Surge Effects and Survival to Hospital Discharge in Critical Care Patients with COVID-19 During the Early Pandemic: A Cohort Study

doi:10.21203/rs.3.rs-111259/v1

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Surge Effects and Survival to Hospital Discharge in Critical Care Patients with COVID-19 During the Early Pandemic: A Cohort Study

https://doi.org/10.21203/rs.3.rs-111259/v1

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published 17 Feb, 2021

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Background

The early months of the COVID-19 pandemic were fraught with much uncertainty and some resource constraint. We assessed the change in survival to hospital discharge over time for intensive care unit patients with COVID-19 during the first three months of the pandemic and the presence of any surge effects on patient outcomes.

Methods

Retrospective cohort study with electronic medical record data of all patients with laboratory-confirmed COVID-19 admitted to intensive care units from February 25, 2020 to May 15, 2020 admitted to intensive care units of 26 hospitals of an integrated delivery system in the Western United States. Patient demographic, comorbidity and severity of illness were measured along with exposure to pharmacologic and medical therapies and hospital outcomes over time. Multivariable logistic regression models were constructed to assess the change in survival to hospital discharge over time during the study period.

Results

Of 620 patients with COVID-19 admitted to the study ICUs (mean age 63.5 years (SD 15.7) and 69% male), 403 (65%) survived to hospital discharge and 217 (35%) died in hospital. Survival to hospital discharge increased over the study period from 60.0% in the first two weeks of patient admission to 67.6% in the last two weeks. In a multivariable logistic regression analysis, the risk-adjusted odds of survival to hospital discharge increased over time (bi-weekly change, adjusted odds ratio [aOR] 1.22, 95%CI 1.04-1.40, P = 0.02). Additionally, an a priori-defined explanatory model showed that after adjusting for both hospital occupancy and COVID positive/PUI percent hospital capacity, and the same set of covariates, the temporal trend in risk-adjusted patient survival to hospital discharge remained the same (bi-weekly change, aOR 1.18, 95% CI 1.00 to 1.38, P = 0.04) and a greater COVID positive/PUI percentage of hospital capacity remained significantly and inversely associated with survival to hospital discharge (aOR 0.95, 95% CI 0.92 to 0.98, P < 0.01).

Conclusions

During the the early COVID-19 pandemic, risk-adjusted survival to hospital discharge increased over time for critical care patients. This may have been partially explained by surge affects, as measured by a greater COVID positive/PUI percentage of hospital capacity.

Critical Care & Emergency Medicine

COVID-19

Critical Care

Outcomes

Health Care Delivery

Surge Effects

Health Services

The coronavirus 2019 (COVID-19) global pandemic caused by SARS CoV-2 stressed Intensive Care Units (ICUs) across the world. Initial reports from China featured 83% survival in hospitalized patients, 61% in ICU patients and only 3% in patients receiving mechanical ventilation.[1, 2] In the Western United States, where the first US case of COVID-19 was reported, investigators in the Seattle area initially reported approximately 50% survival in ICU patients.[3, 4]

During the early months of the COVID-19 pandemic in the United States and Europe many organizations treated “surges” of patients during a time of incomplete understanding of the biology of critical illness related to SARS CoV-2 and a lack of clearly effective COVID-19 specific therapies. Reported data sets in initial publications were often incomplete with half or more of patients lacking hospital outcomes and there was significant uncertainty.[5–7] Social media and pre-print servers like MedRxIV became common and frequently shared sources of information on prognosis, treatments and outcomes. Drug therapy thought to be possibly-effective or touted as effective by political leaders on social medial evolved rapidly over the first several months of the pandemic with much fanfare and limited information.[8–10] There was significant interest in different therapies for hypoxemic respiratory failure, including the utility of delaying or minimizing intubation, using or not using non-invasive positive pressure ventilation and when to use high-flow oxygen.[11, 12] In addition to the ambiguous and evolving clinical situation, the surge nature of the COVID-19 pandemic itself led to resource constraints, rationing in some areas of the world and discussions of crisis standards of care in others and may have adversely affected patient outcomes.[13–15]

With critical care resources, including ventilators, nurses, respiratory therapists and ICU beds, at the center of crisis planning, we were interested changes outcomes of critical care patients with COVID-19 over the first several months of the pandemic. We hypothesized that survival to hospital discharge increased over the initial months of the COVID-19 pandemic in a cohort of COVD-19 positive patients admitted to ICUs of a large integrated delivery system in the Western United States. Additionally, we hypothesized that volume-related “surge effects” may have adversely impacted survival to hospital discharge in COVID-19 positive ICU patients during the initial months of the pandemic.

