ABSTRACT
BACKGROUND: Pneumonia complicated by septic shock is associated with significant morbidity and mortality. Classification and regression tree methodology is an intuitive method for predicting clinical outcomes using binary splits. We aimed to improve the prediction of in-hospital mortality in patients with pneumonia and septic shock using decision tree analysis. METHODS: Classification and regression tree models were applied to all patients with pneumonia-associated septic shock in the international, multicenter Cooperative Antimicrobial Therapy of Septic Shock database between 1996 and 2015. The association between clinical factors (time to appropriate antimicrobial therapy, severity of illness) and in-hospital mortality was evaluated. Accuracy in predicting clinical outcomes, sensitivity, specificity, and area under receiver operating curve of the final model was evaluated in training (n = 2111) and testing datasets (n = 2111). RESULTS: The study cohort contained 4222 patients, and in-hospital mortality was 51%. The mean time from onset of shock to administration of appropriate antimicrobials was significantly higher for patients who died (17.2 h) compared to those who survived (5.0 h). In the training dataset (n = 2111), a tree model using Acute Physiology and Chronic Health Evaluation II Score, lactate, age, and time to appropriate antimicrobial therapy yielded accuracy of 73% and area under the receiver operating curve 0.75. The testing dataset (n = 2111) had accuracy of 69% and area under the receiver operating curve 0.72. CONCLUSIONS: Overall mortality (51%) in patients with pneumonia complicated by septic shock is high. Increased time to administration of antimicrobial therapy, Acute Physiology and Chronic Health Evaluation II Score, serum lactate, and age were associated with increased in-hospital mortality. Classification and regression tree methodology offers a simple prognostic model with good performance in predicting in-hospital mortality.
ABSTRACT
OBJECTIVES: To describe the clinical characteristics and in-hospital mortality of chronic dialysis-dependent end-stage kidney disease patients with septic shock in comparison to septic shock patients not receiving chronic dialysis. METHODS: Using an international, multicenter database, we conducted a retrospective analysis of data collected from 10,414 patients admitted to the intensive care unit (ICU) with septic shock from 1989 to 2013, of which 800 (7.7 %) were chronic dialysis patients. Data on demographic characteristics, sites of infection, microbial pathogens, antimicrobial usage patterns, and in-hospital mortality were aggregated and compared for chronic dialysis and non-dialysis patients. Multivariate time-varying Cox models with and without propensity score matching were constructed to determine the association between dialysis and in-hospital death. RESULTS: Septic shock secondary to central venous catheter infection, peritonitis, ischemic bowel, and cellulitis was more frequent in chronic dialysis patients. The isolation of resistant organisms (10.7 vs. 7.1 %; p = 0.005) and delays in receiving antimicrobials (6.0 vs. 5.0 h) were more common in chronic dialysis patients than in non-dialysis patients. Delayed appropriate antimicrobial therapy was associated with an increased risk of death in chronic dialysis patients (p < 0.0001). In-hospital death occurred in 54.8 and 49.0 % of chronic dialysis and non-dialysis patients, respectively. After propensity score matching, there was no difference in overall survival between chronic dialysis and non-dialysis patients, but survival in chronic dialysis patients decreased over time compared to non-dialysis patients. CONCLUSIONS: The demographic and clinical characteristics of chronic dialysis patients with septic shock differ from those of similar non-dialysis patients. However, there was no significant difference in mortality between the chronic dialysis and non-dialysis patients with septic shock enrolled in this analysis.
