Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom.

  • Harrison D
  • Brady A
  • Parry G
 et al. 
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Abstract

OBJECTIVE: To assess the performance of published risk prediction
models in common use in adult critical care in the United Kingdom
and to recalibrate these models in a large representative database
of critical care admissions. DESIGN: Prospective cohort study. SETTING:
A total of 163 adult general critical care units in England, Wales,
and Northern Ireland, during the period of December 1995 to August
2003. PATIENTS: A total of 231,930 admissions, of which 141,106 met
inclusion criteria and had sufficient data recorded for all risk
prediction models. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS:
The published versions of the Acute Physiology and Chronic Health
Evaluation (APACHE) II, APACHE II UK, APACHE III, Simplified Acute
Physiology Score (SAPS) II, and Mortality Probability Models (MPM)
II were evaluated for discrimination and calibration by means of
a combination of appropriate statistical measures recommended by
an expert steering committee. All models showed good discrimination
(the c index varied from 0.803 to 0.832) but imperfect calibration.
Recalibration of the models, which was performed by both the Cox
method and re-estimating coefficients, led to improved discrimination
and calibration, although all models still showed significant departures
from perfect calibration. CONCLUSIONS: Risk prediction models developed
in another country require validation and recalibration before being
used to provide risk-adjusted outcomes within a new country setting.
Periodic reassessment is beneficial to ensure calibration is maintained.

Author-supplied keywords

  • APACHE; Calibration; Critical Care; Decision Suppo
  • Statistical; Northern Ireland; Prospective Studie
  • standards; Severity of Illness Index; Wales

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Authors

  • David A Harrison

  • Anthony R Brady

  • Gareth J Parry

  • James R Carpenter

  • Kathy Rowan

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