Journal of Surgical Oncology 2014;110:629–635
Assessment of Comorbidities in Surgical Oncology Outcomes PARUL SINHA, MBBS, MS, DORINA KALLOGJERI, MD, MPH, AND JAY F. PICCIRILLO,
MD FACS CPI
Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri
The impact of comorbidities on treatment, postoperative complications, survival, prognosis, and quality of life, makes comorbidities’ assessment essential in surgically managed cancer patients. Multiple indices exist to measure the presence and severity of comorbidities, of which the Charlson Comorbidity Index and the Adult Comorbidity Evaluation‐27 are the most widely employed. Incorporation of comorbidity data in cancer registries facilitates surgical oncology research conduct with an objective to improve cancer care through better‐informed patient counseling and treatment planning.
J. Surg. Oncol. 2014;110:629–635. ß 2014 Wiley Periodicals, Inc.
KEY WORDS: comorbidity; surgical outcomes; cancer; survival; comorbidity assessment
INTRODUCTION As defined by Alvan Feinstein in 1970, comorbidity refers to the presence of “a distinct additional clinical entity” that has existed or may occur during the clinical course of a patient with an index disease [1,2]. Comorbidities, single or multiple, often coexist in cancer patients at the time of their principal diagnosis and can affect the timing of the diagnosis (diagnostic comorbidity), selection of treatment (therapeutic comorbidity), or prognosis (prognostic comorbidity). The prevalence of most cancers and comorbid ailments increase with age [3–6]. Several studies [6–9], including the most recent 2007–2009 [7] estimates obtained from Surveillance, Epidemiology, and End Results (SEER) registries, demonstrate that the probability of developing a new cancer increases with age in both males and females, and is highest in individuals aged 70 years or more. Numerous studies now document the significant independent impact of comorbidities on the treatment choice [10–12], prognosis [13–16], and quality of life [17,18] for cancer patients, regardless of the tumor stage. In patients undergoing surgical management for cancer, comorbidities tend to presage the tolerance for a particular procedure, influence decisions for the extent of resection, and use of adjuvant therapy [19,20]. Comorbidities are also associated with increased postoperative morbidity and mortality, hospital stay, readmission, and treatment costs [10,21–23]. While comorbidities receive consideration in clinical practice for the management of individual cancer patients, less attention has been given to the impact of comorbidity among clinical trials and oncologic studies evaluating outcomes and quality of care for cancer patients. Over the past few years, several measures for the collection of comorbid health information have been proposed. Some measures include comorbid ailments that are relevant to general adult patients [24–26] and some are specific to certain disease conditions [16,26,27]. In this review, the measures for assessment of comorbidities and their application in evaluation of surgical oncology outcomes is discussed.
MEASURES OF COMORBIDITY ASSESSMENT A comorbidity index identifies the important coexistent medical conditions and their severity. Since patients often have multiple medical conditions, an overall score, which represents the burden of the coexistent conditions, is calculated. This overall score facilitates comparison among patients [28]. Multiple valid comorbidity indices exist to quantify the presence and role of comorbidities in cancer patients
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[14,24,25,29]. The comorbidity information can be procured from different data sources: (1) self‐report, (2) charts or medical records, or (3) the administrative claims or financial databases that contain diagnoses and procedures using the International Classification of Diseases, Ninth or Tenth Revision, Clinical Modification (ICD‐9‐CM, ICD‐10‐CM) and Current Procedural Terminology (CPT) coding system. In Table I, a summary of the different indices, which have been used to assess comorbidity in cancer studies, is provided. The most commonly used indices from Table I are discussed below under the three main categories of comorbidity instruments.
Self‐Report‐Based Approach In this method, the patient is the primary source of the comorbid health information [26,30–34]. The comorbidity information is recorded at the time of the cancer diagnosis. The data are provided by the patient and is recorded by the physician or another health care professional.
Chart‐ or Medical Record‐Based Approach In this approach, the comorbidity information is collected through a review of medical charts by the medical staff or cancer registrars [25,35,36]. To ensure standardized and accurate data collection, the cancer registrars and medical staff are trained and validated to review the medical record and identify the cogent comorbidities in a time‐efficient and valid fashion. The Kaplan–Feinstein Index (KFI) [27] was created from the study of patients diagnosed with diabetes mellitus. Using this index, disease conditions are identified from the records and severity is classified as Mild, Moderate, or Severe according to the degree of organ decompensation. The overall comorbidity score is determined by the highest level of organ decompensation. In a situation where two comorbidities from two different organ systems are graded as Moderate, the overall score is Severe. The KFI
*Correspondence to: Jay F. Piccirillo MD FACS CPI, Department of Otolaryngology‐Head and Neck Surgery, 660 S. Euclid Avenue, Campus Box 8115, St. Louis, MO 63110. Fax: þ1‐314‐362‐7522. E‐mail: [email protected] Received 5 April 2014; Accepted 11 June 2014 DOI 10.1002/jso.23723 Published online 6 August 2014 in Wiley Online Library (wileyonlinelibrary.com).
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TABLE I. Measures for Assessment of Comorbidities Used in Cancer Studies
Index
Author et al. (year)
Primary (alternative) data source
General or disease‐specific
Number of items measured
Population (sample size)
Outcome(s) of interest Mortality Physical impairment measurement 5‐yr survival
TIBI‐CaP [93] CIRS [15,41]
Litwin, 2007 Linn, 1968
Self‐report Chart
Disease‐specific General
11 conditions 13 systems
Prostate cancer (2894) Not Reported
KFI [27]
Kaplan & Feinstein, 1974 Charlson, 1987 Greenfield, 1993
Chart
Disease‐specific
12 systems
Adult diabetes (188)
Chart (administrative) Chart
General General
19 conditions 14 systems
General medical patients (608) Patients undergoing total hip replacement (356)
Satariano & Ragland, 1994 Yancik, 1996
Chart (administrative)
Disease‐specific
7 conditions
Breast cancer (936)
Chart
General
24 conditions
Cancer (7600)
ASA [95] WUHNCI [83] ACE‐27 [13] SCI [96] Elixhauser [29]
Reid, 2001 Piccirillo, 2002 Piccirillo, 2004 Colinet, 2005 Elixhauser, 1998
Chart Chart (administrative) Chart (administrative) Chart Administrative
General Disease‐specific General Disease‐specific General
Not Reported 7 conditions 27 conditions 7 conditions 30 conditions
Flemings’ CPI [81] NCI comorbidity index [47] ATC [82] index HNCA [82] Tammemagi [80,97]
Fleming, 1999 Klabunde, 2000
Administrative Administrative
Disease‐specific General
34 conditions 12 conditions
Reid, 2002 Reid, 2002 Tammemagi, 2003 & 2005
Administrative Administrative Administrative
Disease‐specific Disease‐specific Disease‐specific
MACSS [98]
Holman, 2005
Administrative
General
11 conditions 8 conditions 19 conditions for lung, 77 conditions for breast 102 conditions
Surgical patients Head & neck cancer (1094) Cancer (19,268) Lung cancer (735) Adult acute care hospital (1,779,167) Breast cancer (848) Prostate (28,868), Breast (14,943) cancer Head & neck cancer (9386) Head & neck cancer (9386) Lung (1155), Breast (905)
New NCI combined index [99]
Klabunde, 2007
Administrative
General
13 conditions
CCI [24] ICED [26]
Satariano [16] NIA/NCI Collaborative study [94]
Hospital: medical (asthma, acute MI), procedural (surgery for breast cancer, prostate hypertrophy), & psychiatric patients (1,069,770) Breast (26,377), Prostate (53,503), Colorectal (26,460), Lung (33,975)
1‐yr survival Post‐op complications, 1‐ yr quality of life, physical functioning 3‐yr survival Comorbidity assessment in older cancer patients Acute operative risk 5‐yr mortality Survival Overall survival Mortality, length of stay, hospital charges 1‐yr survival 2‐yr non cancer mortality All‐cause mortality All‐cause mortality Survival (Lung) [79], All‐ cause & breast‐cancer specific survival [94] 1‐yr mortality, 30‐day readmissions, length of stay
2‐yr non cancer mortality
TIBI‐CaP, Total Illness Burden Index for Prostate Cancer; CIRS, Cumulative Illness Rating Scale; KFI, Kaplan–Feinstein Index; CCI, Charlson Comorbidity Index; ICED, Index of Coexistent Disease; NIA/NCI, National Institute on Aging/National Cancer Institute; ASA, American Society of Anesthesiology; WUHNCI, Washington University Head and Neck Comorbidity Index; ACE‐27, Adult Comorbidity Evaluation‐27; SCI, Simplified Comorbidity Index; CPI, Comprehensive Prognostic Index; ATC, Alcohol‐Tobacco‐Related Comorbidities Index; HNCA, Head and Neck Cancer Index; MACSS, Multipurpose Australian Comorbidity Scoring System.
