Health status prediction in critically ill children: a pilot study introducing standardized health ratios: A pilot study introducing Standardized Health Ratios

N. de Keijser, G.J. Bonsel, R.J.B.J. Gemke, N. F. De Keizer

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)

Abstract

Performance of intensive care is usually quantified by means of standardized mortality rates, where standardization is directed towards the severity of illness on admission. However, as more critically ill patients survive, functional outcome and quality of life of these patients becomes more important. In a prospective study in a 10-bed tertiary paediatric intensive care unit (ICU), admission and follow-up health status were collected for 209 surviving patients. For this cohort of patients, health status 1 year after admission was also predicted, using the quantified health-utility-index (HUI), as a value between 0 and 1. For this purpose, two alternative multiple regression models were constructed. The most important predictors of 1-year health status were the level of sensation, mobility and cognition on admission to which self-care, systolic blood pressure, oxygen, Glascow Coma Scale, glucose and age may be added. The two alternative predictive models performed equally well (R2 = 0.83 and 0.84 respectively), indicating that health status could be predicted to a significant degree. The concept of relating expected future health status (based on base-line health status), with actual (observed) health status is denoted with the Standardized Health Ratio (SHR). In combination with the Standardized Mortality Ratios (SMR), such a ratio may become a new comprehensive indicator of performance in intensive care medicine.

Original languageUndefined/Unknown
Pages (from-to)192-199
Number of pages8
JournalQuality of life research
Volume6
Issue number2
Publication statusPublished - 1997

Keywords

  • AMC wi-buiten
  • Standardized Health Ratio
  • health status
  • outcome assessment
  • paediatric intensive care performance
  • prediction model

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