Study setting and data collection

We conducted an observational cohort study of all patients with positive SARS CoV-2 polymerase chain reaction assay (COVID-19 positive patients), admitted to an ICU for at least six hours, across twenty-six hospitals in a large healthcare system in the Western United States from February 25, 2020 to May 15, 2020. Data were collected through May 28, 2020 and patients still admitted at the end of the study period were excluded from the study, as were any subsequent readmissions of patients in the cohort. For simplicity of display, patient characteristics and outcomes were summarized in two-week cohorts by date of admission for bivariate analysis and the differences between and trend across the cohorts assessed. For our multivariable models, we modelled admissions by one-week interval for greater precision and report our results on a per-week basis, starting with the first week of the study.

Persons under investigation (PUIs) were defined as those patients in whom a COVID-19 PCR test had been sent, but not yet resulted. We modelled the surge effects of increased hospital volumes in two ways. First, hospital occupancy was defined as inpatient census on day of admission divided by total number of licensed hospital beds. Second, COVID-19 positive/PUI percent hospital capacity was similarly calculated by dividing the sum of COVID-19 positive and PUI censuses on the day of patient admission by number of licensed beds. The day of admission was chosen to summarize volume effects for clarity and reproducibility.

Patient data including socio-demographics, comorbidities, severity of illness, COVID-19 treatments, laboratory, and outcomes data were obtained from the electronic medical record (Epic Systems, Madison, WI). Median household income was imputed from patient’s home ZIP code based on 2006–2010 data from the US Census. Treatments were assessed as binary variables depending upon having received one exposure to the treatment. The Sequential Organ Failure Assessment (SOFA) score was calculated based on the most severe values obtained in the first 24 hours of hospitalization. Exposures to drug and oxygen therapies were measured as binary variables based on any exposure to the therapy. Prone therapy included non-intubated proning based on medical record documentation.

Statistical analysis

We used descriptive statistics to assess the distribution of all variables of interest. Continuous variables are presented as means or median as appropriate, and categorical variables are presented as frequencies. Comparisons between groups were performed by ANVOA or Kruskal-Wallis rank sum tests for continuous variables, and Chi-squared or Fisher exact tests for categorical variables. If there was significant difference between groups, Jonckheere–Terpstra tests and Chi-squared tests for trend were used to evaluate for temporal trend (ordered differences) across sequential bi-weekly time period for continuous and categorical variables, respectively. To determine the temporal pattern and independent factors for survival to hospital discharge, we used multivariable logistic regression analyses, with admission date modelled on continuous basis. The odds ratios with corresponding 95% confidence intervals were presented. Variables including time of admission, age, gender, BMI, race, income, smoking status, marital status, comorbidities and SOFA scores were evaluated and selected for adjusted regression based on the literature and a priori associations and informed by the bivariate associations. The linearity of the continuous variables in relation to the logit of the binary outcome (discharged alive, yes vs no), and final model diagnosis were assessed for linearity assumptions and the overall agreement between observed and fitted values.

Robustness of observed temporal trends from the final multivariable model was evaluated with a hierarchical random effects model to account of hospital-level cluster effects. In our a priori explanatory model on surge effects, the trend in the stress on the hospital system, estimated by percentage of hospital beds filled and filled with COVID-19 positive/person under investigation (PUI) patients, and the association of stress on the hospital system with survival to hospital discharge were evaluated. We also further evaluated whether the temporal trend in risk-adjusted patient survival to hospital discharge was explained by the trend in the hospital occupancy and the COVID/PUI percentage capacity, first individually in the multivariable model and then together, with the same set of covariates as in our main model for risk-adjusted survival to hospital discharge. This project was approved by the Swedish Health Services Institutional Review Board. Statistical analyses were performed in R, version 3.6.3 (R Core Team 2020).

Description of the study cohort

Patients

During the study period, 650 patients with COVID-19 were admitted, 13 patients’ subsequent readmissions were excluded and 17 (2.7%) patients were excluded as they remained hospitalized at the end of the data collection period. Among the 620 patients in our final analysis cohort, the mean age was 63.5 (SD 15.7) years and did not differ between two-week increments (P = 0.51); 430 (69.4%) were male and the median body mass index (BMI) was 28.2 (IQR 24.3–33.1) with significant difference in BMI among two-week cohorts (P < 0.01). 267 (43.1%) patients self-described their primary race/ethnicity as white and 181 (29.2%) as Hispanic or Latinx and the distribution differed by two-week cohorts (P = 0.01). The mean household median annual adjusted gross income in the patient’s home ZIP code was $68,943 (SD $18,019) and differed across two-week cohort (P < 0.01). 31 (5.0%) patients described themselves as current smoked tobacco users and smoking status did not differ significantly across two-week cohort (p = 0.17). 303 (48.9%) patients described themselves as married and there was a significant difference across two-week cohorts in marital status (P < 0.01).

Hypertension by past medical history or problem list entry was present in 195 (31.5%) patients, 174 (28.1%) diabetes and 71 (11.5%) chronic kidney disease of any stage, 277 (44.7%) had no medical comorbidities and the distribution of medical comorbidities was not similar across two-week cohorts (P = 0.04). The mean initial 24-hour SOFA score was 4.0 (SD 3.0) and did not vary significantly across the two-week cohorts (P = 0.10). Additional patient details are summarized by two-week cohort on Table 1.