has been used in several cancer‐related studies to classify comorbidities [37–39]. A modification of KFI resulted in formation of the Adult Comorbidity Evaluation‐27 (ACE‐27) [13,25,36], the most widely used and validated comorbidity instrument for cancer patients. The ACE‐27 added clinical entities, such as diabetes, AIDS, and dementia, which were not included in KFI. The severity grading of each comorbid ailment included in ACE‐27 provides sufficient information for valid prognostic estimates of overall survival of cancer patients [40]. An on‐line comorbidity education program [http://otooutcomes.wustl.edu/Research/ ResearchFocus/Cancer/ComorbidityDataCollection.aspx] intended to train cancer registrars and other health care professionals in the valid use of ACE‐ 27 index and an on‐line comorbidity calculator [http://oto.wustl.edu/ comorbiditycalculator.html] developed through education grants from the National Cancer Institute (NCI) are available. The Charlson Comorbidity Index (CCI) [24] was developed from the study of patients admitted to a general medical unit. It is a weighted index that takes into account the number of comorbid ailments. The weights are calculated using adjusted relative risk for each of the clinically significant comorbid conditions. The scoring system assigns weights of 1, 2, 3, and 6 for each condition and the total score, which is the sum of each individual condition score, determines a patient’s prognostic status. In CCI, of all 19 listed comorbid ailments, severity is accounted only for diabetes, liver and renal disease. Journal of Surgical Oncology
The Index of Coexistent Disease (ICED) [26] was developed as a modified version of an earlier comorbidity instrument [22,23] to measure the severity of comorbidity and its impact on postoperative stay, function, and quality of life. ICED assesses the patient’s status in two separate domains: physiological and functional burden. The severity of dysfunction derived from both domains determines the overall score on a 4‐point scale, indicating 1, No comorbid disease or asymptomatic; 2, Controlled, but mildly symptomatic; 3, Uncontrolled and severely symptomatic; and 4, Life‐threatening disease. The Cumulative Illness Rating Scale (CIRS) [41] rates 13 organ systems according to the severity of dysfunction based on medical records on a scale from 0 to 4, where 0 indicates no dysfunction and four indicates extremely severe dysfunction. CIRS has been used to measure the impact of comorbidities on survival in cancer patients in general [42] and for specific sub‐sites, such as prostate and colon cancer [43,44].
Administrative‐ or Claims‐Based approach In this approach, the primary source of comorbidity data is the primary and secondary diagnosis code fields, as defined by the ICD and CPT codes. A number of indices have been employed by different studies to report comorbidity from administrative or claims data. The
Comorbidity in Surgical Oncology measure by Satariano and Ragland [16], initially developed as a chart‐ based review instrument, was later adapted for use with administrative data. The chart‐based CCI was also modified for use with administrative data by Deyo et al. [45] and Romano et al. [46] (Dartmouth‐Manitoba). These adaptations added ICD‐9 codes to the comorbid conditions in the CCI. The codes assigned by Deyo adaptation represent a stricter interpretation of the original Charlson’s comorbidity definitions [45]. The Dartmouth‐Manitoba/Romano adaptation included broader definitions for conceptually similar conditions that were not explicitly mentioned in the CCI, particularly for the conditions of peripheral vascular disease, cerebrovascular disease, renal disease, and malignancy [46]. The CCI, with these adaptations, was successfully used to predict mortality and other surgical outcomes, such as postoperative complications, length of hospital stay, and inpatient costs. The CCI has also been used as the basis for developing another administrative‐ based instrument, the NCI Comorbidity Index [47], which uses a set of clinical conditions from the inpatient and outpatient administrative records. An administrative claim‐based ACE‐27 measure has also been recently proposed [48]. Based on a cross‐sectional study of 4,300 breast and prostate cancer patients from the “Centers for Disease Control and Prevention Patterns of Care Study,” Fleming et al. [48] found the administrative‐based ACE‐27 to be in fair to moderate agreement with the chart‐based ACE‐27 index. They concluded that ACE‐27 score derived from administrative claims data is a good alternative to chart‐ based ACE‐27 to evaluate the impact of comorbidity on cancer outcomes, when chart‐based review is logistically impossible [48]. Elixhauser et al. [29] identified 30 pre‐existing conditions with a significant impact on short‐term outcomes using administrative data. Their index was used for comorbidity assessment in cancer of the breast, prostate, cervix, and colon [14]. Other measures of comorbidity based on claims data (not listed in Table I) include “simple count of the number of unique diagnoses” and the Chronic Disease Score (CDS) [49]. Melfi et al. [50] reported in an analysis of Medicare claims data that a simple count of the number of unique diagnosis codes listed on the discharge summary was predictive of hospital stay and 30‐day mortality. The CDS uses a pharmacy‐based measure of chronic disease status in which a score is calculated by adding the scores assigned for each class of medications from data on prescription drug use [49].
IMPORTANCE OF COMORBIDITY IN SURGICAL ONCOLOGY Surgery is a mainstay of treatment for solid tumors arising from sites such as the head and neck, breast, lung, urogenital, and gastrointestinal tract. The presence of comorbid illnesses not only impacts the decision to perform surgery but also the key outcomes after surgery, which include postoperative complications, perioperative mortality, duration of hospitalization, adjuvant treatment decision, quality of life, and survival status. The impact of comorbidity on the outcomes in surgical oncology is discussed below.
