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).

 

Illustration of COVID-19

Agilum Releases Additional RWD Survival Rate Analytics for HCQ or CQ and Acetazolamide

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 survival rate by drug treatment regimens for COVID-19 patients with and without comorbid conditions (determined by a pulmonary and/or cardiac diagnosis within the past 12 calendar months). Specifically, this information is grouped into a known COVID-19 regimen: hydroxychloroquine or chloroquine and acetazolamide (“HCQ or CQ and Acetazolamide”). 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:
Drug Protocols of Hydroxychloroquine or Chloroquine and Acetazolamide

Background

Clinicians have observed and reported that chest x-ray and CT scan lung imaging of COVID-19 patients shows patterns consistent with High Altitude Pulmonary Edema (HAPE). Acetazolamide is a common treatment for this condition, and as such, we have witnessed a measurable increase in the use of this medication in combination with hydroxychloroquine or chloroquine in the treatment of COVID-19 patients.

In the continued interest of producing information to support and advance the care of these patients, we seek to provide an individualized look at the real-world data (RWD) surrounding this drug regimen.

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 tables 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 inpatient dispensations of select drugs from March 1, 2020 through June 15, 2020, from hospitals nationwide. Leveraged a current, near-real-time data stream to ensure timeliness and accuracy.
  • Grouped drugs into a specific known COVID-19 regimen: hydroxychloroquine or chloroquine and acetazolamide (“HCQ or CQ and Acetazolamide”).
  • Analyzed patients by age and gender on each regimen for outcomes associated with survival rates.
  • Analysis factors comorbid conditions related to cardiac and pulmonary diseases, using ICD-10 CM diagnosis codes corresponding to the respective clinical classes as defined by the Agency for Healthcare Research and Quality (AHRQ).
  • All patients in this analysis were hospitalized and treated with the drug regimens listed herein. The analysis does not contain patients who were treated outside of these drug regimens.

Disclaimer

Disclaimer

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).

Illustration of COVID-19 Virus

Agilum Releases New York State COVID-19 Drug Utilization Trends Using Real-World Data (RWD) to Help Hospitals Predict Shifting Drug Demand

Updated June 30, 2020

 

By: Dawn DeAngelo, BS, PharmD, Chief Pharmacy Officer, Agilum Healthcare Intelligence, Inc. and
Travis J. Leonardi, RPh, C.P., CEO of Agilum Healthcare Intelligence, Inc.

 

In response to the rapidly evolving COVID-19 pandemic, Agilum Healthcare Intelligence analyzed real-world data (RWD) from over 29% of the hospital beds in New York state to provide hospitals and pharmacists the evidence-based data to support their ability to predict the growing demand of certain drug inventory for the treatment and supportive care for COVID-19.

Agilum’s Comparative Rapid Cycle Analytics™ platform used advanced algorithms to gain insights from its comprehensive longitudinal patient database, including trends on current drug utilization within various cohorts, to provide an understanding of the dynamic changes occurring in the drug supply chain with the evolving drug treatment protocols for COVID-19. Observations were based on:

  • RWD from inpatient and outpatient care, including drug dispensation data
  • Dynamic, real-time, continuously updated utilization trends

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

 

COVID-19 Inpatient and Outpatient Drug Utilization Review Using Real-World Data (RWD)*

Objective

By leveraging Agilum’s Comparative Rapid Cycle Analytics™ (CRCA™) platform, Agilum has focused on key drugs being utilized for COVID-19 patients to identify actual drug utilization trends by analyzing real-world data (RWD) from hospitals in the state of New York. The study methodology and criteria for review were based on the treatment of COVID-19 to demonstrate the dynamic changes occurring in the drug supply chain with the evolving drug treatment protocols.

The data tables contained herein were constructed using RWD to create an analytical approach, which seeks to add near real-time longitudinal patient data to the rapidly evolving body of knowledge pertaining to the care and treatment of patients with COVID-19.

Methodology

  • Examined inpatient and outpatient dispensations of select drugs from hospitals in New York state
  • Analyzed dispensation data from 47 hospitals representing 29% of the hospital beds in New York state
  • Reviewed actual data from March 1 to May 27, 2020
  • Targeted drugs selected due to high utilization to either treat or support COVID-19 patients
    • Hydroxychloroquine/chloroquine (HCQ-CQ) tablets and the injectable forms of midazolam, fentanyl, propofol, cisatracurium and ketamine
  • Quantified the daily patient count and the net charge unit quantity of dispensed drugs per day for all study drugs
  • Estimated the percent increase/change per day to trend the change in utilization for HCQ-CQ
  • Modeled out the patients and drug utilization in dispensed units for the HCQ-CQ only based on the change per day. Cease HCQ-CQ modeling as the trends decrease.

NOTE: With supply chain sources fluctuating, purchases were not included in the analysis to avoid any data gaps.

