What We Are Missing: Using Machine Learning Models to Predict Vitamin C Deficiency in Patients with Metabolic and Bariatric Surgery

Abstract

Purpose: Vitamin C (VC) is implicated in many physiological pathways. Vitamin C deficiency (VCD) can compromise the health of patients with metabolic and bariatric surgery (patients). As symptoms of VCD are elusive and data on VCD in patients is scarce, we aim to characterize patients with measured VC levels, investigate the association of VCD with other lab abnormalities, and create predictive models of VCD using machine learning (ML).

Methods: A retrospective chart review of patients seen from 2017 to 2021 at a tertiary care center in Northeastern USA was conducted. A 1:4 case mix of patients with VC measured to a random sample of patients without VC measured was created for comparative purposes. ML models (BayesNet and random forest) were used to create predictive models and estimate the prevalence of VCD patients.

Results: Of 5946 patients reviewed, 187 (3.1%) had VC measures, and 73 (39%) of these patients had VC<23 μmol/L(VCD. When comparing patients with VCD to patients without VCD, the ML algorithms identified a higher risk of VCD in patients deficient in vitamin B1, D, calcium, potassium, iron, and blood indices. ML models reached 70% accuracy. Applied to the testing sample, a "true" VCD prevalence of ~20% was predicted, among whom ~33% had scurvy levels (VC<11 μmol/L).

Conclusion: Our models suggest a much higher level of patients have VCD than is reflected in the literature. This indicates a high proportion of patients remain potentially undiagnosed for VCD and are thus at risk for postoperative morbidity and mortality.

Keywords: Bariatric surgery; Bayesian network; Logistic regression; Machine learning; Micronutrient; Predictive model; Random forest; Vitamin C deficiency.