Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms.

BACKGROUND: Hemodialysis is a life-saving treatment used to eliminate toxins and metabolites from the body during poisoning. Despite its effectiveness, there needs to be more research on this method precisely, with most studies focusing on specific poisoning. This study aims to bridge the existing knowledge gap by developing a machine-learning prediction model for forecasting the prognosis of the poisoned patient undergoing hemodialysis. METHODS: Using a registry database from 2016 to 2022, this study conducted a retrospective cohort study at Loghman Hakim Hospital. First, the relief feature selection algorithm was used to identify the most important variables influencing the prognosis of poisoned patients undergoing hemodialysis. Second, four machine learning algorithms, including extreme gradient boosting (XGBoost), histgradient boosting (HGB), k-nearest neighbors (KNN), and adaptive boosting (AdaBoost), were trained to construct predictive models for predicting the prognosis of poisoned patients undergoing hemodialysis. Finally, the performance of paired feature selection and machine learning (ML) algorithm were evaluated to select the best models using five evaluation metrics including accuracy, sensitivity, specificity the area under the curve (AUC), and f1-score. RESULT: The study comprised 980 patients in total. The experimental results showed that ten variables had a significant influence on prognosis outcomes including age, intubation, acidity (PH), previous medical history, bicarbonate (HCO3), Glasgow coma scale (GCS), intensive care unit (ICU) admission, acute kidney injury, and potassium. Out of the four models evaluated, the HGB classifier stood out with superior results on the test dataset. It achieved an impressive mean classification accuracy of 94.8%, a mean specificity of 93.5 a mean sensitivity of 94%, a mean F-score of 89.2%, and a mean receiver operating characteristic (ROC) of 92%. CONCLUSION: ML-based predictive models can predict the prognosis of poisoned patients undergoing hemodialysis with high performance. The developed ML models demonstrate valuable potential for providing frontline clinicians with data-driven, evidence-based tools to guide time-sensitive prognosis evaluations and care decisions for poisoned patients in need of hemodialysis. Further large-scale multi-center studies are warranted to validate the efficacy of these models across diverse populations.

Indicators of Quality of Care in Individuals With Traumatic Spinal Cord Injury: A Scoping Review.

STUDY DESIGN: Scoping review. OBJECTIVES: To identify a practical and reproducible approach to organize Quality of Care Indicators (QoCI) in individuals with traumatic spinal cord injury (TSCI). METHODS: A comprehensive literature review was conducted in the Cochrane Central Register of Controlled Trials (CENTRAL) (Date: May 2018), MEDLINE (1946 to May 2018), and EMBASE (1974 to May 2018). Two independent reviewers screened 6092 records and included 262 full texts, among which 60 studies were included for qualitative analysis. We included studies, with no language restriction, containing at least 1 quality of care indicator for individuals with traumatic spinal cord injury. Each potential indicator was evaluated in an online, focused group discussion to define its categorization (healthcare system structure, medical process, and individuals with Traumatic Spinal Cord Injury related outcomes), definition, survey options, and scale. RESULTS: A total of 87 indicators were identified from 60 studies screened using our eligibility criteria. We defined each indicator. Out of 87 indicators, 37 appraised the healthcare system structure, 30 evaluated medical processes, and 20 included individuals with TSCI related outcomes. The healthcare system structure included the impact of the cost of hospitalization and rehabilitation, as well as staff and patient perception of treatment. The medical processes included targeting physical activities for improvement of health-related outcomes and complications. Changes in motor score, functional independence, and readmission rates were reported as individuals with TSCI-related outcomes indicators. CONCLUSION: Indicators of quality of care in the management of individuals with TSCI are important for health policy strategists to standardize healthcare assessment, for clinicians to improve care, and for data collection efforts including registries.