Risultati per: Le cause della polmonite nei bambini

Stroke, Volume 56, Issue Suppl_1, Page ATMP100-ATMP100, February 1, 2025. Introduction:Food insecurity, defined as limited access to nutritious food due to financial challenges, has grown substantially over the past two decades in the United States. Stroke survivors are more likely to experience food insecurity compared to the general population. However, the impact of food insecurity on long-term survival post-stroke remains unclear. Hence, we aimed to determine associations between food insecurity post-stroke and all-cause mortality.Methods:The National Health and Nutrition Examination Survey (NHANES) is an iterative cross-sectional study representative of the US population. Using data from 1999-2018, we assessed associations between food insecurity and all-cause mortality among stroke survivors and the general NHANES population, using linkage to death certificates from the National Death Index. Food security was assessed using ten NHANES questions and dichotomized into food security and food insecurity. A Cox proportional hazards model was used to evaluate associations between food insecurity and mortality adjusting for NHANES weighting, sex, age, race/ethnicity, education, marital status, poverty income ratio, BMI, diabetes, stroke, myocardial infarction, and cancer.Results:Among 101,316 NHANES participants, 2,197 (2.2%) self-reported as stroke survivors (mean age 67.6y, 50.8% female). Stroke survivors were more often food insecure compared to the general NHANES population (17.6% [SE:1.3%] vs. 14.3% [SE:0.4%], p=0.0038). Among 1,754 (78.9%) stroke survivors with linked death records, 801 died during a median follow-up of 105 months (IQR 56-159). The 10-year all-cause mortality rate among stroke survivors was 36.2% (SE: 3.0%) for the food secure and 48.4% (SE: 1.8%) for the food insecure group. In the general NHANES population, the corresponding rates were 10.9% (SE: 0.2%) and 14.2% (SE: 0.4%), respectively (Fig 1). Regression analysis showed that food insecurity was associated with a 31% increased risk of mortality in stroke survivors (HR: 1.31, 95%CI:1.00-1.73, P=0.049) and a 40% increase in the general NHANES population (HR: 1.40, 95%CI:1.25-1.57, P

Stroke, Volume 56, Issue Suppl_1, Page AWP324-AWP324, February 1, 2025. Previous studies identified black race as a risk factor for stroke in cancer settings. Yet, no studies have sought to identify the risk factors associated with stroke as a cause of death in black cancer patients. This study aimed to use advanced machine learning techniques to predict stroke mortality in black cancer patients and to identify significant risk factors. We utilized The Surveillance, Epidemiology, and End Results 17 registry to predict stroke as a cause of death in Black cancer patients from January 1, 2000, to December 31, 2020. Various machine learning models were employed, including Random Forest, Logistic Regression, XGBoost, LightGBM, and Gradient Boosting. The dataset included features such as age, sex, cancer type, year of diagnosis, and treatment modalities. Data preprocessing involved cleaning, feature selection, and 80-20 stratified train-test splitting. The models were combined into a voting ensemble to leverage their strengths. The performance of each model was evaluated using metrics including accuracy, AUC (macro, micro, weighted), precision, recall, and F1 scores. Feature importance was analyzed to identify the most contributing variables in predicting stroke death. A total of 292,680 black cancer patients were included, with a mean age of 57.36 ± 13.68 years. The cohort comprised 152,991 males (52.27%) and 139,689 females (47.73%). Among them, 5,874 patients (2.01%) died from stroke, while 286,806 patients are alive. The voting ensemble model achieved an accuracy of 0.98, with an AUC macro of 0.88, AUC micro of 0.99, and AUC weighted of 0.88. The average precision scores were 0.64 (macro), 0.99 (micro), and 0.98 (weighted). The F1 scores were 0.64 (macro), 0.98 (micro), and 0.97 (weighted). The precision scores were 0.78 (macro), 0.98 (micro), and 0.97 (weighted). Significant predictors of stroke death included an earlier year of diagnosis, older age, lack of any cancer treatment, and specific cancer types (notably gastrointestinal and male genital cancers). Our findings suggest that the voting ensemble machine learning model can effectively predict stroke mortality in black cancer patients, with high accuracy and robust performance metrics. These insights could inform targeted interventions to reduce stroke risk in this population, contributing to improved clinical outcomes and survival rates. Future studies should assess the impact of immunotherapy on stroke risk to further refine treatment approaches.

