Agilum Releases RWD Inpatient Mortality Rate Analytics by Age and Gender

Updated June 30, 2020

Agilum first began publishing RWD on COVID-19 hospital admissions on April 7, 2020. The real-world data (RWD) were refreshed daily and showed notable differences in survival rates and ALOS across recognized drug treatment regimens. This analysis was presumptive in nature and sought to produce RWD in near-real-time to advance the care and treatment of these patients.

As the rate of COVID-19 hospital admissions slows, Agilum will sunset the daily reports and continue to support our customers by providing real-world data and real-world evidence to help improve patient outcomes. 

 

By: William D. Kirsh, DO, MPH, CMIO, Agilum Healthcare Intelligence, Inc. and
Travis J. Leonardi, RPh, C.P., CEO of Agilum Healthcare Intelligence, Inc.

 

Through its Comparative Rapid Cycle Analytics™ (CRCA™) solution, Agilum Healthcare Intelligence seeks to leverage its comprehensive, longitudinal patient database to deliver updates on current and new treatment regimens – providing greater transparency into the resulting outcomes for various cohorts of COVID-19 patients. This data supports more detailed observations into the mortality rate by age and gender for COVID-19 patients with and without comorbid conditions (determined by a pulmonary and/or cardiac diagnosis within the past 12 calendar months). Observations are based on:

  • Real-world data (RWD) from inpatient care including drug dispensation data
  • A representative sampling of patients in hospitals across the United States
  • Dynamic, real-time, continuously updated information

With the rapidly evolving incidence of COVID-19, this report will be refreshed regularly to show near real-time trends of patient outcomes based on specific drug treatment regimens.

COVID-19 Nationwide Real-World Data (RWD) Inpatient Drug Regimen Analysis:
Patient Mortality Rate Risk Factors Analysis

Background

Publicly reported information about COVID-19 has suggested patients vary greatly in their response to the disease. While some patients may display symptoms of a mild cold, or perhaps no symptoms at all, others require hospitalization, including potential ventilation treatments and/or death from the disease. The RWD contained herein seeks to provide basic evidence as to role patient age and gender play in predicting mortality from a hospital admission due to COVID-19.

Objective

By leveraging Agilum’s Comparative Rapid Cycle Analytics™ (CRCA™) platform, Agilum has written protocols to analyze real-world data (RWD) from the inpatient care setting taking place in hospitals nationwide treating patients with COVID-19. The data and graphics contained herein were constructed using RWD to create an analytical approach, as opposed to a clinical study such as a randomized control trial. In doing so, we seek to advance the rapidly evolving body of knowledge pertaining to the care and treatment of patients with COVID-19 using near real-time longitudinal patient data. The data will be updated and republished continuously as available.

Methodology

  • Examined the profiles of patients admitted to hospitals from March 1, 2020 through yesterday for treatment of COVID-19 as defined by the use of certain drug treatment regimens outlined in the Nationwide COVID-19 Real-World Data Survival Rate Analytics report.
  • Analyzed patient age and gender data to determine whether each variable represented an independent risk factor for mortality.
  • Analyzed the relationship between patient age and gender to determine if interaction between the variables contributed significantly to explaining variance in mortality.

 

Observations

  • Patient age and gender have an independent, significant association with mortality (p<0.001 for the main effects of both variables) among the hospitalized COVID-19 population.
  • Older patients are at greater risk of death than are younger patients, and men are at greater risk of death than women.
    But, again, these risks are independent of one another.
  • The interaction of these two factors, however, was not a statistically significant contributor to explaining the variance in mortality, i.e., the strength of the association between gender and mortality did not change reliably across age tiers, as demonstrated by the nearly parallel paths of the lines describing the association of age and mortality.

Disclaimer

  • Based on observation of real-world data analytics, not a clinical study or trial.
  • This information is for observational purposes only and is not a recommendation, endorsement or advice as to any medical or therapeutic treatment option. We advise readers to consult with medical professionals and public health authorities regarding treatment of any COVID-19 infection.
  • The information is subject to change without notice. The information is solely based on data received.
  • All product names and trademarks are the property of their respective owners.

To download the final PDF of this analysis, simply fill out the form below. For more information about this analysis or Agilum Healthcare Intelligence solutions, please call 877.AGLMHCI (245.6424).