Literature DB >> 33861549

Surge and Mortality in ICUs in New York City's Public Healthcare System.

Alexander T Toth1, Kathleen S Tatem, Nicole Hosseinipour, Taylor Wong, Remle Newton-Dame, Gabriel M Cohen, Annie George, Thomas Sessa, Radu Postelnicu, Amit Uppal, Nichola J Davis, Vikramjit Mukherjee.   

Abstract

OBJECTIVES: To evaluate the impact of ICU surge on mortality and to explore clinical and sociodemographic predictors of mortality.
DESIGN: Retrospective cohort analysis.
SETTING: NYC Health + Hospitals ICUs. PATIENTS: Adult ICU patients with coronavirus disease 2019 admitted between March 24, and May 12, 2020.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Hospitals reported surge levels daily. Uni- and multivariable analyses were conducted to assess factors impacting in-hospital mortality. Mortality in Hispanic patients was higher for high/very high surge compared with low/medium surge (69.6% vs 56.4%; p = 0.0011). Patients 65 years old and older had similar mortality across surge levels. Mortality decreased from high/very high surge to low/medium surge in, patients 18-44 years old and 45-64 (18-44 yr: 46.4% vs 27.3%; p = 0.0017 and 45-64 yr: 64.9% vs 53.2%; p = 0.002), and for medium, high, and very high poverty neighborhoods (medium: 69.5% vs 60.7%; p = 0.019 and high: 71.2% vs 59.7%; p = 0.0078 and very high: 66.6% vs 50.7%; p = 0.0003). In the multivariable model high surge (high/very high vs low/medium odds ratio, 1.4; 95% CI, 1.2-1.8), race/ethnicity (Black vs White odds ratio, 1.5; 95% CI, 1.1-2.0 and Asian vs White odds ratio 1.5; 95% CI, 1.0-2.3; other vs White odds ratio 1.5, 95% CI, 1.0-2.3), age (45-64 vs 18-44 odds ratio, 2.0; 95% CI, 1.6-2.5 and 65-74 vs 18-44 odds ratio, 5.1; 95% CI, 3.3-8.0 and 75+ vs 18-44 odds ratio, 6.8; 95% CI, 4.7-10.1), payer type (uninsured vs commercial/other odds ratio, 1.7; 95% CI, 1.2-2.3; medicaid vs commercial/other odds ratio, 1.3; 95% CI, 1.1-1.5), neighborhood poverty (medium vs low odds ratio 1.6, 95% CI, 1.0-2.4 and high vs low odds ratio, 1.8; 95% CI, 1.3-2.5), comorbidities (diabetes odds ratio, 1.6; 95% CI, 1.2-2.0 and asthma odds ratio, 1.4; 95% CI, 1.1-1.8 and heart disease odds ratio, 2.5; 95% CI, 2.0-3.3), and interventions (mechanical ventilation odds ratio, 8.8; 95% CI, 6.1-12.9 and dialysis odds ratio, 3.0; 95% CI, 1.9-4.7) were significant predictors for mortality.
CONCLUSIONS: Patients admitted to ICUs with higher surge scores were at greater risk of death. Impact of surge levels on mortality varied across sociodemographic groups.

Entities:  

Year:  2021        PMID: 33861549     DOI: 10.1097/CCM.0000000000004972

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  9 in total

1.  Predictive Value of an Age-Based Modification of the National Early Warning System in Hospitalized Patients With COVID-19.

Authors:  Ryan C Maves; Stephanie A Richard; David A Lindholm; Nusrat Epsi; Derek T Larson; Christian Conlon; Kyle Everson; Steffen Lis; Paul W Blair; Sharon Chi; Anuradha Ganesan; Simon Pollett; Timothy H Burgess; Brian K Agan; Rhonda E Colombo; Christopher J Colombo
Journal:  Open Forum Infect Dis       Date:  2021-08-10       Impact factor: 3.835

2.  Increasing ICU capacity to accommodate higher demand during the COVID-19 pandemic.

Authors:  Edward Litton; Sue Huckson; Shaila Chavan; Tamara Bucci; Anthony Holley; Evan Everest; Sean Kelly; Steven McGloughlin; Johnny Millar; Nhi Nguyen; Mark Nicholls; Paule Secombe; David Pilcher
Journal:  Med J Aust       Date:  2021-10-22       Impact factor: 12.776

3.  Obesity, Inflammation, and Mortality in COVID-19: An Observational Study from the Public Health Care System of New York City.

Authors:  Leonidas Palaiodimos; Ryad Ali; Hugo O Teo; Sahana Parthasarathy; Dimitrios Karamanis; Natalia Chamorro-Pareja; Damianos G Kokkinidis; Sharanjit Kaur; Michail Kladas; Jeremy Sperling; Michael Chang; Kenneth Hupart; Colin Cha-Fong; Shankar Srinivasan; Preeti Kishore; Nichola Davis; Robert T Faillace
Journal:  J Clin Med       Date:  2022-01-26       Impact factor: 4.241

4.  The implications of living with COVID-19 for intensive care in Australia.

Authors:  Raymond Raper
Journal:  Med J Aust       Date:  2021-11-11       Impact factor: 12.776

5.  How Common SOFA and Ventilator Time Trial Criteria Would Have Performed During the COVID-19 Pandemic: An Observational Simulated Cohort Study.

Authors:  B Corbett Walsh; Deepak Pradhan; Vikramjit Mukherjee; Amit Uppal; Mark E Nunnally; Kenneth A Berkowitz
Journal:  Disaster Med Public Health Prep       Date:  2022-06-09       Impact factor: 5.556

6.  Effect of Coronavirus Disease 2019 (Covid-19), a Nationwide Mass Casualty Disaster on Intensive Care Units: Clinical Outcomes and Associated Cost-of-Care.

Authors:  Allison M Henning; Neal J Thomas; Duane C Williams; David M Shore; Michelle E Memmi; Li Wang
Journal:  Disaster Med Public Health Prep       Date:  2022-06-15       Impact factor: 5.556

7.  Surge in Incidence and Coronavirus Disease 2019 Hospital Risk of Death, United States, September 2020 to March 2021.

Authors:  Bela Patel; Robert E Murphy; Siddharth Karanth; Salsawit Shiffaraw; Richard M Peters; Samuel F Hohmann; Raymond S Greenberg
Journal:  Open Forum Infect Dis       Date:  2022-08-26       Impact factor: 4.423

8.  Mortality over time among COVID-19 patients hospitalized during the first surge of the pandemic: A large cohort study.

Authors:  Izabel Marcilio; Felippe Lazar Neto; Andre Lazzeri Cortez; Anna Miethke-Morais; Hillegonda Maria Dutilh Novaes; Heraldo Possolo de Sousa; Carlos Roberto Ribeiro de Carvalho; Anna Sara Shafferman Levin; Juliana Carvalho Ferreira; Nelson Gouveia
Journal:  PLoS One       Date:  2022-09-28       Impact factor: 3.752

9.  The Contribution of COVID-19-Forced Transformations in Critical Care Delivery to Patient Mortality: Still an Underexplored Association.

Authors:  Lavi Oud
Journal:  J Clin Med Res       Date:  2021-06-25
  9 in total

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