NarxCare Scores Greater Than 300 Are Associated with Adverse Outcomes After Primary THA

Abstract

Background

The association between preoperative prescription drug use (narcotics, sedatives, and stimulants) and complications and/or greater healthcare utilization (length of stay, discharge disposition, readmission, emergency department visits, and reoperation) after total joint arthroplasty has been established but not well quantified. The NarxCare score (NCS) is a weighted scalar measure of overall prescription opioid, sedative, and stimulant use. Higher scores reflect riskier drug-use patterns, which are calculated based on (1) the number of prescribing providers, (2) the number of dispensing pharmacies, (3) milligram equivalence doses, (4) coprescribed potentiating drugs, and (5) overlapping prescription days. The aforementioned factors have not been incorporated into association measures between preoperative prescription drug use and adverse events after THA. In addition, the utility of the NCS as a scalar measure in predicting post-THA complications has not been explored.

Questions/purposes

(1) Is the NarxCare score (NCS) associated with 90-day readmission, reoperation, emergency department visits, length of stay, and discharge disposition after primary THA; and are there NCS thresholds associated with a higher risk for those adverse outcomes if such an association exists? (2) Is there an association between the type of preoperative active drug prescription and the aforementioned outcomes?

Methods

Of 3040 primary unilateral THAs performed between November 2018 and December 2019, 92% (2787) had complete baseline information and were subsequently included. The cohort with missing baseline information (NCS or demographic/racial determinants; 8%) had similar BMI distribution but slightly younger age and a lower Charlson Comorbidity Index (CCI). Outcomes in this retrospective study of a longitudinally maintained institutional database included 90-day readmissions (all-cause, procedure, and nonprocedure-related), reoperations, 90-day emergency department (ED) visits, prolonged length of stay (> 2 days), and discharge disposition (home or nonhome). The association between the NCS category and THA outcomes was analyzed through multivariable regression analyses and a confirmatory propensity score–matched comparison based on age, gender, race, BMI, smoking status, CCI, insurance status, preoperative diagnosis, and surgical approach, which removed significant differences at baseline. A similar regression model was constructed to evaluate the association between the type of preoperative active drug prescription (opioids, sedatives, and stimulants) and adverse outcomes after THA.

Results

After controlling for potentially confounding variables like age, gender, race, BMI, smoking status, CCI, insurance status, preoperative diagnosis, and surgical approach, an NCS of 300 to 399 was associated with a higher odds of 90-day all-cause readmission (odds ratio 2.0 [95% confidence interval 1.1 to 3.3]; p = 0.02), procedure-related readmission (OR 3.3 [95% CI 1.4 to 7.9]; p = 0.006), length of stay > 2 days (OR 2.2 [95% CI 1.5 to 3.2]; p < 0.001), and nonhome discharge (OR 2.0 [95% CI 1.3 to 3.1]; p = 0.002). A score of 400 to 499 demonstrated a similar pattern, in addition to a higher odds of 90-day emergency department visits (OR 2.2 [95% CI 1.2 to 3.9]; p = 0.01). After controlling for potentially confounding variables like age, gender, race, BMI, smoking status, CCI, insurance status, preoperative diagnosis, and surgical approach, we found no clinically important association between an active opioid prescription and 90-day all-cause readmission (OR 1.002 [95% CI 1.001 to 1.004]; p = 0.05), procedure-related readmission (OR 1.003 [95% CI 1.001 to 1.006]; p = 0.02), length of stay > 2 days (OR 1.003 [95% CI 1.002 to 1.005]; p < 0.001), or nonhome discharge (OR 1.002 [95% CI 1.001 to 1.003]; p = 0.019); the large size of the database allowed us to find statistical associations, but the effect sizes are so small that the finding is unlikely to be clinically meaningful. A similarly small association that is unlikely to be clinically important was found between active sedative use and 90-day ED visits (OR 1.002 [95% CI 1.001 to 1.004]; p = 0.02).

