New York City has emerged as the global epicenter for the coronavirus disease 2019 (COVID-19) pandemic. The city's public health system, New York City Health + Hospitals, has been key to the city's response because its vulnerable patient population is disproportionately affected by the disease. As the number of cases rose in the city, NYC Health + Hospitals carried out plans to greatly expand critical care capacity. Primary intensive care unit (ICU) spaces were identified and upgraded as needed, and new ICU spaces were created in emergency departments, procedural areas, and other inpatient units. Patients were transferred between hospitals to reduce strain. Critical care staffing was supplemented by temporary recruits, volunteers, and Department of Defense medical personnel. Supplies needed to deliver critical care were monitored closely and replenished to prevent interruptions. An emergency department action team was formed to ensure that the experience of front-line providers was informing network-level decisions. The steps taken by NYC Health + Hospitals greatly expanded its capacity to provide critical care during an unprecedented surge of COVID-19 cases in NYC. These steps, along with lessons learned, could inform preparations for other health systems during a primary or secondary surge of cases.
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.
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