Background
With an approximately 4.4% lifetime risk, colorectal cancer (CRC) remains the third most commonly diagnosed cancer in the world.1 Although prognosis has improved recently, >40% of patients with CRC still die within 5 years of diagnosis.2 Because of the aging population, the burden of CRC has been predicted to increase in the next decade.3
CRC is largely diagnosed at older age, when comorbidities are common.4 Some studies have examined the prognostic role of comorbidity in patients with CRC,5–16 wherein decreased survival was observed in patients with comorbidities. However, many previous studies could not adjust for important factors such as tumor stage.7,9,11 Furthermore, evidence on the role of comorbidities in recurrence-related outcomes is limited. It is also unclear whether the impact of comorbidities varies by tumor stage or site. In some previous studies, the effect of comorbidity on overall survival (OS) appeared to be more pronounced in patients with early-stage CRC,5,14,15 but further evidence on other outcomes and from population-based studies is needed for a more comprehensive conclusion regarding the effect of comorbidities on specific patient groups.
Comorbidities and old age independently affect CRC prognosis. Therefore, it is possible that the presence of comorbidities could be associated with worse prognosis at younger ages, but evidence on the “age equivalent” of worsening of prognosis in comorbid compared with noncomorbid patients is lacking. Estimating this could provide valuable information for personalized care and enhanced prediction of prognosis for patients with CRC. The goal of this study was to thoroughly assess the impact of comorbidities on OS and disease-free survival (DFS) in a cohort of patients with CRC and to quantify, for the first time, the “age-equivalent effects” of comorbidities with respect to CRC prognosis.
Methods
Study Design and Population
Our study is based on data from 4,602 patients with CRC who were diagnosed in 2003 through 2014 and recruited into the DACHS study, which is an ongoing population-based case-control study in the Rhine-Neckar region of Germany that was initiated primarily to evaluate the effects of endoscopic screening on CRC risk. Cases are also being followed up to evaluate prognostic factors. Patients with first time diagnosis of CRC (ICD-10 codes C18–C20) and aged ≥30 years were eligible. Patients were recruited from all 22 hospitals providing first-line treatment of CRC in the study region of approximately 2 million inhabitants. According to estimates from cancer registries, approximately half of the eligible patients were recruited. Further details of the DACHS study have been described elsewhere.17,18 The DACHS study was approved by the ethics committees of the Medical Faculty of Heidelberg University and the state medical boards of Baden-Wuerttemberg and Rhineland-Palatinate. All participants provided written informed consent.
At baseline, trained interviewers conducted interviews with participants to collect information on lifestyle factors and medical history. Detailed information on treatment and comorbidities were also recorded in medical records. Follow-up started from CRC diagnosis; vital status and cause of death were ascertained from population registries and public health authorities approximately 3, 5, and 10 years after diagnosis. Information on CRC treatment, newly diagnosed cancers, and recurrence was obtained from physicians approximately 3 years after diagnosis using a standardized questionnaire (physicians abstracted the information from their patient databases). Recurrence history for patients who died during or were lost to follow-up was ascertained from the last attending physician.
Inclusion Criteria
Because of limited evidence on the impact of comorbidities within tumor stages, our analyses included patients with stage I–IV CRC (UICC stages).19 Patients who did not undergo surgery for CRC and those who died within 1 month of surgery were excluded (Figure 1).
Selection of analytic sample.
Abbreviation: CRC, colorectal cancer.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 18, 1; 10.6004/jnccn.2019.7346
Ascertainment of Comorbidities
ICD-10 codes for comorbidities diagnosed either before or at the time of CRC diagnosis were abstracted from discharge reports. To ensure comparability with previous studies, we used the Charlson comorbidity index (CCI),20 adapted by Deyo et al,21 to quantify overall comorbidity. Briefly, the CCI comprises 19 weighted comorbidities, with weights ranging from 1 to 6 based on the magnitude of the adjusted 1-year mortality risk.20 Similar to previous studies,13,16 we grouped patients into 4 groups, namely CCI score 0 (no comorbidity), 1, 2, or ≥3 (severe comorbidity).
