Md Nazmul Karim1, Christopher M Reid2, Lavinia Tran1, Andrew Cochrane3, Baki Billah4. 1. School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia. 2. School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia; School of Public Health, Curtin University, Perth, WA, Australia. 3. Department of Cardiothoracic Surgery, Monash Medical Centre, Melbourne, Vic, Australia. 4. School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia. Electronic address: baki.billah@monash.edu.
Abstract
BACKGROUND: The aim of this study was to evaluate the impact of missing values on the prediction performance of the model predicting 30-day mortality following cardiac surgery as an example. METHODS: Information from 83,309 eligible patients, who underwent cardiac surgery, recorded in the Australia and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) database registry between 2001 and 2014, was used. An existing 30-day mortality risk prediction model developed from ANZSCTS database was re-estimated using the complete cases (CC) analysis and using multiple imputation (MI) analysis. Agreement between the risks generated by the CC and MI analysis approaches was assessed by the Bland-Altman method. Performances of the two models were compared. RESULTS: One or more missing predictor variables were present in 15.8% of the patients in the dataset. The Bland-Altman plot demonstrated significant disagreement between the risk scores (p<0.0001) generated by MI and CC analysis approaches and showed a trend of increasing disagreement for patients with higher risk of mortality. Compared to CC analysis, MI analysis resulted in an average of 8.5% decrease in standard error, a measure of uncertainty. The MI model provided better prediction of mortality risk (observed: 2.69%; MI: 2.63% versus CC: 2.37%, P<0.001). CONCLUSION: 'Multiple imputation' of missing values improved the 30-day mortality risk prediction following cardiac surgery.
BACKGROUND: The aim of this study was to evaluate the impact of missing values on the prediction performance of the model predicting 30-day mortality following cardiac surgery as an example. METHODS: Information from 83,309 eligible patients, who underwent cardiac surgery, recorded in the Australia and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) database registry between 2001 and 2014, was used. An existing 30-day mortality risk prediction model developed from ANZSCTS database was re-estimated using the complete cases (CC) analysis and using multiple imputation (MI) analysis. Agreement between the risks generated by the CC and MI analysis approaches was assessed by the Bland-Altman method. Performances of the two models were compared. RESULTS: One or more missing predictor variables were present in 15.8% of the patients in the dataset. The Bland-Altman plot demonstrated significant disagreement between the risk scores (p<0.0001) generated by MI and CC analysis approaches and showed a trend of increasing disagreement for patients with higher risk of mortality. Compared to CC analysis, MI analysis resulted in an average of 8.5% decrease in standard error, a measure of uncertainty. The MI model provided better prediction of mortality risk (observed: 2.69%; MI: 2.63% versus CC: 2.37%, P<0.001). CONCLUSION: 'Multiple imputation' of missing values improved the 30-day mortality risk prediction following cardiac surgery.
Authors: D Scott Kehler; Andrew N Stammers; Navdeep Tangri; Brett Hiebert; Randy Fransoo; Annette S H Schultz; Kerry Macdonald; Nicholas Giacomontonio; Ansar Hassan; Jean-Francois Légaré; Rakesh C Arora; Todd A Duhamel Journal: BMJ Open Date: 2017-08-11 Impact factor: 2.692
Authors: Mitchell N Sarkies; Suzanne Robinson; Tom Briffa; Stephen J Duffy; Mark Nelson; John Beltrame; Louise Cullen; Derek Chew; Julian Smith; David Brieger; Peter Macdonald; Danny Liew; Chris Reid Journal: Health Res Policy Syst Date: 2021-04-21