Treatments

321 (51.8%) received hydroxychloroquine but the percentage was not evenly distributed across the two-week cohorts (P < 0.01), with 127 (75.6%) of patients receiving hydroxychloroquine in the second two-week cohort in March 2020 and only 4 (11.8%) in the final cohort in May 2020. 109 (17.6%) patients received remdesivir with significant difference across the two-week cohorts (P = 0.03), with 4 (11.4%) receiving redesivir in the first two-week cohort and 6 (17.6%) in the final two-week cohort. 88 (14.2%) patients received tocilizumab and 28 (4.5%) dexamethasone, neither of which varied significantly across cohorts (P = 0.2 and P = 0.7, respectively).

507 (81.8%) patients were on room air at some point in their hospitalization and 529 (85.3%) patients received nasal cannula at some point and neither therapy differed significantly by two-week cohort (P = 0.3 for both). 371 (59.8%) patients underwent mechanical ventilation, with 23 (65.7%) patients during the first two-week cohort and significantly decreasing to 14 (41.2%) by the final 2-week cohort (P < 0.01 for the trend). Additionally, overall 296 (47.7%) patients received high-flow nasal cannula. There was a significant increase in the use of high-flow oxygen from the first two-week cohort (10 patients, 28.6%) to the final cohort (18 patients, 52.9%; P < 0.01).

Outcomes

Overall, 403 (65.0%) patients were discharged alive, increased over the study period from 60.0% in the first two weeks of patient admission to 67.6% in the last two weeks (Fig. 1a). Of the 217 patients who died during their hospitalization, 176 (81.1% of deaths) occurred while the patient was in the ICU and 41 (18.9% of deaths) occurred after the patient left the ICU. Of those patients who underwent mechanical ventilation, the median (IQR) time on the ventilator was 9.1 (4.7–14.4) days and this decreased over time across the two-week cohorts (P = 0.02). Median duration of ICU stay was 6.2 (2.7–12.5) days and median duration of hospitalization was 12.7 (7.5–21.8) days and both of these varied significantly by two-week cohort (P < 0.01 and P = 0.01, respectively). Additional outcomes are shown in Table 2.

Trend and predictors of survival to hospital discharge

Univariate analysis

As shown in Table 3, the odds of being discharged alive increased over time during the study period, not adjusting for other covariates (bi-weekly change, OR 1.14, 95% CI 1.00 to 1.28, P = 0.04). Year of age (OR 0.94, 95%CI 0.93–0.95, P < 0.01) and former smoker status (OR 0.51, 95%CI 0.35–0.74, P < 0.01) were both associated with decreased odds of survival to hospital discharge. Patient-designation of primary race/ethnicity both as Asian (OR 1.98, 95%CI 1.03–3.83, P = 0.04) and Hispanic/Lantix (OR 1.63, 95%CI 1.09–2.43, P = 0.02) were associated with greater odds of survival to hospital discharge than White/Caucasian. Household median income based on home ZIP code was associated with survival to hospital discharge ($1000 change, OR 1.02, 95% CI 1.01 to 1.03, P < 0.01). Hospital occupancy was inversely associated with survival to hospital discharge (OR 0.98, 95% CI 0.97 to 0.99, P < 0.01). COVID positive/PUI percent hospital capacity was also inversely associated with survival to hospital discharge (OR 0.94, 95% CI 0.92 to 0.97, P < 0.01).

Multivariable analysis

In the final multivariable logistic regression model, the odds of being discharged alive increased over time throughout the study period, after adjusting for age, gender, BMI, race, income, smoking status, marital status, hypertension, diabetes, chronic kidney disease, coronary artery disease, congestive heart failure, COPD, asthma, and SOFA scores (bi-weekly change, aOR 1.22, 95%CI 1.04–1.420, P = 0.02, Table 3). On average, the risk-adjusted patient survival increased from 60.8% (first two weeks) to 69.5% (last two weeks) over the study period (Fig. 1B). This finding held true after accounting for hospital-level random effects (bi-weekly change, aOR 1.18, 95% CI 1.00 to 1.38, P = 0.049). Other significant predictors of survival to hospital discharge include greater household median income ($1,000 change, aOR 1.02, 95% CI 1.01 to 1.04, P < 0.01), age (yearly change, aOR 0.92, 95% CI 0.90 to 0.94, P < 0.01) and BMI (one-unit change, aOR 0.93, 95% CI 0.90 to 0.96, P < 0.01).

In our a priori defined explanatory models, greater hospital occupancy and higher COVID positive/PUI percent hospital capacity were each inversely associated with survival to hospital discharge (aOR 0.98, 95% CI 0.97 to 1.00, P = 0.04 and aOR 0.94, 95% CI 0.92 to 0.97, P < 0.01, respectively). After adjusting for both hospital occupancy and COVID positive/PUI percent hospital capacity, and the same set of covariates as in the primary model, the temporal trend in risk-adjusted patient survival to hospital discharge remained the same (bi-weekly change, aOR 1.18, 95% CI 1.00 to 1.38, P = 0.04). In this model, hospital occupancy was not significantly independently associated with survival to hospital discharge (P = 0.3), however COVID positive/PUI percent hospital capacity remained significantly inversely associated with survival to hospital discharge (1% increase, aOR 0.95, 95% CI 0.92 to 0.98, P < 0.01).

