There are various methods for reconstructing defects caused by Mohs micrographic surgery (MMS). However, there are limits to the reconstruction methods that can be used if the defect is large. An 85-year-old woman presented with a 2.4×2.2 cm hyperkeratotic plaque on her right temple for 2 years. A skin biopsy was performed for a diagnosis. Histopathology confirmed squamous cell carcinoma, and MMS was performed to completely remove the tumor. A total of three MMS stages were performed intraoperatively to confirm margin clear, resulting in a skin defect measuring 5.0×4.5 cm. To reconstruct the large defect, a splitted full thickness skin graft was performed, taking into account the site, size, and function of the defect. Each skin graft was harvested from the submental area and a tie-over bolster dressing was applied to the recipient site. To date, the surgical site has remained free of surgical complications or tumor recurrence.

There are various methods for reconstructing defects caused by Mohs micrographic surgery (MMS). However, there are limits to the reconstruction methods that can be used if the defect is large. An 85-year-old woman presented with a 2.4×2.2 cm hyperkeratotic plaque on her right temple for 2 years. A skin biopsy was performed for a diagnosis. Histopathology confirmed squamous cell carcinoma, and MMS was performed to completely remove the tumor. A total of three MMS stages were performed intraoperatively to confirm margin clear, resulting in a skin defect measuring 5.0×4.5 cm. To reconstruct the large defect, a splitted full thickness skin graft was performed, taking into account the site, size, and function of the defect. Each skin graft was harvested from the submental area and a tie-over bolster dressing was applied to the recipient site. To date, the surgical site has remained free of surgical complications or tumor recurrence.

There are various methods for reconstructing defects caused by Mohs micrographic surgery (MMS). However, there are limits to the reconstruction methods that can be used if the defect is large. An 85-year-old woman presented with a 2.4×2.2 cm hyperkeratotic plaque on her right temple for 2 years. A skin biopsy was performed for a diagnosis. Histopathology confirmed squamous cell carcinoma, and MMS was performed to completely remove the tumor. A total of three MMS stages were performed intraoperatively to confirm margin clear, resulting in a skin defect measuring 5.0×4.5 cm. To reconstruct the large defect, a splitted full thickness skin graft was performed, taking into account the site, size, and function of the defect. Each skin graft was harvested from the submental area and a tie-over bolster dressing was applied to the recipient site. To date, the surgical site has remained free of surgical complications or tumor recurrence.

There are various methods for reconstructing defects caused by Mohs micrographic surgery (MMS). However, there are limits to the reconstruction methods that can be used if the defect is large. An 85-year-old woman presented with a 2.4×2.2 cm hyperkeratotic plaque on her right temple for 2 years. A skin biopsy was performed for a diagnosis. Histopathology confirmed squamous cell carcinoma, and MMS was performed to completely remove the tumor. A total of three MMS stages were performed intraoperatively to confirm margin clear, resulting in a skin defect measuring 5.0×4.5 cm. To reconstruct the large defect, a splitted full thickness skin graft was performed, taking into account the site, size, and function of the defect. Each skin graft was harvested from the submental area and a tie-over bolster dressing was applied to the recipient site. To date, the surgical site has remained free of surgical complications or tumor recurrence.

Background: Sepsis is a leading cause of intensive care unit (ICU) admission. However, few studies have evaluated how the ICU model affects the outcomes of patients with sepsis. Methods: This post hoc analysis of data from the Management of Severe Sepsis in Asia’s Intensive Care Units II study included 537 patients with sepsis admitted to 27 ICUs in Korea. The outcome measures of interest were compared between the closed ICU group, patients admitted under the full responsibility of an intensivist as the primary attending physician, and the open ICU group. The association between a closed ICU and ICU mortality was evaluated using a logistic regression analysis. Results: Altogether, 363 and 174 enrolled patients were treated in open and closed ICUs, respectively. Compliance with the sepsis bundles did not differ between the two groups; however, the closed ICU group had a higher rate of renal replacement therapy and shorter duration of ventilator support. The closed ICU group also had a lower ICU mortality rate than the open ICU group (24.7% vs. 33.1%). In a logistic regression analysis, management in the closed ICU was significantly associated with a decreased ICU mortality rate even after adjusting for potential confounding factors (adjusted odds ratio, 0.576; 95% CI, 0.342–0.970), and that association was observed for up to 90 days. Conclusions Sepsis management in closed ICUs was significantly associated with improved ICU survival and decreased length of ICU stay, even though the compliance rates for the sepsis bundles did not differ between open and closed ICUs.

