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Incidence Estimation in Post-ICU Populations: Challenges and Possible Solutions When Using Claims Data.

Magdalena Brandl1, Christian Apfelbacher1,2,3, Annette Weiß1

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Gesundheitswesen (Bundesverband Der Arzte Des Offentlichen Gesundheitsdienstes (Germany))
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Summary

Estimating post-intensive care syndrome (PICS) incidence using claims data presents challenges like competing risks and interval censoring. Solutions involve advanced statistical methods and data integration for accurate PICS component incidence.

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Area of Science:

  • Critical Care Medicine
  • Health Services Research
  • Biostatistics

Background:

  • Post-intensive care syndrome (PICS) involves cognitive, physical, and mental health impairments after critical illness.
  • Estimating PICS incidence is difficult due to challenges in recruiting post-intensive care unit (ICU) patients for studies.
  • Claims data offer a potential solution for incidence estimation in hard-to-recruit populations but have inherent limitations.

Purpose of the Study:

  • To describe three key methodological challenges in estimating PICS incidence using claims data.
  • To propose potential solutions for these challenges, enabling more accurate PICS research.
  • To highlight the importance of addressing competing risks, syndrome operationalization, and interval censoring.

Main Methods:

  • Addressing competing risk by death, where death precludes PICS event observation.
  • Operationalizing PICS using International Classification of Diseases, 10th Revision (ICD-10) codes and validating cases.
  • Managing interval censoring, where event dates are known only within a time interval, using advanced statistical methods or data linkage.

Main Results:

  • Claims data present significant methodological hurdles for PICS incidence estimation.
  • Competing risks, syndrome definition complexity, and interval censoring are primary obstacles.
  • Advanced statistical techniques and data integration strategies can overcome these limitations.

Conclusions:

  • Accurate PICS incidence estimation from claims data requires awareness of competing risks.
  • Internal validation criteria are crucial for reliable operationalization of PICS events.
  • Interval censoring can be managed through statistical methods or by combining diverse data sources.