Dataset Information

Risk prediction score for death of traumatised and injured children.

ABSTRACT: BACKGROUND: Injury prediction scores facilitate the development of clinical management protocols to decrease mortality. However, most of the previously developed scores are limited in scope and are non-specific for use in children. We aimed to develop and validate a risk prediction model of death for injured and Traumatised Thai children. METHODS: Our cross-sectional study included 43,516 injured children from 34 emergency services. A risk prediction model was derived using a logistic regression analysis that included 15 predictors. Model performance was assessed using the concordance statistic (C-statistic) and the observed per expected (O/E) ratio. Internal validation of the model was performed using a 200-repetition bootstrap analysis. RESULTS: Death occurred in 1.7% of the injured children (95% confidence interval [95% CI]: 1.57-1.82). Ten predictors (i.e., age, airway intervention, physical injury mechanism, three injured body regions, the Glasgow Coma Scale, and three vital signs) were significantly associated with death. The C-statistic and the O/E ratio were 0.938 (95% CI: 0.929-0.947) and 0.86 (95% CI: 0.70-1.02), respectively. The scoring scheme classified three risk stratifications with respective likelihood ratios of 1.26 (95% CI: 1.25-1.27), 2.45 (95% CI: 2.42-2.52), and 4.72 (95% CI: 4.57-4.88) for low, intermediate, and high risks of death. Internal validation showed good model performance (C-statistic = 0.938, 95% CI: 0.926-0.952) and a small calibration bias of 0.002 (95% CI: 0.0005-0.003). CONCLUSIONS: We developed a simplified Thai pediatric injury death prediction score with satisfactory calibrated and discriminative performance in emergency room settings.

SUBMITTER: Vallipakorn SA

PROVIDER: S-EPMC3939810 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Risk prediction score for death of traumatised and injured children.

Vallipakorn Sakda Arj-ong SA Plitapolkarnpim Adisak A Suriyawongpaisal Paibul P Techakamolsuk Pimpa P Smith Gary A GA Thakkinstian Ammarin A

BMC pediatrics 20140228

<h4>Background</h4>Injury prediction scores facilitate the development of clinical management protocols to decrease mortality. However, most of the previously developed scores are limited in scope and are non-specific for use in children. We aimed to develop and validate a risk prediction model of death for injured and Traumatised Thai children.<h4>Methods</h4>Our cross-sectional study included 43,516 injured children from 34 emergency services. A risk prediction model was derived using a logist ...[more]

PMID: 24575982

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Project description:BACKGROUND:Although sudden cardiac death is a leading cause of death in the United States, most victims of sudden cardiac death are not identified as at risk prior to death. We sought to derive and validate a population-based risk score that predicts sudden cardiac death. METHODS:The Atherosclerosis Risk in Communities (ARIC) Study recorded clinical measures from men and women aged 45-64 years at baseline; 11,335 white and 3780 black participants were included in this analysis. Participants were followed over 10 years and sudden cardiac death was physician adjudicated. Cox proportional hazards models were used to derive race-specific equations to estimate the 10-year sudden cardiac death risk. Covariates for the risk score were selected from available demographic and clinical variables. Utility was assessed by calculating discrimination (Harrell's C-index) and calibration (Hosmer-Lemeshow chi-squared test). The white-specific equation was validated among 5626 Framingham Heart Study participants. RESULTS:During 10 years' follow-up among ARIC participants (mean age 54.4 years, 52.4% women), 145 participants experienced sudden cardiac death; the majority occurred in the highest quintile of predicted risk. Model covariates included age, sex, total cholesterol, lipid-lowering and hypertension medication use, blood pressure, smoking status, diabetes, and body mass index. The score yielded very good internal discrimination (white-specific C-index 0.82; 95% confidence interval [CI], 0.78-0.85; black-specific C-index 0.75; 95% CI, 0.68-0.82) and very good external discrimination among Framingham participants (C-index 0.82; 95% CI, 0.79-0.86). Calibration plots indicated excellent calibration in ARIC (white-specific chi-squared 5.3, P = .82; black-specific chi-squared 4.1, P = .77), and a simple recalibration led to excellent fit within Framingham (chi-squared 2.1, P = 0.99). CONCLUSIONS:The proposed risk scores may be used to identify those at risk for sudden cardiac death within 10 years and particularly classify those at highest risk who may merit further screening.

Project description:Endometriosis is a major health care challenge because many young women with endometriosis go undetected for an extended period, which may lead to pain sensitization. Clinical tools to better identify candidates for laparoscopy-guided diagnosis are urgently needed. Since endometriosis has a strong genetic component, there is a growing interest in using genetics as part of the clinical risk assessment. The aim of this work was to investigate the discriminative ability of a polygenic risk score (PRS) for endometriosis using three different cohorts: surgically confirmed cases from the Western Danish endometriosis referral Center (249 cases, 348 controls), cases identified from the Danish Twin Registry (DTR) based on ICD-10 codes from the National Patient Registry (140 cases, 316 controls), and replication analysis in the UK Biobank (2,967 cases, 256,222 controls). Patients with adenomyosis from the DTR (25 cases) and from the UK Biobank (1,883 cases) were included for comparison. The PRS was derived from 14 genetic variants identified in a published genome-wide association study with more than 17,000 cases. The PRS was associated with endometriosis in surgically confirmed cases [odds ratio (OR) = 1.59, p = 2.57× 10-7] and in cases from the DTR biobank (OR = 1.50, p = 0.0001). Combining the two Danish cohorts, each standard deviation increase in PRS was associated with endometriosis (OR = 1.57, p = 2.5× 10-11), as well as the major subtypes of endometriosis; ovarian (OR = 1.72, p = 6.7× 10-5), infiltrating (OR = 1.66, p = 2.7× 10-9), and peritoneal (OR = 1.51, p = 2.6 × 10-3). These findings were replicated in the UK Biobank with a much larger sample size (OR = 1.28, p < 2.2× 10-16). The PRS was not associated with adenomyosis, suggesting that adenomyosis is not driven by the same genetic risk variants as endometriosis. Our results suggest that a PRS captures an increased risk of all types of endometriosis rather than an increased risk for endometriosis in specific locations. Although the discriminative accuracy is not yet sufficient as a stand-alone clinical utility, our data demonstrate that genetics risk variants in form of a simple PRS may add significant new discriminatory value. We suggest that an endometriosis PRS in combination with classical clinical risk factors and symptoms could be an important step in developing an urgently needed endometriosis risk stratification tool.

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Risk prediction score for death of traumatised and injured children.

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Risk prediction score for death of traumatised and injured children.

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