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1.
Cancer Med ; 11(11): 2204-2215, 2022 06.
Article in English | MEDLINE | ID: covidwho-1729106

ABSTRACT

BACKGROUND: The interaction between cancer diagnoses and COVID-19 infection and outcomes is unclear. We leveraged a state-wide, multi-institutional database to assess cancer-related risk factors for poor COVID-19 outcomes. METHODS: We conducted a retrospective cohort study using the University of California Health COVID Research Dataset, which includes electronic health data of patients tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at 17 California medical centers. We identified adults tested for SARS-CoV-2 from 2/1/2020-12/31/2020 and selected a cohort of patients with cancer. We obtained demographic, clinical, cancer type, and antineoplastic therapy data. The primary outcome was hospitalization within 30d after the first positive SARS-CoV-2 test. Secondary outcomes were SARS-CoV-2 positivity and severe COVID-19 (intensive care, mechanical ventilation, or death within 30d after the first positive test). We used multivariable logistic regression to identify cancer-related factors associated with outcomes. RESULTS: We identified 409,462 patients undergoing SARS-CoV-2 testing. Of 49,918 patients with cancer, 1781 (3.6%) tested positive. Patients with cancer were less likely to test positive (RR 0.70, 95% CI: 0.67-0.74, p < 0.001). Among the 1781 SARS-CoV-2-positive patients with cancer, BCR/ABL-negative myeloproliferative neoplasms (RR 2.15, 95% CI: 1.25-3.41, p = 0.007), venetoclax (RR 2.96, 95% CI: 1.14-5.66, p = 0.028), and methotrexate (RR 2.72, 95% CI: 1.10-5.19, p = 0.032) were associated with greater hospitalization risk. Cancer and therapy types were not associated with severe COVID-19. CONCLUSIONS: In this large, diverse cohort, cancer was associated with a decreased risk of SARS-CoV-2 positivity. Patients with BCR/ABL-negative myeloproliferative neoplasm or receiving methotrexate or venetoclax may be at increased risk of hospitalization following SARS-CoV-2 infection. Mechanistic and comparative studies are needed to validate findings.


Subject(s)
COVID-19 , Neoplasms , Adult , COVID-19/epidemiology , COVID-19 Testing , Hospitalization , Humans , Methotrexate , Neoplasms/epidemiology , Retrospective Studies , SARS-CoV-2
2.
J Am Med Inform Assoc ; 29(5): 864-872, 2022 04 13.
Article in English | MEDLINE | ID: covidwho-1684718

ABSTRACT

OBJECTIVE: The study sought to investigate the disease state-dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. MATERIALS AND METHODS: A covariate-dependent, continuous-time hidden Markov model with 4 states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2. Demographic characteristics, comorbidities, vital signs, and laboratory test results were retrospectively evaluated to infer a patient's clinical progression. RESULTS: The association between patient-level covariates and risk of progression was found to be disease state dependent. Specifically, while being male, being Black or having a medical comorbidity were all associated with an increased risk of progressing from the moderate disease state to the severe disease state, these same factors were associated with a decreased risk of progressing from the severe disease state to the deceased state. DISCUSSION: Recent studies have not included analyses of the temporal progression of COVID-19, making the current study a unique modeling-based approach to understand the dynamics of COVID-19 in hospitalized patients. CONCLUSION: Dynamic risk stratification models have the potential to improve clinical outcomes not only in COVID-19, but also in a myriad of other acute and chronic diseases that, to date, have largely been assessed only by static modeling techniques.


Subject(s)
COVID-19 , Comorbidity , Female , Hospitalization , Humans , Male , Retrospective Studies , Risk Factors , SARS-CoV-2
3.
Journal of the Indian Institute of Science ; : 1-25, 2021.
Article in English | EuropePMC | ID: covidwho-1451155

ABSTRACT

The study of epidemics is useful for not only understanding outbreaks and trying to limit their adverse effects, but also because epidemics are related to social phenomena such as government instability, crime, poverty, and inequality. One approach for studying epidemics is to simulate their spread through populations. In this work, we describe an integrated multi-dimensional approach to epidemic simulation, which encompasses: (1) a theoretical framework for simulation and analysis;(2) synthetic population (digital twin) generation;(3) (social contact) network construction methods from synthetic populations, (4) stylized network construction methods;and (5) simulation of the evolution of a virus or disease through a social network. We describe these aspects and end with a short discussion on simulation results that inform public policy.

