SPINE Volume 42, Number 20, pp 1538–1544 ß 2017 Wolters Kluwer Health, Inc. All rights reserved.
CERVICAL SPINE
Predictors for Patient Discharge Destination After Elective Anterior Cervical Discectomy and Fusion John Di Capua, MHS, BS, Sulaiman Somani, BS, Jun S. Kim, MD, Nathan J. Lee, BS, Parth Kothari, BS, Kevin Phan, BS,y,z Nahyr Lugo-Fagundo, BS,§ and Samuel K. Cho, MD
Study Design. Retrospective study of prospectively collected data. Objective. To identify risk factors for nonhome patient discharge after elective anterior cervical discectomy and fusion (ACDF). Summary of Background Data. ACDF is one of the most performed spinal procedures and this is expected to increase in the coming years. To effectively deal with an increasing patient volume, identifying variables associated with patient discharge destination can expedite placement applications and subsequently reduce hospital length of stay. Methods. The 2011 to 2014 ACS-NSQIP database was queried using Current Procedural Terminology (CPT) codes 22551 or 22554. Patients were divided into two cohorts based on discharge destination. Bivariate and multivariate logistic regression analyses were employed to identify predictors for patient discharge destination and extended hospital length of stay. Results. A total of 14,602 patients met the inclusion criteria for the study of which 498 (3.4%) had nonhome discharge. Multivariate logistic regression found that Hispanic versus Black race/ ethnicity (odds ratio, OR ¼0.21, 0.05–0.91, P ¼0.037), American Indian or Alaska Native, Asian, Native Hawaiian or Pacific Islander versus Black race/ethnicity (OR ¼ 0.52, 0.34– 0.80, p-value ¼ 0.003), White versus Black race/ethnicity (OR ¼ 0.55, 0.42–0.71), elderly age 65 years (OR ¼ 3.32, 2.72–4.06),
From the Department of Orthopedics Surgery, Icahn School of Medicine at Mount Sinai, New York, NY; yNeuroSpine Surgery Research Group, University of New South Wales, Sydney, Australia; zFaculty of Medicine, University of New South Wales, Sydney, Australia; and §Ponce Health Sciences University, Ponce, Puerto Rico. Acknowledgment date: July 1, 2016. First revision date: December 22, 2016. Acceptance date: February 8, 2017. The manuscript submitted does not contain information about medical device(s)/drug(s). No funds were received in support of this work. Relevant financial activities outside the submitted work: grants. Address correspondence and reprint requests to Samuel K. Cho, MD, Department of Orthopedics Surgery, Icahn School of Medicine at Mount Sinai, 5 East 98th Street, Box 1188, New York, NY 10029; E-mail: [email protected] DOI: 10.1097/BRS.0000000000002140
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obesity (OR ¼ 0.77, 0.63–0.93, P ¼ 0.008), diabetes (OR ¼ 1.32, 1.06 – 1.65, P ¼ 0.013), independent versus partially/totally dependent functional status (OR ¼ 0.11, 0.08–0.15), operation time 4 hours (OR ¼ 2.46, 1.87– 3.25), cardiac comorbidity (OR ¼ 1.38, 1.10 – 1.72, P ¼ 0.005), and ASA Class 3 (OR ¼ 2.57, 2.05–3.20) were predictive factors in patient discharge to a facility other than home. In addition, multivariate logistic regression analysis also found nonhome discharge to be the most predictive variable in prolonged hospital length of stay. Conclusion. Several predictive factors were identified in patient discharge to a facility other than home, many being preoperative variables. Identification of these factors can expedite patient discharge applications and potentially can reduce hospital stay, thereby reducing the risk of hospital acquired conditions and minimizing health care costs. Key words: anterior cervical discectomy and fusion, cervical, complications, discharge destination, home, length of stay, rehabilitation, risk factors. Level of Evidence: 3 Spine 2017;42:1538–1544
A
