Risultati per: Scoperte le cause biologiche della depressione

Al via la piattaforma online di psiconcologia ‘In buona salute’

Objectives
To evaluate how postmortem anthropometric malnutrition (PAM) measures align with expert panel attribution of malnutrition as a causal or significant condition in under-5 mortality (U5M).

Design
Cohort study using data from the Child Health and Mortality Prevention Surveillance network, incorporating clinical records, postmortem anthropometrics, minimally invasive tissue sampling, clinical abstraction and verbal autopsy to determine multiple causes of death.

Setting/participants
1405 deaths of children aged 1–59 months from six African countries between 2016 and 2023.

Primary and secondary outcome measures
PAM was determined using z-scores from the WHO Child Growth Standards: underweight (weight-for-age

Andreoni, ‘sovrainfezioni da più batteri a causa del prolungarsi del suo stato’

Il 30% più veloci per chi ne soffre

Background
Studies on avoidable mortality in adults with intellectual disabilities are limited, as are studies on causes of death.

Objectives
We aimed to quantify mortality rates, and causes, and identify factors (i.e., age, sex, Scottish Index of Multiple Deprivation (SIMD)) related to avoidable mortality in adults with intellectual disabilities.

Design
A record linkage national cohort study.

Setting
A cohort of adults with intellectual disabilities with or without co-occurring autism, aged 25+ years and a randomly selected comparison group aged 25+ years without intellectual disabilities or autism identified from Scotland’s Census, 2011. Census records were linked to the National Records of Scotland Statutory Register of Deaths database to ascertain all deaths from 2011 to 2019.

Participants
We analysed data on 14 477 adults with intellectual disabilities aged 25+ years and a randomly selected comparison group of 506 207 adults aged 25+ without intellectual disabilities identified from Scotland’s Census 2011.

Primary and secondary outcome measures
We ran 2 tests and t-tests to investigate individual characteristics and differences in age at death for adults with intellectual disabilities compared with peers in the general population. Cox proportional hazard models were fitted to calculate risk of mortality (all-cause, avoidable, treatable, preventable) unadjusted and adjusted for age, sex and SIMD. We then calculated mortality rates, using crude and indirect standardisation methods.

Results
During the 8.5-year follow-up, 23.7% (crude death rate of 3033.3 per 100 000) of adults with intellectual disabilities died compared with 13.8% of controls. The median age at death among adults aged 25+ with intellectual disabilities was 65.0 years compared with 80.0 years for adults without intellectual disabilities. For all-cause mortality, the age-standardised mortality ratio (SMR) in the population with intellectual disabilities was 3.1 (95% CI 3.0 to 3.2). The SMRs were higher for the youngest age groups, women and in the most affluent areas. This was also the case for SMRs for avoidable, treatable and preventable deaths. For the population of adults with intellectual disabilities, 31.7% of recorded deaths were considered avoidable, 21.1% were treatable and 19.9% were preventable. In the controls, 18.2% of deaths were considered avoidable, 8.8% treatable and 14.7% preventable. Down syndrome and dementia were the two most commonly recorded underlying causes of death for people with intellectual disabilities while malignant neoplasm of bronchus and lung and acute myocardial infarction were most commonly recorded in the general population.

Conclusions
Risk of all-cause, avoidable, treatable and preventable mortality was higher for adults with intellectual disabilities than their peers. The highest SMRs were observed for youngest adults, women and individuals living in the most affluent areas.

New England Journal of Medicine, Volume 392, Issue 7, Page 719-721, February 2025.

Clinical manifestation A 16-year-old Chinese woman presented with a 7-year history of recurrent abdominal pain, haematemesis, melena and anaemia. Her previous oesophagogastroduodenoscopies (OGD) revealed nodular gastritis with discontinuous mucosal folds of the corpus. Gastric mucosa bleeding was described once and sclerosant injection together with titanium clip were performed. Biopsies were taken multiple times which indicated increase of eosinophils in lamina propria (up to 100/HPF). She was diagnosed eosinophilic gastritis and treated with oral prednisolone. After weaning of prednisolone, the symptoms relapsed twice. She was allergic to both penicillin and cephalosporin antibiotics, having a history of urticaria and allergic rhinitis as well. Blood tests suggested mild iron-deficiency anaemia (haemoglobin 106 g/L, Fe 5.1 µmol/L, ferritin 5.6 µg/L) and elevated IgE level (IgE 478.0 KU/L). Peripheral blood eosinophil count, autoimmune antibodies, tumour markers were all negative and there was no evidence for specific pathogen infection. OGD revealed smooth peninsula-shaped areas of the corpus…

Via a 2 fase del progetto Oms Europa coordinato in Italia da Iss

Annals of Internal Medicine, Ahead of Print.

Annals of Internal Medicine, Volume 178, Issue 2, Page JC14, February 2025.

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.

Farmaco regolatore orexina ruolo chiave anche in psicopatologia

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