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- Omer, M, et al.
(författare)
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Birth order and pediatric traumatic brain injury
- 2022
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1, s. 14451-
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Tidskriftsartikel (refereegranskat)abstract
- Pediatric traumatic brain injury (TBI) is a significant problem of public health importance worldwide. Large population-based studies on the effect of birth order on health phenomena are exceedingly rare. This study examines the relationship between birth order and risk for pediatric TBI among sibling groups. We performed a retrospective cohort study following 59,469 Finnish newborns from 1987 until age 18 years. Data on first diagnosis of TBI was recorded within the 1987 Finnish Birth Cohort (FBC). Compared with first born siblings, later born siblings had an increased risk of TBI during the follow-up period (hazard ratio [HR] 1.02; 95% confidence interval [CI] 0.91–1.14 for second born, HR 1.09; 95% CI 0.95 1.26 for third born, HR 1.28; 95% CI 1.08–1.53 for fourth or higher). When adjusted for sex and maternal age at child’s birth, HRs (95% CIs) for TBI during the follow-up period were 1.12 (0.99–1.26) for second born, 1.31 (1.12–1.53) for third born and 1.61 (1.33–1.95) for fourth born or higher children, respectively. Within this large register-based population-wide study, order of birth modified risk for pediatric TBI among sibling groups. Taken together, these study findings may serve to stimulate further inquiry into genetic, psychological, or psychosocial factors which underlie differences in risk and depth of effect within and between sibling groups.
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| 2. |
- Raj, R, et al.
(författare)
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Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm
- 2022
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Ingår i: NPJ digital medicine. - : Springer Science and Business Media LLC. - 2398-6352. ; 5:1, s. 96-
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Tidskriftsartikel (refereegranskat)abstract
- Intensive care for patients with traumatic brain injury (TBI) aims to optimize intracranial pressure (ICP) and cerebral perfusion pressure (CPP). The transformation of ICP and CPP time-series data into a dynamic prediction model could aid clinicians to make more data-driven treatment decisions. We retrained and externally validated a machine learning model to dynamically predict the risk of mortality in patients with TBI. Retraining was done in 686 patients with 62,000 h of data and validation was done in two international cohorts including 638 patients with 60,000 h of data. The area under the receiver operating characteristic curve increased with time to 0.79 and 0.73 and the precision recall curve increased with time to 0.57 and 0.64 in the Swedish and American validation cohorts, respectively. The rate of false positives decreased to ≤2.5%. The algorithm provides dynamic mortality predictions during intensive care that improved with increasing data and may have a role as a clinical decision support tool.
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