This study aimed to develop a machine learning-based 2-year risk prediction model for early identification of patients with rapid progressive immunoglobulin A nephropathy (IgAN). We also assessed the model’s performance to predict the long-term kidney-related outcome of patients. Methods: A retrospective cohort of 1,301 patients with biopsy-proven IgAN from two tertiary hospitals was used to derive and externally validate a random forest-based prediction model predicting primary outcome (30% decline in estimated glomerular filtration rate from baseline or end-stage kidney disease requiring renal replacement therapy) and secondary outcome (improvement of proteinuria) within 2 years after kidney biopsy. Results: For the 2-year prediction of primary outcomes, precision, recall, area-under-the-curve, precision-recall-curve, F1, and Brier score were 0.259, 0.875, 0.771, 0.242, 0.400, and 0.309, respectively. The values for the secondary outcome were 0.904, 0.971, 0.694, 0.903, 0.955, and 0.113, respectively. From Shapley Additive exPlanations analysis, the most informative feature identifying both outcomes was baseline proteinuria. When Kaplan-Meier analysis for 10-year kidney outcome risk was performed with three groups by predicting probabilities derived from the 2-year primary outcome prediction model (low, moderate, and high), high (hazard ratio [HR], 13.00; 95% confidence interval [CI], 9.52–17.77) and moderate (HR, 12.90; 95% CI, 9.92–16.76) groups showed higher risks compared with the low group. From the 2-year secondary outcome prediction model, low (HR, 1.66; 95% CI, 1.42–1.95) and moderate (HR, 1.42; 95% CI, 0.99–2.03) groups were at greater risk for 10-year prognosis than the high group. Conclusion: Our machine learning-based 2-year risk prediction models for the progression of IgAN showed reliable performance and effectively predicted long-term kidney outcome.

This study aimed to develop a machine learning-based 2-year risk prediction model for early identification of patients with rapid progressive immunoglobulin A nephropathy (IgAN). We also assessed the model’s performance to predict the long-term kidney-related outcome of patients. Methods: A retrospective cohort of 1,301 patients with biopsy-proven IgAN from two tertiary hospitals was used to derive and externally validate a random forest-based prediction model predicting primary outcome (30% decline in estimated glomerular filtration rate from baseline or end-stage kidney disease requiring renal replacement therapy) and secondary outcome (improvement of proteinuria) within 2 years after kidney biopsy. Results: For the 2-year prediction of primary outcomes, precision, recall, area-under-the-curve, precision-recall-curve, F1, and Brier score were 0.259, 0.875, 0.771, 0.242, 0.400, and 0.309, respectively. The values for the secondary outcome were 0.904, 0.971, 0.694, 0.903, 0.955, and 0.113, respectively. From Shapley Additive exPlanations analysis, the most informative feature identifying both outcomes was baseline proteinuria. When Kaplan-Meier analysis for 10-year kidney outcome risk was performed with three groups by predicting probabilities derived from the 2-year primary outcome prediction model (low, moderate, and high), high (hazard ratio [HR], 13.00; 95% confidence interval [CI], 9.52–17.77) and moderate (HR, 12.90; 95% CI, 9.92–16.76) groups showed higher risks compared with the low group. From the 2-year secondary outcome prediction model, low (HR, 1.66; 95% CI, 1.42–1.95) and moderate (HR, 1.42; 95% CI, 0.99–2.03) groups were at greater risk for 10-year prognosis than the high group. Conclusion: Our machine learning-based 2-year risk prediction models for the progression of IgAN showed reliable performance and effectively predicted long-term kidney outcome.

This study aimed to develop a machine learning-based 2-year risk prediction model for early identification of patients with rapid progressive immunoglobulin A nephropathy (IgAN). We also assessed the model’s performance to predict the long-term kidney-related outcome of patients. Methods: A retrospective cohort of 1,301 patients with biopsy-proven IgAN from two tertiary hospitals was used to derive and externally validate a random forest-based prediction model predicting primary outcome (30% decline in estimated glomerular filtration rate from baseline or end-stage kidney disease requiring renal replacement therapy) and secondary outcome (improvement of proteinuria) within 2 years after kidney biopsy. Results: For the 2-year prediction of primary outcomes, precision, recall, area-under-the-curve, precision-recall-curve, F1, and Brier score were 0.259, 0.875, 0.771, 0.242, 0.400, and 0.309, respectively. The values for the secondary outcome were 0.904, 0.971, 0.694, 0.903, 0.955, and 0.113, respectively. From Shapley Additive exPlanations analysis, the most informative feature identifying both outcomes was baseline proteinuria. When Kaplan-Meier analysis for 10-year kidney outcome risk was performed with three groups by predicting probabilities derived from the 2-year primary outcome prediction model (low, moderate, and high), high (hazard ratio [HR], 13.00; 95% confidence interval [CI], 9.52–17.77) and moderate (HR, 12.90; 95% CI, 9.92–16.76) groups showed higher risks compared with the low group. From the 2-year secondary outcome prediction model, low (HR, 1.66; 95% CI, 1.42–1.95) and moderate (HR, 1.42; 95% CI, 0.99–2.03) groups were at greater risk for 10-year prognosis than the high group. Conclusion: Our machine learning-based 2-year risk prediction models for the progression of IgAN showed reliable performance and effectively predicted long-term kidney outcome.