Establishing the Minimal Clinically Important Difference, Patient Acceptable Symptomatic State, and Substantial Clinical Benefit of the PROMIS Upper Extremity Questionnaire After Rotator Cuff Repair

Authors

Eric D Haunschild, Ron Gilat, MD, Michael C Fu, MD, MHS, Tracy Tauro, Hailey P Huddleston, Adam B Yanke, MD, Brian Forsythe, Nikhil N Verma, MD, Brian J Cole, MD, MBA

Journal

American Journal of Sports Medicine. 2020 Oct 26;363546520964957. doi: 10.1177/0363546520964957.

Abstract

Background

The Patient-Reported Outcome Measurement Information System Upper Extremity (PROMIS UE) questionnaire has been validated as an effective and efficient outcome measure after rotator cuff repair (RCR). However, definitions of clinically significant outcomes used in interpreting this outcome measure have yet to be defined.

Purpose

To define clinically significant outcomes of the PROMIS UE questionnaire in patients undergoing arthroscopic RCR.

Study design

Cohort study (diagnosis); Level of evidence, 3.

Methods

We reviewed charts of consecutive patients undergoing RCR in our institution between 2017 and 2018 and included patients who were administered the PROMIS UE before surgery and 12 months after surgery. At 12 months postoperatively, patients were asked domain-specific anchor questions regarding their function and satisfaction after surgery, which were then used to determine the minimal clinically important difference (MCID), Patient Acceptable Symptomatic State (PASS), and substantial clinical benefit (SCB) using receiver operating characteristic and area under the curve (AUC) analysis. Univariate and multivariate logistic regression analysis was utilized to identify patient factors associated with clinically significant outcomes.

Results

A total of 105 patients with RCR and minimum 12-month postoperative PROMIS UE were included in the analysis. The defined clinically significant outcomes were 4.87 for the MCID using a distribution-based method, 7.95 for the SCB (sensitivity, 0.708; specificity, 0.833; AUC, 0.760), and 39.00 for the PASS (sensitivity, 0.789; specificity, 0.720; AUC, 0.815). Among respondents, 79.0%, 62.9%, and 64.8% achieved the MCID, SCB, and PASS score thresholds, respectively. Workers' compensation was negatively associated with achievement of the PASS. Lower preoperative PROMIS UE scores were associated with obtaining the MCID (odds ratio [OR], 0.871; P = .001) and the SCB (OR, 0.900; P = .040), whereas higher preoperative scores were predictive of achieving the PASS (OR, 1.111; P = .020).

Conclusion

This study defines the clinically significant outcomes for the PROMIS UE after RCR, of which the majority of patients achieved the MCID, PASS, and SCB at 12 months after surgery. These thresholds should be considered in future study design and interpretation of PROMIS UE in patients with RCR.

Keywords

MCID, PASS; PROMIS; SCB; rotator cuff repair.

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About the Author

Dr. Michael Fu is an orthopedic surgeon and shoulder specialist at the Hospital for Special Surgery (HSS) in New York City (NYC) and New Jersey (NJ), the best hospital for orthopedics as ranked by U.S. News & World Report. Dr. Fu treats the entire spectrum of shoulder conditions, including rotator cuff tears, shoulder instability, and shoulder arthritis. Dr. Fu was educated at Columbia University and Yale School of Medicine, followed by orthopedic surgery residency at HSS and sports medicine & shoulder surgery fellowship at Rush University Medical Center in Chicago. He has been a team physician for the Chicago Bulls, Chicago White Sox, DePaul University, and NYC’s PSAL.

Disclaimer: All materials presented on this website are the opinions of Dr. Michael Fu and any guest writers, and should not be construed as medical advice. Each patient’s specific condition is different, and a comprehensive medical assessment requires a full medical history, physical exam, and review of diagnostic imaging. If you would like to seek the opinion of Dr. Michael Fu for your specific case, we recommend contacting our office to make an appointment.

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Delay to Arthroscopic Rotator Cuff Repair Is Associated With Increased Risk of Revision Rotator Cuff Surgery

For additional coverage and information on this research from the Hospital for Special Surgery (HSS) , please click here.

Authors

Michael C. Fu, MD, MHS, Evan A. O'Donnell, MD, Samuel A. Taylor, MD, Oluwatobi M. Aladesuru, AB, Ryan C. Rauck, MD, Joshua S. Dines, MD, David M. Dines, MD, Russell F. Warren, MD, Lawrence V. Gulotta, MD

Journal

Orthopedics. 2020 Oct 1;1-5. doi: 10.3928/01477447-20200923-02.

