The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom*
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.
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Cites
Le Gall, J.-R. (1993) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.270.24.2957 · Cited by 7,773
Why we need observational studies to evaluate the effectiveness of health care
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Two Further Applications of a Model for Binary Regression
Cox, D. R. (1958) · Biometrika · doi:10.2307/2333203 · Cited by 557
Verification of forecasts expressed in terms of probability
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078%3c0001:vofeit%3e2.0.co;2 · cited 1×
“Brier’s score Brier’s score, B (22), was developed in relation to meteorological forecasts; it is an overall measure of the accuracy of predictions: ● Perfect prediction, B 0.”
Background
Cited by
Kramer, A. A., & Zimmerman, J. E. (2007) · Critical Care Medicine · doi:10.1097/01.ccm.0000275267.64078.b0 · Cited by 886
Wijeysundera, D. N., Karkouti, K., Dupuis, J.-Y., Rao, V., Chan, C. T., Granton, J. T., & Beattie, W. S. (2007) · JAMA · doi:10.1001/jama.297.16.1801 · Cited by 469
Use of Brier score to assess binary predictions
Rufibach, K. (2010) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.11.009 · Cited by 454
Feng, J., Phillips, R. V., Malenica, I., Bishara, A., Hubbard, A. E., Celi, L. A., & Pirracchio, R. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00611-y · Cited by 423
Pirracchio, R., Petersen, M. L., Carone, M., Rigon, M. R., Chevret, S., & van der Laan, M. J. (2015) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(14)70239-5 · Cited by 374
Metadata
- Authors
- Harrison, D. A., Brady, A. R., Parry, G. J., Carpenter, J. R., & Rowan, K.
- Venue
- Critical Care Medicine
- Published
- 2006-05
- Volume
- 34
- Issue
- 5
- Pages
- 1378-1388
- ISSN
- 0090-3493 (ISSN-L)
- OpenAlex
- W2048993934
- Cited by
- 168
- References
- 4
- FWCI
- 11.29
- Type
- journal article
- Language
- en
- Open access
- closed
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- Harrison, D. A., Brady, A. R., Parry, G. J., Carpenter, J. R., & Rowan, K. (2006). Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom*. Critical Care Medicine. https://doi.org/10.1097/01.ccm.0000216702.94014.75
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