Impact on Primary Treatment Selection and Adjuvant Therapy Decisions Comorbid conditions that affect the treatment decision‐making process of cancer patients are referred to as therapeutic comorbidities. Particular comorbid ailments, such as chronic pulmonary disease, may impact the use of a particular surgical approach, such as a partial laryngectomy, since the anticipated complications of the surgery may be prohibitively high in patients with pulmonary disease. After primary treatment with surgery, comorbidities can also influence the choice of adjuvant treatment with chemotherapy and or radiation therapy. A bias towards non‐surgical treatment for cancer patients with higher levels of comorbidity severity was observed in studies involving patients with different cancers [51–54]. For example, Connor et al. [55] observed that Journal of Surgical Oncology
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among patients with Stage III laryngeal cancer, those with more comorbid ailments were more likely to receive non‐surgical treatment. McCulloch et al. [20] observed the same relationship between increased severity of comorbidity and increased use of non‐surgical treatments among patient with gastric and esophageal cancer patients, and Tetsche et al. [19] observed similar findings among patients with ovarian cancer. This relationship between increased severity of comorbidity and non‐ surgical treatment is not found in all studies. For example, Maas et al. [56] found no association of high comorbidities with non‐surgical treatment in ovarian cancer, nor did a study of 358 patients with cancers of the oral cavity, breast, gastrointestinal tract, and urogenital tract by Chaudhary et al. [10] The latter study [10] however reported delays in administration or administration of less toxic treatments due to comorbidities than what was standard in at least 29% of the patients requiring adjuvant therapy [10]. Failure of patients to receive the standard adjuvant treatment was also observed in ovarian [19], colon [57,58], and breast cancer [59].
Impact on Postoperative Complications and Duration of Hospitalization An increase in the rate of postoperative complications and duration of hospital stay after major surgery in patients with higher comorbidities is well‐recognized. In a study using data from Nationwide Inpatient Sample, Genther and Gourin [60] graded comorbidity using the Romano adaptation of the Charlson comorbidity index in 61,740 surgically treated elderly patients with head and neck cancer [60]. Advanced comorbidity was present in 18% of the patients and was significantly associated with acute medical complications (odds ratio 3.7), in‐hospital mortality (odds ratio 3.6), increased length of hospitalization, and hospital‐related costs [60]. An increased odds ratio between 3.9 and 4.6 was also reported for postoperative complications, increased complication severity, and increased hospital stay for comorbidity assessed by CCI [61] and ACE‐27 [17] in head and neck cancer patients following microvascular reconstruction. A two‐ to fourfold higher 30‐ day postoperative mortality was observed in colon cancer patients with comorbidity compared to patients without comorbidity [2]. Using the CCI index in 4,171 patients with lobectomy for Stage I lung cancer, Rueth et al. [62] observed an increased incidence of postoperative complications (odds ratio of 1.38 for a CCI score of 1 and 1.83 for a CCI score of 2) in patients with comorbidities. The impact of comorbidities on postoperative complications has also been investigated in patients with renal tumors. In a study of 1,092 patients undergoing partial or radical nephrectomy for localized renal tumors, Tomaszewski et al. [63] observed a 1.9 times increased odds of incurring any complications in patients >75 years old and a CCI score of >2 compared with patients without these criteria.
Impact on Quality of Life Studies evaluating the effect of comorbidity on the quality of life in surgically treated cancer patients are scarce. In head and neck cancer, Terrell et al. [64] and Borggreven et al. [65] reported diminished quality of life in patients having preoperative comorbidities, which were either self‐reported or were chart‐based; a standard index was not applied for grading the severity in these studies. In prostate cancer, the impact of comorbid conditions on quality of life after radical prostatectomy has been investigated. In a study of 2,415 men with prostate cancer treated with radical prostatectomy, Karakiewicz et al. [34] reported a strong association of increased severity of comorbidities with decreased sexual and urinary function and general health‐related quality of life; data collection for comorbidity information was based on self‐report. In another multi‐institutional study of 691 patients with prostate cancer, the effect of baseline comorbidities as assessed by ICED was independently associated with poorer sexual function after radical prostatectomy but
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comorbidities were not associated with urinary incontinence or bowel functioning [18].
Impact on Survival Comorbid health conditions that impact on prognosis for cancer patients are referred to as Prognostic Comorbidities. Advanced comorbidity influences mortality for adult cancer patients in two ways. First, the direct effects of comorbid illness will impact survival. Second, the indirect effects of comorbid illness will impact a patient’s ability to tolerate cancer therapy and mount a successful host response. Cancer statistics are difficult to interpret with regards to the cause of death. Strict attribution of mortality to the index cancer or to the comorbid ailment(s) is difficult since the two may be inter‐related. Nevertheless, it is intuitive that deaths specific to cancer occur more commonly among patients with aggressive tumor types while deaths attributable to comorbid illness occur more frequently among patients with more indolent, slowly progressive types. Thus, the prognostic impact of comorbid illness is more important among patients with less aggressive tumors [66]. Studies on cancer of various sites, such as colon [51], breast [67], lung [38,54], cervix [68], and head and neck [13,69,70], have found patients with comorbidities to have poorer survival. Most of these studies comprise primarily surgically treated patients, but some have a mix of both surgically and non‐surgically treated patients. For example, in an analysis of 3,152 patients with non‐ small cell lung carcinoma who underwent surgical resection, Lüchtenborg et al. [54] observed severe comorbidity (CCI 3þ) to be independently associated with significantly higher death rates. They also observed the stage‐specific 5‐year survival to be significantly lower in patients with severe comorbidity than in patients without comorbid disease such as 38% for T1 tumors and CCI 3þ versus 69% for T1 tumors and CCI 0 [54]. Land et al. [71] found an association between comorbidity and cancer‐specific mortality in women with breast cancer. In 62,591 women diagnosed with localized breast cancer, the presence of comorbidity increased the risk of all‐cause mortality by a hazard ratio of 1.45 (CI 95% 1.40–1.51) for CCI 1, 1.52 (95% CI 1.45–1.60) for CCI 2, and 2.21 (95% CI 2.08–2.35) for CCI 3þ. Hazard ratios for breast cancer‐specific mortality were 1.30 (95% CI, 1.24–1.36) for CCI 1, 1.31 (95% CI 1.23–1.39) for CCI 2, and 1.79 (95% CI, 1.66–1.93) for CCI 3þ [71]. In a study on head and neck cancer treated with surgery (radiation) in 73% patients, the median disease‐free interval (11.1 vs. 21.6 months) and tumor‐specific survival (13.7 vs. 57.6 months) was found to be significantly lower for patients with advanced comorbidity, independent of the tumor stage [69]. The prognostic value of comorbidity may be diminished for cancer types which afflict mainly the younger and healthier population like the human papillomavirus (HPV)‐driven oropharynx cancer. In a study of 171 HPV‐positive oropharynx cancer patients, only 4% had severe comorbidities and the severity of comorbidity as assessed by ACE‐27 was not found to be prognostic for overall or disease‐specific survival [72].
Impact on Prognostication and Inclusion in Current Staging Systems Comorbidity has been demonstrated to have a prognostic impact on cancer‐related outcomes, regardless of the tumor stage and treatment‐ related factors. The physiologic burden of pre‐existing comorbid ailments can increase tumor aggressiveness by an unfavorable alteration of the host’s responses to cancer [13,15,69]. The presence of severe comorbidities can also modify treatment decisions, sometimes resulting in less optimal chances of disease control [13,15]. These explanations are postulated to account for the negative prognostication from comorbidities even on cancer‐specific outcomes. A statistical technique of “conjunctive consolidation” was described to incorporate comorbidity into the TNM classification for staging cancer [73,74]. This Journal of Surgical Oncology
technique has been used in the development of expanded staging systems for a variety of cancers and demonstrates improved prognostic precision over TNM classification alone.
