1387 C OPYRIGHT 2014
BY
T HE J OURNAL
OF
B ONE
AND J OINT
S URGERY, I NCORPORATED
Differences in Short-Term Complications Between Unicompartmental and Total Knee Arthroplasty A Propensity Score Matched Analysis Kyle R. Duchman, MD, Yubo Gao, PhD, Andrew J. Pugely, MD, Christopher T. Martin, MD, and John J. Callaghan, MD Investigation performed at the Department of Orthopaedic Surgery and Rehabilitation, University of Iowa Hospitals and Clinics, Iowa City, Iowa
Background: Knee arthroplasty has emerged as an effective treatment for end-stage gonarthrosis. Although total knee arthroplasty remains the gold standard, unicompartmental knee arthroplasty is an appropriate alternative for select patients. We sought to use a large, heterogeneous national database to identify differences in thirty-day complication rates between unicompartmental and total knee arthroplasty as well as to identify risk factors for complications. Methods: Patients in the ACS NSQIP (American College of Surgeons National Surgical Quality Improvement Program) database who had undergone total or unicompartmental knee arthroplasty from 2005 to 2011 were identified. CPT (Current Procedural Terminology) codes were used to select cases of elective primary knee arthroplasty. Statistical models employing univariate and multivariate logistic regression identified risk factors associated with the thirty-day incidence of morbidity and mortality after total and unicompartmental knee arthroplasty. Propensity score matching addressed demographic differences between the total and unicompartmental knee arthroplasty cohorts. Results: A total of 29,333 patients were identified; 27,745 (94.6%) underwent total knee arthroplasty and 1588 (5.41%) underwent unicompartmental knee arthroplasty. Prior to matching, the total knee arthroplasty cohort was 63.7% female and had a mean BMI of 32.8 ± 7.3 kg/m2, whereas the values for the unicompartmental cohort were 55.3% and 31.5 ± 6.5 kg/m2 (p < 0.0001). The mean ages of these cohorts were 67.2 ± 10.1 and 64.0 ± 10.7 years, respectively (p < 0.0001). A previously developed and implemented propensity score matching algorithm was used to address the demographic differences. Following matching, the total complication rate did not differ significantly between the total and unicompartmental knee arthroplasty cohorts (5.29% compared with 4.16%, p = 0.35), whereas the rate of deep venous thrombosis (1.50% compared with 0.50%, p = 0.02) and the duration of hospital stay (3.4 compared with 2.2 days, p < 0.0001) were significantly higher in the total knee arthroplasty cohort. Conclusions: Comparison of total and unicompartmental knee arthroplasty revealed no differences in overall short-term (thirty-day) morbidity and mortality. Although this study does not address long-term subjective outcomes or implant survival, these findings should provide helpful information for surgeons counseling patients considering total and/or unicompartmental knee arthroplasty. Level of Evidence: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.
Peer Review: This article was reviewed by the Editor-in-Chief and one Deputy Editor, and it underwent blinded review by two or more outside experts. It was also reviewed by an expert in methodology and statistics. The Deputy Editor reviewed each revision of the article, and it underwent a final review by the Editor-in-Chief prior to publication. Final corrections and clarifications occurred during one or more exchanges between the author(s) and copyeditors.
O
ver one-half million total knee arthroplasties (TKAs) are performed in the United States annually, making TKA the most commonly performed inpatient musculoskeletal procedure1,2. Estimates from 2005 projected a 600% increase in
TKAs over the following two decades3, making TKA utilization and the treatment of gonarthrosis an important topic with regard to sustainability4,5. Unicompartmental knee arthroplasty (UKA) has long been recognized as an alternative procedure for symptomatic
Disclosure: None of the authors received payments or services, either directly or indirectly (i.e., via his or her institution), from a third party in support of any aspect of this work. One or more of the authors, or his or her institution, has had a financial relationship, in the thirty-six months prior to submission of this work, with an entity in the biomedical arena that could be perceived to influence or have the potential to influence what is written in this work. No author has had any other relationships, or has engaged in any other activities, that could be perceived to influence or have the potential to influence what is written in this work. The complete Disclosures of Potential Conflicts of Interest submitted by authors are always provided with the online version of the article.
