bims-covirf Biomed News
on COVID19 risk factors
Issue of 2020‒10‒04
thirteen papers selected by
Catherine Rycroft

  1. medRxiv. 2020 Sep 14. pii: 2020.09.12.20193391. [Epub ahead of print]
    Lusczek ER, Ingraham NE, Karam B, Proper J, Siegel L, Helgeson E, Lotfi-Emran S, Zolfaghari EJ, Jones E, Usher M, Chipman J, Dudley RA, Benson B, Melton GB, Charles A, Lupei MI, Tignanelli CJ.
      BACKGROUND: There is limited understanding of heterogeneity in outcomes across hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of distinct clinical phenotypes may facilitate tailored therapy and improve outcomes.OBJECTIVE: Identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.
    DESIGN, SETTINGS, AND PARTICIPANTS: Retrospective analysis of 1,022 COVID-19 patient admissions from 14 Midwest U.S. hospitals between March 7, 2020 and August 25, 2020.
    METHODS: Ensemble clustering was performed on a set of 33 vitals and labs variables collected within 72 hours of admission. K-means based consensus clustering was used to identify three clinical phenotypes. Principal component analysis was performed on the average covariance matrix of all imputed datasets to visualize clustering and variable relationships. Multinomial regression models were fit to further compare patient comorbidities across phenotype classification. Multivariable models were fit to estimate the association between phenotype and in-hospital complications and clinical outcomes. Main outcomes and measures: Phenotype classification (I, II, III), patient characteristics associated with phenotype assignment, in-hospital complications, and clinical outcomes including ICU admission, need for mechanical ventilation, hospital length of stay, and mortality.
    RESULTS: The database included 1,022 patients requiring hospital admission with COVID-19 (median age, 62.1 [IQR: 45.9-75.8] years; 481 [48.6%] male, 412 [40.3%] required ICU admission, 437 [46.7%] were white). Three clinical phenotypes were identified (I, II, III); 236 [23.1%] patients had phenotype I, 613 [60%] patients had phenotype II, and 173 [16.9%] patients had phenotype III. When grouping comorbidities by organ system, patients with respiratory comorbidities were most commonly characterized by phenotype III (p=0.002), while patients with hematologic (p<0.001), renal (p<0.001), and cardiac (p<0.001) comorbidities were most commonly characterized by phenotype I. The adjusted odds of respiratory (p<0.001), renal (p<0.001), and metabolic (p<0.001) complications were highest for patients with phenotype I, followed by phenotype II. Patients with phenotype I had a far greater odds of hepatic (p<0.001) and hematological (p=0.02) complications than the other two phenotypes. Phenotypes I and II were associated with 7.30-fold (HR: 7.30, 95% CI: (3.11-17.17), p<0.001) and 2.57-fold (HR: 2.57, 95% CI: (1.10-6.00), p=0.03) increases in the hazard of death, respectively, when compared to phenotype III.
    CONCLUSION: In this retrospective analysis of patients with COVID-19, three clinical phenotypes were identified. Future research is urgently needed to determine the utility of these phenotypes in clinical practice and trial design.
  2. J Am Med Inform Assoc. 2020 Sep 28. pii: ocaa246. [Epub ahead of print]
    Gao X, Dong Q.
      OBJECTIVE: Estimating the hospitalization risk for people with comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance. Traditional biostatistical methods for risk estimations require: (i) the number of infected people who were not hospitalized, which may be severely undercounted since many infected people were not tested; (ii) comorbidity information for people not hospitalized, which may not always be readily available. We aim to overcome these limitations by developing a Bayesian approach to estimate the risk ratio of hospitalization for COVID-19 patients with comorbidities.MATERIALS AND METHODS: We derived a Bayesian approach to estimate the posterior distribution of the risk ratio using the observed frequency of comorbidities in COVID-19 patients in hospitals and the prevalence of comorbidities in the general population. We applied our approach to two large-scale datasets in the United States: 2491 patients in the COVID-NET, and 5700 patients in New York hospitals.
    RESULTS: Our results consistently indicated that cardiovascular diseases carried the highest hospitalization risk for COVID-19 patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity, respectively.
    DISCUSSION: Our approach only needs (i) the number of hospitalized COVID-19 patients and their comorbidity information, which can be reliably obtained using hospital records, and (ii) the prevalence of the comorbidity of interest in the general population, which is regularly documented by public health agencies for common medical conditions.
