The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is ...essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care.
This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia.
Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years range, 21-88 years; 64% 36/55 male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years range, 27-88 years; 55% 22/40 male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87-1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease.
We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
When DNA is damaged, or DNA replication goes awry, cells activate checkpoints to allow time for damage to be repaired and replication to complete. In Saccharomyces cerevisiae, the DNA damage ...checkpoint, which responds to lesions such as double-strand breaks, is activated when the lesion promotes the association of the sensor kinase Mec1 and its targeting subunit Ddc2 with its activators Ddc1 (a member of the 9-1-1 complex) and Dpb11. It has been more difficult to determine what role these Mec1 activators play in the replication checkpoint, which recognizes stalled replication forks, since Dpb11 has a separate role in DNA replication itself. Therefore, we constructed an in vivo replication-checkpoint mimic, which recapitulates Mec1-dependent phosphorylation of the effector kinase Rad53, a crucial step in checkpoint activation. In the natural replication checkpoint, Mec1 phosphorylation of Rad53 requires Mrc1, a replisome component. The replication-checkpoint mimic requires co-localization of Mrc1-LacI and Ddc2-LacI, and is independent of both Ddc1 and Dpb11. We show that these activators are also dispensable for Mec1 activity and cell survival in the natural replication checkpoint, but that Ddc1 is absolutely required in the absence of Mrc1. We propose that co-localization of Mrc1 and Mec1 is the minimal signal required to activate the replication checkpoint. The Skp1-Cul1-F box complex (SCF) associates with any one of a number of F box proteins, which serve as substrate binding adaptors. The human F box protein βTRCP directs the conjugation of ubiquitin to a variety of substrate proteins, leading to the destruction of the substrate by the proteasome. To identify βTRCP substrates, we employed a recently-developed technique, called Ligase Trapping, wherein a ubiquitin ligase is fused to a ubiquitin-binding domain to “trap” ubiquitinated substrates. 88% of the candidate substrates that we examined were bona fide substrates, comprising twelve previously validated substrates, eleven new substrates and three false positives. One βTRCP substrate, CReP, is a Protein Phosphatase 1 (PP1) specificity subunit that targets the translation initiation factor eIF2α to promote the removal of a stress-induced inhibitory phosphorylation and increase cap-dependent translation. We found that CReP is targeted by βTRCP for degradation upon DNA damage. Using a stable CReP allele, we show that depletion of CReP is required for the full induction of eIF2α phosphorylation upon DNA damage, and contributes to keeping the levels of translation low as cells recover from DNA damage.
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