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

Hospital morbidity data (HMD), or administrative claims data, are increasingly being used to study important clinical outcomes including in-hospital mortality [1, 2], re-admissions [2, 3], and post-operative complications [4]. These routinely collected data are both readily available and cover large populations offering advantages in regulatory and surveillance settings in that the data have been collected in a reasonably consistent manner over a number of years, and will continue to be collected, using similar procedures, into the future. However, in comparison with clinical data (usually retrieved from individual patient chart review) these data may lack detail on co-morbidities, severity scores, and timing of diagnoses [5‐7]. Moreover, databases that have restricted coding spaces are often limited to a minimum set of data [8]. In addition, HMD do not routinely include important risk factors such as weight, height and detailed smoking history. Nonetheless, owing to their many advantages, researchers have tried to improve these data, validate them [9, 10], and augment them with additional information in order to use them in health care research [11].

Total joint replacement (TJR) is among the most common elective surgical procedures performed in developed countries [12]. The incidence of this procedure has risen over recent years mainly because of the ageing population and increases in the prevalence of risk factors such as obesity [13]. It has been estimated that the demand for total joint replacement will continue to grow [12]. Although primary total joint replacement is considered one of the safest and most effective surgical procedures [14], the procedure is nevertheless associated with short- and long-term complications that can also be life-threatening [15, 16]. These adverse outcomes are more frequent in older patients [16], particularly men [15], and in the obese [3, 17, 18], and a thorough understanding of potential complications in these groups is important for the delivery of high quality and safe medical care. To study these outcomes, researchers have used existing large databases including joint replacement registries and hospital morbidity data. The latter have frequently been used to characterize the rates of immediate postoperative outcomes of both primary [15‐18] and revision total joint replacement [15, 19]. Methods to improve existing data sources, such as HMD, to predict complications following TJR have never been documented.

In an earlier analysis, we have shown that major comorbidities (such as myocardial infarction and cancer) and major operations (such as TJR and coronary artery bypass graft surgery) are more likely to be recorded in the Western Australia (WA) HMD than conditions of less serious nature such as dyslipedemia [9]. In this current study, we assessed the validity and recording of the diagnosis of obesity in this HMD system, and we evaluated whether its augmentation with actual weight and height (both measured by clinical staff) could improve its ability to predict major in-hospital complications following TJR.

In a cohort of men who had had a primary TJR, we found that actual weight independently predicted major in-hospital complications following the procedure showing a dose–response effect, whereas a record of obesity diagnosis in hospital morbidity data did not. Adding actual weight and height to a HMD system makes the latter a better prognostic tool for this major health outcome.

The utility of hospital morbidity data as a resource for medical research has been keenly investigated in recent years [1‐11]. While clinical data usually retrieved from patients’ files are considered the gold standard for accurate clinical information, these are costly and time consuming to obtain and often large clinical databases for comparative purposes are not easily available. Therefore, claims data or HMD are being increasingly used to assess clinical outcomes, and monitor, evaluate, and improve the quality of care. However, differences in HMD-based-outcome among patients may or may not indicate differences in quality of care that the patients received because these differences may be attributed to many factors including differences in age and co-morbid conditions, but also differences in the quality of the data [5‐7]. Since the ability of these routinely collected data to predict adverse outcomes may largely depend on the extent and accuracy of the data on each patient’s clinical condition when care began, researchers have tried to validate, improve and augment them with additional information in order to use them in health care research. Increasingly, studies show how the augmentation of administrative data with minimal clinical information may improve the former’s predictive power [27, 28]. In a retrospective study of 46,769 patients in 30 acute care hospitals, Pine et al. demonstrated how the addition of laboratory data to hospital administrative datasets could provide accurate predictions of inpatient mortality from acute myocardial infarction, cerebrovascular accident, congestive heart failure or pneumonia with significant improvements in models’ discrimination [27]. Another study [28] showed how models using claims data to predict mortality following cardiac bypass surgery can be improved with the addition of minimal clinical variables. Methods to improve HMD to predict complications following TJR have never been documented and this was the focus of our current study.

Postoperative complications following a total joint replacement procedure are not uncommon in elderly patients and in the obese [3, 15‐18]. The impact of obesity on surgical outcomes is achieving significant attention because of the rapidly increasing prevalence of this condition worldwide [29]. In our elderly cohort, 25% of the patients who underwent TJR were obese. However, the WA HMD failed to report this condition among 70% of our obese study population. In earlier analyses, we have shown that body weight is an important risk factor for various adverse outcomes in patients undergoing TJR. We found that, compared with patients with normal weight, the overweight or obese were significantly more likely to develop in-hospital major complications [18], to stay longer in hospital, and to be readmitted within 5 years of this procedure [3]. Nevertheless, HMD systems do not include the weight and height of patients as variables whose recording is mandatory. In this analysis, we found that obesity was under-reported in HMD and was selectively recorded for more severely ill patients. When assessing postoperative complications, HMD alone produced inferior predictive models compared with those that also accounted for the actual weight and height of the patients. The inclusion of actual weight and height in the HMD makes the HMD a better prognostic tool to assess major complications among patients undergoing TJR.

Strengths of this study include its population-based provenance, the longitudinal design and the integration of clinical data with validated HMD. For each participant, any significant morbidity or health-related outcome was retrieved from the linked data in the period 1970 through to 2007 and this enabled us to better account for patient co-morbidities. However, the study has some limitations. HMD may not differentiate complications from co-existing conditions [30]. Our method of retrieving (from the TJR-index admission) only the diagnoses that were reported for the first time for every patient may have misclassified some diagnoses as co-morbidities. Furthermore, HMD systems may be disadvantaged by under-coding or over-coding. We had no access to patients’ charts and, therefore, we could not validate these conditions against these charts. Moreover, classification of a complication as major or minor may differ among studies and our data did not allow us to assess risk of individual conditions. This study also did not account for other surgical and intervention-related factors (such as type of anesthesia) that may also be associated with postoperative complications.

Body weight is an important risk factor for numerous health outcomes and there is increasing evidence to support a correlation between obesity and adverse outcomes in patients undergoing a TJR. The lack of validity of the HMD-recorded diagnosis of obesity limits its use in health research. 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.