Abstract
Background
While adequate pain relief is central to patient recovery and satisfaction, opioid use is associated with side effects, adverse drug events and opioid use disorder and therefore is under increased scrutiny. Enhanced surgical recovery protocols include multimodal pain management as a key process, but the impact of opioid dose as an independent variable has not been examined.
Methods
Retrospective analysis of 51,824 hip and knee arthroplasty encounters in a large healthcare system.
Results
Overall, patients receiving treatment with lower doses of opiates had shorter median length of stay (p < 0.001); this earlier discharge had no negative consequences on readmission rates. In particular, patients discharged on day 1 received a lower median morphine milligram equivalent (MME) per day than those who were not discharged (32.5 [IQR: 19.0–50.0] versus 45.0 [26.7–71.2], respectively, p < 0.001). The probability of discharge on day 1 was 41.2% and 19.6% for those patients on lower versus higher MME/day, respectively. Similarly, there was a reduction in odds of readmission of 15.2% (95% CI 5.8–23.6%) for patients on lower doses of MME/day.
Conclusion
Lower MME/day following joint arthroplasty is linked to the probability of discharge on both days 1 and 2 post-surgery as well as reduced odds of readmission. These findings persisted even when adjusting for all other factors, including participation in the enhanced surgical recovery program, the use of a multi-modal analgesic regimen, the presence of complications, patient demographics, and other baseline characteristics. Efforts to reduce opioid use in the peri- and immediate post-operative period, regardless of the mechanism, demonstrated a significant effect on patient outcomes.
Keywords: Opioids, Narcotics, Surgical recovery, Total knee arthroplasty, Total hip arthroplasty, Length of stay, Re-admissions
Graphical abstract
1. Introduction
Adequate pain relief is central to patient recovery and satisfaction after surgery.1 The use of perioperative opioids for pain management after surgery is under increased scrutiny as opioid related complications and death continue to rise.2,3 Opioid use is associated with side effects and adverse drug events that have been shown to increase length of stay and discharge to extended care facilities.4, 5, 6, 7 Thus, providers are challenged to explore mechanisms for treating pain while reducing opioid use without negatively affecting clinical outcomes.
Multimodal analgesia, which involves a variety of protocols that use different classes of analgesics and/or different sites of administration, might control pain as well as single-drug therapy, with the benefit of decreasing opioid use and related complications.8, 9, 10, 11 Certain multimodal pain management protocols have been linked to decreased postoperative hospital length of stay.12, 13, 14, 15 However, variation in the components and implementation of these protocols has complicated discussion about which drug combinations or processes are required to achieve these outcomes, and whether the results are generalizable to all types of surgeries.
Enhanced recovery protocols have bolstered the adoption of multimodal analgesic regimens alongside various other initiatives. These enhanced recovery protocols have been associated with reduced cost and reduced length of stay for joint arthroplasty patients.16 Yet, since these programs can contain up to twenty different components of care, it is difficult to determine which interventions or combinations are the most effective for achieving optimal results.17,18
In this retrospective analysis, we utilized a large real-world data set to investigate the relationship between opioid use and post-operative length of stay and readmission rates for hip and knee arthroplasty patients in community hospitals associated within a large healthcare system. This population was chosen as these procedures are performed frequently and pain is usually managed with opioids. This association was investigated in the presence of a system-wide enhanced recovery protocol with local variation in implementation maturity and adoption of protocol elements to assess the influence of program components and patient comorbidities on opioid use and outcomes.
2. Materials and methods
Data were obtained from the clinical data warehouse of a large healthcare system that includes over 180 affiliated medical centers across the United States. Data were included from all adult patient encounters classified as hip or knee joint arthroplasty procedures with discharge dates between June 1, 2016 and March 24, 2019. With these parameters, data from a total of 98 facilities were included in this analysis. Records with missing values for either outcomes or covariates of interest were excluded.
Outcomes of interest were length of stay and 30-day readmission rate. Length of stay was analyzed primarily as a discrete ordinal quantity, taking values of 1, 2, 3, or 4 or more days in hospital, as defined by midnight census. Continuous length of stay in hours was also used in a secondary confirmatory analysis. Readmission was defined as readmission within 30 days to any affiliated facility.
