| M. Sami Walid, MD, PhD, Aaron C.M. Barth, BA, Mohammed Ajjan, MD, Leon A. Hyer, PhD, and Joe Sam Robinson Jr., MD Georgia Neurosurgical Institute, Macon, Ga. The authors reported no conflicts for disclosure. Correspondence to: Abbreviations: Received: Oct. 27, 2007 Key Words: spine, opioid dependence, length of stay |
Abstract
Patients dependent on opioids frequently are viewed as demanding patients who
experience longer hospitalizations, consequently incurring higher healthcare
costs. To determine the validity of this view, 300 patients admitted to a
single institution for spine surgery (cervical decompression and fusion,
lumbar spine decompression and fusion, or lumbar discectomy) were screened
for opioid use. The 150 patients (50 percent) identified as using opioid
painkillers were studied. These patients were interviewed using the Walid-Robinson
Opioid Dependence Questionnaire. The authors performed two analyses to determine
the impact of opioid dependence, OD, on hospital length of stay, LOS. In
the first analysis two status types (+OD and –OD) were identified, and in
the second analysis three groups (+OD, –OD and SubOD) were identified. Correlation,
covariance and regression analyses were performed. The authors identified
20 percent of patients in the +OD group, 45 percent in the –OD group, and
35 percent in the SubOD group. There was no significant correlation between
OD and LOS. However, there was a very significant correlation between LOS
and the number of OD criteria present. After controlling for type of surgery,
age, ethnicity and number of previous spine surgeries, neither analysis revealed
a significant difference in LOS. Based on the number of OD criteria present
and the type of surgery, age, ethnicity and number of previous spine surgeries,
the authors devised an equation that predicted LOS for 64 percent of patients
in the sample. The authors concluded that OD does not impact LOS after spine
surgery.
Introduction
Healthcare providers commonly consider OD patients hospitalized with acute
pain to be difficult patients, a perception that may be fueled by the anxiety
exhibited by these patients. This anxiety may be rooted in the patients’
distrust of the medical community, concern about being stigmatized and fear
that their pain will be undertreated or that their opioid therapy may be
altered or discontinued (2). The anxiety can be substantial enough to complicate
routine hospital care. One result has been thought to be longer hospital
stays for OD patients than for non-OD patients, subsequently raising the
cost of care for OD patients.
To date there are no studies that assess the relationship of OD status to hospital LOS. In the current climate of mounting budget deficits and debate regarding healthcare reform, expenditure must be monitored and effectuated wisely. Considering that the average cost for a patient to stay in our neurosurgical ward is $805 per day, the financial benefit to both the patient and to healthcare providers of reducing LOS is considerable. Perioperative conditions, including opioid use, are worth studying to determine whether they significantly impact hospitalization time.
Materials and Methods
After Institutional Review Board approval was obtained, 300 patients admitted
to a rural medical center in central Georgia for spine surgery between 2006
and 2007 were screened for the use of opioids. Physician assistants interviewed
the 150 patients who were found to be using an opioid medication for pain
relief. The sample was a mixed-gender group that was middle-aged, evenly
distributed in relation to surgery type, and mostly Caucasian (Table 1).
The tool of measurement used was the WRODQ (4), which defines “dependence”
as the presence of at least three of six criteria—strong desire, binge use,
withdrawal, tolerance, neglect and use despite harm-in accordance with the
WHO (7) and the DSM-IV-TR guidelines.
We defined OD status as +OD with three or more criteria and as –OD with zero-to-two
criteria (categorical variable). We defined OD groups as +OD for three or more
criteria, SubOD for two criteria and –OD for zero-to-one criterion (categorical
variable). In addition, we counted the total number (zero to six) of WRODQ
questions on OD status that were answered affirmatively (numerical variable).
LOS data (numerical variable) were collected after patient discharge from the
hospital.
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We performed several statistical tests using SPSS software. Pearson correlation analysis was performed for LOS, the two OD status types, the three OD groups and the WRODQ. Analysis of covariance was performed on LOS where the fixed variables were OD groups of two and three categories, controlling for type of surgery, age, ethnicity and number of previous spine surgeries. Lastly, linear regression analysis was performed to create an equation for the prediction of LOS using the most significant factors as predictors. These included type of surgery (ToS), patient age (A), ethnicity (E), number of previous spine surgeries (NoPSS) and number of OD criteria present (WRODQ). The resultant equation is:
LOS = 1.529 × ToS + 0.02 × A + 0.087 × NoPSS + 0.299 × E + 0.11 × WRODQ – 2.951
The values for the five variables are defined as follows:
| ToS = | 1 for LMD |
| 2 for CDF | |
| 3 for LDF | |
| A = | age in years |
| NoPSS = | number of previous spine surgeries |
| E = | 1 for Caucasian patients |
| 2 for African-American patients | |
| WRODQ = | the number (1–6) of criteria met (affirmative responses) on the WRODQ |
Results
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Pearson correlation analysis (Table 3) showed no significant correlation between
OD status and LOS (r = 0.065,
p > 0.1) or between LOS and OD groups (r = 0.130, p > 0.1). However,
there was a very significant correlation between LOS and the number of OD criteria
met (r = 0.231, p < 0.01).
