PloS one · 2023
Clinical predictors of mortality in patients with pseudomonas aeruginosa infection.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Frem Jim Abi, Doumat George, Kazma Jamil, Gharamti Amal, Kanj Souha S, Abou Fayad Antoine G, Matar Ghassan M, Kanafani Zeina A
Аннотация
BACKGROUND: Infections caused by Pseudomonas aeruginosa are difficult to treat with a significant cost and burden. In Lebanon, P. aeruginosa is one of the most common organisms in ventilator-associated pneumonia (VAP). P. aeruginosa has developed widespread resistance to multiple antimicrobial agents such as fluoroquinolones and carbapenems. We aimed at identifying risk factors associated for P. aeruginosa infections as well as identifying independent risk factors for developing septic shock and in-hospital mortality.
METHODS: We used a cross-sectional study design where we included patients with documented P. aeruginosa cultures who developed an infection after obtaining written consent. Two multivariable regression models were used to determine independent predictors of septic shock and mortality.
RESULTS: During the observed period of 30 months 196 patients were recruited. The most common predisposing factor was antibiotic use for more than 48 hours within 30 days (55%). The prevalence of multi-drug resistant (MDR) P. aeruginosa was 10%. The strongest predictors of mortality were steroid use (aOR = 3.4), respiratory failure (aOR = 7.3), identified respiratory cultures (aOR = 6.0), malignancy (aOR = 9.8), septic shock (aOR = 18.6), and hemodialysis (aOR = 30.9).
CONCLUSION: Understanding resistance patterns and risk factors associated with mortality is crucial to personalize treatment based on risk level and to decrease the emerging threat of antimicrobial resistance.
METHODS: We used a cross-sectional study design where we included patients with documented P. aeruginosa cultures who developed an infection after obtaining written consent. Two multivariable regression models were used to determine independent predictors of septic shock and mortality.
RESULTS: During the observed period of 30 months 196 patients were recruited. The most common predisposing factor was antibiotic use for more than 48 hours within 30 days (55%). The prevalence of multi-drug resistant (MDR) P. aeruginosa was 10%. The strongest predictors of mortality were steroid use (aOR = 3.4), respiratory failure (aOR = 7.3), identified respiratory cultures (aOR = 6.0), malignancy (aOR = 9.8), septic shock (aOR = 18.6), and hemodialysis (aOR = 30.9).
CONCLUSION: Understanding resistance patterns and risk factors associated with mortality is crucial to personalize treatment based on risk level and to decrease the emerging threat of antimicrobial resistance.