Abstract

Purpose

The purpose of this study was to determine the association between time from the diagnosis of rotator cuff tear to repair and the rate of subsequent revision surgery for re-tear.

Methods

A national insurance database was queried from 2007 to 2016 for patients who underwent arthroscopic rotator cuff repair after a diagnosis of rotator cuff tear with minimum 5-year follow-up. On the basis of time from diagnosis to repair, patients were stratified into an early (<6 weeks), a routine (between 6 weeks and 12 months), or a delayed (>12 months) repair cohort. The rates of subsequent revision rotator cuff repair were compared pairwise between cohorts with Pearson's chi-square tests. Multivariate logistic regression was used to adjust for patient demographics and comorbidity burden.

Results

A total of 2759 patients were included, with 1510 (54.7%) undergoing early repair, 1104 (40.0%) undergoing routine repair, and 145 (5.3%) having delayed repair. The overall revision rate at 5-year follow-up was 9.6%. The revision rate was higher in the delayed group (15.2%) relative to the early (9.9%) and routine (8.3%) groups (P=.048 and P=.007, respectively). On multivariate analysis, delayed repair was associated with increased odds of revision surgery (odds ratio, 1.97; P=.009) compared with routine repair.

Conclusions

Delayed rotator cuff repair beyond 12 months of diagnosis was associated with an increased risk of undergoing subsequent revision rotator cuff repair while controlling for age and comorbidity burden.

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About the Author

Dr. Michael Fu is an orthopedic surgeon and shoulder specialist at the Hospital for Special Surgery (HSS) in New York City (NYC) and New Jersey (NJ), the best hospital for orthopedics as ranked by U.S. News & World Report. Dr. Fu is an expert at shoulder rotator cuff repair surgery, shoulder instability surgery, and shoulder replacement. Dr. Fu was educated at Columbia University and Yale School of Medicine, followed by orthopedic surgery residency at HSS and sports medicine & shoulder surgery fellowship at Rush University Medical Center in Chicago. He has been a team physician for the Chicago Bulls, Chicago White Sox, DePaul University, and NYC’s PSAL.

Disclaimer: All materials presented on this website are the opinions of Dr. Michael Fu and any guest writers, and should not be construed as medical advice. Each patient’s specific condition is different, and a comprehensive medical assessment requires a full medical history, physical exam, and review of diagnostic imaging. If you would like to seek the opinion of Dr. Michael Fu for your specific case, we recommend contacting our office to make an appointment.

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Development of Supervised Machine Learning Algorithms for Prediction of Satisfaction at Two Years Following Total Shoulder Arthroplasty

Authors

Evan M. Polce, BS, Kyle N. Kunze, MD, Michael Fu, MD, Grant E. Garrigues, MD, Brian Forsythe, MD, Gregory P. Nicholson, MD, Brian J. Cole, MD MBA, Nikhil N. Verma, MD

Journal

Journal of Shoulder and Elbow Surgery. 2020 Sep 30. DOI:https://doi.org/10.1016/j.jse.2020.09.007.

Abstract

Background

Patient satisfaction after primary anatomic and reverse total shoulder arthroplasty (TSA) represents an important metric for gauging patient perception of their care and surgical outcome. Although TSA confers improvement in pain and function for most patients, inevitably some will remain unsatisfied postoperatively. The purpose of the present study was to (1) train supervised machine learning (SML) algorithms to predict satisfaction after TSA and (2) develop a clinical tool for individualized assessment of patient-specific risk factors.

Methods

A retrospective review of primary anatomic and reverse TSA patients between January 2014 and February 2018 was performed. A total of 16 demographic, clinical, and patient-reported outcomes were evaluated for predictive value. Five SML algorithms underwent three iterations of 10-fold cross-validation on a training set (80% of cohort). Assessment by discrimination, calibration, Brier score, and decision curve analysis was performed on an independent testing set (remaining 20% of cohort). Global and local model behavior were evaluated with global variable importance plots and local interpretable model-agnostic explanation, respectively.

