Chris A. Anthony

Performance of PROMIS Instruments in Patients With Shoulder Instability

Background: Shoulder instability is a relatively common condition occurring in 2% of the population. PROMIS (Patient-Reported Outcome Measurement Information System) was developed by the National Institutes of Health in an effort to advance patient-reported outcome (PRO) instruments by developing question banks for major health domains. Purpose: To compare PROMIS instruments to current PRO instruments in patients who would be undergoing operative intervention for recurrent shoulder instability. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A total of 74 patients with a primary diagnosis of shoulder instability who would be undergoing surgery were asked to fill out the American Shoulder and Elbow Surgeons shoulder assessment form (ASES), Marx shoulder activity scale (Marx), Short Form–36 Health Survey Physical Function subscale (SF-36 PF), Western Ontario Shoulder Instability Index (WOSI), PROMIS physical function computer adaptive test (PF CAT), and PROMIS upper extremity item bank (UE). Correlation between PRO instruments was defined as excellent (>0.7), excellent-good (0.61-0.7), good (0.4-0.6), and poor (0.2-0.3). Results: Utilization of the PROMIS UE demonstrated excellent correlation with the SF-36 PF (r = 0.78, P < .01) and ASES (r = 0.71, P < .01); there was excellent-good correlation with the EQ-5D (r = 0.66, P < .01), WOSI (r = 0.63, P < .01), and PROMIS PF CAT (r = 0.63, P < .01). Utilization of the PROMIS PF CAT demonstrated excellent correlation with the SF-36 PF (r = 0.72, P < .01); there was excellent-good correlation with the ASES (r = 0.67, P < .01) and PROMIS UE (r = 0.63, P < .01). When utilizing the PROMIS UE, ceiling effects were present in 28.6% of patients aged 18 to 21 years. Patients, on average, answered 4.6 ± 1.8 questions utilizing the PROMIS PF CAT. Conclusion: The PROMIS UE and PROMIS PF CAT demonstrated good to excellent correlation with common shoulder and upper extremity PRO instruments as well as the SF-36 PF in patients with shoulder instability. In patients aged ≤21 years, there were significant ceiling effects utilizing the PROMIS UE. While the PROMIS PF CAT appears appropriate for use in adults of any age, our findings demonstrate that the PROMIS UE has significant ceiling effects in patients with shoulder instability who are ≤21 years old, and we do not recommend use of the PROMIS UE in this population.

Can We Predict Discharge Status After Total Joint Arthroplasty? A Calculator to Predict Home Discharge

BACKGROUND Postoperative discharge to a skilled nursing facility after total joint arthroplasty (TJA) is associated with increased costs, complications, and readmission. The purpose of this study was to identify the risk factors for discharge to a location other than home to build a calculator to predict discharge disposition after TJA. METHODS The American College of Surgeons National Surgical Quality Improvement Program database was queried from 2011 to 2013 to identify patients who underwent primary total hip or total knee arthroplasty. Risk factors were compared between patients discharging home vs a facility. Predictors of facility discharge were converted to discrete values to develop a simple numerical calculator. RESULTS After primary TJA, patients discharged to a facility were typically older (70.9 vs 64.3, P < .001), female (69.5% vs 55.7%, P < .001), had an elevated American Society of Anesthesiologist (ASA) class, and were more likely to be functionally dependent before surgery (3.8% vs 1.1%, P < .001). Patient age, preoperative functional status, nonelective THA for hip fracture, and ASA class were most predictive of facility discharge. After development of a predictive model, scores exceeding 40 and 80 points resulted in a facility discharge probability of 75% and 99%, respectively. CONCLUSION In patients undergoing TJA, advanced age, elevated ASA class, and functionally dependent status before surgery strongly predicted facility discharge. Given that facility discharge imposes a significant cost and morbidity burden after TJA, patients, surgeons, and hospitals may use this simple calculator to target this susceptible patient population.

Outpatient blood pressure monitoring using bi–directional text messaging

To diagnose hypertension, multiple blood pressure (BP) measurements are recommended. We randomized patients into three groups: EMR-only (patients recorded BP measurements in an electronic medical record [EMR] web portal), EMR + reminders (patients were sent text message reminders to record their BP measurements in the EMR), and bi-directional text messaging (patients were sent a text message asking them to respond with their current BP). Subjects were asked to complete 14 measurements. Automated messages were sent to each patient in the bi-directional text messaging and EMR + reminder groups twice daily. Among 121 patients, those in the bi-directional text messaging group reported the full 14 measurements more often than both the EMR-only group (P < .001) and the EMR + reminders group (P = .038). Also, the EMR + reminders group outperformed the EMR-only group (P < .001). Bi-directional automated text messaging is an effective way to gather patient BP data. Text-message-based reminders alone are an effective way to encourage patients to record BP measurements.

