Implementation of a nutritional assessment tool for patients undergoing surgery

Freebody, Susan

doi:10.1016/s1361-3111(98)80007-1

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…This study is the first to report that adding actual weight and height to HMD may significantly improve the model discrimination for major complications in an elderly patient population. Since the standard hospital practice is to measure the weight and height of patients [31], our study suggests making actual weight and height mandatory variables in any hospital morbidity data system. Identification of patients who are at increased risk for developing postoperative complications following TJR may assist hospitals in assessing casemix, quality of care, and resource allocation, as well as this may assist clinicians in selecting patients for surgery, and informing patients about their individual level of risk.…”

Section: Discussionmentioning

confidence: 99%

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

Mnatzaganian

Ryan

Norman

et al. 2012

BMC Health Serv Res

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BackgroundRoutinely 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.MethodsThe 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.ResultsThe 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.ConclusionBody 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.

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Section: Discussionmentioning

confidence: 99%