    CONCLUSION: We developed a novel Bayesian approach to estimate the hospitalization risk for people with comorbidities infected with the SARS-CoV-2 virus.
    Keywords:  Bayesian; COVID-19; Comorbidity; Hospitalization; Risk Ratio; SARS-CoV-2
  3. Ann Med. 2020 Sep 30. 1-31
    Halalau A, Imam Z, Karabon P, Mankuzhy N, Shaheen A, Tu J, Carpenter C.
      Background: Identification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients.Methods: A retrospective multi-center (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between March 1 and April 1, 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration.Results: Compared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both P < 0.0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both P < 0.0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)).Conclusions: The COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19. Key Points:Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)?In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)), respectively.The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19.
  4. Diabetes Metab Syndr. 2020 Sep 28. pii: S1871-4021(20)30381-7. [Epub ahead of print]14(6): 1897-1904
    Soeroto AY, Soetedjo NN, Purwiga A, Santoso P, Kulsum ID, Suryadinata H, Ferdian F.
      BACKGROUND AND AIMS: Corona virus diseases 2019 (COVID-19) pandemic spread rapidly. Growing evidences that overweight and obesity which extent nearly a third of the world population were associated with severe COVID-19. This study aimed to explore the association and risk of increased BMI and obesity with composite poor outcome in COVID-19 adult patients.METHODS: We conducted a systematic literature search from PubMed and Embase database. We included all original research articles in COVID-19 adult patients and obesity based on classification of Body Mass Index (BMI) and composite poor outcome which consist of ICU admission, ARDS, severe COVID-19, use of mechanical ventilation, hospital admission, and mortality.
    RESULTS: Sixteen studies were included in meta-analysis with 9 studies presented BMI as continuous outcome and 10 studies presented BMI as dichotomous outcome (cut-off ≥30 kg/m2). COVID-19 patients with composite poor outcome had higher BMI with mean difference 1.12 (95% CI, 0.67-1.57, P < 0.001). Meanwhile, obesity was associated with composite poor outcome with odds ratio (OR) = 1.78 (95% CI, 1.25-2.54, P < 0.001) Multivariate meta-regression showed the association between BMI and obesity on composite poor outcome were affected by age, gender, DM type 2, and hypertension.
    CONCLUSION: Obesity is a risk factor of composite poor outcome of COVID-19. On the other hand, COVID-19 patients with composite poor outcome have higher BMI. BMI is an important routine procedure that should always be assessed in the management of COVID-19 patients and special attention should be given to patients with obesity.
    Keywords:  Body mass index; Covid-19; Obesity; Poor outcome
  5. J Clin Endocrinol Metab. 2020 12 01. pii: dgaa535. [Epub ahead of print]105(12):
    Wang X, Liu Z, Li J, Zhang J, Tian S, Lu S, Qi M, Ma J, Qiu B, Dong W, Xu Y.
      PURPOSE: Coronavirus disease 2019 (COVID-19) has become a topic of concern worldwide; however, the impacts of type 2 diabetes mellitus (T2DM) on disease severity, therapeutic effect, and mortality of patients with COVID-19 are unclear.METHODS: All consecutive patients with COVID-19 admitted to the Renmin Hospital of Wuhan University from January 11 to February 6, 2020, were included in this study.
    RESULTS: A total of 663 patients with COVID-19 were included, while 67 patients with T2DM accounted for 10.1% of the total. Compared with patients with COVID-19 without T2DM, those with T2DM were older (aged 66 years vs 57 years; P < 0.001) and had a male predominance (62.7% vs 37.3%; P = 0.019) and higher prevalence of cardiovascular diseases (61.2% vs 20.6%; P < 0.001) and urinary diseases (9% vs 2.5%; P = 0.014). Patients with T2DM were prone to developing severe (58.2% vs 46.3%; P = 0.002) and critical COVID-19 (20.9% vs 13.4%; P = 0.002) and having poor therapeutic effect (76.1% vs 60.4%; P = 0.017). But there was no obvious difference in the mortality between patients with COVID-19 with and without T2DM (4.5% vs 3.7%; P = 0.732). Multivariate logistic regression analysis identified that T2DM was associated with poor therapeutic effect in patients with COVID-19 (odd ratio [OR] 2.99; 95% confidence interval [CI], 1.07-8.66; P = 0.04). Moreover, having a severe and critical COVID-19 condition (OR 3.27; 95% CI, 1.02-9.00; P = 0.029) and decreased lymphocytes (OR 1.59; 95% CI, 1.10-2.34; P = 0.016) were independent risk factors associated with poor therapeutic effect in patients with COVID-19 with T2DM.
