Prognosis, risk factors and prediction modeling of recurrent pseudomonas aeruginosa infections.

Background: Pseudomonas aeruginosa is widely present in clinical practice and can cause recurrent infections. However, the prognosis for recurrence and the risk factors leading to its recurrence are unclear.

Aim: The study was aim to explore the prognosis and risk factors for recurrent P. aeruginosa infections, and construct a nomogram predictive model.

Methods: A retrospective study was conducted at a tertiary hospital in China, including patients with recurrent P. aeruginosa infections hospitalized between June 1, 2019, and May 30, 2023. Logistic regression was used to evaluate risk factors, and a nomogram model was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUROC) and calibration curves.

Founds: Patients with recurrent P. aeruginosa infection at 60 days and thereafter have poorer prognosis. Chronic structural lung disease, cerebrovascular disease, long-term exposure to corticosteroids or immunosuppressants, antimicrobial exposure in the past 90 days, bedridden for >3 months, indwelling urinary catheter, CRPA in the first positive culture, and inadequate initial antimicrobial therapy were the risk factors for recurrent P. aeruginosa infections and were included in the nomogram model. The AUROC of the model was 0.879 (95 % CI: 0.851∼0.907), and the calibration curve closely approximated the ideal curve.

Conclusions: Recurrent P. aeruginosa infections at 60 days result in a poor prognosis. We constructed a reliable nomogram model based on risk factors for recurrent P. aeruginosa infections. The proper utilization of this tool can contribute to reducing recurrent P. aeruginosa infections, improving patient prognosis, and alleviating the healthcare burden.