The Journal of Bone & Joint Surgery

The Journal of Bone & Joint Surgery, Volume 80, Issue 8

Articles | August 01, 1998

The Correlation of Comorbidity with Function of the Shoulder and Health Status of Patients Who Have Glenohumeral Degenerative Joint Disease*

RICHARD ROZENCWAIG, M.D.†; ARTHUR VAN NOORT, M.D.†; MICHAEL J. MOSKAL, M.D.†; KEVIN L. SMITH, M.D.†; JOHN A. SIDLES, PH.D.†; FREDERICK A. MATSEN III, M.D.†, SEATTLE, WASHINGTON

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Investigation performed at the Department of Orthopaedics, University of Washington, Seattle

J Bone Joint Surg Am, 1998 Aug 01;80(8):1146-53

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Abstract

We studied the effect of comorbidities on function of the shoulder and health status in a group of eighty-five consecutive patients who had glenohumeral degenerative joint disease of sufficient severity to meet one surgeon's criteria for the performance of shoulder arthroplasty. A questionnaire was used to identify the comorbidities, such as other diseases, social factors, or a work-related injury, for each patient.The number of functions on the Simple Shoulder Test that the patient could perform had a significant negative correlation with the number of comorbidities (r = -0.32, intercept = 4.6 per cent, slope = -0.6, and p = 0.0031). Each parameter on the Short Form-36 (except for physical role function) had a significant negative correlation with the number of comorbidities (p < 0.05). This negative relationship was strongest for general health perception (r = -0.42) and vitality (r = -0.35).We concluded that the number of comorbidities has a quantitative effect on function of the shoulder. In the evaluation of the functional status of patients and the effectiveness of treatment, the effects of comorbidity must be controlled. The results of the present study demonstrate that the scores on the Short Form-36 are quantitatively related to the number of comorbidities. The six parameters that are unrelated to function of the shoulder (physical function, social function, emotional role function, mental health, vitality, and general health perception) may provide a practical way to integrate the effects of all potential comorbidities on individual patients. Future clinical research will be strengthened by efforts to measure the impact of comorbidities and by strategies to control for their effects.

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Introduction

Comorbidities are factors other than the primary orthopaedic diagnosis under consideration that have a major influence on the patient's function and health status^{4,5,8-11,17-19,32,43,44,49,51,54,57,61-65}. They may include medical diagnoses (such as diabetes, heart disease, and depression), social factors (such as occupation, a relevant legal claim, and Workers' Compensation status), and personal factors (such as age, nutrition, smoking, and gender). Comorbidities may also influence the presentation of an orthopaedic disorder as well as the results of its treatment. Since the quality and cost of health care have become major issues for the public and for third-party payers, assessment of the severity of an illness and measurement of the effect of comorbidities have become increasingly important in the documentation of the value of heath care⁵⁴. Hospital payments based on diagnosis-related groups alone that are unadjusted for the severity of the illness and attendant comorbidities may unfairly and adversely discriminate against certain health-care providers²⁷.

Accounting for comorbidities that may independently affect the status at presentation and the outcomes of care of patients who have musculoskeletal disorders is a relatively new endeavor. There are several difficulties in the assessment of comorbidities. First, the potentially important factors that should be included in the analysis must be determined. The list of such factors is extensive, and each addition renders the investigation more complex. Second, the investigator must decide how each of the comorbidities should be weighted. For example, the investigator must establish the relative importance of depression compared with that of asthma or a pending related legal claim or the effect of being malnourished compared with the presence of hypertension or a work-related injury. Third, the severity of each comorbidity must be quantitated. For example, the difference between diet-controlled diabetes and severe insulin-dependent diabetes or between exercise-induced asthma and severe asthma necessitating the use of steroids and supplemental oxygen must be determined. Fourth, individual variations in attitude and coping skills must be acknowledged when the investigator attempts to determine the effects of identical comorbidities on different patients. For example, some patients are incapacitated by rheumatoid arthritis, while others thrive despite it. Finally, all of these factors must be combined in a manner that facilitates analysis. The collection of complete data on a sufficient number of patients in a quantitative manner that enables the application of statistical analysis is a daunting task.

In our first effort in this complex domain, we tested two hypotheses: (1) the number of comorbidities is negatively correlated with self-assessed function of the shoulder in patients who have glenohumeral degenerative joint disease, and (2) the number of comorbidities is reflected by the self-assessed health status.

