Metabolic Health Index (MHI): Assessment of Comorbidity in Bariatric Patients Based on Biomarkers

Predicting pregnancy at the first year following metabolic-bariatric surgery: development and validation of machine learning models

BACKGROUND

Metabolic-bariatric surgery (MBS) is the last effective way to lose weight whom around half of the patients are women of reproductive age. It is recommended an interval of 12 months between surgery and pregnancy to optimize weight loss and nutritional status. Predicting pregnancy up to 12 months after MBS is important for evaluating reproductive health services in bariatric centers; therefore, this study aimed to present a prediction model for pregnancy at the first year following MBS using machine learning (ML) algorithms.

METHODS

In a nested case-control study of 473 women with a history of pregnancy after MBS during 2009-2023, predisposing factors in pregnancy within 12 months after MBS were identified and subsequently, several ML models, including the classification algorithms and decision trees, as well as regression analyses, were applied to predict pregnancy up to 12 months after MBS.

RESULTS

The highest area under the curve (AUC) was 0.920 ± 0.014 (95%CI 0.906, 0.927) for the C5.0 decision tree with sensitivity and specificity of 0.762 ± 0.044 (95%CI 0.739, 0.801) and 0.916 ± 0.028 (95%CI 0.883, 0.922), respectively. This model considered thirteen important factors to predict pregnancy at the first 12 months following MB, including menstrual irregularity, marital status, a history of abnormal fetal development, age, infertility type, parity, gravidity, fertility treatment, presurgery body mass index (BMI), infertility, infertility duration, polycystic ovary syndrome (PCOS), and type 2 diabetes (T2DM).

CONCLUSION

Developing the ML models, which predict pregnancy within 12 months after MBS, can help bariatric surgeons and obstetricians to prevent and manage suboptimal surgical response and adverse pregnancy outcomes.

The Metabolic Health Index Identifies Patients That Will Benefit From Metabolic Surgery

INTRODUCTION

Metabolic syndrome is a modern world's major health hazard related to comorbidities like type 2 diabetes and cardiovascular disease. Bariatric surgery is well known to lower this health risk in patients with obesity. There is a need for an objective measure to assess the intended reduction in health hazard and indirectly the eligibility for bariatric surgery. The Metabolic Health Index (MHI) quantitatively summarizes the cumulative impact of the metabolic syndrome on health status on a scale from 1 to 6. This study describes the use of the MHI as a supportive tool in the decision for and outcome assessment of bariatric surgery.

METHODS

The general usability of the MHI was tested by extending its application to patient data of five other bariatric centers in the Netherlands. Retrospective laboratory and national bariatric quality registry data of 11,501 patients were collected.

RESULTS

The quantification of (improvement in) metabolic health burden as measured by the MHI was independent of the dataset that was used to derive the MHI model. Patients with MHI > 2.8 prior to surgery improved significantly more in MHI 12 mo after surgery compared to patients with MHI ≤ 2.8 (1.1 compared to 0.4 MHI points, respectively; P < 0.001).

CONCLUSIONS

The MHI is robust between centers and is suitable for general use in clinical decision-making. As changes in MHI over time reflect metabolic health alterations, it is suitable as an outcome measure of surgery. An MHI cut-off value of 2.8 helps to predict the likelihood of significant improvement after surgery, independent of body mass index and known metabolic comorbidities.

Current Applications of Artificial Intelligence in Bariatric Surgery

The application of artificial intelligence technologies is growing in several fields of healthcare settings. The aim of this article is to review the current applications of artificial intelligence in bariatric surgery. We performed a review of the literature on Scopus, PubMed and Cochrane databases, screening all relevant studies published until September 2021, and finally including 36 articles. The use of machine learning algorithms in bariatric surgery is explored in all steps of the clinical pathway, from presurgical risk-assessment and intraoperative management to complications and outcomes prediction. The models showed remarkable results helping physicians in the decision-making process, thus improving the quality of care, and contributing to precision medicine. Several legal and ethical hurdles should be overcome before these methods can be used in common practice.

A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives

Artificial intelligence (AI) is a revolution in data analysis with emerging roles in various specialties and with various applications. The objective of this scoping review was to retrieve current literature on the fields of AI that have been applied to metabolic bariatric surgery (MBS) and to investigate potential applications of AI as a decision-making tool of the bariatric surgeon. Initial search yielded 3260 studies published from January 2000 until March 2021. After screening, 49 unique articles were included in the final analysis. Studies were grouped into categories, and the frequency of appearing algorithms, dataset types, and metrics were documented. The heterogeneity of current studies showed that meticulous validation, strict reporting systems, and reliable benchmarking are mandatory for ensuring the clinical validity of future research.