COMPARISON OF COMORBIDITY ASSESSMENT MEASURES There are certain advantages and disadvantages to each of the comorbidity assessment approaches. The self‐report‐based approach provides information about the functional impact of the comorbidity, which cannot be provided by the other two approaches. However, the use of self‐report‐based comorbidity may be sub‐optimal due to a high possibility of under‐reporting, especially in the elderly. In head and neck cancer, Mukerji et al. [75] compared the self‐report to the comorbidity severity assessed with chart‐based ACE 27. Moderate level of consistency (k ¼ 0.50) was noted, with greater inconsistencies in elderly patients and those with severe comorbidities. Self‐report‐based approach is also not feasible to capture the comorbidities for the purpose of conducting retrospective studies. The chart‐based approach can retrospectively obtain information on comorbidities through access of the medical records. The data collected this way is shown to be very accurate. However, the collection of comorbidity information from the medical record requires the training of health care professionals so that information is collected in a valid and accurate manner. The administrative‐based approach has the advantage of allowing collection of information for a large number of patients over wide geographic areas in a less tedious and costly manner compared to the chart‐based approach, but lack of sensitivity to the severity of medical conditions decreases the clinical utility of this approach. In addition, the ICD codes undergo revisions, which may make it difficult to accurately compare trends over time. However, preliminary studies have shown that ICD‐10 version of the Charlson comorbidity [76] and Elixhauser [77] scores yields similar prevalence and prognosis information for in‐ hospital mortality in comparison with the ICD‐9‐CM coding algorithm [76,77]. Therefore, it has been suggested that comorbidity scores derived from ICD‐10 administrative data represent a valid approach to adjust for comorbidity [76,77]. Moreover, comparison of outcomes between different health systems may be difficult if the usage of ICD versions is not uniform. For instance, ICD‐9 is used in United States whereas ICD‐ 10 is already implemented in European nations and other countries such as Canada, Australia, China, etc., some of which have developed their own versions with addition of new codes. Biases related to misclassification of ailments are recognized as another disadvantage with administrative‐based approach. Misclassifications arise when complications from the treatment or disease progression are coded as comorbidities [78]. Another disadvantage is of incomplete or inaccurate data collections, when information for services not billed to the insurance system is not available [78] or when the information is not available for all patients in a tumor registry. And finally, lack of sensitivity to medical conditions is reflected by a significant majority of patients (65%), across different studies, being coded as having no comorbidities. Of the individual measures for comorbidity assessment enumerated in Table I, no particular index has been established as a single gold standard for application in cancer patients. The choice of measure is often guided by availability and accessibility of relevant data. Studies have compared the common indices for the criteria of validity, reliability, feasibility, and generalizability but these comparisons are not restricted to assessment of comorbidity in surgical oncology [14,79]. Hence, even though surgery was one of the therapeutic modalities in studies appraised by the available comparative reviews, the comorbidity indices were not compared exclusively for the surgically treated cohort. For comparison of comorbidity indices in cancer studies, the principle of validity relates to the following [14,28]: (1) Content validity: the extent and relevance of the content of all items to measure what is
Comorbidity in Surgical Oncology intended to measure; (2) Face validity: the extent to which the measure makes sense; and (3) Predictive validity: the extent to which the measure is able to predict outcomes of interest. One of the comparative reviews for comorbidity indices in cancer studies identified eight indices, which scored moderately well on the above‐mentioned validity criteria [14]. These included CCI [24], Satariano [16], Elixhauser [29] and Tammemagi [80] approaches, Fleming’s CPI [81], NCI Comorbidity Index [47], Alcohol‐Tobacco Related Comorbidities Index [82], and the WUHNCI [83]; all had administrative as the primary or secondary data source. Of these eight indices, three (CCI, Elixhauser approach, and NCI Index) demonstrated usefulness in relation to non‐site specific cancer [14]. Amongst the primarily chart‐based indices, ACE‐27 scored high on all criteria [14]. In a comparison of general versus disease‐specific claims‐based indices [35], of which two, the CCI and the NCI Index, are general indices and two, WUHNCI and the Head and Neck Cancer (HNCA) Index are disease‐specific, no apparent advantage of a disease‐ specific index over the general was observed. The authors proposed that general versus disease‐specific comorbidity indices may not differ in their predictive validities for overall survival.
ANCILLARY MEASURES OF POSTOPERATIVE OUTCOMES Comorbidity indices help in accurate interpretation of observational studies by stratifying patients into groups with similar risk for certain outcomes; however, the incorporation of comorbidity assessment in clinical practice is currently limited. For patients undergoing major surgical intervention for cancer or other conditions, a preoperative subjective estimate of organ system diseases by the American Society of Anesthesiology (ASA) score is generally used to predict postoperative risk and mortality [84,85]. However, the ASA score is not deemed to accurately reflect a patient’s physiologic reserve, and there is a growing interest instead, in the assessment of “frailty” [84,86,87]. Frailty is identified through the assessment of five criteria: weight loss, gait speed, grip strength, physical activity, and physical exhaustion [84,88]. It is shown to be useful in the prediction of postoperative complications and mortality in comorbid and/or elderly patients undergoing major surgical intervention for gynecologic and colorectal malignancy [86,89]. The domain of frailty often overlaps with comorbidity [90] but may actually complement comorbidity measures to weigh the risks and benefits of surgery, and facilitate patient counseling and decision‐making in surgical oncology illnesses. Furthermore, preoperative patient‐reported outcomes of function and QOL have also been demonstrated to carry a predictive value for the postoperative outcomes of both short‐term mortality and long‐term survival after surgery in patients with gastrointestinal, gynecologic and genitourinary malignancies [91,92]. Studies suggest that incorporation of QOL data in the preoperative assessment may improve postoperative outcomes by identifying patients who can benefit from timely interventions to promote their general well‐being [91,92]. In comparison to the comorbidities information gleaned through chart reviews or administrative‐based data, QOL assessment may be methodologically difficult due to suboptimal compliance in completion of the questionnaires. It may also be significantly influenced by patient factors such attitude and self‐perceptions. Assessment of patient‐centered outcomes is also fraught with the problem of selecting the appropriate instrument, since a variety of instruments and QOL domains are documented across different studies.
CONCLUSION A multitude of studies and clinical judgment confirm the importance and impact of concurrent medical conditions or comorbidities at the time of cancer diagnosis on treatment selection and outcomes. In surgical Journal of Surgical Oncology
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oncology, comorbidities especially impact treatment decisions, postoperative complications, quality of life, survival, and prognosis. Three primary approaches are available for the measurement of the severity of concomitant medical conditions: self‐report‐based, chart‐based, and claims‐based. With a variety of measures available, investigators and clinicians may find selection of an appropriate measure to be challenging. It is recommended to use the approach for which the data are available, and which will most suitably answer the research hypothesis. The ACE‐27 and CCI indices and their modifications are the most widely applied measures of comorbidity in cancer surgery. Accurate evaluationof comorbidities and incorporation in cancer registries will improve the classification of the cancer patient and conduct of clinical research. Improvement in clinical research will improve the care of cancer patients through better assessment of treatment effectiveness, patient counseling, and treatment planning, including informed management decisions and considerations of likely postoperative outcomes.