J Bone Joint Surg Am. 2014;96:1387-94
d
http://dx.doi.org/10.2106/JBJS.M.01048
1388 TH E JO U R NA L O F B O N E & JO I N T SU RG E RY J B J S . O RG V O L U M E 9 6-A N U M B E R 16 A U G U S T 20, 2 014 d
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unicompartmental arthritis. Market estimates suggest that UKA accounts for approximately 8% of the knee arthroplasties performed in the United States, with continued increases in annual utilization rates6,7. Internationally, joint registrars cite similar rates of UKA utilization, ranging from 8% to 12%7. Historically, early implant failure plagued first-generation UKA designs and limited their widespread adoption8,9. Longterm durability has improved with modern UKA implant designs, but long-term survivorship still fails to match that of TKA6,10-13. In small series, UKA has demonstrated accelerated recovery, improved early knee motion, and decreased duration of hospital stay compared with TKA14-18. Over the last several years, the ACS NSQIP (American College of Surgeons National Surgical Quality Improvement Program) database has emerged as a resource for analysis of surgical outcomes in both general and orthopaedic surgery. The purpose of the present study was to use the NSQIP database to identify the incidence of, and risk factors for, thirty-day morbidity and mortality after UKA and TKA as well as to perform a propensity score matched analysis to determine differences in complication rates. Materials and Methods Data Collection
T
his study was deemed exempt by our institutional review board. Patients who underwent primary knee arthroplasty from 2005 to 2011 were identified from the ACS NSQIP participant use data files. Patients were initially identified with use of CPT (Current Procedural Terminology) code 27447 for primary TKA and 27446 for primary UKA. ICD-9 (International Classification of Diseases, Ninth Revision) diagnosis codes and CPT codes were used to ensure that only elective cases of primary arthroplasty were included. Cases involving emergency treatment, wound infection, major ligament reconstruction, bilateral surgery, or prosthesis revision (CPT codes 27486, 27487, and 27488) were excluded. The ACS NSQIP collects, reports, and analyzes clinical data from over 480 hospitals within the United States across a variety of surgical specialties, including orthopaedic surgery. Use of the NSQIP to evaluate short-term complications has been extensively validated in the general surgery and ortho19-24 paedic literature . Surgical clinical reviewers use strict definitions to collect patient demographics, comorbidities, laboratory values, operative variables, and postoperative thirty-day morbidity and mortality data. Data collection continues for thirty days regardless of inpatient status, and high data fidelity is ensured through chart review, surgeon contact, and patient telephone communication. 19 These techniques and routine auditing ensure data disagreement rates of 250 preoperative and postoperative patient characteristics. Notable preoperative patient data categories include demographics, preoperative health and comorbidities, laboratory values, and perioperative variables. Notable demographics include age, sex, and race. Preoperative health and comorbidities charted include functional status (competence with activities of daily living), body mass index (BMI), recent weight loss (10% of total body weight over a six-month period), diabetes mellitus (oral or insulindependent), smoking status, alcohol consumption, chronic obstructive pulmonary disease (COPD), coronary artery disease (including chronic heart failure or history of myocardial infarction), peripheral vascular disease, history of stroke or transient ischemic attack, dialysis, corticosteroid use, bleeding disorders, preoperative blood transfusion (defined as a transfusion of one or more units of blood within three days of surgery), an open wound (defined as any laceration or ulceration identified on the body and not necessarily isolated to the operatively treated limb in question), radiation therapy (within ninety days
D I F F E R E N C E S I N S H O R T -T E R M C O M P L I C AT I O N S B E T W E E N U N I C O M PA R T M E N TA L A N D T O TA L K N E E A R T H R O P L A S T Y
prior to surgery), chemotherapy (within thirty days prior to surgery), a recent operation (within thirty days prior to the index procedure), and preoperative sepsis. Preoperative laboratory values captured include white blood-cell count (WBC), hematocrit, platelet count, serum creatinine, blood urea nitrogen, serum albumin, and the international normalized ratio (INR). Operative variables include ASA (American Society of Anesthesiologists) classification, wound class, intraoperative blood transfusions, duration of the operation, and resident involvement.
