Quality of data collected for severity of illness scores in the Dutch National Intensive Care Evaluation (NICE) registry

Intensive Care Med. 2002 May;28(5):656-9. doi: 10.1007/s00134-002-1272-z. Epub 2002 Apr 13.

Abstract

Objective: To analyse the quality of data used to measure severity of illness in the Dutch National Intensive Care Evaluation (NICE) registry, after implementation of quality improving procedures.

Design: Data were re-abstracted from the paper records of patients or the Patient Data Management System and compared to the data contained in the registry. The re-abstracted data were considered to be the gold standard.

Setting: ICUs of nine Dutch hospitals that had been collecting data for the NICE registry for at least 1 year.

Measurement and results: The mean percentages of inaccurate and incomplete data, per hospital, over all variables, were 6.1%+/-4.4 (SD) and 2.7%+/-4.4 (SD), respectively. The mean difference in severity of illness scores between registry data and re-abstracted data was 0.2 points for APACHE II and 0.4 points for SAPS II. The mean difference in predicted mortality according to APACHE II and SAPS II between registry data and re-abstracted data was 0.4% and 0.02%, respectively.

Conclusions: The current data quality of the NICE registry is good and justifies evaluative research. These positive results might be explained by the implementation of several quality assurance procedures in the NICE registry, such as training and automatic data checks. Electronic supplementary material to this paper can be obtained by using the Springer LINK server located at http://dx.doi.org/10.1007/s00134-002-1272-z

Publication types

  • Comparative Study

MeSH terms

  • APACHE
  • Documentation / standards*
  • Health Services Research
  • Humans
  • Intensive Care Units / standards*
  • Netherlands
  • Outcome Assessment, Health Care
  • Quality Assurance, Health Care
  • Registries / standards*
  • Registries / statistics & numerical data
  • Severity of Illness Index*
  • Statistics, Nonparametric