In a large cohort of patients with COVID-19 admitted to the ICU, survival to hospital discharge increased over time during the first three months of the COVID-19 pandemic in the United States. To our knowledge this is one of the most complete reporting of outcomes for COVID patients hospitalized in the ICU with only 17 (2.7%) patients remaining hospitalized at the end of data collection and the first to report outcomes over time. This cohort includes patients from ICUs across the Western United States and is similar in age and sex to those reported previously, includes more Hispanic people than other cohorts and lower burden of many comorbidities, including hypertension and coronary artery disease, which have been associated with COVID-19 survival and were included in our model.[5, 7, 16–20] Additionally, this study supports the previously reported inverse association of age and BMI with survival to hospital discharge.[7, 18, 21] The association between median household income and COVID-19 outcomes also has been reported and may reflect access to care or biases in care delivery.[22] Both BMI and median ZIP code household income were included in our multivariable models.

The exact mechanism by which each week was associated with increased survival to hospital discharge is not clear from our study. Changes in evidence-based therapies over time seem unlikely to have played a significant role. Despite early promising data and initial enthusiasm for hydroxycholorquine, later trials and metanalysis have not conclusively demonstrated a benefit.[23–27] Our organization was a significant early contributor to remdesivir trial enrollment based on initial data and our experience with the first COVID-19 patient in North America.[4, 28–30] This may have accounted for the 17.6% of patients who received remdesivir in this cohort, which is higher than reported elsewhere.[6, 7] However, changes in remdesivir by-week are unlikely to have caused the improved survival to hospital discharge as later trials and metanalysis have not definitively shown remdesivir to have an effect on survival to hospital discharge, especially in an ICU cohort.[27, 31, 32] An increase in the use of steroids over time could have contributed to increased survival, but our study period antedated the body of steroid literature reported over the Summer and Fall of 2020 and the use of steroids remained low and did not increase over the study period.

Similar to a lack of obvious change in medication therapy driving the increased survival to hospital discharge over time, changes in the therapy for hypoxemic respiratory failure over time seem unlikely to account for the changes in mortality. The use of mechanical ventilation decreased over time in our cohort as the use of high-flow oxygen increased. Initial reports emphasized an early-intubation strategy for COVID-19, a strategy that ultimately proved controversial and has not been associated with improved outcomes.[1, 5, 11, 12] Over the study period, evidence emerged about the benefits of high-flow oxygen and non-intubated proning on oxygenation, but despite effects on oxygenation these therapies also have not been associated with improved survival.[33–35] Similarly, non-invasive positive pressure ventilation did not vary over the study period and its use likewise has not been definitively associated with improved survival.[36]

Our secondary hypothesis focused on “surge effects” and the idea that high-volume stressors on care delivery could have driven the observed increase in survival over time. In our explanatory model, the percentage of hospital beds occupied by COVID-19/PUI positive patients was independently inversely associated with survival to hospital discharge. The initial time periods of the COVID-19 pandemic featured diagnostic and therapeutic uncertainty and initial reports were of very high ICU mortality, especially in mechanically ventilated patients.[1, 3, 37] It was uncertain in early weeks of the pandemic if our hospitals would have enough resources to care for all of the patients who would present. In March, Italian hospitals developed strategies for rationing limited resources in the face of models predicting a surge that would overstress their hospital system.[13, 38, 39] Our organization also developed scare resource triage plans based off of those created by the Northwest Healthcare Response Network in Washington State.[15] Ultimately we did not have to enact scare resource triage, but the surge in COVID-19 positive patient volumes may have had an unmeasured effects on patient care more subtle than formally enacting scarce resource triage or crisis standards of care. To that end our preliminary internal data show numeric increases in catheter-associated urinary tract infections and central line bloodstream infection rates. The National Healthcare Surveillance Network data for healthcare associated infections will be able to shed more light on this question over time. Interestingly, overall hospital volumes were not independently associated with changes in survival to hospital discharge, independent of COVID-19/PUI volumes, in this cohort of critical care patients, which might speak to more focused effects within the ICU, rather than hospital-wide effects.

