Characteristics and Outcomes of Mechanically Ventilated Patients at Adult ICU of Selected Public Hospitals in Addis Ababa, Ethiopia

Alemayehu, Micheal; Azazh, Aklilu; Hussien, Heyria; Baru, Ararso

doi:10.2147/oaem.s369752

Cited by 4 publications

(8 citation statements)

References 17 publications

(48 reference statements)

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“…While some pooled ORs lacked significance, closer examination revealed significant associations and trends in individual studies. Admission with a cardiovascular diseases were consistently emerged as a significant factor associated with mortality in four prior studies, with adjusted ORs (AORs) ranging from 1.04 to 5.05 (12,22–24). Another critical determinant of mortality is the presence of sepsis or septic shock, as evidenced by AORs ranging from 1.938 to 7.4 in previous studies (11,24).…”

Section: Resultsmentioning

confidence: 99%

“…The use of sedation emerged as a factor influencing mortality, with one study reporting an increased risk of mortality for those utilizing sedation (24), while another study in Ethiopia indicates that sedation was a protective factor against mortality (25). The meta-analysis findings also supported sedation as a protective factor in the overall assessment.…”

Section: Discussionmentioning

confidence: 99%

“…Sedation was also mentioned as a significant positive predictor of mortality in one study (24), and as a significant negative predictor in another one study (25); thus, the AOR ranges from 0.42 to 3.95. Finally, MODS has been identified as another relevant factor in mortality, with AORs ranging from 0.24 to 8.07 in two distinct studies (24,25).…”

Section: Factors Associated With Mortalitymentioning

confidence: 91%

“…Admission with a cardiovascular diseases were consistently emerged as a significant factor associated with mortality in four prior studies, with adjusted ORs (AORs) ranging from 1.04 to 5.05 (12,(22)(23)(24). Another critical determinant of mortality is the presence of sepsis or septic shock, as evidenced by AORs ranging from 1.938 to 7.4 in previous studies (11,24). Furthermore, a GCS level of ≤8 was identified as a predictor of mortality in two studies, with ORs ranging from 0.85 to 6.3 (12,(22)(23)(24)(25).…”

Section: Factors Associated With Mortalitymentioning

confidence: 99%

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Prevalence of mortality and associated factors among ICU admitted adult patients with mechanical ventilation in Ethiopia; Systematic review and meta-analysis

Alamaw,

Abebe,

Abate

et al. 2024

Shock

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Introduction The global demand for intensive care has risen, given its effectiveness in lowering mortality rates. Mechanical ventilation (MV) is integral to intensive care but introduces risks such as ventilator-associated complications. Ethiopia experiences a high intensive care unit (ICU) mortality rate. Objective This systematic review and meta-analysis aim to comprehensively synthesize evidence on the mortality of adults undergoing MV in Ethiopia and identify associated factors. Methods The study extensively searched databases and grey literature for research on MV outcomes, trends, and associated factors in adult ICUs. Adhering to the 2020 PRISMA checklist, a systematic review and meta-analysis sought to establish the mortality rate and key determinants among adult ICU patients on MV. The search incorporated keywords and MeSH terms, excluding studies with unsound methodologies or missing data. Data extraction, quality assessment, and analysis followed established protocols, including the JBI tool for methodological quality evaluation. STATA version 17.0 facilitated analysis, assessing heterogeneity, publication bias, and performing sensitivity and meta-regression analyses. Results The pooled mortality rate among adult ICU patients undergoing MV was 48.61% (95% CI: 40.82, 56.40%). Significant mortality-contributing factors included medical diagnosis, Glasgow Coma Scale (GCS) score, sepsis/septic shock, sedation use, multiple organ dysfunction syndrome (MODS), and cardiovascular disease. Although some pooled odds ratios seemed insignificant, closer examination revealed significant associations in individual studies. Conclusion The study underscores the urgent need for further research, improved ICU infrastructure, and healthcare personnel training in Ethiopia to enhance outcomes for mechanically ventilated patients. Identified factors offer valuable insights for targeted interventions, guiding tailored treatment strategies to reduce mortality. This study contributes to understanding mortality and associated factors in MV patients, informing initiatives to improve critical care outcomes in Ethiopia.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Factors Associated With Mortalitymentioning

confidence: 91%

Section: Factors Associated With Mortalitymentioning

confidence: 99%

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Prevalence of mortality and associated factors among ICU admitted adult patients with mechanical ventilation in Ethiopia; Systematic review and meta-analysis

Alamaw,

Abebe,

Abate

et al. 2024

Shock

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“…This finding agrees with the study conducted in southern India in which 53.5% of the nurses had poor knowledge [ 11 ]. On the other hand, the study conducted in Sri Lanka and Eastern India showed a higher knowledge level among nurses, which are 73% and 69.7%, and had good knowledge regarding mechanical ventilation [ 6 , 12 ]. This discrepancy could be attributed to the sociodemographic difference and the difference in the type of ICU.…”