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Assessment of Comorbidities in Surgical Oncology Outcomes PARUL SINHA, MBBS, MS, DORINA KALLOGJERI, MD, MPH, AND JAY F. PICCIRILLO,
MD FACS CPI
Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri
The impact of comorbidities on treatment, postoperative complications, survival, prognosis, and quality of life, makes comorbidities’ assessment essential in surgically managed cancer patients. Multiple indices exist to measure the presence and severity of comorbidities, of which the Charlson Comorbidity Index and the Adult Comorbidity Evaluation‐27 are the most widely employed. Incorporation of comorbidity data in cancer registries facilitates surgical oncology research conduct with an objective to improve cancer care through better‐informed patient counseling and treatment planning.
J. Surg. Oncol. 2014;110:629–635. ß 2014 Wiley Periodicals, Inc.
KEY WORDS: comorbidity; surgical outcomes; cancer; survival; comorbidity assessment
INTRODUCTION As defined by Alvan Feinstein in 1970, comorbidity refers to the presence of “a distinct additional clinical entity” that has existed or may occur during the clinical course of a patient with an index disease [1,2]. Comorbidities, single or multiple, often coexist in cancer patients at the time of their principal diagnosis and can affect the timing of the diagnosis (diagnostic comorbidity), selection of treatment (therapeutic comorbidity), or prognosis (prognostic comorbidity). The prevalence of most cancers and comorbid ailments increase with age [3–6]. Several studies [6–9], including the most recent 2007–2009 [7] estimates obtained from Surveillance, Epidemiology, and End Results (SEER) registries, demonstrate that the probability of developing a new cancer increases with age in both males and females, and is highest in individuals aged 70 years or more. Numerous studies now document the significant independent impact of comorbidities on the treatment choice [10–12], prognosis [13–16], and quality of life [17,18] for cancer patients, regardless of the tumor stage. In patients undergoing surgical management for cancer, comorbidities tend to presage the tolerance for a particular procedure, influence decisions for the extent of resection, and use of adjuvant therapy [19,20]. Comorbidities are also associated with increased postoperative morbidity and mortality, hospital stay, readmission, and treatment costs [10,21–23]. While comorbidities receive consideration in clinical practice for the management of individual cancer patients, less attention has been given to the impact of comorbidity among clinical trials and oncologic studies evaluating outcomes and quality of care for cancer patients. Over the past few years, several measures for the collection of comorbid health information have been proposed. Some measures include comorbid ailments that are relevant to general adult patients [24–26] and some are specific to certain disease conditions [16,26,27]. In this review, the measures for assessment of comorbidities and their application in evaluation of surgical oncology outcomes is discussed.
MEASURES OF COMORBIDITY ASSESSMENT A comorbidity index identifies the important coexistent medical conditions and their severity. Since patients often have multiple medical conditions, an overall score, which represents the burden of the coexistent conditions, is calculated. This overall score facilitates comparison among patients [28]. Multiple valid comorbidity indices exist to quantify the presence and role of comorbidities in cancer patients
ß 2014 Wiley Periodicals, Inc.
[14,24,25,29]. The comorbidity information can be procured from different data sources: (1) self‐report, (2) charts or medical records, or (3) the administrative claims or financial databases that contain diagnoses and procedures using the International Classification of Diseases, Ninth or Tenth Revision, Clinical Modification (ICD‐9‐CM, ICD‐10‐CM) and Current Procedural Terminology (CPT) coding system. In Table I, a summary of the different indices, which have been used to assess comorbidity in cancer studies, is provided. The most commonly used indices from Table I are discussed below under the three main categories of comorbidity instruments.
Self‐Report‐Based Approach In this method, the patient is the primary source of the comorbid health information [26,30–34]. The comorbidity information is recorded at the time of the cancer diagnosis. The data are provided by the patient and is recorded by the physician or another health care professional.
Chart‐ or Medical Record‐Based Approach In this approach, the comorbidity information is collected through a review of medical charts by the medical staff or cancer registrars [25,35,36]. To ensure standardized and accurate data collection, the cancer registrars and medical staff are trained and validated to review the medical record and identify the cogent comorbidities in a time‐efficient and valid fashion. The Kaplan–Feinstein Index (KFI) [27] was created from the study of patients diagnosed with diabetes mellitus. Using this index, disease conditions are identified from the records and severity is classified as Mild, Moderate, or Severe according to the degree of organ decompensation. The overall comorbidity score is determined by the highest level of organ decompensation. In a situation where two comorbidities from two different organ systems are graded as Moderate, the overall score is Severe. The KFI
*Correspondence to: Jay F. Piccirillo MD FACS CPI, Department of Otolaryngology‐Head and Neck Surgery, 660 S. Euclid Avenue, Campus Box 8115, St. Louis, MO 63110. Fax: þ1‐314‐362‐7522. E‐mail: [email protected] Received 5 April 2014; Accepted 11 June 2014 DOI 10.1002/jso.23723 Published online 6 August 2014 in Wiley Online Library (wileyonlinelibrary.com).
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TABLE I. Measures for Assessment of Comorbidities Used in Cancer Studies
Index
Author et al. (year)
Primary (alternative) data source
General or disease‐specific
Number of items measured
Population (sample size)
Outcome(s) of interest Mortality Physical impairment measurement 5‐yr survival
TIBI‐CaP [93] CIRS [15,41]
Litwin, 2007 Linn, 1968
Self‐report Chart
Disease‐specific General
11 conditions 13 systems
Prostate cancer (2894) Not Reported
KFI [27]
Kaplan & Feinstein, 1974 Charlson, 1987 Greenfield, 1993
Chart
Disease‐specific
12 systems
Adult diabetes (188)
Chart (administrative) Chart
General General
19 conditions 14 systems
General medical patients (608) Patients undergoing total hip replacement (356)
Satariano & Ragland, 1994 Yancik, 1996
Chart (administrative)
Disease‐specific
7 conditions
Breast cancer (936)
Chart
General
24 conditions
Cancer (7600)
ASA [95] WUHNCI [83] ACE‐27 [13] SCI [96] Elixhauser [29]
Reid, 2001 Piccirillo, 2002 Piccirillo, 2004 Colinet, 2005 Elixhauser, 1998
Chart Chart (administrative) Chart (administrative) Chart Administrative
General Disease‐specific General Disease‐specific General
Not Reported 7 conditions 27 conditions 7 conditions 30 conditions
Flemings’ CPI [81] NCI comorbidity index [47] ATC [82] index HNCA [82] Tammemagi [80,97]
Fleming, 1999 Klabunde, 2000
Administrative Administrative
Disease‐specific General
34 conditions 12 conditions
Reid, 2002 Reid, 2002 Tammemagi, 2003 & 2005
Administrative Administrative Administrative
Disease‐specific Disease‐specific Disease‐specific
MACSS [98]
Holman, 2005
Administrative
General
11 conditions 8 conditions 19 conditions for lung, 77 conditions for breast 102 conditions
Surgical patients Head & neck cancer (1094) Cancer (19,268) Lung cancer (735) Adult acute care hospital (1,779,167) Breast cancer (848) Prostate (28,868), Breast (14,943) cancer Head & neck cancer (9386) Head & neck cancer (9386) Lung (1155), Breast (905)
New NCI combined index [99]
Klabunde, 2007
Administrative
General
13 conditions
CCI [24] ICED [26]
Satariano [16] NIA/NCI Collaborative study [94]
Hospital: medical (asthma, acute MI), procedural (surgery for breast cancer, prostate hypertrophy), & psychiatric patients (1,069,770) Breast (26,377), Prostate (53,503), Colorectal (26,460), Lung (33,975)
1‐yr survival Post‐op complications, 1‐ yr quality of life, physical functioning 3‐yr survival Comorbidity assessment in older cancer patients Acute operative risk 5‐yr mortality Survival Overall survival Mortality, length of stay, hospital charges 1‐yr survival 2‐yr non cancer mortality All‐cause mortality All‐cause mortality Survival (Lung) [79], All‐ cause & breast‐cancer specific survival [94] 1‐yr mortality, 30‐day readmissions, length of stay
2‐yr non cancer mortality
TIBI‐CaP, Total Illness Burden Index for Prostate Cancer; CIRS, Cumulative Illness Rating Scale; KFI, Kaplan–Feinstein Index; CCI, Charlson Comorbidity Index; ICED, Index of Coexistent Disease; NIA/NCI, National Institute on Aging/National Cancer Institute; ASA, American Society of Anesthesiology; WUHNCI, Washington University Head and Neck Comorbidity Index; ACE‐27, Adult Comorbidity Evaluation‐27; SCI, Simplified Comorbidity Index; CPI, Comprehensive Prognostic Index; ATC, Alcohol‐Tobacco‐Related Comorbidities Index; HNCA, Head and Neck Cancer Index; MACSS, Multipurpose Australian Comorbidity Scoring System.