In addition to any surge effect based on patient volume, the timing of the surge in our cohort towards the beginning of the pandemic may be relevant. As the high patient volumes that define a surge occurred early in our dataset, the newness of the COVID-19 as a disease, the care teams’ unfamiliarity with its treatment and the social milieu of the early pandemic may have contributed in addition to or instead of any volume-related “surge effect” and our data are unable to address that difference. At the start of the COVID-19 pandemic, the clinical course of the illness was uncertain and early reports were of poor survivability, especially in elderly intubated patients.[1] Anecdotally, in some of our critical care teams, this led to discussions with patients and surrogates on earlier transitions to comfort-focused care than we would routinely recommend for other patients with acute respiratory distress syndrome (ARDS) or other causes of critical illness or respiratory failure. As learning about the biology and clinical course of COVID-19 associated respiratory compromise became more-clear, providers may have reverted to anchoring on survivability estimates based on other patients with ARDS in discussions with patients and surrogates and this change may have been associated with increased survival to hospital discharge. Further work is needed move beyond these hypotheses and to separate early-pandemic effects from surge effects and their respective independent contributions to survival to hospital discharge.

Limitations

Our study has several important limitations. First, our cohort is from ICUs in the Western United States and may not represent changes in survival to hospital discharge observed in other regions or countries, as the Western United States had the first cases in America, but less of an intense peak than seen in New York City or Italy. Thus, the generalizability of our learnings may be limited. Second, we focused on the initial months of the outbreak and extrapolation of our findings to other points in time are limited. Third, our study looked at patients admitted to the ICU. We focused on the ICU as it is a critical resource constraint in the pandemic and of great interest to the critical care community. However, it is possible that changes in the severity of illness of patients admitted to the ICU varied over time. That the SOFA score did not vary over time speaks against this, though there could have been other unmeasured confounders. Fourth, we were limited by variables we can extract from the electronic medical record and were not able to explore daily patient counts or drug exposures with more subtlety and it’s very possible that more refined variable collection could add additional information. Fifth, we chose to model surge effects in a linear manner, and it’s quite possible that there are threshold effects or that a non-linear relationship both between overall hospital volume or COVID-19/PUI volume may well exist. Finally, though we tested for independent associations between hospital occupancy percentage and COVID positive/PUI percentage of hospital capacity in our explanatory models, our ability to fully explain the change in survival to hospital discharge over time is limited by the lack of more detailed data on behaviors of patients, families, providers and care teams over time and imprecision in modelling the surge and we summarized volume simply by the volume on day of admission and did not attempt to model it in more complex ways. Additional qualitative investigation is needed to address the mechanistic reasons for the observed changes, especially differentiating early pandemic effects from surge effects.

Over the course of the early months of the 2020 COVID-19 pandemic in patients admitted to the ICUs of a large integrated delivery system in the Western United States, survival to hospital discharge increased over time. This appears to have been partially explained by the surge effects of increased COVID-19/PUI volume.

Acute respiratory distress syndrome (ARDS)

Body mass index (BMI)

Intensive Care Unit (ICU)

Person under investigation (PUI)

Sequential Organ Failure Assessment (SOFA)

Ethics approval and consent to participate:

Waived by IRB approval based on minimal risk.

Consent for publication:

N/A

Availability of data and materials:

The dataset used during the current study is available from the corresponding author on reasonable request.

Competing interests:

No financial or non-financial competing interests exist. All study authors are employees of Providence family of health care delivery organizations.

Funding:

No extramural funding was received for this work.

Authors’ contributions:

CD: Designed the study, participated in the analysis, prepared the manuscript and read and approves the final version.

RS: Designed the study, participated in the analysis, helped prepare the manuscript and read and approves the final version.

SC: Designed the study, participated in the analysis, did the statistical analysis, prepared the manuscript and read and approves the final version.

AR: Designed the study, helped with the analysis and read and approves the final version.

GP: Designed the study, helped with the analysis and read and approves the final version.

JG: Designed the study, helped with the analysis and read and approves the final version.

AV: Designed the study, helped with the analysis and read and approves the final version.

DH: Designed the study, helped with the analysis and read and approves the final version.

TG: Designed the study, helped with the analysis and read and approves the final version.

Acknowledgements:

Anya Pryor helped to pull the dataset from the electronic medical record.

