Construction of predictive model for the risk of acute lactic acidosis in patients with ischemic stroke during the ICU stay: A study based on the medical information Mart for intensive care database.

BACKGROUND

This study aims to identify the factors influencing the risk of lactic acidosis (LA) in patients with ischemic stroke (IS) and to develop a predictive model for assessing the risk of LA in IS patients during their stay in the intensive care unit (ICU).

METHODS

A retrospective cohort design was employed, with data collected from the Medical Information Mart for Intensive Care (MIMIC)-III and MIMIC-IV databases spanning from 2001 to 2019. LA was defined as pH < 7.35 and lactate ≥ 2 mmol/L. The total sample was randomly divided into a training set and a testing set at a 7:3 ratio. Predictive variables were selected using bidirectional stepwise regression to build the final model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves.

RESULTS

The study included 531 patients, of whom 50 (13.47 %) developed LA. The predictive factors included in the model were hypertension, weight, heart rate, Charlson comorbidity index (CCI), Sequential Organ Failure Assessment (SOFA) score, white blood cell (WBC) count, insulin use, sodium bicarbonate administration, and renal replacement therapy (RRT).. The model demonstrated an area under the ROC curve (AUC) of 0.785 [95 % confidence interval (CI): 0.717-0.854] for the training dataset, and 0.721 (95 % CI: 0.615-0.826) for the testing dataset.

CONCLUSION

The predictive model developed for assessing the risk of LA in IS patients demonstrates encouraging predictive performance. It can play a crucial role in managing acid-base balance during ICU stays and assist in the prevention and management of LA in these patients.