Conclusion

Preoperative prescription drug use, as reflected by higher NCSs, has a dose-response association with adverse outcomes after THA. Surgeons may use the preoperative NCS to initiate and guide a patient-centered discussion regarding possible postoperative risks associated with prescription drug-use patterns (sedatives, opioids, or stimulants). An interdisciplinary approach can then be initiated to mitigate unfavorable patterns of prescription drug use and subsequently lower patient NCSs. However, given its nature and its reflection of drug-use patterns rather than patients’ current health status, the NCS does not qualify as a basis for surgical denial or ineligibility.

Level of Evidence

Level III, diagnostic study.

Introduction

An estimated 16.9 million individuals (6.2% of the US population) self-reported that they misused prescription drugs in 2018, including stimulants, sedatives, and opioids [35]. Indeed, the financial burden of prescription opioid abuse alone exceeds USD 504 billion annually [37]. Previous studies have highlighted an association between prescription opioid consumption before THA and higher 30-day and 90-day readmission rates, all-cause emergency department (ED) visits, wound complications, and 90-day, 1-year, and 3-year revision rates [24, 39]. In addition, preoperative use of opioids and benzodiazepines has been associated with an 86% and 81% increase, respectively, in postoperative morphine milliequivalent requirements [11]. However, an assessment of healthcare utilization (length of stay [LOS], nonhome discharge, early readmission, ED visits, and reoperation) after THA based on a preoperative scalar evaluation of the combined drug-use patterns (dose, duration, and dispensation of narcotics, sedatives, and stimulants) has not, to our knowledge, been performed.

A quantitative association between opioid use and healthcare utilization after THA has been impeded, in part, by the lack of reliable and readily available data regarding patient-specific prescription drug use. Hilario et al. [21] reported that more than 12.7% of patients with opioid dependence underreported their opioid consumption. Similar patterns have been described among patients with sedative and stimulant abuse [9, 13, 15, 29]. Interestingly, although patient-specific information regarding prescription drug-use patterns is available through the state-specific prescription drug monitoring program, more than 90.9% of orthopaedic surgeons do not routinely query the prescription drug monitoring program for patterns of prescription drug use [27]. As such, there is a critical need to assess the association between preoperative prescription drug use and complications, including increased healthcare utilization after THA using a scalar and readily available modality that can be routinely implemented in a clinical setting.

The NarxCare score (NCS) is a Health Information Technology for Economic and Clinical Health (HITECH) Act–supported quantifiable reflection of prescription drug monitoring program data regarding patient-specific prescription drug use (sedatives, stimulants, and opioids combined) and is currently integrated into patients’ electronic medical records across 43 states in the United States [1-3, 16, 23, 36]. This score provides a scalar (quantitative rather than qualitative) method of assessing patient-specific prescription drug-use patterns, which may influence postoperative healthcare utilization. Many recent studies have dichotomized patients into those who use prescription drugs and those who do not [5, 11, 31]. Such categorization may disregard several attributes within the group of patients who use those drugs, including use pattern, dose, number of prescribing providers, dispensing pharmacies, and overlapping prescriptions, all of which contribute to high-risk drug-use patterns accounted for by the NCS.

We therefore asked: (1) Is the NarxCare score (NCS) associated with 90-day readmission, reoperation, emergency department visits, length of stay, and discharge disposition after primary THA; and are there NCS thresholds associated with higher risk of adverse effects of those healthcare parameters, if such an association exists? (2) Is there an association between the type of preoperative active drug prescription and the aforementioned outcomes?

Patients and Methods

Study Design and Setting

We retrospectively reviewed the Ortho Minimal Data Set Episode of Care (OME) database for patients who underwent primary THA between November 2018 and December 2019 [6, 10, 12]. The OME is a longitudinally maintained institutional cohort database that captures more than 97% of orthopaedic elective surgical interventions occurring in our integrated healthcare system and records preoperative demographic characteristics, comorbidities, perioperative details (surgical details, LOS, and discharge disposition), 90-day readmission, reoperation, and ED visits occurring in the healthcare system. Furthermore, we conducted a chart review of included patients to ensure completeness of 90-day data and capture potential events that may have occurred outside of the healthcare system.