The primary predictor variable was morphine milligram equivalent per day (MME/day), defined as total morphine milligram equivalents administered during the hospital stay divided by length of stay in days. This was based on documentation of opioid administrations in the electronic health record.
Other covariates included in models of length of stay were patient age, sex, race, body mass index (BMI), predicted length of stay (via GMLOS), procedure type (hip/knee), case mix index, ASA level (1–4), and binary indicators of complications, diabetes diagnosis, each of three multimodal pain management protocol medications (NSAID, acetaminophen, gabapentin), participation in a goal-directed fluid management protocol, and preoperative consumption of a carbohydrate drink. The multimodal pain management protocol, goal-directed fluid management protocol, and preoperative carbohydrate drink were components of an enhanced recovery protocol that was active in the affiliated facilities and these variables are included in the model to account for the effects of this protocol. The enhanced recovery program, including the multimodal pain management protocol, was in use across all hospitals in the data set, but the prescribing of opioids was dictated by surgeon and patient preference. The same covariates were included in modeling readmission, except that predicted length of stay was replaced by observed length of stay.
Continuation ratio ordinal logistic regression19,20 was used to estimate the covariate-adjusted association between MME per day and probability of discharge on a given day, conditional on the sub-cohort of patients still in the hospital on that day. These conditional probabilities were then used to calculate unconditional probabilities of discharge by day. Using the same covariates, the association between MME per day and continuous length of stay was estimated using ordinary least squares linear regression. Logistic regression was used to estimate the association between MME per day and odds of readmission.
Model predictions are adjusted to median or reference-category values for continuous and discrete covariates, respectively. For the following models, those values are as follows: Patient Age = 68.3, Patient BMI = 30.4, Patient Race = White, Patient Sex = Female, Case Mix Index = 2.05, ASA Level = 3, GMLOS = 2.4, Joint = Knee, Complications = No, Diabetes = No, NSAID = Yes, acetaminophen = Yes, gabapentin = Yes, Carbohydrate Drink = No, Goal-directed fluid = No. In all models the effect of MME per day was not assumed to be linear, but was instead modeled flexibly using restricted cubic spline functions. Correspondingly, the models estimate the effect of a reduction in MME per day from its observed 75th percentile to its observed 25th percentile (64 and 23 MME/day, respectively), in a method similar to that described by Dunn et al., 2010.21 Predictor adequacy was assessed by comparing the full model to a set of multivariable models, each fitted with one of the predictors of interest removed. Expressed as percent, adequacy was calculated using ratios of explained variation via the −2 log likelihood ratio chi-squared statistic. In short, this used models with one variable removed to determine how much reduction in explained variation in the total model was attributable to the variable of interest.
The statistical package R (version 3.6.1) was used to preprocess the datasets, as well as perform the analyses for this study.
3. Results
A total of 51,824 patient encounters classified as hip or knee procedures with discharge dates between June 1, 2016 and March 24, 2019 were included in this analysis. The median [IQR] length of stay, in hours, was 54.3 [32.6–76.2] and the median [IQR] MME/day was 40.0 [23.5–64.0]. The majority of these encounters (96.4%) were not readmitted to an affiliated facility. Additional demographics and other selected variables are presented in Table 1.
Table 1.
Summaries of demographic and selected other variables.