Infection following Anterior Cruciate Ligament Reconstruction: An Analysis of 6,389 Cases

Abstract Infection following anterior cruciate ligament reconstruction (ACLR) is rare. Previous authors have concluded that diabetes, tobacco use, and previous knee surgery may influence infection rates following ACLR. The purpose of this study was to identify a cohort of patients undergoing ACLR and define (1) the incidence of infection after ACLR from a large multicenter database and (2) the risk factors for infection after ACLR. We identified patients undergoing elective ACLRs in the American College of Surgeons National Surgical Quality Improvement Program database between 2007 and 2013. The primary outcome was any surgical site infection within 30 days of surgery. We performed univariate and multivariate analyses comparing infected and noninfected cases to identify risk factors for infection. In total, 6,398 ACLRs were available for analysis of which 39 (0.61%) were diagnosed with a postoperative infection. Univariate analysis identified preoperative dyspnea, low hematocrit, operative time > 1 hour, and hospital admission following surgery as predictors of postoperative infection. Diabetes, tobacco use, age, and body mass index (BMI) were not associated with infection (p > 0.05). After multivariate analysis, the only independent predictor of postoperative infection was hospital admission following surgery (odds ratio, 2.67; 95% confidence interval, 1.02‐6.96; p = 0.04). Hospital admission following surgery was associated with an increased incidence of infection in this large, multicenter cohort. Smoking, elevated BMI, and diabetes did not increase the risk infection in the present study. Surgeons should optimize outpatient operating systems and practices to aid in same‐day discharges following ACLR.

Performance of PROMIS for Healthy Patients Undergoing Meniscal Surgery.

Background: The Patient-Reported Outcomes Measurement Information System (PROMIS) was developed as an extensive question bank with multiple health domains that could be utilized for computerized adaptive testing (CAT). In the present study, we investigated the use of the PROMIS Physical Function CAT (PROMIS PF CAT) in an otherwise healthy population scheduled to undergo surgery for meniscal injury with the hypotheses that (1) the PROMIS PF CAT would correlate strongly with patient-reported outcome instruments that measure physical function and would not correlate strongly with those that measure other health domains, (2) there would be no ceiling effects, and (3) the test burden would be significantly less than that of the traditional measures. Methods: Patients scheduled to undergo meniscal surgery completed the PROMIS PF CAT, Knee injury and Osteoarthritis Outcome Score (KOOS), Marx Knee Activity Rating Scale, Short Form-36 (SF-36), and EuroQol-5 Dimension (EQ-5D) questionnaires. Correlations were defined as high (≥0.7), high-moderate (0.61 to 0.69), moderate (0.4 to 0.6), moderate-weak (0.31 to 0.39), or weak (⩽0.3). If ≥15% respondents to a patient-reported outcome measure obtained the highest or lowest possible score, the instrument was determined to have a significant ceiling or floor effect. Results: A total of 107 participants were analyzed. The PROMIS PF CAT had a high correlation with the SF-36 Physical Functioning (PF) (r = 0.82, p < 0.01) and KOOS Sport (r = 0.76, p < 0.01) scores; a high-moderate correlation with the KOOS Quality-of-Life (QOL) (r = 0.63, p < 0.01) and EQ-5D (r = 0.62, p < 0.01) instruments; and a moderate correlation with the SF-36 Pain (r = 0.60, p < 0.01), KOOS Symptoms (r = 0.57, p < 0.01), KOOS Activities of Daily Living (ADL) (r = 0.60, p < 0.01), and KOOS Pain (r = 0.60, p < 0.01) scores. The majority (89%) of the patients completed the PROMIS PF CAT after answering only 4 items. The PROMIS PF CAT had no floor or ceiling effects, with 0% of the participants achieving the lowest and highest score, respectively. Conclusions: The PROMIS PF CAT correlates strongly with currently used patient-reported outcome measures of physical function and demonstrates no ceiling effects for patients with meniscal injury requiring surgery. It may be a reasonable alternative to more burdensome patient-reported outcome measures.

