Role of SFGSI, microbial culture and qSOFA as predictive factors in determining the survival rate in Fournier Gangrene patient.

OBJECTIVES: To examine the simplified Fournier Gangrene Severe Index Score (SFGSI) and the number of species in culture findings for predicting death in Fournier Gangrene (FG) patients in terms of their predictive power. METHODS: From January 2017 to July 2022, the medical records of individuals undergoing emergency surgery for FG were obtained. A total of 80 patients were examined for clinical data such as age, gender, laboratory parameters, etiology, isolated bacteria, and mortality rate. RESULTS: We identified a statistically significant mean difference between SFGSI (p<0.0001) and quickSOFA (qSOFA) scores (p=0.002) in determining the survival rate of FG patients. The sensitivity and specificity of the SFGSI score in predicting mortality were 90.1% and 88.3% respectively, whereas the sensitivity and specificity of the qSOFA score were 88.2% and 86.2%. E. Coli comprised 56.2% of the bacteria, followed by S. Haemolyticus, S. Aureus, P. Aeruginosa, and K. Pneumoniae. On the basis of bacterial culture results, P. Aeruginosa had the highest fatality rate (100%) followed by S. Aureus (75%), S. Haemolyticus (30%), and E. Coli (20%), in that order. CONCLUSION: The survival rate of FG patients can be predicted using the sensitivity and specificity of the SFGSI and qSOFA scores together. P. Aeruginosa-infected patients have the greatest mortality rate (100%) compared to the other groups.

Indonesian prostate cancer risk calculator (IPCRC): an application for predicting prostate cancer risk (a multicenter study).

AIM: to develop a prediction risk model of prostate cancer based on Indonesia population. METHODS: we included all benign prostate hyperthrophy (BPH) and PCa patients who had prostate biopsy and prostatectomy between January 2009 and December 2013 from 5 urology centers in Indonesia. The relationship between the possibility of PCa with the following variables including: age; PSA level, prostate volume (by transabdominal ultrasound or transrectal ultrasound) and digital rectal examination (DRE) finding. We calculated a predictive scoring equation to predict the possibility of PCa using chi-square analysis, Kolmogorov-Smirnov test, multiple logistic regression and ROC curve. Then, we designed an application for predicting prostate cancer risk called Indonesian Prostate Cancer Risk Calculator (IPCRC). RESULTS: there were 784 PCa and 1173 BPH patients were used for developing the risk calculator in our study. The mean ages, PSA and prostate volume are 66.9±8.1 years old; 72.4±248.9 ng/ml and 49.6±28.2 ml, respectively. Abnormal DRE was found in 637 PCa and 56 BPH. We included age, PSA level, abnormal DRE finding (all showed significant p<0.05 in univariate model). Additionally, although not significant, we included prostate volume (p=0.157) due to its clinical importance. The corrected ROC analysis showed AUC 0.935, sensitivity of 90.1% and specificity 80% in predicting the prostate cancer in our population. CONCLUSION: we have developed the Indonesian Prostate Cancer Risk Calculator which includes age, PSA, DRE, and prostate volume as its variables. Future prospective study to validate the risk calculator is needed.