A Multicenter Evaluation of Survival After In-Hospital Cardiac Arrest in Coronavirus Disease 2019 Patients

Outcomes after cardiac arrest in Medical Intensive Care Unit: A propensity score matching analysis of COVID-19 MICU vs non COVID-19 MICU cardiac arrest

AIM

To assess whether there were differences in resuscitation efforts and outcomes for medical intensive care unit (MICU) in-hospital cardiac arrest (IHCA) during the COVID-19 pandemic when compared to pre-pandemic.

METHODS

Comparing COVID-19 MICU-IHCA patients (03/2020 to 10/2020) to non-COVID-19 MICU IHCA (01/2014 to 12/2018) at Clevleand Clinic Health System (CCHS) of NE Ohio. Propensity score matching analysis (PSMA) was used to create comparable groups.

RESULTS

There werea total of 516 patients, 51 in COVID-19 MICU IHCA cohort and 465 in the non-COVID-19 MICU IHCA cohort. The mean (SD) age of the study population was 60.9 (16) years and 56% were males. In 92.1% (n=475) patients, initial arrest rhythm was non-shockable. At the time of ICU admission, compared to the non-COVID-19 MICU-IHCA cohort, the COVID-19 MICU IHCA cohort had a lower mean APACHE III score (70 [32.9] vs 101.3 [39.6], P=<0.01). The COVID-19 cohort had a higher rate of survival to hospital discharge (12 [23.5%] vs 59 [12.7%], P=0.03). Upon PSMA, the algorithm selected 40 COVID-19 patients and 200 non-COVID-19 patients. Imbalances in baseline characteristics, comorbidities, and APACHE III were well-balanced after matching. Survival rate after matching became non-significant; (10 [25%) vs 42 [21%], P=0.67). Further, there were no significant differences in ICU or hospital length-of-stay or neurological outcomes at discharge for survivors in the two matched cohorts.

CONCLUSION

It is imperative that COVID-19 patients receive unbiased and unrestricted resuscitation measures, without any discouragement.

Predicting neurological outcomes after in-hospital cardiac arrests for patients with Coronavirus Disease 2019

Background

Machine learning models are more accurate than standard tools for predicting neurological outcomes in patients resuscitated after cardiac arrest. However, their accuracy in patients with Coronavirus Disease 2019 (COVID-19) is unknown. Therefore, we compared their performance in a cohort of cardiac arrest patients with COVID-19.

Methods

We conducted a retrospective analysis of resuscitation survivors in the Get With The Guidelines®-Resuscitation (GWTG-R) COVID-19 registry between February 2020 and May 2021. The primary outcome was a favorable neurological outcome, indicated by a discharge Cerebral Performance Category score ≤ 2. Pre- and peri-arrest variables were used as predictors. We applied our published logistic regression, neural network, and gradient boosted machine models developed in patients without COVID-19 to the COVID-19 cohort. We also updated the neural network model using transfer learning. Performance was compared between models and the Cardiac Arrest Survival Post-Resuscitation In-Hospital (CASPRI) score.

Results

Among the 4,125 patients with COVID-19 included in the analysis, 484 (12 %) patients survived with favorable neurological outcomes. The gradient boosted machine, trained on non-COVID-19 patients was the best performing model for predicting neurological outcomes in COVID-19 patients, significantly better than the CASPRI score (c-statistic: 0.75 vs 0.67, P < 0.001). While calibration improved for the neural network with transfer learning, it did not surpass the gradient boosted machine in terms of discrimination.

Conclusion

Our gradient boosted machine model developed in non-COVID patients had high discrimination and adequate calibration in COVID-19 resuscitation survivors and may provide clinicians with important information for these patients.

Out-of-hospital and in-hospital cardiac arrest during the COVID-19 pandemic: changes in demographics, outcomes and management

During the COVID-19 pandemic, prehospital and hospital services were put under great stress because of limited resources and increased workloads. One expected effect was the increased number of out-of-hospital (OHCA) and in-hospital (IHCA) cardiac arrests that occurred during 2020 compared to previous years. Both direct and indirect mechanisms were involved. In the former case, although the exact mechanisms by which Sars-Cov-2 causes cardiac arrest (CA) are still unknown, severe hypoxia, a dysregulated immune host response and sepsis are probably implicated and are often seen in COVID-19 patients with poor outcomes. In the latter case, the strain on hospitals, changes in treatment protocols, governments' actions to limit the spread of the disease and fear of the contagion naturally affected treatment efficacy and disrupted the CA chain of survival; as expected in OHCA, only a small proportion of patients were positive to COVID-19, and yet reported outcomes were worse during the pandemic. CA patient characteristics were reported, along with modifications in patient management. In this review, we summarise the evidence to date regarding OHCA and IHCA epidemiology and management during the COVID-19 pandemic.