Stroke, Volume 56, Issue Suppl_1, Page ATP262-ATP262, February 1, 2025. Introduction:Fibrocartilaginous embolism (FCE) is a rare etiology of spinal cord infarction that occurs when nucleus pulposus material from intervertebral discs spontaneously embolizes into a spinal artery. Younger patients are at higher risk because arterial vascularization of the nucleus pulposus is present from early life through adolescence after which it regresses.Methods:Case reportResults:An 11yo F presented with sudden onset back pain and lower extremity sensory disturbances following an ATV ride that quickly evolved into an ascending paralysis with associated urinary retention. Exam showed 4/5 strength in proximal UEs, 2/5 grip strength on the right, and 0/5 strength in LEs with associated areflexia and decreased sensation to light touch and pinprick but preserved vibratory sense. CSF studies were unremarkable with no albuminocytologic dissociation or lymphocytic pleocytosis. EMG/NCS revealed absent F-waves consistent with possible early demyelinating polyradiculopathy. She was started on IVIG given concern for AIDP. Extensive work-up including TSH, ESR, CRP, B1, B12, heavy metals, C. jejuni Ag, stool cx, blood cx, ANCA vasculitis panel, RMSF and Arbovirus Abs, and AchR and MuSK Abs was unremarkable. MRI revealed diffusion restriction in the cervicothoracic cord without enhancement (Fig 1). She had no improvement of her symptoms after completing 5 days of IVIG. She also developed long-tract signs including +Babinski on the left and LE spasticity. Repeat imaging 4 days from prior revealed adjacent vertebral body with area of T2 hyperintensity concerning for bony infarct (Fig 2). Given her rapid onset of symptoms, lack of improvement after treatment with IVIG, and development of long-tract signs, she was given a diagnosis of spinal cord infarction. FCE was deemed the etiology given the presence of adjacent bony infarct suggesting embolic phenomenon and the lack of evidence of CNS inflammation, infection, or other etiology. Repeat EMG/NCS showed evidence of severe reduction in the amplitudes of the compound motor action potentials which can be seen in compromise of the motor neuron population due to spinal cord infarction (Fig 3). She was discharged to inpatient rehab.Conclusion:This report alerts clinicians to FCE as a rare etiology of ischemic myelopathy that should be considered in patients who present with sudden, painful onset followed by “stroke-in-evolution” pattern of progression that may resemble the ascending paralysis seen in AIDP.

Stroke, Volume 56, Issue Suppl_1, Page AWP269-AWP269, February 1, 2025. Introduction:All-cause dementia remains a significant public health concern, with stroke recognized as a key risk factor. Few studies have applied Machine Learning (ML) models to accurately predict cognitive impairment and dementia, yet none have specifically focused on post-stroke dementia risk prediction. This study aims to compare the efficacy of ML approaches and traditional biostatistical methods for predicting the incidence of one-year post-stroke all-cause dementia using electronic health record (EHR) data.Methods:We analyzed de-identified data extracted from the TriNetX network, covering 60 healthcare organizations. This study included patients aged 20+ who experienced their first stroke (any type) in 2018 (baseline). We excluded those with dementia history, lacking data 3 years after stroke onset, or without relevant health data within 3 years preceding stroke. We developed four models: Logistic regression (LR) with backward selection, regularized LR (LASSO and Ridge regression), and Random Forest (RF). The primary outcome was the incidence of all-cause dementia within one year post-stroke. Covariates included demographics, comorbidities, medications, laboratory measures, and vital signs. Model performance was evaluated using accuracy and the area under the curve (AUC) of the receiver operating characteristic (ROC).Results:The final cohort comprised 55,888 adults, of whom 8% developed all-cause dementia within the subsequent year. The sample was 48.4% female, with a distribution of 8.7% aged 20-44, 37.2% aged 45-64, and 54.0% aged 65+. About 64% were non-Hispanic Whites. Among those who developed dementia, 49.7% were female and 80.5% were 65+. They had slightly higher systolic blood pressure, lower BMI, higher rates of comorbidities, and medication use (Table 1). Performance metrics for the models were as follows: LR with backward selection (accuracy: 92.07%; AUC: 0.8033), LASSO regression (92.09%;0.8000), Ridge regression (92.04%; 0.8026), and RF (92.20%; 0.7828) (Table 2).Conclusion:This study demonstrated the feasibility of using ML models to accurately predict post-stroke all-cause dementia incidence. All models showed high accuracy and robust discriminative ability, with the RF model achieving the best accuracy and traditional LR displaying the highest AUC. ML approaches can effectively learn from the data to identify individuals at higher risk of post-stroke dementia, potentially enabling targeted interventions and improved patient care.

Studio su cinque Paesi, è la principale causa di polmoniti

Ricerca italiana apre alla diagnosi con un prelievo

New England Journal of Medicine, Volume 392, Issue 4, January 23, 2025.