Results

The study cohort consisted of 413 patients, of which 331 (82.6%) were satisfied at two-years postoperatively. The support vector machine (SVM) model demonstrated the best relative performance on the independent testing set not used for model training (c-statistic=0.80, calibration intercept=0.20, calibration slope=2.32, Brier score=0.11). The most important factors for predicting satisfaction were baseline single assessment numeric evaluation (SANE) score, exercise and activity, workers compensation status, diagnosis, symptom duration prior to surgery, body mass index, age, smoking status, anatomic vs. reverse TSA, and diabetes. The SVM algorithm was incorporated into an open-access digital application for patient-level explanations of risk and predictions available here: https://orthopedics.shinyapps.io/SatisfactionTSA/

Conclusion

The best performing SML model demonstrated excellent discrimination and adequate calibration for predicting satisfaction following TSA and was used to create an open-access, clinical-decision making tool. However, rigorous external validation in different geographic locations and patient populations is essential prior to assessment of clinical utility. Given that this tool is based on partially modifiable risk factors it may enhance shared decision making and allow for periods of targeted, preoperative health optimization efforts.

Keywords

Total shoulder arthroplasty, satisfaction, classification, feature selection, cross-validation, supervised machine learning (SML), support vector machine (SVM)

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About the Author

Dr. Michael Fu is an orthopedic surgeon and shoulder specialist at the Hospital for Special Surgery (HSS), the best hospital for orthopedics as ranked by U.S. News & World Report. Dr. Fu treats the entire spectrum of shoulder conditions, including rotator cuff tears, shoulder instability, and shoulder arthritis. Dr. Fu was educated at Columbia University and Yale School of Medicine, followed by orthopedic surgery residency at HSS and sports medicine & shoulder surgery fellowship at Rush University Medical Center in Chicago. He has been a team physician for the Chicago Bulls, Chicago White Sox, DePaul University, and NYC’s PSAL.

Disclaimer: All materials presented on this website are the opinions of Dr. Michael Fu and any guest writers, and should not be construed as medical advice. Each patient’s specific condition is different, and a comprehensive medical assessment requires a full medical history, physical exam, and review of diagnostic imaging. If you would like to seek the opinion of Dr. Michael Fu for your specific case, we recommend contacting our office to make an appointment.

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The Effect of Patient Characteristics and Comorbidities on the Rate of Revision Rotator Cuff Repair

Authors:

Evan A. O’Donnell, MD, Michael C. Fu, MD, MHS, Alex E. White, MD, Samuel A. Taylor, MD, Joshua S. Dines, MD, David M. Dines, MD, Russell F. Warren, MD, Lawrence V. Gulotta, MD

Journal:

Arthroscopy: The Journal of Arthroscopic and Related Surgery, 2020-09-01, Volume 36, Issue 9, Pages 2380-2388

Abstract:

Purpose

To describe the national rates of failed primary rotator cuff repair (RCR) requiring revision repair, using numerous patient characteristics previously defined in orthopaedic literature, including smoking history, diabetes mellitus (DM), hyperlipidemia (HLD), vitamin D deficiency, and osteoporosis to determine which factors independently affect the success of primary RCR.

Methods

A combined public and private national insurance database was searched from 2007 to 2016 for all patients who underwent RCR. Current Procedural Terminology codes were used to identify RCRs. Laterality modifiers for the primary surgery were used to identify subsequent revision RCRs. All patients who did not have a linked laterality modifier for the RCR Current Procedural Terminology code were excluded from the study. Basic demographics were recorded. International Classification of Diseases Ninth Revision codes were used to identify patient characteristics including Charlson Comorbidity Index, smoking status, DM, obesity, HLD, vitamin D deficiency, and osteoporosis. Patient age categorized as <60, 60-69, 70-74, or 75+ years old. Dichotomous data were analyzed with χ 2 testing. Multivariable logistic regression was used to characterize independent associations with revision RCR.

Results

Included in the study were 41,467 patients (41,844 shoulders, 52.7% male patients) who underwent primary arthroscopic RCR. Of all arthroscopic RCRs, 3072 patients (3463 shoulders, 53.5% male patients) underwent revision RCR (8.38%). In both primary and revision RCR, patients age 60 to 69 years were most prevalent, accounting for 38.4% and 37.6% of the cohorts, respectively. The average time from primary RCR to revision was 414.9 days (median 214.0 days). Increasing age and male sex (odds ratio [OR] 1.10, P = .019, 95% confidence interval [CI] 1.02-1.19) were significantly predictive of revision RCR. Of the remaining patient characteristics, smoking most strongly predicted revision RCR (OR 1.36, P < .001, CI 1.23-1.49). Obesity (OR 1.32, P < .001, CI 1.21-1.43), hyperlipidemia (OR 1.09, P = .032, CI 1.01-1.18), and vitamin D deficiency (OR 1.18, P < .001, CI 1.08-1.28) also increased risk of revision RCR significantly. DM was found to be protective against revision surgery (OR 0.84, P < .001, CI 0.76-0.92). Overall comorbidity burden as measured by the Charlson Comorbidity Index was not predictive of revision RCR.