| Variable | Median [IQR]/Count (%) |
|---|---|
| n = 51,824 | |
| Age, years | 68.3 [61.4–74.5] |
| Sex | |
| F | 30801 (59.4%) |
| M | 21023 (40.6%) |
| Race | |
| White | 45777 (88.3%) |
| Black | 3847 (7.4%) |
| Other | 2200 (4.2%) |
| BMI, kg/m2 | 30.4 [26.6–35.1] |
| Procedure | |
| Knee | 32510 (62.7%) |
| Hip | 19314 (37.3%) |
| MME/day | 40.0 [23.3–64.0] |
| LOS, hours | 54.3 [32.6–76.2] |
| LOS, days | 2.0 [1.0–3.0] |
| Readmission | |
| No | 49955 (96.4%) |
| Yes | 1869 (3.6%) |
Overall length of stay varied by MME/day. Patients receiving doses at or below the observed median MME/day (40.0) had a median [interquartile range] length of stay of 51.7 [31.0–60.7] hours. Patients receiving doses above 40 MME/day had a median length of stay of 56.0 [37.1–78.4] hours. The difference in length of stay was statistically significant (p < 0.001). Readmission also varied by MME/day. Among patients receiving doses at or below the observed median of 40 MME per day, 929/26,232 (3.5%) were readmitted within 30 days, compared with 940/25,592 (3.7%) readmitted among patients receiving doses above 40 MME per day (p = 0.422).
The initial findings suggested that there were differences in outcomes based on MME/day that necessitated further analysis. Accordingly, we divided all patient encounters into discharge cohorts by length of stay and calculated the median MME/day and 30-day readmission for patients discharged or not discharged in each cohort. Among all patients, those discharged on day 1 had a lower MME/day (median [IQR]) than those who were not discharged (32.5 [19.0–50.0] versus 45.0 [26.7–71.2], respectively, p < 0.001). A similar profile was seen for those patients discharged on day 2 (43.5 [26.0–67.0] versus 48.0 [27.5–77.0], p < 0.001), but not for those discharged on day 3. This is illustrated in Fig. 1. Patients discharged on day 1 or 2 had lower readmissions than those not discharged on days 1 or 2 (Table 2).
Fig. 1.
Daily MME (median [IQR]) by discharge cohort.
Cohorts represent total number of patients in the hospital and available for discharge each day. LOS≥1 day includes all patients. LOS≥2 days represents all patients not discharged on day 1. LOS≥3 represents all patients not discharged on day 2. Discharged patients are represented in the top section of each bar (green); patients who are not discharged (orange) make up the entire population eligible for discharge in the subsequent cohort. Within-cohort Wilcoxon rank sum test (p-values all < 0.001).
Table 2.
30-day readmission (number (%)) by discharge cohort.
| Cohort |
||||||
|---|---|---|---|---|---|---|
| LOS≥1 |
LOS≥2 |
LOS≥3 |
||||
| Discharged | Not Discharged | Discharged | Not Discharged | Discharged | Not Discharged | |
| 30-day readmission N (%) | 437 (2.5%) | 1432 (4.2%) | 538 (2.9%) | 894 (5.9%) | 459 (4.6%) | 435 (8.4%) |
Cohorts represent total number of patients in the hospital and available for discharge each day. LOS≥1 day includes all patients. LOS≥2 days represents all patients not discharged on day 1. LOS≥3 represents all patients not discharged on day 2. Patients who are not discharged make up the entire population eligible for discharge in the subsequent cohort.
Based on these results, we used an ordinal logistic regression model to estimate the covariate-adjusted association between MME per day and probability of discharge on a given day, conditional on the sub-cohort of patients still in the hospital on that day. We defined comparator groups to investigate this association at what we defined as a low amount of MME/day (23.0 MME/day) and a high amount of MME/day (64.0 MME/day); these values were derived from the 25th and 75th percentile of the entire study population, respectively.21 From the ordinal logistic regression model, the odds of discharge on day 1 were lower with higher values of MME per day (OR: 0.33; 95% CI 0.31–0.35). When MME/day was removed from the model, there was a reduction of 19.5% in the variation explained by the full model, suggesting that this variable is a major contributor to the odds of discharge on day 1.
The estimated covariate-adjusted probabilities of discharge after one day were 41.2% (95% CI 40.1–42.3%) for patients at the low dose of MME/day (23.0) versus 19.6% (95% CI 18.8–20.4%) at a higher dose of MME per day (64.0). For patients not discharged after one day (LOS≥2 days), the corresponding low-vs-high MME/day probabilities of discharge after two days were 64.8% (95% CI 63.7–65.9%) for low MME/day versus 53.1% (95% CI 51.9–54.2%) for high MME/day. For patients not discharged after two days (LOS≥3 days), the probabilities of discharge after three days were 76.5% (95% CI 75.3–77.7%) for low MME/day versus 73.7% (95% CI 72.5–74.9%) for high MME/day.