Observation Summary

  • Drug utilization and patient trends were identified for HCQ-CQ and showed an increase in the utilization and patients mid-March, with a 20% median increase per day towards the end of the month when analyzing March 1-31, 2020 data. After March 31st, there is a flattening of the curve where the change in utilization in the beginning of April was slightly under 2% and now appears to be trending down.
  • The various drugs used for supportive therapy for COVID-19 are in high demand. In the NY study hospitals, the target study drugs show a shift in utilization trends towards the latter part of the month, as expected.
  • In the last 1-2 weeks of March, utilization patterns shifted on a daily basis for supportive medications, but there continues to be some cyclical decrease of a few of the drugs on the weekend, typically used in surgery. This would be expected because of a decrease in surgical cases on the weekend.
  • As these patterns are changing, the patterned utilization trends are evolving, causing the supply chain of the drugs to be greatly impacted.
  • Related to the drug supply chain, comments from the field indicate hospitals are now challenged with using different vial sizes to accommodate the change to the drugs being administered in higher doses or IV bags, vial sizes that they typically did not use in the past. The relevance is that, for those vial sizes that are on allocation, and/or controlled substances that have limits in place for reorder points, it becomes a challenge to order those vial sizes with controls set in place. Consideration needs to be made at a wholesaler and federal level for special considerations.

Hospitals

Representing 29% of the hospital beds in New York state

NOTE: Bed data updated and sourced from Definitive Healthcare as of April 25, 2020.

Drug Utilization Observations

Summary of the Drug Utilization Review for Hydroxychloroquine/Chloroquine (HCQ-CQ)

  • Drug charge data was reviewed for 47 hospitals in New York state, of which 41 had dispensation data for HCQ and CQ.
  • Charge data represents the net quantity of the drug unit doses dispensed. Credits were accounted for in the dispensed quantity.
  • No exclusions were made for the use of HCQ-CQ in analyzing the drug utilization.
  • The table and the graph represent the daily count of the patients that are on the drugs HCQ or CQ and the net dispensed quantities.
  • Please note that modeling for HCQ-CQ was initially incorporated into the review but now stopped with the decreasing utilization of HCQ-CQ.
  • Real-world data is showing that the patterned utilization is leveling off as the COVID-19 cases do and is trending down.

New York State Study Hospitals Daily Patient and Dispensed Units of HCQ-CQ March 1-May 27

NOTE: Values for dispensed units and patients are on opposing axes.

New York State Study Hospitals Daily Patient and Dispensed Units of HCQ-CQ

Summary of the Drug Utilization Review for Supportive Medications

  • Drug charge data was reviewed for 47 hospitals in New York state, of which all 47 had dispensation data for the supportive medications: midazolam, propofol, ketamine, cisatracurium and fentanyl.
  • Charge data represents the net quantity of the drug unit doses dispensed. Credits were accounted for in the dispensed quantity.
  • The table and the graph represent the daily count of the patients and dispensed units for the supportive therapy drugs.
  • As mentioned above, the patterned utilization trends are evolving, causing the supply chain of the drugs to be impacted.
  • There is evidence of weekend utilization fluctuations still, but a general trend towards increased utilization overall.
  • Utilization trends are now showing a decrease in utilization after a steady increase over the last couple weeks in March. Expectations were that the patterned utilization would level off as the COVID-19 cases do. When the trending utilization leveled off, it was difficult to consider a modeling method to anticipate utilization patterns for the supportive therapy drugs.

New York State Study Hospitals Daily Patient and Dispensed Units of Midazolam March 1-May 27

NOTE: Values for dispensed units and patients are on opposing axes.

New York State Study Hospitals Daily Patient and Dispensed Units of Midazolam

New York State Study Hospitals Daily Patient and Dispensed Units of Propofol March 1-May 27

NOTE: Values for dispensed units and patients are on opposing axes.

New York State Study Hospitals Daily Patient and Dispensed Units of Propofol

New York State Study Hospitals Daily Patient and Dispensed Units of Fentanyl March 1-May 27

NOTE: Values for dispensed units and patients are on opposing axes.

New York State Study Hospitals Daily Patient and Dispensed Units of Fentanyl

New York State Study Hospitals Daily Patient and Dispensed Units of Ketamine March 1-May 27

NOTE: Values for dispensed units and patients are on opposing axes.

New York State Study Hospitals Daily Patient and Dispensed Units of Ketamine

New York State Study Hospitals Daily Patient and Dispensed Units of Cisatracurium March 1-May 27

NOTE: Values for dispensed units and patients are on opposing axes.

New York State Study Hospitals Daily Patient and Dispensed Units of Cisatracurium

New York State Study Hospitals Daily Patient and Dispensed Units Data Table

New York State Study Hospitals Daily Patient and Dispensed Units Data Table

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

Illustration of COVID-19

Agilum Releases Nationwide COVID-19 Real-World Data (RWD) Survival Rate Analytics

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 has been updated to support more detailed observations into the survival rate and average length of stay (ALOS) by drug treatment regimens 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 Inpatient Drug Regimen Analysis Using Real-World Data (RWD)

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 tables 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 inpatient dispensations of select drugs from March 1, 2020 through June 15, 2020, from hospitals nationwide. Leveraged a current, near-real-time data stream in order to improve timeliness and accuracy.
  • Grouped drugs into known COVID-19 drug treatment regimens.
  • Analyzed patients by age and gender on each regimen for outcomes associated with average length of stay (ALOS) and survival rates.
  • Analysis has been updated to address comorbid conditions related to cardiac and pulmonary diseases, using ICD-10 CM diagnosis codes corresponding to the respective clinical classes as defined by the Agency for Healthcare Research and Quality (AHRQ).
  • All patients in this analysis were hospitalized and treated with the drug regimens listed herein. The analysis does not contain patients who were treated outside of these drug regimens.

Drug Regimen Legend
HCQ: hydroxychloroquine
CQ: chloroquine
Azith: Azithromycin

Disclaimer

Disclaimer

Disclaimer

Profile of Patient Populations

Disclaimer

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Disclaimer

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).