nterior cervical discectomy and fusion (ACDF) is one of the most commonly performed spinal procedures for patients who present with cervical spondylosis; ACDF is also indicated for cervical realignment, trauma, and neoplasm.1–3 Patients who require ACDF generally enjoy significant improvement in clinical symptoms, quick recovery times, and minimal surgical risk.1,2,4–6 The total number of ACDF cases rose by 800% between 1990 and 2004.2,6,7 Considering the high success and low complications rate of ACDF,1–3 the aging population in the United States8 and high prevalence of age-related degeneration of the cervical spine,1 the rates of ACDF are expected to continually rise.2,7,9 Identifying clear predictive factors for patient discharge after surgery is essential to improving patient recovery, physician workflow, and reducing hospital costs. It is estimated the average cost of each additional day spent in the hospital is approximately $1000 USD at baseline.10–12 Early recognition of patient discharge allows clinicians to make necessary and timely patient placement preparations and October 2017
Copyright © 2017 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
CERVICAL SPINE has the potential to reduce patient length of stay (LOS) in the hospital, thereby improving the patient experience and limiting their exposure to hospital acquired conditions.12,13 Similar analyses have been conducted in orthopedic trauma,14,15 total joint arthroplasty,13,16,17 and lumbar spinal fusion.11 To the author’s knowledge, this is the first study to analyze predictive factors for patient discharge to a facility other than home after elective ACDF.
Patient Discharge After Cervical Discectomy and Fusion Di Capua et al
The ACS-NSQIP database from 2011 to 2014 was used in this study. Adult patients (18 years) undergoing ACDF (3 levels fused) were identified based on Current Procedural Terminology (CPT) codes 22551 or 22554. 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, before surgeries within 30 days, cases requiring cardiopulmonary resuscitation (CPR) before surgery, any patients undergoing a nonelective procedure, ambulatory cases or cases with diagnoses of cervical spine, trauma or injury to spine, or neoplasm of spine were excluded to reduce the risk of confounding variables.
(30 days before 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 before surgery) and American Society of Anesthesiology (ASA) physical status classification (3). Intraoperative variables included bone graft, osteotomy, intervertebral device insertion, operative time (4 hours), and total relative value units (TRVU). Thirty-day postoperative outcome variables include mortality, LOS 5 days (95th percentile for LOS), 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), renal complication (progressive renal insufficiency or acute renal failure), urinary tract infection, cardiac complication (cardiac arrest requiring CPR 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.19 Discharge destination is coded in the ACS-NSQIP database as follows: (i) skilled care, not home (e.g., transitional care unit, subacute hospital, ventilator bed, and skilled nursing home), (ii) unskilled facility, not home (e.g., nursing home or assisted facility—if not patient’s home preoperatively), (iii) facility which was home (e.g., return to chronic care, unskilled facility, or assisted livingwhich was the patient’s home preoperatively), (iv) home, (v) separate acute care (e.g., transfer to another acute care facility), and (vi) rehabilitation.19 Two cohorts were created of patients discharged to home and patients not discharged to home. Discharge destinations other than home included skilled and nonskilled care facility (which were not patient’s home preoperatively), separate acute care, and rehab.
