Adherence to Recommended Metabolic Monitoring of Children and Adolescents Taking Second-Generation Antipsychotics

OBJECTIVE

Clinical guidelines recommend periodic monitoring for adverse metabolic effects associated with second-generation antipsychotic medications. The authors sought to evaluate adherence to the guideline-recommended metabolic monitoring schedule for children and adolescents prescribed second-generation antipsychotics.

METHODS

The authors used a national electronic medical records database for a retrospective study of children and adolescents ages 1-17 years (N=9,620) who were prescribed second-generation antipsychotics in January 2010-December 2018. Adherence to guideline-recommended monitoring of body mass index (BMI), blood glucose, and cholesterol was categorized as full, partial, and no monitoring. Full monitoring of patients was defined as strict metabolic monitoring, following the guideline-recommended schedule. Patients who received any monitoring, but not meeting the full monitoring criteria, were considered partially monitored. Three multinomial logistic regression models were fitted for each metabolic parameter to identify predictors associated with monitoring status.

RESULTS

BMI was the metabolic parameter with the highest adherence to guideline-recommended monitoring (full monitoring, 4.7% of patients; partial monitoring, 44.8%), followed by blood glucose (full monitoring, 6.5%; partial monitoring, 29.4%) and cholesterol (full monitoring, 0.8%; partial monitoring, 22.4%). Being Black (vs. non-Black), having a comorbid mood disorder (vs. none), receiving olanzapine as the index second-generation antipsychotic (vs. aripiprazole), and receiving an antidepressant as a concurrent medication (vs. none) were associated with a higher likelihood of receiving both full and partial monitoring of all three metabolic parameters.

CONCLUSIONS

Both full and partial adherence to guideline-recommended monitoring of children and adolescents prescribed second-generation antipsychotics were poor. However, children and adolescents at increased metabolic risk tended to be more closely monitored.

High-throughput detection allied with machine learning for precise monitoring of significant serum metabolic changes in Helicobacter pylori infection

High-throughput detection of large-scale samples is the foundation for rapidly accessing massive metabolic data in precision medicine. Machine learning is a powerful tool for uncovering valuable information hidden within massive data. In this work, we achieved the extraction of a single fingerprinting of 1 μL serum within 5 s through a high-throughput detection platform based on functionalized nanoparticles. We quickly obtained over a thousand serum metabolic fingerprintings (SMFs) including those of individuals with Helicobacter pylori (HP) infection. Combining four classical machine learning models and enrichment analysis, we attempted to extract and confirm useful information behind these SMFs. Based on all fingerprint signals, all four models achieved area under the curve (AUC) values of 0.983-1. In particular, orthogonal partial least squares discriminant analysis (OPLS-DA) model obtained value of 1 in both the discovery and validation sets. Fortunately, we identified six significant metabolic features, all of which can greatly contribute to the monitoring of HP infection, with AUC values ranging from 0.906 to 0.985. The combination of these six significant metabolic features can enable the precise monitoring of HP infection in serum, with over 95 % of accuracy, specificity and sensitivity. The OPLS-DA model displayed optimal performance and the corresponding scatter plot visualized the clear distinction between HP and HC. Interestingly, they exhibit a consistent reduction trend compared to healthy controls, prompting us to explore the possible metabolic pathways and potential mechanism. This work demonstrates the potential alliance between high-throughput detection and machine learning, advancing their application in precision medicine.

Impact of the AACAP practice parameters on the metabolic adverse event monitoring for second-generation antipsychotics (SGAs) in children and adolescents

INTRODUCTION

The objective of our study was to evaluate the impact of the publication of the American Academy of Child and Adolescent Psychiatry (AACAP) practice parameters for SGA metabolic monitoring in 2011 on SGA metabolic monitoring uptake among pediatric SGA recipients.

METHODS

This was a retrospective study of children 1-17 years of age who initiated SGA treatment from Jan 2010 to December 2018 using a national Electronic Medical Records database. A segmented regression of interrupted time-series (ITS) analysis was conducted to analyze the change of metabolic monitoring rates for Body Mass Index (BMI), Blood Glucose (BG), and Total Cholesterol (CHL) 9 quarters pre- and 26 quarters post-the publication of the AACAP practice parameters.