The second most important predictor in the day of discharge model was patient age. There was an association between lower age and earlier discharge. Among all patients, those discharged on day 1 had a median [IQR] age of 66.2 [59.3–71.8]; those that were not discharged on day 1 had a median age of 69.4 [62.7–75.6]. A similar profile was seen for the cohort with a length of stay of at least two days (68.1 [61.6–74.0] versus 71.1 [64.5–77.5] for those discharged and not discharged, respectively), but not for those with a length of stay of at least three days (70.9 [64.6–77.1] versus 71.4 [64.4–78.5]). The odds of discharge on day 1 was lower with increasing age (OR: 0.49; 95% CI 0.47–0.51). When age was removed from the model, there was a reduction of 16% in the variation explained by the full model, suggesting that this variable is a contributor to the odds of discharge on day 1.The odds of discharge on day 1 for the remaining predictors in the model, ranked in descending order of importance, are presented in Table 3.
Table 3.
Odds of discharge on day 1. Odds ratios compare variables at level A & B, with A as reference/baseline value (indicated B:A).
| Variable | Odds Ratio (95% CI) |
|---|---|
| MME Per Day - 64:23 | 0.35 (0.33–0.36) |
| Age - 74.5:61.4 | 0.49 (0.47–0.51) |
| Sex - M:F | 1.48 (1.44–1.52) |
| Case Mix Index - 2.067:2.054 | 0.99 (0.99–1.00) |
| ASA Level | |
| ASA Level - 2:1 | 0.74 (0.66–0.84) |
| ASA Level - 3:1 | 0.53 (0.47–0.60) |
| ASA Level - 4:1 | 0.39 (0.33–0.46) |
| Diabetes - Yes:No | 0.80 (0.77–0.83) |
| BMI - 35.1:26.6 | 0.92 (0.88–0.95) |
| Joint - Hip:Knee | 1.19 (1.15–1.22) |
In the readmission model, the covariate-adjusted odds of readmission within 30-days were greater with increased MME/day (OR: 1.19, 95% CI 1.07–1.33). However, several other variables also were associated with readmission about equally (Joint, Sex) or more strongly (gabapentin, Case Mix Index, Age, LOS, ASA); see Table 4. The strongest predictor of 30-day readmission was ASA level, with increasing ASA levels associated with increased odds of readmission. Length of stay was the second most important predictor in the readmission model, followed by patient age. When MME/day was removed from the model, there was a reduction of 1.9% in the variation explained by the full model, suggesting that this variable is a contributor to the odds of 30-day readmission. Removal of ASA or LOS from the model reduce the variation explained by the full model by 16.4% and 14.4%, respectively, suggesting that these two variables have greater contribution to the odds of 30-day readmission.
Table 4.
Odds of readmission, variables ranked in decreasing order of predictor importance.Odds ratios compare variables at level A & B, with A as reference/baseline value (indicated B:A).
| Variable | Odds Ratio (95% CI) |
|---|---|
| ASA Level | |
| ASA Level - 2:1 | 2.00 (1.03–3.89) |
| ASA Level - 3:1 | 3.48 (1.79–6.77) |
| ASA Level - 4:1 | 5.50 (2.73–11.12) |
| Length of Stay | |
| LOS - 2 days:1 day | 1.06 (0.93–1.21) |
| LOS - 3 days:1 day | 1.45 (1.26–1.68) |
| LOS - 4+ days:1 day | 2.17 (1.85–2.54) |
| Age, years - 74.5:61.4 | 1.40 (1.22–1.60) |
| Case Mix Index - 2.067:2.054 | 1.01 (1.01–1.02) |
| Gabapentin - No:Yes | 0.79 (0.72–0.88) |
| Sex - M:F | 1.20 (1.09–1.32) |
| Joint - Hip:Knee | 1.21 (1.09–1.33) |
| MME Per Day - 64:23 | 1.19 (1.07–1.32) |
From the logistic regression model, the estimated covariate-adjusted reduction in odds of readmission of 15.2% (95% CI 5.8–23.6%) when comparing low (23.0) to high (64.0) reference values of MME per day. Thus, patients at lower values of MME/day had both a higher probability of discharge on day one and reduced odds of readmission.