*One or more of the authors has recieved or will recieve benefits for persnal or professional use from a commercial party related directly or indirectly to the subject of this article. In addition, benefits have been or will be directed to a research fund or foundation, educational institution, or other non-profit organization with which one or more of the authors is associated. Funds were recieved in total or partial support of the research or clinical study presented in this article. The funding sources were the Bristo-Myers Squibb/Zimmer Institutional Award and the E.A. Codman Endowment for Shoulder Research at the University of Washington.

†Department of Orthopaedics, University of Washington, Box 356500, 1959 N.E. Pacific, Seattle, Washington 98195-6500. E-mail address for Dr. Matsen:matsen@u.washington.edu.

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TABLE I FREQUENCY OF THE ASSESSED COMORBIDITIES

Comorbidity	No. of Patients
High blood pressure	36
Heart problems	22
Pneumonia	11
Diabetes	9
Work-related injury	7
Cancer	7
Anxiety or depression	7
Thyroid dysfunction	6
Liver disease or hepatitis	5
Smoking	5
Asthma	4
Seizures	4
Legal claim	1
Lung disease	1
Sexually transmitted disease	1
Tuberculosis	0
Other	4

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TABLE I FREQUENCY OF THE ASSESSED COMORBIDITIES

Comorbidity	No. of Patients
High blood pressure	36
Heart problems	22
Pneumonia	11
Diabetes	9
Work-related injury	7
Cancer	7
Anxiety or depression	7
Thyroid dysfunction	6
Liver disease or hepatitis	5
Smoking	5
Asthma	4
Seizures	4
Legal claim	1
Lung disease	1
Sexually transmitted disease	1
Tuberculosis	0
Other	4

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TABLE II DATA REGARDING THE SIMPLE SHOULDER TEST

Function	No. of Patients Answering Yes (N = 85)
Able to place arm comfortably by side	55 (65%)
Able to carry 20 lbs. (9.1 kg) at side	51 (60%)
Able to toss underhand 10 yds. (9.1 m)	43 (51%)
Able to place coin at shoulder level	42 (49%)
Able to place 1 lb. (0.5 kg) at shoulder level	36 (42%)
Able to do usual work	32 (38%)
Able to place hand behind head	23 (27%)
Able to tuck in back of shirt	18 (21%)
Able to place 8 lbs. (3.6 kg) above head	8 (9%)
Able to sleep comfortably	6 (7%)
Able to wash back of contralateral shoulder	4 (5%)
Able to throw overhand 20 yds. (18.3 m)	2 (2%)

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TABLE II DATA REGARDING THE SIMPLE SHOULDER TEST

Function	No. of Patients Answering Yes (N = 85)
Able to place arm comfortably by side	55 (65%)
Able to carry 20 lbs. (9.1 kg) at side	51 (60%)
Able to toss underhand 10 yds. (9.1 m)	43 (51%)
Able to place coin at shoulder level	42 (49%)
Able to place 1 lb. (0.5 kg) at shoulder level	36 (42%)
Able to do usual work	32 (38%)
Able to place hand behind head	23 (27%)
Able to tuck in back of shirt	18 (21%)
Able to place 8 lbs. (3.6 kg) above head	8 (9%)
Able to sleep comfortably	6 (7%)
Able to wash back of contralateral shoulder	4 (5%)
Able to throw overhand 20 yds. (18.3 m)	2 (2%)

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TABLE III EVALUATION OF HEALTH STATUS WITH THE SHORT FORM-36

*And standard deviation. †With respect to age and gender-matched controls.

Parameter	Average Score* (points)	Average Scaled Score*† (per cent)
Physical role function	33 ± 41	47 ± 59
Comfort	37 ± 19	51 ± 27
Physical function	58 ± 26	76 ± 36
Emotional role function	68 ± 42	81 ± 50
Social function	73 ± 27	84 ± 33
Vitality	53 ± 22	87 ± 37
Mental health	74 ± 21	95 ± 27
General health perception	68 ± 19	101 ± 30

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TABLE III EVALUATION OF HEALTH STATUS WITH THE SHORT FORM-36

*And standard deviation. †With respect to age and gender-matched controls.

