PURPOSE: Compared with radical retropubic prostatectomy (RRP), laparoscopic surgery significantly reduces postoperative discomfort, the length of the hospital stay and the length of the convalescent period. However, the procedure of laparoscopic radical prostatectomy (LRP) is difficult to master. Therefore, we considered the surgical technique that is minimally invasive like LRP and it does not require a considerable learning curve and brings good results, like conventional RRP. MATERIALS AND METHODS: From January to May 2006 at our institution, we performed LaMRRP on 7 patients who were diagnosed with clinically localized prostate cancer. Under general anesthesia, a 5cm midline incision was made suprapubically and a 10mm trocar is introduced extraperitoneally at the umbilicus for the camera. The assistant port consisted of a 5mm trocar that was placed for blood suction and for vesicourethral anastomosis. We performed the operation under direct vision through the small window and using a video monitor. The surgical procedures followed the steps employed in conventional RRP. RESULTS: We successfully performed the operation in all cases without any extension of the incision. The mean patient age is 68.4 years old (range: 59-75). The mean operating time was 320 min (range: 290-360). The mean blood loss was 1,380ml (range: 1,150-1,800). There were no major complications. The postoperative pain was noticeably reduced compared with conventional RRP. The urethral catheter was left in place for 18 days (range: 14-25) in all the patients and postoperative cystography showed no leakage. CONCLUSIONS: We could perform LaMRRP with using enhanced surgical views, and there was no long learning curve and no additional expense. The results of LaMRRP were not significantly different from that of conventional RRP. Therefore, LaMRRP could be a useful method for the treatment of localized prostate cancer.
Anesthesia, General ; Humans ; Laparoscopy* ; Laparotomy* ; Learning Curve ; Length of Stay ; Pain, Postoperative ; Prostate* ; Prostatectomy* ; Prostatic Neoplasms* ; Suction ; Surgical Instruments ; Umbilicus ; Urinary Catheters

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

OBJECTIVES: The study is investigated socioeconomic variations in self-rated health status and contribution of health behavioral factors in Korea. METHODS: A nationally representative sample (2,800 men and 3,230 women aged 20-64 years) from the 2005 Korea National Health and Nutrition Surveys was analyzed using logistic regression. RESULTS: Self-rated health was lower among lower socioeconomic groups compared with higher socioeconomic groups, with gender being irrelevant. This association was attenuated when health behavioral and socio-demographic factors were adjusted. When each health behavioral factor was considered separately, mediators such as smoking in men, and stress or exercise in women explained a large part of the decreased socioeconomic health inequalities. CONCLUSIONS: In Korea, subjective health inequalities arise from different socioeconomic status, but this difference is decreased by health behavioral factors. Therefore, socioeconomic inequity in self-rated health status can be corrected more effectively by promotional health behaviors.

Objectives: This study aimed to identify the current patterns of cancer incidence and estimate the projected cancer incidence and mortality between 2020 and 2035 in Korea. Methods: Data on cancer incidence cases were extracted from the Korean Statistical Information Service from 2000 to 2017, and data on cancer-related deaths were extracted from the National Cancer Center from 2000 to 2018. Cancer cases and deaths were classified according to the International Classification of Diseases, 10th edition. For the current patterns of cancer incidence, age-standardized incidence rates (ASIRs) and age-standardized mortality rates were investigated using the 2000 mid-year estimated population aged over 20 years and older. A joinpoint regression model was used to determine the 2020 to 2035 trends in cancer. Results: Overall, cancer cases were predicted to increase from 265 299 in 2020 to 474 085 in 2035 (growth rate: 1.8%). The greatest increase in the ASIR was projected for prostate cancer among male (7.84 vs. 189.53 per 100 000 people) and breast cancer among female (34.17 vs. 238.45 per 100 000 people) from 2000 to 2035. Overall cancer deaths were projected to increase from 81 717 in 2020 to 95 845 in 2035 (average annual growth rate: 1.2%). Although most cancer mortality rates were projected to decrease, those of breast, pancreatic, and ovarian cancer among female were projected to increase until 2035. Conclusions These up-to-date projections of cancer incidence and mortality in the Korean population may be a significant resource for implementing cancer-related regulations or developing cancer treatments.

Background: The increasing rate of excess body weight (EBW) in the global population has led to growing health concerns, including cancer-related EBW. We aimed to estimate the population attributable fraction (PAF) of cancer incidence and deaths linked to EBW in Korean individuals from 2015 to 2030 and to compare its value with various body mass index cutoffs. Methods: Levin’s formula was used to calculate the PAF; the prevalence rates were computed using the Korean National Health and Nutrition Examination Survey data, while the relative risks of specific cancers related to EBW were estimated based on the results of Korean cohort studies. To account for the 15-year latency period when estimating the PAF in 2020, the prevalence rates from 2015 and attributable cases or deaths from 2020 were used. Results: The PAF attributed to EBW was similar for both cancer incidence and deaths using either the World Health Organization (WHO) Asian-Pacific region standard or a modified Asian standard, with the WHO standard yielding the lowest values. In the Korean population, the PAFs of EBW for cancer incidence were 2.96% in men and 3.61% in women, while those for cancer deaths were 0.67% in men and 3.06% in women in 2020. Additionally, PAFs showed a gradual increase in both sexes until 2030. Conclusion The EBW continues to have a significant impact on cancer incidence and deaths in Korea. Effective prevention strategies targeting the reduction of this modifiable risk factor can substantially decrease the cancer burden.

Background: The increasing rate of excess body weight (EBW) in the global population has led to growing health concerns, including cancer-related EBW. We aimed to estimate the population attributable fraction (PAF) of cancer incidence and deaths linked to EBW in Korean individuals from 2015 to 2030 and to compare its value with various body mass index cutoffs. Methods: Levin’s formula was used to calculate the PAF; the prevalence rates were computed using the Korean National Health and Nutrition Examination Survey data, while the relative risks of specific cancers related to EBW were estimated based on the results of Korean cohort studies. To account for the 15-year latency period when estimating the PAF in 2020, the prevalence rates from 2015 and attributable cases or deaths from 2020 were used. Results: The PAF attributed to EBW was similar for both cancer incidence and deaths using either the World Health Organization (WHO) Asian-Pacific region standard or a modified Asian standard, with the WHO standard yielding the lowest values. In the Korean population, the PAFs of EBW for cancer incidence were 2.96% in men and 3.61% in women, while those for cancer deaths were 0.67% in men and 3.06% in women in 2020. Additionally, PAFs showed a gradual increase in both sexes until 2030. Conclusion The EBW continues to have a significant impact on cancer incidence and deaths in Korea. Effective prevention strategies targeting the reduction of this modifiable risk factor can substantially decrease the cancer burden.