Predicting mortality in intensive care unit patients infected with Klebsiella pneumoniae: A retrospective cohort study.

Introduction: Although several models to predict intensive care unit (ICU) mortality are available, their performance decreases in certain subpopulations because specific factors are not included. Moreover, these models often involve complex techniques and are not applicable in low-resource settings. We developed a prediction model and simplified risk score to predict 14-day mortality in ICU patients infected with Klebsiella pneumoniae.

Methodology: A retrospective cohort study was conducted using data of ICU patients infected with Klebsiella pneumoniae at the largest tertiary hospital in Northern Vietnam during 2016-2018. Logistic regression was used to develop our prediction model. Model performance was assessed by calibration (area under the receiver operating characteristic curve-AUC) and discrimination (Hosmer-Lemeshow goodness-of-fit test). A simplified risk score was also constructed.

Results: Two hundred forty-nine patients were included, with an overall 14-day mortality of 28.9%. The final prediction model comprised six predictors: age, referral route, SOFA score, central venous catheter, intracerebral haemorrhage surgery and absence of adjunctive therapy. The model showed high predictive accuracy (AUC = 0.83; p-value Hosmer-Lemeshow test = 0.92). The risk score has a range of 0-12 corresponding to mortality risk 0-100%, which produced similar predictive performance as the original model.

Conclusions: The developed prediction model and risk score provide an objective quantitative estimation of individual 14-day mortality in ICU patients infected with Klebsiella pneumoniae. The tool is highly applicable in practice to help facilitate patient stratification and management, evaluation of further interventions and allocation of resources and care, especially in low-resource settings where electronic systems to support complex models are missing.