The Danish Hip Arthroscopy Registry: Registration Completeness and Patient Characteristics Between Responders and Non-Responders

Engaging and following physical injury survivors at risk for developing posttraumatic stress disorder symptoms: A 25 site US national study

INTRODUCTION

Early intervention for patients at risk for Posttraumatic Stress Disorder (PTSD) relies upon the ability to engage and follow trauma-exposed patients. Recent requirements by the American College of Surgeons Committee on Trauma (College) have mandated screening and referral for patients with high levels of risk for the development of PTSD or depression. Investigations that assess factors associated with engaging and following physically injured patients may be essential in assessing outcomes related to screening, intervention, and referral.

METHODS

This investigation was a secondary analysis of data collected as part of a United States level I trauma center site randomized clinical trial. All 635 patients were ages ≥18 and had high PTSD symptom levels (i.e., DSM-IV PTSD Checklist score ≥35) at the time of the baseline trauma center admission. Baseline technology use, demographic, and injury characteristics were collected for patients who were followed up with over the course of the year after physical injury. Regression analyses were used to assess the associations between technology use, demographic and injury characteristics, and the attainment of follow-up outcome assessments.

RESULTS

Thirty-one percent of participants were missing one or more 3-, 6- or 12-month follow-up outcome assessments. Increased risk of missing one or more outcome assessments was associated with younger age (18-30 versus ≥55 Relative Risks [RR] = 1.78, 95 % Confidence Interval [CI] = 1.09, 2.91), lack of cell phone (RR = 1.32, 95 % CI = 1.01, 1.72), no internet access (RR = 1.47, 95 % CI = 1.01, 2.16), public versus private insurance (RR = 1.47, 95 % CI = 1.12, 1.92), having no chronic medical comorbidities (≥4 versus none, RR = 0.28, 95 % CI = 0.20, 0.39), and worse pre-injury mental health function (RR = 0.99, 95 % CI = 0.98, 0.99).

CONCLUSIONS

This multisite investigation suggests that younger and publicly insured and/or uninsured patients with barriers to cell phone and internet access may be particularly vulnerable to lapses in trauma center follow-up. Clinical research informing trauma center-based screening, intervention, and referral procedures could productively explore strategies for patients at risk for not engaging and adhering to follow-up care and outcome assessments.

Predictors of Postoperative Patient-Reported Outcome Measure Response Rates Among Patients With Rotator Cuff Repair

BACKGROUND

Patient-reported outcome measures (PROMs) contribute to evaluating and improving the quality of patient care. Patient outcomes after rotator cuff repair (RCR) have been researched; however, the relationship between PROM response rates and individual and health care correlates has not been thoroughly investigated.

PURPOSE

To examine differences in individual and health care factors among patients who had undergone RCR based on their PROM response rates.

STUDY DESIGN

Case-control study; Level of evidence, 3.

METHODS

Shoulder-specific and general PROMs were solicited via email and text message of all patients who underwent RCR between 2016 and 2020. Three subgroups were classified: (1) complete responders completed all 1-year postoperative PROMs, (2) partial responders answered enough questions to produce ≥1 usable score, and (3) nonresponders did not respond to a single measure. Correlates were assessed using analysis of variance and chi-square tests. Adjusted multinomial logistic regression models identified predictors of 1-year PROM response.

RESULTS

Of 2195 patients included at the 1-year follow-up, 34% were complete responders; 11%, partial responders; and 55%, nonresponders. Patients had a mean age of 61.8 years, 63% were men, and 90% were White. Pre- and postoperative PROM scores were similar across responder groups. In stepwise selection, 1-year responses (complete or partial) were associated with older age, later year of surgery, White race, and having workers' compensation insurance. The strongest predictor of PROM response was having workers' compensation insurance.

CONCLUSION

Patients with workers' compensation insurance compared with other insurance types responded to PROMs at disproportionately higher rates. This could distort postoperative PROM scores in the population studied because there are known differences among patients with this insurance status.

Young or Old Age and Non-White Race Are Associated With Poor Patient-Reported Outcome Measure Response Compliance After Orthopaedic Surgery

Purpose

To investigate orthopaedic patient compliance with patient-reported outcome measures (PROMs) and identify factors that improve response rates.

Methods

Our search strategy comprised a combination of key words and database-specific subject headings for the concepts of orthopaedic surgical procedures, compliance, and PROMs from several research databases from inception to October 11, 2022. Duplicates were removed. A total of 97 studies were included. A table was created for the remaining articles to be appraised and analyzed. The collected data included study characteristics, follow-up/compliance rate, factors that increase/decrease compliance, and type of PROM. Follow-up/compliance rate was determined to be any reported response rate. The range and average used for analysis was based on the highest or lowest number reported in the specific article.

