Evidence map›Paper›PMID 38045250›Full record

ArticleResearch square2023

Predictive models for occurrence of expansive intracranial hematomas and surgical evacuation outcomes in traumatic brain injury patients in Uganda: A prospective cohort study.

Larrey Kasereka Kamabu, Ronald Oboth, Godfrey Bbosa, Ssenyondwa John Baptist, Martin N Kaddumukasa, Daniel Deng, Hervé Monka Lekuya, Louange Maha Kataka, Joel Kiryabwire, Galukande Moses and 3 more

Open access · greenAbstract readPreprint
In one paragraph

Article in Research square, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 3 countries.

Larrey Kasereka KamabuMakerere University.
Ronald ObothMakerere University.
Godfrey BbosaMakerere University College of Health Sciences.
Ssenyondwa John BaptistMakerere University.
Martin N KaddumukasaMakerere University.
Daniel DengDuke University.
Hervé Monka LekuyaMakerere University.
Louange Maha KatakaFaculty of Medicine, Université Catholique du Graben.
Joel KiryabwireMakerere University.
Galukande MosesMakerere University.
Martha SajatovicUniversity Hospitals Cleveland Medical Center & Case Western Reserve University School of Medicine.
Mark KaddumukasaMakerere University.
Anthony T FullerDuke University.
Makerere University · UGDuke University · USUniversité Catholique du Graben · CDUniversity Hospitals of Cleveland · US

Funding

Research training and mentorship to strengthen brain health across the lifespan in Uganda.D43NS118560 · NINDS · MAKERERE UNIVERSITY COLLEGE OF HEALTH SCIENCES · PI KADDUMUKASA, MARK, KATABIRA, ELLY T · 2020 to 2024
$1.1M
NINDS NIH HHS D43 NS118560
6 · The paper itself

Abstract

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.

Indexed as

and neurosurgical outcomesoccurrencepredictive modelsTraumatic expansive intracranial hematomas

Identifiers

PMID38045250
PMCPMC10690308
OpenAlexW4389002721

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.