The Seasonal Variability of Surgical Site Infections in Knee and Hip Arthroplasty

BACKGROUND Surgical site infections (SSIs) after total knee (TKA) and total hip (THA) arthroplasty are devastating to patients and costly to healthcare systems. The purpose of this study is to investigate the seasonality of TKA and THA SSIs at a national level. METHODS All data were extracted from the National Readmission Database for 2013 and 2014. Patients were included if they had undergone TKA or THA. We modeled the odds of having a primary diagnosis of SSI as a function of discharge date by month, payer status, hospital size, and various patient co-morbidities. SSI status was defined as patients who were readmitted to the hospital with a primary diagnosis of SSI within 30 days of their arthroplasty procedure. RESULTS There were 760,283 procedures (TKA 424,104, THA 336,179) in our sample. Our models indicate that SSI risk was highest for patients discharged from their surgery in June and lowest for December discharges. For TKA, the odds of a 30-day readmission for SSI were 30.5% higher at the peak compared to the nadir time (95% confidence interval [CI] 20-42). For THA, the seasonal increase in SSI was 19% (95% CI 9-30). Compared to Medicare, patients with Medicaid as the primary payer had a 49% higher odds of 30-day SSI after TKA (95% CI 32-68). CONCLUSION SSIs following TKA and THA are seasonal peaking in summer months. Payer status was also a significant risk factor for SSIs. Future studies should investigate potential factors that could relate to the associations demonstrated in this study.

The AAHKS Clinical Research Award: What Are the Costs of Knee Osteoarthritis in the Year Prior to Total Knee Arthroplasty?

BACKGROUND Despite American Academy of Orthopaedic Surgeons Clinical Practice Guidelines (CPGs) related to the non-arthroplasty management of osteoarthritis (OA) of the knee, non-recommended treatments remain in common use. We sought to determine the costs associated with non-arthroplasty management of knee OA in the year prior to total knee arthroplasty (TKA) and stratify them by CPG recommendation status. METHODS The Humana database was reviewed from 2007 to 2015 for primary TKA patients. Costs for hyaluronic acid (HA) and corticosteroid injections, physical therapy, braces, wedge insoles, opioids, non-steroidal anti-inflammatories, and tramadol in the year prior to TKA were calculated. Cost was defined as reimbursement paid by the insurance provider. Costs were analyzed relative to the overall non-inpatient costs for knee OA and categorized based on CPG recommendations. RESULTS In total 86,081 primary TKA patients were analyzed and 65.8% had at least one treatment in the year prior to TKA. Treatments analyzed made up 57.6% of the total non-inpatient cost of knee OA in the year prior to TKA. Only 3 of the 8 treatments studied have a strong recommendation for their use (physical therapy, non-steroidal anti-inflammatories, tramadol) and costs for these interventions represented 12.2% of non-inpatient knee OA cost. In contrast, 29.3% of the costs are due to HA injections alone, which are not supported by CPGs. CONCLUSION In the year prior to TKA, over half of the non-inpatient costs associated with knee OA are from injections, therapy, prosthetics, and prescriptions. Approximately 30% of this is due to HA injections alone. If only interventions recommend by the CPG are utilized then costs associated with knee OA could be decreased by 45%.

Opioid Demand Before and After Anterior Cruciate Ligament Reconstruction

Background: Surgeons and health care systems have received a call to action in an effort to curtail the current opioid epidemic. Purpose: To (1) define the natural history of opioid demand after anterior cruciate ligament reconstruction (ACLR), (2) consider how filling preoperative opioid prescriptions affects opioid demand after ACLR, and (3) evaluate the effect of additional procedures during ACLR and patient age on postoperative opioid demand. Study Design: Cohort study; Level of evidence, 3. Methods: ACLRs performed in the Humana database between 2007 and 2014 were identified using Current Procedural Terminology code 29888. Patients were considered preoperative opioid users if they had filled an opioid prescription in the 3 months preceding surgery. Patients were defined as “chronic” opioid users if they had filled a prescription preoperatively at 1 to 3 months from surgery. Further categorization was performed by identifying patients who only underwent ACLR with no other procedures, those who underwent ACLR with meniscus repair, those who underwent ACLR with meniscectomy, and those who underwent ACLR with microfracture. Categorization by age was also performed. The relative risk (RR) of postoperative opioid use was calculated, and 95% CIs were determined. Results: Over the course of the study period, 4946 ACLRs were performed. At 3 months after their procedure, 7.24% of patients were still filling opioid prescriptions. At 9 and 12 months postoperatively, 4.97% and 4.71% of patients, respectively, were still filling opioid prescriptions. Nearly 35% of patients (1716/4946) were filling opioid pain prescriptions in the 3 months before ACLR. Those filling preoperative opioid prescriptions were 5.35 (95% CI, 4.15-6.90) times more likely to be filling opioid prescriptions at 3 months after ACLR than nonusers (15.38% vs 2.88%, respectively). Those filling opioid prescriptions chronically before surgery were at a 10.50 (95% CI, 7.53-14.64) times increased risk of filling postoperative opioid prescriptions at 5 months. At 5 months postoperatively, patients undergoing ACLR with microfracture had a 1.96 (95% CI, 1.34-2.87) increased risk of filling opioid prescriptions compared with ACLR alone, 2.38 (95% CI, 1.48-3.82) increased risk compared with ACLR with meniscus repair, and 1.51 (95% CI, 1.04-2.19) increased risk compared with ACLR with meniscectomy. Patients younger than 25 years of age had an increased risk of filling opioid prescriptions after ACLR at all time points of the study. Conclusion: Opioid demand after ACLR dropped significantly in the vast majority of patients by the third postoperative month. Surprisingly, 35% of patients undergoing ACLR were observed to be using opioid medication preoperatively, and this study found preoperative opioid use to be a strong predictor of postoperative opioid demand with a 5- to 7-fold increased risk in this patient population. Patients who were filling opioid prescriptions 1 to 3 months from their surgical date were at the highest risk for postoperative opioid utilization. Patients undergoing ACLR with microfracture were at an increased risk of filling opioid prescriptions. Patients less than 25 years of age were at an elevated risk of filling opioid prescriptions at all time points postoperatively.