Parameter	Average Score* (points)	Average Scaled Score*† (per cent)
Physical role function	33 ± 41	47 ± 59
Comfort	37 ± 19	51 ± 27
Physical function	58 ± 26	76 ± 36
Emotional role function	68 ± 42	81 ± 50
Social function	73 ± 27	84 ± 33
Vitality	53 ± 22	87 ± 37
Mental health	74 ± 21	95 ± 27
General health perception	68 ± 19	101 ± 30

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TABLE IV CORRELATION BETWEEN HEALTH STATUS AND NUMBER OF COMORBIDITIES

Parameter	Correlation Coefficient	Intercept	Slope	P Value
Physical function	-0.29	87.8%	-7.4	0.0074
Social function	-0.27	94.1%	-6.4	0.0132
Physical role function	-0.18	58.7%	-7.6	0.1015
Emotional role function	-0.28	96.2%	-9.9	0.0105
Mental health	-0.29	102.9%	-5.5	0.0085
Vitality	-0.35	100.1%	-9.2	0.0010
Comfort	-0.29	59.4%	-5.5	0.0077
General health perception	-0.42	115.0%	-9.0	0.0001

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TABLE IV CORRELATION BETWEEN HEALTH STATUS AND NUMBER OF COMORBIDITIES

Parameter	Correlation Coefficient	Intercept	Slope	P Value
Physical function	-0.29	87.8%	-7.4	0.0074
Social function	-0.27	94.1%	-6.4	0.0132
Physical role function	-0.18	58.7%	-7.6	0.1015
Emotional role function	-0.28	96.2%	-9.9	0.0105
Mental health	-0.29	102.9%	-5.5	0.0085
Vitality	-0.35	100.1%	-9.2	0.0010
Comfort	-0.29	59.4%	-5.5	0.0077
General health perception	-0.42	115.0%	-9.0	0.0001

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Fig. 1 Regression analysis plot of the general health perception score, expressed as a percentage of the expected value for age and gender-matched controls, as a function of the number of comorbidities. Regression equation: general health perception score = 115.0 - (9.0 x number of comorbities).

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Fig. 2 Regression analysis plot of the vitality score, expressed as a percentage of the expected value for age and gender-matched controls, as a function of the number of comorbidities. Regression equation: vitality score = 100.1 - (9.2 x number of comorbidities).

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Fig. 3 Regression analysis plot of the comfort score, expressed as a percentage of the expected value for age and gender-matched controls, as a function of the number of comorbidities. Regression equation: general health perception score = 59.4 - (5.5 x number of comorbities.

Review of the Literature

It is known that the prevalence of comorbidity in the non-institutionalized elderly population is substantial⁷. Comorbidity appears to be the most powerful, yet regularly overlooked, predictor of survival of patients who have out-of-hospital ventricular fibrillation²². According to Schellevis et al., one in five patients who are older than sixty-five years has one chronic disease, while one in seven has more than one⁶⁴. Berkanovic and Hurwicz reported that 54 per cent (155) of 288 patients who had rheumatoid arthritis had other chronic conditions; more importantly, 20 per cent (fifty-eight) considered at least one of these comorbidities to be as severe as the arthritis⁴.

Greater comorbidity and poorer preoperative functional status are associated with less patient satisfaction after operative treatment³⁶. Comorbidity affects the ability to recover from illness and influences the utilization of resources^{23,25,29,50,55,58,73,74}. Oldridge et al. demonstrated that the number of diagnoses listed on the patient's hospital claim was strongly associated with mortality in a group of patients who had had an operation on the lumbar spine⁵³.

The methodology of measuring comorbidity remains imprecise. Many of the current comorbidity scores rely solely on information from computerized discharge databases. While convenient, this approach frequently underestimates the number of comorbidities^54,60. Reviews of the medical records can elucidate twice as many comorbidities⁶⁶. Some authors have shown that specific questionnaires are even more sensitive^1,32,34,58. Self-assessment questionnaires eliminate intraobserver and interobserver variability³⁰. However, even if the number of coexisting diagnoses could be determined reliably, the severity of their impact on the patient would remain undetermined^{13,26,49,54,61-63,67,70}.

Gonnella et al. defined the severity of specific comorbidities according to three stages based on manifestations and complications of the diseases^15,16. The Severity of Illness Index comprises seven indicators: primary diagnosis, interaction with other diagnoses, rate of response to treatment, residual impairment, complications, dependence on medical care, and procedures^25-28. In an attempt to refine the evaluation of a comorbidity's impact, Charlson et al. emphasized the importance of the physician's judgment when reviewing medical records to assess the severity of the illness and the comorbidity, functional status, and prognosis^8-10.