We then estimated the probabilities of various lengths of stay when grouped by low and high levels of MME per day, taking into account the contributions of all clinical and demographic variables to the likelihood of discharge. In the low MME/day group, the highest probability of discharge was on days 1 and 2 (41.2% and 39.1%, respectively) (Fig. 2). In contrast, the highest probability of discharge in the high MME/day group was on day 2 (46.5%); the probability of discharge on day 1 in this group was 19.6% (Fig. 2).
Fig. 2.
Estimated probabilities of discharge by day for low and high observed MME per day.
Estimated probabilities of discharge by day for low and high reference levels of MME/day (23.0 vs. 64.0, respectively).
4. Discussion
The amount of opioids used following joint arthroplasty is associated with the day of discharge, with lower amounts of opioid use linked to the probability of earlier discharge on both days 1 and 2 post-surgery. This association persisted even when adjusting for all other factors, including participation in various components of an enhanced surgical recovery program, the use of a multi-modal analgesic regimen, the presence of complications, patient demographics, and other baseline characteristics. The amount of opioids per day was the most important predictor in our model; removing this variable from the model resulted in a reduction of nearly 20% in the variation explained by the full model. While a considerably much weaker predictor of readmission, lower MME/day was associated with a reduction in the odds of readmission as well, suggesting that patients are being appropriately discharged. The decreased risk of readmission may be due to increased likelihood of low dose patients going home on less or no opioids as this correlation has been established in the literature.22 This suggests that efforts to reduce opioid use in the peri- and immediate post-operative period, regardless of the mechanism by which this reduction is encouraged or independent of other ancillary treatments, might have a tangible effect on patient outcomes.
Opioid dose per day is the strongest predictor of earlier discharge in this analysis, with patient age and case-mix nearing a similar level of influence. As expected, our study population was skewed towards older adults with a median age of 68.3 years and an interquartile range of 61.4–74.5. Even with this population, we still observed a strong effect of age on day of discharge. Notably, this effect appeared to be modulated by amounts of opioids used, as patients with a younger median age and lower MME/day were discharged earlier than similarly aged patients with higher MME/day. However, there was a tendency within our data set for MME per day to be negatively associated with age; patients in the 25th percentile of age (approximately 61 years) could be expected to receive 29% (95% CI 28%–30%) more MME per day than patients in the 75th percentile of age (approximately 75 years). Additional work will be needed to determine if limiting opioid exposure for patients in a younger age bracket can affect the day of discharge without introducing any negative consequences. Similarly, further investigation is needed to better understand the interaction between age and MME/day, and whether treating older patients with even lower dosages of opiates also might produce comparable or even greater benefits.
We confirmed the effect of MME/day on length of stay in another linear regression model with continuous length of stay as the outcome. This model indicated that the estimated reduction in median length of stay between low (23.0) and high (64.0) reference values of MME/day is 19.8% (95% CI 19.1–20.5%). While lower MME/day increased the probability of an earlier discharge and shorter length of stay, there was no corresponding increase in the odds of readmissions. Previous studies have shown that the rates of 30-day readmission are strongly influenced by enhanced recovery protocols.23,24 In our analysis, MME/day was not the strongest predictor of readmission; factors such as ASA level, length of stay, patient age, and case mix index had a greater influence on the odds of readmission. However, when taking all these factors into account through the regression model, higher MME/day was associated with an approximate 19% increase in the odds of readmission. While our data set was limited to those patients that were readmitted to an affiliated facility, these results suggest that efforts to reduce opioid use alone might provide equal or better results for patients in terms of length of stay while modestly decreasing the odds of a readmission.