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Table 1

Demographic and clinical characteristics of patients.
	Overall (n = 620)	First Period (n = 35)	Second Period (n = 168)	Third Period (n = 164)	Fourth Period (n = 125)	Fifth Period (n = 94)	Sixth Period (n = 34)	p-value
Date Range	Mar 01 - May 15	Mar 01 - Mar 14	Mar15 - Mar 28	Mar 29 - April 11	April 12 - April 25	April 26 - May 09	May 10 - May 15
Hospital occupancy (%)^a, median (IQR)	54.5 (45.0–63.4)	70.7 (63.8–85.8)	55.35 (44.1–61.9)	48.1 (41.7–55.0)	53.7 (43.0–61.1)	59.4 (52.8–65.6)	61.7 (51.1–72.4)	< 0.001
COVID-19 positive/ PUI census (%)^b, median (IQR)	9.8 (6.6–14.8)	4.7 (1.2–7.7)	10.5 (8.0–14.9)	12.0 (8.2–15.7)	9.2 (5.6–16.7)	8.1 (3.9–14.3)	7.2 (4.1–9.6)	< 0.001
COVID-19 positive census (%)^c, median (IQR)	4.6 (1.7–7.7)	0.4 (0.0–2.1)	2.1 (0.8–4.5)	5.6 (3.3–8.2)	6.3 (3.4–10.7)	5.8 (2.7–10.7)	4.9 (1.6–6.8)	< 0.001
Age (years), mean (SD)	63.5 (15.7)	63.7 (15.7)	63.1 (14.6)	63.4 (15.5)	65.8 (14.7)	61.9 (17.2)	61.7 (19.6)	0.514
Male Sex, n (%)	430 (69.4)	21 (60.0)	119 (70.8)	124 (75.6)	83 (66.4)	58 (61.7)	25 (73.5)	0.155
BMI, median (IQR)	28.2 (24.3–33.1)	28.3 (23.5–32.5)	30.0 (25.8–34.1)	27.2 (23.6–31.2)	28.1 (24.4–33.9)	27.2 (22.9–32.5)	28.7 (24.3–35.3)	0.002
Race/ Ethnicity, n (%)								0.012
White or Caucasian	267 (43.1)	22 (62.9)	88 (52.4)	54 (32.9)	53 (42.4)	36 (38.3)	14 (41.2)
Asian	53 (8.5)	5 (14.3)	13 (7.7)	15 (9.1)	9 (7.2)	10 (10.6)	1 (2.9)
Black or African American	31 (5.0)	0 (0.0)	11 (6.5)	11 (6.7)	6 (4.8)	2 (2.1)	1 (2.9)
Hispanic or Latino	181 (29.2)	3 (8.6)	37 (22.0)	52 (31.7)	41 (32.8)	34 (36.2)	14 (41.2)
Other/ Unknown	88 (14.2)	5 (14.3)	19 (11.3)	32 (19.5)	16 (12.8)	12 (12.8)	4 (11.8)
Median Income, mean (SD)	68943.4 (18019.3)	60106.7 (18987.3)	66875.7 (17983.1)	67368.2 (16857.2)	71623.9 (17064.6)	76244.6 (18939.9)	65814.1 (16360.8)	< 0.001
Smoking Status, n (%)								0.170
Never Smoker	368 (59.4)	21 (60.0)	96 (57.1)	102 (62.2)	71 (56.8)	56 (59.6)	22 (64.7)
Current Smoker	31 (5.0)	1 (2.9)	6 (3.6)	7 (4.3)	10 (8.0)	6 (6.4)	1 (2.9)
Former Smoker	174 (28.1)	12 (34.3)	60 (35.7)	41 (25.0)	32 (25.6)	20 (21.3)	9 (26.5)
Unknown	47 (7.6)	1 (2.9)	6 (3.6)	14 (8.5)	12 (9.6)	12 (12.8)	2 (5.9)
Marital Status, n (%)								0.005
Not married	279 (45.0)	13 (37.1)	67 (39.9)	62 (37.8)	65 (52.0)	52 (55.3)	20 (58.8)
Married	303 (48.9)	22 (62.9)	94 (56.0)	88 (53.7)	53 (42.4)	33 (35.1)	13 (38.2)
Unknown	38 (6.1)	0 (0.0)	7 (4.2)	14 (8.5)	7 (5.6)	9 (9.6)	1 (2.9)
Hypertension, n (%)	195 (31.5)	12 (34.3)	50 (29.8)	45 (27.4)	41 (32.8)	36 (38.3)	11 (32.4)	0.588
Diabetes, n (%)	174 (28.1)	9 (25.7)	41 (24.4)	51 (31.1)	31 (24.8)	33 (35.1)	9 (26.5)	0.420
Chronic Kidney Disease, n (%)	71 (11.5)	4 (11.4)	13 (7.7)	14 (8.5)	23 (18.4)	7 (7.4)	10 (29.4)	0.001
Coronary Artery Disease, n (%)	53 (8.5)	5 (14.3)	13 (7.7)	12 (7.3)	11 (8.8)	7 (7.4)	5 (14.7)	0.588
Congestive Heart Failure, n (%)	48 (7.7)	5 (14.3)	13 (7.7)	6 (3.7)	11 (8.8)	11 (11.7)	2 (5.9)	0.138
COPD, n (%)	43 (6.9)	3 (8.6)	11 (6.5)	6 (3.7)	13 (10.4)	8 (8.5)	2 (5.9)	0.341
Asthma, n (%)	32 (5.2)	4 (11.4)	11 (6.5)	4 (2.4)	9 (7.2)	4 (4.3)	0 (0.0)	0.108
Cirrhosis, n (%)	9 (1.5)	1 (2.9)	1 (0.6)	3 (1.8)	2 (1.6)	2 (2.1)	0 (0.0)	0.802
ESRD, n (%)	17 (2.7)	0 (0.0)	2 (1.2)	4 (2.4)	6 (4.8)	3 (3.2)	2 (5.9)	0.319
Total Comorbidities, n (%)								0.026
0	277 (44.7)	10 (28.6)	74 (44.0)	74 (45.1)	63 (50.4)	40 (42.6)	16 (47.1)
1	163 (26.3)	10 (28.6)	52 (31.0)	52 (31.7)	19 (15.2)	24 (25.5)	6 (17.6)
2+	180 (29.0)	15 (42.9)	42 (25.0)	38 (23.2)	43 (34.4)	30 (31.9)	12 (35.3)
Initial SOFA Score, mean (SD)	3.97 (2.99)	3.34 (2.63)	3.61 (3.04)	4.33 (3.02)	4.16 (2.79)	3.72 (2.95)	4.62 (3.48)	0.104
WHO at admission (%)								0.001
2	14 (2.3)	0 (0.0)	3 (1.8)	2 (1.2)	4 (3.2)	5 (5.3)	0 (0.0)
3	133 (21.5)	10 (28.6)	38 (22.6)	30 (18.3)	23 (18.4)	26 (27.7)	6 (17.6)
4	293 (47.3)	18 (51.4)	85 (50.6)	80 (48.8)	56 (44.8)	35 (37.2)	19 (55.9)
5	62 (10.0)	3 (8.6)	6 (3.6)	10 (6.1)	24 (19.2)	17 (18.1)	2 (5.9)
6	33 (5.3)	3 (8.6)	10 (6.0)	10 (6.1)	5 (4.0)	3 (3.2)	2 (5.9)
7	82 (13.2)	1 (2.9)	26 (15.5)	31 (18.9)	13 (10.4)	6 (6.4)	5 (14.7)
8	3 (0.5)	0 (0.0)	0 (0.0)	1 (0.6)	0 (0.0)	2 (2.1)	0 (0.0)
^a Inpatient census on day of admission / total number of licensed hospital beds x 100
^b Sum of COVID-19 positive and PUI censuses on the day of patient admission / total number of licensed hospital beds x 100
^c Sum of COVID-19 positive censuses on the day of patient admission / total number of licensed hospital beds x 100