has been used in several cancer‐related studies to classify comorbidities [37–39]. A modification of KFI resulted in formation of the Adult Comorbidity Evaluation‐27 (ACE‐27) [13,25,36], the most widely used and validated comorbidity instrument for cancer patients. The ACE‐27 added clinical entities, such as diabetes, AIDS, and dementia, which were not included in KFI. The severity grading of each comorbid ailment included in ACE‐27 provides sufficient information for valid prognostic estimates of overall survival of cancer patients [40]. An on‐line comorbidity education program [http://otooutcomes.wustl.edu/Research/ ResearchFocus/Cancer/ComorbidityDataCollection.aspx] intended to train cancer registrars and other health care professionals in the valid use of ACE‐ 27 index and an on‐line comorbidity calculator [http://oto.wustl.edu/ comorbiditycalculator.html] developed through education grants from the National Cancer Institute (NCI) are available. The Charlson Comorbidity Index (CCI) [24] was developed from the study of patients admitted to a general medical unit. It is a weighted index that takes into account the number of comorbid ailments. The weights are calculated using adjusted relative risk for each of the clinically significant comorbid conditions. The scoring system assigns weights of 1, 2, 3, and 6 for each condition and the total score, which is the sum of each individual condition score, determines a patient’s prognostic status. In CCI, of all 19 listed comorbid ailments, severity is accounted only for diabetes, liver and renal disease. Journal of Surgical Oncology
The Index of Coexistent Disease (ICED) [26] was developed as a modified version of an earlier comorbidity instrument [22,23] to measure the severity of comorbidity and its impact on postoperative stay, function, and quality of life. ICED assesses the patient’s status in two separate domains: physiological and functional burden. The severity of dysfunction derived from both domains determines the overall score on a 4‐point scale, indicating 1, No comorbid disease or asymptomatic; 2, Controlled, but mildly symptomatic; 3, Uncontrolled and severely symptomatic; and 4, Life‐threatening disease. The Cumulative Illness Rating Scale (CIRS) [41] rates 13 organ systems according to the severity of dysfunction based on medical records on a scale from 0 to 4, where 0 indicates no dysfunction and four indicates extremely severe dysfunction. CIRS has been used to measure the impact of comorbidities on survival in cancer patients in general [42] and for specific sub‐sites, such as prostate and colon cancer [43,44].
Administrative‐ or Claims‐Based approach In this approach, the primary source of comorbidity data is the primary and secondary diagnosis code fields, as defined by the ICD and CPT codes. A number of indices have been employed by different studies to report comorbidity from administrative or claims data. The
Comorbidity in Surgical Oncology measure by Satariano and Ragland [16], initially developed as a chart‐ based review instrument, was later adapted for use with administrative data. The chart‐based CCI was also modified for use with administrative data by Deyo et al. [45] and Romano et al. [46] (Dartmouth‐Manitoba). These adaptations added ICD‐9 codes to the comorbid conditions in the CCI. The codes assigned by Deyo adaptation represent a stricter interpretation of the original Charlson’s comorbidity definitions [45]. The Dartmouth‐Manitoba/Romano adaptation included broader definitions for conceptually similar conditions that were not explicitly mentioned in the CCI, particularly for the conditions of peripheral vascular disease, cerebrovascular disease, renal disease, and malignancy [46]. The CCI, with these adaptations, was successfully used to predict mortality and other surgical outcomes, such as postoperative complications, length of hospital stay, and inpatient costs. The CCI has also been used as the basis for developing another administrative‐ based instrument, the NCI Comorbidity Index [47], which uses a set of clinical conditions from the inpatient and outpatient administrative records. An administrative claim‐based ACE‐27 measure has also been recently proposed [48]. Based on a cross‐sectional study of 4,300 breast and prostate cancer patients from the “Centers for Disease Control and Prevention Patterns of Care Study,” Fleming et al. [48] found the administrative‐based ACE‐27 to be in fair to moderate agreement with the chart‐based ACE‐27 index. They concluded that ACE‐27 score derived from administrative claims data is a good alternative to chart‐ based ACE‐27 to evaluate the impact of comorbidity on cancer outcomes, when chart‐based review is logistically impossible [48]. Elixhauser et al. [29] identified 30 pre‐existing conditions with a significant impact on short‐term outcomes using administrative data. Their index was used for comorbidity assessment in cancer of the breast, prostate, cervix, and colon [14]. Other measures of comorbidity based on claims data (not listed in Table I) include “simple count of the number of unique diagnoses” and the Chronic Disease Score (CDS) [49]. Melfi et al. [50] reported in an analysis of Medicare claims data that a simple count of the number of unique diagnosis codes listed on the discharge summary was predictive of hospital stay and 30‐day mortality. The CDS uses a pharmacy‐based measure of chronic disease status in which a score is calculated by adding the scores assigned for each class of medications from data on prescription drug use [49].
IMPORTANCE OF COMORBIDITY IN SURGICAL ONCOLOGY Surgery is a mainstay of treatment for solid tumors arising from sites such as the head and neck, breast, lung, urogenital, and gastrointestinal tract. The presence of comorbid illnesses not only impacts the decision to perform surgery but also the key outcomes after surgery, which include postoperative complications, perioperative mortality, duration of hospitalization, adjuvant treatment decision, quality of life, and survival status. The impact of comorbidity on the outcomes in surgical oncology is discussed below.
