Alien Registration- Bergeron, Antoinette (Lewiston, Androscoggin County)

https://digitalmaine.com/alien_docs/30126/thumbnail.jp

Prediction modelling for trauma using comorbidity and 'true' 30-day outcome

BACKGROUND: Prediction models for trauma outcome routinely control for age but there is uncertainty about the need to control for comorbidity and whether the two interact. This paper describes recent revisions to the Trauma Audit and Research Network (TARN) risk adjustment model designed to take account of age and comorbidities. In addition linkage between TARN and the Office of National Statistics (ONS) database allows patient's outcome to be accurately identified up to 30 days after injury. Outcome at discharge within 30 days was previously used. METHODS: Prospectively collected data between 2010 and 2013 from the TARN database were analysed. The data for modelling consisted of 129 786 hospital trauma admissions. Three models were compared using the area under the receiver operating curve (AuROC) for assessing the ability of the models to predict outcome, the Akaike information criteria to measure the quality between models and test for goodness-of-fit and calibration. Model 1 is the current TARN model, Model 2 is Model 1 augmented by a modified Charlson comorbidity index and Model 3 is Model 2 with ONS data on 30 day outcome. RESULTS: The values of the AuROC curve for Model 1 were 0.896 (95% CI 0.893 to 0.899), for Model 2 were 0.904 (0.900 to 0.907) and for Model 3 0.897 (0.896 to 0.902). No significant interaction was found between age and comorbidity in Model 2 or in Model 3. CONCLUSIONS: The new model includes comorbidity and this has improved outcome prediction. There was no interaction between age and comorbidity, suggesting that both independently increase vulnerability to mortality after injury

Prevalence of amyloid‐β pathology in distinct variants of primary progressive aphasia

International audienceObjective: To estimate the prevalence of amyloid positivity, defined by positron emission tomography (PET)/cerebrospinal fluid (CSF) biomarkers and/or neuropathological examination, in primary progressive aphasia (PPA) variants.Methods: We conducted a meta-analysis with individual participant data from 1,251 patients diagnosed with PPA (including logopenic [lvPPA, n = 443], nonfluent [nfvPPA, n = 333], semantic [svPPA, n = 401], and mixed/unclassifiable [n = 74] variants of PPA) from 36 centers, with a measure of amyloid-β pathology (CSF [n = 600], PET [n = 366], and/or autopsy [n = 378]) available. The estimated prevalence of amyloid positivity according to PPA variant, age, and apolipoprotein E (ApoE) ε4 status was determined using generalized estimating equation models.Results: Amyloid-β positivity was more prevalent in lvPPA (86%) than in nfvPPA (20%) or svPPA (16%; p < 0.001). Prevalence of amyloid-β positivity increased with age in nfvPPA (from 10% at age 50 years to 27% at age 80 years, p < 0.01) and svPPA (from 6% at age 50 years to 32% at age 80 years, p < 0.001), but not in lvPPA (p = 0.94). Across PPA variants, ApoE ε4 carriers were more often amyloid-β positive (58.0%) than noncarriers (35.0%, p < 0.001). Autopsy data revealed Alzheimer disease pathology as the most common pathologic diagnosis in lvPPA (76%), frontotemporal lobar degeneration-TDP-43 in svPPA (80%), and frontotemporal lobar degeneration-TDP-43/tau in nfvPPA (64%).Interpretation: This study shows that the current PPA classification system helps to predict underlying pathology across different cohorts and clinical settings, and suggests that age and ApoE genotype should be considered when interpreting amyloid-β biomarkers in PPA patients. Ann Neurol 2018;84:737-748