โก Quick Summary
A recent study developed an artificial intelligence (AI) platform to predict prolonged dependence on mechanical ventilation in patients with critical orthopaedic trauma. The eXGBM model demonstrated superior performance, achieving an AUC of 0.949 and offering a promising tool for optimizing patient care in critical settings.
๐ Key Details
- ๐ Dataset: 1,400 patients with critical orthopaedic trauma
- ๐งฉ Features used: Respiratory rate, lower limb fracture, glucose, PaO2, PaCO2
- โ๏ธ Technology: Machine learning models including eXGBM, LightGBM, RF, SVM, and others
- ๐ Performance: eXGBM: AUC 0.949, Recall 0.892, Accuracy 0.871
๐ Key Takeaways
- ๐ค AI application can significantly enhance predictions for mechanical ventilation dependence.
- ๐ฅ Prolonged mechanical ventilation is a critical issue in ICU settings, affecting patient outcomes.
- ๐ eXGBM model outperformed other models in key metrics, establishing its reliability.
- ๐ Online AI calculator is available for assessing individual risk of prolonged ventilation.
- ๐ External validation confirmed the model’s effectiveness in a separate patient cohort.
- ๐ก Key features influencing predictions were identified, aiding targeted interventions.
- ๐ Stratification of patients into high-risk and low-risk groups can guide clinical decisions.
- ๐ Study published in BMC Musculoskeletal Disorders, highlighting its significance in critical care.
๐ Background
Prolonged dependence on mechanical ventilation is a prevalent challenge in intensive care units (ICUs), particularly for patients with critical orthopaedic trauma. This condition not only complicates patient management but also increases the risk of ventilator-associated complications. The need for effective predictive tools is paramount to enhance patient outcomes and optimize resource allocation in critical care settings.
๐๏ธ Study
The study analyzed a cohort of 1,400 patients who required mechanical ventilation due to critical orthopaedic trauma. Researchers employed various machine learning techniques, including logistic regression, extreme gradient boosting machine (eXGBM), and others, to develop predictive models. The patients were divided into training and validation cohorts to ensure robust model development and validation.
๐ Results
Among the models developed, the eXGBM model achieved the highest performance score of 50, followed closely by the LightGBM model with a score of 48. The eXGBM model excelled in several key metrics, including a recall of 0.892 and a Brier score of 0.088. External validation further confirmed its efficacy, yielding an AUC of 0.893.
๐ Impact and Implications
The implications of this study are profound. By utilizing an AI-driven approach to predict prolonged dependence on mechanical ventilation, healthcare providers can make informed decisions regarding patient management. This tool not only enhances the accuracy of risk assessments but also facilitates timely interventions, ultimately improving patient outcomes and optimizing resource use in critical care environments.
๐ฎ Conclusion
This study highlights the transformative potential of artificial intelligence in critical care settings. The successful development and validation of the eXGBM model provide a valuable resource for clinicians, enabling better stratification of patients at risk for prolonged mechanical ventilation. As we continue to integrate AI into healthcare, the future looks promising for enhancing patient care and outcomes.
๐ฌ Your comments
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An artificial intelligence application to predict prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma: an establishment and validation study.
Abstract
BACKGROUND: Prolonged dependence on mechanical ventilation is a common occurrence in clinical ICU patients and presents significant challenges for patient care and resource allocation. Predicting prolonged dependence on mechanical ventilation is crucial for improving patient outcomes, preventing ventilator-associated complications, and guiding targeted clinical interventions. However, specific tools for predicting prolonged mechanical ventilation among ICU patients, particularly those with critical orthopaedic trauma, are currently lacking. The purpose of the study was to establish and validate an artificial intelligence (AI) platform to assess the prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma.
METHODS: This study analyzed 1400 patients with critical orthopaedic trauma who received mechanical ventilation, and the prolonged dependence on mechanical ventilation was defined as not weaning from mechanical ventilation for โงโ7 days. Patients were randomly classified into a training cohort and a validation cohort based on the ratio of 8:2. Patients in the training cohort were used to establish models using machine learning techniques, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), random forest (RF), support vector machine (SVM), and light gradient boosting machine (LightGBM), whereas patients in the validation cohort were used to validate these models. The prediction performance of these models was evaluated using discrimination and calibration. A scoring system was used to comprehensively assess and compare the prediction performance of the models, based on ten evaluation metrics. External validation of the model was performed in 122 patients with critical orthopaedic trauma from a university teaching hospital. Furthermore, the optimal model was deployed as an AI calculator, which was accessible online, to assess the risk of prolonged dependence on mechanical ventilation.
RESULTS: Among the developed models, the eXGBM model had the highest score of 50, followed by the LightGBM model (48) and the RF model (37). In detail, the eXGBM model outperformed other models in terms of recall (0.892), Brier score (0.088), log loss (0.291), and calibration slope (0.999), and the model was the second best in terms of area under the curve value (0.949, 95%: 0.933-0.961), accuracy (0.871), F1 score (0.873), and discrimination slope (0.647). The SHAP revealed that the most important five features were respiratory rate, lower limb fracture, glucose, PaO2, and PaCO2. External validation of the eXGBM model also demonstrated favorable prediction performance, with an AUC value of 0.893 (95%CI: 0.819-0.967). The eXGBM model was successfully deployed as an AI platform, which was at https://prolongedmechanicalventilation-lqsfm6ecky6dpd4ybkvohu.streamlit.app/ . By simply clicking the link and inputting features, users were able to obtain the risk of experiencing prolonged dependence on mechanical ventilation for individuals. Based on the risk of prolonged dependence on mechanical ventilation, patients were stratified into the high-risk or the low-risk groups, and corresponding therapeutic interventions were recommended, accordingly.
CONCLUSIONS: The AI model shows potential as a valuable tool for stratifying patients with a high risk of prolonged dependence on mechanical ventilation. The AI model may offer a promising approach for optimizing patient care and resource allocation in critical care settings.
CLINICAL TRIAL NUMBER: Not applicable.
Author: [‘Jiang W’, ‘Liu T’, ‘Sun B’, ‘Zhong L’, ‘Han Z’, ‘Lu M’, ‘Lei M’]
Journal: BMC Musculoskelet Disord
Citation: Jiang W, et al. An artificial intelligence application to predict prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma: an establishment and validation study. An artificial intelligence application to predict prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma: an establishment and validation study. 2024; 25:1089. doi: 10.1186/s12891-024-08245-9