Purpose: We investigated the clinical features, current negative-pressure wound therapy (NPWT) management strategies, and outcomes of pelvic-perineal soft tissue infection after open pelvic fractures. Materials and Methods: We analyzed the data of patients admitted to our trauma center with pelvic-perineal soft tissue after open pelvic fractures over a 7-year period. We investigated the injury severity score (ISS), medical costs, number of NPWTs, time required to reach definite wound coverage, complications, fracture classifications, transfusion requirements, interventions, length of stay (LOS) in hospital and intensive care unit (ICU), and prognosis. Results: Twenty patients with open pelvic fractures were treated with NPWT, and one patient who underwent NPWT died of pelvic sepsis during ICU treatment. The median LOS in hospital and medical costs were 98 [56–164] days and 106400 [65600–171100] USD, respectively. Patients treated with instillation NPWT (iNPWT, n=10) had a shorter NPWT duration (24 [13–39] vs. 46 [42–91] days, p=0.023), time to definite wound coverage (30 [21–43] vs. 49 [42–93] days, p=0.026), and hospital LOS (56 [43–72] vs. 158 [101–192] days, p=0.001), as well as lower medical costs (67800 [42500–102500] vs. 144200 [110400–236000] USD, p=0.009) compared to those treated with conventional NPWT. Conclusion NPWT is a feasible method for treating pelvic soft tissue infections in patients with open pelvic fractures. iNPWT can reduce the duration of NPWT, hospital LOS, and medical costs.

Purpose: We investigated the clinical features, current negative-pressure wound therapy (NPWT) management strategies, and outcomes of pelvic-perineal soft tissue infection after open pelvic fractures. Materials and Methods: We analyzed the data of patients admitted to our trauma center with pelvic-perineal soft tissue after open pelvic fractures over a 7-year period. We investigated the injury severity score (ISS), medical costs, number of NPWTs, time required to reach definite wound coverage, complications, fracture classifications, transfusion requirements, interventions, length of stay (LOS) in hospital and intensive care unit (ICU), and prognosis. Results: Twenty patients with open pelvic fractures were treated with NPWT, and one patient who underwent NPWT died of pelvic sepsis during ICU treatment. The median LOS in hospital and medical costs were 98 [56–164] days and 106400 [65600–171100] USD, respectively. Patients treated with instillation NPWT (iNPWT, n=10) had a shorter NPWT duration (24 [13–39] vs. 46 [42–91] days, p=0.023), time to definite wound coverage (30 [21–43] vs. 49 [42–93] days, p=0.026), and hospital LOS (56 [43–72] vs. 158 [101–192] days, p=0.001), as well as lower medical costs (67800 [42500–102500] vs. 144200 [110400–236000] USD, p=0.009) compared to those treated with conventional NPWT. Conclusion NPWT is a feasible method for treating pelvic soft tissue infections in patients with open pelvic fractures. iNPWT can reduce the duration of NPWT, hospital LOS, and medical costs.

Purpose: We investigated the clinical features, current negative-pressure wound therapy (NPWT) management strategies, and outcomes of pelvic-perineal soft tissue infection after open pelvic fractures. Materials and Methods: We analyzed the data of patients admitted to our trauma center with pelvic-perineal soft tissue after open pelvic fractures over a 7-year period. We investigated the injury severity score (ISS), medical costs, number of NPWTs, time required to reach definite wound coverage, complications, fracture classifications, transfusion requirements, interventions, length of stay (LOS) in hospital and intensive care unit (ICU), and prognosis. Results: Twenty patients with open pelvic fractures were treated with NPWT, and one patient who underwent NPWT died of pelvic sepsis during ICU treatment. The median LOS in hospital and medical costs were 98 [56–164] days and 106400 [65600–171100] USD, respectively. Patients treated with instillation NPWT (iNPWT, n=10) had a shorter NPWT duration (24 [13–39] vs. 46 [42–91] days, p=0.023), time to definite wound coverage (30 [21–43] vs. 49 [42–93] days, p=0.026), and hospital LOS (56 [43–72] vs. 158 [101–192] days, p=0.001), as well as lower medical costs (67800 [42500–102500] vs. 144200 [110400–236000] USD, p=0.009) compared to those treated with conventional NPWT. Conclusion NPWT is a feasible method for treating pelvic soft tissue infections in patients with open pelvic fractures. iNPWT can reduce the duration of NPWT, hospital LOS, and medical costs.