Impact on Primary Treatment Selection and Adjuvant Therapy Decisions Comorbid conditions that affect the treatment decision‐making process of cancer patients are referred to as therapeutic comorbidities. Particular comorbid ailments, such as chronic pulmonary disease, may impact the use of a particular surgical approach, such as a partial laryngectomy, since the anticipated complications of the surgery may be prohibitively high in patients with pulmonary disease. After primary treatment with surgery, comorbidities can also influence the choice of adjuvant treatment with chemotherapy and or radiation therapy. A bias towards non‐surgical treatment for cancer patients with higher levels of comorbidity severity was observed in studies involving patients with different cancers [51–54]. For example, Connor et al. [55] observed that Journal of Surgical Oncology
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among patients with Stage III laryngeal cancer, those with more comorbid ailments were more likely to receive non‐surgical treatment. McCulloch et al. [20] observed the same relationship between increased severity of comorbidity and increased use of non‐surgical treatments among patient with gastric and esophageal cancer patients, and Tetsche et al. [19] observed similar findings among patients with ovarian cancer. This relationship between increased severity of comorbidity and non‐ surgical treatment is not found in all studies. For example, Maas et al. [56] found no association of high comorbidities with non‐surgical treatment in ovarian cancer, nor did a study of 358 patients with cancers of the oral cavity, breast, gastrointestinal tract, and urogenital tract by Chaudhary et al. [10] The latter study [10] however reported delays in administration or administration of less toxic treatments due to comorbidities than what was standard in at least 29% of the patients requiring adjuvant therapy [10]. Failure of patients to receive the standard adjuvant treatment was also observed in ovarian [19], colon [57,58], and breast cancer [59].
Impact on Postoperative Complications and Duration of Hospitalization An increase in the rate of postoperative complications and duration of hospital stay after major surgery in patients with higher comorbidities is well‐recognized. In a study using data from Nationwide Inpatient Sample, Genther and Gourin [60] graded comorbidity using the Romano adaptation of the Charlson comorbidity index in 61,740 surgically treated elderly patients with head and neck cancer [60]. Advanced comorbidity was present in 18% of the patients and was significantly associated with acute medical complications (odds ratio 3.7), in‐hospital mortality (odds ratio 3.6), increased length of hospitalization, and hospital‐related costs [60]. An increased odds ratio between 3.9 and 4.6 was also reported for postoperative complications, increased complication severity, and increased hospital stay for comorbidity assessed by CCI [61] and ACE‐27 [17] in head and neck cancer patients following microvascular reconstruction. A two‐ to fourfold higher 30‐ day postoperative mortality was observed in colon cancer patients with comorbidity compared to patients without comorbidity [2]. Using the CCI index in 4,171 patients with lobectomy for Stage I lung cancer, Rueth et al. [62] observed an increased incidence of postoperative complications (odds ratio of 1.38 for a CCI score of 1 and 1.83 for a CCI score of 2) in patients with comorbidities. The impact of comorbidities on postoperative complications has also been investigated in patients with renal tumors. In a study of 1,092 patients undergoing partial or radical nephrectomy for localized renal tumors, Tomaszewski et al. [63] observed a 1.9 times increased odds of incurring any complications in patients >75 years old and a CCI score of >2 compared with patients without these criteria.
Impact on Quality of Life Studies evaluating the effect of comorbidity on the quality of life in surgically treated cancer patients are scarce. In head and neck cancer, Terrell et al. [64] and Borggreven et al. [65] reported diminished quality of life in patients having preoperative comorbidities, which were either self‐reported or were chart‐based; a standard index was not applied for grading the severity in these studies. In prostate cancer, the impact of comorbid conditions on quality of life after radical prostatectomy has been investigated. In a study of 2,415 men with prostate cancer treated with radical prostatectomy, Karakiewicz et al. [34] reported a strong association of increased severity of comorbidities with decreased sexual and urinary function and general health‐related quality of life; data collection for comorbidity information was based on self‐report. In another multi‐institutional study of 691 patients with prostate cancer, the effect of baseline comorbidities as assessed by ICED was independently associated with poorer sexual function after radical prostatectomy but
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comorbidities were not associated with urinary incontinence or bowel functioning [18].
Impact on Survival Comorbid health conditions that impact on prognosis for cancer patients are referred to as Prognostic Comorbidities. Advanced comorbidity influences mortality for adult cancer patients in two ways. First, the direct effects of comorbid illness will impact survival. Second, the indirect effects of comorbid illness will impact a patient’s ability to tolerate cancer therapy and mount a successful host response. Cancer statistics are difficult to interpret with regards to the cause of death. Strict attribution of mortality to the index cancer or to the comorbid ailment(s) is difficult since the two may be inter‐related. Nevertheless, it is intuitive that deaths specific to cancer occur more commonly among patients with aggressive tumor types while deaths attributable to comorbid illness occur more frequently among patients with more indolent, slowly progressive types. Thus, the prognostic impact of comorbid illness is more important among patients with less aggressive tumors [66]. Studies on cancer of various sites, such as colon [51], breast [67], lung [38,54], cervix [68], and head and neck [13,69,70], have found patients with comorbidities to have poorer survival. Most of these studies comprise primarily surgically treated patients, but some have a mix of both surgically and non‐surgically treated patients. For example, in an analysis of 3,152 patients with non‐ small cell lung carcinoma who underwent surgical resection, Lüchtenborg et al. [54] observed severe comorbidity (CCI 3þ) to be independently associated with significantly higher death rates. They also observed the stage‐specific 5‐year survival to be significantly lower in patients with severe comorbidity than in patients without comorbid disease such as 38% for T1 tumors and CCI 3þ versus 69% for T1 tumors and CCI 0 [54]. Land et al. [71] found an association between comorbidity and cancer‐specific mortality in women with breast cancer. In 62,591 women diagnosed with localized breast cancer, the presence of comorbidity increased the risk of all‐cause mortality by a hazard ratio of 1.45 (CI 95% 1.40–1.51) for CCI 1, 1.52 (95% CI 1.45–1.60) for CCI 2, and 2.21 (95% CI 2.08–2.35) for CCI 3þ. Hazard ratios for breast cancer‐specific mortality were 1.30 (95% CI, 1.24–1.36) for CCI 1, 1.31 (95% CI 1.23–1.39) for CCI 2, and 1.79 (95% CI, 1.66–1.93) for CCI 3þ [71]. In a study on head and neck cancer treated with surgery (radiation) in 73% patients, the median disease‐free interval (11.1 vs. 21.6 months) and tumor‐specific survival (13.7 vs. 57.6 months) was found to be significantly lower for patients with advanced comorbidity, independent of the tumor stage [69]. The prognostic value of comorbidity may be diminished for cancer types which afflict mainly the younger and healthier population like the human papillomavirus (HPV)‐driven oropharynx cancer. In a study of 171 HPV‐positive oropharynx cancer patients, only 4% had severe comorbidities and the severity of comorbidity as assessed by ACE‐27 was not found to be prognostic for overall or disease‐specific survival [72].
Impact on Prognostication and Inclusion in Current Staging Systems Comorbidity has been demonstrated to have a prognostic impact on cancer‐related outcomes, regardless of the tumor stage and treatment‐ related factors. The physiologic burden of pre‐existing comorbid ailments can increase tumor aggressiveness by an unfavorable alteration of the host’s responses to cancer [13,15,69]. The presence of severe comorbidities can also modify treatment decisions, sometimes resulting in less optimal chances of disease control [13,15]. These explanations are postulated to account for the negative prognostication from comorbidities even on cancer‐specific outcomes. A statistical technique of “conjunctive consolidation” was described to incorporate comorbidity into the TNM classification for staging cancer [73,74]. This Journal of Surgical Oncology
technique has been used in the development of expanded staging systems for a variety of cancers and demonstrates improved prognostic precision over TNM classification alone.