RESULTS

The analytical cohort included 9620 children and adolescents who initiated SGA treatment during the study period. The ITS results showed that the publication of the AACAP practice parameter for SGA metabolic monitoring was associated with a 12.61 percentage points (p < 0.0002) immediate increase in BMI monitoring rate, (increased from 29.10% in Q4 2011 to 40.10% in Q3 2012). There was a positive trend of BMI monitoring rate prior to the publication of AACAP practice parameters, which continued during the post-publication period. Neither immediate nor sustained changes in the association of monitoring rates for BG and CHL were observed after the issuance of the guidelines.

CONCLUSION

The publication of AACAP practice parameters for SGA monitoring was associated with a significant improvement in the monitoring for BMI, but not for BG and CHL in children and adolescents.

Postprandial concentration of circulating branched chain amino acids are able to predict the carbohydrate content of the ingested mixed meal

BACKGROUND

The amount of the macronutrients protein and carbohydrate (CHO) in a mixed meal is known to affect each other's digestion, absorption, and subsequent metabolism. While the effect of the amount of dietary protein and fat on the glycemic response is well studied, the ability of postprandial plasma amino acid patterns to predict the meal composition is unknown.

OBJECTIVE

To study the postprandial plasma amino acid patterns in relation to the protein, CHO, and fat content of different mixed meals and to investigate if these patterns can predict the macronutrient meal composition.

DESIGN

Ten older adults were given 9 meals with 3 different levels (low, medium, and high) of protein, CHO, and fat in different combinations, taking the medium content as that of a standardized western meal. We monitored the postprandial plasma response for amino acids, glucose, insulin, and triglycerides for 8 h and the areas under the curve (AUC) were subsequently calculated. Multiple regression analysis was performed to determine if amino acid patterns could predict the meal composition.

RESULTS

Increasing meal CHO content reduced the postprandial plasma response of several amino acids including all branched chain amino acids (BCAA) (leucine; q < 0.0001, isoleucine; q = 0.0035, valine; q = 0.0022). The plasma BCAA patterns after the meal significantly predicted the meal's CHO content (leucine; p < 0.0001, isoleucine; p = 0.0003, valine; p = 0.0008) along with aspartate (p < 0.0001), tyrosine (p < 0.0001), methionine (p = 0.0159) and phenylalanine (p = 0.0332). Plasma citrulline predicted best the fat content of the meal (p = 0.0024).

CONCLUSIONS

The postprandial plasma BCAA patterns are lower with increasing meal CHO content and are strong predictors of a mixed meal protein and CHO composition, as are plasma citrulline for the fat content. We hypothesize that postprandial plasma amino acid concentrations can be used to predict the meal's macronutrient composition.

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.

Cystinosis: Therapy adherence and metabolic monitoring in patients treated with immediate-release cysteamine

Background

Cystinosis is a metabolic disease caused by intracellular accumulation of cystine within lysosomes. Development of symptoms can be delayed significantly by a life-long therapy with cysteamine, a drug that enters the lysosome and reacts with cystine thereby enabling its export from the organelle.

Methods

During a period of 16 years, blood samples of 330 cystinosis patients were analyzed to investigate therapeutic adherence and metabolic control in patients treated with immediate-release cysteamine. The accepted therapeutic goal is to measure intracellular cystine levels in white blood cells every 3 months and to keep them below 0.5 nmol cystine/mg protein (= 1 nmol hemicystine/mg protein).

Results

42% of measurements were within the desired 3-month interval, 38% were done every 3–5 months, 11% every 6–8 months, 5% every 9–12 months and 4% after a 12-month interval only. 64.4% of the measurements were higher than the therapeutic target value. Median cystine levels increased with longer control intervals.

Conclusions

The majority of the cystinosis patients showed insufficient metabolic adjustment. Intracellular cystine levels were not done as often as recommended and were not within therapeutic range. Poor therapy adherence is likely to be caused by gastrointestinal side effects of immediate-release cysteamine. Incorrect intervals between drug intake and blood sampling could contribute to the results.

