ICU strain and outcome in COVID-19 patients—A multicenter retrospective observational study

Demoule, Alexandre; Fartoukh, Muriel; Louis, Guillaume; Azoulay, Élie; Nemlaghi, Safaa; Jullien, Edouard; Desnos, Cyrielle; Clerc, Sébastien; Yvin, Elise; Mellati, Nouchan; Charron, Cyril; Voiriot, Guillaume; Picard, Yoann; Vieillard‐Baron, Antoine; Darmon, Michaël

doi:10.1371/journal.pone.0271358

Cited by 6 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the Korean government mobilized infectious wards and intensive care beds to prepare for further epidemics, an unprecedented increase of COVID-19 patients since June 2021 (Figure S 1 ) may induce a shortage in the capability for critical care management, which may eventually increase the mortality of emergent COVID-19 and non-COVID-19 disease patients [ 46 , 47 ].…”

Section: Discussionmentioning

confidence: 99%

Excess mortality during the Coronavirus disease pandemic in Korea

Han,

Jang,

2023

BMC Public Health

View full text Add to dashboard Cite

Background Although the ongoing epidemics of Coronavirus disease 2019 (COVID-19) may have affected the mortality trend of the nation, the national level assessment of excess mortality (changes in overall mortality in the entire population) is still scarce in Korea. Therefore, this study evaluated the excess mortality during the COVID-19 pandemic in Korea using the certified mortality data. Methods Monthly mortality and population data from January 2013 to June 2022 was obtained from the National Health Insurance Service database and Statistics Korea. A quasi-Poisson interrupted time-series model adjusted for age structure, population, seasonality, and long-term trends was used to estimate the counterfactual projections (expected) of mortality during the COVID-19 pandemic (March 2020 to June 2022). The absolute difference (observed—expected) and ratio (observed / expected) of mortality were calculated. Stratified analysis based on pandemic years (years 2020, 2021, and 2022), sex, and age groups (aged 0–4, 5–19, 20–64, and ≥ 65 years) were conducted. Results An 8.7% increase in mortality was observed during the COVID-19 pandemic [absolute difference: 61,277 persons; ratio (95% confidence interval (CI)): 1.087 (1.066, 1.107)]. The gap between observed and estimated mortality became wider with continuation of the pandemic [ratio (95% CI), year 2020: 1.021 (1.003, 1.040); year 2021: 1.060 (1.039, 1.080), year 2022: 1.244 (1.219, 1.270)]. Although excess mortality across sex was similar, the adult [aged 20–64, ratio (95% CI): 1.059 (1.043, 1.076)] and elderly [aged 65-, ratio (95% CI): 1.098 (1.062, 1.135)] population showed increased excess mortality during the pandemic. Conclusions Despite Korea's successful quarantine policy response, the continued epidemic has led to an excess mortality. The estimated mortality exceeded the number of deaths from COVID-19 infection. Excess mortality should be monitored to estimate the overall impact of the pandemic on a nation.

show abstract

Section: Discussionmentioning

confidence: 99%

Excess mortality during the Coronavirus disease pandemic in Korea

Han,

Jang,

2023

BMC Public Health

View full text Add to dashboard Cite

show abstract

“…The recent COVID-19 pandemic strained ICU resources throughout the world with adverse effects to patient outcomes (35, 36). At the hospital examined in this study, two findings associated with the higher intensity model were particularly helpful during local surges.…”

Section: Discussionmentioning

confidence: 99%

Patient Outcomes and Unit Composition With Transition to a High-Intensity ICU Staffing Model: A Before-and-After Study

