Unplanned Readmissions Following Outpatient Hand and Elbow Surgery

Noureldin, Mohamed MD; Habermann, Elizabeth B. PhD; Ubl, Daniel S. BA; Kakar, Sanjeev MD

Journal of Bone & Joint Surgery - American Volume: 5 April 2017 - Volume 99 - Issue 7 - p 541–549
doi: 10.2106/JBJS.15.01423
Scientific Articles
Disclosures

Background: Unplanned readmission following surgery is a quality metric that helps surgeons assess initiatives targeted at improving patient care. We utilized the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database to determine the rates, causes, and predictors of unplanned 30-day readmissions after outpatient elective hand and elbow surgery.

Methods: The ACS-NSQIP database was queried using hand-and-elbow-specific Current Procedural Terminology (CPT) codes to retrospectively identify patients who had undergone outpatient hand or elbow surgery in 2012 and 2013. Patients who required an unplanned readmission to the hospital within 30 days were compared with those who were not readmitted. Preoperative patient characteristics, intraoperative variables, complications, and mortality were compared between the cohorts. Cox proportional hazard models were used to determine independent associations with 30-day unplanned readmission.

Results: A total of 14,106 outpatient hand or elbow surgery procedures were identified between 2012 and 2013, and 169 (1.2%) of them were followed by an unplanned readmission. The leading causes of readmission included postoperative infections (19.5%), pain (4.7%), thromboembolic events (4.1%), and pulmonary complications (3.0%). The causes of approximately 1/3 of the readmissions were missing from the database, and these readmissions were likely unrelated to the principal procedure. Independent predictors of readmission included an age of 70 to 84 years (hazard ratio [HR] = 2.83, 95% confidence interval [CI] = 1.67 to 4.78), smoking (HR = 2.23, 95% CI = 1.57 to 3.18), a lower hematocrit (HR = 2.19, 95% CI = 1.38 to 3.46), renal dialysis (HR = 3.32, 95% CI = 1.60 to 6.91), and an elbow procedure (with or without a hand procedure) (HR = 2.19, 95% CI = 1.57 to 3.04).

Conclusions: The prevalence of unplanned readmission following outpatient hand and elbow surgery is low. Several modifiable factors, including preoperative smoking and anemia, are associated with unplanned readmission. These data may be helpful in developing quality-control initiatives to target unplanned readmissions following hand and elbow procedures.

Level of Evidence: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.

1Department of Orthopedic Surgery, University of Toledo College of Medicine, Toledo, Ohio

2Departments of Health Sciences Research (E.B.H. and D.S.U.) and Orthopedic Surgery (S.K.), Mayo Clinic, Rochester, Minnesota

E-mail address for S. Kakar: Kakar.Sanjeev@mayo.edu

Article Outline

Approximately 8 million outpatient musculoskeletal procedures are performed annually in the United States, and this number continues to rise1. Previously reported readmission rates range from 0.6% to 3.0% following outpatient procedures across multiple specialties2-5. However, there is a paucity of literature on the rate and causes of unplanned readmissions following outpatient hand and elbow surgery. Readmission within the first 30 days following hospital discharge is a quality metric monitored by the Centers for Medicare & Medicaid Services (CMS). This is mandated by the Patient Protection and Affordable Care Act, by which hospitals may be penalized for higher-than-standardized rates of early readmission6. The CMS started monitoring unplanned readmission rates for specific conditions such as heart failure and then extended the program to include other specialties such as orthopaedic surgery7. It is anticipated that the program will expand to assess hospital performance in other specialties6-8. The rate of 30-day unplanned readmission following orthopaedic procedures was recently estimated to be 5.4%9.

It is the responsibility of hospitals and physicians to continuously look for opportunities to improve patient care and associated outcomes. Thus, there is increasing interest in recognizing factors associated with unplanned readmissions and identifying patients at high risk for readmission in order to decrease readmission rates10 and mitigate the associated increase in resource utilization11. The purpose of this study was to use a large multicenter registry of prospectively collected data to identify the rates, underlying causes, and risk-adjusted predictors of unplanned readmissions within 30 days after outpatient hand and elbow surgery.