Indirect calorimetry in critically ill mechanically ventilated patients: Comparison of E-sCOVX with the deltatrac

BACKGROUND & AIMS

Indirect calorimetry is recommended to measure energy expenditure (EE) in critically ill, mechanically ventilated patients. The most validated system, the Deltatrac® (Datex-Ohmeda, Helsinki, Finland) is no longer in production. We tested the agreement of a new breath-by-breath metabolic monitor E-sCOVX® (GE healthcare, Helsinki, Finland), with the Deltatrac. We also compared the performance of the E-sCOVX to commonly used predictive equations.

METHODS

We included mechanically ventilated patients eligible to undergo indirect calorimetry. After a stabilization period, EE was measured simultaneously with the Deltatrac and the E-sCOVX for 2 h. Agreement and precision of the E-sCOVX was tested by determining bias, limits of agreement and agreement rates compared to the Deltatrac. Performance of the E-sCOVX was also compared to four predictive equations: the 25 kcal/kg, Penn State University 2003b, Faisy, and Harris-Benedict equation.

RESULTS

We performed 29 measurements in 16 patients. Mean EE-Deltatrac was 1942 ± 274 kcal/day, and mean EE-E-sCOVX was 2177 ± 319 kcal/day (p < 0.001). E-sCOVX overestimated EE with a bias of 235 ± 149 kcal/day, being 12.1% of EE-Deltatrac. Limits of agreement were -63 to +532 kcal/day. The 10% and 15% agreement rates of EE-E-sCOVX compared to the Deltatrac were 34% and 72% respectively. The bias of E-sCOVX was lower than the bias of the 25 kcal/kg-equation, but higher than bias of the other equations. Agreement rates for E-sCOVX were similar to the equations. The Faisy-equation had the highest 15% agreement rate.

CONCLUSION

The E-sCOVX metabolic monitor is not accurate in estimating EE in critically ill mechanically ventilated patients when compared to the Deltatrac, the present reference method. The E-sCOVX overestimates EE with a bias and precision that are clinically unacceptable.

Cardio-metabolic risk and its management in a cohort of clozapine-treated outpatients

OBJECTIVE

To comprehensively assess cardio-metabolic risk factors and their management in a large sample of outpatients treated with clozapine.

METHODS

Observational cross-sectional study of all clozapine users attending specialized clozapine monitoring outpatient clinics in three public hospitals in Sydney, Australia were approached to participate over the one-year period 01/10/2015-30/09/2016. Cardio-metabolic risk factors including metabolic syndrome, risk for future development of diabetes, smoking, physical activity, nutrition, and prescribed medications were assessed at face-to-face interview and through medical record review. Among patients who had cardio-metabolic risk factors, the proportion receiving appropriate management was assessed.

RESULTS

Of 451 registered clozapine clinic attenders, 92.2% completed questionnaires and anthropometric measurements. 58.3% met criteria for metabolic syndrome. 79.6% were overweight or obese. 55.9% had blood pressure meeting metabolic syndrome criteria. 46.6% had elevated fasting blood glucose and 55.2% had elevated blood triglycerides. 43.6% were current smokers. Only 10% achieved recommended weekly physical activity levels. Unhealthy food categories were highly consumed. 32.1% were on additional antipsychotics. In the majority of individuals, cardio-metabolic risk factors were untreated or under-treated.

CONCLUSIONS

Clozapine use was associated with very high rates of cardiovascular and metabolic risk factors, which were frequently under-treated. Management of both physical and mental health should be prioritized. Polypharmacy should be rationalized. Future research should investigate the effectiveness of smoking cessation and lifestyle interventions in this high-risk population.

Seven unconfirmed ideas to improve future ICU practice

With imprecise definitions, inexact measurement tools, and flawed study execution, our clinical science often lags behind bedside experience and simply documents what appear to be the apparent faults or validity of ongoing practices. These impressions are later confirmed, modified, or overturned by the results of the next trial. On the other hand, insights that stem from the intuitions of experienced clinicians, scientists and educators-while often neglected-help place current thinking into proper perspective and occasionally point the way toward formulating novel hypotheses that direct future research. Both streams of information and opinion contribute to progress. In this paper we present a wide-ranging set of unproven 'out of the mainstream' ideas of our FCCM faculty, each with a defensible rationale and holding clear implications for altering bedside management. Each proposition was designed deliberately to be provocative so as to raise awareness, stimulate new thinking and initiate lively dialog.