Background: Examining factors that increase risk of death in veterans with multiple sclerosis (MS) may help reduce MS-related mortality. We sought to determine predictors of mortality in veterans with MS attending an outpatient clinic.
Methods: Review of electronic medical records of 226 veterans with MS regularly followed up from January 1, 2000, through December 31, 2014.
Results: Mortality at the end of the 15-year study period was 14%. Patients with MS died prematurely, with a standardized mortality rate of 1.35 relative to the general (Oklahoma) population. The main causes of death documented were MS disease itself (57% of cases), infection (43%), and cancer and respiratory failure (18% each). Cox regression analysis using the whole cohort showed that progressive MS type; older age at entry into the study; presence of sensory, cerebellar, or motor (weakness and/or ataxia) concerns on presentation; more disability on presentation; higher body-mass index; being diabetic; never received disease-modifying therapy; and presence of pressure ulcers or neurogenic bladder were significant predictors of higher mortality.
Conclusions: Initial presentation by MS type (progressive MS), higher level of disability, and associated motor, sensory, and cerebellar concerns are significant predictors of MS-related mortality. The main causes of death were MS disease itself, infection, respiratory disease, and cancer. More attention should be given to preventive strategies that delay mortality, such as yearly immunization and aggressively treating MS-related complications and diabetes mellitus.
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http://dx.doi.org/10.7224/1537-2073.2016-067 | DOI Listing |
Glob Epidemiol
June 2025
Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Brazil.
Unlabelled: COVID-19 is no longer a global health emergency, but it remains challenging to predict its prognosis.
Objective: To develop and validate an instrument to predict COVID-19 progression for critically ill hospitalized patients in a Brazilian population.
Methodology: Observational study with retrospective follow-up.
JACC Adv
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Background: Liver synthetic dysfunction predicts outcomes in cardiac intensive care unit (CICU) patients.
Objectives: The purpose of this study was to evaluate the associations between the severity and extent of admission liver function test (LFT) abnormalities and mortality in a mixed CICU population.
Methods: This historical cohort study included unique CICU patients from 2007 to 2018 with available data for admission LFT values.
BMC Nephrol
January 2025
Department of Clinical Pharmacy, King Khalid University, 61421, Abha, Saudi Arabia.
Background: Chronic kidney disease (CKD) is a prevalent global health issue affecting millions of patients worldwide, impacting quality of life, impeding physical and psychological well-being, causing financial stress, and increasing mortality rates. This study aimed to highlight the prevalence of CKD and its associated risk factors across Saudi Arabia.
Method: This is a cross-sectional study conducted from 2015 to 2022, using data from 42 branches of a major network of diagnostic laboratories in Saudi Arabia, covering the country's 13 administrative areas.
BMC Cardiovasc Disord
January 2025
The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Rd Dalian, Liaoning, Liaoning, 116011, China.
Purpose: Catheter ablation (CA) for atrial fibrillation (AF) in heart failure patients with preserved ejection fraction (HFPEF) has shown promising results in reducing mortality and improving heart function. However, previous studies have been limited by a lack of control groups and significant heterogeneity in their methodologies.
Hypothesis: CA for AF in HFPEF patients may not increase the complications and had similarly the rate of freedom from AF vs.
BMC Emerg Med
January 2025
Department of Surgery, Mayo Clinic, Rochester, MN, US.
Background: Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these settings, though its predictive accuracy remains under debate. This study evaluates the effectiveness of machine learning (ML) models in predicting triage decisions and compares their performance to the KTS.
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