    CONCLUSIONS: T2DM influenced the disease severity and therapeutic effect and was one of the independent risk factors for poor therapeutic effect in patients with COVID-19.
    Keywords:  coronavirus disease 2019; disease severity; risk factors; therapeutic effect; type 2 diabetes mellitus
  6. Clin Med Insights Circ Respir Pulm Med. 2020 ;14 1179548420959165
    Pranata R, Supriyadi R, Huang I, Permana H, Lim MA, Yonas E, Soetedjo NNM, Lukito AA.
      Objective: The aim of the study was to evaluate the association between chronic kidney disease (CKD) and new onset renal replacement therapy (RRT) with the outcome of Coronavirus Disease 2019 (COVID-19) in patients.Methodology: A systematic literature search from several databases was performed on studies that assessed CKD, use of RRT, and the outcome of COVID-19. The composite of poor outcome consisted of mortality, severe COVID-19, acute respiratory distress syndrome (ARDS), need for intensive care, and use of mechanical ventilator.
    Results: Nineteen studies with a total of 7216 patients were included. CKD was associated with increased composite poor outcome (RR 2.63 [1.33, 5.17], P = .03; I 2 = 51%, P = .01) and its subgroup, consisting of mortality (RR 3.47 [1.36, 8.86], P = .009; I 2 = 14%, P = .32) and severe COVID-19 (RR 2.89 [0.98, 8.46], P = .05; I 2 = 57%, P = .04). RRT was associated with increased composite poor outcome (RR 18.04 [4.44, 73.25], P < .001; I 2 = 87%, P < .001), including mortality (RR 26.02 [5.01, 135.13], P < .001; I 2 = 60%, P = .06), severe COVID-19 (RR 12.95 [1.93, 86.82], P = .008; I 2 = 81%, P < .001), intensive care (IC) (RR 14.22 [1.76, 114.62], P < .01; I 2 = 0%, P < .98), and use of mechanical ventilator (RR 34.39 [4.63, 255.51], P < .0005).
    Conclusion: CKD and new-onset RRT were associated with poor outcome in patients with COVID-19.
    Keywords:  COVID-19; Chronic kidney disease; Coronavirus; SARS-CoV-2; mortality; renal replacement therapy; severity
  7. medRxiv. 2020 Sep 23. pii: 2020.09.22.20199802. [Epub ahead of print]
    Patanavanich R, Glantz SA.
      BACKGROUND: Smoking impairs lung immune functions and damages upper airways, increasing risks of contracting and severity of infectious diseases.METHODS: We searched PubMed and Embase for studies published from January 1-May 25, 2020. We included studies reporting smoking behavior of COVID-19 patients and progression of disease, including death. We used a random effects meta-analysis and used meta-regression and lowess regressions to examine relationships in the data.
    RESULTS: We identified 47 peer-reviewed papers with a total of 31,871 COVID-19 patients, 5,759 (18.1%) experienced disease progression and 5,734 (18.0%) with a history of smoking. Among smokers, 29.2% experienced disease progression, compared with 21.1% of non-smokers. The meta-analysis confirmed an association between smoking and COVID-19 progression (OR 1.56, 95% CI 1.32-1.83, p=0.001). Smoking was associated with increased risk of death from COVID-19 (OR 1.19, 95% CI 1.05-1.34, p=0.007). We found no significant difference (p=0.432) between the effects of smoking on COVID-19 disease progression between adjusted and unadjusted analyses, suggesting that smoking is an independent risk factor for COVID-19 disease progression. We also found the risk of having COVID-19 progression among younger adults (p=0.023), with the effect most pronounced among people under about 45 years old.
    CONCLUSIONS: Smoking is an independent risk for having severe progression of COVID-19, including mortality. The effects seem to be higher among young people. Smoking prevention and cessation should remain a priority for the public, physicians, and public health professionals during the COVID-19 pandemic.
  8. Addiction. 2020 Oct 02.
    Simons D, Shahab L, Brown J, Perski O.
      AIMS: To estimate the association of smoking status with rates of i) infection, ii) hospitalisation, iii) disease severity, and iv) mortality from SARS-CoV-2/COVID-19 disease.DESIGN: Living rapid review of observational and experimental studies with random-effects hierarchical Bayesian meta-analyses. Published articles and pre-prints were identified via MEDLINE and medRxiv.
