Measuring disability following injury is important. Understanding the factors that impact on disability is necessary to inform trauma care, and enable adequate risk-adjustment for benchmarking and monitoring. The presence of comorbidities has been identified as an important predictor of mortality following injury 1 2 3 . Others have found that age is a more important predictor, and comorbidity offers little prediction benefit compared to models using age alone 4 5 6 . The impact of comorbidity on the prediction of disability outcomes has not been well explored. Untangling the effect of comorbidity has been identified as an important challenge for measuring injury-related disability 7 8 .

De Groot et al., identified numerous methods for measuring comorbidity, and highlighted many issues with the development and validation of comorbidity indices 9 . Of note were the lack of a consistent definition of comorbidity, variation in populations used to develop comorbidity measures, limited validation undertaken, and few measures developed with disability in mind 9 . Several studies have reported associations between comorbidity and disability 7 10 11 12 13 14 15 16 but none have directly compared indices.

This study compared different approaches to modelling the impact of comorbidity, collected as part of the routine hospital discharge data, on 12-month disability outcomes in an orthopaedic injury population to inform the development of risk adjustment models for disability outcomes. A key additional aim was to establish whether the inclusion of comorbidity provides additional important predictive value in addition to adjustment for age alone.

The potential for comorbidity to impact on the long term disability experienced by injury survivors is clear but how best to adjust for comorbidity has not been well explored. This study of 13,519 injury survivors found that comorbidity, mapped to commonly used indices from routinely collected ICD-10 diagnosis codes, is an important predictor of functional recovery, providing additional predictive value over adjustment for age alone.

The findings confirm that comorbidity impairs patient recovery after injury. However, measurement of comorbidity remains a challenge. A lack of defined criteria for what constitutes a comorbidity, and absence of an established gold standard for measuring comorbidity 3 9 , have led to a variety of methods and indices being used in injury research. Routinely collected discharge data, patient self-report and medical record review are common sources of comorbidity information 27 28 . Each source has strengths and weaknesses with self-report criticised due to the potential for recall bias, difficulty in data collection with cognitive deficits, and prohibitive costs in large studies 28 . Medical record review is considered the most comprehensive method, with documented conditions corresponding well to established comorbidity indices, but is resource intensive 28 . In the current study, routinely collected discharge data were used to map ICD-10 diagnoses to comorbidity indices. Routinely collected discharge data have been described as “inevitably imperfect” 28 due to the coding of only a subset of recorded conditions and a focus on coding for maximising reimbursement in many settings. However, routinely collected discharge data provide a less resource intensive method for capturing comorbidities and have been shown to agree with medical record review for key conditions such as diabetes, cancer, chronic lung disease and alcohol abuse 29 . Studies directly comparing various data sources in trauma are absent, but Fan et al., in their study of >10,000 Veterans Affairs patients, found comparable prediction of health-related quality of life when comparing routinely collected discharge data with record review and self-report 27 .

Our findings are consistent with previous studies that have found comorbidity based on patient or proxy self-report 7 12 13 15 16 and registry data, 11 to be an important predictor of longer term functional or health-related quality of life outcome following injury. In contrast to previous studies, comorbidity provided additional predictive value over adjustment only for age 3 4 5 . Adjustment for the specific conditions showed little improvement over adjustment based on the number of conditions represented for the ICD-10 chapters, FCI and the six frequent chronic conditions reported by Haagsma et al., supporting methods of adjustment previously used 10 11 15 16 .

Discrimination and calibration statistics of the various models revealed relatively little difference in model prediction of functional recovery, with ICD-10 chapter and the six frequent chronic condition-based models demonstrating slightly better performance than FCI and CCI models. A potential explanation for the improved performance of the ICD-10 and the six frequent chronic condition models is that they used all of the available ICD-10 codes, and therefore all of the comorbidity information available, while the FCI and CCI restricted the conditions included in the model to a specific subset. In data not shown, only diabetes, heart disease, stroke, neurological disease, PVD and visual impairment were significant predictors of recovery in the FCI models. For the six frequent chronic condition models, diabetes, heart disease and “other” conditions remained significant. The ICD-10 chapters related to ear, respiratory, skin, musculoskeletal, digestive, genitourinary and congenital conditions failed to reach significance in the model.