Ghali et al. attempted to assign weights to different comorbidity variables according to their prognostic association with the outcome¹⁴. The Index of Co-Existent Disease measures the physiological severity of comorbidity as well as general physical impairment^17,18. Barsky et al. proposed another comorbidity score, based on the medical records, which rates the severity of each diagnosis; the threat to life; the number of organ systems involved; and the seriousness of treatment, disability, and complications². The Duke University Severity of Illness checklist is a chart audit system based on estimates of the severity of symptoms, treatability, prognosis, and complications^37,54.

No one system for documenting comorbidity is likely to meet all of the needs of investigators and specific populations. Interestingly, none of the mentioned studies included social factors, such as a pending Workers' Compensation claim or another pending legal claim, as comorbidities, even though it is widely recognized that such factors have a major impact on the patient's health status at the time of presentation as well as on the cost and effectiveness of care.

Materials and Methods

The present study included eighty-five consecutive patients who had all been managed by the same surgeon (F. A. M., III) because of degenerative glenohumeral joint disease of sufficient severity to meet that surgeon's criteria for the performance of total shoulder arthroplasty. This method of patient selection reduced the potential for confounding variables due to the application of different diagnostic criteria, heterogeneous diagnoses, various degrees of involvement, and variations in the method of applying study instruments. The average age of the twenty women and sixty-five men was sixty-four years (range, twenty-nine to eighty-one years). For patients who had bilateral involvement, one shoulder was selected at random for inclusion in the study; involvement of the contralateral shoulder was not considered a comorbidity.

Because there is currently no standardized method for documenting all aspects of comorbidity, we selected a binary (yes or no) inventory of potential comorbid conditions. All patients completed three standardized questionnaires when they were first seen at the surgeon's office. The first questionnaire concerned general medical problems and social issues. The patient was asked if he or she was currently being managed for asthma, diabetes, heart problems, high blood pressure, cancer, liver disease or hepatitis, anxiety or depression, pneumonia, seizures, thyroid dysfunction, tuberculosis, lung disease, sexually transmitted disease, or other illnesses. The patient was also asked if he or she had been injured on the job, had a pending legal claim, or was currently a smoker. Each yes answer was counted as a comorbidity.

The second questionnaire was the Simple Shoulder Test, a standardized self-assessment inventory of function of the shoulder^39,45-48. With the Simple Shoulder Test, the patient assesses his or her ability to perform twelve functions that were derived from an evaluation of the common functional limitations of patients who are seen for problems related to the shoulder. In addition to its face validity, this instrument has been shown to have test-retest reproducibility and to be sensitive to a wide variety of disorders involving the shoulder, practical within the context of busy practices, sensitive to the change in function of the shoulder resulting from treatment, and able to identify failures of treatment^39,45-48.

The third questionnaire was the Short Form-36^40-42,71,72, which assesses eight parameters: physical function, social function, physical role function, emotional role function, mental health, vitality, comfort, and general health perception. The Short Form-36 is the most frequently used measure of health status in the United States^43,44. It has been employed to demonstrate the health status of control populations as well as populations with defined medical and psychological conditions. Furthermore, it has been used to demonstrate the effectiveness of orthopaedic treatment^{3,6,24,31,33-35,56,68,69,71,72}. Only two of its eight parameters (physical role function and comfort) are significantly affected by disorders related to the shoulder and their treatment^{12,20,21,24,44-46,48}.

The score for each patient for each of the eight parameters on the Short Form-36 was determined as described previously^40-42,71,72. In addition, each score was expressed as a percentage of the expected value for age and gender-matched controls derived from a large population-based sample⁵⁹; these scores are referred to as scaled scores in the current report.

Linear regression analysis was used to determine the correlation between comorbidity (the number of comorbidities) and function of the shoulder (the number of functions that the patient could perform as determined with the Simple Shoulder Test). Linear regression analysis was also used to determine the correlation between comorbidity and each of the eight parameters on the Short Form-36 (expressed as a percentage of the expected value for age and gender-matched controls).

Results

The average number of comorbidities was 1.5 (range, zero to six) per patient. The most common comorbidities were high blood pressure (thirty-six patients), heart problems (twenty-two), pneumonia (eleven), and diabetes (nine) (Table I).

All patients had severely impaired function of the shoulder and were able to perform only an average of 3.8 of the twelve functions listed on the Simple Shoulder Test. The number of patients who were able to perform each function ranged from two (2 per cent) for throwing overhand to fifty-five (65 per cent) for placing the arm comfortably at the side (Table II).

