Predicting hypovitaminosis C with LASSO algorithm in adult critically ill patients in surgical intensive care units: a bi-center prospective cohort study

• Published in: Scientific reports Vol. 14; no. 1; p. 5073
• Main Authors: Hu, Jie, Zhang, Jingwen, Li, Dawei, Hu, Xin, Li, Qi, Wang, Wenwen, Su, Jianguo, Wu, Di, Kang, Hongjun, Zhou, Feihu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.03.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/499; 692/699; 692/700; Albumin; Algorithms; Ascorbic acid; Clinical trials; Cohort analysis; Hospitals; Humanities and Social Sciences; Intensive care units; multidisciplinary; Nomograms; Patients; Prediction models; Science; Science (multidisciplinary); Sepsis; Vascular diseases; Vitamin C; Vitamin deficiency
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-54826-y

Vitamin C played pleiotropic roles in critical illness and vitamin C insufficiency was predictive of the development of multiple organ failure. Currently, the prevalence of vitamin C insufficiency in Chinese critically ill patients is rarely determined and there are no established bedside tools to predict hypovitaminosis C. To develop a nomogram to identify patients with high risk of hypovitaminosis C, we performed a bi-center prospective cohort study at two ICUs of the first and sixth medical center in PLA General Hospital, Beijing, China from May 6th to July 31st, 2021 We identified 322 eligible patients. 62.4% patients were hypovitaminosis C. 7 features, including source of infection, the level of serum albumin, age, male gender, sepsis, vascular disease, and wasting of vitamin C by the kidney, were selected using LASSO algorithm and therefore included in the nomogram. In the testing set, our model showed moderate discrimination ability with areas under the curve of 0.75 [0.64–0.84]. Variable importance evaluated by SHAP value highlighted two novel important predictors, i.e., abdominal infection and the level of serum albumin. In conclusion, we first reported a high burden of vitamin C insufficiency in Chinese adult patient in the ICU. We also constructed a prediction model to timely identify patients with high risk of hypovitaminosis C, which allows the clinicians to choose appropriate candidates for Vitamin C repletion in clinical practice or clinical trials.