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Predictors for Non-Home Patient Discharge Following Elective Adult Spinal Deformity Surgery

Study Design: Retrospective cohort study. Objectives: Adult spinal deformity (ASD) surgery encompasses a wide variety of spinal disorders and is associated with a morbidity rate between 20% and 80%. The utilization of spinal surgery has increased and this trend is expected to continue. To effectively deal with an increasing patient volume, identifying variables associated with patient discharge destination can expedite placement and reduce length of stay. Methods: The 2013-2014 American College of Surgeons National Surgical Quality Improvement Program database was queried using Current Procedural Terminology and International Classification of Diseases, Ninth Revision diagnosis codes relevant to ASD. Patients were divided based on discharge destination. Bivariate and multivariate logistic regression analyses were employed to identify predictors for patient discharge destination and hospital length of stay. Results: A total of 4552 patients met inclusion criteria, of which 1102 (24.2%) had non-home discharge. Multivariate regression revealed total relative value unit (odds ratio [OR] = 1.01, 95% confidence interval [CI] = 1.00-1.01); female sex (OR = 1.54, 95% CI = 1.32-1.81); American Indian, Alaska Native, Asian, Native Hawaiian, or Pacific Islander versus black race (OR = 0.52, 95% CI = 0.35-0.78, P = .002); age ≥65 years (OR = 3.72, 95% CI = 3.19-4.35); obesity (OR = 1.18, 95% CI = 1.01-1.38, P = .034); partially/totally functionally dependent (OR = 2.11, 95% CI = 1.49-2.99); osteotomy (OR = 1.42, 95% CI = 1.12-1.80, P = .004) pelvis fixation (OR = 2.38, 95% CI = 1.82-3.11); operation time ≥4 hours (OR = 1.74, 95% CI = 1.47-2.05); recent weight loss (OR = 7.66, 95% CI = 1.52-38.65; P = .014); and American Society of Anesthesiologists class ≥3 (OR = 1.80, 95% CI = 1.53-2.11) as predictors of non-home discharge. P values were <.001 unless otherwise noted. Additionally, multivariate regression found non-home discharge to be a significant variable in prolonged length of stay. Conclusions: The authors suggest these results can be used to inform patients preoperatively of expected discharge destination, anticipate patient discharge needs postoperatively, and reduce health care costs and morbidity associated with prolonged LOS.


Introduction
With prevalence rates as high as 60% in the elderly population, 1 adult spinal deformity (ASD) has become a common pathology that has the potential to greatly affect a patient's quality of life. Surgical treatment, which is indicated for patients with worsening neurological symptoms and sagittal malalignment, involves spinal decompression and restoration of global sagittal balance. 2,3 However, postoperative complication and revision rates remain high. 2,4 In fact, the United States Bone and Joint Initiative estimates nearly $50 billion in hospital discharge costs associated with ASD. 5 Advances in surgical and anesthetic techniques have made the option of ASD surgery available to a greater portion of the elderly population, a growing demographic in the United States. [6][7][8] This is evident by an increase in the annual number of discharges for spinal fusion procedures, which increased by 137% between 1998 and 2008. 9 In order to adapt to growing surgical volume, health care providers and administrators may find themselves obligated to minimize costs by streamlining discharges and reducing length of stay (LOS) in order to minimize third-party payer pressure. Patient discharge destination is closely related to hospital LOS. Although previous studies in ASD have identified patient comorbidities and intraoperative complications as risk factors for increased LOS, [10][11][12] bed availability at rehabilitation centers, insurance approval, and social support resources remain potential contributory factors for greater LOS. By identifying predictive factors for non-home patient discharge early, it may be possible to help mitigate associated discharge and LOS costs. Prior literature for predicting factors for non-home discharge exists, albeit sparsely, for other spinal surgeries, 13,14 but to the authors' knowledge, none have investigated this issue following ASD surgery. As such, this study seeks to identify preoperative and operative variables that are predictive of non-home discharge destination in ASD patients.