Study Population

All 3040 patients who underwent unilateral primary elective THA during the study period were eligible for inclusion. Six percent (194 of 3040) of patients did not have available preoperative NCSs, and 2% (59 of 3040) of patients had incomplete demographic information (unspecified race or gender). Therefore, 92% (2787 of 3040) of the patients had complete baseline information and were included in the analysis (Fig. 1). On retrospective review of patients' records, all included patients had available 90-day data, which was the minimum duration for reporting the primary and secondary endpoints of this investigation.

Fig. 1.

This flowchart illustrates how patients were selected for this study.

The mean age was 65 ± 11 years, 57% (1598 of 2787) of patients were women, and 85% (2376 of 2787) of patients were white (see Appendix 1; Supplemental Digital Content 1, http://links.lww.com/CORR/A537). Thirty-nine percent (1085 of 2787) of patients had never taken prescribed opioids, sedatives, or stimulants per PDMP-sourced data (that is, those who were naïve to prescription drugs), while 2% (61 of 2787) of patients had an NCS ≥ 500 (Fig. 2). In general, patients 45 to 64 years old, women, those with morbid obesity, and those who had a Charlson Comorbidity Index (CCI) score ≥ 5 exhibited the highest preoperative NCSs. Patients who experienced 90-day all-cause readmission (NCS of 164 ± 167), procedure-related readmission (NCS of 204 ± 178), or 90-day ED visits (NCS of 161 ± 169) and those with LOS longer than 2 days (NCS of 172 ± 175) or nonhome discharge (NCS of 168 ± 172) demonstrated the highest preoperative NCSs (p < 0.001 each; Appendix 1). Compared with the included cohort, patients who were excluded (8%) had similar rates of 90-day readmission (included 6% [175 of 2787] versus excluded 4% [10 of 253]; p = 0.18), 90-day ED visits (included 7% [201 of 2787] versus excluded 6% [14 of 253]; p = 0.39), LOS longer than 2 days (included 18% [501 of 2787] versus excluded 13% [33 of 253]; p = 0.06), and nonhome discharge (included 14% [387 of 2787] versus excluded 12% [30 of 253]; p = 0.42). Excluded patients also exhibited a similar BMI distribution relative to included patients despite having a greater proportion of patients in the younger age groups and lower CCI categories (see Appendix 2; Supplemental Digital Content 2, http://links.lww.com/CORR/A538).

Fig. 2.

This graph shows the distribution of the study sample, based on the preoperative NCS category.

The most common preoperative diagnosis was osteoarthritis (n = 2421), followed by avascular necrosis (n = 154), hip dysplasia (n = 67), hip fracture (n = 46), posttraumatic arthritis (n = 33), femoroacetabular impingement (n = 15), oncologic (n = 15), and other (n = 36). All nontumor cases had endstage osteoarthritic changes. Cemented femoral stem fixation accounted for 6% (157 of 2787) of patients.

The Admission NCS

The electronic medical records (EMRs) of enrolled patients were queried for updated NCSs as of the time of the index surgical admission. The NCS uses the NarxCare platform (Appriss Health) to query the state-specific prescription drug monitoring program (PDMP) at each patient encounter and provides numerical scores between 0 (patients who were naïve to prescription drugs) and 999, with higher scores indicating a greater risk of prescription drug overdose [3, 16, 23]. This score is integrated into the patients’ EMR from which the most updated scores could be directly accessed, similar to documented allergies and personal identifiers (Fig. 3). Additionally, previous NCSs recorded at prior visits are available (which allows for longitudinal recording of score variation). The NCS has been adopted into the EMR on a state level in 43 states in the United States. NCS numerical values are computed through algorithmic analysis of current and past prescriptions extracted from PDMP data. The NCS accounts for (1) the number of prescribing providers, (2) the number of dispensing pharmacies, (3) milligram equivalence doses, (4) coprescribed potentiating drugs, and (5) overlapping prescription days for opioid, sedative, and stimulant drugs [3, 16].

Fig. 3.

The most common and accessible location of the updated NarxCare score within patients’ charts.

