Predicting hypovitaminosis C with LASSO algorithm in adult critically ill patients in surgical intensive care units: a bi-center prospective cohort study
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View abstract on PubMed
Summary
This summary is machine-generated.Vitamin C insufficiency is common in Chinese ICU patients, affecting over 60%. Researchers developed a nomogram using infection source and albumin levels to predict this deficiency, aiding targeted Vitamin C repletion.
Area Of Science
- Critical Care Medicine
- Nutritional Biochemistry
- Clinical Prediction Modeling
Background
- Vitamin C has crucial roles in critical illness, and its deficiency predicts multiple organ failure.
- Prevalence of vitamin C insufficiency in Chinese critically ill patients is largely unknown.
- Lack of bedside tools to predict hypovitaminosis C hinders timely intervention.
Purpose Of The Study
- To determine the prevalence of vitamin C insufficiency in Chinese critically ill patients.
- To develop and validate a predictive nomogram for identifying patients at high risk of hypovitaminosis C.
Main Methods
- A bi-center prospective cohort study involving 322 critically ill patients in Beijing, China.
- Utilized LASSO regression to select predictive features for the nomogram.
- Evaluated model performance using area under the curve (AUC) and SHAP values.
Main Results
- A high prevalence of vitamin C insufficiency was observed in 62.4% of the study population.
- A nomogram was constructed incorporating infection source, serum albumin, age, gender, sepsis, vascular disease, and kidney wasting.
- The model demonstrated moderate discrimination (AUC 0.75), with abdominal infection and serum albumin identified as key predictors.
Conclusions
- This study first reports the significant burden of vitamin C insufficiency among Chinese adult ICU patients.
- The developed nomogram provides a tool for timely identification of patients at risk of hypovitaminosis C.
- The findings support targeted Vitamin C repletion strategies in clinical practice and research.
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