Comparison of seven comorbidity scores on four-month survival of lung cancer patients

The median age at lung cancer diagnosis is 70 years [1]. Given the increasing probability of developing comorbidities with age, the prevalence of comorbidity is higher in lung cancer than in other cancers, with at least 50 to 70% of patients having at least one comorbidity at diagnosis [2, 3].

The negative affect of comorbidities on patient survival are well described [4‐6]. Since the development of the Charlson Comorbidity Index (CCI) [7], several comorbidity scores have been developed and are currently used. They all differ by the origin of the initial data source (administrative data or physician-reported data), their purpose (measuring comorbidity, measuring the impact of comorbidity and physical function), and comorbidity measures (organ or system-based approaches, counts of individual conditions and weighted indices) [8] (Table S1 Supplementary Materials 1). Some have been developed using lung cancer patient cohorts [9‐11]. Despite the large number of comorbidity scores available, the CCI is the most studied and used comorbidity index in the medical literature [12, 13].

Certain comorbidity scores are based on the International Statistical Classification of Diseases and Related Health Problems (ICD-10), as Quan et al. published ICD-10 codes relative to comorbidities in 2005 [14]. They include the CCI (updated by Quan et al. in 2011 [7, 15]), CCI for lung cancer (named later CCI-lung) (Klabunde et al. in 2007) [16], age-adjusted CCI (ACCI) [17], Elixhauser score (updated in 2009 by Van Wallraven et al. [18, 19]), Elixhauser for lung cancer (Elixhauser-lung) (Mehta et al. [20]), National Cancer Institute Combined Index (NCI) [1], and NCI for lung cancer (NCI-lung) (Klabunde et al. in 2007 [16, 21]). They differ in terms of the type of comorbidities considered, the cohort used for validation, and their initial purpose [22].

Yang et al. found that the ACCI was better at predicting three-year overall survival than the CCI and Elixhauser score in a cohort of resected lung-cancer patients [23] based on administrative data coded using the ICD-9. However, they only compared the ACCI, CCI, and Elixhauser score. More recently, Mehta et al. proposed an Elixhauser score adapted to the cancer type (breast, lung, prostate, and colorectal). The cancer-specific Elixhauser score appears to be a better prognostic score for two-year survival than the cancer-specific NCI (developed by Klabunde et al.) [20].

Although the CCI is the most widely used comorbidity score, it would be informative to assess which score is more predictive of mortality in cohorts with administrative data. Here, we compared the seven comorbidity scores available using administrative data coded using the ICD-10 in predicting four-month survival of our cohort of hospitalized lung-cancer patients.

The CCI has been shown to be associated with poorer survival for all TNM stage lung-cancer patients [12, 26] and is the most widely used comorbidity score. Here, we performed the first study to compare seven comorbidity scores on a cohort of lung-cancer patients. Sarfati et al. suggested that the CCI, cancer-specific NCI, and Elixhauser score may be the preferred comorbidity scores when using administrative data [22]. In this study, we used the ICD-10 to identify comorbidity from administrative data and found the Elixhauser score to be the best score for predicting four-month mortality.

The Elixhauser score has already been compared to the CCI for patients with cancers other than lung cancer and found to be a better prognosis score for colorectal and oral cancer patients [27, 28]. Mehta et al. found that the lung cancer-specific Elixhauser performed better than the lung cancer-specific NCI and Elixhauser score [29]. The outcome of the aforementioned study was two-year non-cancer mortality, which had a consequence on the statistical analyses because the authors had to consider competing risks. In addition, they studied comorbidities prior to the lung cancer diagnosis. More interestingly, they also compared these scores to the individual Charlson and Elixhauser comorbidity scores. The Elixhauser individual comorbidity scores performed better than the Charlson individual comorbidity scores. However, scores have been shown to be good substitutes for individual comorbidity variables in health services research [30]. In a paper published by Yang et al., the ACCI predicted overall three-year survival better than the CCI or Elixhauser score [23]. In contrast to Mehta et al., they did not discriminate between death from cancer and other causes, but they did consider comorbidities prior to the diagnosis of lung cancer.

These scores differ not only in the way they were constructed (origin of the cohort and outcome chosen), but also in the weight assigned to each comorbidity; some use the beta coefficient obtained from the regression and others the hazard ratio. The beta coefficients and hazard ratios are related to each other by an exponential relationship, and although the use of beta coefficients is preferred when using a summary score [31], we calculated the comorbidity scores as they were described and published in the original papers.

There are several possible explanations concerning the better performance of the Elixhauser score. The Elixhauser score was developed using a short-term outcome: in-hospital mortality. The median overall survival in our cohort was four months, which is short relative to the other scores (i.e., the CCI), which were constructed using a long-term outcome, such as one- or two-year mortality. This result corroborates another publication concerning in-hospital mortality of non-cancer patients, in which the Elixhauser score outperformed the CCI [32]. Another possibility is the number of comorbidities taken into account in the Elixhauser score, which is more than for the other scores. Lung cancer patients have the most comorbidities at diagnosis relative to patients with other types of cancer, especially due to tobacco exposure [3, 33]. This could explain why the Elixhauser score best fit our cohort in predicting four-month mortality.

This study had several limitations. We assessed comorbidities that occurred both before and after lung cancer diagnosis and did not distinguish between death from lung cancer and that from other causes. Extension of this paper results should be done with one caution. Despite we had 71% of men and 74.2% of patients with metastatic status at diagnosis, which is similar to literature, age have a non-significant effect on survival. This may be due to the inclusion criteria which is hospitalized patients and therefore frailty ones with the shortest survivals, and high comorbidity burden (Median Elixhauser score of 6). Because performance test has been performed on the same data used to train the model there will be a need for external validity of the results. There may have also been unknown confounders. Moreover, this was a retrospective monocentric study.

The use of ICD-10 codes to identify the comorbidities was a strength of our study, as they can be used to query easily available structured datasets and allow the comparison of comorbidity scores, as well as sensitivity analyses, which confirmed the superiority of the Elixhauser score for estimating four-month survival in our cohort.

Despite the extensive use of the CCI in the literature, other comorbidity scores are available, including scores based on administrative data coded using the ICD-10. In this original study, in which we compared seven comorbidity scores using administrative data, the Elixhauser score was the comorbidity score best suited to hospitalized lung-cancer patients for predicting four-month mortality. It could be informative to repeat these analyses with a longer follow-up of the patients.