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Materials and Methods

Data Source

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) offers a large database of surgical procedure and outcomes data that were prospectively collected at >450 hospitals across the United States 12-14. The details about the methodology of data collection, including sampling, abstraction, and collected variables, have been previously described12,13,15. Briefly, >200 variables related to patient demographics, preoperative comorbidities, intraoperative data, 30-day postoperative outcomes, and mortality are collected. Annual audits are conducted to ensure that the reliability of the data collection is at least 95%16. The database uses Current Procedural Terminology (CPT) codes17 for primary and concomitant procedures and captures readmissions regardless of whether they were to the index or another hospital.

Thirty-day readmissions were identified using the readmission variable in the database. A readmission event is specified as having been planned or unplanned at the time of discharge, and whether the reason for the readmission was likely related to the principal operation is documented. Data abstractors are able to communicate with hospitals and patients directly to ascertain whether a readmission occurred. The relatedness variable was used to determine whether a readmission was likely related to the index procedure, and the reasons for those that were likely related to the index procedure were identified. Reasons for readmissions that were likely unrelated to the index procedure were not reported in the 2012 database. Preoperative laboratory data are expressed as continuous variables in the database; we defined anemia as a hematocrit of <40.6% and hypoalbuminemia as an albumin level of <3.5 g/dL, which is consistent with previously published literature18. The ACS-NSQIP database has been used previously to address outcomes of hand surgery, and this use has been validated19-21.

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Study Population

All musculoskeletal CPT codes were reviewed to identify those specifically related to hand and elbow surgery. These hand-and-elbow-specific codes (see Appendix) were then used to query the ACS-NSQIP database to retrospectively identify patients who had undergone outpatient hand or elbow surgery in 2012 and 2013. A list of hand CPT codes has been previously published22. Patients who had undergone any concomitant surgery unrelated to the hand or elbow were excluded from the analysis. Patients for whom an available postoperative ICD-9 (International Classification of Diseases, Ninth Revision) diagnosis code was unrelated to a hand or elbow diagnosis (n = 47) were also excluded. Identified patients were separated into 2 cohorts: unplanned readmission versus no or planned readmission.

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Statistical Analysis

Preoperative patient characteristics (demographics, comorbidities, and laboratory values), intraoperative data (e.g., operative time and type of anesthesia), and postoperative outcomes (e.g., length of hospital stay, 30-day postoperative complications, and mortality) were compared between the study cohorts using the chi-square or Fisher exact test for categorical variables and the Student t test or Wilcoxon rank-sum test for continuous variables. Cox proportional hazard models accounting for the time to the event (time from discharge to readmission) were used to determine the independent predictors of unplanned readmissions—as readmissions were within 30 days after the procedure, not discharge—while controlling for patient demographics and preoperative comorbidities. Covariates were selected for inclusion in adjusted analyses if they were found to be significant with univariate comparisons or if they were clinically relevant in the context of readmission. Patients were censored at 30 days following the procedure, if they had died prior to readmission, or had not been discharged within 30 days after the index procedure. In the 30-day postoperative period, there were 5 deaths (4 of which were not preceded by a readmission) compared with 169 readmissions. Consequently, death was not considered a significant competing event (an event that occurs instead of the failure event of interest). The ACS-NSQIP database does not specify whether a death was attributed to the index surgery. No collinearity between variables was detected during the building of the multivariable models. A p value of ≤0.05 with false-discovery-rate adjustment was used to denote significance. The statistical analyses were performed using SAS software (version 9.3; SAS Institute).

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Results

A total of 14,106 surgical procedures carried out in 2012 and 2013 were identified. The rate of unplanned readmissions was 1.2% (95% Poisson approximation confidence interval [CI] = 1.00% to 1.37%) per 30 person-days. The median time to unplanned readmission was 14 days (interquartile range, 5 to 20 days), and these readmissions were homogeneously distributed throughout the 30-day postoperative period at a relatively linear rate (see Appendix). Of the readmitted patients, 32.0% (54) returned to the operating room.

