[Visualization analysis of predictive model of acute kidney injury in patients with sepsis by online dynamic nomogram: research on development and validation of application].

OBJECTIVE

To explore the risk factors of septic acute kidney injury (sAKI) in patients with sepsis, construct a predictive model for sAKI, verify the predictive value of the model, and develop a dynamic nomogram to help clinical doctors identify patients with high-risk sAKI earlier and more easily.

METHODS

A cross-sectional study was conducted. A total of 245 patients with sepsis admitted to intensive care unit (ICU) of the Affiliated Hospital of Jining Medical University from May 2013 to November 2023 were enrolled as the research subjects. The patients were divided into sAKI group and non-sAKI group based on whether they suffered from sAKI during ICU hospitalization. The differences of the demographic, clinical and laboratory indicators of patients between the two groups were compared. Logistic ordinal regression analysis was performed to analyze the imbalanced variables between the two groups, and to construct a sAKI predictive model. The predictive value of the sAKI predictive model was evaluated through 5-fold cross validation, calibration curve, and decision curve analysis (DCA), and to develop an online dynamic nomogram for the predictive model.

RESULTS

A total of 245 patients were enrolled in the final analysis. 110 (44.9%) patients developed sAKI during ICU hospitalization and 135 (55.1%) patients did not develop sAKI. Compared with the non-sAKI group, the patients in the sAKI group had higher ratios of female, hypertension, invasive mechanical ventilation (IMV), renal replacement therapy (RRT), vasopressin usage, and neutrophil count (NEU), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum creatinine (SCr), uric acid (UA), Na, K, procalcitonin (PCT), acute physiology and chronic health evaluation II (APACHE II) score, and sequential organ failure assessment (SOFA) score. Multivariate Logistic ordinal regression analysis showed that female [odd ratio (OR) = 2.208, 95% confidence interval (95%CI) was 1.073-4.323, P = 0.020], hypertension (OR = 2.422, 95%CI was 1.255-5.073, P = 0.012), vasopressin usage (OR = 2.888, 95%CI was 1.380-6.679, P = 0.002), and SCr (OR = 1.015, 95%CI was 1.009-1.024, P < 0.001) were independent risk factors for sAKI in septic patients, and a sAKI predictive model was constructed: ln[P/(1+P)] = -4.665+0.792×female+0.885×hypertension+1.060×vasopressin usage+0.015×SCr. The 5-fold cross validation showed that the average area under the receiver operator characteristic curve (AUC) was 0.860, indicating the sAKI predictive model had a good performance. The calibration curve analysis showed that the calibration degree of the sAKI predictive model was good. DCA showed that the net profit of the sAKI predictive model was relatively high. A static nomogram and an online dynamic nomogram were constructed for the sAKI predictive model. Compared with the static nomogram, the dynamic nomogram allowed for manual selection of corresponding patient characteristics and viewing the corresponding sAKI risk directly.

CONCLUSIONS

Female, hypertension, vasopressin usage, and SCr are the main risk factors for sAKI in patients with sepsis. The sAKI predictive model constructed based on these factors can help clinical doctors identifying high-risk patients as early as possible, and intervene in a timely manner to provide preventive effects. Compared with the common static nomogram, online dynamic nomogram can make predictive models clearer, more intuitive, and easier.