Objective: The objective of the present study was to explore the correlation between the advanced lung cancer inflammation index (ALI) and in-hospital mortality among patients diagnosed with community-acquired pneumonia (CAP).
Methods: Data from the Medical Information Mart for Intensive Care-IV database were adopted to analyze the in-hospital mortality of ICU patients with CAP. Upon admission to the ICU, fundamental data including vital signs, critical illness scores, comorbidities, and laboratory results, were collected. The in-hospital mortality of all CAP patients was documented. Multivariate logistic regression (MLR) models and restricted cubic spline (RCS) analysis together with subgroup analyses were conducted.
Results: This study includes 311 CAP individuals, involving 218 survivors as well as 93 nonsurvivors. The participants had an average age of 63.57 years, and the females accounted for approximately 45.33%. The in-hospital mortality was documented to be 29.90%. MLR analysis found that ALI was identified as an independent predictor for in-hospital mortality among patients with CAP solely in the Q1 group with ALI ≤ 39.38 (HR: 2.227, 95% CI: 1.026-4.831, = 0.043). RCS analysis showed a nonlinear relationship between the ALI and in-hospital mortality, with a turning point at 81, and on the left side of the inflection point, a negative correlation was observed between ALI and in-hospital mortality (HR: 0.984, 95% CI: 0.975-0.994, = 0.002). The subgroup with high blood pressure showed significant interaction with the ALI.
Conclusion: The present study demonstrated a nonlinear correlation of the ALI with in-hospital mortality among individuals with CAP. Additional confirmation of these findings requires conducting larger prospective investigations.
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http://dx.doi.org/10.1002/agm2.12334 | DOI Listing |
Interact J Med Res
January 2025
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: Incorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain.
Objective: This study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century.
J Med Internet Res
January 2025
Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.
Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.
JMIR Aging
January 2025
Department of Geriatrics, Guangdong Provincial Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, No. 106, Zhongshan 2nd Road, Yuexiu District, Guangzhou, China, 0898-66571684.
Background: The utility of aging metrics that incorporate cognitive and physical function is not fully understood.
Objective: We aim to compare the predictive capacities of 3 distinct aging metrics-motoric cognitive risk syndrome (MCR), physio-cognitive decline syndrome (PCDS), and cognitive frailty (CF)-for incident dementia and all-cause mortality among community-dwelling older adults.
Methods: We used longitudinal data from waves 10-15 of the Health and Retirement Study.
Neurology
February 2025
Department of Medicine and Geriatrics, Tuen Mun Hospital, Hong Kong, People's Republic of China.
Background And Objectives: Mitochondrial disorders are multiorgan disorders resulting in significant morbidity and mortality. We aimed to characterize death-associated factors in an international cohort of deceased individuals with mitochondrial disorders.
Methods: This cross-sectional multicenter observational study used data provided by 26 mitochondrial disease centers from 8 countries from January 2022 to March 2023.
Am J Public Health
January 2025
Teeraboon Lertwanichwattana and Ram Rangsin are with Phramongkutklao College of Medicine, Bangkok, Thailand. Supattra Srivanichakorn, Sairat Noknoy, and Sirinapa Siriporn Na Ratchaseema are with the Royal College of Family Physicians of Thailand, Bangkok. Nittaya Phanuphak is with the Institute of HIV Research and Innovation, Bangkok. Kitti Wongthavarawat is with the National Science and Technology Development Agency, Bangkok. Arunotai Siriussawakul, Varalak Srinonprasert, and Pattara Leelahavarong are with the Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok. Parawee Chevaisrakul and Putthapoom Lumjiaktase are with the Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok. Aree Kumpitak is with the Thai Network of People Living With HIV, Bangkok. Nopphan Phromsri is with the Human Settlement Foundation, Bangkok. Yupadee Sirisinsuk is with the Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok. Pongtorn Kietdumrongwong is with the Bangkok Dusit Medical Services, Bangkok. Apinun Aramrattana is with the Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
To determine the overall mortality and risk factors of COVID-19 patients who were admitted to the Home Isolation (HI) program in Bangkok, Thailand, during the epidemic crisis in 2021. We conducted a retrospective cohort study using the data from a government telehealth application from July to December 2021. The vital status was verified from the government database on September 20, 2022.
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