Acute cerebral infarctions are rare in children, however, they can occur as a complication of a Mycoplasma pneumoniae (MP) infection due to direct invasion, vasculitis, or a hypercoagulable state. We report on the case of a 5-year-old boy who had an extensive stroke in multiple cerebrovascular territories 10 days after the diagnosis of MP infection. Based on the suspicion that the cerebral infarction was associated with a macrolide-resistant MP infection, the patient was treated with levofloxacin, methyl-prednisolone, intravenous immunoglobulin, and enoxaparin. Despite this medical management, cerebral vascular narrowing progressed and a decompressive craniectomy became necessary for the patient's survival. According to laboratory tests, brain magnetic resonance imaging, and clinical manifestations, the cerebral infarction in this case appeared to be due to the combined effects of hypercoagulability and cytokineinduced vascular inflammation.
Brain ; Cerebral Infarction* ; Child ; Child, Preschool ; Decompressive Craniectomy ; Diagnosis ; Enoxaparin ; Humans ; Immunoglobulins ; Inflammation ; Levofloxacin ; Magnetic Resonance Imaging ; Male ; Mycoplasma pneumoniae* ; Mycoplasma* ; Pneumonia, Mycoplasma* ; Stroke ; Thrombophilia ; Thrombosis ; Vasculitis

Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality. Methods: This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing. Results: Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700–1.000). Conclusions: The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.

Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality. Methods: This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing. Results: Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700–1.000). Conclusions: The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.

Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality. Methods: This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing. Results: Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700–1.000). Conclusions: The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.

Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality. Methods: This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing. Results: Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700–1.000). Conclusions: The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.

Phacomatosis pigmentovascularis (PPV) is a rare syndrome characterized by concurrent nevus flammeus (capillary malformation) and pigmentary nevus. According to current research, the major pathophysiologic mechanism in PPV is venous dysplasia with resultant compensatory collateral channels and venous hypertension. Arterial involvement is rare. We herein report our experience on renovascular hypertension, intermittent claudication, and severe rhabdomyolysis due to diffuse stenosis of multiple arteries in a patient with PPV type IIb associated with SWS.
Arteries ; Constriction, Pathologic ; Humans ; Hypertension ; Hypertension, Renovascular ; Intermittent Claudication ; Intracranial Aneurysm ; Neurocutaneous Syndromes* ; Nevus ; Port-Wine Stain ; Rhabdomyolysis* ; Sturge-Weber Syndrome* ; Vascular Diseases