Purpose: We investigated the clinical features, current negative-pressure wound therapy (NPWT) management strategies, and outcomes of pelvic-perineal soft tissue infection after open pelvic fractures. Materials and Methods: We analyzed the data of patients admitted to our trauma center with pelvic-perineal soft tissue after open pelvic fractures over a 7-year period. We investigated the injury severity score (ISS), medical costs, number of NPWTs, time required to reach definite wound coverage, complications, fracture classifications, transfusion requirements, interventions, length of stay (LOS) in hospital and intensive care unit (ICU), and prognosis. Results: Twenty patients with open pelvic fractures were treated with NPWT, and one patient who underwent NPWT died of pelvic sepsis during ICU treatment. The median LOS in hospital and medical costs were 98 [56–164] days and 106400 [65600–171100] USD, respectively. Patients treated with instillation NPWT (iNPWT, n=10) had a shorter NPWT duration (24 [13–39] vs. 46 [42–91] days, p=0.023), time to definite wound coverage (30 [21–43] vs. 49 [42–93] days, p=0.026), and hospital LOS (56 [43–72] vs. 158 [101–192] days, p=0.001), as well as lower medical costs (67800 [42500–102500] vs. 144200 [110400–236000] USD, p=0.009) compared to those treated with conventional NPWT. Conclusion NPWT is a feasible method for treating pelvic soft tissue infections in patients with open pelvic fractures. iNPWT can reduce the duration of NPWT, hospital LOS, and medical costs.

Purpose: We investigated the clinical features, current negative-pressure wound therapy (NPWT) management strategies, and outcomes of pelvic-perineal soft tissue infection after open pelvic fractures. Materials and Methods: We analyzed the data of patients admitted to our trauma center with pelvic-perineal soft tissue after open pelvic fractures over a 7-year period. We investigated the injury severity score (ISS), medical costs, number of NPWTs, time required to reach definite wound coverage, complications, fracture classifications, transfusion requirements, interventions, length of stay (LOS) in hospital and intensive care unit (ICU), and prognosis. Results: Twenty patients with open pelvic fractures were treated with NPWT, and one patient who underwent NPWT died of pelvic sepsis during ICU treatment. The median LOS in hospital and medical costs were 98 [56–164] days and 106400 [65600–171100] USD, respectively. Patients treated with instillation NPWT (iNPWT, n=10) had a shorter NPWT duration (24 [13–39] vs. 46 [42–91] days, p=0.023), time to definite wound coverage (30 [21–43] vs. 49 [42–93] days, p=0.026), and hospital LOS (56 [43–72] vs. 158 [101–192] days, p=0.001), as well as lower medical costs (67800 [42500–102500] vs. 144200 [110400–236000] USD, p=0.009) compared to those treated with conventional NPWT. Conclusion NPWT is a feasible method for treating pelvic soft tissue infections in patients with open pelvic fractures. iNPWT can reduce the duration of NPWT, hospital LOS, and medical costs.

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.

Melorheostosis is a rare type of sclerosing bone dysplasia with an incidence of approximately 0.9 per million individuals. This disease predominantly affects the appendicular skeleton, with rare involvement of the axial skeleton. Patients with spinal melorheostosis may present with symptoms such as scoliosis, stiffness, back pain, progressive myelopathy, radiculopathy, and vertebrobasilar insufficiency. Surgical management for spinal melorheostosis has been reported, but it remains exceedingly rare. Here, we present the case of a 67-year-old woman with incidental findings on thoracic vertebral imaging from a preoperative chest computed tomography scan performed for shoulder surgery. The patient had experienced gait disturbances and mild, motion-related back pain for approximately 2 to 3 years, along with a recent symptom of mild tingling sensations in both feet. A diagnosis of spinal melorheostosis was considered based on the characteristic imaging findings. In this case, rather than prioritizing the severity of the current symptoms, we focused on the location and extent of osteosclerotic lesions, which are directly associated with the potential development of neurological complications. Therefore, we opted for surgical treatment involving decompression and screw fixation. The patient’s symptoms were relieved without significant surgical complications over a 1-year follow-up period.

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.