Variable Definition
Statistical Analysis
Patient demographic variables included sex, age (50, 51– 60, 61–70, 71–80, and 80 years) and race/ethnicity (White, Black, Hispanic, and other). Other race/ethnicity included American Indian, Alaska Native, Asian, Native Hawaiian, Pacific Islander, or Unknown/Not Reported. Preoperative variables included operation year, obesity (30 kg/m2), diabetes (noninsulin dependent diabetes mellitus or insulin dependent diabetes mellitus), current smoking (within 1 year of surgery), dyspnea (30 days before surgery), functional status before surgery (independent or partially/totally dependent 30 days before surgery), pulmonary comorbidity (ventilator dependent 48 hours before surgery or history of chronic obstructive pulmonary disease 30 days before surgery), cardiac comorbidity (use of hypertensive medication or history of chronic heart failure 30 days before surgery), renal comorbidity (acute renal failure 24 hours before surgery or dialysis treatment 2 weeks before surgery), steroid use for chronic condition
Patients were divided into two cohorts based on discharge destination. A bivariate analysis was performed on patient demographic, preoperative, intraoperative, and postoperative characteristics using Pearson x2 test. Fischer 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 patient discharge to a destination other than home. 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 0.05. The C-statistic, which is the area under the receiver operating characteristic (ROC) curve, was also retrieved from the multivariate logistic regression analysis and determined the accuracy of this model. The area under this curve measures the ability of the model to correctly classify those with the complication and those without. SAS Studio
MATERIALS AND METHODS Data Source This was a retrospective study of prospectively collected data in 2011 to 2014 ACS-NSQIP 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 2011–2014 ACS-NSQIP database.18 ACS-NSQIP data are collected prospectively by dedicated clinical abstractors at each institution on more than 150 demographic, preoperative, intraoperative, and 30-day postoperative variables.19
Inclusion and Exclusion Criteria
Spine
www.spinejournal.com
1539
Copyright © 2017 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
CERVICAL SPINE Version 3.4 (SAS Institute Inc., Cary, NC) was used for all statistical analysis.
RESULTS Study Population A total of 14,598 patients met the inclusion criteria for the study of which, 498 (3.4%) were discharged to a location other than home. Of the patients discharged to a facility other than home, 238 (47.8%) patients were discharged to a rehab facility, 214 (43.0%) patients were discharged to a skilled care facility, and 46 (9.2%) patients were discharged to an ‘‘other’’ facility. Other includes an unskilled facility that was not home or separate acute care. The mean age of all patients requiring ACDF was 53.3 years and the median age was 53 years. Patients discharged to a location other than home were more likely to be older, Black, diabetic, dyspnea, partially or totally functionally dependent, pulmonary comorbid, cardiac comorbid, renal comorbid, use steroids, have recent weight loss, bleeding disorder, preoperative RBC transfusion, ASA class 3, operation time 4 hours and prolonged LOS. Patients discharged home were more likely to smoke (Table 1).
Unadjusted Analysis There were statistically significant differences in 30-day unadjusted morbidity and mortality between the two patient cohorts. Patients who were not discharged home experienced a higher rate of mortality (3.0% vs. 0.0%), pulmonary complication (13.3% vs. 0.6%), venous thromboembolism (1.4% vs. 0.2%), urinary tract infection (4.4% vs. 0.3%), cardiac complication (3.6% vs. 0.1%), intra/ postoperative RBC transfusion (4.0% vs. 0.3%), sepsis (4.0% vs. 0.2%), reoperation (7.1% vs. 1.0%), and unplanned readmission (7.3% vs. 2.1%). All P values are < 0.001 unless otherwise stated (Table 2).