Data Source
This was a retrospective study of prospectively collected data in the 2013-2014 ACS-NSQIP (American College of Surgeons National Surgical Quality Improvement Program) database. ACS-NSQIP is a large national database with risk-adjusted 30-day postoperative morbidity and mortality outcomes. Over 500 hospitals that vary in size, socioeconomic location, and academic affiliation contributed data to the database. 15 ACS-NSQIP data is collected prospectively by dedicated clinical abstractors at each institution on more than 150 demographic, preoperative, intraoperative, and 30-day postoperative variables. The success of quality improvement initiatives based on ACS-NSQIP data have been validated in the Veterans Administration system and private sector. 16 Table 1. Cases with missing preoperative data, emergency cases, patients with a wound class of 2, 3, or 4, an open wound on their body, current sepsis, current pneumonia, prior surgeries within 30 days, cases requiring cardiopulmonary resuscitation prior to surgery, any patients undergoing a nonelective procedure, or cases with diagnoses of cervical spine, trauma, or injury to spine, or neoplasm of spine were excluded in order to reduce the risk of confounding variables.

Variable Definition
Patient demographic variables included sex, age (65 years), and race (white, black, Hispanic, and other). Other race included American Indian, Alaska Native, Asian, Native Hawaiian, Pacific Islander, or Unknown/Not Reported. Preoperative variables included obesity (30 kg/m 2 ), diabetes (non-insulin-dependent diabetes mellitus or insulindependent diabetes mellitus), current smoking (within 1 year of surgery), dyspnea (30 days prior to surgery), functional status prior to surgery (independent or partially/totally dependent 30 days prior to surgery), pulmonary comorbidity (ventilator dependent 48 hours prior to surgery or history of chronic obstructive pulmonary disease 30 days prior to surgery), cardiac comorbidity (use of hypertensive medication or history of chronic heart failure 30 days prior to surgery), renal comorbidity (acute renal failure 24 hours prior to surgery or dialysis treatment 2 weeks prior to surgery), steroid use for chronic condition (30 days prior to surgery), 10% loss of body weight (in the last 6 months), bleeding disorder (chronic, active condition), preoperative transfusion of 1 unit of whole/ packed red blood cells (RBCs) (72 hours prior to surgery), and American Society of Anesthesiologists (ASA) physical status classification (3).
Intraoperative variables included operation year (2013-2014), fusion length (long fusion is 4 levels during an anterior approach and 7 levels during a posterior approach), surgical approach (anterior, posterior, or combined), pelvic fixation, osteotomy, intervertebral device, operative time (4 hours), and total relative value units (TRVUs). TRVU was used as a loose surrogate for surgical complexity as certain deformity cases such as those involving pedicle subtraction osteotomies or vertebral column resections are at greater risk for morbidity. Thirty-day postoperative outcome variables include mortality, any postoperative complication, LOS (5 days), wound complication (superficial or deep surgical site infection, organ space infection, or wound dehiscence), pulmonary complication (pneumonia, unplanned reintubation, or duration of ventilator-assisted respiration 48 hours), venous thromboembolism (pulmonary embolism or deep vein thrombosis), renal complication (progressive renal insufficiency or acute renal failure), urinary tract infection, cardiac complication (cardiac arrest requiring cardiopulmonary resuscitation or myocardial infarction), intra-/postoperative transfusion, sepsis, reoperation (related to initial procedure), and unplanned readmission (related to initial procedure). ACS-NSQIP provides further information on variable characteristics. 18 Discharge destination is coded in the ACS-NSQIP database as follows: (1) skilled care, not home (eg, transitional care unit, subacute hospital, ventilator bed, skilled nursing home); (2) unskilled facility, not home (eg, nursing home or assisted facility-if not patient's home preoperatively); (3) facility which was home (eg, return to chronic care, unskilled facility, or assisted living, which was the patient's home preoperatively); (4) home; (5) separate acute care (eg, transfer to another acute care facility); and (6) rehab. 18 Patients were split up as 2 cohorts: those that were discharged home and those patients that were not discharged home. Destinations other than home included skilled and non-skilled care facility (which were not patient's home preoperatively), nursing homes, assisted living, and rehabilitation centers.