Of the eight parameters on the Short Form-36, physical role function and comfort were the most severely affected, both in terms of the average score and in terms of the average percentage of the expected value for age and gender-matched controls (scaled score). The average score and the average scaled score were 33 points and 47 per cent for physical role function and 37 points and 51 per cent for comfort (Table III).

The number of functions that the patient could perform, as indicated by the Simple Shoulder Test, had a significant negative correlation with the number of comorbidities (r = -0.32, intercept = 4.6 per cent, slope = -0.6, and p = 0.0031). This result suggests that, in the absence of comorbidities, the average patient would have been able to perform 4.6 functions rather than the 3.8 that we observed. Stepwise regression analysis revealed that, of the individual comorbidities that were examined, only high blood pressure and thyroid dysfunction had a significant negative correlation with the number of functions that the patient could perform (r = -0.40 and -0.33, respectively). The lack of other correlations may be due to the low prevalence of some of the comorbidities.

Each of the parameters on the Short Form-36 (except for physical role function) had a significant negative correlation with the number of comorbidities (Table IV). This negative relationship was strongest for the scaled general health perception score (r = -0.42) (Fig .1) and the scaled vitality score (r = -0.35) (Fig .2). The intercept of both of these plots was at least 100 per cent, suggesting that, without comorbidities, the scores for these parameters would have been close to control values. A weaker correlation was observed for the scaled comfort score (r = -0.29) (Fig. 3). This intercept was only 59 per cent, which suggests that, even if the comorbidity effect had been removed, the comfort score for these patients would have been only about 60 per cent of control values.

Stepwise regression analysis indicated a significant negative correlation between the scaled physical function score and anxiety or depression (r = -0.43), between the scaled emotional role function score and a work-related injury (r = -0.42), between the scaled mental health score and anxiety or depression and a work-related injury (r = -0.36 and -0.33, respectively), between the scaled vitality score and anxiety or depression (r = -0.44), between the scaled comfort score and anxiety or depression (r = -0.34), and between the scaled general health perception score and anxiety or depression (r = -0.38).

Discussion

We tested the hypothesis that, in a large homogeneous population of patients who had glenohumeral degenerative joint disease of sufficient severity to necessitate shoulder arthroplasty, the number of comorbidities (medical, social, and personal factors) was negatively correlated with function of the shoulder (as reflected by the Simple Shoulder Test). The results indicated a highly significant negative correlation. The intercept of the linear regression model was 4.6 per cent, which suggests that if the effects of comorbidities had been removed the average patient in this cohort would have been able to perform 4.6 functions of the Simple Shoulder Test instead of the observed average of 3.8. The negative slope of 0.6 infers that, with each additional comorbidity, a loss of 0.6 shoulder function is predicted by the linear regression analysis.

We also tested the hypothesis that the number of comorbidities was negatively correlated with the parameters of the Short Form-36. Previous studies^45,46,48, as well as the current data, indicated that conditions related to the shoulder adversely affect the physical role function and comfort parameters of the Short Form-36 but not the other six parameters. In the present study, these six parameters, especially the general health perception and vitality parameters, had a strong negative correlation with the number of comorbidities, and linear regression analysis indicated intercepts of 87.8 to 115.0 per cent for each. This suggests that, if the effects of comorbidity had been removed, the health status parameters for this group of patients would have been similar to those for controls. The negative slopes, ranging from 5.5 to 9.9, suggest that each comorbidity may be associated with the loss of approximately 6 to 10 per cent of the control value for each of the parameters on the Short Form-36.

The present study was limited by the simplifications imposed by ignoring the severity of involvement of each of the comorbidities, the limited list of comorbidities, and the use of simple linear regression analysis. However, none of these simplifications appear to have injected bias into the study or to have invalidated the testing of the hypotheses.

The results of the present study indicate that the six parameters on the Short Form-36 that are not substantially affected by an abnormality of the shoulder may provide a practical method for integrating the effects of all comorbidities on an individual patient^20,38,43,44.

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Topics

comorbidity ; health status ; shoulder region

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RICHARD ROZENCWAIG, ARTHUR VAN NOORT, MICHAEL J. MOSKAL, KEVIN L. SMITH, JOHN A. SIDLES, FREDERICK A. MATSEN III; The Correlation of Comorbidity with Function of the Shoulder and Health Status of Patients Who Have Glenohumeral Degenerative Joint Disease*. The Journal of Bone & Joint Surgery. 1998 Aug;80(8):1146-53.

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