Conclusions

Smoking, obesity, vitamin D deficiency, and HLD are shown to be independent risk factors for failure of primary RCR requiring revision RCR. However, despite the suggestions of previous studies, DM, osteoporosis, and overall comorbidity burden did not demonstrate independent associations in this study.

Level of Evidence

IV, Case Series

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About the Author

Dr. Michael Fu is an orthopedic surgeon and shoulder specialist at the Hospital for Special Surgery (HSS) in New York City (NYC) and New Jersey (NJ), the best hospital for orthopedics as ranked by U.S. News & World Report. Dr. Fu is an expert at shoulder rotator cuff repair surgery, shoulder instability surgery, and shoulder replacement. Dr. Fu was educated at Columbia University and Yale School of Medicine, followed by orthopedic surgery residency at HSS and sports medicine & shoulder surgery fellowship at Rush University Medical Center in Chicago. He has been a team physician for the Chicago Bulls, Chicago White Sox, DePaul University, and NYC’s PSAL.

Disclaimer: All materials presented on this website are the opinions of Dr. Michael Fu and any guest writers, and should not be construed as medical advice. Each patient’s specific condition is different, and a comprehensive medical assessment requires a full medical history, physical exam, and review of diagnostic imaging. If you would like to seek the opinion of Dr. Michael Fu for your specific case, we recommend contacting our office to make an appointment.

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PROMIS Physical Function Underperforms Psychometrically Relative to American Shoulder and Elbow Surgeons Score in Patients Undergoing Anatomic Total Shoulder Arthroplasty

Authors

Michael C. Fu, MD, MHS, Brenda Chang, MS, MPH, Alexandra C. Wong, BS, Benedict U. Nwachukwu, MD, MBA, Russell F. Warren, MD, David M. Dines, MD, Joshua S. Dines, MD, Frank A. Cordasco, MD, MS, Stephen Lyman, PhD, Lawrence V. Gulotta, MD

Journal

Journal of Shoulder and Elbow Surgery. 2019 Sep;28(9):1809-1815.

Abstract

Background

The purpose of this study was to evaluate the psychometric properties of the Patient-Reported Outcomes Measurement Information System (PROMIS) physical function computer adaptive test (PF-CAT) relative to the American Shoulder and Elbow Surgeons (ASES) score in patients with glenohumeral osteoarthritis undergoing primary anatomic total shoulder arthroplasty (TSA).

Methods

A retrospective study of an institutional TSA registry was performed. Preoperative PROMIS PF-CAT and ASES scores were collected. Floor and ceiling effects were determined, and convergent validity was established through Pearson correlations. Rasch partial credit modeling was used for psychometric analysis of the validity of PF-CAT and ASES question items. Person-item maps were generated to characterize the distribution of question responses along the latent dimension of shoulder disability.

Results

Responses from 179 patients (184 shoulders) were included. PF-CAT had a moderate correlation to ASES (r = 0.487; P < .001), with no floor or ceiling effects; ASES had a 1.1% floor effect and no ceiling effect. With iterative Rasch model item-reduction analysis eliminating poorly fitting question items, all possible PF-CAT items were eliminated after 6 iterations. With ASES, just 1 function question item was dropped. Person-item maps showed ASES to be superior to PROMIS PF-CAT psychometrically, with sequential and improved coverage of the latent dimension of shoulder disability.

Conclusion

Despite moderate correlation with ASES, PROMIS PF-CAT demonstrated inferior validity and psychometric properties in patients undergoing TSA. PF-CAT should not replace the ASES in this population of patients. KeywordsASES; PROMIS; arthroplasty; computer adaptive test; osteoarthritis; patient-reported outcomes; physical function; shoulder.

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About the Author

Dr. Michael Fu is an orthopedic surgeon and shoulder specialist at the Hospital for Special Surgery (HSS), the best hospital for orthopedics as ranked by U.S. News & World Report. Dr. Fu treats the entire spectrum of shoulder conditions, including rotator cuff tears, shoulder instability, and shoulder arthritis. Dr. Fu was educated at Columbia University and Yale School of Medicine, followed by orthopedic surgery residency at HSS and sports medicine & shoulder surgery fellowship at Rush University Medical Center in Chicago. He has been a team physician for the Chicago Bulls, Chicago White Sox, DePaul University, and NYC’s PSAL.

Disclaimer: All materials presented on this website are the opinions of Dr. Michael Fu and any guest writers, and should not be construed as medical advice. Each patient’s specific condition is different, and a comprehensive medical assessment requires a full medical history, physical exam, and review of diagnostic imaging. If you would like to seek the opinion of Dr. Michael Fu for your specific case, we recommend contacting our office to make an appointment.