Outcomes The ACS NSQIP reports the duration of hospital stay and surgical complications within thirty days postoperatively across the following categories: nosocomial surgical site infections (SSIs) as described by the CDC (Centers for Disease 25 Control and Prevention) definitions (superficial SSIs, deep SSIs, organ-space SSIs, and wound dehiscence), systemic infections (pneumonia, urinary tract infection, sepsis, septic shock), respiratory (unplanned reintubation), hematologic (deep venous thrombosis, pulmonary embolism, and postoperative blood transfusion [defined as any blood transfusion within seventy-two hours]), renal (renal insufficiency and acute renal failure), neurologic (stroke, coma lasting more than twenty-four hours, and peripheral nerve injury), cardiac (cardiac arrhythmic arrest and myocardial infarction), implant failure, reoperation within thirty days, and mortality. Complications were reported in aggregate, and patients with multiple complications were analyzed once.
Statistical Analysis Multiple statistical methods were employed. Unadjusted thirty-day complication rates were reported independently for TKA and UKA. Although classified as a complication by NSQIP metrics, postoperative blood transfusion was excluded as a complication during statistical analysis because of the high variability in institutional protocols for blood transfusion after knee arthroplasty during the time frame of this study (2005 to 2011). Transfusion rates during this time period were 26-29 highly variable throughout the literature . Two separate forms of advanced analysis, multivariate logistic regression and propensity score matching, were used. Multivariate logistic regression was used to identify risk factors for complications after TKA and UKA. Propensity scores were used to minimize selection bias between the TKA and UKA cohorts, allowing comparison of thirty-day complication rates. Each form of advanced analysis required univariate comparison of all variables followed by selective inclusion of variables. All statistical analysis was performed with use of SAS software (version 9.3; SAS Institute, Cary, North Carolina). For the multivariate logistic regression analysis, both the TKA and UKA cohorts were separated into ‘‘complication’’ and ‘‘no complication’’ groups on the basis of the presence or absence of any complication within the thirty days. Patient demographics, preoperative health and comorbidities, laboratory values, and perioperative variables were compared between the two cohorts with use of univariate means and the Student two-tailed t test (for continuous variables) or chi-square analysis (for categorical variables). A p value cutoff of £0.1 was used for inclusion in the multivariate model. Univariate variables meeting this cutoff included age, sex, race, BMI, recent weight loss, diabetes mellitus, smoking, dialysis, corticosteroids, a bleeding disorder, preoperative transfusion, an open wound, preoperative sepsis, WBC, hematocrit, platelet count, serum creatinine, and ASA class. Additional variables such as alcohol use, peripheral vascular disease, history of transient ischemic attack, chemotherapy, another recent operation, serum albumin, and INR all had a p value of £0.1 but lacked >80% chart completion and were excluded to avoid excess case attrition and population skewing. With the dependent (outcome) variable set as ‘‘any complication,’’ multivariate logistic regression analysis was used to identify independent risk factors. Pearson correlation testing ensured minimal collinearity between included variables. All analyses were conducted separately for the TKA and UKA cohorts as well as for a combined UKA plus TKA cohort. Propensity scores provided a method to control for selection bias between the TKA and UKA cohorts by matching the UKA cohort with a subset of the TKA cohort. It has been well described in the medical literature for use in
1389 TH E JO U R NA L O F B O N E & JO I N T SU RG E RY J B J S . O RG V O L U M E 9 6-A N U M B E R 16 A U G U S T 20, 2 014 d
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D I F F E R E N C E S I N S H O R T -T E R M C O M P L I C AT I O N S B E T W E E N U N I C O M PA R T M E N TA L A N D T O TA L K N E E A R T H R O P L A S T Y
TABLE I Characteristics of the TKA and UKA Cohorts TKA P Value Variable*
Unadjusted, N = 27,745
Matched, N = 1588
UKA, N = 1588
Unadjusted
Matched
BY
T HE J OURNAL
OF
B ONE
AND J OINT
S URGERY, I NCORPORATED