    SETTING: Community or hospital. No restrictions on location.
    PARTICIPANTS: Adults who received a SARS-CoV-2 test or a COVID-19 diagnosis.
    MEASUREMENTS: Outcomes were SARS-CoV-2 infection, hospitalisation, disease severity and mortality stratified by smoking status. Study quality was assessed (i.e. 'good', 'fair' and 'poor').
    FINDINGS: Version 7 (searches up to 25 August 2020) included 233 studies with 32 'good' and 'fair' quality studies included in meta-analyses. Fifty-seven studies (24.5%) reported current, former and never smoking status. Recorded smoking prevalence among people with COVID-19 was generally lower than national prevalence. Current compared with never smokers were at reduced risk of SARS-CoV-2 infection (RR=0.74, 95% Credible Interval (CrI) = 0.58-0.93, τ = 0.41). Data for former smokers were inconclusive (RR=1.05, 95% CrI = 0.95-1.17, τ = 0.17) but favoured there being no important association (21% probability of RR ≥1.1). Former compared with never smokers were at somewhat increased risk of hospitalisation (RR=1.20, CrI = 1.03-1.44, τ = 0.17), greater disease severity (RR=1.52, CrI = 1.13-2.07, τ = 0.29), and mortality (RR=1.39, 95% CrI = 1.09-1.87, τ = 0.27). Data for current smokers were inconclusive (RR=1.06, CrI = 0.82-1.35, τ = 0.27; RR=1.25, CrI = 0.85-1.93, τ = 0.34; RR=1.22, 95% CrI = 0.78-1.94, τ = 0.49 respectively) but favoured there being no important associations with hospitalisation and mortality (35% and 70% probability of RR ≥1.1, respectively) and a small but important association with disease severity (79% probability of RR ≥1.1).
    CONCLUSIONS: Compared with never smokers, current smokers appear to be at reduced risk of SARS-CoV-2 infection while former smokers appear to be at increased risk of hospitalisation, increased disease severity and mortality from COVID-19. However, it is uncertain whether these associations are causal.
    Keywords:  COVID-19; SARS-CoV-2; e-cigarettes; hospitalisation; infection; living review; mortality; nicotine replacement therapy; smoking; tobacco
  9. Metabolism. 2020 Sep 28. pii: S0026-0495(20)30242-0. [Epub ahead of print] 154378
    Huang Y, Yao LU, Huang YM, Wang M, Ling W, Sui Y, Zhao HL.
      BACKGROUND: Obesity is common in patients with coronavirus disease 2019 (COVID-19). The effects of obesity on clinical outcomes of COVID-19 warrant systematical investigation.OBJECTIVE: This study explores the effects of obesity with the risk of severe disease among patients with COVID-19.
    METHODS: Body mass index (BMI) and degree of visceral adipose tissue (VAT) accumulation were used as indicators for obesity status. Publication databases including preprints were searched up to August 10, 2020. Clinical outcomes of severe COVID-19 included hospitalization, a requirement for treatment in an intensive care unit (ICU), invasive mechanical ventilation (IMV), and mortality. Risks for severe COVID-19 outcomes are presented as odds ratios (OR) and 95% confidence interval (95%CI) for cohort studies with BMI-defined obesity, and standardized mean difference (SMD) and 95%CI for controlled studies with VAT-defined excessive adiposity.
    RESULTS: A total of 45, 650 participants from 30 studies with BMI-defined obesity and 3 controlled studies with VAT-defined adiposity were included for assessing the risk of severe COVID-19. Univariate analyses showed significantly higher ORs of severe COVID-19 with higher BMI: 1.76 (95%: 1.21, 2.56, P = 0.003) for hospitalization, 1.67 (95%CI: 1.26, 2.21, P<0.001) for ICU admission, 2.19 (95%CI: 1.56, 3.07, P<0.001) for IMV requirement, and 1.37 (95%CI: 1.06, 1.75, P = 0.014) for death, giving an overall OR for severe COVID-19 of 1.67 (95%CI: 1.43, 1.96; P<0.001). Multivariate analyses revealed increased ORs of severe COVID-19 associated with higher BMI: 2.36 (95%CI: 1.37, 4.07, P = 0.002) for hospitalization, 2.32 (95%CI: 1.38, 3.90, P = 0.001) for requiring ICU admission, 2.63 (95%CI: 1.32, 5.25, P = 0.006) for IMV support, and 1.49 (95%CI: 1.20, 1.85, P<0.001) for mortality, giving an overall OR for severe COVID-19 of 2.09 (95%CI: 1.67, 2.62; P<0.001). Compared to non-severe COVID-19 patients, severe COVID-19 cases showed significantly higher VAT accumulation with a SMD of 0.49 for hospitalization (95% CI: 0.11, 0.87; P = 0.011), 0.57 (95% CI: 0.33, 0.81; P<0.001) for requiring ICU admission and 0.37 (95% CI: 0.03, 0.71; P = 0.035) for IMV support. The overall SMD for severe COVID-19 was 0.50 (95% CI: 0.33, 0.68; P<0.001).
