Development of prognostic models for Health-Related Quality of Life following traumatic brain injury

Background Traumatic brain injury (TBI) is a leading cause of impairments affecting Health-Related Quality of Life (HRQoL). We aimed to identify predictors of and develop prognostic models for HRQoL following TBI. Methods We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Core study, including patients with a clinical diagnosis of TBI and an indication for computed tomography presenting within 24 h of injury. The primary outcome measures were the SF-36v2 physical (PCS) and mental (MCS) health component summary scores and the Quality of Life after Traumatic Brain Injury (QOLIBRI) total score 6 months post injury. We considered 16 patient and injury characteristics in linear regression analyses. Model performance was expressed as proportion of variance explained (R-2) and corrected for optimism with bootstrap procedures. Results 2666 Adult patients completed the HRQoL questionnaires. Most were mild TBI patients (74%). The strongest predictors for PCS were Glasgow Coma Scale, major extracranial injury, and pre-injury health status, while MCS and QOLIBRI were mainly related to pre-injury mental health problems, level of education, and type of employment. R-2 of the full models was 19% for PCS, 9% for MCS, and 13% for the QOLIBRI. In a subset of patients following predominantly mild TBI (N = 436), including 2 week HRQoL assessment improved model performance substantially (R-2 PCS 15% to 37%, MCS 12% to 36%, and QOLIBRI 10% to 48%). Conclusion Medical and injury-related characteristics are of greatest importance for the prediction of PCS, whereas patient-related characteristics are more important for the prediction of MCS and the QOLIBRI following TBI.Development and application of statistical models for medical scientific researchAnalysis and support of clinical decision makin

Estimating the effectiveness and cost-effectiveness of establishing additional endovascular Thrombectomy stroke Centres in England::a discrete event simulation

Background We have previously modelled that the optimal number of comprehensive stroke centres (CSC) providing endovascular thrombectomy (EVT) in England would be 30 (net 6 new centres). We now estimate the relative effectiveness and cost-effectiveness of increasing the number of centres from 24 to 30. Methods We constructed a discrete event simulation (DES) to estimate the effectiveness and lifetime cost-effectiveness (from a payer perspective) using 1 year’s incidence of stroke in England. 2000 iterations of the simulation were performed comparing baseline 24 centres to 30. Results Of 80,800 patients admitted to hospital with acute stroke/year, 21,740 would be affected by the service reconfiguration. The median time to treatment for eligible early presenters (< 270 min since onset) would reduce from 195 (IQR 155–249) to 165 (IQR 105–224) minutes. Our model predicts reconfiguration would mean an additional 33 independent patients (modified Rankin scale [mRS] 0–1) and 30 fewer dependent/dead patients (mRS 3–6) per year. The net addition of 6 centres generates 190 QALYs (95%CI − 6 to 399) and results in net savings to the healthcare system of £1,864,000/year (95% CI -1,204,000 to £5,017,000). The estimated budget impact was a saving of £980,000 in year 1 and £7.07 million in years 2 to 5. Conclusion Changes in acute stroke service configuration will produce clinical and cost benefits when the time taken for patients to receive treatment is reduced. Benefits are highly likely to be cost saving over 5 years before any capital investment above £8 million is required

Prognosis in Moderate and Severe Traumatic Brain Injury: A Systematic Review of Contemporary Models and Validation Studies

Analysis and support of clinical decision makin

SARS-CoV-2-specific CD4(+) and CD8(+) T cell responses can originate from cross- reactive CMV- specific T cells

Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2) specific CD4(+ )and CD8(+) T cells in SARS- CoV- 2-unexposed donors has been explained by the presence of T cells primed by other coronaviruses. However, based on the relatively high frequency and prevalence of cross-reactive T cells, we hypothesized cytomegalovirus (CMV) may induce these cross-reactive T cells. Stimulation of pre-pandemic cryo-preserved peripheral blood mononuclear cells (PBMCs) with SARS- CoV- 2 peptides revealed that frequencies of SARS- CoV- 2-specific T cells were higher in CMVseropositive donors. Characterization of these T cells demonstrated that membrane-specific CD4(+ )and spike-specific CD8(+) T cells originate from cross-reactive CMVspecific T cells. Spike-specific CD8(+ )T cells recognize SARS- CoV- 2 spike peptide FVSNGTHWF (FVS) and dissimilar CMV pp65 peptide IPSINVHHY (IPS) presented by HLA- B*35:01. These dual IPS/FVS-reactive CD8(+) T cells were found in multiple donors as well as severe COVID- 19 patients and shared a common T cell receptor (TCR), illustrating that IPS/FVS- cross-reactivity is caused by a public TCR. In conclusion, CMVspecific T cells cross react with SARS-CoV- 2, despite low sequence homology between the two viruses, and may contribute to the pre-existing immunity against SARS-CoV- 2

SARS-CoV-2-specific CD4+ and CD8+ T cell responses can originate from cross-reactive CMV-specific T cells

Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) specific CD4+ and CD8+ T cells in SARS-CoV-2-unexposed donors has been explained by the presence of T cells primed by other coronaviruses. However, based on the relatively high frequency and prevalence of cross-reactive T cells, we hypothesized cytomegalovirus (CMV) may induce these cross-reactive T cells. Stimulation of pre-pandemic cryo-preserved peripheral blood mononuclear cells (PBMCs) with SARS-CoV-2 peptides revealed that frequencies of SARS-CoV-2-specific T cells were higher in CMV-seropositive donors. Characterization of these T cells demonstrated that membrane-specific CD4+ and spike-specific CD8+ T cells originate from cross-reactive CMV-specific T cells. Spike-specific CD8+ T cells recognize SARS-CoV-2 spike peptide FVSNGTHWF (FVS) and dissimilar CMV pp65 peptide IPSINVHHY (IPS) presented by HLA-B*35:01. These dual IPS/FVS-reactive CD8+ T cells were found in multiple donors as well as severe COVID-19 patients and shared a common T cell receptor (TCR), illustrating that IPS/FVS-cross-reactivity is caused by a public TCR. In conclusion, CMV-specific T cells cross-react with SARS-CoV-2, despite low sequence homology between the two viruses, and may contribute to the pre-existing immunity against SARS-CoV-2