Differences in Short-Term Complications Between Unicompartmental and Total Knee Arthroplasty A Propensity Score Matched Analysis Kyle R. Duchman, MD, Yubo Gao, PhD, Andrew J. Pugely, MD, Christopher T. Martin, MD, and John J. Callaghan, MD Investigation performed at the Department of Orthopaedic Surgery and Rehabilitation, University of Iowa Hospitals and Clinics, Iowa City, Iowa
Background: Knee arthroplasty has emerged as an effective treatment for end-stage gonarthrosis. Although total knee arthroplasty remains the gold standard, unicompartmental knee arthroplasty is an appropriate alternative for select patients. We sought to use a large, heterogeneous national database to identify differences in thirty-day complication rates between unicompartmental and total knee arthroplasty as well as to identify risk factors for complications. Methods: Patients in the ACS NSQIP (American College of Surgeons National Surgical Quality Improvement Program) database who had undergone total or unicompartmental knee arthroplasty from 2005 to 2011 were identified. CPT (Current Procedural Terminology) codes were used to select cases of elective primary knee arthroplasty. Statistical models employing univariate and multivariate logistic regression identified risk factors associated with the thirty-day incidence of morbidity and mortality after total and unicompartmental knee arthroplasty. Propensity score matching addressed demographic differences between the total and unicompartmental knee arthroplasty cohorts. Results: A total of 29,333 patients were identified; 27,745 (94.6%) underwent total knee arthroplasty and 1588 (5.41%) underwent unicompartmental knee arthroplasty. Prior to matching, the total knee arthroplasty cohort was 63.7% female and had a mean BMI of 32.8 ± 7.3 kg/m2, whereas the values for the unicompartmental cohort were 55.3% and 31.5 ± 6.5 kg/m2 (p < 0.0001). The mean ages of these cohorts were 67.2 ± 10.1 and 64.0 ± 10.7 years, respectively (p < 0.0001). A previously developed and implemented propensity score matching algorithm was used to address the demographic differences. Following matching, the total complication rate did not differ significantly between the total and unicompartmental knee arthroplasty cohorts (5.29% compared with 4.16%, p = 0.35), whereas the rate of deep venous thrombosis (1.50% compared with 0.50%, p = 0.02) and the duration of hospital stay (3.4 compared with 2.2 days, p < 0.0001) were significantly higher in the total knee arthroplasty cohort. Conclusions: Comparison of total and unicompartmental knee arthroplasty revealed no differences in overall short-term (thirty-day) morbidity and mortality. Although this study does not address long-term subjective outcomes or implant survival, these findings should provide helpful information for surgeons counseling patients considering total and/or unicompartmental knee arthroplasty. Level of Evidence: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.
Peer Review: This article was reviewed by the Editor-in-Chief and one Deputy Editor, and it underwent blinded review by two or more outside experts. It was also reviewed by an expert in methodology and statistics. The Deputy Editor reviewed each revision of the article, and it underwent a final review by the Editor-in-Chief prior to publication. Final corrections and clarifications occurred during one or more exchanges between the author(s) and copyeditors.
O
ver one-half million total knee arthroplasties (TKAs) are performed in the United States annually, making TKA the most commonly performed inpatient musculoskeletal procedure1,2. Estimates from 2005 projected a 600% increase in
TKAs over the following two decades3, making TKA utilization and the treatment of gonarthrosis an important topic with regard to sustainability4,5. Unicompartmental knee arthroplasty (UKA) has long been recognized as an alternative procedure for symptomatic
Disclosure: None of the authors received payments or services, either directly or indirectly (i.e., via his or her institution), from a third party in support of any aspect of this work. One or more of the authors, or his or her institution, has had a financial relationship, in the thirty-six months prior to submission of this work, with an entity in the biomedical arena that could be perceived to influence or have the potential to influence what is written in this work. No author has had any other relationships, or has engaged in any other activities, that could be perceived to influence or have the potential to influence what is written in this work. The complete Disclosures of Potential Conflicts of Interest submitted by authors are always provided with the online version of the article.