Within this study population, patients may or may not have participated in an enhanced recovery protocol. This protocol was designed to improve outcomes and reduce length of stay. Upon further investigation of this issue, we determined that adherence to the components of the enhanced recovery protocol varied and did not always fully align with the designation of participation in the protocol. Therefore, we included the measurable aspects of this program as covariates within our model. Taking these into account through the regression model, higher MME/day was associated with an approximate 67% decrease in the odds of discharge on day 1. While the covariates were statistically significant, the effect size for the individual components of the enhanced recovery program (e.g., multimodal pain management protocol and preoperative carbohydrate drink) were small and thus appear to have less clinical influence on length of stay than opioid dose. Similar results were seen in a model that included participation in the overall enhanced surgical recovery program as a covariate. This makes the influences of the dose of opioids more remarkable as it is know that each individual enhanced surgical recovery component has limited impact, but only in combination is the benefit on length of stay, readmissions, and complications seen. Opioids standout as a single independent predictor of outcomes among enhanced surgical recovery protocols.
This analysis was limited to hip and knee arthroplasty encounters, as these procedures commonly use opioids for pain management after surgery. In our patient population, approximately 37% were hip procedures and 63% were knee procedures. While we primarily investigated the relationship between MME/day and day of discharge in all procedures, there were no substantial difference in this relationship between hip or knee procedures. While there could be more complex interactions between procedure type and other variables, this suggests that opioid use remains a key factor contributing to day of discharge. In addition, there is a known confounder related to general discharge practices following joint surgery whereby patients usually do not need more than 3 days recovery in the hospital. The lack of a similar effect of MME/day on the odds of discharge on day 3 might be due this practice and we might be observing a type of “ceiling effect” that masks potential differences. Additionally for the Medicare population, 3 midnights are required in the hospital before the patient can qualify for skill nursing facility or rehabilitation placement, so patients not ready for discharge to home on day 3 could have been transferred to another care setting. Additional analysis will be needed to confirm these findings in other surgical patient populations.
Previous studies have shown that the use of opioids after arthroplasty can be limited or eliminated while still controlling pain. Alternative methods of analgesia during or after surgery have shown to be equal or better at controlling pain for patients.15,25,26 Multimodal analgesia, as part of enhanced recovery protocols, has been shown to help reduce pain and lower opioid consumption following joint arthroplasty.16,27 A randomized controlled trial that reduced initial opioid prescription after discharge found no increase in pain scores and no difference in patient-recorded outcomes.28 Our findings provide further evidence that institutions and providers should focus on the development, implementation, and evaluation of programs specifically designed with the goal of reducing opioid use following joint arthroplasty.
The primarily limitation to this study is that it is retrospective in nature so that determinations of “cause and effect” cannot be made. Variation at the local facility or surgeon level (as there were approximately 750 surgeons operating at 98 facilities for the encounters included in this data set) could have influenced the default treatment plan in terms of receipt of enhanced recovery protocol components or initial opioid prescription. We accounted for these treatment covariates within our model and are thus reasonably certain that it is the lower level of MME/day that is driving the earlier discharge. However, the exact mechanism by which opioid use was reduced, such as through the use of multi-modal analgesia or other enhanced recovery protocol components, remains unknown. We also did not have access to data regarding preoperative opioid use for the over 50,000 patient encounters in our data set. This is an important variable that could influence patient outcomes and we would like to include it in future studies.
While the multimodal pain management protocol was in use across all hospitals in this data set, the prescribing of opioids was influenced by surgeon preference and patient preference and nursing practice. It is this difference in preference in practice that is a likely driver of the variation in total opioid use seen in this analysis. Regardless of method used, reduction in opioid prescribing should have a positive impact on clinical outcomes for patients; prescribers should consider maximizing the use of multi-modal pain management protocols and limit exposure to high dose and intravenous pain medications to expedite recovery.
5. Conclusion
In conclusion, we found that the probability of an earlier discharge following joint arthroplasty and a reduction in the odds of readmission were associated with the receipt of lower amounts of opioids following surgery. This was observed in the presence of various components of an enhanced recovery protocol, suggesting that programs specifically aimed at reducing post-surgical opioid use, regardless of mechanism, may have a positive effect on patient outcomes while limiting opioid exposure and the associated negative consequences.
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