Construction and validation of an early warning model for predicting the acute kidney injury in elderly patients with sepsis

Abstract

Background Sepsis-induced acute kidney injury (S-AKI) is a significant complication and is associated with an increased risk of mortality, especially in elderly patients with sepsis. However, there are no reliable and robust predictive models to identify high-risk patients likely to develop S-AKI. We aimed to develop a nomogram to predict S-AKI in elderly sepsis patients and help physicians make personalized management within 24 h of admission. Methods A total of 849 elderly sepsis patients from the First Affiliated Hospital of Xi’an Jiaotong University were identified and randomly divided into a training set (75%, n = 637) and a validation set (25%, n = 212). Univariate and multivariate logistic regression analyses were performed to identify the independent predictors of S-AKI. The corresponding nomogram was constructed based on those predictors. The calibration curve, receiver operating characteristics (ROC)curve, and decision curve analysis were performed to evaluate the nomogram. The secondary outcome was 30-day mortality and major adverse kidney events within 30 days (MAKE30). MAKE30 were a composite of death, new renal replacement therapy (RRT), or persistent renal dysfunction (PRD). Results The independent predictors for nomogram construction were mean arterial pressure (MAP), serum procalcitonin (PCT), and platelet (PLT), prothrombin time activity (PTA), albumin globulin ratio (AGR), and creatinine (Cr). The predictive model had satisfactory discrimination with an area under the curve (AUC) of 0.852–0.858 in the training and validation cohorts, respectively. The nomogram showed good calibration and clinical application according to the calibration curve and decision curve analysis. Furthermore, the prediction model had perfect predictive power for predicting 30-day mortality (AUC = 0.813) and MAKE30 (AUC = 0.823) in elderly sepsis patients. Conclusion The proposed nomogram can quickly and effectively predict S-AKI risk in elderly sepsis patients within 24 h after admission, providing information for clinicians to make personalized interventions.

An early warning model for predicting major adverse kidney events within 30 days in sepsis patients

“…Recognition of patients at high risk for renal outcomes within 24 h after ICU admission could allow providers to avoid risk factors, such as nephrotoxic drugs and radiocontrast dye. Classical theories propose that the elderly have high mortality, PRD, and RRT in sepsis patients because various factors may contribute to the decline in renal function with advancing age, encompassing the atrophy and degeneration of renal tubular epithelial cells, a thickened basement membrane, and reduced renal blood flow (13). In line with our study, previous research findings have demonstrated a substantial rise in the incidence of sepsis (58-65%) among elderly patients, with age being identified as an independent predictor of sepsis mortality (14).…”

Artificial intelligence and machine learning’s role in sepsis-associated acute kidney injury

“…Traditional statistical analysis has been instrumental in identifying risk factors for SA-AKI across a range of comorbidities, infections, medications, and other determinants [4,[34][35][36][37][38][39][40][41][42]. Key comorbid conditions found to significantly predict SA-AKI include hypertension, diabetes mellitus, chronic kidney disease, cardiovascular disease, liver disease, and coronary artery disease [4].…”

“…Traditional statistical approaches to predicting SA-AKI through various studies have been used [34][35][36][37][38][39][40][41][42]. These studies (Table 1) utilized well-established methods such as logistic regression analysis, least absolute shrinkage and selection operator (LASSO) regression for variable selection, and calibration plots [34][35][36][37][38][39][40][41][42].…”

“…Traditional statistical approaches to predicting SA-AKI through various studies have been used [34][35][36][37][38][39][40][41][42]. These studies (Table 1) utilized well-established methods such as logistic regression analysis, least absolute shrinkage and selection operator (LASSO) regression for variable selection, and calibration plots [34][35][36][37][38][39][40][41][42]. These models identified key predictors like diabetes, chronic kidney disease, cardiovascular disease, and specific lab values such as creatinine and procalcitonin.…”

Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury

“…The study identified several independent predictor variables including age, emergency ICU admission, post-surgical cases, admission diagnosis, AKI etiology, disease severity score, mechanical ventilation, use of boosters and blood outcomes such as albumin, potassium, and pH ( 8 ). In a study analyzing 30-day mortality in elderly patients with sepsis, a multivariate logistic regression-based analysis was conducted and resulted in a more accurate prediction with an AUC of 0.831 ( 9 ). Additionally, a multivariate prediction model for ICU and in-hospital death in AKI patients undergoing continuous renal replacement therapy found to be more accurate than SOFA, APACHE-II, and SAP-II scores ( 10 ).…”