The NCS is a proprietary analytic that includes the weighted mean of scaled values, with 50% of weighting being dependent on the dose in milligram equivalencies while the rest of the factors account for the remaining 50%. As such, the NCS provides a readily available quantitative evaluation that reflects patients’ net prescription drug-use patterns. NCSs were also calculated for individual prescription drug types (opioids, sedatives, and stimulants) using a similar algorithm [3, 16]. Drug type–specific scores were extracted for included patients. The last digit of drug type–specific NCSs reflects the number of active prescriptions, with 0 representing no active prescriptions at the time of admission regardless of the score’s overall numerical value.

Outcomes of Interest

The primary outcome for this investigation was all-cause 90-day postoperative readmission.

Secondary outcomes included 90-day procedure- and nonprocedure-related readmission, ED visits, and reoperation. The aforementioned healthcare utilization parameters were obtained through retrospective study of a longitudinally maintained institutional database and confirmed through retrospective chart review to ensure completeness of the available 90-day follow-up. Classification into procedure-related (surgical) versus nonprocedure-related (medical) causes of readmission conformed to those described by Schairer et al. [32]. Furthermore, the proportions of prolonged LOS, defined as a duration of hospitalization longer than 2 days postoperatively, and nonhome discharge were recorded. The association between the NCS category and the type of active drug prescription and the aforementioned outcomes were evaluated.

Ethical Approval

We obtained institutional review board approval for this study from the Cleveland Clinic, Cleveland, OH, USA (approval number 20-007). The study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology statement.

Data Analysis and NCS Stratification

We conducted a univariate analysis to outline the overall distribution of patient demographics, comorbidities, and outcomes. Descriptive statistics were computed for NCSs as a continuous variable (mean and SD) per risk factor or outcome. Patients were then stratified into seven preoperative NCS categories: 0 (patients who were naïve to prescription drugs), 1 to 99, 100 to 199, 200 to 299, 300 to 399, 400 to 499, and ≥ 500 [3, 16, 23]. A multivariable regression analysis was conducted to evaluate independent associations between the preoperative NCS category and study outcomes while adjusting for potential confounders (age group, gender, race, BMI category, smoking status, preoperative diagnosis, insurance status, surgical approach, and underlying comorbidities using the CCI). There was no threshold of statistical significance for inclusion in the multivariable model; instead, all aforementioned factors were included based on previous reports describing a potential association with increased postoperative complications and healthcare utilization after THA [11, 18, 24, 26, 39]. The NCS at which a higher odds of the primary outcome could be demonstrated was then used as a high-risk designation threshold.

To confirm such an association's independence, we conducted a propensity score–matched comparison of primary and secondary outcome proportions after THA between a cohort of patients with NCSs below the identified high-risk threshold and those with NCSs equal to or above this threshold, at a ratio of 3:1. The multivariable regression model detected a higher risk of 90-day readmission (the primary outcome) starting with the NCS category of 300 to 399. As such, an NCS of 300 was used as a threshold for the confirmatory propensity score–matched comparison (≥ 300 versus < 300). Propensity score matching accounted for age, gender, race, BMI, smoking status, insurance status, surgical approach, preoperative diagnosis, and CCI category. A nearest-neighbor match without replacement was implemented, which was demonstrated to induce the same balance in baseline covariates as optimal matching [4]. The matching process eliminated baseline differences between the compared cohorts (see Appendix 3; Supplemental Digital Content 3, http://links.lww.com/CORR/A539).

An additional multivariable regression model was constructed to explore the association between the type of active drug prescription at the index surgical admission, as indicated by a nonzero last digit in the corresponding drug-specific NCS (opioids, sedatives, and stimulants) and adverse events after THA. This regression analysis accounted for the same aforementioned potential confounders and used a reference cohort of patients with no active prescriptions at the time of the index surgical admission. All statistical analyses were implemented in R (R Foundation for Computation Science) along with the MatchIt package for propensity score matching [22].

