic users [17].

Our findings showed that central obesity measurements were barely conducted, which is consistent with the literature. The results of a study analysing data from a metropolitan mental health hospital reported an extremely low level of WC monitoring, being recorded in only 7.0% of screened records [19]. Similarly, previous work showed that WC was not recorded in the reviewed files, despite interventions applied for this purpose [14, 20]. Weight gain, especially when manifested as central obesity, remains a significant long-term health issue in patients taking antipsychotics, even with normal BMI scores [21].

This study revealed a low rate of BG monitoring of less than 2.0%, despite diabetes being independent of weight gain. Previous work reported various levels of BG monitoring from 15.0 to 46.0% [22, 23], which is markedly higher than our recorded rate. The observed increase in monitoring patterns reported in previous studies may have resulted from either the close monitoring provided by specialised centres or interventions (a predesigned monitoring form). However, the reasons for this low monitoring rate of the BG observed in this study are not fully clear. Furthermore, in this study, most patients were prescribed second-generation antipsychotics, which appeared to have no impact on metabolic monitoring. Our findings showed that patients were more likely to receive cardiometabolic monitoring for co-existing morbidities, which could be considered more important determinants of cardiometabolic monitoring among patients prescribed antipsychotics.

We found no correlation between the use of antipsychotics with known metabolic liabilities (quetiapine, risperidone, or olanzapine) and cardiometabolic monitoring, which is inconsistent with the recommendations of NICE/RCP emphasising preferential monitoring based on the administered antipsychotic. Similar reports showed that the use of second-generation antipsychotics was significantly associated with low HbA1c testing, calling for increased metabolic screening among such patients [23,24,25].

The use of a pre-piloted data extraction tool reduced data sampling bias. Also, the sample size was adequate to ensure the precision of the observed results and estimations. However, this study has several limitations related to the used design. The study design was retrospective hence that relied on the accuracy of data recording and data coding in EMIS web. The generalisability of the results may be limited due to the small number of participating sites. 

Future research should target health promotion strategies in primary care for monitoring and follow-up interventions, mainly for unrecorded variables, e.g., WC and BG. Routine physical health monitoring, referrals, and treatment for patients with schizophrenia, the current system of fragmented mental and physical health services could be transformed into a system focusing on early interventions. The under-monitoring observed in this study can be explained by barriers impacting efficient monitoring for cardiometabolic abnormalities. Addressing these barriers would improve cardiometabolic monitoring practices for antipsychotics in primary care. Also, qualitative research targeting prospective healthcare providers in primary care regarding the importance of cardiometabolic monitoring among antipsychotic users is warranted.