Predictive models for occurrence of expansive intracranial hematomas and surgical evacuation outcomes in traumatic brain injury patients in Uganda: A prospective cohort study.
| العنوان: | Predictive models for occurrence of expansive intracranial hematomas and surgical evacuation outcomes in traumatic brain injury patients in Uganda: A prospective cohort study. |
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| المؤلفون: | Kamabu LK; Makerere University., Oboth R; Makerere University., Bbosa G; Makerere University College of Health Sciences., Baptist SJ; Makerere University., Kaddumukasa MN; Makerere University., Deng D; Duke University., Lekuya HM; Makerere University., Kataka LM; Faculty of Medicine, Université Catholique du Graben., Kiryabwire J; Makerere University., Moses G; Makerere University., Sajatovic M; University Hospitals Cleveland Medical Center & Case Western Reserve University School of Medicine., Kaddumukasa M; Makerere University., Fuller AT; Duke University. |
| المصدر: | Research square [Res Sq] 2023 Nov 25. Date of Electronic Publication: 2023 Nov 25. |
| نوع المنشور: | Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101768035 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: Res Sq Subsets: PubMed not MEDLINE |
| مستخلص: | Background: Hematoma expansion is a common manifestation of acute intracranial hemorrhage (ICH) which is associated with poor outcomes and functional status. Objective: We determined the prevalence of expansive intracranial hematomas (EIH) and assessed the predictive model for EIH occurrence and surgical evacuation outcomes in patients with traumatic brain injury (TBI) in Uganda. Methods: We recruited adult patients with TBI with intracranial hematomas in a prospective cohort study. Data analysis using logistic regression to identify relevant risk factors, assess the interactions between variables, and developing a predictive model for EIH occurrence and surgical evacuation outcomes in TBI patients was performed. The predictive accuracies of these algorithms were compared using the area under the receiver operating characteristic curve (AUC). A p-values of < 0.05 at a 95% Confidence interval (CI) was considered significant. Results: A total of 324 study participants with intracranial hemorrhage were followed up for 6 months after surgery. About 59.3% (192/324) had expansive intracranial hemorrhage. The study participants with expansive intracranial hemorrhage had poor quality of life at both 3 and 6-months with p < 0.010 respectively. Among the 5 machine learning algorithms, the random forest performed the best in predicting EIH in both the training cohort (AUC = 0.833) and the validation cohort (AUC = 0.734). The top five features in the random forest algorithm-based model were subdural hematoma, diffuse axonal injury, systolic and diastolic blood pressure, association between depressed fracture and subdural hematoma. Other models demonstrated good discrimination with AUC for intraoperative complication (0.675) and poor discrimination for mortality (0.366) after neurosurgical evacuation in TBI patients. Conclusion: Expansive intracranial hemorrhage is common among patients with traumatic brain injury in Uganda. Early identification of patients with subdural hematoma, diffuse axonal injury, systolic and diastolic blood pressure, association between depressed fracture and subdural hematoma, were crucial in predicting EIH and intraoperative complications. Competing Interests: Competing interests All authors have declared no conflict of interest. |
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| معلومات مُعتمدة: | D43 NS118560 United States NS NINDS NIH HHS |
| فهرسة مساهمة: | Keywords: Traumatic expansive intracranial hematomas; and neurosurgical outcomes; occurrence; predictive models |
| تواريخ الأحداث: | Date Created: 20231204 Latest Revision: 20231214 |
| رمز التحديث: | 20240628 |
| مُعرف محوري في PubMed: | PMC10690308 |
| DOI: | 10.21203/rs.3.rs-3626631/v1 |
| PMID: | 38045250 |
| قاعدة البيانات: | MEDLINE |
| DOI: | 10.21203/rs.3.rs-3626631/v1 |
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