Multivariate Analysis Multivariate logistic regression analysis revealed several factors to be significantly and independently associated with patient discharge other than home. It was found that Hispanic versus Black race/ethnicity (odds ratio, OR ¼ 0.21, 0.05–0.91, P ¼ 0.037), American Indian, Alaska Native, Asian, Native Hawaiian, Pacific Islander versus Black race/ethnicity (OR ¼ 0.52, 0.34–0.80, P ¼ 0.003), White versus Black race/ethnicity (OR ¼ 0.55, 0.42–0.71), 65 years of age (OR ¼ 3.32, 2.72–4.06), obese (OR ¼ 0.77, 0.63–0.93, P ¼ 0.008), diabetic (OR ¼ 1.32, 1.06–1.65, P ¼ 0.013), independent versus partially or totally dependent functional status (OR ¼ 0.11, 0.08–0.15), operation time 4 hours (OR ¼ 2.46, 1.87–3.25), cardiac comorbidity (OR ¼ 1.38, 1.10–1.72, P ¼ 0.005), and ASA Class 3 (OR ¼ 2.57, 2.05–3.20) were predictive factors in patient discharge to a facility other than home (Table 3). All 1540
www.spinejournal.com
Patient Discharge After Cervical Discectomy and Fusion Di Capua et al
P-values are < 0.001 unless otherwise stated. Moreover, the C statistic for this multivariate logistic regression model was 0.79. A multivariate logistic regression model was also utilized to identify predictors for prolonged LOS. The model identified Hispanic versus Black race/ethnicity (OR ¼ 1.07, 0.51–2.46, P ¼ 0.856), American Indian, Alaska Native, Asian, Native Hawaiian, Pacific Islander versus Black race/ethnicity (OR ¼ 0.75, 0.52–1.08, P ¼ 0.119), White versus Black race/ethnicity (OR ¼ 0.56, 0.43–0.71), diabetes (OR ¼ 1.25, 1.00–1.54, P ¼ 0.046), independent versus partially or totally dependent functional status (OR ¼ 0.38, 0.25–0.56), operation time 4 hours (OR ¼ 3.83, 3.01– 4.86), pulmonary comorbidity (OR ¼ 1.36, 1.05–1.75, P ¼ 0.019), cardiac comorbidity (OR ¼ 1.38, 1.14–1.68, P ¼ 0.001), bleeding disorder (OR ¼ 2.25, 1.27–4.01, P ¼ 0.006), preoperative RBC transfusion (OR ¼ 11.57, 1.16–115.17, P ¼ 0.037), ASA Class 3 (OR ¼ 1.82, 1.49–2.22 and discharge to a facility other than home (OR ¼ 17.03, 13.67–21.21) to be predictive factors for prolonged LOS (Table 4). All P-values are < 0.001 unless otherwise stated. Discharge to a destination other than home was the most predictive factor for prolonged hospital LOS. The C statistic for this multivariate logistic regression model was 0.80.
DISCUSSION This retrospective analysis of the 2011–2014 ACS-NSQIP database identified several risk factors for patient discharge to a facility other than home after elective ACDF. ACSNSQIP 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.18–22 The success of quality improvement initiatives based on ACS-NSQIP data have been validated by the decreased mortality rates in the VA system, and decreased surgical site infection rates in the private sector.21,23 To our knowledge, this is the first large cohort analysis using a national database to identify predictors for patient discharge. Discharge planning is an interdisciplinary team activity and begins soon after surgery to ensure efficient hospitalization and utilization of resources.24 Our multivariate logistic regression analysis revealed several predictive preoperative and intraoperative factors for patient discharge destination after elective ACDF (Table 3). Significant variables include race, age 65 years, body mass index (BMI) 30 kg/m2, diabetes, partially or totally dependent functional status, cardiac comorbidity, ASA Class 3, and prolonged operative time. Patients who were partially or totally functionally dependent had over a nine times greater odds of being discharged to a facility other than home, making functional status the strongest predictor. Functional status, as defined as a decreased ability to perform activities of daily living, is associated with increased frailty and surgical complications.25–27 It has been suggested this October 2017
Copyright © 2017 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
CERVICAL SPINE
Patient Discharge After Cervical Discectomy and Fusion Di Capua et al
TABLE 1. Patient Demographic, Preoperative, and Intraoperative Characteristics Between