Table 2

Treatments and (ICU) LOS by two-week cohorts.
	Overall (n = 620)	First Period (n = 35)	Second Period (n = 168)	Third Period (n = 164)	Fourth Period (n = 125)	Fifth Period (n = 94)	Sixth Period (n = 34)	p-value
Date Range	Mar 01 - May 15	Mar 01 - Mar 14	Mar15 - Mar 28	Mar 29 - April 11	April 12 - April 25	April 26 - May 09	May 10 - May 15
Hydroxychloroquine, n (%)	321 (51.8)	15 (42.9)	127 (75.6)	99 (60.4)	59 (47.2)	17 (18.1)	4 (11.8)	< 0.001
Remdesivir, n (%)	109 (17.6)	4 (11.4)	31 (18.5)	33 (20.1)	11 (8.8)	24 (25.5)	6 (17.6)	0.029
Tocilizumab, n (%)	88 (14.2)	1 (2.9)	22 (13.1)	27 (16.5)	23 (18.4)	11 (11.7)	4 (11.8)	0.220
Dexamethasone, n (%)	28 (4.5)	0 (0.0)	10 (6.0)	6 (3.7)	7 (5.6)	4 (4.3)	1 (2.9)	0.655
Room Air, n (%)	507 (81.8)	31 (88.6)	145 (86.3)	133 (81.1)	95 (76.0)	75 (79.8)	28 (82.4)	0.260
Nasal Cannula, n (%)	529 (85.3)	32 (91.4)	150 (89.3)	141 (86.0)	103 (82.4)	75 (79.8)	28 (82.4)	0.252
Mask, n (%)	411 (66.3)	25 (71.4)	119 (70.8)	105 (64.0)	79 (63.2)	60 (63.8)	23 (67.6)	0.676
CPAP, n (%)	78 (12.6)	6 (17.1)	22 (13.1)	23 (14.0)	13 (10.4)	12 (12.8)	2 (5.9)	0.709
BIPAP, n (%)	87 (14.0)	4 (11.4)	24 (14.3)	16 (9.8)	20 (16.0)	17 (18.1)	6 (17.6)	0.448
High-flow Nasal Cannula, n (%)	296 (47.7)	10 (28.6)	56 (33.3)	86 (52.4)	76 (60.8)	50 (53.2)	18 (52.9)	< 0.001
Mechanical Ventilation, n (%)	371 (59.8)	23 (65.7)	131 (78.0)	98 (59.8)	65 (52.0)	40 (42.6)	14 (41.2)	< 0.001
Prone Ventilation, n (%)	356 (57.4)	16 (45.7)	97 (57.7)	97 (59.1)	87 (69.6)	47 (50.0)	12 (35.3)	0.002
Duration of Mechanical Ventilation (days), median (IQR)	9.1 (4.7–14.4)	8.6 (3.5–17.0)	9.9 (7.0–15.4)	7.4 (4.1–12.1)	8.9 (3.9–15.9)	8.7 (4.0–15.7)	3.9 (2.5–9.6)	0.018
ICU LOS (days), median (IQR)	6.2 (2.7–12.5)	4.8 (3.4–13.7)	9.1 (3.5–13.6)	6.0 (2.5–11.4)	6.2 (2.3–14.1)	5.0 (2.6–9.1)	4.0 (1.5–7.4)	0.004
LOS (days), median (IQR)	12.7 (7.5–21.8)	10.7 (7.1–28.4)	14.1 (8.1–23.0)	13.6 (8.3–22.1)	13.4 (7.2–21.6)	10.0 (5.9–17.02)	8.7 (5.5–16.1)	0.009

