Mohamad Habous1, Raanan Tal2, Alaa Tealab3, Tarek Soliman4, Mohammed Nassar1, Zenhom Mekawi1, Saad Mahmoud1, Osama Abdelwahab4, Mohamed Elkhouly1, Hatem Kamr1, Abdallah Remeah1, Saleh Binsaleh5, David Ralph6, John Mulhall2. 1. Urology and Andrology Department, Elaj Medical Centers, Jeddah, Saudi Arabia. 2. Sexual and Reproductive Medicine Program, Urology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 3. Urology, Zagazig University, Benha, Egypt. 4. Urology, Benha University, Benha, Egypt. 5. Division of Urology, Department of Surgery, Faculty of Medicine, King Saud University, Riyadh, Saudi Arabia. 6. St Peters Andrology Centre and The Institute of Urology, University College London Hospital (UCLH), London, UK.
Abstract
OBJECTIVES: To re-evaluate the role of diabetes mellitus (DM) as a risk factor for penile implant infection by exploring the association between glycated haemoglobin (HbA1c) levels and penile implant infection rates and to define a threshold value that predicts implant infection. PATIENTS AND METHODS: We conducted a multicentre prospective study including all patients undergoing penile implant surgery between 2009 and 2015. Preoperative, perioperative and postoperative management were identical for the entire cohort. Univariate analysis was performed to define predictors of implant infection. The HbA1c levels were analysed as continuous variables and sequential analysis was conducted using 0.5% increments to define a threshold level predicting implant infection. Multivariable analysis was performed with the following factors entered in the model: DM, HbA1C level, patient age, implant type, number of vascular risk factors (VRFs), presence of Peyronie's disease (PD), body mass index (BMI), and surgeon volume. A receiver operating characteristic (ROC) curve was generated to define the optimal HbA1C threshold for infection prediction. RESULTS: In all, 902 implant procedures were performed over the study period. The mean patient age was 56.6 years. The mean HbA1c level was 8.0%, with 81% of men having a HbA1c level of >6%. In all, 685 (76%) implants were malleable and 217 (24%) were inflatable devices; 302 (33.5%) patients also had a diagnosis of PD. The overall infection rate was 8.9% (80/902). Patients who had implant infection had significantly higher mean HbA1c levels, 9.5% vs 7.8% (P < 0.001). Grouping the cases by HbA1c level, we found infection rates were: 1.3% with HbA1c level of <6.5%, 1.5% for 6.5-7.5%, 6.5% for 7.6-8.5%, 14.7% for 8.6-9.5%, 22.4% for >9.5% (P < 0.001). Patient age, implant type, and number of VRFs were not predictive. Predictors defined on multivariable analysis were: PD, high BMI, and high HbA1c level, whilst a high-volume surgeon had a protective effect and was associated with a reduced infection risk. Using ROC analysis, we determined that a HbA1c threshold level of 8.5% predicted infection with a sensitivity of 80% and a specificity of 65%. CONCLUSION: Uncontrolled DM is associated with increased risk of infection after penile implant surgery. The risk is directly related to the HbA1c level. A threshold HbA1c level of 8.5% is suggested for clinical use to identify patients at increased infection risk.
OBJECTIVES: To re-evaluate the role of diabetes mellitus (DM) as a risk factor for penile implant infection by exploring the association between glycated haemoglobin (HbA1c) levels and penile implant infection rates and to define a threshold value that predicts implant infection. PATIENTS AND METHODS: We conducted a multicentre prospective study including all patients undergoing penile implant surgery between 2009 and 2015. Preoperative, perioperative and postoperative management were identical for the entire cohort. Univariate analysis was performed to define predictors of implant infection. The HbA1c levels were analysed as continuous variables and sequential analysis was conducted using 0.5% increments to define a threshold level predicting implant infection. Multivariable analysis was performed with the following factors entered in the model: DM, HbA1C level, patient age, implant type, number of vascular risk factors (VRFs), presence of Peyronie's disease (PD), body mass index (BMI), and surgeon volume. A receiver operating characteristic (ROC) curve was generated to define the optimal HbA1C threshold for infection prediction. RESULTS: In all, 902 implant procedures were performed over the study period. The mean patient age was 56.6 years. The mean HbA1c level was 8.0%, with 81% of men having a HbA1c level of >6%. In all, 685 (76%) implants were malleable and 217 (24%) were inflatable devices; 302 (33.5%) patients also had a diagnosis of PD. The overall infection rate was 8.9% (80/902). Patients who had implant infection had significantly higher mean HbA1c levels, 9.5% vs 7.8% (P < 0.001). Grouping the cases by HbA1c level, we found infection rates were: 1.3% with HbA1c level of <6.5%, 1.5% for 6.5-7.5%, 6.5% for 7.6-8.5%, 14.7% for 8.6-9.5%, 22.4% for >9.5% (P < 0.001). Patient age, implant type, and number of VRFs were not predictive. Predictors defined on multivariable analysis were: PD, high BMI, and high HbA1c level, whilst a high-volume surgeon had a protective effect and was associated with a reduced infection risk. Using ROC analysis, we determined that a HbA1c threshold level of 8.5% predicted infection with a sensitivity of 80% and a specificity of 65%. CONCLUSION: Uncontrolled DM is associated with increased risk of infection after penile implant surgery. The risk is directly related to the HbA1c level. A threshold HbA1c level of 8.5% is suggested for clinical use to identify patients at increased infection risk.
Authors: Eric Chung; Carlo Bettocchi; Paulo Egydio; Chris Love; Daniar Osmonov; Sean Park; David Ralph; Zhong Cheng Xin; Gerald Brock Journal: Nat Rev Urol Date: 2022-06-16 Impact factor: 16.430
Authors: Mohamad M Osman; Linda M Huynh; Farouk M El-Khatib; Maxwell Towe; Huang-Wei Su; Robert Andrianne; Gregory Barton; Gregory Broderick; Arthur L Burnett; Jeffrey D Campbell; Jonathan Clavell-Hernandez; Jessica Connor; Martin Gross; Ross Guillum; Amy I Guise; Georgios Hatzichristodoulou; Gerard D Henry; Tung-Chin Hsieh; Lawrence C Jenkins; Christopher Koprowski; Kook B Lee; Aaron Lentz; Ricardo M Munarriz; Daniar Osmonov; Shu Pan; Kevin Parikh; Sung Hun Park; Amir S Patel; Paul Perito; Hossein Sadeghi-Nejad; Maxime Sempels; Jay Simhan; Run Wang; Faysal A Yafi Journal: Int J Impot Res Date: 2020-03-20 Impact factor: 2.896