Results

Risk of 90-day Adverse Outcomes by NCS Category

After controlling for potentially confounding variables like age, gender, race, BMI, smoking status, surgical approach, preoperative diagnosis, insurance status, and CCI, we found that patients with an NCS between 300 and 399 had a higher odds of all-cause 90-day readmission (odds ratio 2.0 [95% confidence interval 1.1 to 3.3]; p = 0.02), procedure-related 90-day readmission (OR 3.3 [95% CI 1.4 to 7.9]; p = 0.006), LOS longer than 2 days (OR 2.2 [95% CI 1.5 to 3.2]; p < 0.001), and nonhome discharge disposition (OR 2.0 [95% CI 1.3 to 3.1]; p = 0.002) than patients with an NCS of zero (patients who have no PDMP-documented opioid, sedative, or stimulant use) did (Fig. 4). Similarly, the cohort with an NCS between 400 and 499 exhibited a greater odds of all-cause 90-day readmission (OR 2.0 [95% CI 1.1 to 3.9]; p = 0.03), LOS longer than 2 days (OR 2.2 [95% CI 1.3 to 3.5]; p = 0.001), and nonhome discharge disposition (OR 3.6 [95% CI 2.2 to 6.0]; p < 0.001), in addition to a higher odds of 90-day ED visits (OR 2.2 [95% CI 1.2 to 3.9]; p = 0.01), than did the zero NCS group. Increasing the NCS category to ≥ 500 maintained similar trends of higher odds of procedure-related readmission, LOS longer than 2 days, and nonhome discharge disposition, despite showing no difference in the odds of all-cause 90-day readmission and 90-day ED visits (Table 1). Such patterns may be attributable to the small sample size of the NCS ≥ 500 cohort, rendering it relatively underpowered. Nonprocedure-related 90-day readmission and any 90-day reoperation did not exhibit an association with any NCS category (Table 1). Among the potential confounders accounted for by the multivariable regression model, age, gender, BMI, CCI, insurance status, surgical approach, and preoperative diagnosis were significantly associated with primary and secondary outcomes (see Appendix 4; Supplemental Digital Content 4, http://links.lww.com/CORR/A549).

Fig. 4.

A-D These spline regression models outline the association between the NCS as a continuous variable and (A) 90-day all-cause readmission, (B) a length of stay longer than 2 days, (C) nonhome discharge disposition, and (D) 90-day ED visits.

Table 1.

Multivariate logistic regression outlining the independent association between NarxCare score (NCS) category and 90-day healthcare utilization while adjusting for potential confounding risk factors

NCS score category	LOS > 2 days		Nonhome discharge		All-cause 90-day readmission		Procedure-related 90-day readmission		Nonprocedure-related 90-day readmission		90-day ED visits		90-day reoperation
	Odds ratio (95% CI)	p value	Odds ratio (95% CI)	p value	Odds ratio (95% CI)	p value	Odds ratio (95% CI)	p value	Odds ratio (95% CI)	p value	Odds ratio (95% CI)	p value	Odds ratio (95% CI)	p value
0	Reference
1-99	0.9 (0.7-1.3)	0.87	0.9 (0.7-1.4)	0.99	1.1 (0.7-1.8)	0.64	1.4 (0.6-3.2)	0.45	1.0 (0.6 - 1.8)	0.93	1.2 (0.8-1.8)	0.427	1.1 (0.4-3.5)	0.85
100-199	1.0 (0.7-1.4)	0.94	1.0 (0.7-1.5)	0.94	1.2 (0.7-1.9)	0.48	2.3 (1.1-5.0)	0.03	0.8 (0.4-1.5)	0.46	1.3 (0.8-2.0)	0.274	0.9 (0.2-3.3)	0.82
200-299	1.8 (1.2-2.5)	0.002	1.3 (0.8-12.0)	0.28	1.5 (0.9-2.5)	0.15	2.7 (1.1-6.3)	0.02	1.0 (0.5-2.1)	0.93	1.2 (0.7-2.0)	0.493	2.2 (0.7-7.0)	0.2
300-399	2.2 (1.5-3.2)	< 0.001	2.0 (1.3-3.1)	0.002	2.0 (1.1-3.3)	0.02	3.3 (1.4-7.9)	0.006	1.4 (0.7-2.7)	0.33	1.4 (0.8-2.4)	0.203	2.6 (0.8-8.6)	0.11
400-499	2.2 (1.3-3.5)	0.001	3.6 (2.2-6.0)	< 0.001	2.0 (1.1-3.9)	0.03	2.9 (0.9-8.4)	0.06	1.7 (0.7-3.7)	0.21	2.2 (1.2-3.9)	0.01	1.8 (0.4-9.1)	0.49
≥ 500	4.4 (2.4-8.1)	< 0.001	3.0 (1.5-5.8)	0.002	1.9 (0.8-4.9)	0.17	6.2 (2.0-19.2)	0.002	0.4 (0.1-3.0)	0.37	1.9 (0.8-4.4)	0.12	1.3 (0.1-12.5)	0.84

