Construction of anomogram for predicting the prognosis of patients with sepsis-associated acute kidney injury.
10.3760/cma.j.cn121430-20230813-00621
- Author:
Li ZHAO
1
;
Yan LIU
2
;
Man CHEN
1
;
Li CHEN
1
;
Shenglin ZHOU
1
;
Xue BAI
1
;
Jicheng ZHANG
1
Author Information
1. Department of Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China.
2. Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing 100029, China. Corresponding author: Zhang Jicheng, Email: wszjc7861@126.com.
- Publication Type:Journal Article
- MeSH:
Humans;
Aged;
Cystatin C;
Retrospective Studies;
Intensive Care Units;
Sepsis/diagnosis*;
Acute Kidney Injury/therapy*;
Prognosis;
ROC Curve;
Fibrinogen
- From:
Chinese Critical Care Medicine
2023;35(12):1255-1261
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To explore the risk factors for poor prognosis in sepsis-associated acute kidney injury (SA-AKI) and establish a nomogram predictive model.
METHODS:The clinical data of patients with SA-AKI admitted to the department of critical care medicine of Shandong Provincial Hospital Affiliated to Shandong First Medical University from January 2019 to September 2022 were retrospectively analyzed, including demographic information, worst values of blood cell counts and biochemical indicators within 24 hours of SA-AKI diagnosis, whether the patient received renal replacement therapy (RRT), mechanical ventilation, vasopressor therapy during hospitalization, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), fibrinogen-to-albumin ratio (FAR) within 24 hours of diagnosis, acute kidney injury (AKI) staging, total length of hospital stay, length of intensive care unit (ICU) stay, and others. According to the 28-day outcome, the patients were divided into survival group and death group, and the indicators between the two groups were compared. Univariate and multivariate Logistic regression analyses were used to screen for risk factors associated with mortality in SA-AKI patients. A nomogram predictive model for SA-AKI prognosis was constructed based on the identified risk factors. Receiver operator characteristic curve (ROC curve) and calibration plots were generated to evaluate the predictive value of the nomogram model for SA-AKI prognosis.
RESULTS:A total of 113 SA-AKI patients were included, with 67 in the survival group and 46 in the death group. The 28-day mortality among SA-AKI patients was 40.7%. The comparison between the two groups showed that there were statistically significant differences in age ≥ 65 years, AKI stage, mechanical ventilation, vasopressors, RRT, length of ICU stay, and laboratory indicators cystatin C (Cys C), fibrinogen (Fib), and FAR. Multivariate Logistic regression analysis showed that age ≥ 65 years [odds ratio (OR) = 7.967, 95% confidence interval (95%CI) was 1.803-35.203, P = 0.006], cystatin C (OR = 7.202, 95%CI was 1.756-29.534, P = 0.006), FAR (OR = 2.444, 95%CI was 1.506-3.968, P < 0.001), and RRT (OR = 7.639, 95%CI was 1.391-41.951, P = 0.019) were independent risk factors for mortality in SA-AKI patients. ROC curve analysis showed that the area under the ROC curve (AUC) for age ≥ 65 years, cystatin C, FAR, and RRT in predicting SA-AKI patient mortality were 0.713, 0.856, 0.911, and 0.701, respectively. A nomogram predictive model for SA-AKI patient prognosis was constructed based on age ≥ 65 years, cystatin C, FAR, and RRT, with an AUC of 0.967 (95%CI was 0.932-1.000) according to ROC curve analysis. The calibration plot indicated good consistency between predicted and actual probabilities.
CONCLUSIONS:Age ≥ 65 years, cystatin C, FAR, and RRT are independent risk factors for mortality in SA-AKI patients. The nomogram predictive model based on these four factors can accurately predict SA-AKI patient prognosis, helping physicians adjust treatment strategies in a timely manner and improve patient outcomes.