    CONCLUSIONS: Obesity increases risk for hospitalization, ICU admission, IMV requirement and death among patients with COVID-19. Further, excessive visceral adiposity appears to be associated with severe COVID-19 outcomes. These findings emphasize the need for effective actions by individuals, the public and governments to increase awareness of the risks resulting from obesity and how these are heightened in the current global pandemic.
    Keywords:  Coronavirus disease 2019; Intensive care; Invasive mechanical ventilation; Mortality; Obesity; Visceral adipose tissue
  10. Cureus. 2020 Aug 29. 12(8): e10114
    Kelada M, Anto A, Dave K, Saleh SN.
      A worldwide outbreak of coronavirus disease 2019 (COVID-19), identified as being caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), was classified as a Public Health Emergency of International Concern by the World Health Organisation (WHO) on January 30, 2020. Initial sex-disaggregated mortality data emerging from the Wuhan province of China identified male sex as a risk factor for increased COVID-19 mortality.   In this systematic review, we aimed to assess the role of sex in the risk of mortality from COVID-19 in adult patients through comparison of clinical markers and inflammatory indexes.   A systematic search was conducted on the following databases: PubMed, WHO COVID-19 database, Ovid MEDLINE, and Web of Science between the dates of June 15, 2020, and June 30, 2020. Key search terms used included: "sex", "gender", "SARS-COV-2", "COVID" and "mortality". We accepted the following types of studies concerning adult COVID-19 patients: retrospective cohort, observational cohort, case series, and applied research. Further studies were extracted from reference searching. The risk of bias was determined using the National Institutes of Health Quality Assessment Tool for Observational Cohort, Cross-Sectional Studies, and Case Series.  We identified a total of 16 studies published between January 2020 and June 2020 for analysis in this systematic review. Our study population consisted of 11 cohort studies, four case series, and one genetic study, including a total of 76,555 participants. Ten of the studies included in this review observed a higher risk of mortality among males compared to females, and eight of these studies found this risk to be statistically significant.    Sex-disaggregated COVID-19 mortality data identifies male patients with comorbidities as being at an increased risk of mortality worldwide. Further investigation revealed differences in immune response regulated by sex hormones, angiotensin-converting enzyme 2 (ACE2) expression, and health behaviours as contributing factors to increased risk of mortality from COVID-19 among males.    Nine out of the 16 studies included were conducted in China. In order to comprehensively assess sex-differences in the risk of mortality from COVID-19, more studies will need to be conducted worldwide. Sex-disaggregated COVID-19 data published in the medical literature is limited, however it has become evident that male sex is an important risk factor for mortality. Further exploration into the impact of sex on this pandemic is required in order to develop targeted therapies, as well as public health policies, and to prevent sex bias in treatment.
    Keywords:  covid-19; gender; mortality; review; sex
  11. J Clin Oncol. 2020 Sep 28. JCO2001580
    Brar G, Pinheiro LC, Shusterman M, Swed B, Reshetnyak E, Soroka O, Chen F, Yamshon S, Vaughn J, Martin P, Paul D, Hidalgo M, Shah MA.
      PURPOSE: SARS-CoV-2 (COVID-19) is a systemic infection. Patients with cancer are immunocompromised and may be vulnerable to COVID-related morbidity and mortality. The objectives of this study were to determine if patients with cancer have worse outcomes compared with patients without cancer and to identify demographic and clinical predictors of morbidity and mortality among patients with cancer.METHODS: We used data from adult patients who tested positive for COVID-19 and were admitted to two New York-Presbyterian hospitals between March 3 and May 15, 2020. Patients with cancer were matched 1:4 to controls without cancer in terms of age, sex, and number of comorbidities. Using Kaplan-Meier curves and the log-rank test, we compared morbidity (intensive care unit admission and intubation) and mortality outcomes between patients with cancer and controls. Among those with cancer, we identified demographic and clinical predictors of worse outcomes using Cox proportional hazard models.
