Application of machine learning methodology to assess the performance of DIABETIMSS program for patients with type 2 diabetes in family medicine clinics in Mexico

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

Introduction

Many transformations and uncertainties, such as the fourth industrial revolution and pandemics, have propelled healthcare acceptance and deployment of health information systems (HIS). External and internal determinants aligning with the global course influence their deployments. At the epic is digitalization, which generates endless data that has permeated healthcare. The continuous proliferation of complex and dynamic healthcare data is the digitalization frontier in healthcare that necessitates attention.

Objective

This study explores the existing body of information on HIS for healthcare through the data lens to present a data-driven paradigm for healthcare augmentation paramount to attaining a sustainable and resilient HIS.

Method

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: PRISMA-compliant in-depth literature review was conducted systematically to synthesize and analyze the literature content to ascertain the value disposition of HIS data in healthcare delivery.

Results

This study details the aspects of a data-driven paradigm for robust and sustainable HIS for health care applications. Data source, data action and decisions, data sciences techniques, serialization of data sciences techniques in the HIS, and data insight implementation and application are data-driven features expounded. These are essential data-driven paradigm building blocks that need iteration to succeed.

Discussions

Existing literature considers insurgent data in healthcare challenging, disruptive, and potentially revolutionary. This view echoes the current healthcare quandary of good and bad data availability. Thus, data-driven insights are essential for building a resilient and sustainable HIS. People, technology, and tasks dominated prior HIS frameworks, with few data-centric facets. Improving healthcare and the HIS requires identifying and integrating crucial data elements.

Conclusion

The paper presented a data-driven paradigm for a resilient and sustainable HIS. The findings show that data-driven track and components are essential to improve healthcare using data analytics insights. It provides an integrated footing for data analytics to support and effectively assist health care delivery.

Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review

PURPOSE

The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments.

METHODS

We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarized the epidemiological discipline, geographical location, expertize of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results.

RESULTS

Of the 89 publications included, 33% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated from outside the United States and explored up to seven different epidemiological disciplines in 2021-2022. Double-robustness, bias reduction, and model misspecification were the main motivations that drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial, and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE.

CONCLUSIONS

There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits and adoption of TMLE.

[The estimated economic impact of cardiovascular and diabetes mellitus complications 2019-2028]

Background

In Mexico, diabetes mellitus (DM) and diseases cardiovascular, register an upward trend.

Objective

To estimate the number of complications due to cardiovascular events (CVD) and complications derived from DM (CDM) accumulated in beneficiaries of the Mexican Institute of Social Security (IMSS) from 2019 to 2028, as well as the expense for medical and economic benefits in a scenario baseline and one of change in metabolic profile due to lack of medical follow-up during the COVID-19 pandemic.

Material and methods

The number of CVD and CDM was estimated from 2019, with a 10-year risk projection using the ESC CVD Risk Calculator and United Kingdom Prospective Diabetes Study, considering risk factors registered in the institutional databases.

Results

From 2019 to 2028, cumulative CVD cases were estimated at 2 million and those of CDM in 960 thousand, with an impact on medical spending of 439,523 million pesos and on the economic benefits of 174,085 millions. When considering the COVID-19 pandemic, CVD events and CDM increased by 589 thousand, with an increase in spending of 93,787 million pesos for medical care and 41,159 million for economic benefits.

Conclusions

Without a comprehensive intervention in the management of CVD and CDM, the cost by both diseases will continue to increase, with financial pressures getting older.

Effect of Metformin on Glycemic Control Regarding Carriers of the SLC22A1/OCT1 (rs628031) Polymorphism and Its Interactions with Dietary Micronutrients in Type 2 Diabetes

PURPOSE

Differences in metformin effect on glycemic control in type 2 Diabetes (T2D) have been associated with diet, obesity, years since T2D diagnosis and genetic factors, such as the Met408Val (rs628031) SLC22A1/OCT1 gene polymorphism. This study aimed to analyze the effect of metformin and diet on glycemic control and its association with the Met408Val polymorphism in patients with T2D from western Mexico.

PATIENTS AND METHODS

A total of 240 T2D adult patients were enrolled in this cross-sectional study. Anti-hyperglycemic therapy, dietary intake, body composition and glycemic profile were recorded and the determination of genotypes of SLC22A1/OCT1 gene (rs628031) was performed using an allelic discrimination assay.

RESULTS

The type of metformin therapy was 47% monotherapy, 45% dual therapy (metformin+glibenclamide or metformin+insulin) and 8% triple therapy (metformin+glibenclamide+insulin). Individuals with metformin monotherapy had a higher glycemic control frequency (%HbA1c <7.0) compared with the dual and triple treatment schemes (77% vs 35% and 15%, respectively; p<0.001). Interestingly, a high potassium intake was documented in the three anti-hyperglycemic therapies and a lower intake of micronutrients, including calcium, magnesium, and zinc. An interaction was found between calcium intake and carriers of the risk allele A (408Val) with %HbA1c (P interaction=0.028), and potassium intake with the TyG index (P interaction=0.027). In addition, there was a positive correlation between calcium intake and %HbA1c (r=0.682; p=0.010), and potassium intake vs TyG index (r=0.593; p=0.033) in risk allele A (408Val) carriers with metformin monotherapy. Genotype frequencies were GG homozygotes (76.6%), GA heterozygotes (21.5%) and AA homozygotes (1.9%). The allele frequency was 87.4% for the ancestral allele G and 12.6% for the risk allele A.

CONCLUSION

These findings suggest a differing effect of metformin on glycemic control regarding calcium and potassium intake and the Met408Val SLC22A1/OCT1 gene polymorphism in T2D patients.

Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model's efficiency. Models trained on tidy datasets achieved almost perfect models.

Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis

OBJECTIVE

We aimed to identify machine learning (ML) models for type 2 diabetes (T2DM) prediction in community settings and determine their predictive performance.

METHOD

Systematic review of ML predictive modelling studies in 13 databases since 2009 was conducted. Primary outcomes included metrics of discrimination, calibration, and classification. Secondary outcomes included important variables, level of validation, and intended use of models. Meta-analysis of c-indices, subgroup analyses, meta-regression, publication bias assessments and sensitivity analyses were conducted.

RESULTS

Twenty-three studies (40 prediction models) were included. Studies with high-, moderate-, and low- risk of bias were 3, 14, and 6 respectively. All studies conducted internal validation whereas none conducted external validation of their models. Twenty studies provided classification metrics to varying extents whereas only 7 studies performed model calibration. Eighteen studies reported information on both the variables used for model development and the feature importance. Twelve studies highlighted potential applicability of their models for T2DM screening. Meta-analysis produced a good pooled c-index (0.812). Sources of heterogeneity were identified through subgroup analyses and meta-regression. Issues pertaining to methodological quality and reporting were observed.

CONCLUSIONS

We found evidence of good performance of ML models for T2DM prediction in the community. Improvements to methodology, reporting and validation are needed before they can be used at scale.