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What Associations Exist Between Comorbidity Indices and Postoperative Adverse Events After Total Shoulder Arthroplasty?

Authors

Michael C. Fu, MD, MHS, Nathaniel T. Ondeck, MD, Benedict U. Nwachukwu, MD, MBA, Grant H. Garcia, MD, Lawrence V. Gulotta, MD, Nikhil N. Verma, MD, Jonathan N. Grauer, MD

Journal

Clinical Orthopaedics and Related Research. 2019 Apr; 477(4): 881–890.

Abstract

Background

Comorbidity indices like the modified Charlson Comorbidity Index (mCCI) and the modified Frailty Index (mFI) are commonly reported in large database outcomes research. It is unclear if they provide greater association and discriminative ability for postoperative adverse events after total shoulder arthroplasty (TSA) than simple variables.

Questions/Purposes

Using a large research database to examine postoperative adverse events after anatomic and reverse TSA, we asked: (1) Which demographic/anthropometric variable among age, sex, and body mass index (BMI) has the best discriminative ability as measured by receiver operating characteristics (ROC)? (2) Which comorbidity index, among the American Society of Anesthesiologists (ASA) classification, the mCCI, or the mFI, has the best ROC? (3) Does a combination of a demographic/anthropometric variable and a comorbidity index provide better ROC than either variable alone?

Methods

Patients who underwent TSA from 2005 to 2015 were identified from the National Surgical Quality Improvement Program (NSQIP). This multicenter database with representative samples from more than 600 hospitals in the United States was chosen for its prospectively collected data and documented superiority over administrative databases. Of an initial 10,597 cases identified, 70 were excluded due to missing age, sex, height, weight, or being younger than 18 years of age, leaving a total of 10,527 patients in the study. Demographics, medical comorbidities, and ASA scores were collected, while BMI, mCCI and mFI were calculated for each patient. Though all required data variables were found in the NSQIP, the completeness of data elements was not determined in this study, and missing data were treated as being the null condition. Thirty-day outcomes included postoperative severe adverse events, any adverse events, extended length of stay (LOS, defined as > 3 days), and discharge to a higher level of care. ROC analysis was performed for each variable and outcome, by plotting its sensitivity against one minus the specificity. The area under the curve (AUC) was used as a measure of model discriminative ability, ranging from 0 to 1, where 1 represents a perfectly accurate test, and 0.5 indicates a test that is no better than chance.

Results

Among demographic/anthropometric variables, age had a higher AUC (0.587–0.727) than sex (0.520–0.628) and BMI (0.492–0.546) for all study outcomes (all p < 0.050), while ASA (0.580–0.630) and mFI (0.568–0.622) had higher AUCs than mCCI (0.532–0.570) among comorbidity indices (all p < 0.050). A combination of age and ASA had higher AUCs (0.608–0.752) than age or ASA alone for any adverse event, extended LOS, and discharge to higher level of care (all p < 0.05). Notably, for nearly all variables and outcomes, the AUCs showed fair or moderate discriminative ability at best.

Conclusion

Despite the use of existing comorbidity indices adapted to large databases such as the NSQIP, they provide no greater association with adverse events after TSA than simple variables such as age and ASA status, which have only fair associations themselves. Based on database-specific coding patterns, the development of database- or NSQIP-specific indices may improve their ability to provide preoperative risk stratification.

Level of Evidence

Level III, diagnostic study.

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About the Author

Dr. Michael Fu is an orthopedic surgeon and shoulder specialist at the Hospital for Special Surgery (HSS), the best hospital for orthopedics as ranked by U.S. News & World Report. Dr. Fu treats the entire spectrum of shoulder conditions, including rotator cuff tears, shoulder instability, and shoulder arthritis. Dr. Fu was educated at Columbia University and Yale School of Medicine, followed by orthopedic surgery residency at HSS and sports medicine & shoulder surgery fellowship at Rush University Medical Center in Chicago. He has been a team physician for the Chicago Bulls, Chicago White Sox, DePaul University, and NYC’s PSAL.

Disclaimer: All materials presented on this website are the opinions of Dr. Michael Fu and any guest writers, and should not be construed as medical advice. Each patient’s specific condition is different, and a comprehensive medical assessment requires a full medical history, physical exam, and review of diagnostic imaging. If you would like to seek the opinion of Dr. Michael Fu for your specific case, we recommend contacting our office to make an appointment.

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