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Evaluation of machine learning methodology for the prediction of healthcare resource utilization and healthcare costs in patients with critical limb ischemia—is preventive and personalized approach on the horizon?

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Abstract

Background

Critical limb ischemia (CLI) is a severe stage of peripheral arterial disease and has a substantial disease and economic burden not only to patients and families, but also to the society and healthcare systems. We aim to develop a personalized prediction model that utilizes baseline patient characteristics prior to CLI diagnosis to predict subsequent 1-year all-cause hospitalizations and total annual healthcare cost, using a novel Bayesian machine learning platform, Reverse Engineering Forward Simulation™ (REFS™), to support a paradigm shift from reactive healthcare to Predictive Preventive and Personalized Medicine (PPPM)-driven healthcare.

Methods

Patients ≥ 50 years with CLI plus clinical activity for a 6-month pre-index and a 12-month post-index period or death during the post-index period were included in this retrospective cohort of the linked Optum-Humedica databases. REFS™ built an ensemble of 256 predictive models to identify predictors of all-cause hospitalizations and total annual all-cause healthcare costs during the 12-month post-index interval.

Results

The mean age of 3189 eligible patients was 71.9 years. The most common CLI-related comorbidities were hypertension (79.5%), dyslipidemia (61.4%), coronary atherosclerosis and other heart disease (42.3%), and type 2 diabetes (39.2%). Post-index CLI-related healthcare utilization included inpatient services (14.6%) and ≥ 1 outpatient visits (32.1%). Median annual all-cause and CLI-related costs per patient were $30,514 and $2196, respectively. REFS™ identified diagnosis of skin and subcutaneous tissue infections, cellulitis and abscess, use of nonselective beta-blockers, other aftercare, and osteoarthritis as high confidence predictors of all-cause hospitalizations. The leading predictors for total all-cause costs included region of residence and comorbid health conditions including other diseases of kidney and ureters, blindness of vision defects, chronic ulcer of skin, and chronic ulcer of leg or foot.

Conclusions

REFS™ identified baseline predictors of subsequent healthcare resource utilization and costs in CLI patients. Machine learning and model-based, data-driven medicine may complement physicians’ evidence-based medical services. These findings also support the PPPM framework that a paradigm shift from post-diagnosis disease care to early management of comorbidities and targeted prevention is warranted to deliver a cost-effective medical services and desirable healthcare economy.

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Abbreviations

CLI:

Critical limb ischemia

PAD:

Peripheral artery disease

US:

United States

PPPM:

Predictive, Preventive and Personalized Medicine

REFS:

Reverse Engineering Forward Simulation

EHR:

Electronic health record

ICD-9-CM:

International Classification of Diseases, Ninth Revision, Clinical Modification

ED:

Emergency department

CCI:

Charlson Comorbidity Index

SD:

Standard deviation

IQR:

Interquartile range

AUC:

Area under the receiver operating curve

EF:

Edge frequency

AOR:

Average odds ratios

APCC:

Average percentage change in costs

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Acknowledgments

Carole Alison Chrvala, PhD of Health Matters, Inc., funded by GNS Healthcare, is acknowledged for her assistance with writing and editing this manuscript.

Funding

This study was funded by Janssen Pharmaceuticals (Titusville, NJ). The publication of study results was not contingent on the sponsor’s approval or censorship of the manuscript.

Author information

Authors and Affiliations

  1. New York University Langone Medical Center, Center for the Prevention of Cardiovascular Disease, New York, NY, 10016, USA

    Jeffrey S. Berger

  2. Janssen Scientific Affairs, LLC, Raritan, NJ, 08869, USA

    Lloyd Haskell, Concetta Crivera & Jeffrey R. Schein

  3. Mt. Sinai Health System, New York, NY, 10029, USA

    Windsor Ting

  4. Jobst Vascular Institute, Toledo, OH, 43606, USA

    Fedor Lurie

  5. Health Economics and Outcomes Research, GNS Healthcare, 196 Broadway, Cambridge, MA, 02139, USA

    Shun-Chiao Chang, Luke A. Mueller, Kenneth Elder, Kelly Rich & Veronica Alas

Authors
  1. Jeffrey S. Berger
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Corresponding author

Correspondence to Shun-Chiao Chang.

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Conflict of interest

Authors Haskell, Crivera, and Schein have direct financial relationships with Janssen Pharmaceuticals. Authors Berger, Ting, and Lurie have indirect financial relationships with Janssen Pharmaceuticals. Authors Chang, Meuller, Elder, Rich, and Alas declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Berger, J.S., Haskell, L., Ting, W. et al. Evaluation of machine learning methodology for the prediction of healthcare resource utilization and healthcare costs in patients with critical limb ischemia—is preventive and personalized approach on the horizon?. EPMA Journal 11, 53–64 (2020). https://doi.org/10.1007/s13167-019-00196-9

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  • DOI: https://doi.org/10.1007/s13167-019-00196-9

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