The prevalence of osteoarthritis, rheumatoid arthritis and obesity in the study population was 1%, 0.1% and 1% (Table 2). The reported prevalence of these diseases in the Australian population is 15%, 30 2%, 31 and 19-22% 32 , respectively. In contrast, the prevalence of heart disease in the study population was 6%, slightly higher than the reported Australian population prevalence for heart, stroke and vascular conditions of 4% 33 . The prevalence of diabetes in the study population was 6%, also slightly higher than the reported 4% national prevalence 34 . The differences in sample prevalence of diseases relative to population prevalence could be explained by the demographic profile of injury patients. In general, the trauma population tends to be younger and healthier than the general population. A number of prospective studies have shown pre-injury quality of life to be higher than age and gender matched population norms 35 36 37 and hence would be expected to have a lower prevalence of chronic diseases. However, the prevalence of some of these conditions (e.g. arthritis) is substantially lower than expected, particularly given the prevalence of elderly patients in the study sample and this is likely to reflect the ICD-10 coding practices and directives.

As noted in the methods, coders are directed to code conditions that have impacted on care provided during the patient’s hospital stay, and this will underestimate the true prevalence of chronic conditions. The finding that the models performed similarly is more likely to reflect the types of conditions coded from the medical record and the importance of these conditions to patient care. All indices included conditions such as diabetes and heart disease which require ongoing clinical management during a patient’s admission.

The overarching purpose of this study was to explore the relative contribution of each comorbidity measurement approach, and its contribution over age alone, to prediction of functional outcome. Importantly, despite models including age, injuries sustained and comorbidity, the capacity to discriminate between recovered/non-recovered patients was only in the acceptable range (AUC ≈ 0.70), confirming the importance of other factors. Numerous injury, personal and environmental factors have the capacity to influence the recovery of an individual. This is well demonstrated in the literature where factors such as compensable status 10 11 15 16 38 39 , level of education 11 13 16 , the intent of injury 11 , gender 11 13 16 40 41 , social circumstances 13 16 18 , intent 11 , mechanism of injury 11 , and the level of designation of the trauma centre of management 11 42 have been shown to be important. While adjustment for comorbidity is important, the contribution of other factors cannot be under-estimated and should be part of any risk-adjustment processes.

The strengths of this study are the large number of cases (>13,000), high follow-up rate at 12-months (87% of all survivors to hospital discharge), and the use of a hospital discharge data to identify comorbidities which has been shown to have low error rates in diagnoses audited 43 . However, there were study limitations.

The study focused on orthopaedic trauma cases rather than all admitted trauma cases. While many cases had also sustained non-orthopaedic injuries, the results may not reflect cases without orthopaedic injury and this should be considered when interpreting the findings. Nevertheless, the orthopaedic registry was selected as the data source specifically for this study because this population includes a higher proportion of elderly patients with comorbidities than other injury populations such as major trauma. There were differences between included cases and those lost to follow-up at 12-months with a bias towards older patients with comorbidity in the group followed-up and included in this study. Secondly, there are challenges in collecting comorbidity information from patients from large populations and particularly where cognitive deficit (e.g. head injury and pre-existing dementia) are prevalent. Therefore, we were not able to assess the agreement between self-report and ICD-10 coding, or fully evaluate the relationship between the FCI and the six frequent chronic conditions not prevalent in the ICD-10 codes, highlighting issues with adapting self-report based indices (e.g. the six frequent chronic conditions described by Haagsma et al.) to ICD-10 based datasets. In future, the development of privacy protecting record linkage systems may enable primary care data to be linked to trauma and hospital discharge datasets to permit evaluation of the impact of conditions not deemed to influence in-hospital care.

Each of the participating hospitals’ coders used the same coding standard but the potential for variable interpretation of the medical record and the coding directives remains, despite regular auditing of the hospital discharge data. Additionally, ICD-10-AM coding is done in Australia for reimbursement purposes. In other jurisdictions, where ICD-10 coding is not used for reimbursement or where systems limit the number of codes recorded, the number and distribution of codes may differ. Whether this would impact on ICD-10 based comorbidity adjustment is unknown but warrants consideration in the interpretation of the findings and for further research. Finally, this study focused on functional outcome. The possibility that the relationship between comorbidity and other outcomes such as health-related quality of life, return to work and pain differs from the relationship between comorbidity and functional status will be examined in further research using data from VOTOR.

Mapping of ICD-10 codes to comorbidity indices showed that comorbidity is an important predictor of long term functional outcome following orthopaedic trauma, independent of age and injuries sustained. Adjustment for comorbidity is indicated when assessing risk-adjusted functional outcomes over time or across jurisdictions.

The authors declare that they have no competing interests.

All authors contributed to the conception and design and interpretation of data. BJG analysed the data for this study. BJG drafted the article and all authors reviewed it critically for important intellectual content. All authors approved the final version for submission.