Modulation of the Association Between Age and Death by Risk Factor Burden in Critically Ill Patients With COVID-19

OBJECTIVES: Older age is a key risk factor for adverse outcomes in critically ill patients with COVID-19. However, few studies have investigated whether preexisting comorbidities and acute physiologic ICU factors modify the association between age and death. DESIGN: Multicenter cohort study. SETTING: ICUs at 68 hospitals across the United States. PATIENTS: A total of 5,037 critically ill adults with COVID-19 admitted to ICUs between March 1, 2020, and July 1, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary exposure was age, modeled as a continuous variable. The primary outcome was 28-day inhospital mortality. Multivariable logistic regression tested the association between age and death. Effect modification by the number of risk factors was assessed through a multiplicative interaction term in the logistic regression model. Among the 5,037 patients included (mean age, 60.9 yr [± 14.7], 3,179 [63.1%] male), 1,786 (35.4%) died within 28 days. Age had a nonlinear association with 28-day mortality (p for nonlinearity <0.001) after adjustment for covariates that included demographics, preexisting comorbidities, acute physiologic ICU factors, number of ICU beds, and treatments for COVID-19. The number of preexisting comorbidities and acute physiologic ICU factors modified the association between age and 28-day mortality (p for interaction <0.001), but this effect modification was modest as age still had an exponential relationship with death in subgroups stratified by the number of risk factors. CONCLUSIONS: In a large population of critically ill patients with COVID-19, age had an independent exponential association with death. The number of preexisting comorbidities and acute physiologic ICU factors modified the association between age and death, but age still had an exponential association with death in subgroups according to the number of risk factors present. Additional studies are needed to identify the mechanisms underpinning why older age confers an increased risk of death in critically ill patients with COVID-19.

[1]  G. French,et al.  Impact of Hospital Strain on Excess Deaths During the COVID-19 Pandemic — United States, July 2020–July 2021 , 2021, MMWR. Morbidity and mortality weekly report.

[2]  S. Bagshaw,et al.  Association between preoperative frailty and outcomes among adults undergoing cardiac surgery: a prospective cohort study , 2021, CMAJ open.

[3]  Samantha K. Brenner,et al.  Hospital-Level Variation in Death for Critically Ill Patients with COVID-19 , 2021, American journal of respiratory and critical care medicine.

[4]  P. Jat,et al.  Mechanisms of Cellular Senescence: Cell Cycle Arrest and Senescence Associated Secretory Phenotype , 2021, Frontiers in Cell and Developmental Biology.

[5]  Samantha K. Brenner,et al.  d-dimer and Death in Critically Ill Patients With Coronavirus Disease 2019 , 2021, Critical care medicine.

[6]  Eefje G. J. Roelofsen,et al.  Association between Clinical Frailty Scale score and hospital mortality in adult patients with COVID-19 (COMET): an international, multicentre, retrospective, observational cohort study , 2021, The Lancet Healthy Longevity.

[7]  M. Zeegers,et al.  Demographic risk factors for COVID-19 infection, severity, ICU admission and death: a meta-analysis of 59 studies , 2021, BMJ Open.

[8]  N. Rosenthal,et al.  Risk Factors Associated With In-Hospital Mortality in a US National Sample of Patients With COVID-19 , 2020, JAMA network open.

[9]  H. Tayebi-Khosroshahi,et al.  Risk Factors for COVID-19. , 2020, Le infezioni in medicina.

[10]  J. Pell,et al.  Is older age associated with COVID-19 mortality in the absence of other risk factors? General population cohort study of 470,034 participants , 2020, PloS one.

[11]  A. Mebazaa,et al.  Clinical characteristics and day-90 outcomes of 4244 critically ill adults with COVID-19: a prospective cohort study , 2020, Intensive Care Medicine.

[12]  M. Méndez-Bailon,et al.  Clinical Characteristics and Risk Factors for Mortality in Very Old Patients Hospitalized With COVID-19 in Spain , 2020, The journals of gerontology. Series A, Biological sciences and medical sciences.

[13]  L. Brammer,et al.  Risk Factors for Intensive Care Unit Admission and In-hospital Mortality among Hospitalized Adults Identified through the U.S. Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET) , 2020, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[14]  M. Antonelli,et al.  Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy. , 2020, JAMA internal medicine.

[15]  Anand Srivastava,et al.  Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US. , 2020, JAMA internal medicine.