Discharge Destination Groups (N ¼ 14,598) Category Sex Male Female Age 65 years Race/Ethnicity White Other Black Hispanic Obese Diabetes Dyspnea Functional status Independent Partially/Totally dependent Pulmonary comorbidity Cardiac comorbidity Renal comorbidity Smoke Steroid Recent weight loss Bleeding disorder Preoperative RBC transfusion ASA Class 3 Operation time 4 Hours Operation year 2011 2012 2013 2014 Osteotomy Intervertebral device Length of stay 5 days
Discharge Discharge Destination Other Destination Other Than Home (N) Than Home (%)
Discharge Destination Home (%)
P
240 258 269
48.2 51.8 54.0
6786 7314 2685
48.1 51.9 19.0
376 37 83 2 222 150 51
75.5 7.4 16.7 0.4 44.6 30.1 10.2
11,509 1207 1241 143 6242 1982 727
81.6 8.6 8.8 1.0 44.3 14.1 5.2
CERVICAL SPINE
Predictors for Patient Discharge Destination After Elective Anterior Cervical Discectomy and Fusion John Di Capua, MHS, BS, Sulaiman Somani, BS, Jun S. Kim, MD, Nathan J. Lee, BS, Parth Kothari, BS, Kevin Phan, BS,y,z Nahyr Lugo-Fagundo, BS,§ and Samuel K. Cho, MD
Study Design. Retrospective study of prospectively collected data. Objective. To identify risk factors for nonhome patient discharge after elective anterior cervical discectomy and fusion (ACDF). Summary of Background Data. ACDF is one of the most performed spinal procedures and this is expected to increase in the coming years. To effectively deal with an increasing patient volume, identifying variables associated with patient discharge destination can expedite placement applications and subsequently reduce hospital length of stay. Methods. The 2011 to 2014 ACS-NSQIP database was queried using Current Procedural Terminology (CPT) codes 22551 or 22554. Patients were divided into two cohorts based on discharge destination. Bivariate and multivariate logistic regression analyses were employed to identify predictors for patient discharge destination and extended hospital length of stay. Results. A total of 14,602 patients met the inclusion criteria for the study of which 498 (3.4%) had nonhome discharge. Multivariate logistic regression found that Hispanic versus Black race/ ethnicity (odds ratio, OR ¼0.21, 0.05–0.91, P ¼0.037), American Indian or Alaska Native, Asian, Native Hawaiian or Pacific Islander versus Black race/ethnicity (OR ¼ 0.52, 0.34– 0.80, p-value ¼ 0.003), White versus Black race/ethnicity (OR ¼ 0.55, 0.42–0.71), elderly age 65 years (OR ¼ 3.32, 2.72–4.06),
From the Department of Orthopedics Surgery, Icahn School of Medicine at Mount Sinai, New York, NY; yNeuroSpine Surgery Research Group, University of New South Wales, Sydney, Australia; zFaculty of Medicine, University of New South Wales, Sydney, Australia; and §Ponce Health Sciences University, Ponce, Puerto Rico. Acknowledgment date: July 1, 2016. First revision date: December 22, 2016. Acceptance date: February 8, 2017. The manuscript submitted does not contain information about medical device(s)/drug(s). No funds were received in support of this work. Relevant financial activities outside the submitted work: grants. Address correspondence and reprint requests to Samuel K. Cho, MD, Department of Orthopedics Surgery, Icahn School of Medicine at Mount Sinai, 5 East 98th Street, Box 1188, New York, NY 10029; E-mail: [email protected] DOI: 10.1097/BRS.0000000000002140
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obesity (OR ¼ 0.77, 0.63–0.93, P ¼ 0.008), diabetes (OR ¼ 1.32, 1.06 – 1.65, P ¼ 0.013), independent versus partially/totally dependent functional status (OR ¼ 0.11, 0.08–0.15), operation time 4 hours (OR ¼ 2.46, 1.87– 3.25), cardiac comorbidity (OR ¼ 1.38, 1.10 – 1.72, P ¼ 0.005), and ASA Class 3 (OR ¼ 2.57, 2.05–3.20) were predictive factors in patient discharge to a facility other than home. In addition, multivariate logistic regression analysis also found nonhome discharge to be the most predictive variable in prolonged hospital length of stay. Conclusion. Several predictive factors were identified in patient discharge to a facility other than home, many being preoperative variables. Identification of these factors can expedite patient discharge applications and potentially can reduce hospital stay, thereby reducing the risk of hospital acquired conditions and minimizing health care costs. Key words: anterior cervical discectomy and fusion, cervical, complications, discharge destination, home, length of stay, rehabilitation, risk factors. Level of Evidence: 3 Spine 2017;42:1538–1544
