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Open Access Research article

Use of routine hospital morbidity data together with weight and height of patients to predict in-hospital complications following total joint replacement

George Mnatzaganian12*, Philip Ryan23, Paul E Norman4, David C Davidson5 and Janet E Hiller12

Author Affiliations

1 Faculty of Health Sciences, Australian Catholic University, Fitzroy, Victoria, Australia

2 School of Population Health and Clinical Practice, Discipline of Public Health, The University of Adelaide, South Australia, Australia

3 Data Management and Analysis Centre, The University of Adelaide, South Australia, Australia

4 School of Surgery, University of Western Australia, Perth, Australia

5 Emeritus Consultant Orthopaedic Surgeon, Royal Adelaide Hospital, South Australia, Australia

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BMC Health Services Research 2012, 12:380  doi:10.1186/1472-6963-12-380

Published: 1 November 2012

Abstract

Background

Routinely collected data such as hospital morbidity data (HMD) are increasingly used in studying clinical outcomes among patients undergoing total joint replacement (TJR). These data are readily available and cover large populations. However, since these data were not originally collected for the purpose of health research, a rigorous assessment of their quality is required. We assessed the accuracy of the diagnosis of obesity in HMD and evaluated whether the augmentation of HMD with actual weight and height of patients could improve their ability to predict major in-hospital complications following total joint replacement in men.

Methods

The electronic records of 857 participants in the Health In Men Study (HIMS) who had had TJR were linked with Western Australia HMD. HMD-recorded diagnosis of obesity was validated using the actual weight and height obtained from HIMS. In-hospital major complications were modelled using multivariable logistic regressions that either included the actual weight and height or HMD-recorded obesity. Model discrimination was calculated using area under ROC curve.

Results

The HMD failed to detect 70% of the obese patients. Only 64 patients (7.5%) were recorded in HMD as obese although 216 (25%) were obese [BMI: ≥30kg/m2] (sensitivity: 0.2, positive predictive value: 0.7). Overall, 174 patients (20%) developed an in-hospital major complication which was significantly higher in the overweight and obese comparing with patients with normal weight. HMD-recorded obesity was not independently associated with major complications, whereas a dose–response relationship between weight and these complications was observed (P=0.004). Using the actual weight and height of the participants instead of HMD-recorded diagnosis of obesity improved model discrimination by 9%, with areas under ROC curve of: 0.69, 95% CI: 0.64-0.73 for the model with HMD-recorded obesity compared with 0.75, 95% CI: 0.70-0.79 for the model with actual weight and height, P<0.001.

Conclusion

Body weight is an important risk factor for in-hospital complications in patients undergoing TJR. HMD systems do not include weight and height as variables whose recording is mandatory. Augmenting HMD with patients’ weight and height may improve prediction of major complications following TJR. Our study suggests making these variables mandatory in any hospital morbidity data system.