    RESULTS: We included 585 patients who were COVID-19 positive, of whom 117 had active malignancy, defined as those receiving cancer-directed therapy or under active surveillance within 6 months of admission. Presenting symptoms and in-hospital complications were similar between the cancer and noncancer groups. Nearly one half of patients with cancer were receiving therapy, and 45% of patients received cytotoxic or immunosuppressive treatment within 90 days of admission. There were no statistically significant differences in morbidity or mortality (P = .894) between patients with and without cancer.
    CONCLUSION: We observed that patients with COVID-19 and cancer had similar outcomes compared with matched patients without cancer. This finding suggests that a diagnosis of active cancer alone and recent anticancer therapy do not predict worse COVID-19 outcomes and therefore, recommendations to limit cancer-directed therapy must be considered carefully in relation to cancer-specific outcomes and death.
  12. Clin Infect Dis. 2020 Sep 28. pii: ciaa1468. [Epub ahead of print]
    Bhargava A, Sharma M, Riederer K, Fukushima EA, Szpunar SM, Saravolatz L.
      BACKGROUND: Racial disparities are central in the national conversation about Covid-19. Black/African Americans are contracting and dying from COVID-19 disproportionately. We assessed risk factors for death from COVID-19 among black inpatients at an urban center in Detroit, MI.METHODS: This was a retrospective, single-center cohort study. We reviewed the electronic medical records of patients positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, the virus that causes COVID-19) on qualitative polymerase-chain-reaction assay, who were admitted between 3/8-5/6/2020. The primary outcome was in-hospital mortality.
    RESULTS: The case fatality rate was 29.1% (122/419). The mean duration of symptoms prior to hospitalization was 5.3 (3.9) days. Patients who died were older (mean [SD] age, 68.7 [14.8] years vs 60.3 [16.0] years; p <0.0001), had dementia (35 [28.7%] vs 34 [11.4%]; p <0.0001), hemiplegia (14 [11.5%] vs 12 [4.0%]; p=0.004), malignancy (11 [9.0%] vs 12 [4.0%]; p=0.04), and moderate-severe liver disease (4 [3.3%] vs 1 [0.3%]; p=0.01). The incidence of AMS on presentation was higher among patients who died than those who survived, 43% vs. 20.0%, respectively (p<0.0001). From multivariable analysis, the odds of death increased with age (≥60 yrs.), admission from a nursing facility, Charlson score, altered mental status, higher C-reactive protein on admission, need for mechanical ventilation, presence of shock, and acute respiratory distress syndrome.
    CONCLUSIONS: These demographic, clinical and laboratory factors should help healthcare providers identify black patients at highest risk for severe COVID-19-associated outcomes. Early and aggressive interventions among this at-risk population can help mitigate adverse outcomes.
    Keywords:  COVID-19; Predictors; Risk factors; mortality
  13. Cancer Cell. 2020 Sep 15. pii: S1535-6108(20)30481-5. [Epub ahead of print]
    Westblade LF, Brar G, Pinheiro LC, Paidoussis D, Rajan M, Martin P, Goyal P, Sepulveda JL, Zhang L, George G, Liu D, Whittier S, Plate M, Small CB, Rand JH, Cushing MM, Walsh TJ, Cooke J, Safford MM, Loda M, Satlin MJ.
      Patients with cancer may be at increased risk of severe coronavirus disease 2019 (COVID-19), but the role of viral load on this risk is unknown. We measured SARS-CoV-2 viral load using cycle threshold (CT) values from reverse-transcription polymerase chain reaction assays applied to nasopharyngeal swab specimens in 100 patients with cancer and 2,914 without cancer who were admitted to three New York City hospitals. Overall, the in-hospital mortality rate was 38.8% among patients with a high viral load, 24.1% among patients with a medium viral load, and 15.3% among patients with a low viral load (p < 0.001). Similar findings were observed in patients with cancer (high, 45.2% mortality; medium, 28.0%; low, 12.1%; p = 0.008). Patients with hematologic malignancies had higher median viral loads (CT = 25.0) than patients without cancer (CT = 29.2; p = 0.0039). SARS-CoV-2 viral load results may offer vital prognostic information for patients with and without cancer who are hospitalized with COVID-19.
    Keywords:  cancer; coronavirus disease 2019 (COVID-19); cycle threshold (C(T)); hematologic malignancy; mortality; severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); solid tumor; viral load