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

BACKGROUND

New or worsening cognitive, physical and/or mental health impairments after acute care for critical illness are referred to as "post-intensive care syndrome" (PICS). Little is known about the incidence of its components, since it is challenging to recruit patients after intensive care unit (ICU) treatment for observational studies. Claims data are particularly suited to achieve incidence estimates in difficult-to-recruit groups. However, some limitations remain when using claims data for empirical research on the outcome of ICU treatment. The objective of this article is to describe three challenges and possible solutions for the estimation of the incidence of PICS based on claims data METHODOLOGICAL CHALLENGES: THE PRESENCE OF COMPETING RISK BY DEATH, INVESTIGATING A SYNDROME AND DEALING WITH INTERVAL CENSORING: First, in (post) ICU populations the assumption of independence between the event of interest (diagnosis of PICS component) and the competing event (death) is violated. Competing risk is an event whose occurrence precludes the event of interest to be observed, and in ICU populations, death is a frequent secondary event. Methods that estimate incidence in the presence of competing risks are well-established but have not been applied to the scenario described above. Second, PICS is a complex syndrome and represented by various ICD-10 (International Classification of Diseases, 10th Revision) disease codes. The operationalization of this syndrome (case identification) and the validation of cases are particularly challenging. Third, another major challenge is that the exact date of the event of interest is not available in claims data. It is only known that the event occurred within a certain interval. This feature is called interval censoring. Recently, methods have been developed that address informative censoring due to competing risks in the presence of interval censoring. We will discuss how these methods could be used to tackle the problem when estimating PICS components. Alternatively, it could be possible to assign an exact date for each diagnosis by combining the diagnosis with the exact date of prescriptions of the respective medicines and/or medical services.

CONCLUSION

Estimating incidence in post-ICU populations entails various methodological issues when using claims data. Investigators need to be aware of the presence of competing risks. The application of internal validation criteria to operationalize the event of interest is crucial to achieve reliable incidence estimates. The problem of interval censoring can be solved either by statistical methods or by combining information from different sources.