J Bone Joint Surg Am. 2014;96:1387-94
d
http://dx.doi.org/10.2106/JBJS.M.01048
1388 TH E JO U R NA L O F B O N E & JO I N T SU RG E RY J B J S . O RG V O L U M E 9 6-A N U M B E R 16 A U G U S T 20, 2 014 d
d
d
unicompartmental arthritis. Market estimates suggest that UKA accounts for approximately 8% of the knee arthroplasties performed in the United States, with continued increases in annual utilization rates6,7. Internationally, joint registrars cite similar rates of UKA utilization, ranging from 8% to 12%7. Historically, early implant failure plagued first-generation UKA designs and limited their widespread adoption8,9. Longterm durability has improved with modern UKA implant designs, but long-term survivorship still fails to match that of TKA6,10-13. In small series, UKA has demonstrated accelerated recovery, improved early knee motion, and decreased duration of hospital stay compared with TKA14-18. Over the last several years, the ACS NSQIP (American College of Surgeons National Surgical Quality Improvement Program) database has emerged as a resource for analysis of surgical outcomes in both general and orthopaedic surgery. The purpose of the present study was to use the NSQIP database to identify the incidence of, and risk factors for, thirty-day morbidity and mortality after UKA and TKA as well as to perform a propensity score matched analysis to determine differences in complication rates. Materials and Methods Data Collection
T
his study was deemed exempt by our institutional review board. Patients who underwent primary knee arthroplasty from 2005 to 2011 were identified from the ACS NSQIP participant use data files. Patients were initially identified with use of CPT (Current Procedural Terminology) code 27447 for primary TKA and 27446 for primary UKA. ICD-9 (International Classification of Diseases, Ninth Revision) diagnosis codes and CPT codes were used to ensure that only elective cases of primary arthroplasty were included. Cases involving emergency treatment, wound infection, major ligament reconstruction, bilateral surgery, or prosthesis revision (CPT codes 27486, 27487, and 27488) were excluded. The ACS NSQIP collects, reports, and analyzes clinical data from over 480 hospitals within the United States across a variety of surgical specialties, including orthopaedic surgery. Use of the NSQIP to evaluate short-term complications has been extensively validated in the general surgery and ortho19-24 paedic literature . Surgical clinical reviewers use strict definitions to collect patient demographics, comorbidities, laboratory values, operative variables, and postoperative thirty-day morbidity and mortality data. Data collection continues for thirty days regardless of inpatient status, and high data fidelity is ensured through chart review, surgeon contact, and patient telephone communication. 19 These techniques and routine auditing ensure data disagreement rates of 250 preoperative and postoperative patient characteristics. Notable preoperative patient data categories include demographics, preoperative health and comorbidities, laboratory values, and perioperative variables. Notable demographics include age, sex, and race. Preoperative health and comorbidities charted include functional status (competence with activities of daily living), body mass index (BMI), recent weight loss (10% of total body weight over a six-month period), diabetes mellitus (oral or insulindependent), smoking status, alcohol consumption, chronic obstructive pulmonary disease (COPD), coronary artery disease (including chronic heart failure or history of myocardial infarction), peripheral vascular disease, history of stroke or transient ischemic attack, dialysis, corticosteroid use, bleeding disorders, preoperative blood transfusion (defined as a transfusion of one or more units of blood within three days of surgery), an open wound (defined as any laceration or ulceration identified on the body and not necessarily isolated to the operatively treated limb in question), radiation therapy (within ninety days
D I F F E R E N C E S I N S H O R T -T E R M C O M P L I C AT I O N S B E T W E E N U N I C O M PA R T M E N TA L A N D T O TA L K N E E A R T H R O P L A S T Y
prior to surgery), chemotherapy (within thirty days prior to surgery), a recent operation (within thirty days prior to the index procedure), and preoperative sepsis. Preoperative laboratory values captured include white blood-cell count (WBC), hematocrit, platelet count, serum creatinine, blood urea nitrogen, serum albumin, and the international normalized ratio (INR). Operative variables include ASA (American Society of Anesthesiologists) classification, wound class, intraoperative blood transfusions, duration of the operation, and resident involvement.