The readmitted cohort tended to be ≥50 years old and were more likely to be smokers; to have diabetes mellitus, hypertension, chronic obstructive pulmonary disease, congestive heart failure, a history of dialysis, dyspnea, and bleeding diatheses; to be on steroids; to have a lower hematocrit; and to have hypoalbuminemia (all p ≤ 0.01) (Table I).

Intraoperative parameters that were linked to an increased risk of readmission were an ASA (American Society of Anesthesiologists) class of ≥3 and prolonged operative time (p < 0.01). Furthermore, readmitted patients were more likely to be functionally dependent on caregivers (p < 0.01) and less likely to have undergone a nerve block procedure (local or regional) (p = 0.05) compared with the cohort that was not readmitted (Table II).

Postoperative outcomes also differed between the study cohorts (Table II). The readmitted patients demonstrated higher rates of pulmonary embolism, deep vein thrombosis, sepsis or septic shock, surgical site infection, and wound-healing problems (all p < 0.01). They also had a higher rate of reoperations (p < 0.01).

The most common reason for unplanned readmission was surgical site infection (19.5%) followed by postoperative pain (4.7%), a thromboembolic event (deep vein thrombosis or pulmonary embolism) (4.1%), and a pulmonary complication (including pneumonia and asthma) (3.0%) (Table III). Infection was the leading cause of readmission in both the early (first week) and late (after the first week) postoperative periods, whereas pain was a predominant cause of readmission only in the early postoperative period (10.4%). The causes of 51 (30.2%) of the readmissions were missing from the database, and these were unlikely related to the index procedure. We accounted for all readmitted patients by including them in the denominator when calculating the percentages of the different causes of readmission (Table III).

A small percentage of the readmitted patients (3.6%, n = 6) returned to the hospital because of a complication that had occurred while the patient was in the hospital or had presented as a preoperative comorbidity. Five of these 6 patients had an infected wound (2 superficial infections and 3 deep infections) and were later readmitted for the same reason. One patient had developed a urinary tract infection in the hospital and was readmitted for the same reason later.

When examining the readmission rates for the most common hand and elbow procedures reported in the database, we found that operations to treat proximal ulnar, phalangeal, and comminuted distal radial fractures were associated with the highest readmission rates (2.7%, 2.2%, and 1.4%, respectively) (Table IV).

The multivariable analyses demonstrated that an age of 70 to 84 years (hazard ratio [HR] = 2.83, 95% CI = 1.67 to 4.78), smoking (HR = 2.23, 95% CI = 1.57 to 3.18), anemia (HR = 2.19, 95% CI = 1.38 to 3.46), and renal dialysis (HR = 3.32, 95% CI = 1.60 to 6.91) were independently associated with unplanned readmission following hand and elbow surgery. Furthermore, patients who had undergone an elbow procedure (regardless of its type) had a higher risk of unplanned readmission (HR = 2.19, 95% CI = 1.57 to 3.04) (Table V).

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Discussion

The CMS defines quality metrics as tools that help measure or quantify health-care processes, outcomes, and patient perceptions, among other factors, to provide high-quality health care. The CMS monitors two 30-day quality metrics: 30-day readmission rate and 30-day mortality rate23. There is a lack of information pertaining to the rate, predictors, and causes of unplanned readmissions following hand and elbow surgery. The present study of a national database demonstrated a 1.2% rate of unplanned 30-day readmission following outpatient hand and elbow surgery. The leading causes of readmission related to surgery were infection, pain, thromboembolic events (deep vein thrombosis or pulmonary embolism), and pulmonary complications. This study provides benchmark data that can be used to develop quality-control initiatives to decrease rates of unplanned readmissions.

