Prehospital management of earthquake crush injuries: A collective review

Abu-Zidan, FikriM; Idris, Kamal; Cevik, ArifAlper

doi:10.4103/tjem.tjem_201_23

Turk J Emerg Med

2023

DOI: 10.4103/tjem.tjem_201_23

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Prehospital management of earthquake crush injuries: A collective review

FikriM Abu-Zidan,

Kamal Idris,

ArifAlper Cevik

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2024

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Cited by 4 publications

References 60 publications

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The loneliness of the local orthopaedic surgeon in disaster zones

Hernigou,

Homma,

Herard

et al. 2024

International Orthopaedics (SICOT)

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The loneliness of the local orthopaedic surgeon in disaster zones

Hernigou,

Homma,

Herard

et al. 2024

International Orthopaedics (SICOT)

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Interpretable machine learning model for early prediction of acute kidney injury in patients with rhabdomyolysis

Zhang,

Liang,

et al. 2024

Emerg Crit Care Med

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Background Rhabdomyolysis (RM) is a complex set of clinical syndromes. RM-induced acute kidney injury (AKI) is a common illness in war and military operations. This study aimed to develop an interpretable and generalizable model for early AKI prediction in patients with RM. Methods Retrospective analyses were performed on 2 electronic medical record databases: the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care III database. Data were extracted from the first 24 hours after patient admission. Data from the two datasets were merged for further analysis. The extreme gradient boosting (XGBoost) model with the Shapley additive explanation method (SHAP) was used to conduct early and interpretable predictions of AKI. Results The analysis included 938 eligible patients with RM. The XGBoost model exhibited superior performance (area under the receiver operating characteristic curve [AUC] = 0.767) compared to the other models (logistic regression, AUC = 0.711; support vector machine, AUC = 0.693; random forest, AUC = 0.728; and naive Bayesian, AUC = 0.700). Conclusion Although the XGBoost model performance could be improved from an absolute perspective, it provides better predictive performance than other models for estimating the AKI in patients with RM based on patient characteristics in the first 24 hours after admission to an intensive care unit. Furthermore, including SHAP to elucidate AKI-related factors enables individualized patient treatment, potentially leading to improved prognoses for patients with RM.

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Uric acid in predicting the traumatic rhabdomyolysis induced acute kidney injury; a systematic review and meta-analysis

Safari,

Ghasemi,

Yousefifard

et al. 2024

BMC Nephrol

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Objective The objective of this systematic review and meta-analysis was to assess the value of uric acid in predicting acute kidney injury caused by traumatic rhabdomyolysis. Methods The search was conducted in MEDLINE, Scopus, Embase and Web of Science until November 1, 2023. Based on the inclusion and exclusion criteria, the articles were included by two independent researchers. Data regarding study design, patient characteristics, number of patients with and without AKI, mean and SD of uric acid and prognostic characteristics of uric acid were extracted from relevant studies. STATA version 17.0 was used to compute pooled measures of standardized mean differences, odds ratios, and diagnostic accuracy. I2 and chi-square tests were used to assess heterogeneity between studies. Results We found 689 non-redundant studies, 44 of them were potentially relevant. Six articles met the inclusion criteria and were included in the review. The results of the meta-analysis confirmed that there was a significant correlation between serum uric acid levels and the occurrence of AKI (SMD = 1.61, 95% CI = 0.69 to 2.54, I2 = 96.94%; p value = 0.001). There were no significant publication biases. Conclusion According to this meta-analysis, uric acid levels could be considered as a predictor of acute kidney injury following traumatic rhabdomyolysis.

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Prehospital management of earthquake crush injuries: A collective review

Cited by 4 publications

References 60 publications

The loneliness of the local orthopaedic surgeon in disaster zones

The loneliness of the local orthopaedic surgeon in disaster zones

Interpretable machine learning model for early prediction of acute kidney injury in patients with rhabdomyolysis

Uric acid in predicting the traumatic rhabdomyolysis induced acute kidney injury; a systematic review and meta-analysis

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