Outcomes The ACS NSQIP reports the duration of hospital stay and surgical complications within thirty days postoperatively across the following categories: nosocomial surgical site infections (SSIs) as described by the CDC (Centers for Disease 25 Control and Prevention) definitions (superficial SSIs, deep SSIs, organ-space SSIs, and wound dehiscence), systemic infections (pneumonia, urinary tract infection, sepsis, septic shock), respiratory (unplanned reintubation), hematologic (deep venous thrombosis, pulmonary embolism, and postoperative blood transfusion [defined as any blood transfusion within seventy-two hours]), renal (renal insufficiency and acute renal failure), neurologic (stroke, coma lasting more than twenty-four hours, and peripheral nerve injury), cardiac (cardiac arrhythmic arrest and myocardial infarction), implant failure, reoperation within thirty days, and mortality. Complications were reported in aggregate, and patients with multiple complications were analyzed once.
Statistical Analysis Multiple statistical methods were employed. Unadjusted thirty-day complication rates were reported independently for TKA and UKA. Although classified as a complication by NSQIP metrics, postoperative blood transfusion was excluded as a complication during statistical analysis because of the high variability in institutional protocols for blood transfusion after knee arthroplasty during the time frame of this study (2005 to 2011). Transfusion rates during this time period were 26-29 highly variable throughout the literature . Two separate forms of advanced analysis, multivariate logistic regression and propensity score matching, were used. Multivariate logistic regression was used to identify risk factors for complications after TKA and UKA. Propensity scores were used to minimize selection bias between the TKA and UKA cohorts, allowing comparison of thirty-day complication rates. Each form of advanced analysis required univariate comparison of all variables followed by selective inclusion of variables. All statistical analysis was performed with use of SAS software (version 9.3; SAS Institute, Cary, North Carolina). For the multivariate logistic regression analysis, both the TKA and UKA cohorts were separated into ‘‘complication’’ and ‘‘no complication’’ groups on the basis of the presence or absence of any complication within the thirty days. Patient demographics, preoperative health and comorbidities, laboratory values, and perioperative variables were compared between the two cohorts with use of univariate means and the Student two-tailed t test (for continuous variables) or chi-square analysis (for categorical variables). A p value cutoff of £0.1 was used for inclusion in the multivariate model. Univariate variables meeting this cutoff included age, sex, race, BMI, recent weight loss, diabetes mellitus, smoking, dialysis, corticosteroids, a bleeding disorder, preoperative transfusion, an open wound, preoperative sepsis, WBC, hematocrit, platelet count, serum creatinine, and ASA class. Additional variables such as alcohol use, peripheral vascular disease, history of transient ischemic attack, chemotherapy, another recent operation, serum albumin, and INR all had a p value of £0.1 but lacked >80% chart completion and were excluded to avoid excess case attrition and population skewing. With the dependent (outcome) variable set as ‘‘any complication,’’ multivariate logistic regression analysis was used to identify independent risk factors. Pearson correlation testing ensured minimal collinearity between included variables. All analyses were conducted separately for the TKA and UKA cohorts as well as for a combined UKA plus TKA cohort. Propensity scores provided a method to control for selection bias between the TKA and UKA cohorts by matching the UKA cohort with a subset of the TKA cohort. It has been well described in the medical literature for use in
1389 TH E JO U R NA L O F B O N E & JO I N T SU RG E RY J B J S . O RG V O L U M E 9 6-A N U M B E R 16 A U G U S T 20, 2 014 d
d
d
D I F F E R E N C E S I N S H O R T -T E R M C O M P L I C AT I O N S B E T W E E N U N I C O M PA R T M E N TA L A N D T O TA L K N E E A R T H R O P L A S T Y
TABLE I Characteristics of the TKA and UKA Cohorts TKA P Value Variable*
Unadjusted, N = 27,745
Matched, N = 1588
UKA, N = 1588
Unadjusted
Matched
