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Predicting Long Term Outcome in Patients Treated With Continuous Flow Left Ventricular Assist Device: The PennâColumbia Risk Score
Background: Predicting which patients are unlikely to benefit from continuous flow left ventricular assist device (LVAD) treatment is crucial for the identification of appropriate patients. Previously developed scoring systems are limited to past eras of device or restricted to specific devices. Our objective was to create a risk model for patients treated with continuous flow LVAD based on the preimplant variables. Methods and Results: We performed a retrospective analysis of all patients implanted with a continuous flow LVAD between 2006 and 2014 at the University of Pennsylvania and included a total of 210 patients (male 78%; mean age, 56±15; mean followâup, 465±486 days). From all plausible preoperative covariates, we performed univariate Cox regression analysis for covariates affecting the odds of 1âyear survival following implantation (P<0.2). These variables were included in a multivariable model and dropped if significance rose above P=0.2. From this base model, we performed stepâwise forward and backward selection for other covariates that improved power by minimizing Akaike Information Criteria while maximizing the Harrell Concordance Index. We then used KaplanâMeier curves, the logârank test, and Cox proportional hazard models to assess internal validity of the scoring system and its ability to stratify survival. A final optimized model was identified based on clinical and echocardiographic parameters preceding LVAD implantation. Oneâyear mortality was significantly higher in patients with higher risk scores (hazard ratio, 1.38; P=0.004). This hazard ratio represents the multiplied risk of death for every increase of 1 point in the risk score. The risk score was validated in a separate patient cohort of 260 patients at Columbia University, which confirmed the prognostic utility of this risk score (P=0.0237). Conclusion: We present a novel risk score and its validation for prediction of longâterm survival in patients with current types of continuous flow LVAD support
Whole-genome sequencing reveals host factors underlying critical COVID-19
Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease