 |
|
Development of a New Head and Neck Cancer–Specific Comorbidity Index
Jay F. Piccirillo, MD;
Peter D. Lacy, MB, FRCSI;
Arindam Basu, MD, MPH;
Edward L. Spitznagel, PhD
Arch Otolaryngol Head Neck Surg. 2002;128:1172-1179.
ABSTRACT
| |
Background Most patients with head and neck squamous cell carcinoma are older and
may have coexistent or comorbid diseases.
Objectives To determine the prognostic impact of individual comorbid conditions
in patients with head and neck cancer, to combine the individual comorbid
conditions to form a new a head and neck–specific comorbidity instrument,
and to compare it with the Modified Kaplan-Feinstein Index to determine if
the new disease-specific instrument offers any improvement in survival prediction
over a general comorbidity index.
Design Retrospective review of medical records.
Population The study population comprised 1153 patients with biopsy-proven, newly
diagnosed squamous cell carcinoma of the oral cavity, oropharynx, or larynx.
Results Seven comorbid conditions (congestive heart disease, cardiac arrhythmia,
peripheral vascular disease, pulmonary disease, renal disease, cancer controlled,
and cancer uncontrolled) were significantly related to survival. These comorbid
conditions were assigned integer weights to reflect their relative prognostic
importance and combined to create the new Washington University Head and Neck
Comorbidity Index (WUHNCI). Survival was significantly related to levels of
comorbidity severity as defined by the WUHNCI. The WUHNCI predicted survival
better than the Modified Kaplan-Feinstein Index despite containing far fewer
ailments.
Conclusions Comorbidity is an important feature of the patient with head and neck
cancer. The WUHNCI can be used for retrospective review or prospective outcomes
research.
INTRODUCTION
MOST PATIENTS with head and neck squamous cell carcinoma are older and
many have coexistent, nonneoplastic (comorbid) diseases.1 These
conditions may be mild and may not affect survival rates. Examples of mild
comorbidity include hypertension that is controlled by medication or a diagnosis
of peptic ulcer disease. However, some comorbidities or combinations of comorbidities
may be so severe that they can affect survival rates. These comorbidities
are referred to as prognostic comorbidity2 and include recent myocardial infarction or ventricular
arrhythmia, severe hypertension, severe hepatic disease, and recent severe
stroke. These conditions may affect the selection of initial treatment and
patient outcome. For example, a patient may not be offered a supraglottic
laryngectomy because his or her preexisting lung disease is so severe that
the aspiration associated with a partial larynx may lead to life-threatening
postoperative pneumonia. Another patient who is "too sick" to tolerate a preferred
treatment may be given a less effective or even palliative treatment. In several
studies of cancer prognosis, the presence of comorbidity was found to dramatically
affect survival and the evaluation of treatment effectiveness, even after
controlling for TNM stage.2-6
A number of validated instruments have been developed to classify comorbidity.
The Kaplan-Feinstein Comorbidity Index (KFI)2 was
developed from a study of the impact of comorbidity on 5-year survival for
a cohort of male patients with diabetes mellitus. Another validated comorbidity
instrument is the Charlson Comorbidity Index (CCI).7 This
instrument was created from a study of patients admitted to a general medical
unit of a teaching hospital.
These 2 comorbid instruments were developed from the study of outcomes
for general medical patients, not patients with cancer. While various comorbid
ailments are common to all populations, the frequency distribution and the
relative prognostic impact of each condition to the primary disease process
may vary. Furthermore, within a cohort of patients with cancer, the relative
impact of individual comorbidities may vary across different cancer types.
For example, head and neck cancer is primarily a disease of the elderly, and
the pattern of comorbidities in such a patient population may be quite different
than that of patients with cervical cancer.
The purposes of the present study were to (1) determine the prognostic
impact of individual comorbid conditions in patients with head and neck cancer,
(2) combine the individual comorbid conditions to form a new a head and neck–specific
comorbidity instrument, and (3) compare it with the KFI to determine if the
new disease-specific instrument offers any improvement in prediction of survival
over a general comorbidity index.
PATIENTS AND METHODS
STUDY POPULATION
The study population comprised 1153 patients identified from the pathology
records of Barnes-Jewish Hospitals (St Louis, Mo), who had biopsy-proven,
newly diagnosed squamous cell carcinoma of the oral cavity, oropharynx, or
larynx and had received their initial treatment at Washington University Medical
Center (St Louis) between January 1, 1980, and December 31, 1991. Medical
records from the Washington University Department of Otolaryngology–Head
and Neck Surgery, the Mallinckrödt Institute of Radiology (St Louis),
and Barnes-Jewish Hospitals were used to collect baseline demographic and
clinical information, initial treatment, and follow-up data. Full 5-year follow-up
data were available for 1094 patients (95%) who composed the inception cohort.
COLLECTION OF DATA
Initial "zero time" was defined as the date of commencement of first
antineoplastic therapy to the primary tumor or the date the decision was made
not to treat a patient. If no such decision was made, the date of diagnosis
was used as zero time.
Information for each patient was recorded on a specially designed data
extraction form. Data collected before treatment or at zero time included
basic demographic information; a description of symptoms and classification
of symptoms into a symptom severity system; tobacco habits; medical history
with a detailed documentation of comorbid conditions; symptom type and duration;
presence of synchronous tumors; results of pertinent radiographic and laboratory
studies; pathologic description of the biopsy specimen; and complete anatomic
description of the tumor. Data collected after zero time included details
of initial therapy, complications, duration to last follow-up, survival duration,
and tumor status at last follow-up or death.
CLASSIFICATION OF DATA
To ensure scientific accuracy and high-quality data, specific techniques
applied in this study were devised and maintained in a coding handbook, which
was referenced during coding sessions. Three different coders (all physicians)
extracted the data with systematic interobserver consistency checks. Only
minor differences in coding were seen, and in general, these did not cause
migration of subjects between categories and were believed to be clinically
insignificant.
Symptom Severity Stage
Previous research demonstrated that cancer-related symptom severity
is a measure of the biological index of disease.8 In
particular, dysphagia, otalgia, neck lump, and weight loss were identified
as independent predictors of survival. Therefore, the presence of these 4
symptoms at zero time was noted, and each patient was classified into 1 of
4 symptom-severity stages (none, mild, moderate, or severe). Symptom-severity
stage none is defined by the absence of all 4 symptoms; mild, 1 of the 4 symptoms was recorded as present; moderate, 2 of the 4 symptoms were recorded as present;
or severe, 3 or 4 symptoms were recorded as present.
TNM Cancer Stage
The TNM classification assigned to the patient by the attending physician
at zero time was used when this was clearly defined in the medical record.
Otherwise, all information obtained prior to zero time, including clinical,
endoscopic, and radiographic findings, was used for classification based on
the 1992 American Joint Committee on Cancer criteria.9 If
more than 1 physician staged a tumor differently and the medical record lacked
sufficient anatomic data to identify which stage was correct, then the stage
assigned by the most senior physician was recorded. Occasionally, a patient's
TNM classification was changed from that which was recorded in the medical
record, but only when it was clear that the original TNM classification was
incorrect.
Histopathologic Grade
The grade of the primary tumor was recorded for all patients using the well, moderately, and poorly differentiated
grades of the Broder system.10 As undifferentiated implies that the tissue of origin is unknown, no tumors
of this histopathologic grade were included. The histopathologic grade was
recorded from the biopsy specimens. When 2 or more conflicting reports of
histopathologic grade were reported (eg, from multiple biopsy specimens),
the most anaplastic grade was recorded. If the grade was not recorded, it
was classified as well differentiated.
Classification of Comorbidity Based on Modified Kaplan-Feinstein Index
The Modified Kaplan-Feinstein Index (MKFI) was used to classify the
overall severity of comorbidity. The MKFI is based on the original KFI,2 which was first described in 1974. The MKFI has been
used in a variety of studies, including head and neck cancer.1 One
of us (J.F.P.), along with other health services researchers at Washington
University, modified the KFI to reflect additional diseases and conditions
not included in the original KFI. These diseases and conditions include dementia,
Parkinson disease, human immunodeficiency virus and acquired immunodeficiency
syndrome, peripheral vascular disease, and obesity. In addition, because the
original KFI was developed from the study of patients with diabetes mellitus,
this condition was not listed as a comorbid ailment. The terms "cancer controlled"
and "cancer uncontrolled" were defined as follows:
- Cancer controlled: in the case of solid tumors,
a cancer that has been treated and there is no evidence of residual disease
or recurrence; in the case of lymphoma and leukemia, cancer controlled implies
the above or the lymphoma or leukemia is present but indolent and the patient
is not currently receiving treatment.
- Cancer uncontrolled: the situation in which the
patient has a synchronous tumor at the time of diagnosis, received treatment
for a previous cancer but the tumor has persisted, or the patient's tumor
responded to treatment initially but there is recurrence of the previous cancer
at the time the index cancer is diagnosed.
The original KFI and the MKFI categorizes patients into 1 of 4 overall
comorbidity groups (none, mild, moderate, or severe) based on the existence
of specific diseases and conditions. Cogent individual comorbid ailments are
classified according to their severity of organ decompensation: grade 1, mild;
grade 2, moderate; or grade 3, severe. The overall comorbidity severity score
is defined by the grade of the highest ranked single ailment or, in the case
in which there are two grade 2 ailments in different organ systems, the score
is grade 3 (severe).
At the time of original chart abstraction, an MKFI score was determined
by a review of the comorbid ailments. This score was determined without reference
to survival or other important end points. The MKFI is available from the
Clinical Outcomes Research Office's Web site (http://oto.wustl.edu/clinepi).
Development of the WUHNCI: a New Disease-Specific Instrument
At the time of original medical record review, a special 132-item comorbidity
form was used to record the presence and severity of individual medical comorbidities.
Only conditions present at or before the time of diagnosis were recorded.
The comorbidity form and a full description of the coding criteria for these
specific comorbid conditions are available from one of us (J.F.P.).
The first step in the development of the WUHNCI was the identification
of common comorbid conditions among the 132 conditions listed on the comorbidity
form. Any condition affecting less than 1% of the cohort was considered too
uncommon and was excluded from further analysis. Next, the prognostic effect
of each of the common (ie, prevalence greater than 1%) comorbid conditions
was determined. A series of cross-tabulation tables of individual comorbid
conditions on 5-year survival was performed, and  2 analysis
was used to assess the statistical significance of the observed relationships.
The comorbid ailments that affected survival at a P value
of .1 or less were considered potential independently significant prognostic
variables. Variables that achieved this level of significance were entered
into a multivariable logistic regression model to determine which comorbid
conditions, when controlling for the other significant comorbidities, affected
survival. Those comorbidities that maintained independent prognostic significance
at a P value of .1 or less were then included in
the WUHNCI. Because each comorbid condition had a different impact on survival,
each condition was weighted in the final WUHNCI according to its prognostic
impact. The whole integer weighting for each comorbid condition was determined
by the magnitude of the parameter estimate from the multivariable logistic
regression model. The parameter estimate is a measure of how much change will
result in the dependent variable (eg, 5-year overall survival) with every
unit change in the independent variable (eg, specific comorbid ailment). The
WUHNCI comorbidity score is calculated as the sum of the weights of each of
the comorbid conditions that are present within the patient.
Initial Treatment
Data collected on each patient's initial and subsequent treatment(s)
included type of treatment (ie, radiotherapy, surgery, chemotherapy, or combination),
timing of radiotherapy (ie, preoperative or postoperative), and type of surgical
procedure. Postoperative radiotherapy was coded as a combined initial treatment
when the pretreatment decision to use combined therapy was explicit in the
medical record.
FOLLOW-UP AND OUTCOME
Follow-up information was obtained from medical records from the Department
of Otolaryngology–Head and Neck Surgery and the Mallinckrödt Institute
of Radiology (Washington University School of Medicine) and Barnes-Jewish
Hospital, Barnes Hospital Oncology Data Services, and the Equifax National
Death Search (Arlington, Va) service. When needed for individual patients,
additional information was obtained from other hospitals and consulting or
referring physicians. Follow-up was maintained until a patient's death was
documented or until the end of the study period (December 1, 1997). The primary
outcome measure was 5-year overall survival. Approval from the Human Studies
Committee of Washington University School of Medicine was obtained before
commencement of the study.
DATA ANALYSIS
The information contained on the data extraction forms was entered into
a Paradox database (Borland International, Scotts Valley, Calif), using a
data entry screen that was identical to the extraction form. Equipped with
internal validity checks, the specially designed screens facilitated reliable
and efficient data entry. Periodic review for internal consistency and comparison
of separate databases provided verification of the entered data. Sorting,
tabulation, and statistical analyses including the (2, bivariate
odds ratio and 95% confidence limits, and multivariate analysis were performed
using the SAS statistical analysis software system, release 6.12 (SAS Institute
Inc, Cary, NC).
The utility of the WUHNCI and comparison of its predictive ability with
the MKFI were assessed from 4 different logistic regression models. All models
contained age, sex, race, symptom stage, and TNM stage. In model 1, no comorbidity
measure was added. In model 2, the WUHNCI alone was added to the model. In
model 3, the MKFI alone was added. In model 4, both the WUHNCI and the MKFI
were entered into the model. For each of these models, the log-likelihood
ratio 2 test and the c-statistic
were used to compare the prognostic performance. Among pairs of patients in
which 1 patient lives and 1 dies, the c-statistic11 reflects the proportion in which a higher risk is
assigned to the patient who died than to the one who lived. The c-statistic is graphically represented as the area under the receiver-operating
characteristic curve.12-13 The
values for the c-statistic range from 0.5 (no discrimination)
to 1.0 (perfect discrimination). According to Ohman et al,14 a
predictive model with a c-statistic less than 0.6
has no clinical value, 0.6 to 0.7 has limited value, 0.7 to 0.8 has modest
value, and greater than 0.8 has discrimination adequate for genuine clinical
utility.
The validity of the methodology for identifying the cogent comorbid
factors and assignment of integer weights was assessed using a split-half
analysis approach. In this technique, the database was divided randomly into
2 groups. The cogent comorbid variables and integer weights were determined
from the patients contained within one half of the cohort. The model was then
tested in the other half of the cohort using the same multivariable analytic
techniques as described for the evaluation of the WUHNCI.
RESULTS
The cohort of 1094 patients had a mean ± SD (range) age of 62.1
± 11.2 (16-100) years. The median age was 62 years, mode age was 65,
and the 25% to 75% interquartile range was 55.0 to 69.8 years. There were
785 men (72%), and 918 (84%) were white. Mean ± SD survival was 68
± 56 months, median survival was 56 months, and the overall 5-year
survival rate was 47% (514 of 1094 patients).
Table 1 shows the relationship
between baseline features of the patient population and 5-year survival rates.
There was a statistically significant relationship between survival and age
group, race, symptom severity, TNM stage, and degree of histological differentiation.
Sex, history of cigarette smoking, and type of initial treatment did not significantly
affect survival.
|
|
|
|
Table 1. Description of Patient Population and 5-Year Survival Rates
|
|
|
In Table 2, the prevalence
of individual comorbid conditions is given. The 5 most common conditions (percentage
frequency) were pulmonary disease (17.9%), other cancer controlled (8.6%),
diabetes mellitus (7.9%), myocardial infarction (6.7%), and peptic ulcer disease
(5.2%). Interestingly, the frequency of hypertension and rheumatological and
psychiatric illness was very low. The comorbid conditions with a prevalence
of less than 1% were excluded from further analysis.
|
|
|
|
Table 2. Prevalence of Individual Comorbid Conditions
|
|
|
The impact of the common comorbid conditions on survival is demonstrated
in Table 3. The following 7 comorbid
conditions were significantly related to survival: congestive heart disease,
cardiac arrhythmia, peripheral vascular disease, pulmonary disease, renal
disease, other cancer controlled, and other cancer uncontrolled. Survival
was related to myocardial infarction, although this effect did not achieve
statistical significance (P = .08). Because multiple
comorbid variables affected survival, the next step was to perform a multivariable
analysis.
|
|
|
|
Table 3. Impact of Individual Comorbid Ailments on 5-Year Survival
|
|
|
Table 4 demonstrates the
results of multivariable analysis of the impact of the 8 comorbid conditions
on survival. As can be seen by the P value and 95%
confidence limits around the odds ratio, congestive heart failure, cardiac
arrhythmia, peripheral vascular disease, pulmonary disease, renal disease,
other cancer uncontrolled, and renal disease were all significantly associated
with survival. Other cancer controlled approached significance (P = .07) and was retained in the model building. Myocardial infarction
was not significant (P = .5) and was dropped from
further analysis.
|
|
|
|
Table 4. Multivariable Logistic Regression Analysis of Comorbid Factors
|
|
|
The prognostic importance of each of the 7 significant comorbid conditions
is demonstrated by the parameter estimate. The parameter estimates for the
2 comorbid conditions with the weakness impact (pulmonary disease [0.396]
and other cancer controlled [0.442]) were selected as the baseline value for
the determination of the whole integer weights for all 7 conditions. Pulmonary
disease and other cancer controlled were both assigned the integer weight
of 1. Whole integer weights were then assigned to the other 5 comorbid conditions
based on the relationship of the parameter estimates. For example, the parameter
estimate for congestive heart disease was 1.064 and a whole integer value
of 2 was selected as the weight for this comorbid condition because 1.064
is approximately twice as large as 0.396 (the parameter estimate of pulmonary
disease) and 0.442 (the parameter estimate of other cancer controlled).
In Table 5, the WUHNCI score
is given with the whole integer weights for all 7 comorbid conditions. In Table 6, the prognostic impact of each
of the values of the WUHNCI and the 4-category consolidated index are given.
As demonstrated by the 2 statistic, there is a strong prognostic
impact of comorbidity. As the level of comorbidity increases, the survival
rate decreases. This relationship is further demonstrated in Figure 1.
|
|
|
|
Table 5. Washington University Head and Neck Comorbidity Index*
|
|
|
|
|
|
|
Table 6. Relationship Between Washington University Head and Neck Comorbidity
Index and 5-Year Survival*
|
|
|
|
|
|
|
Survival curves for 1094 patients according to severity of comorbidity
(log rank = 77.17; P<.001). WUHNCI indicates Washington
University Head and Neck Comorbidity Index.
|
|
|
The prognostic impact of the newly created WUHNCI was compared in a
multivariable logistic regression model with the other significant demographic,
clinical, and tumor factors. As shown in Table 7, all demographic, clinical, and tumor factors were significantly
related to 5-year survival.
|
|
|
|
Table 7. Multivariable Analysis of Factors Affecting 5-Year Survival
|
|
|
The c-statistic for the overall model, including
the WUHNCI, was 0.754. This compares with a c-statistic
of 0.756 for a model that contained the same demographic, clinical, and tumor
factors, but included the significant comorbid ailments as individual conditions
rather than being weighted and combined as was done to create the WUHNCI.
Because the c-statistic for the WUHNCI is very close
to the c-statistic for the unrounded multivariate
model, replacing parameter estimate values with rounded integer weights captures
nearly all the prognostic information of the comorbid conditions individually.
The performance of the WUHNCI was compared with that of the MKFI. In Table 8, the performance of 4 models is
given. For all 4 models, age, sex, race, symptom severity stage, and TNM stage
were included. For model 1, no comorbid factors were added. For model 2, the
WUHNCI was added. For model 3, the MKFI was added, and for model 4, both the
WUHNCI and MKFI were added. As shown in Table 8, either the WUHNCI or the MKFI adds significantly to the
predictive power of a model that does not contain a comorbidity factor. The
likelihood ratio and c-statistic shows that the WUHNCI
performs significantly better than the MKFI, since omitting the MKFI from
the model containing both terms doesn't drop the likelihood ratio by a statistically
significant amount or change the c-statistic. Whereas,
omitting the WUHNCI from the model that contains both terms does drop the
likelihood ratio 2 by a significant amount and changes the c-statistic.
|
|
|
|
Table 8. Comparison of the Predictive Ability of 4 Different Multivariable
Models
|
|
|
The split-half analysis identified the same 7 cogent comorbid factors
as the original research, although the integer weights were slightly different
(congestive heart failure [1], cardiac arrhythmia [3], peripheral vascular
disease [4], pulmonary disease [1], renal disease [4], other cancer controlled
[1], and other cancer uncontrolled [1]). The predictive model containing age,
sex, race, symptom severity, TNM stage, and the new comorbidity variable,
as tested in the other half of the cohort, performed quite well (likelihood
ratio 2, 116.514; c-statistic, 0.766).
The new comorbidity variable performed better than the MKFI.
COMMENT
Patients with head and neck cancers often have other diseases, illnesses,
and conditions in addition to their index tumor. These other conditions are
often referred to as comorbidities. The present study identifies the 7 important
prognostic comorbidities for patients with head and neck cancer. These comorbidities
are congestive heart failure, cardiac arrhythmia, peripheral vascular disease,
pulmonary disease, renal disease, previous history of cancer now controlled,
and previous history of cancer now uncontrolled. This study also describes
the creation of a disease-specific composite comorbidity index, the WUHNCI.
This index was found to predict survival better than the MKFI despite containing
far fewer comorbid ailments.
General comorbidity instruments were developed and are intended to be
used across a wide range of clinical conditions. However, disease-specific
comorbidity instruments are intended for an individual ailment or cluster
of closely related ailments. Despite their apparent lack of specificity, general
instruments perform well as predictors of important outcomes for specific
diseases. For example, Singh et al15 found
that the severity of comorbidity, as defined by the CCI,7 was
significantly related to 5-year survival for a cohort of patients with head
and neck cancer.
However, some investigators have found that disease-specific instruments
perform better in specific disease conditions than a general instrument. For
example, Polanczyk and colleagues16 recently
reported that a disease-specific index performed better than CCI in predicting
in-hospital mortality for patients with congestive cardiac failure. In another
study, Fleming et al17 indicated that the weights
assigned to comorbid ailments identified within their own data set were different
from the CCI. He concluded from this that the impact of comorbidities on survival
could be disease specific. And finally, Ghali and coworkers18 found
in a study of coronary artery bypass patients in Massachusetts that using
weights derived from the study with CCI-defined comorbid conditions substantially
improved the ability of the model to predict mortality compared with the CCI.
These 3 studies suggest that disease-specific measures may perform better
than a general comorbidity measure. However, there are at least 2 methodological
problems with these studies that undermine the conclusion that disease-specific
instruments will necessarily do better. First, all 3 studies were based on
secondary data analyses of administrative and financial databases. Charlson
used primary data to develop and validate the CCI, and it is possible that
the CCI may not work as well with secondary data as with primary data. Second,
the calibration and testing of the disease-specific comorbidity indexes was
done on the same data set. This resulted in a higher 2 estimate
and c-statistic than could possibly be obtained from
iterations in which the instruments were developed and tested in separate
populations. For this same reason, the WUHNCI must be tested prospectively
in a separate cohort of patients with head and neck cancer, ideally not from
Barnes-Jewish Hospital.11 Only through testing
in a separate population can the validity of the WUHNCI be scientifically
assessed.
It is interesting to note that alcohol-related conditions were not identified
as a significant comorbid factor in the present study. Clearly, alcohol use
and abuse are important features in the development and prognosis of head
and neck cancer. Deleyiannis et al19 noted
that alcoholism and a history of alcohol-related systemic health problems
were associated with an increased risk of death among a cohort of 649 patients
with head and neck cancer. In that study, a detailed alcohol history was obtained
by trained clinical research interviewers as part of another National Cancer
Institute–sponsored research project. The association between alcohol
use and mortality was independent of age, site of cancer, anatomic stage,
histopathologic grade, smoking, and type of antineoplastic treatment.
There are several possible reasons why alcohol-related conditions were
not found to be important in this project. First, the amount of alcohol consumed
and the implications of this consumption were all derived from a retrospective
review of medical records, not from in-person interviews. It is likely that
there was a fair degree of underreporting and misclassification of alcohol
use and abuse in the medical records. The classification of alcohol abuse
for this project required a fairly high level of alcohol use and abuse (a
patient experienced 1 or more episodes of delirium tremens or seizure, recurrent
episodes or hospitalizations for alcohol-associated ailments, or nutritionally
caused cachexia or anemia). Despite the failure to include alcohol-related
conditions in the final comorbidity model, we recognize alcohol use and abuse
as important factors for many patients with head and neck cancer.
The WUHNCI can be used for both the retrospective review of the medical
records of patients with head and neck cancer or as part of a prospective
outcomes research project. In addition, the WUHNCI can be used with administrative
data sets, such as hospital discharge records or the Surveillance, Epidemiology,
and End Results (SEER)-Medicare linked database. To be used with administrative
data sets, the International Classification of Diseases,
Ninth Revision codes for each of the 7 comorbid ailments that are part
of the WUHNCI must be specified (available from the authors). Overall comorbidity
is then determined from an analysis of the primary and secondary diagnoses
represented on the hospital discharge "face sheet." Because comorbidity is
an important aspect of the patient with cancer and the assessment of the quality
of cancer, measures of comorbidity should be included in all ongoing outcomes
studies. Since there are general and disease-specific comorbidity indexes
available, the continued exclusion of comorbidity adjustment from clinical
research can no longer be justified.
AUTHOR INFORMATION
Accepted for publication April 22, 2002.
This research was supported in part by the American Cancer Society Junior
Clinical Research Award (JCRA-1) and the National Cancer Institute, Bethesda,
Md (R01 CA62072).
Corresponding author and reprints: Jay F. Piccirillo, MD, Department
of Otolaryngology–Head and Neck Surgery, Washington University School
of Medicine, Box 8115, 660 S Euclid Ave, St Louis, MO 63110 (e-mail: piccirij{at}msnotes.wustl.edu).
From the Clinical Outcomes Research Office, Departments of Otolaryngology–Head
and Neck Surgery (Drs Piccirillo, Lacy, and Basu) and Mathematics (Dr Spitznagel),
Washington University School of Medicine, St Louis, Mo.
REFERENCES
| |
1. Piccirillo JF. Importance of comorbidity in head and neck cancer. Laryngoscope. 2000;110:593-602.
FULL TEXT
|
WEB OF SCIENCE
| PUBMED
2. Kaplan MH, Feinstein AR. The importance of classifying initial co-morbidity in evaluating the
outcome of diabetes mellitus. J Chronic Dis. 1974;27:387-404.
FULL TEXT
|
WEB OF SCIENCE
| PUBMED
3. Piccirillo JF, Wells CK, Sasaki CT, Feinstein AR. New clinical severity staging system for cancer of the larynx: five-year
survival rates. Ann Otol Rhinol Laryngol. 1994;103:83-92.
WEB OF SCIENCE
| PUBMED
4. Feinstein AR, Wells CK. A clinical-severity staging system for patients with lung cancer. Medicine (Baltimore). 1990;69:1-33.
PUBMED
5. Feinstein AR, Schimpff CR, Andrews JF, Wells CK. Cancer of the larynx: a new staging system and a re-appraisal of prognosis
and treatment. J Chronic Dis. 1977;30:277-305.
FULL TEXT
|
WEB OF SCIENCE
| PUBMED
6. Greenfield S, Aronow HU, Elashoff RM, Watanbe D. Flaws in mortality data: the hazards of ignoring comorbid disease. JAMA. 1988;260:2253-2255.
FREE FULL TEXT
7. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal
studies: development and validation. J Chronic Dis. 1987;40:373-383.
FULL TEXT
|
WEB OF SCIENCE
| PUBMED
8. Pugliano FA, Piccirillo JF, Zequeira MR, Fredrickson JM, Perez CA, Simpson JR. Symptoms as an index of biologic behavior in head and neck cancer. Otolaryngol Head Neck Surg. 1999;120:380-386.
FULL TEXT
|
WEB OF SCIENCE
| PUBMED
9. American Joint Committee on Cancer. Manual for Staging of Cancer. Philadelphia, Pa: JB Lippincott; 1992.
10. Broder AC. The grading of carcinoma. Minn Med. 1925;8:726-730.
11. Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med. 1999;130:515-524.
FREE FULL TEXT
12. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic
(ROC) curve. Radiology. 1982;143:29-36.
FREE FULL TEXT
13. Harrell FE, Lee KL, Mark DB. Tutorials in biostatistics: multivariable prognostic models: issues
in developing models, evaluating assumptions and adequacy, and measuring and
reducing errors. Stat Med. 1996;15:361-387.
FULL TEXT
|
WEB OF SCIENCE
| PUBMED
14. Ohman EM, Granger CB, Harrington RA, Lee KL. Risk stratification and therapeutic decision making in acute coronary
syndromes. JAMA. 2000;284:876-878.
FREE FULL TEXT
15. Singh B, Bhaya M, Zimbler M, et al. Impact of comorbidity on outcome of young patients with head and neck
squamous cell carcinoma. Head Neck Surg. 1998;20:1-7.
16. Polanczyk CA, Rohde LE, Philbin EA, Di Salvo TG. A new casemix adjustment index for hospital mortality among patients
with congestive heart failure. Med Care. 1998;36:1489-1499.
FULL TEXT
|
WEB OF SCIENCE
| PUBMED
17. Fleming ST, Rastogi A, Dmitrienko A, Johnson KD. A comprehensive prognostic index to predict survival based on multiple
comorbidities. Med Care. 1999;37:601-614.
FULL TEXT
|
WEB OF SCIENCE
| PUBMED
18. Ghali WA, Hall RE, Rosen AK, Ash AS, Moskowitz MA. Searching for an improved clinical comorbidity index for use with ICD-9-CM
administrative data. J Clin Epidemiol. 1996;49:273-278.
FULL TEXT
|
WEB OF SCIENCE
| PUBMED
19. Deleyiannis FW, Thomas DB, Vaughan TL, Davis S. Alcoholism: independent predictor of survival in patients with head
and neck cancer. J Natl Cancer Inst. 1996;88:542-549.
FREE FULL TEXT
 CiteULike  Connotea  Del.icio.us  Digg  Reddit  Technorati  Twitter
What's this?
THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES
|
Validation of the Washington University Head and Neck Comorbidity Index in a Cohort of Older Patients
Sanabria et al.
Arch Otolaryngol Head Neck Surg 2008;134:603-607.
ABSTRACT
| FULL TEXT
Genetic Polymorphisms and Head and Neck Cancer Outcomes: A Review
Hopkins et al.
Cancer Epidemiol. Biomarkers Prev. 2008;17:490-499.
ABSTRACT
| FULL TEXT
Life After Total Laryngectomy: A Measure of Long-term Survival, Function, and Quality of Life
Woodard et al.
Arch Otolaryngol Head Neck Surg 2007;133:526-532.
ABSTRACT
| FULL TEXT
Using Comorbidity Indexes to Predict Costs for Head and Neck Cancer
Hollenbeak et al.
Arch Otolaryngol Head Neck Surg 2007;133:24-27.
ABSTRACT
| FULL TEXT
Colorectal cancer screening in young patients with poor health and severe comorbidity.
Sultan et al.
Arch Intern Med 2006;166:2209-2214.
ABSTRACT
| FULL TEXT
Analysis of clinical decision-making in multi-disciplinary cancer teams
Blazeby et al.
Ann Oncol 2006;17:457-460.
ABSTRACT
| FULL TEXT
Comparing Microvascular Outcomes at a Large Integrated Health Maintenance Organization With Flagship Centers in the United States
Lueg
Arch Otolaryngol Head Neck Surg 2004;130:779-785.
ABSTRACT
| FULL TEXT
Clinical Predictors of Quality of Life in Patients With Head and Neck Cancer
Terrell et al.
Arch Otolaryngol Head Neck Surg 2004;130:401-408.
ABSTRACT
| FULL TEXT
|