We found surgical site infection to be the leading cause of unplanned readmission (19.5%). Similar findings were reported by Merkow et al., who studied the underlying causes of readmissions across multiple surgical specialties24. Although the majority of hospitals in the United States follow the measures for preventing surgical site infection recommended by the Surgical Care Improvement Project (SCIP), Tillman et al. reported that adherence to these protocols led to no significant improvements in surgical site infection rates25. Interestingly, a recent report showed poor patient compliance with preoperative disinfection protocols26. Improving such compliance may be one way to decrease the rate of surgical site infection and thereby improve readmission rates. Providing patients with thorough counseling and information about the importance of preoperative protocols and burdens associated with postoperative infections may prove helpful.

The present study showed thromboembolic events (deep vein thrombosis and pulmonary embolism) to be one of the leading causes of readmission (4.1%). Because of their ambulatory status, patients treated with upper-limb surgery do not receive routine thromboprophylaxis against deep vein thrombosis and, given the rarity of deep vein thrombosis as shown in this study, it does not appear necessary in most cases. However, directed deep vein thrombosis prophylaxis on the basis of an increased risk for deep vein thrombosis may help decrease postoperative thromboembolic events27 and associated readmissions. Pulmonary complications such as pneumonia and asthma were also leading causes of readmission in our study, and early detection of postoperative pneumonia by utilizing existing scoring systems and new guidelines28 as well as early empirical therapy may help prevent associated unplanned readmissions.

Our risk-adjusted multivariable analysis demonstrated that older age (70 to 84 years), smoking, preoperative anemia, renal dialysis, and elbow procedures are significant independent predictors of readmission following hand and elbow surgery. Age is a known risk factor for hospital readmission29. Smoking is associated with increased risks of perioperative complications, infection, wound-healing problems, nonunion, and delayed union30. It was demonstrated that cessation of smoking for a brief period before the operation may help to mitigate associated perioperative risks30.

An ASA class of 3 or higher indicates lower baseline health and severe systemic disease, which make the patient more susceptible to postoperative complications31 and more likely to be readmitted to the hospital. The value of hypoalbuminemia in predicting readmissions has been demonstrated in the context of other surgical procedures32-35. In the present study, these 2 variables approached significance with adjusted p values of 0.08 and 0.07, respectively.

Renal dialysis and associated unplanned readmissions represent a substantial burden on the health-care system. It was demonstrated that patients undergoing hemodialysis were admitted to hospitals an average of 2 times per year, and about 36% of them were readmitted within 30 days36,37. Coordinating the health care of these patients with other providers such as physicians and dialysis facilities can decrease the rates of readmission37. Adjustments of hemoglobin and erythropoietin levels and administration of vitamin D within the first week following hospital discharge have been shown to decrease the rate of readmission of patients on renal dialysis38.

Our finding that readmitted patients had higher rates of comorbidities and higher ASA levels suggests that these patients may benefit from care by in-hospital multidisciplinary teams; thus, performing the procedure in an inpatient setting may be appropriate for this patient population. In fact, there is increasing evidence that the care provided by in-hospital multidisciplinary teams is associated with decreased adverse events and improved outcomes39.

Open treatment of proximal ulnar fractures and open treatment of phalangeal fractures were among the procedures associated with the highest readmission rates (2.7% and 2.2%, respectively). Additionally, the readmission rates following open treatment of distal radial fractures ranged from 0.9% to 1.4% depending on the degree of comminution. Curtin and Hernandez-Boussard demonstrated a 30-day readmission rate of 8% following internal fixation of distal radial fractures40. They included unplanned emergency room visits in their analysis even if the patient had not been readmitted to the hospital. This may explain the higher rate of readmission in their study compared with our rate. Schick et al.21 and Jiang et al.20 used the ACS-NSQIP database to characterize the 30-day complication profile following distal radial fractures. Although they did not explicitly report the hospital readmission rate, they did report the prevalence of returning to the operating room in both of their cohorts (combined readmitted and not readmitted), which ranged from 1.0% to 1.2%, and demonstrated that a return to the operation room was the most common major complication following operative treatment of a distal radial fracture21. Lipira et al. reported a similar result in a cohort of patients who had undergone hand surgery19. In the present study, 32.0% of the readmitted patients eventually returned to the operating room.

