The comparison of patient outcomes between healthcare providers requires effective risk adjustment for patient characteristics. In particular, comorbidities are important predictors of outcome
1 2. Comorbidity summary measures have been developed to help classify patients according to their overall disease burden [
1‐
4].
The most commonly used summary measure is the Charlson Comorbidity Index (CCI) [
4]. Charlson et al. identified 17 diseases that optimally predict one-year mortality when assigned a weight between 1 (e.g. peripheral vascular disease) and 6 (e.g. metastatic cancer) [
1]. Although the CCI is commonly used [
4] and has been widely validated [
5], it was developed in the 1980s and has been criticized as outdated [
6]. A number of meta-analyses have found that an alternative summary measure proposed by Elixhauser et al. [
2] has superior predictive properties
3 4. In particular, the Elixhauser Method (EM) predicts mortality more effectively than CCI amongst patients with fractures of the cervical spine [
7] and proximal humerus [
8]. However, although older adults with hip fractures have a high comorbid disease burden, it is unclear which summary measure optimally predicts mortality in this population. The EM is similar to the CCI (nine categories overlap the two measures: diabetes [uncomplicated and complicated], congestive heart failure, HIV, metastatic cancer, renal disease, chronic pulmonary disease, rheumatic disease, and peripheral vascular disease) but includes almost twice as many diagnostic categories [
9].
A number of algorithms have been developed to determine CCI and EM from administrative databases based on ICD-9 [
10‐
12] and ICD-10 [
9] diagnostic codes. Although Khan
et al [
13] have developed an algorithm for calculating CCI in Read-coded databases; there is no equivalent translation for EM. This is important because Read codes are used by General Practitioners throughout the United Kingdom National Health Service (NHS) [
14] and are the basis on which a number of national primary care datasets have developed. These include the Clinical Practice Research Datalink (CPRD) GOLD [
15] and The Health Improvement Network (THIN) [
16] databases.
The aims of this study were to: (1) develop coding algorithms for calculating CCI and EM in Read-coded databases, (2) describe the comorbidity characteristics of a hip fracture cohort with matched controls, and (3) compare the predictive properties of the CCI (both original and modified versions) and the EM.