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. 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.
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. 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. 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.
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.