ABSTRACT
OBJECTIVE: To determine risk factors for mortality among COVID-19 patients admitted to a system of community hospitals in the United States. DESIGN: Retrospective analysis of patient data collected from the routine care of COVID-19 patients. SETTING: System of >180 acute-care facilities in the United States. PARTICIPANTS: All admitted patients with positive identification of COVID-19 and a documented discharge as of May 12, 2020. METHODS: Determination of demographic characteristics, vital signs at admission, patient comorbidities and recorded discharge disposition in this population to construct a logistic regression estimating the odds of mortality, particular for those patients characterized as not being critically ill at admission. RESULTS: In total, 6,180 COVID-19+ patients were identified as of May 12, 2020. Most COVID-19+ patients (4,808, 77.8%) were admitted directly to a medical-surgical unit with no documented critical care or mechanical ventilation within 8 hours of admission. After adjusting for demographic characteristics, comorbidities, and vital signs at admission in this subgroup, the largest driver of the odds of mortality was patient age (OR, 1.07; 95% CI, 1.06-1.08; P < .001). Decreased oxygen saturation at admission was associated with increased odds of mortality (OR, 1.09; 95% CI, 1.06-1.12; P < .001) as was diabetes (OR, 1.57; 95% CI, 1.21-2.03; P < .001). CONCLUSIONS: The identification of factors observable at admission that are associated with mortality in COVID-19 patients who are initially admitted to non-critical care units may help care providers, hospital epidemiologists, and hospital safety experts better plan for the care of these patients.
Subject(s)
COVID-19/pathology , Vital Signs , Age Factors , Aged , Aged, 80 and over , COVID-19/mortality , Female , Humans , Logistic Models , Male , Middle Aged , Oxygen/blood , Patient Admission/statistics & numerical data , Retrospective Studies , Risk Factors , United States/epidemiologyABSTRACT
Coronavirus disease 2019 (COVID-19) has migrated to regions that were initially spared, and it is likely that different populations are currently at risk for illness. Herein, we present our observations of the change in characteristics and resource use of COVID-19 patients over time in a national system of community hospitals to help inform those managing surge planning, operational management, and future policy decisions.
Subject(s)
COVID-19/epidemiology , COVID-19/therapy , Hospitalization/statistics & numerical data , Adult , Age Factors , Aged , Aged, 80 and over , COVID-19/ethnology , COVID-19/mortality , Female , Hispanic or Latino/statistics & numerical data , Hospitals, Community , Humans , Male , Middle Aged , SARS-CoV-2/isolation & purification , Virginia/epidemiology , Young AdultABSTRACT
MOTIVATION: A significant and stubbornly intractable problem in genome sequence analysis has been the de novo identification of transcription factor binding sites in promoter regions. Although theoretically pleasing, probabilistic methods have faced difficulties due to model mismatch and the nature of the biological sequence. These problems result in inference in a high dimensional, highly multimodal space, and consequently often display only local convergence and hence unsatisfactory performance. ALGORITHM: In this article, we derive and demonstrate a novel method utilizing a sequential Monte Carlo-based expectation-maximization (EM) optimization to improve performance in this scenario. The Monte Carlo element should increase the robustness of the algorithm compared to classical EM. Furthermore, the parallel nature of the sequential Monte Carlo algorithm should be more robust than Gibbs sampling approaches to multimodality problems. RESULTS: We demonstrate the superior performance of this algorithm on both semi-synthetic and real data from Escherichia coli. AVAILABILITY: http://sigproc-eng.cam.ac.uk/ approximately ej230/smc_em_tfbsid.tar.gz
Subject(s)
Algorithms , Sequence Alignment/methods , Sequence Analysis, DNA/methods , Transcription Factors/chemistry , Transcription Factors/genetics , Base Sequence , Binding Sites , Likelihood Functions , Models, Genetic , Models, Statistical , Molecular Sequence Data , Monte Carlo Method , Pattern Recognition, Automated/methods , Protein BindingABSTRACT
This paper will give a complete methodological approach to the processing of oligonucleotide microarray data from postmortem tissue, particularly brain matter. Attention will be drawn to each of the important stages in the process; specifically the quality control, gene expression value calculation, multiple hypothesis testing and correlation analyses. We shall initially discuss the theoretical foundations of each individual method and subsequently apply the ensemble to a sample data set to illustrate and visualise important points.