Statistical Analysis
We assessed the distribution of the 4 comorbidity groups according to patient and tumor characteristics. Associations of CCI scores with OS (mortality from any cause), CRC-specific survival (CSS; mortality from CRC), non–CRC-specific survival (nCSS; mortality from causes other than CRC), DFS (recurrence of CRC, or mortality from any cause), and recurrence-free survival (RFS; recurrence of or mortality from CRC) were examined using Cox proportional hazards regression. Time was estimated from CRC diagnosis to the respective endpoints or end of follow-up, whichever occurred first.
Because participants entered the study at different times, we accounted for specific left-truncation, by incorporating “delayed entry time” into the Cox models. Two adjustment levels defined a priori were applied: (1) adjustment for sex, age, tumor stage, tumor site, period of diagnosis, years of school education, smoking status, body mass index (BMI), lifetime physical activity, lifetime alcohol consumption, and use of nonsteroidal anti-inflammatory drugs and statins (ie, model 1), and (2) additional adjustment for initiation of chemo(radio)therapy (ie, model 2). The covariates were incorporated into the models as categorical variables, as listed in supplemental eTable 1, available with this article at JNCCN.org. We assessed the proportional hazards assumption for all covariates by adding time-dependent interaction terms to the covariates and checking whether their effects were statistically significant. Time-dependent interaction terms (with age, tumor stage, year of diagnosis, BMI, and chemo[radio]therapy) were added to the model in case of violation of the proportional assumption. Subgroup analyses according to tumor site and stage were also performed, and stage-specific results for OS were furthermore illustrated with adjusted survival curves. We moreover investigated the associations of individual comorbidities with CRC prognosis. Results from model 1 are reported as main results and those from model 2 were used to assess potential mediating effects of chemo(radio)therapy initiation. In sensitivity analyses, we used the Fine-Gray model22 to account for non-CRC mortality (a competing event for CSS and RFS) and CRC mortality (a competing event for nCSS).
In addition to hazard ratios (HRs), we calculated age advancement periods (AAPs) to quantify “age-equivalent effects” of comorbidities following an approach that was previously introduced as “risk or rate advancement periods,”23 which is increasingly used in epidemiology to quantify the age advancement of the risk of diseases for which risk increases with age.24–28 A detailed description of the calculation of AAPs is provided in supplemental eAppendix 1. In brief, AAPs are derived as ratios of the regression coefficient for the risk factor under investigation (here, CCI scores 1, 2, or ≥3 vs CCI 0) to the regression coefficient for age (where age is included in the model as a linear term, in years). Regression coefficients from model 1 were used to derive AAPs to quantify the extent (age difference) to which worse survival rates occur earlier (at younger ages) in comorbid versus noncomorbid patients. All analyses were conducted using SAS 9.4 (SAS Institute Inc). Statistical tests were 2-sided, with α=0.05.
Results
Characteristics of Study Participants
Of 4,916 patients with CRC diagnosed in 2003–2014, 4,829 underwent CRC surgery and 4,811 survived for ≥1 month. Of these, those with missing data on any of the covariates of interest were excluded (n=209), resulting in a total of 4,602 patients included in the main analysis (Figure 1). We excluded 104 patients from the analyses of recurrence-related outcomes because they had recurrence before the interview date. A total of 1,807 (39%) deaths occurred during a median follow-up period of 5.1 years, of which 1,044 (58%) were caused by CRC.
Descriptive statistics of the 4,602 patients are summarized in supplemental eTables 2 and 3. More than 43% of the patients had CCI ≥1 (eTable 2). The 2 most common comorbidities were various cardiovascular diseases (CVDs; 18% overall) and diabetes (16%). Median age of the participants was 69 years. Comorbidity was particularly common in men, older and underweight/obese patients, former smokers, those recently diagnosed, those with right-sided colon cancer and stage I–II disease, and nonrecipients of chemo(radio)therapy (supplemental eTable 3).