4.
Sci Rep ; 11(1): 19543, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1447319

ABSTRACT

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.


Subject(s)
Budgets , COVID-19/pathology , COVID-19/virology , Machine Learning , Outcome Assessment, Health Care , SARS-CoV-2/isolation & purification , Humans
5.
J Allergy Clin Immunol ; 148(5): 1176-1191, 2021 11.
Article in English | MEDLINE | ID: covidwho-1401557

ABSTRACT

BACKGROUND: The risk of severe coronavirus disease 2019 (COVID-19) varies significantly among persons of similar age and is higher in males. Age-independent, sex-biased differences in susceptibility to severe COVID-19 may be ascribable to deficits in a sexually dimorphic protective attribute that we termed immunologic resilience (IR). OBJECTIVE: We sought to examine whether deficits in IR that antedate or are induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection independently predict COVID-19 mortality. METHODS: IR levels were quantified with 2 novel metrics: immune health grades (IHG-I [best] to IHG-IV) to gauge CD8+ and CD4+ T-cell count equilibrium, and blood gene expression signatures. IR metrics were examined in a prospective COVID-19 cohort (n = 522); primary outcome was 30-day mortality. Associations of IR metrics with outcomes in non-COVID-19 cohorts (n = 13,461) provided the framework for linking pre-COVID-19 IR status to IR during COVID-19, as well as to COVID-19 outcomes. RESULTS: IHG-I, tracking high-grade equilibrium between CD8+ and CD4+ T-cell counts, was the most common grade (73%) among healthy adults, particularly in females. SARS-CoV-2 infection was associated with underrepresentation of IHG-I (21%) versus overrepresentation (77%) of IHG-II or IHG-IV, especially in males versus females (P < .01). Presentation with IHG-I was associated with 88% lower mortality, after controlling for age and sex; reduced risk of hospitalization and respiratory failure; lower plasma IL-6 levels; rapid clearance of nasopharyngeal SARS-CoV-2 burden; and gene expression signatures correlating with survival that signify immunocompetence and controlled inflammation. In non-COVID-19 cohorts, IR-preserving metrics were associated with resistance to progressive influenza or HIV infection, as well as lower 9-year mortality in the Framingham Heart Study, especially in females. CONCLUSIONS: Preservation of immunocompetence with controlled inflammation during antigenic challenges is a hallmark of IR and associates with longevity and AIDS resistance. Independent of age, a male-biased proclivity to degrade IR before and/or during SARS-CoV-2 infection predisposes to severe COVID-19.


Subject(s)
COVID-19/immunology , HIV Infections/epidemiology , HIV-1/physiology , Respiratory Insufficiency/epidemiology , SARS-CoV-2/physiology , Sex Factors , T-Lymphocytes/immunology , Adult , Aged , COVID-19/epidemiology , COVID-19/mortality , Cohort Studies , Disease Resistance , Female , Humans , Immunocompetence , Interleukin-6/blood , Longitudinal Studies , Male , Middle Aged , Prospective Studies , Severity of Illness Index , Survival Analysis , Transcriptome/immunology , United States/epidemiology , Viral Load
6.
Qatar Med J ; 2020(2): 26, 2020.
Article in English | MEDLINE | ID: covidwho-891655

ABSTRACT

Our article outlines a perspective on COVID-19 reactivation with considerations of implored commentary on behalf of the medical community regarding open discourse about this subject. Such a topic is paramount in elucidating parameters that pertain to testing, and subsequent public health population dynamics once uneventful cases pass. We argue that what some may refer to as a reinfection or reactivation of the virus, is actually a result of prolonged shedding of the virus complemented with occasional false positives/negatives and lab errors. This article was written with the perspective of informing in addition to engage discussions that distill salient, evidence-based characterization of COVID-19. We hope to recruit fellow academics in medicine who see trends in their own respective communities about people who re-test, and to explore their clinical outcomes.

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