Independent analyses of covariance showed no significant difference (p > 0.716) in the LOS between the two OD status types (p = 0.3) or between the three OD groups (p = 0.439) after controlling for type of surgery, age, ethnicity and number of previous spine surgeries. It appears that neither OD status nor OD group impacts LOS.
The model of the pLOS equation predicted LOS for 64 percent of patients studied (R2 = 0.64). The interactive graph for LOS and pLOS was developed to verify the weak points of the equation (Figure 1). As shown in the graph, the model holds true for LOS of zero-to-four days, but conformance degrades thereafter.
Discussion
Healthcare providers and recipients remain keenly attuned to the cost of delivering
healthcare. With the aging of the population and changes in medical technology
and utilization, national health expenditures are expected to increase, and
the healthcare share of gross domestic product is expected to climb to 20 percent
by 2015 (3).
This study addressed the immediate impact of opioid dependence on hospital LOS in spine surgery patients in an attempt to assist healthcare providers in the decision-making process. Hospital LOS is dependent on many medical, social, psychological and institutional factors.
Zheng and colleagues studied the factors predicting hospital LOS in 112 patients undergoing revision posterior LDF with segmental instrumentation (8). The number of levels fused and age were the most significant factors predicting LOS. Opioid dependence was not evaluated in that study as a potential determinant of LOS.
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Opioid dependence is “a clusterof physiological, behavioral and cognitive phenomena of variable intensity, in which the use of a psychoactive drug (or drugs) takes on a high priority” as defined by the WHO Expert Committee on Addiction-Producing Drugs (6). If opiates are given for pain, an estimated 5 percent to 15 percent of patients will become addicted (Volkow N: What do we know and what don’t we know about opiate analgesic abuse? Keynote address presented at the 24th Annual Scientific Meeting of the American Pain Society, Boston, Mass., Wednesday, March 30, 2005). In our study, 50 percent of patients screened were using opioids, and 20 percent of these patients were found to be OD.
In a previous study (4), we examined the prevalence of OD status in patients admitted for spine surgery and determined that 20 percent of surgery patients on opioids before spine surgery could be classified as OD. We found no significant correlation (r = 0.085, p > 0.1) between OD status and LOS. Regression analysis showed that type of surgery (p = 0.0), patient age (p = 0.016) and ethnicity (p = 0.032) were the most significant variables for LOS. In another study of OD status in patients before spine surgery (5), we determined the diagnostic and predictive values of pain parameters (pain intensity upon admission scored at eight or higher; length of pain suffering, 24 months or longer; and a WRPI score of 660 or higher). We found the WRPI score of 660 or higher to be the most accurate and efficient measure (80 percent). It had a positive predictive value twice that of the other parameters (56 percent compared with 24 percent and 28 percent, respectively), although these values were not high enough to be reliable predictors of opioid dependence in this category of patients due to a sensitivity of less than 90 percent and a prevalence rate of less than 50 percent.
Neuropathic pain patients, together with cancer and peripheral vascular disease patients, are among those patients who are highly susceptible to opioid dependence. Opioids also are prescribed widely for patients with acute and chronic back pain, including those with disc hernia and spinal stenosis.
In our current study, we addressed the impact of preoperative OD status on postoperative hospital LOS. This study showed that five variables (type of surgery, age, ethnicity, number of previous spine surgeries, and number of OD criteria) were statistically significant as determinants of LOS. Age is a key factor, largely because of comorbidities. Ethnicity is a marker for numerous other factors which, if carefully dissected, would likely produce truer operable variables. The ethnicity variable requires further investigation because there were no Hispanics or other ethnic minorities in our sample. As in our earlier study (5), the number of previous spine surgeries was found to be significantly correlated with LOS. The number of previous spine surgeries is an important factor that reflects the chronic nature of back problems.
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This study has several limitations. First, we used a sample of convenience. This may have asserted a bias on the type of patients in the study, perhaps due to the rural area. We also did not covary for other variables of influence, such as medical comorbidities. New surgeries introduced in the past few years such as X-STOP and kyphoplasty were excluded due to the very small numbers of patients in the sample during the study period. We also did not consider postsurgical factors, like the type and amount of postoperative analgesia. However, we believe that this study improves on our previous ones and adds to the extant literature.
Conclusions
Opioid dependence does not impact hospital LOS after LMD, CDF or LDF spine
surgery. Hospital LOS after spine surgery can be estimated in 64 percent
of patients by a formula that uses the most significant factors (type of
surgery, age, ethnicity, number of previous spine surgeries and number of
OD criteria met). We suggest further research on OD status and other hospital
markers related to cost and functional outcomes.
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