[16]  K. Bhaskaran,et al.  OpenSAFELY: factors associated with COVID-19 death in 17 million patients , 2020, Nature.

[17]  J. Hewitt,et al.  The effect of frailty on survival in patients with COVID-19 (COPE): a multicentre, European, observational cohort study , 2020, The Lancet Public Health.

[18]  A. Acquaviva,et al.  Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients , 2020, Scientific Reports.

[19]  D. Brodie,et al.  Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study , 2020, The Lancet.

[20]  Kenar D. Jhaveri,et al.  Acute kidney injury in patients hospitalized with COVID-19 , 2020, Kidney International.

[21]  Eun Ji Kim,et al.  Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. , 2020, JAMA.

[22]  Huihui Ren,et al.  Clinical characteristics and outcomes of patients with severe covid-19 with diabetes , 2020, BMJ Open Diabetes Research & Care.

[23]  J. Xiang,et al.  Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study , 2020, The Lancet.

[24]  E. Dong,et al.  An interactive web-based dashboard to track COVID-19 in real time , 2020, The Lancet Infectious Diseases.

[25]  H. Cohen,et al.  Cancer statistics for adults aged 85 years and older, 2019 , 2019, CA: a cancer journal for clinicians.

[26]  T. Murphy,et al.  The Combined Effects of Frailty and Cognitive Impairment on Post-ICU Disability among Older ICU Survivors. , 2019, American journal of respiratory and critical care medicine.

[27]  T. Iwashyna,et al.  Association of frailty with short-term outcomes, organ support and resource use in critically ill patients , 2018, Intensive Care Medicine.

[28]  C. Franceschi,et al.  Inflammaging: a new immune–metabolic viewpoint for age-related diseases , 2018, Nature Reviews Endocrinology.

[29]  Tyler J. VanderWeele,et al.  Sensitivity Analysis in Observational Research: Introducing the E-Value , 2017, Annals of Internal Medicine.

[30]  G. Bernard,et al.  Frailty and Subsequent Disability and Mortality among Patients with Critical Illness , 2017, American journal of respiratory and critical care medicine.

[31]  M. Gong,et al.  Assessing the Usefulness and Validity of Frailty Markers in Critically Ill Adults , 2017, Annals of the American Thoracic Society.

[32]  A. Hidalgo,et al.  Aging: A Temporal Dimension for Neutrophils. , 2016, Trends in immunology.

[33]  Lena Osterhagen,et al.  Multiple Imputation For Nonresponse In Surveys , 2016 .

[34]  M. Sjoding,et al.  Association of Intensive Care Unit Admission With Mortality Among Older Patients With Pneumonia. , 2015, JAMA.

[35]  A. Hungin,et al.  High prevalence of undetected heart failure in long-term care residents: findings from the Heart Failure in Care Homes (HFinCH) study , 2012, European journal of heart failure.

[36]  A. Díaz,et al.  A Molecular Mechanism for TNF-α–Mediated Downregulation of B Cell Responses , 2012, The Journal of Immunology.

[37]  Nicola Orsini,et al.  Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software. , 2012, American journal of epidemiology.

[38]  François Mariotti,et al.  Dose‐response analyses using restricted cubic spline functions in public health research , 2010, Statistics in medicine.

[39]  N. Sharpless,et al.  Expression of p16INK4a in peripheral blood T‐cells is a biomarker of human aging , 2009, Aging cell.

[40]  P. Harris,et al.  Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support , 2009, J. Biomed. Informatics.

[41]  J. Vincent,et al.  The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure , 1996, Intensive Care Medicine.

[42]  N. Powe,et al.  Barriers to health care access among the elderly and who perceives them. , 2004, American journal of public health.

[43]  Trevillore E. Raghunathan,et al.  IVEware: Imputation and Variance Estimation Software User Guide , 2002 .

[44]  John Van Hoewyk,et al.  A multivariate technique for multiply imputing missing values using a sequence of regression models , 2001 .

[45]  F. Rengo,et al.  Quality control of spirometry in the elderly. The SA.R.A. study. SAlute Respiration nell'Anziano = Respiratory Health in the Elderly. , 2000, American journal of respiratory and critical care medicine.

[46]  L. Rubenstein,et al.  Old People in the Emergency Room: Age‐Related Differences in Emergency Department Use and Care , 1987, Journal of the American Geriatrics Society.