Predicting acquisition of carbapenem-resistant Gram-negative pathogens in intensive care units

Abstract

Background: Infections by multidrug-resistant Gram-negative (MDRGN) bacteria are among the greatest contemporary health concerns, especially in intensive care units (ICUs), and may be associated with increased hospitalization time, morbidity, costs, and mortality.

Aim: The study aimed to predict carbapenem-resistant MDRGN acquisition in ICUs, to determine its risk factors, and to assess the impact of this acquisition on mortality rate.

Methods: A matched case-control study was performed in patients admitted to the ICU at a large Brazilian hospital over a five-year period. Cases were defined as patients who acquired carbapenem-resistant MDRGN bacteria during hospitalization. Controls were defined as patients who had no detection of carbapenem-resistant MDRGN bacteria. Cases were matched to controls according to the admission period. Risk factors were identified by multiple logistic regression using a stepwise selection method.

Findings: In total, 343 cases and 1029 controls were analysed. The 30-day mortality rate for subjects with ICU-associated carbapenem-resistant MDRGN was 37.6%. Five variables were identified as statistically significant and more relevant for the acquisition of multidrug-resistant strains: increased Simplified Acute Physiology Score 3, patients with severe chronic obstructive pulmonary disease and exposure to haemodialysis catheter, central venous catheter, or mechanical ventilation. Models developed displayed good results with an accuracy of ∼90%. Patients who acquired MDRGN were 2.72 times more likely to die than non-MDRGN acquisition patients.

Conclusion: Finding risk factors and developing predictive models may benefit patients through early detection and by controlling the spread of MDR. The presence of mechanical ventilation and central venous catheter were the main risk factors demonstrated, and their use requires special attention.

Keywords: Carbapenem resistance; Gram-negative; Healthcare-associated infection; Intensive care units; Multi-drug resistance; Predictive models.