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Comparison of the newly developed Fournier’s gangrene mortality prediction model with existing models

BACKGROUND: Many predictive factors and scoring systems associated with Fournier’s gangrene have been proposed, including comorbidities, vital signs, biochemical and hematological parameters, and demographic characteristics of the patient. The aim of this study was to determine the strengths of the...

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Detalles Bibliográficos
Autores principales: Çomçalı, Bülent, Ceylan, Cengiz, Özdemir, Buket Altun, Ağaçkıran, İbrahim, Akıncı, Felat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Kare Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10443128/
https://www.ncbi.nlm.nih.gov/pubmed/35485517
http://dx.doi.org/10.14744/tjtes.2020.68137
Descripción
Sumario:BACKGROUND: Many predictive factors and scoring systems associated with Fournier’s gangrene have been proposed, including comorbidities, vital signs, biochemical and hematological parameters, and demographic characteristics of the patient. The aim of this study was to determine the strengths of the scoring systems that have been formed by revealing these factors from a wider perspective and in a larger patient population. METHODS: The patient population included 144 patients, 21 of whom died. Age, biochemical and hematological parameters, Uludag Fournier’s Gangrene Severity Index (UFGSI), Fournier’s Gangrene Severity Index (FGSI), and Age-Adjusted Charlson Comorbidity Index (ACCI) scores were analyzed using the Mann Whitney U-test due to their non-parametric distribution. Categorical data such as comorbidities, gender, need for positive inotropes, diversion ostomy status, and UFGSI grading status was analyzed with the Chi-square test, and independent risk factors were determined from the significant data emerging from univariate and multivariate logistic regression analysis. Their strengths were compared using the logistic regression model (Fournier’s Gangrene Mortality Prediction Model: FGMPM) created through ROC analysis of the FGSI, UFGSI, and ACCI scores. RESULTS: The results of the statistical analyses showed that albumin (p<0.001) and need for positive inotropic support (p<0.001) were independent risk factors for mortality and ROC analysis revealed that the created FGMPM regression model (AUC: 0.995) was a stronger model than the FGSI (AUC: 0.874), UFGSI (0.893), and ACCI (0.788) scoring systems. CONCLUSION: The results of this study revealed that albumin and the need for positive inotropic support are independent risk factors for mortality. It is thought that the determination of these two parameters can be used to predict mortality more practically than the parameters used in the UFGSI and FGSI.