LOS = length of stay; ED = emergency department.

After controlling for potentially confounding variables (age, gender, race, BMI, smoking, CCI category, preoperative diagnosis, surgical approach, and insurance status) through propensity score matching, a confirmatory comparison supported that patients with an NCS ≥ 300 demonstrated higher rates of all-cause 90-day readmission, procedure-related readmission, 90-day ED visits, LOS longer than 2 days, and nonhome discharge than did those with scores < 300 (see Appendix 3; Supplemental Digital Content 3, http://links.lww.com/CORR/A539).

Association Between Active Drug Prescription Type on the Risk of 90-day Adverse Outcomes

After controlling for potentially confounding variables like on age, gender, race, BMI, smoking status, CCI, insurance status, preoperative diagnosis, and surgical approach, we found no clinically important association between an active opioid prescription and 90-day all-cause readmission (OR 1.002 [95% CI 1.001 to 1.004]; p = 0.05), procedure-related readmission (OR 1.003 [95% CI 1.001 to 1.006]; p = 0.02), length of stay >2 days (OR 1.003 [95% CI 1.002 to 1.005]; p < 0.001), or nonhome discharge (OR 1.002 [95% CI 1.001 to 1.003]; p = 0.019); the large size of the database allowed us to find statistical associations, but the effect sizes are so small that the finding is unlikely to be clinically meaningful. There was no association between active opioid prescription and 90-day nonprocedure-related readmission, reoperation, or ED visits. We found a small and probably clinically unimportant association between active sedative use and 90-day ED visits (OR 1.002 [95% CI 1.001 to 1.004]; p = 0.02); we found no association between sedative use and all-cause readmission, procedure-related readmission, nonprocedure-related readmission, reoperation, LOS longer than 2 days, and nonhome discharge. There was no association between active stimulant use and any of the evaluated outcomes.

Discussion

The association between preoperative prescription drug use and complications as well as increased healthcare utilization after THA has been established [5, 7, 17, 24, 25, 39]. Such investigations suggest dose-dependent and duration-of-consumption-dependent relationships between preoperative prescription drug intake and postoperative complications. However, the aforementioned associations have not been quantitatively characterized (that is, by using a scalar variable) given the dichotomous nature of designating preoperative prescription drug use or abuse. The present study found an incremental risk of most healthcare utilization parameters as preoperative NCS values increased. In addition, above a threshold of 300, there was a more consistent pattern of higher healthcare utilization, including 90-day all-cause readmission, procedure-related readmission, ED visits, LOS longer than 2 days, and a nonhome discharge. Such findings suggest that a preoperative NCS of 300 may be used as a quantitative threshold to indicate a high risk of healthcare utilization after THA and may warrant a more intensive preoperative protocol of patient counseling and the implementation of an interdisciplinary intervention that targets mitigating hazardous opioid, sedative, and stimulant drug-use patterns.

Limitations

The findings of this study should be viewed in the context of its limitations. NCSs are generated through a patented algorithm, which may obscure score-specific computational details [2, 3, 16]. However, the elements used by such an algorithm have been disclosed and outline that the NCS is a weighted score that places the greatest emphasis on dosage as well as recent use while accounting for overall prescription patterns [16]. The NCS is a Health Information Technology for Economic and Clinical Health (HITECH) Act–supported algorithm that reflects combined sedatives, stimulants, and opioids PDMP-reported data and is currently integrated into patients' electronic medical records across 43 states in the United States. Although the NCS has not been extensively used in orthopaedic surgery, it has been previously used to assess drug use among kidney transplant recipients, ED admissions, and spine surgery and was found to correlate with physician-assessed drug use [16, 20, 30]. As such, further score validation may be required for a more comprehensive interpretation of its implications.