Comorbidity and CRC Prognosis
Tables 1 and 2 show adjusted HRs for the associations of overall comorbidity and CRC prognosis. HRs for CCI scores 1, 2, and ≥3, compared with CCI 0, were 1.25, 1.53, and 2.30 (P<.001) for OS, 1.05, 1.30, and 1.56 (P<.001) for CSS, 1.20, 1.48, and 2.03 (P<.001) for DFS, and 1.70, 1.98, and 3.62 (P<.001) for nCSS. The corresponding AAP estimates were 5.0, 9.7, and 18.9 years for all-cause mortality, 2.1, 11.5, and 19.1 years for CRC-specific mortality, 5.5, 11.7, and 21.0 years for worse DFS, and 5.9, 7.6, and 14.5 years for non-CRC mortality (Table 2). Noncomorbid patients aged 70 years, for example, had similar OS rates as 65-, 60-, and 51-year-old patients with CCI scores 1, 2, and ≥3, respectively (Figure 2). Compared with patients with CCI 0, those with CCI scores 2 and ≥3 had 28% and 37% significantly poorer RFS, respectively. In sensitivity analyses that accounted for competing events in the associations of comorbidity with CSS, RFS, and non-CRC mortality, HRs were attenuated (supplemental eTable 4). Supplemental eTable 1 shows adjusted HRs for the associations of the covariates adjusted for in the multivariable models with OS and CSS.
Association of Overall Comorbidity With Survival Outcomes
AAPs for Overall Comorbidity and Survival Outcomes
Age-equivalent effect of comorbidities on all-cause mortality.
Abbreviations: CCI, Charlson comorbidity index score; HR, hazard ratio.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 18, 1; 10.6004/jnccn.2019.7346
Subgroup Analyses by Tumor Site and Stage
Comorbidity was associated with poorer OS and CSS in all tumor sites, with comparable HRs (Table 1). The association of comorbidity with RFS, however, seemed pronounced in patients with rectal cancer (Table 3). In stage-specific analyses (Table 4 and Figure 3), comorbidity was associated with decreased OS and CSS in patients with stage I–III disease only. HRs for both OS and CSS decreased steadily with increasing stage, whereas comorbidity was associated with increased non-CRC mortality in all stages, and HRs increased with increasing stage (Table 4). Significant interaction between comorbidity and stage was observed for OS (Pinteraction<.001). When we restricted our analyses to stage I–III, the associations of comorbidity with the investigated outcomes were stronger than those reported for all stages, but the AAP estimates were mostly comparable (Table 2).
Association of Comorbidity With Recurrence-Free Survival
Associations of Overall Comorbidity With Survival Outcomes, Stratified by Tumor Stage
Adjusted survival curves for comorbidity and colorectal cancer prognosis by tumor stage: (A) I, (B) II, (C) III, and (D) IV.
Abbreviations: CCI, Charlson comorbidity index score; OS, overall survival.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 18, 1; 10.6004/jnccn.2019.7346
Subgroup Analyses by Individual Comorbidities
Table 5 summarizes the associations of individual comorbidities with survival outcomes, overall and restricted to patients with stage I–III. Heart failure, renal disease, diabetes, and other cancers were consistently associated with decreased OS. Comorbidities that had the most significant impact on OS were renal disease and heart failure; each had an AAP estimate of approximately 20 years. Renal disease, heart failure, myocardial infarction (stage I–III only), and other cancers were associated with poorer CSS. Heart failure, peripheral vascular disease, stroke, renal disease, diabetes, and other cancers were associated with increased non-CRC mortality.
Associations of Individual Comorbidities With Survival Outcomes
Discussion
We aimed to thoroughly evaluate the impact of comorbidities on CRC prognosis. Comorbidity was associated with substantially poorer prognosis, especially in patients with stage I–III disease. Besides the conventional quantification of comorbidity effects by HRs, we also calculated AAPs to estimate the “age-equivalent effects” of comorbidity in CRC prognosis. Patients with CCI scores of 1, 2, and ≥3 experienced the same levels of all-cause mortality as noncomorbid patients aged 5, 10, and 19 years older, respectively.
