Nomogram for predicting 90-day mortality in patients with Acinetobacter baumannii-caused hospital-acquired and ventilator-associated pneumonia in the respiratory intensive care unit

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Abstract
Objective: We built a prediction model of mortality risk in patients the with Acinetobacter baumannii (AB)-caused hospital-acquired (HAP) and ventilator-associated pneumonia (VAP). Methods: In this retrospective study, 164 patients with AB lower respiratory tract infection were admitted to the respiratory intensive care unit (RICU) from January 2019 to August 2021 (29 with HAP, 135 with VAP) and grouped randomly into a training cohort (n = 115) and a validation cohort (n = 49). Least absolute shrinkage and selection operator regression and multivariate Cox regression were used to identify risk factors of 90-day mortality. We built a nomogram prediction model and evaluated model discrimination and calibration using the area under the receiver operating characteristic curve (AUC) and calibration curves, respectively. Results: Four predictors (days in intensive care unit, infection with carbapenem-resistant AB, days of carbapenem use within 90 days of isolating AB, and septic shock) were used to build the nomogram. The AUC of the two groups was 0.922 and 0.823, respectively. The predictive model was well-calibrated; decision curve analysis showed the proposed nomogram would obtain a net benefit with threshold probability between 1% and 100%. Conclusions: The nomogram model showed good performance, making it useful in managing patients with AB-caused HAP and VAP.
Funding Information
  • The Science and Technology Development Program of Suzhou (SLJ202008, SLT201930, SYS2020025)

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