The analyzed cohort was not limited to a single preoperative diagnosis, which may contribute to our sample’s heterogeneity. However, preoperative diagnosis was included in the multivariable regression model to account for potential diagnosis-based differences while maintaining sample size and improving the current study’s external validity. Of the included cohort, 94% received cementless THA, which precluded an assessment of fixation method as a variable [30]. While the regression model accounted for patients’ insurance status as a measure of socioeconomic determinants, patients’ income was not included in the model. Patient income may be a more precise reflection of socioeconomic status, a determinant that has been associated with post-THA complications [33, 38]. However, insurance type is a reliable proxy of patients’ socioeconomic status, and the incorporation of additional socioeconomic indicators may only contribute to collinearity within the regression model [28]. Furthermore, a total of 253 patients were excluded from the analysis due to the absence of preoperative NCSs or certain demographic data. Compared with the analyzed cohort, excluded patients were generally healthier and younger. Although such differences may result in some selection bias, the excluded percent was very small, and there was no difference in the incidence of the primary outcomes between the included and excluded groups. Moreover, full 90-day follow-up information was available for 92% of the included cohort, thereby mitigating the potential risk of selection bias to some degree [14, 34]. The follow-up period for the current investigation was limited to 90 days after surgery; readers need to recognize that adverse events that occurred after 90 days would therefore not be included here. The 90-day adverse events (readmission, ED visits, and reoperations) were captured through an intrainstitutional centralized data collection tool. This creates a potential for missed documentation of readmission and/or reoperation events that took place outside this healthcare system. However, this healthcare system spans the entire region of northeast Ohio, and patients were instructed to the contact their physician or the system-wide electronic-based triage system and present to the nearest center whenever necessary. Furthermore, the occurrence of adverse events (or lack thereof) was confirmed through chart review to ensure the capture of all parameters of increased healthcare utilization regardless of the site of presentation.

The number of patients with NCSs ≥ 500 and those with active sedative prescriptions at the time of surgical admission was low, which may have rendered the analysis underpowered to detect potential associations in these groups, such as all-cause 90-day readmission and ED visits.

Risk of 90-day Adverse Outcomes by NCS Category

After controlling for potentially confounding demographic and clinical variables, we found that patients with an NCS equal to or above 300 were much more likely to have the types of complications that surgeons and patients very much would prefer to avoid after hip replacement, including prolonged length of stay, nonhome discharge, 90-day readmission, and ED visits. One small study retrospectively reviewed 90-day complications among 14,734 patients who underwent THA while stratifying patients as to whether they were naïve to opioids, had used them for 3 months or less, for 3 to 6 months, or for longer than 6 months, to reflect the duration of preoperative opioid consumption [24]. The authors reported that individuals who used opioids for longer than 6 months exhibited a higher risk of 90-day all-cause ED visits than did patients who had not used opioids. Conversely, those who had used opioids for 3 months or less or 3 to 6 months did not demonstrate a higher risk of adverse events. A similar investigation corroborated the association between the duration of opioid consumption and postoperative readmission by demonstrating greater 30-day readmission rates among patients who consumed opioids for more than 60 days preoperatively than those with a 1- to 30-day history of preoperative opioid consumption and those who had not used opioids at all [39]. Although these findings outline the importance of use duration rather than a simple, dichotomous characterization (those who have used opioids versus those who have not), several salient drug-use patterns were not accounted for, including dosage, coprescriptions, and dispensing patterns. Furthermore, clinical translation of these findings is challenging without a systematic assessment of prescription drug use. The present study expanded on a known association by providing a quantifiable parameter that considers the role of the dose and pattern of preoperative drug consumption in the association between preoperative drug consumption and increased healthcare utilization after THA. This was accomplished using a widely available quantitative parameter in patients’ EMRs. Furthermore, this investigation adds value by using the overall prescription drug NCSs rather than relying on an evaluation merely limited to opioids. Such assessment is crucial given the common coprescription of controlled medications, with more than 40% of patients who have been prescribed opioids given a concurrent sedative prescription [19].