Use of PROMIS for Patients Undergoing Primary Total Shoulder Arthroplasty

Background: The Patient-Reported Outcomes Measurement Information System (PROMIS) consists of question banks for health domains through computer adaptive testing (CAT). Hypothesis: For patients with glenohumeral arthritis, (1) there would be high correlation between traditional patient-reported outcome (PRO) measures and the PROMIS upper extremity item bank (PROMIS UE) and PROMIS physical function CAT (PROMIS PF CAT), and (2) PROMIS PF CAT would not demonstrate ceiling effects. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: Sixty-one patients with glenohumeral osteoarthritis were included. Each patient completed the American Shoulder and Elbow Surgeons (ASES) assessment form, Marx Shoulder Activity Scale, Short Form–36 physical function scale (SF-36 PF), EuroQol 5 Dimensions (EQ-5D) questionnaire, Western Ontario Osteoarthritis Shoulder (WOOS) index, PROMIS PF CAT, and the PROMIS UE. Correlation was defined as high (>0.7), moderate (0.4-0.6), or weak (0.2-0.3). Significant floor and ceiling effects were present if more than 15% of individuals scored the lowest or highest possible total score on any PRO. Results: The PROMIS PF demonstrated excellent correlation with the SF-36 PF (r = 0.81, P < .0001) and good correlation with the ASES (r = 0.62, P < .0001), EQ-5D (r = 0.64, P < .001), and WOOS index (r = 0.51, P < .01). The PROMIS PF demonstrated low correlation with the Marx scale (r = 0.29, P = .02). The PROMIS UE demonstrated good correlation with the ASES (r = 0.55, P < .0001), SF-36 (r = 0.53, P < .01), EQ-5D (r = 0.48, P < .01), and WOOS (r = 0.34, P <.01), and poor correlation with the Marx scale (r = 0.06, P = .62). There were no ceiling or floor effects observed. The mean number of items administered by the PROMIS PRO was 4. Conclusion: These data suggest that for a patient population with operative shoulder osteoarthritis, PROMIS UE and PROMIS PF CAT may be valid alternative PROs. Additionally, PROMIS PF CAT offers a decreased question burden with no ceiling effects.

Delivery of Patient-Reported Outcome Instruments by Automated Mobile Phone Text Messaging

Background: Patient-reported outcome (PRO) instruments allow patients to interpret their health and are integral in evaluating orthopedic treatments and outcomes. The purpose of this study was to define: (1) correlation between PROs collected by automated delivery of text messages on mobile phones compared with paper delivery; and (2) patient use characteristics of a technology platform utilizing automated delivery of text messages on mobile phones. Methods: Paper versions of the 12-Item Short Form Health Survey (SF-12) and the short form of the Disabilities of the Arm, Shoulder and Hand (QuickDASH) were completed by patients in orthopedic hand and upper extremity clinics. Over the next 48 hours, the same patients also completed the mobile phone portion of the study outside of the clinic which included text message delivery of the SF-12 and QuickDASH, assigned in a random order. Correlations between paper and text message delivery of the 2 PROs were assessed. Results: Among 72 patients, the intraclass correlation coefficient (ICC) between the written and mobile phone delivery of QuickDASH was 0.91 (95% confidence interval [CI], 0.85-0.95). The ICC between the paper and mobile phone delivery of the SF-12 physical health composite score was 0.88 (95% CI, 0.79-0.93) and 0.86 (95% CI, 0.75-0.92) for the SF-12 mental health composite score. Conclusions: We find that text message delivery using mobile phones permits valid assessment of SF-12 and QuickDASH scores. The findings suggest that software-driven automated delivery of text communication to patients via mobile phones may be a valid method to obtain other PRO scores in orthopedic patients.