Predictors of Clinical Inertia and Type 2 Diabetes: Assessment of Primary Care Physicians and Their Patients

A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data

OBJECTIVE

To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of health (SDOH).

METHODS

This prognostic study with a retrospective cohort design used OneFlorida data (linked electronic health records (EHRs) from 1240 practices/clinics in Florida). The study cohort included adults (aged ≥ 18) with type 2 diabetes, HbA1C ≥ 7% (53 mmol/mol), ≥one ambulatory visit, and ≥one antihyperglycemic medication prescribed (excluded patients prescribed insulin before HbA1C). The outcome was therapeutic inertia, defined as absence of treatment intensification within six months after HbA1C ≥ 7% (53 mmol/mol). The predictors were patient, provider, and healthcare system factors. Machine learning methods included gradient boosting machines (GBM), random forests (RF), elastic net (EN), and least absolute shrinkage and selection operator (LASSO). The DeLong test compared the discriminative ability (represented by C-statistics) between models.

RESULTS

The cohort included 31,087 patients with type 2 diabetes (mean age = 58.89 (SD = 13.27) years, 50.50% male, 58.89% White). The therapeutic inertia prevalence was 39.80% among the 68,445 records. GBM outperformed (C-statistic from testing sample = 0.84, 95% CI = 0.83-0.84) RF (C-statistic = 0.80, 95% CI = 0.79-0.80), EN (C-statistic = 0.80, 95% CI = 0.80-0.81), and LASSO (C-statistic = 0.80, 95% CI = 0.80-0.81), p < 0.05. Area-level SDOH significantly increased the discriminative ability versus models without SDOH (C-statistic for GBM = 0.84, 95% CI = 0.84-0.85 vs. 0.84, 95% CI = 0.83-0.84), p < 0.05.

CONCLUSIONS

Using EHRs of patients with type 2 diabetes from a large state, machine learning predicted therapeutic inertia (prevalence = 40%). The model's ability to predict patients at high risk of therapeutic inertia is clinically applicable to diabetes care.

[Evaluation of healthcare for patients with diabetes in primary care: A systematic review]

OBJECTIVE

Have the most current evidence in relation to the evaluation of medical healthcare for patients with diabetes in primary care.

METHOD

During the review process, we followed the recommendations to improve the publication of systematic reviews and meta-analyses and the preferred reporting points for PRISMA systematic reviews. The bibliographic search was carried out in Cumulative Index to Nursing and Allied Health Literature (CINAHL), SCOPUS, Scielo, MedLine/ PubMed, Cochrane databases and in the Google Scholar search engine, with free and controlled language, using the MeSh search terms: «Physicians, Primary Care», «Diabetes Mellitus, Type2». Eight selected articles were analyzed. The articles were selected based on their relevance, published in peer-reviewed academic journals and published between 2019 and 2023.

RESULTS

The main study tool represents interventions in knowledge and practice about the care of patients with diabetes among primary care physicians. The most important discussion topics extracted in the analyzed articles refer to knowledge, clinical inertia, patients' housing challenges, adherence intervention programs, and a self-care application for patients with diabetes.

CONCLUSIONS

The findings of this study indicate the need to improve medical health care through knowledge, attitudes and practices in primary care regarding patients with diabetes. In this way, it could be considered a useful tool to promote medical healthcare in primary care.