Multivariable Risk Prediction Models It's All about the Performance
*Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom. †Departments of Anesthesia and Clinical Epidemiology and Biostatistics, Michael DeGroote School of Medicine, Faculty of Health Sciences, McMaster University, Population Health Research Institute, Hamilton, Ontario, Canada. . Anesthesiology
(Impact Factor: 5.88).
06/2013; 118(6):1252-1253. DOI: 10.1097/ALN.0b013e31828e13e9
Available from: Robert Raw
- "Legrand M, Payen D: Case scenario: Hemodynamic management of postoperative acute kidney injury. AnestHesioLogy 2013; 118:1446–54 2 "
Anesthesiology 01/2014; 120(1):245-6. DOI:10.1097/ALN.0000000000000026 · 5.88 Impact Factor
Available from: William N Whiteley
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To determine whether the predictions of functional outcome after ischemic stroke made at the bedside using a doctor’s clinical experience were more or less accurate than the predictions made by clinical prediction models (CPMs).
Methods and Findings
A prospective cohort study of nine hundred and thirty one ischemic stroke patients recruited consecutively at the outpatient, inpatient and emergency departments of the Western General Hospital, Edinburgh between 2002 and 2005. Doctors made informal predictions of six month functional outcome on the Oxford Handicap Scale (OHS). Patients were followed up at six months with a validated postal questionnaire. For each patient we calculated the absolute predicted risk of death or dependence (OHS≥3) using five previously described CPMs. The specificity of a doctor’s informal predictions of OHS≥3 at six months was good 0.96 (95% CI: 0.94 to 0.97) and similar to CPMs (range 0.94 to 0.96); however the sensitivity of both informal clinical predictions 0.44 (95% CI: 0.39 to 0.49) and clinical prediction models (range 0.38 to 0.45) was poor. The prediction of the level of disability after stroke was similar for informal clinical predictions (ordinal c-statistic 0.74 with 95% CI 0.72 to 0.76) and CPMs (range 0.69 to 0.75). No patient or clinician characteristic affected the accuracy of informal predictions, though predictions were more accurate in outpatients.
CPMs are at least as good as informal clinical predictions in discriminating between good and bad functional outcome after ischemic stroke. The place of these models in clinical practice has yet to be determined.
PLoS ONE 10/2014; 9(10):e110189. DOI:10.1371/journal.pone.0110189 · 3.23 Impact Factor
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ABSTRACT: The Charlson score is a commonly used measure of comorbidity; however, there is little empirical research into the optimal implementation when studying cancer surgery outcomes using administrative data. We compared four alternative Charlson score implementations, including and excluding metastatic cancer and varying the look-back periods.
Nine years of linked administrative data were used to identify patients undergoing surgery for cancer of the colon, rectum, or lung in New South Wales, Australia. Four binary outcomes of 30- and 365-day mortality, length of stay greater than 21 days, and emergency readmission within 28 days were compared between groups of similar hospitals. Hospital risk adjustment models were compared for alternative Charlson score implementations.
Excluding metastatic cancer from the Charlson score improved model performance for short-term outcomes, but there was no implementation that was consistently optimal. Incorporating a look-back period reduced the number of patients for analysis but did not improve hospital risk adjustment.
Charlson scores for hospital risk adjustment of short-term outcomes of cancer surgery should be calculated excluding metastatic cancer as a separate comorbidity. We found no clear best performing implementation and found no benefit in incorporating any look-back period.
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Journal of Clinical Epidemiology 12/2014; 68(4). DOI:10.1016/j.jclinepi.2014.12.002 · 3.42 Impact Factor
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