Table 3

Multivariable logistic regression analyses for independent predictors associated with survival to hospital discharge.
	Univariate Analysis (n = 620)				Multivariable Analysis				Multivariable Analysis*
	OR	Lower 95% CI	Upper 95% CI	p-value	OR	Lower 95% CI	Upper 95% CI	p-value	OR	Lower 95% CI	Upper 95% CI	p-value
Admission date (bi-weekly)	1.07	1.00	1.14	0.042	1.11	1.02	1.20	0.017	1.09	1.00	1.19	0.049
Age (years)	0.94	0.93	0.95	< 0.001	0.92	0.90	0.93	< 0.001	0.91	0.89	0.93	< 0.001
Male Sex (Reference: Female)	0.92	0.64	1.32	0.648	0.88	0.55	1.40	0.596	0.90	0.56	1.47	0.687
BMI	0.98	0.96	1.01	0.157	0.93	0.90	0.96	< 0.001	0.93	0.90	0.96	< 0.001
Race and Ethnicity (Reference: White or Caucasian)
Asian	1.98	1.03	3.83	0.041	1.46	0.66	3.21	0.349	1.24	0.53	2.91	0.619
Black or African American	0.86	0.41	1.83	0.702	0.67	0.28	1.63	0.378	0.58	0.23	1.46	0.243
Hispanic or Latino	1.63	1.09	2.43	0.017	0.65	0.38	1.10	0.108	0.69	0.39	1.23	0.207
Other/Unknown	2.01	1.18	3.43	0.010	1.69	0.85	3.37	0.136	1.74	0.84	3.59	0.135
Median Gross Income ($1,000)	1.02	1.01	1.03	< 0.001	1.02	1.01	1.04	< 0.001	1.02	1.00	1.04	0.112
Smoking Status (Reference: Never Smoker)
Current Smoker	1.23	0.53	2.82	0.633	1.13	0.4	3.19	0.819	1.18	0.40	3.50	0.764
Former Smoker	0.51	0.35	0.74	< 0.001	0.74	0.47	1.18	0.208	0.73	0.45	1.19	0.206
Unknown	0.58	0.31	1.07	0.081	0.7	0.32	1.51	0.365	0.72	0.32	1.64	0.436
Marital Status (Reference: Not married)
Married	1.23	0.87	1.72	0.241	1.49	0.97	2.3	0.067	1.50	0.95	2.38	0.081
Unknown	1.72	0.8	3.67	0.165	0.84	0.33	2.18	0.727	0.88	0.32	2.40	0.804
Hypertension (Reference: No)	0.83	0.58	1.18	0.297	1.14	0.72	1.81	0.583	1.21	0.74	2.00	0.449
Diabetes (Reference: No)	0.81	0.56	1.16	0.253	0.82	0.51	1.32	0.422	0.84	0.50	1.38	0.485
Chronic Kidney Disease (Reference: No)	0.58	0.35	0.96	0.033	1.49	0.79	2.81	0.223	1.56	0.80	3.03	0.188
Coronary Artery Disease (Reference: No)	0.88	0.49	1.57	0.663	1.28	0.62	2.63	0.499	1.18	0.56	2.52	0.659
Congestive Heart Failure (Reference: No)	0.46	0.26	0.84	0.011	0.70	0.34	1.44	0.338	0.78	0.37	1.65	0.523
COPD (Reference: No)	0.6	0.32	1.11	0.104	0.90	0.41	2.00	0.804	0.90	0.39	2.09	0.800
Asthma (Reference: No)	1.98	0.84	4.67	0.1162	0.90	0.32	2.56	0.848	0.764	0.263	2.222	0.621
Initial SOFA Score	0.83	0.78	0.88	< 0.001	0.83	0.77	0.89	< 0.001	0.820	0.760	0.885	< 0.001
*Mixed effects logistic regression model including admission time, baseline patient characteristics as fixed effects, and a hospital indicator variable as a random effect.

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Surge Effects and Survival to Hospital Discharge in Critical Care Patients with COVID-19 During the Early Pandemic: A Cohort Study

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Abstract

Figures

Background

Methods

Study setting and data collection

Statistical analysis

Results

Description of the study cohort

Patients

Treatments

Outcomes

Trend and predictors of survival to hospital discharge

Univariate analysis

Multivariable analysis

Discussion

Limitations

Conclusions

List Of Abbreviations

Declarations

References

Tables

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