A
nterior cervical discectomy and fusion (ACDF) is one of the most commonly performed spinal procedures for patients who present with cervical spondylosis; ACDF is also indicated for cervical realignment, trauma, and neoplasm.1–3 Patients who require ACDF generally enjoy significant improvement in clinical symptoms, quick recovery times, and minimal surgical risk.1,2,4–6 The total number of ACDF cases rose by 800% between 1990 and 2004.2,6,7 Considering the high success and low complications rate of ACDF,1–3 the aging population in the United States8 and high prevalence of age-related degeneration of the cervical spine,1 the rates of ACDF are expected to continually rise.2,7,9 Identifying clear predictive factors for patient discharge after surgery is essential to improving patient recovery, physician workflow, and reducing hospital costs. It is estimated the average cost of each additional day spent in the hospital is approximately $1000 USD at baseline.10–12 Early recognition of patient discharge allows clinicians to make necessary and timely patient placement preparations and October 2017
Copyright © 2017 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
CERVICAL SPINE has the potential to reduce patient length of stay (LOS) in the hospital, thereby improving the patient experience and limiting their exposure to hospital acquired conditions.12,13 Similar analyses have been conducted in orthopedic trauma,14,15 total joint arthroplasty,13,16,17 and lumbar spinal fusion.11 To the author’s knowledge, this is the first study to analyze predictive factors for patient discharge to a facility other than home after elective ACDF.
Patient Discharge After Cervical Discectomy and Fusion Di Capua et al
The ACS-NSQIP database from 2011 to 2014 was used in this study. Adult patients (18 years) undergoing ACDF (3 levels fused) were identified based on Current Procedural Terminology (CPT) codes 22551 or 22554. 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, before surgeries within 30 days, cases requiring cardiopulmonary resuscitation (CPR) before surgery, any patients undergoing a nonelective procedure, ambulatory cases or cases with diagnoses of cervical spine, trauma or injury to spine, or neoplasm of spine were excluded to reduce the risk of confounding variables.
(30 days before 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 before surgery) and American Society of Anesthesiology (ASA) physical status classification (3). Intraoperative variables included bone graft, osteotomy, intervertebral device insertion, operative time (4 hours), and total relative value units (TRVU). Thirty-day postoperative outcome variables include mortality, LOS 5 days (95th percentile for LOS), 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), renal complication (progressive renal insufficiency or acute renal failure), urinary tract infection, cardiac complication (cardiac arrest requiring CPR 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.19 Discharge destination is coded in the ACS-NSQIP database as follows: (i) skilled care, not home (e.g., transitional care unit, subacute hospital, ventilator bed, and skilled nursing home), (ii) unskilled facility, not home (e.g., nursing home or assisted facility—if not patient’s home preoperatively), (iii) facility which was home (e.g., return to chronic care, unskilled facility, or assisted livingwhich was the patient’s home preoperatively), (iv) home, (v) separate acute care (e.g., transfer to another acute care facility), and (vi) rehabilitation.19 Two cohorts were created of patients discharged to home and patients not discharged to home. Discharge destinations other than home included skilled and nonskilled care facility (which were not patient’s home preoperatively), separate acute care, and rehab.