Nell’ultima settimana sono 841mila gli italiani a letto

Recently, in Canada, Thornton et al observed a higher proportion of anti-hepatitis E virus (HEV) IgG among persons with cystic fibrosis having received (20.7%) or not (19.3%) a lung transplantation compared with a non-cystic fibrosis population (10.7%).1 In order to understand the difference in the seropositivity rate between these populations, they focused their research on the use of pancreatic enzyme replacement therapy (PERT).1 Indeed, pancreatic insufficiency is quite common in patients with cystic fibrosis, requiring PERT. PERT is porcine derived. Pork is one of the main reservoirs of HEV.2 Zoonotic transmission of viruses through PERT was previously recognised.3 Thornton et al detected HEV RNA in 44% of PERT capsules obtained from different formulations and produced by four different manufacturers.1 In their study, 3 out of 29 lung transplant patients with cystic fibrosis have detectable HEV RNA and developed chronic…

Case presentation A woman in her 70s presented to an Australian centre with right upper quadrant pain, fevers and weight loss. She was born in Greece, having lived in Australia for 50 years. Significant background included chronic lymphocytic leukaemia (CLL), treated with venetoclax and rituximab. Her white cell count was 3.20×109/L with normal liver function tests, bilirubin of 6 µmol/L and C reactive protein of 15 mg/L. Cross-sectional imaging with CT found intrahepatic, extrahepatic, common bile duct dilatation (CBD) to 11 mm, pancreatic duct dilatation to 6 mm (a ‘double duct’ sign) and prominent para-aortic, coeliac and porta lymph nodes (figure 1A). Tumour markers including CA19-9 were normal. She was treated with intravenous ceftriaxone and metronidazole for suspected cholangitis. MR cholangiopancreatography was performed finding widespread upper abdominal lymph nodes suggestive of metastatic disease, with a 4 mm ampullary lesion (figure 1B). A CT chest was performed, showing…

Objective
The study investigates the association between oral microbiome diversity and all-cause mortality.

Design
Population-based cohort study.

Setting
US National Health and Nutrition Examination Survey (2009–2010 and 2011–2012).

Participants
A total of 8224 participants who had valid data on the oral microbiome diversity and survival through 31 December 2019 were included in this study.

Primary and secondary outcome measures
Oral microbiome diversity was measured using the observed number of amplicon sequence variant (ASV) and grouped into quartiles. Cox proportional hazards regression models were used to estimate the HR and 95% CI for all-cause mortality according to the quartiles of ASV number, adjusted for potential confounders.

Results
Among the 8224 participants (mean (SD) age: 42.0 (15.1) years; 49.9% male; 37.2% white, 23.8% black, 27.2% Hispanic and 11.8% other race/ethnicity), the median follow-up time was 108 months (IQR, 95–120 months) and 429 (5.2%) deaths were identified. Participants with a higher ASV number were more likely to be poor, non-Hispanic black or Hispanic, uninsured and current smokers, more likely to have poor self-rated oral health and periodontitis and less likely to use dental floss. However, compared with the lowest quartile of the ASV number, a suggestive association was observed for the second quartile (HR=0.80, 95% CI: 0.60 to 1.08), a significant reduction in all-cause mortality was observed for the third (HR=0.55, 95% CI: 0.37 to 0.82) and the fourth (HR=0.58, 95% CI: 0.38 to 0.89) quartile. The dose–response association for all-cause mortality risk was curvilinear; the protective association plateaued when the number of ASVs was larger than 120.

Conclusion
Despite being linked to greater socioeconomic disadvantages and poorer oral health, higher oral microbiome diversity was significantly associated with a substantial reduction in all-cause mortality.

Esibizione in Cardiochirurgia Pediatrica del Salesi

Clinical presentation A gentleman in his early 40s with a background of schizophrenia on clozapine presented with a 2-month history of rectal bleeding, diarrhoea, weight loss, a microcytic anaemia and a quantitative faecal immunochemical test (qFIT) result >400 µg Hb/g. Colonoscopy demonstrated multiple large polypoid lesions in the rectum and in the sigmoid colon; the sigmoid was unable to be passed by the colonoscope due to narrowing of the lumen (figure 1). Prior to histology being reported, CT colonography was performed to further assess the colon. It reported four malignant-appearing lesions in the rectum and sigmoid with suspicious sigmoid and retroperitoneal lymph nodes (figure 2). Question What is the diagnosis? Answer Histology reported Michaelis-Gutmann bodies, diagnostic of colonic malakoplakia (figure 3). Malakoplakia is a granulomatous condition associated with immunosuppression which may present with nodules, polyps or masses at colonoscopy….

Il progetto promuove corretti stili vita nella scuola primaria

Il progetto promuove corretti stili vita nella scuola primaria