PURPOSE: To investigate the clinical courses between phacoemulsification (PE) and PE with combined trabeculectomy (phacotrabeculectomy, PETL) in closed-angle patients with re-elevated intraocular pressure (IOP) after laser peripheral iridotomy (LPI). METHODS: Closed-angle patients whose IOP re-elevated between 19 and 38 mm Hg after LPI were included. Medical records of 26 patients in the PE group and 21 patients in the PETL group who were followed for more than 12 months after surgery were reviewed for clinical course. RESULTS: The IOP courses after surgery showed no statistical difference during the study period except at 1 and 7 days after surgery, in which IOP in the PETL group were lower than that in the PE group. The number of anti-glaucoma drugs also showed no significant difference except at 6 months, when the number was greater in the PE group. Success rates for IOP below 18 mm Hg at 3 years were 96.2% in the PE group, higher than the 69.8% in the PETL group (Log Rank test, p = 0.015). Postoperative complications were found in 2 patients in the PE group and in 8 patients in the PETL group (Fisher's exact test, p = 0.028). CONCLUSIONS: We suggest that PE is a viable surgical alternative to PETL in closed-angle patients who have mildly re-elevated IOP after LPI.
Humans ; Intraocular Pressure ; Medical Records ; Phacoemulsification ; Postoperative Complications ; Trabeculectomy

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: This study aims to evaluate and explain the socioeconomic inequalities of all-cause mortality after breast cancer surgery in South Korea. METHODS: This population based study included all 8868 females who underwent radical mastectomy for breast cancer between January 2002 and June 2003. Follow-up for mortality continued from January 2002 to June 2006. The patients were divided into 4 socioeconomic classes according to their socioeconomic status as defined by the National Health Insurance contribution rate. The relationship between socioeconomic status and all-cause mortality after breast cancer surgery was assessed using the Cox proportional hazards model with adjusting for age, the Charlson's index score, emergency hospitalization, the type of hospital and the hospital ownership. RESULTS: Those in the lowest socioeconomic status group had a significantly higher hazard ratio of 2.09 (95% CI =1.50 - 2.91) compared with those in the highest socioeconomic group after controlling for all the identifiable confounding variables. For all-cause mortality after radical mastectomy, all the other income groups showed significantly higher 3-year mortality rates than did the highest income group. CONCLUSIONS: The socioeconomic status of breast cancer patients should be considered as an independent prognostic factor that affects all-cause mortality after radical mastectomy, and this is possibly due to a delayed diagnosis, limited access or minimal treatment leading to higher mortality. This study may provide tangible support to intensify surveillance and treatment for breast cancer among low socioeconomic class women.
Adult ; Aged ; Breast Neoplasms/diagnosis/mortality/*surgery ; Cohort Studies ; Female ; Humans ; Mastectomy, Radical/mortality ; Middle Aged ; *Mortality ; Prognosis ; Republic of Korea/epidemiology ; Retrospective Studies ; Risk Factors ; Socioeconomic Factors

Cellulitis and deep vein thrombosis (DVT) have similar symptoms (lower extremity pain, erythema, and swelling) and there is the potential for misdiagnosis. In cases of cellulitis, DVT should be ruled out, as the symptoms of cellulitis might mask those of DVT, leading to serious complications such as pulmonary thromboembolism. The reported incidence of DVT in patients with cellulitis is low, especially patients with progression to pulmonary thromboembolism. We present a case of pulmonary thromboembolism following cellulitis of the lower leg in a 54-year-old male.
Cellulitis* ; Diagnostic Errors ; Erythema ; Extremities ; Humans ; Incidence ; Leg* ; Male ; Masks ; Middle Aged ; Pulmonary Embolism ; Venous Thrombosis*

We find out the clusters with high toxoplasmosis risk to discuss the geographical pattern in Gyodong-myeon and Samsan-myeon of Ganghwa-gun, Cheorwon-gun, and Goseong-gun, Korea. Seroepidemiological data of toxoplasmosis surveyed using rapid diagnostic tests for the residents in the areas in 2019 were analyzed to detect clusters of the infection. The cluster was investigated using the SaTScan program which is based on Kulldorff’s scan statistic. The clusters were found with P-values in each region analyzed in the program, and the risk and patient incidence of specific areas can be examined by the values such as relative risk and log likelihood ratio. Jiseok-ri and Insa-ri were found to be a cluster in Gyodong-myeon and Seokmo-ri was the cluster in Samsan-myeon. Yangji-ri and Igil-ri were found to be a cluster in Cheorwon-gun and Madal-ri and Baebong-ri were the cluster in Goseong-gun. This findings can be used to monitor and prevent toxoplasmosis infections occurring in vulnerable areas.

We find out the clusters with high toxoplasmosis risk to discuss the geographical pattern in Gyodong-myeon and Samsan-myeon of Ganghwa-gun, Cheorwon-gun, and Goseong-gun, Korea. Seroepidemiological data of toxoplasmosis surveyed using rapid diagnostic tests for the residents in the areas in 2019 were analyzed to detect clusters of the infection. The cluster was investigated using the SaTScan program which is based on Kulldorff’s scan statistic. The clusters were found with P-values in each region analyzed in the program, and the risk and patient incidence of specific areas can be examined by the values such as relative risk and log likelihood ratio. Jiseok-ri and Insa-ri were found to be a cluster in Gyodong-myeon and Seokmo-ri was the cluster in Samsan-myeon. Yangji-ri and Igil-ri were found to be a cluster in Cheorwon-gun and Madal-ri and Baebong-ri were the cluster in Goseong-gun. This findings can be used to monitor and prevent toxoplasmosis infections occurring in vulnerable areas.