Variable Definition
Statistical Analysis
Patient demographic variables included sex, age (50, 51– 60, 61–70, 71–80, and 80 years) and race/ethnicity (White, Black, Hispanic, and other). Other race/ethnicity included American Indian, Alaska Native, Asian, Native Hawaiian, Pacific Islander, or Unknown/Not Reported. Preoperative variables included operation year, obesity (30 kg/m2), diabetes (noninsulin dependent diabetes mellitus or insulin dependent diabetes mellitus), current smoking (within 1 year of surgery), dyspnea (30 days before surgery), functional status before surgery (independent or partially/totally dependent 30 days before surgery), pulmonary comorbidity (ventilator dependent 48 hours before surgery or history of chronic obstructive pulmonary disease 30 days before surgery), cardiac comorbidity (use of hypertensive medication or history of chronic heart failure 30 days before surgery), renal comorbidity (acute renal failure 24 hours before surgery or dialysis treatment 2 weeks before surgery), steroid use for chronic condition
Patients were divided into two cohorts based on discharge destination. A bivariate analysis was performed on patient demographic, preoperative, intraoperative, and postoperative characteristics using Pearson x2 test. Fischer 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 patient discharge to a destination other than home. 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 0.05. The C-statistic, which is the area under the receiver operating characteristic (ROC) curve, was also retrieved from the multivariate logistic regression analysis and determined the accuracy of this model. The area under this curve measures the ability of the model to correctly classify those with the complication and those without. SAS Studio
MATERIALS AND METHODS Data Source This was a retrospective study of prospectively collected data in 2011 to 2014 ACS-NSQIP 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 2011–2014 ACS-NSQIP database.18 ACS-NSQIP data are collected prospectively by dedicated clinical abstractors at each institution on more than 150 demographic, preoperative, intraoperative, and 30-day postoperative variables.19
Inclusion and Exclusion Criteria
Spine
www.spinejournal.com
1539
Copyright © 2017 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
CERVICAL SPINE Version 3.4 (SAS Institute Inc., Cary, NC) was used for all statistical analysis.
RESULTS Study Population A total of 14,598 patients met the inclusion criteria for the study of which, 498 (3.4%) were discharged to a location other than home. Of the patients discharged to a facility other than home, 238 (47.8%) patients were discharged to a rehab facility, 214 (43.0%) patients were discharged to a skilled care facility, and 46 (9.2%) patients were discharged to an ‘‘other’’ facility. Other includes an unskilled facility that was not home or separate acute care. The mean age of all patients requiring ACDF was 53.3 years and the median age was 53 years. Patients discharged to a location other than home were more likely to be older, Black, diabetic, dyspnea, partially or totally functionally dependent, pulmonary comorbid, cardiac comorbid, renal comorbid, use steroids, have recent weight loss, bleeding disorder, preoperative RBC transfusion, ASA class 3, operation time 4 hours and prolonged LOS. Patients discharged home were more likely to smoke (Table 1).
Unadjusted Analysis There were statistically significant differences in 30-day unadjusted morbidity and mortality between the two patient cohorts. Patients who were not discharged home experienced a higher rate of mortality (3.0% vs. 0.0%), pulmonary complication (13.3% vs. 0.6%), venous thromboembolism (1.4% vs. 0.2%), urinary tract infection (4.4% vs. 0.3%), cardiac complication (3.6% vs. 0.1%), intra/ postoperative RBC transfusion (4.0% vs. 0.3%), sepsis (4.0% vs. 0.2%), reoperation (7.1% vs. 1.0%), and unplanned readmission (7.3% vs. 2.1%). All P values are < 0.001 unless otherwise stated (Table 2).