The percentage of the readmissions that were due to a preexisting complication captured by the ACS-NSQIP was low (3.6%). This finding is consistent with the observation by Merkow et al. that only 2.3% of readmissions (across multiple specialties) were due to a complication similar to one that had existed preoperatively24. This suggests that the majority of the readmissions were not due to an exacerbation or failure of treatment of a preexisting condition. Rather, they were due to newly developed complications following the surgery. The percentage of readmissions due to a hospital-acquired condition was low in the present study, in contrast to the finding by Raines et al. that 42% of readmissions following inpatient total joint arthroplasty were attributed to hospital-acquired conditions41. These findings highlight the difference in hospital-acquired conditions between patients undergoing outpatient ambulatory upper-extremity surgery and those undergoing inpatient procedures in other subspecialties.

Finally, we detected no particular peak in readmissions within the first 30 days following the index procedures, as indicated by a relatively linear Kaplan-Meier curve (see Appendix). This may suggest that readmissions were not due to failure in coordinating postoperative discharges or to the short-stay nature of outpatient surgery.

The limitations of this study are inherent to any study employing the ACS-NSQIP database or any large database. While these databases are excellent for examining trends of complications, their greatest limitation is their lack of granularity. Additionally, it may be difficult to ascertain the reason for readmission as it could be multifactorial. However, the coded reasons for readmissions are manually abstracted by clinical abstractors and have been previously validated by comparison with physician chart reviews24. Indeed, the ACS-NSQIP database captures readmissions regardless of whether they were to the index or another hospital, which is not usually the case with single-institution-based readmission studies42. The utilization of CPT codes for identifying surgical procedures could be perceived as another limitation as there is the potential of data-entry error. However, annual audits are performed to ensure that the reliability of the data collection for the ACS-NSQIP database is at least 95%16. The database does not offer information about hospital surgical volume; therefore, it is not possible to identify the impact of surgical volume on readmissions. However, previous studies have shown that the volume of surgery should not be used as a surrogate for quality of surgical care43. It has been suggested that hospitals participating in the ACS-NSQIP tend to be larger institutions with an interest in quality improvement44. Participating institutions represent about 10% of the registered hospitals in United States45. The causes of approximately 1/3 of the readmissions were missing from the database; however, these readmissions were likely unrelated to the principal procedure as the database allows specification of the relatedness for all readmission events using a dedicated variable.

Despite these limitations, the present study of >14,000 patients treated at >450 hospitals over a 2-year period provides benchmark data for the short-term rates, underlying reasons, and predictors of readmission following outpatient hand and elbow surgery.

In conclusion, although the rate of unplanned readmission following outpatient hand and elbow surgery is low, understanding the factors related to these readmissions is important to further study and mitigate the problem and thereby improve patient outcomes and decrease resource utilization. Readmissions are mostly due to newly developed complications rather than an exacerbation of preexisting conditions. Several modifiable factors are associated with unplanned readmission. These data provide evidence to help hand surgeons develop quality-control initiatives to decrease unplanned readmissions following outpatient upper-extremity surgery.

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Appendix Cited Here...

A table showing the CPT codes used to retrieve the hand and elbow cohort from the database and a figure demonstrating the Kaplan-Meier curve for the time from discharge to unplanned readmission following outpatient hand and elbow procedures are available with the online version of this article as a data supplement at jbjs.org (http://links.lww.com/JBJS/B590).

Investigation performed at the Mayo Clinic, Rochester, Minnesota

Disclosure: No funds were received in support of this work. No benefits in any form have been or will be received from any commercial party related directly or indirectly to the subject of this manuscript. On the Disclosure of Potential Conflicts of Interest forms, which are provided with the online version of the article, one or more of the authors checked “yes” to indicate that the author had a relevant financial relationship in the biomedical arena outside the submitted work (http://links.lww.com/JBJS/B589).

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