The prevalence of comorbidities in our study (43%) is higher than reported in a study from Japan (25%)14 but lower than an estimate from the United States (58%) in patients with comparable age.29 In particular, CVDs, diabetes, chronic obstructive pulmonary disease, and renal disease are higher in our study than in Japan but lower than in the United States. These variations might reflect differences in assessment of comorbidities across countries. Several previous studies have investigated the effect of overall comorbidity on OS or CSS.5–16,30,31 Most found poorer prognosis in comorbid patients with CRC. However, many were small in size6,8,10,30 and could not adjust for stage.7,9,11 Our study, which addressed these limitations, showed even shorter OS and CSS in comorbid patients. In contrast to some studies that showed no significant association for CSS in comorbid patients,12,14,16,30 we found a significant decrease in CSS also, even after accounting for competing events. Binary grouping of CCI score (≥1 vs 0)14 or small sample size30 might explain why some of the previous studies found no significant association for CSS. Our estimates are comparable to those from a meta-analysis of 5 cohort studies, which observed a 41% and 103% increased all-cause mortality in patients with CRC and CCI scores of 1 to 2 and ≥3 compared with patients with a CCI score of 0, respectively.32 Evidence regarding to what extent comorbidity impacts non-CRC mortality is limited.33 In our study, comorbidity was associated with increased non-CRC mortality, and furthermore, HRs were higher than those of OS and CSS, suggesting that comorbidity affects prognosis in patients with CRC mainly through non-CRC deaths (presumably directly comorbidity-related).
Previous evidence on whether the effect of comorbidity on CRC prognosis varies by tumor stage has been inconclusive. Results from some studies suggested that the prognostic impact of comorbidity decreases with increasing stage.5,14,15,29 Our study confirmed this trend and found no association in patients with stage IV CRC. Patients with stage IV disease have very poor survival, with death almost exclusively being due to the metastasized tumor itself. It is, therefore, plausible that the additional impact of comorbidity on prognosis in these patients may be very small. Conversely, for non-CRC mortality, the effect of comorbidity increased with increasing tumor stage. Although this has not been investigated previously, patients with advanced-stage disease might receive more aggressive treatments and thus have increased risk of adverse effects and complications, which could increase noncancer mortality.
Evidence on the impact of comorbidity on recurrence-related outcomes is sparse. The few available studies assessed DFS only and found significantly shorter DFS in comorbid patients with CRC.14,31 We evaluated to what extent comorbidity impacts RFS, overall and in subgroups according to tumor site. We observed significantly poorer RFS in moderately and severely comorbid patients and stronger associations in patients with rectal cancer. Reasons for this differential impact of comorbidity are unclear and call for further investigation into whether comorbidities affect therapeutic response at specific tumor sites or affect tumor biology, as results from a study suggested that comorbidities could accelerate CRC progression.34 Evidence showing that right-sided CRC has poorer tumor features could also play a role,35 as our data showed pronounced impact of comorbidity in tumor stages and sites with good prognosis.
Previous studies have shown that clusters of individual comorbidities impact CRC prognosis differently.30,33,36 We found renal disease to be the comorbidity with the strongest impact on CRC prognosis, followed by heart failure, and each advanced all-cause mortality by approximately 20 years. Renal disease includes end-stage renal disease, which is a contraindication of administration of chemo(radio)therapy according to the S3 guideline in Germany.37 Renal disease also mostly results from complications of debilitating chronic diseases, including heart failure and diabetes,38 and could thus represent accumulation of effects of several severe comorbidities. These factors might explain why it had a stronger prognostic impact than CVDs and other comorbidities.