We emphasize that our study’s findings do not afford any reasonable basis for denying patients surgery on the grounds of a high NCS alone. Rather, a high NCS should trigger a discussion of potential mitigation strategies, not a decision to cancel (or not offer) surgery. Indeed, within the present study, patients who demonstrate any substantial risk of adverse outcomes based on their NCS (≥ 300; n = 436) only accounted for 25% of all patients with a documented NCS > 0 (that is, those with documented opioid, sedative, or stimulant use). The remaining 75% of this patient subset could have otherwise been non-discriminately labeled as at-risk patients for being preoperative “drug users” without a detailed quantitative assessment. These findings provide a message analogous to that of Cancienne et al. [8] when characterizing the risk of postoperative complications based on glycemic control. Those authors found that within the population of patients with diabetes, those with a hemoglobin A1C level of 7.5% were at particularly high risk of developing adverse events; this allows surgeons to consider risk on a sliding-scale basis rather than considering it dichotomously (those with diabetes versus those without). Along the same lines vis-à-vis the NCS, patients with high NCS scores can either elect to reschedule surgery until a more favorable drug-use pattern has been established (perhaps using our proposed preoperative target of NCS < 300), or patients and surgeons may elect to proceed with surgery being well informed of the potential risks. Although the latter may not be ideal, higher NCS should not act as a barrier or a sole basis for denying surgery.

Association Between Active Drug Prescription Type on the Risk of 90-day Adverse Outcomes

After controlling for relevant confounding variables, we found no clinically important association between an active opioid or sedative prescription and complications after THA, and no association at all for stimulants. The large size of the database allowed us to find some statistical associations, but the effect sizes are so small that the findings are unlikely to be clinically meaningful. The lack of a meaningful association between the presence of active preoperative opioid, sedative, or stimulant prescriptions and postoperative complications may indicate that the longer-term drug-use patterns are of more relevance to postoperative outcomes than whether prescription drugs were consumed in the immediate preoperative period. Furthermore, a considerable proportion of patients receive some form of opioid medications in the immediate postoperative period. Therefore, most patients, including those in the non-active prescription reference cohort, are likely to receive prescription opioids perioperatively, limiting the relevance of an “active use” status assessed during the immediate preoperative visit or the index operative admission Further investigation is warranted to better characterize the association between preoperative active drug prescriptions and complications after THA.

Conclusion

The present study used a quantitative parameter that accounts for several aspects of preoperative prescription drug-use patterns (opioids, sedatives, and stimulants). This analysis demonstrates a clinically relevant association between higher preoperative NCS values and increased healthcare utilization within 90 days after THA. Specifically, patients with an NCS equal to or above 300 demonstrated consistently elevated risk of complications. Identifying such patients in preoperative visits should prompt a more detailed assessment of prescription drug (opioid, sedative, or stimulant) use patterns with subsequent emphasis on patient understanding of associated risks. These patients may then benefit from a thoughtful preoperative plan to adjust drug use, stemming from the common interest of mitigating the risk of postoperative complications. We emphasize that the NCS should not be used to determine patients’ operative eligibility. Such cutoffs are recommended as a basis to begin an informed discussion rather than to be used to allow or deny surgery. This discussion should facilitate a patient-centered interdisciplinary approach to minimize drug use–related risk, and patients may elect to postpone surgery until better drug-use patterns are established. However, surgeons should be cognizant that postponing surgery may contribute to greater pain levels and an associated increase in opioid consumption unless a comprehensive interdisciplinary approach to decrease prescription drug–related risk is implemented. Such approaches may potentially mitigate complications and increase practice efficiency given the widespread availability of the EMR-integrated NCS. Further investigations are needed to assess the role of active interventions to regulate preoperative drug use on NCS values and the subsequent outcomes, which would reflect the value—or lack thereof—of implementing potential NCS-based healthcare policies before THA.