Proper

Wacker

Shaker

et al. 2023

Critical Care Explorations

View full text Add to dashboard Cite

IMPORTANCE: Provider staffing models for ICUs are generally based on pragmatic necessities and historical norms at individual institutions. A better understanding of the role that provider staffing models play in determining patient outcomes and optimizing use of ICU resources is needed. OBJECTIVES: To explore the impact of transitioning from a low- to high-intensity intensivist staffing model on patient outcomes and unit composition. DESIGN, SETTING, AND PARTICIPANTS: This was a prospective observational before-and-after study of adult ICU patients admitted to a single community hospital ICU before (October 2016–May 2017) and after (June 2017–November 2017) the transition to a high-intensity ICU staffing model. MAIN OUTCOMES AND MEASURES: The primary outcome was 30-day all-cause mortality. Secondary outcomes included in-hospital mortality, ICU length of stay (LOS), and unit composition characteristics including type (e.g., medical, surgical) and purpose (ICU-specific intervention vs close monitoring only) of admission. RESULTS: For the primary outcome, 1,219 subjects were included (779 low-intensity, 440 high-intensity). In multivariable analysis, the transition to a high-intensity staffing model was not associated with a decrease in 30-day (odds ratio [OR], 0.90; 95% CI, 0.61–1.34; p = 0.62) or in-hospital (OR, 0.89; 95% CI, 0.57–1.38; p = 0.60) mortality, nor ICU LOS. However, the proportion of patients admitted to the ICU without an ICU-specific need did decrease under the high-intensity staffing model (27.2% low-intensity to 17.5% high-intensity; p < 0.001). CONCLUSIONS AND RELEVANCE: Multivariable analysis showed no association between transition to a high-intensity ICU staffing model and mortality or LOS outcomes; however, the proportion of patients admitted without an ICU-specific need decreased under the high-intensity model. Further research is needed to determine whether a high-intensity staffing model may lead to more efficient ICU bed usage.

show abstract

“…Across the included 39 studies, there were 37 unique measures as shown in Tables 1 and 2. Of these, eleven (three from LMIC) tracked total hospital or ICU caseload unadjusted for baseline bed capacity (14,16,20,22,(25)(26)(27)(28)(29)(30)(31). Caseload was counted as either total census of all patients, total COVID-19-only patients, total positive or person under investigation, or only new admissions on the day of admission (30), daily (20,26,28), weekly (14,25), monthly (29), or quarterly (22).…”

Section: Measures Of Hospital Surge Strainmentioning

confidence: 99%

Measures and Impact of Caseload Surge During the COVID-19 Pandemic: A Systematic Review*

Neupane,

De Jonge,

Angelo

et al. 2024

Critical Care Medicine

View full text Add to dashboard Cite

Objectives: COVID-19 pandemic surges strained hospitals globally. We performed a systematic review to examine measures of pandemic caseload surge and its impact on mortality of hospitalized patients. Data Sources: PubMed, Embase, and Web of Science. Study Selection: English-language studies published between December 1, 2019, and November 22, 2023, which reported the association between pandemic “surge”-related measures and mortality in hospitalized patients. Data Extraction: Three authors independently screened studies, extracted data, and assessed individual study risk of bias. We assessed measures of surge qualitatively across included studies. Given multidomain heterogeneity, we semiquantitatively aggregated surge–mortality associations. Data Synthesis: Of 17,831 citations, we included 39 studies, 17 of which specifically described surge effects in ICU settings. The majority of studies were from high-income countries (n = 35 studies) and included patients with COVID-19 (n = 31). There were 37 different surge metrics which were mapped into four broad themes, incorporating caseloads either directly as unadjusted counts (n = 11), nested in occupancy (n = 14), including additional factors (e.g., resource needs, speed of occupancy; n = 10), or using indirect proxies (e.g., altered staffing ratios, alternative care settings; n = 4). Notwithstanding metric heterogeneity, 32 of 39 studies (82%) reported detrimental adjusted odds/hazard ratio for caseload surge–mortality outcomes, reporting point estimates of up to four-fold increased risk of mortality. This signal persisted among study subgroups categorized by publication year, patient types, clinical settings, and country income status. Conclusions: Pandemic caseload surge was associated with lower survival across most studies regardless of jurisdiction, timing, and population. Markedly variable surge strain measures precluded meta-analysis and findings have uncertain generalizability to lower-middle-income countries (LMICs). These findings underscore the need for establishing a consensus surge metric that is sensitive to capturing harms in everyday fluctuations and future pandemics and is scalable to LMICs.

show abstract

ICU strain and outcome in COVID-19 patients—A multicenter retrospective observational study

Cited by 6 publications

References 33 publications

Excess mortality during the Coronavirus disease pandemic in Korea

Excess mortality during the Coronavirus disease pandemic in Korea

Patient Outcomes and Unit Composition With Transition to a High-Intensity ICU Staffing Model: A Before-and-After Study

Measures and Impact of Caseload Surge During the COVID-19 Pandemic: A Systematic Review*

Contact Info

Product

Resources

About