Our study is the first to quantify the “age-equivalent effects” of comorbidities in CRC prognosis. Our estimates of AAPs, which range from approximately 2 to 6 years for patients with CCI score of 1 to approximately 14 to 21 years for patients with CCI score ≥3, show how comorbidities strongly impact CRC prognosis. For example, patients aged 70 years without comorbidities had a similar OS rate as 51-year-old patients with CCI ≥3. This underlines the important role of comorbidities in the prediction of prognosis and in therapeutic decisions, in addition to age and tumor characteristics. Despite the strong effect of comorbidities on CRC prognosis, prognostic tools still focus on tumor characteristics only.39,40 Further studies using this concept could provide useful evidence for enhanced treatment of comorbid patients and on the need to incorporate comorbidities into nomograms for enhanced prediction of CRC prognosis. Future studies should also evaluate “age-equivalent effects” of other relevant clinicopathologic factors in CRC prognosis.
Several possible mechanisms exist through which comorbidities might affect CRC prognosis. Severe comorbidities could obscure CRC signs, leading to late diagnosis and worse outcomes.41,42 Also, despite the established benefits of neoadjuvant and adjuvant treatments, comorbid patients receive such treatments less often because of potentially increased risk of chemotoxicities.12,29,30 The associations in our study, however, persisted after adjusting for initiation of chemo(radio)therapy, suggesting that this mechanism might play a minor role. Lastly, diagnosis of CRC, like other cancer diagnoses, could lead to reduced adherence to treatment regimen for long-term conditions.43 Medical care for patients with cancer might therefore benefit from more comprehensive, personalized consideration of both cancer and patient characteristics, including comorbidities.
Our study has limitations despite its unique strengths. Although we made extensive efforts to comprehensively include all patients across the study region, approximately 50% of the eligible patients were recruited. There was also an age gradient, with higher recruitment rates at younger ages. However, we did not set an upper age limit, which has merits when aiming to assess the impact of comorbidities. Nevertheless, the most comorbid patients who would not be able to actively participate in a study requiring an interview of approximately 1 hour are most likely underrepresented, which limited our ability to assess the effects of most severe comorbidities. Our results might therefore be less generalizable to populations with severe comorbidities. Although we adjusted for a large number of confounding factors, potential residual confounding cannot be excluded, which might affect the accuracy of our AAP estimates. We used comorbidities documented in medical records only for our analyses, because data on self-reported comorbidities were not complete. Comorbidity status could also change with cancer progression or aging, but, like previous studies, its impact could only be evaluated with baseline data in our study. The resulting inaccuracy of comorbidity classification most likely has led to underestimation of comorbidity effects.
Conclusions
In the era of personalized oncology, patient characteristics often receive less attention as prognostic factors but could be equally as important as tumor characteristics in the enhanced personalized care of patients with CRC. Our findings corroborate and expand prior evidence showing that comorbidities are important prognostic characteristics that require careful consideration in CRC care. Comorbid patients at substantially younger ages experience comparable survival rates to older patients without comorbidity. This first derivation of age-advancement periods may enhance the empirical basis for treatment decisions in comorbid patients and highlight the need to incorporate comorbidities into prognostic nomograms for CRC.
Acknowledgments
We wish to thank Ute Handte-Daub, Ansgar Brandhorst, and Petra Bächer for their excellent technical assistance. We are particularly grateful to the study participants and interviewers who assisted in the data collection. We also gratefully appreciate the cooperation of the following clinics and institutions: Chirurgische Universitätsklinik Heidelberg, Klinik am Gesundbrunnen Heilbronn, St. Vincentiuskrankenhaus Speyer, St. Josefskrankenhaus Heidelberg, Chirurgische Universitätsklinik Mannheim, Diakonissenkrankenhaus Speyer, Krankenhaus Salem Heidelberg, Kreiskrankenhaus Schwetzingen, St. Marienkrankenhaus Ludwigshafen, Klinikum Ludwigshafen, Stadtklinik Frankenthal, Diakoniekrankenhaus Mannheim, Kreiskrankenhaus Sinsheim, Klinikum am Plattenwald Bad Friedrichshall, Kreiskrankenhaus Weinheim, Kreiskrankenhaus Eberbach, Kreiskrankenhaus Buchen, Kreiskrankenhaus Mosbach, Enddarmzentrum Mannheim, Kreiskrankenhaus Brackenheim, and Cancer Registry of Rhineland-Palatinate, Mainz.
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