Section: Discussionmentioning

confidence: 99%

Knowledge Regarding Mechanical Ventilation and Practice of Ventilatory Care among Nurses Working in Intensive Care Units in Selected Governmental Hospitals in Addis Ababa, Ethiopia: A Descriptive Cross-Sectional Study

Hassen

Alemayehu²,

Aniley

et al. 2023

Critical Care Research and Practice

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Introduction. Mechanical ventilation (MV) is a backbone and major supportive modality in intensive care units (ICUs) even though it has side effects and complications. Knowledge of nurses about mechanical ventilators and good practice of nursing care for the ventilated patient plays a crucial role in improving the effectiveness of mechanical ventilation, preventing harm, and optimizing the patient outcome. This study intended to assess the knowledge regarding MV and the practice of ventilator care among nurses working in the ICU. Method. A descriptive cross-sectional study design was conducted. All nurses working in the intensive care unit of selected governmental hospitals were included in the study. The data were collected from March 1 to 30, 2021 with structured and pretested self-administered questionnaires. The collected data were evaluated with SPSS version 26 software. The variables, which have an independent association with poor outcomes, were identified based on OR, with 95% CI and a p value less than 0.05. Results. Of 146 nurses who participated in the study, 51.4% were males. About 71.4% had a BSc in nursing and 57.5% of them had training related to MV. More than half (51.4%) of nurses had poor knowledge regarding MV and the majority (58.9%) of them had poor practice in ventilatory care. The educational level (AOR, 5.1; 95% CI, 1.190–22.002) was positively associated with knowledge. Likewise, the educational level (AOR 5.0 (1.011–24.971)) and work experience (AOR 4.543 (1.430–14.435)) were positively associated with the practice of nurses. Conclusions. Knowledge regarding mechanical ventilators and the practice of ventilatory care among nurses in the selected public hospitals was poor. The educational levels were found statistically associated with both the knowledge and practice of nurses. To improve nursing care offered for MV patients, upgrading the educational level of intensive care nurses plays a vital role.

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Machine Learning Model Utilization for Mortality Prediction in Mechanically Ventilated ICU Patients

Liu,

Xu,

Wang

et al. 2024

Preprint

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Background: The requirement of mechanical ventilation has increased in recent years. Patients in the intensive care unit (ICU) who undergo mechanical ventilation often experience serious illness and critical physiological states, contributing to a high risk of mortality. Prediction of mortality for mechanically ventilated ICU patients offers an early alert mechanism to help physicians implement more targeted treatments to mitigate the risk of mortality. Methods: We extracted medical information of patients with invasive mechanical ventilation in ICU admission from the Medical Information Mart for Intensive Care III (MIMIC III) dataset. This includes demographics, disease severity, diagnosis, and laboratory test results. Patients who met inclusion criteria were randomly divided into the train set (n=11,549, 70%), the test set (n=2,475, 15%), and the validation set (n=2,475, 15%). The oversampling method was utilized to resolve the imbalance dataset. After literature research, clinical expertise and ablation study, we selected 12 variables for model establishment, which were less than the 66 features in the best existing literature. We proposed a deep learning neural network model to predict the ICU mortality of mechanically ventilated patients, and established 7 baseline machine learning (ML) models for comparison, including k-nearest neighbors (KNN), logistic regression, decision tree, random forest, bagging, XGBoost, and support vector machine (SVM). Area under the Receiver Operating Characteristic Curve (AUROC) was used as an evaluation metric for model performance. Results: By using 16,499 mechanically ventilated patients in the MIMIC III database, the deep learning model demonstrated the best performance, showing a 7.06% improvement over the best existing literature. The neural network achieved an AUROC score of 0.879 with 95% Confidence Interval (CI) [0.861-0.896] and an accuracy score of 0.859 on the test set. These results far exceeded those of the best existing literature. Conclusions: The proposed model demonstrated an exceptional ability to predict ICU mortality among mechanically ventilated patients. Through SHAP analysis, we found that respiratory failure is a significant indicator of the mortality prediction compared to other related respiratory dysfunction diseases. We also incorporated mechanical ventilation duration as a variable for the first time in our prediction model. We observed that patients with higher mortality rates tended to have longer mechanical ventilation times. This highlights the model's potential in guiding clinical decisions by indicating that longer mechanical ventilation may not necessarily enhance patient survival chances.

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Characteristics and Outcomes of Mechanically Ventilated Patients at Adult ICU of Selected Public Hospitals in Addis Ababa, Ethiopia

Cited by 4 publications

References 17 publications

Prevalence of mortality and associated factors among ICU admitted adult patients with mechanical ventilation in Ethiopia; Systematic review and meta-analysis

Prevalence of mortality and associated factors among ICU admitted adult patients with mechanical ventilation in Ethiopia; Systematic review and meta-analysis

Knowledge Regarding Mechanical Ventilation and Practice of Ventilatory Care among Nurses Working in Intensive Care Units in Selected Governmental Hospitals in Addis Ababa, Ethiopia: A Descriptive Cross-Sectional Study

Machine Learning Model Utilization for Mortality Prediction in Mechanically Ventilated ICU Patients

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