A new prediction model for acute kidney injury in patients with sepsis.

BACKGROUND

Acute kidney injury is common in patients with sepsis and contributes to poor prognosis and mortality. Early identification of high-risk patients can provide evidence for clinical decision-making.

METHODS

We developed a prediction model based on a cohort of 15,726 patients with sepsis from the Medical Information Mart for Intensive Care III critical care database. Logistic regression analysis was applied to develop a prediction model incorporating the selected risk factors. Discrimination and calibration of the prediction model were assessed using the C-index and calibration plot.

RESULTS

Risk factors in the prediction model included diabetes mellitus, chronic kidney disease, congestive heart failure, chronic liver disease, hyperbicarbonemia, hyperglycemia, low blood pH, prolonged clotting time, hypotension, and hyperlactatemia. The model showed great discrimination with a C-index of 0.711 (95% CI, 0.702-0.721) and appropriate calibration. A medium C-index value of 0.712 (95% CI, 0.697-0.727) could still be reached in the validation cohort. Negative and positive predictive values for the optimal cutoff value of ≥6 points were 56.8% and 72.3% in the training cohort and 57.3% and 72.6% in the validation cohort, respectively.

CONCLUSIONS

This prediction model allows clinicians to quickly assess the risk of sepsis-associated acute kidney injury (SA-AKI) at an early stage. Accordingly, clinicians can implement more medical measures that are considered beneficial to patients with sepsis.