Multivariate Analysis Multivariate logistic regression analysis revealed several factors to be significantly and independently associated with patient discharge other than home. It was found that Hispanic versus Black race/ethnicity (odds ratio, OR ¼ 0.21, 0.05–0.91, P ¼ 0.037), American Indian, Alaska Native, Asian, Native Hawaiian, Pacific Islander versus Black race/ethnicity (OR ¼ 0.52, 0.34–0.80, P ¼ 0.003), White versus Black race/ethnicity (OR ¼ 0.55, 0.42–0.71), 65 years of age (OR ¼ 3.32, 2.72–4.06), obese (OR ¼ 0.77, 0.63–0.93, P ¼ 0.008), diabetic (OR ¼ 1.32, 1.06–1.65, P ¼ 0.013), independent versus partially or totally dependent functional status (OR ¼ 0.11, 0.08–0.15), operation time 4 hours (OR ¼ 2.46, 1.87–3.25), cardiac comorbidity (OR ¼ 1.38, 1.10–1.72, P ¼ 0.005), and ASA Class 3 (OR ¼ 2.57, 2.05–3.20) were predictive factors in patient discharge to a facility other than home (Table 3). All 1540
www.spinejournal.com
Patient Discharge After Cervical Discectomy and Fusion Di Capua et al
P-values are < 0.001 unless otherwise stated. Moreover, the C statistic for this multivariate logistic regression model was 0.79. A multivariate logistic regression model was also utilized to identify predictors for prolonged LOS. The model identified Hispanic versus Black race/ethnicity (OR ¼ 1.07, 0.51–2.46, P ¼ 0.856), American Indian, Alaska Native, Asian, Native Hawaiian, Pacific Islander versus Black race/ethnicity (OR ¼ 0.75, 0.52–1.08, P ¼ 0.119), White versus Black race/ethnicity (OR ¼ 0.56, 0.43–0.71), diabetes (OR ¼ 1.25, 1.00–1.54, P ¼ 0.046), independent versus partially or totally dependent functional status (OR ¼ 0.38, 0.25–0.56), operation time 4 hours (OR ¼ 3.83, 3.01– 4.86), pulmonary comorbidity (OR ¼ 1.36, 1.05–1.75, P ¼ 0.019), cardiac comorbidity (OR ¼ 1.38, 1.14–1.68, P ¼ 0.001), bleeding disorder (OR ¼ 2.25, 1.27–4.01, P ¼ 0.006), preoperative RBC transfusion (OR ¼ 11.57, 1.16–115.17, P ¼ 0.037), ASA Class 3 (OR ¼ 1.82, 1.49–2.22 and discharge to a facility other than home (OR ¼ 17.03, 13.67–21.21) to be predictive factors for prolonged LOS (Table 4). All P-values are < 0.001 unless otherwise stated. Discharge to a destination other than home was the most predictive factor for prolonged hospital LOS. The C statistic for this multivariate logistic regression model was 0.80.
DISCUSSION This retrospective analysis of the 2011–2014 ACS-NSQIP database identified several risk factors for patient discharge to a facility other than home after elective ACDF. ACSNSQIP 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.18–22 The success of quality improvement initiatives based on ACS-NSQIP data have been validated by the decreased mortality rates in the VA system, and decreased surgical site infection rates in the private sector.21,23 To our knowledge, this is the first large cohort analysis using a national database to identify predictors for patient discharge. Discharge planning is an interdisciplinary team activity and begins soon after surgery to ensure efficient hospitalization and utilization of resources.24 Our multivariate logistic regression analysis revealed several predictive preoperative and intraoperative factors for patient discharge destination after elective ACDF (Table 3). Significant variables include race, age 65 years, body mass index (BMI) 30 kg/m2, diabetes, partially or totally dependent functional status, cardiac comorbidity, ASA Class 3, and prolonged operative time. Patients who were partially or totally functionally dependent had over a nine times greater odds of being discharged to a facility other than home, making functional status the strongest predictor. Functional status, as defined as a decreased ability to perform activities of daily living, is associated with increased frailty and surgical complications.25–27 It has been suggested this October 2017
Copyright © 2017 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
CERVICAL SPINE
Patient Discharge After Cervical Discectomy and Fusion Di Capua et al
TABLE 1. Patient Demographic, Preoperative, and Intraoperative Characteristics Between
Discharge Destination Groups (N ¼ 14,598) Category Sex Male Female Age 65 years Race/Ethnicity White Other Black Hispanic Obese Diabetes Dyspnea Functional status Independent Partially/Totally dependent Pulmonary comorbidity Cardiac comorbidity Renal comorbidity Smoke Steroid Recent weight loss Bleeding disorder Preoperative RBC transfusion ASA Class 3 Operation time 4 Hours Operation year 2011 2012 2013 2014 Osteotomy Intervertebral device Length of stay 5 days
Discharge Discharge Destination Other Destination Other Than Home (N) Than Home (%)
Discharge Destination Home (%)
P
240 258 269
48.2 51.8 54.0
6786 7314 2685
48.1 51.9 19.0
376 37 83 2 222 150 51
75.5 7.4 16.7 0.4 44.6 30.1 10.2
11,509 1207 1241 143 6242 1982 727
81.6 8.6 8.8 1.0 44.3 14.1 5.2