COMPARISON OF COMORBIDITY ASSESSMENT MEASURES There are certain advantages and disadvantages to each of the comorbidity assessment approaches. The self‐report‐based approach provides information about the functional impact of the comorbidity, which cannot be provided by the other two approaches. However, the use of self‐report‐based comorbidity may be sub‐optimal due to a high possibility of under‐reporting, especially in the elderly. In head and neck cancer, Mukerji et al. [75] compared the self‐report to the comorbidity severity assessed with chart‐based ACE 27. Moderate level of consistency (k ¼ 0.50) was noted, with greater inconsistencies in elderly patients and those with severe comorbidities. Self‐report‐based approach is also not feasible to capture the comorbidities for the purpose of conducting retrospective studies. The chart‐based approach can retrospectively obtain information on comorbidities through access of the medical records. The data collected this way is shown to be very accurate. However, the collection of comorbidity information from the medical record requires the training of health care professionals so that information is collected in a valid and accurate manner. The administrative‐based approach has the advantage of allowing collection of information for a large number of patients over wide geographic areas in a less tedious and costly manner compared to the chart‐based approach, but lack of sensitivity to the severity of medical conditions decreases the clinical utility of this approach. In addition, the ICD codes undergo revisions, which may make it difficult to accurately compare trends over time. However, preliminary studies have shown that ICD‐10 version of the Charlson comorbidity [76] and Elixhauser [77] scores yields similar prevalence and prognosis information for in‐ hospital mortality in comparison with the ICD‐9‐CM coding algorithm [76,77]. Therefore, it has been suggested that comorbidity scores derived from ICD‐10 administrative data represent a valid approach to adjust for comorbidity [76,77]. Moreover, comparison of outcomes between different health systems may be difficult if the usage of ICD versions is not uniform. For instance, ICD‐9 is used in United States whereas ICD‐ 10 is already implemented in European nations and other countries such as Canada, Australia, China, etc., some of which have developed their own versions with addition of new codes. Biases related to misclassification of ailments are recognized as another disadvantage with administrative‐based approach. Misclassifications arise when complications from the treatment or disease progression are coded as comorbidities [78]. Another disadvantage is of incomplete or inaccurate data collections, when information for services not billed to the insurance system is not available [78] or when the information is not available for all patients in a tumor registry. And finally, lack of sensitivity to medical conditions is reflected by a significant majority of patients (65%), across different studies, being coded as having no comorbidities. Of the individual measures for comorbidity assessment enumerated in Table I, no particular index has been established as a single gold standard for application in cancer patients. The choice of measure is often guided by availability and accessibility of relevant data. Studies have compared the common indices for the criteria of validity, reliability, feasibility, and generalizability but these comparisons are not restricted to assessment of comorbidity in surgical oncology [14,79]. Hence, even though surgery was one of the therapeutic modalities in studies appraised by the available comparative reviews, the comorbidity indices were not compared exclusively for the surgically treated cohort. For comparison of comorbidity indices in cancer studies, the principle of validity relates to the following [14,28]: (1) Content validity: the extent and relevance of the content of all items to measure what is
Comorbidity in Surgical Oncology intended to measure; (2) Face validity: the extent to which the measure makes sense; and (3) Predictive validity: the extent to which the measure is able to predict outcomes of interest. One of the comparative reviews for comorbidity indices in cancer studies identified eight indices, which scored moderately well on the above‐mentioned validity criteria [14]. These included CCI [24], Satariano [16], Elixhauser [29] and Tammemagi [80] approaches, Fleming’s CPI [81], NCI Comorbidity Index [47], Alcohol‐Tobacco Related Comorbidities Index [82], and the WUHNCI [83]; all had administrative as the primary or secondary data source. Of these eight indices, three (CCI, Elixhauser approach, and NCI Index) demonstrated usefulness in relation to non‐site specific cancer [14]. Amongst the primarily chart‐based indices, ACE‐27 scored high on all criteria [14]. In a comparison of general versus disease‐specific claims‐based indices [35], of which two, the CCI and the NCI Index, are general indices and two, WUHNCI and the Head and Neck Cancer (HNCA) Index are disease‐specific, no apparent advantage of a disease‐ specific index over the general was observed. The authors proposed that general versus disease‐specific comorbidity indices may not differ in their predictive validities for overall survival.
ANCILLARY MEASURES OF POSTOPERATIVE OUTCOMES Comorbidity indices help in accurate interpretation of observational studies by stratifying patients into groups with similar risk for certain outcomes; however, the incorporation of comorbidity assessment in clinical practice is currently limited. For patients undergoing major surgical intervention for cancer or other conditions, a preoperative subjective estimate of organ system diseases by the American Society of Anesthesiology (ASA) score is generally used to predict postoperative risk and mortality [84,85]. However, the ASA score is not deemed to accurately reflect a patient’s physiologic reserve, and there is a growing interest instead, in the assessment of “frailty” [84,86,87]. Frailty is identified through the assessment of five criteria: weight loss, gait speed, grip strength, physical activity, and physical exhaustion [84,88]. It is shown to be useful in the prediction of postoperative complications and mortality in comorbid and/or elderly patients undergoing major surgical intervention for gynecologic and colorectal malignancy [86,89]. The domain of frailty often overlaps with comorbidity [90] but may actually complement comorbidity measures to weigh the risks and benefits of surgery, and facilitate patient counseling and decision‐making in surgical oncology illnesses. Furthermore, preoperative patient‐reported outcomes of function and QOL have also been demonstrated to carry a predictive value for the postoperative outcomes of both short‐term mortality and long‐term survival after surgery in patients with gastrointestinal, gynecologic and genitourinary malignancies [91,92]. Studies suggest that incorporation of QOL data in the preoperative assessment may improve postoperative outcomes by identifying patients who can benefit from timely interventions to promote their general well‐being [91,92]. In comparison to the comorbidities information gleaned through chart reviews or administrative‐based data, QOL assessment may be methodologically difficult due to suboptimal compliance in completion of the questionnaires. It may also be significantly influenced by patient factors such attitude and self‐perceptions. Assessment of patient‐centered outcomes is also fraught with the problem of selecting the appropriate instrument, since a variety of instruments and QOL domains are documented across different studies.
CONCLUSION A multitude of studies and clinical judgment confirm the importance and impact of concurrent medical conditions or comorbidities at the time of cancer diagnosis on treatment selection and outcomes. In surgical Journal of Surgical Oncology
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oncology, comorbidities especially impact treatment decisions, postoperative complications, quality of life, survival, and prognosis. Three primary approaches are available for the measurement of the severity of concomitant medical conditions: self‐report‐based, chart‐based, and claims‐based. With a variety of measures available, investigators and clinicians may find selection of an appropriate measure to be challenging. It is recommended to use the approach for which the data are available, and which will most suitably answer the research hypothesis. The ACE‐27 and CCI indices and their modifications are the most widely applied measures of comorbidity in cancer surgery. Accurate evaluationof comorbidities and incorporation in cancer registries will improve the classification of the cancer patient and conduct of clinical research. Improvement in clinical research will improve the care of cancer patients through better assessment of treatment effectiveness, patient counseling, and treatment planning, including informed management decisions and considerations of likely postoperative outcomes.
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