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INTRODUCTION: The medication regimen complexityintensive care unit (MRC-ICU) score was developed prior to the existence of COVID-19 and has demonstrated an association with increased mortality, ICU length of stay, fluid balance, drug interactions, and quantity and quality of pharmacist interventions. Previous reports have questioned the ability of traditional predictors of mortality in critically ill patients to predict death in patients with COVID-19. The purpose of this study was to assess if MRC-ICU could predict mortality patients with COVID-19. METHOD(S): A single-center, observational study was conducted from August 2020 to January 2021. The primary outcome of this study was the area under the receiver operating characteristic (AUROC) for mortality for the 48- hour MRC-ICU. Age, sequential organ failure assessment (SOFA), and World Health Organization (WHO) COVID-19 Severity Classification were also assessed. Logistic regression was also performed to predict mortality as well as WHO Severity Classification at 7 days. RESULT(S): A total of 149 patients were included. The median SOFA score was 8 (IQR 5 - 11) and median MRC-ICU score at 48 hours was 15 (IQR 7 - 21). The inhospital mortality rate was 36% (n = 54). The AUROC for MRC-ICU was 0.71 (95% Confidence Interval (CI), 0.62 - 0.78) compared to 0.66 for age, 0.81 SOFA, and 0.72 for the WHO Severity Classification. In univariate analysis, age, SOFA, MRC-ICU, and WHO Severity Classification all demonstrated significant association with mortality, while SOFA, MRC-ICU, and WHO Severity Classification demonstrated significant association with WHO Severity Classification at 7 days. A multiple logistic regression model for mortality was developed using these four predictors. CONCLUSION(S): In the first analysis of medication-related variables as a predictor of severity and mortality in COVID-19, MRC-ICU demonstrated acceptable predictive ability;however, SOFA was the strongest predictor in both AUROC and regression analysis.
ABSTRACT
Introduction: The novel coronavirus has disrupted chronic pain patients’ care, on-going clinical studies, interrupted daily routines and pain management plans, as well as halted social/extracurricular activities. These disturbances may contribute to increased pain intensity, worsening disability, and deteriorating mood in a population with mental and physical health comorbidities. COVID-19 presented a unique opportunity to observe patients’ pain experience, including quality-of-life (QoL) and daily activities, as well as identify and characterize individuals who are potentially susceptible to changes during a substantial stressor. Methods: As part of on-going multi-site Boston Scientific studies prospectively observing up to 1700 chronic leg and back pain patients’ responses to spinal cord stimulation (SCS), we used smartphones to collect daily self-reported pain intensity, mood, sleep, medications, and activities. We also obtained in-clinic questionnaires and objective measures from smartwatches, sleep sensors, weekly voice recordings, and SCS usage. To evaluate changes during COVID-19, we defined two 6-week periods: “COVID” (03/6/2020—04/17/2020), “Pre-COVID” (12/20/2019- 01/30/2020). Since patients may be differentially impacted, we performed multivariate analyses integrating changes in self-reported variables between periods, which were normalized and K-means clustered to identify sub-cohorts. We also administered questions to assess patients’ emotional state during the pandemic, analyzed with natural language processing (NLP). Results: In our results (Figures 1-4), we found no differences in self-reports between pre-COVID and COVID for the entire cohort (n=70/159). However, clustering identified 3 sub-cohorts: individuals whose pain worsened (pain-susceptible), whose activities decreased (ADLsusceptible), and whose mood, sleep, medication, and activities remained the same or improved (QoL-resilient) during COVID. Partial correlations between changes in self-reports also showed differences as a function of period and sub-cohort. Sensor data indicated that NLP-identified fear related speech content during COVID was lower for the QoL-resilient group, who also had greater watch step counts during pre-COVID, supporting the idea that they had the best overall wellbeing or initial behaviors of the 3 groups. There were no differences in clinical assessments or SCS usage between sub-cohorts or periods. Conclusion: Our results indicate the existence of 3 patient sub-cohorts that diverge in their behaviors during COVID-19. We find each sub-cohort has a characteristic signature that allows us to predict the response an individual patient had to the pandemic. These findings demonstrate the importance of multi-dimensional digital monitoring with important implications for telemedicine, clinical trials and neuromodulation system management. Disclosure: Richard Rauck, MD: Boston Scientific: Contracted Research: Self, Medtronic: Contracted Research: Self, Mainstay: Contracted Research: Self, Saluda: Contracted Research: Self, Stimwave: Contracted Research: Self, SPR Therapeutics: Contracted Research: Self, Nevro: Contracted Research: Self, Neuros: Contracted Research: Self, Sara Berger, PhD: IBM: Employee:, Guillermo Cecchi, PhD: IBM: Employee:, Carla Agurto, PhD: IBM: Employee:, Elif Eyigoz, PhD: IBM: Employee:, Kristen Lechleiter, MS: Boston Scientific: Employee:, Dat Huynh, PhD: Boston Scientific: Employee:, Brad Hershey, BS: Boston Scientific: Employee:, Eric Loudermilk, MD: None, Julio Paez, MD: None, Louis Bojrab, MD: None, John Noles, MD: None, Todd Turley, MD: None, Mohab Ibrahim, MD: None, Amol Patwardhan, MD: None, James Scowcroft, MD: Nevro: Contracted Research:, Boston Scientific: Contracted Research:, Saluda: Contracted Research:, Rene Przkora, MD: Boston Scientific, Abbott, Nevro, Medtronic: Contracted Research:, Nathan Miller, MD: None, Gassan Chaiban, MD: Boston Scientific: Consulting Fee:, Matt McDonald, MS: Boston Scientific: Salary/Employee: Self, Jeffrey Rogers, PhD: IBM: Employee: [Formul presented] [Formula presented] [Formula presented]