Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission

El-Menyar, Ayman; Naduvilekandy, Mashhood; Asim, Mohammad; Rizoli, Sandro; Al-Thani, Hassan

doi:10.1016/j.compbiomed.2024.108880

Computers in Biology and Medicine

2024

DOI: 10.1016/j.compbiomed.2024.108880

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Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission

Ayman El-Menyar,

Mashhood Naduvilekandy,

Mohammad Asim

et al.

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Cited by 2 publications

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Multisource Data Framework for Prehospital Emergency Triage in Real-Time IoMT-Based Telemedicine Systems

Ahmed Jasim,

Ata,

Hussein Salman

2024

International Journal of Medical Informatics

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Multisource Data Framework for Prehospital Emergency Triage in Real-Time IoMT-Based Telemedicine Systems

Ahmed Jasim,

Ata,

Hussein Salman

2024

International Journal of Medical Informatics

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Association between serum anion gap and 28-day mortality in critically ill patients with infective endocarditis: a retrospective cohort study from MIMIC IV database

Huang,

Ao,

Zhen

et al. 2024

BMC Cardiovasc Disord

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Background The relationship between serum anion gap (AG) and 28-day mortality in critically ill patients with infective endocarditis is currently not well established. Objective This study aims to investigate the impact of serum AG on 28-day mortality in critically ill patients with infective endocarditis. Methods A retrospective cohort study was conducted involving 449 participants diagnosed with infective endocarditis and admitted to intensive care units (ICU). Vital signs, laboratory parameters and comorbidity were collected for all participants to analyze the association between anion gap levels and 28-day mortality. Results A total of 449 critically ill patients with infective endocarditis (IE) were included in the study. The mean age was 57 years, and 64% were male. The overall 28-day mortality rate was 20%. A greater AG on admission were significantly associated with increased 28-day mortality in unadjusted analysis (hazard ratio [HR] 1.13; 95% confidence interval [CI] 1.09–1.18; p < 0.001). After adjusting for all confounders, the association remained significant (adjusted HR 1.07; 95% CI 1.02–1.13; p = 0.003). When AG was converted into categorial variables (quartiles), the risk of 28-day mortality in the greatest Q4 group was significantly higher compared with that in the lowest Q1 group (model 4: HR = 2.62, 95%CI: 1.17–5.83, p = 0.019). Subgroup analysis showed consistent results across different groups. Conclusion A greater AG on admission were independently associated with increased 28-day mortality in critically ill patients with IE. These findings suggest that the AG can serve as a prognostic marker in this population, aiding in risk stratification and guiding clinical management.

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Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission

Cited by 2 publications

References 42 publications

Multisource Data Framework for Prehospital Emergency Triage in Real-Time IoMT-Based Telemedicine Systems

Multisource Data Framework for Prehospital Emergency Triage in Real-Time IoMT-Based Telemedicine Systems

Association between serum anion gap and 28-day mortality in critically ill patients with infective endocarditis: a retrospective cohort study from MIMIC IV database

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