Biomedical subjects

Hakan Gulkesen

Publications and source records attributed to Hakan Gulkesen.

2 recordsLinked to original sources

PubMed2005

Comparison of training modalities for performing laparoscopic radical prostatectomy: experience with 1,000 patients.

PURPOSE: We report a detailed analysis of different training modalities on the transferability of laparoscopic radical prostatectomy to generations of surgeons. MATERIAL AND METHODS: The first generation surgeon with experience with 600 cases and the second generation surgeon with 150 were trained in open retropubic radical prostatectomy and laparoscopy, whereas the third generation surgeon with 150 cases was trained only laparoscopically. The fourth generation of surgeons with a total of 50 cases was trained in our fellowship program. We analyzed groups of 50 operations. The groups were comparable with respect to patient age, prostate weight and pathological tumor stage. RESULTS: We observed a continual decrease in operative time between (322 to 247 minutes.) and within (332 to 196 minutes.) the analyzed groups. This result was also expressed in a decrease in the time required for anastomosis. A significant decrease was observed for the initial transfusion rate (4% to 10%). No difference was found in the complication rate (ie conversion in 8% to 0% of cases). Pathological outcomes (ie positive margins for pT2/pT3) were comparable in the first 3 surgeon groups (14.9%, 14.2% and 22%, respectively) and available functional results (followup greater than 2 years) did not reveal any influence of the learning curve. A learning curve was observed only for overall operative time and the time required for anastomosis but it was shown to be significantly shorter for the following generations. CONCLUSIONS: Based on a specific training program the personal level of education has a minor impact on the results and reproducibility of the laparoscopic radical prostatectomy technique.

Clinical Competence↗

PubMed2004

An application of a genetic algorithm in conjunction with other data mining methods for estimating outcome after hospitalization in cancer patients.

BACKGROUND: We investigated which factors predicted the risk of in-hospital mortality in a general population of cancer patients with non-terminal disease and whether employing the genetic algorithm technique would be useful in this regard. MATERIAL/METHODS: A total of 201 cancer patients, including all cases of in-hospital mortality over a 2-year period, as well as a control group of subjects discharged during the same period, all having an Eastern Cooperative Oncology Group (ECOG) performance status of of < or =3 at the time of admission, were retrospectively evaluated. Indicators of in-hospital mortality were determined by multivariate logistic regression, recursive partitioning analysis, neural network, and genetic algorithm (GA) techniques. The performance of the different techniques were compared by a number of measures, including receiver operating curve (ROC) analysis. RESULTS: All four analysis methods selected a combination of six explanatory variables to explain the risk of in-hospital mortality: lactate dehydrogenase (LDH), alanine transaminase (ALT), hemoglobin (Hb), white blood cell counts (Wbc), type of cancer, and reason for admission. Compared with the other 3 methods, GA selected the least number of explanatory variables, i.e. LDH and reason for admission, with similar fraction of cases explained (78.6%), and yielded a fitness score of 0.52. CONCLUSIONS: LDH is an important indicator of in-hospital mortality for hospitalized cancer patients not in terminal stage. GA reliably predicted in-hospital mortality and was shown to be as efficient as the other data mining techniques employed in this study. Its use in a clinical setting for prognostication in oncology appears promising.

Adolescent↗