Statistical Analysis
Bivariate analyses were performed on patient demographic, preoperative, intraoperative, and postoperative characteristics using Pearson's w 2 test. Fischer's exact test was used where appropriate. Multivariable logistic regression models were employed, adjusting for patient demographic, preoperative, and intraoperative variables, to identify risk factors for nonhome patient discharge. Another multivariable logistic regression model was utilized to identify predictors for prolonged LOS. Both regression models utilized a stepwise entry and removal criteria, set to a significance level of .05. The c-statistic, which is the area under the receiver operating characteristic curve, was also retrieved from the multivariate logistic regression analysis and determined the accuracy of this model. SAS Studio Version 3.4 (SAS Institute Inc, Cary, NC) was used for all statistical analysis.

Study Population
A total of 4552 patients met the inclusion criteria for the study, of which 3450 (75.8%) were discharged to home and 1102 (24.2%) were discharged to a destination other than home ( Table 2). Of patients discharged to a destination other than home, 522 (47.4%) were discharged to a rehabilitation facility, 548 (49.7%) were discharged to a skilled care facility, and 32 (2.9%) were discharged to an "other" facility. Other includes an unskilled facility that was not home or separate acute care.
All P values were <.001 unless otherwise noted.

Multivariate Analysis
Multivariate logistic regression analysis (

Discussion
This retrospective analysis of the 2013-2014 ACS-NSQIP database identified several risk factors for patient discharge to a facility other than home following elective ASD surgery. Identified factors for non-home discharge were increasing TRVU, female sex, American Indian, Alaska Native, Asian, Native Hawaiian, or Pacific Islander versus black race, 65 years of age, obesity, partially or totally functionally dependent, osteotomy, pelvis fixation, operation time 4 hours, recent weight loss, and ASA class 3. ACS-NSQIP is a well-established database in the surgical literature and contains preoperative, intraoperative, and 30-day postoperative patient data from over 500 medical centers across the United States. [17][18][19][20] The success    of quality improvement initiatives based on ACS-NSQIP data has been validated in the Veterans Administration system and private sector. 16,17 To our knowledge, this is the first large cohort analysis using a national database to identify predictors for patient discharge in the ASD surgery population. Approximately 5 million adults in the United States are disabled to some degree from spine-related disorders. 7 The number of surgical candidates is expected to increase as advances in surgical and anesthetic techniques make the option of surgery available to a larger population of patients. Therefore, efficient planning of patient discharge may be a contributory factor to improving patient satisfaction and hospital flow. 21 Discharge to a destination other than home may also be a surrogate for higher degrees of patient illness, 22 which was observed from the presence of significantly greater postoperative morbidity in these patients. On the other hand, these postoperative complications may have risen in the care facilities that were not previously the patient's home where, for example, health care teams may not know the details of a patient's medical history. 23,24 In an analysis of 15 092 patients undergoing lumbar spine fusion, Aldebeyan et al identified female gender, advanced age, body mass index 35, diabetes, congestive heart failure, hypertension, ASA class >1, multilevel surgery, operation time 259 minutes, postoperative morbidity, and nonelective surgery to be significant factors for requiring an inpatient admission to a facility other than home. 13 The results of the present analysis support the findings of Aldebeyan et al while also contributing new factors significantly associated with discharge destination in the ASD surgery population. In the present analysis, the identified predictors for discharge destination can be separated based on their interventional capacities, for example, as modifiable or nonmodifiable variables. Modifiable patient risk factors can be actively addressed by a patient and their surgeon before surgery in order to mitigate postoperative risk, whereas nonmodifiable risk factors set a patient's baseline predisposition and ideally can be identified by the health care team early. Potentially modifiable predictive variables for patient discharge include TRVU, body mass index, functional status, use of osteotomy, pelvic fixation, operation time, and recent weight loss. Nonmodifiable variables include sex, race, age, and possibly ASA class. Physicians should consider a patient's constellation of modifiable and nonmodifiable factors when initiating patient placement.
There may be a relationship between patient discharge destination and hospital LOS. LOS following any surgical procedure is of great importance to health care system expenditure and a patient's sense of well-being. 12,13 At baseline, it costs approximately US$1000 to keep a patient in the hospital per day. 12,13,25 Patients discharged to a destination other than home were more likely to experience a LOS 5 days (53.0% vs 16.6%). A multivariate logistic regression model revealed patients discharged to a destination other than home had a 3.74 greater odds of hospital LOS 5 days (Table 5). Unfortunately, the ACS-NSQIP database does not provide an explanation for a patient's extended hospital stay or clinical reasoning behind patient discharge destination, making the direction of causality between LOS and discharge destination unclear. The direction of causality likely changes on a case-bycase basis, yet our results suggest that patients may experience an extended hospital LOS due to delays associated with nonhome discharge. Brasel et al found that delays in discharge, which led to extended hospital stays, were predominantly linked to lack of beds in rehabilitation centers. 13,26 They defined delayed discharge as a patient discharged 1 day following medical clearance and timely discharge as a patient discharged on the same day as medical clearance. 26 Additionally, their analysis showed that patients who experienced a delayed discharge incurred $15 000 more in hospital costs in comparison to patients who were discharged in a timely manner. 26 Another plausible explanation is that patients who had more extensive surgeries (eg, osteotomies, long fusions extending to the pelvis, etc) were more likely to stay in the hospital longer and needed extra recuperation time at a monitored facility other than home later on. Furthermore, patients who had more preoperative medical comorbidities (ASA class 3) would have required more supervision and assistance postoperatively and, therefore, would be more fitting to be discharged to a rehab or skilled nursing facility.
There are several limitations that must be addressed in this work. Because the ACS-NSQIP database classifies cases based on CPT codes, differences between procedural techniques cannot be accounted for in this study. Adjustment for more surgery-specific variables, such as type of osteotomy used and type of spinal deformity, were unable to be performed. TRVU was used as a loose surrogate for surgical complexity in an attempt to control for those cases where patients underwent technically demanding osteotomies such as pedicle subtraction or vertebral column resections. Although it offers a large patient size, ACS-NSQIP overrepresents academic medical centers and therefore is less able to fully represent all US hospitals. The ACS-NSQIP database does not include non-US hospitals, thus potentially limiting the applicability of the findings to non-US centers. However, the variables identified in the analysis are commonly used variables that can be assessed in any patient. Contributing hospital is kept anonymous, limiting the ability to adjust for institution size, patient volume, academic affiliation, and surgeon experience. Additionally, longterm complications are not captured in the NSQIP database, leading to a potential underestimation of risk. Finally, a major limitation of this study is due to its retrospective nature, which limits the ability to determine the direction of causality. For example, it is unclear whether a postoperative complication occurred in the hospital following surgery or at the patient's final destination. The authors are unable to include the occurrence of postoperative complications in the multivariate logistic regression models, which could potentially influence a patient's overall LOS and disposition, thus introducing a potential confounding bias.
Despite these limitations, this is the first national study of predictors for patient discharge to a facility other than home following elective ASD surgery. Identified predictors include increasing TRVU, female sex, American Indian, Alaska Native, Asian, Native Hawaiian, or Pacific Islander versus black race, 65 years of age, obesity, partially or totally functionally dependent, osteotomy, pelvis fixation, operation time 4 hours, recent weight loss, and ASA class 3. Additionally, the study evaluated predictive factors for prolonged hospital LOS and found discharge destination to be a significant variable. The authors suggest there is a potential for the results of this study be used by health care teams to inform patients preoperatively of expected discharge disposition, anticipate patient discharge needs postoperatively, and reduce health care costs and morbidity associated with prolonged LOS.

Authors' Note
This study was qualified as exempt by the Mount Sinai Hospital Institutional Review Board.

Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.