Results

The range of compliance reported was 11.3% to 100%. The overall response rate was 68.6%. The average baseline (preoperative/previsit) response rate was 76.6%. Most studies (77%) had greater than 50% compliance. Intervention/reminder of any type (most commonly phone call or mail) resulted in improved compliance from 44.6% to 70.6%. Young and elderly non-White male patients had the lowest compliance rate. When directly compared, phone call (71.5%) resulted in a greater compliance rate than electronic-based (53.2%) or paper-based (57.6%) surveys.

Conclusions

The response rates for PROMs vary across the orthopaedic literature. Patient-specific factors, such as age (young or old) and race (non-White), may contribute to poor PROM response rate. Reminders and interventions significantly improve PROM response rates.

Clinical Relevance

PROMs are important tools in many aspects of medicine. The data generated from these tools not only provide information about individual patient outcomes but also make hypothesis-driven comparisons possible. Understanding the factors that affect patient compliance with PROMs is vital to our accurate understanding of patient outcomes and the overall advancement of medical care.

Stratified care in hip arthroscopy: can we predict successful and unsuccessful outcomes? Development and external temporal validation of multivariable prediction models

OBJECTIVE

Although hip arthroscopy is a widely adopted treatment option for hip-related pain, it is unknown whether preoperative clinical information can be used to assist surgical decision-making to avoid offering surgery to patients with limited potential for a successful outcome. We aimed to develop and validate clinical prediction models to identify patients more likely to have an unsuccessful or successful outcome 1 year post hip arthroscopy based on the patient acceptable symptom state.

METHODS

Patient records were extracted from the Danish Hip Arthroscopy Registry (DHAR). A priori, 26 common clinical variables from DHAR were selected as prognostic factors, including demographics, radiographic parameters of hip morphology and self-reported measures. We used 1082 hip arthroscopy patients (surgery performed 25 April 2012 to 4 October 2017) to develop the clinical prediction models based on logistic regression analyses. The development models were internally validated using bootstrapping and shrinkage before temporal external validation was performed using 464 hip arthroscopy patients (surgery performed 5 October 2017 to 13 May 2019).

RESULTS

The prediction model for unsuccessful outcomes showed best and acceptable predictive performance on the external validation dataset for all multiple imputations (Nagelkerke R2 range: 0.25-0.26) and calibration (intercept range: -0.10 to -0.11; slope range: 1.06-1.09), and acceptable discrimination (area under the curve range: 0.76-0.77). The prediction model for successful outcomes did not calibrate well, while also showing poor discrimination.

CONCLUSION

Common clinical variables including demographics, radiographic parameters of hip morphology and self-reported measures were able to predict the probability of having an unsuccessful outcome 1 year after hip arthroscopy, while the model for successful outcome showed unacceptable accuracy. The externally validated prediction model can be used to support clinical evaluation and shared decision making by informing the orthopaedic surgeon and patient about the risk of an unsuccessful outcome, and thus when surgery may not be appropriate.

Acetabular retroversion does not affect outcome in primary hip arthroscopy for femoroacetabular impingement

PURPOSE

The surgical treatment of femoral-acetabular impingement syndrome (FAIS) in patients with acetabular retroversion (AR) is arthroscopical or by a reverse periacetabular osteotomy (PAO). The purpose of the present study was to investigate the results after arthroscopic treatment of FAIS in patients with and without radiographic signs of AR in a large, prospective cohort from the Danish Hip Arthroscopy Registry (DHAR). The hypothesis was there is no difference in clinical outcome between the two groups.

METHODS

Data on 4914 hip arthroscopies performed during 2012-2019 were obtained from DHAR. Patients with radiographic signs of osteoarthritis (Tönnis > 1), hip dysplasia (CEA < 25°), other hip pathologies or previous hip surgery were excluded. The clinical outcomes for patients with AR [defined by a positive posterior wall sign (PWS) in combination with a positive Ischial Spine Sign (ISS)] and patients without AR (no PWS, no ISS) were analyzed 1 and 2 years after surgery. The primary outcomes were the six domains of the Copenhagen Hip and Groin Outcome score (HAGOS), while secondary outcomes were the Hip Sports Activity Scale (HSAS), a visual analogue pain scale (VAS) and a numeric rating scale (NRS) for pain.

RESULTS

A total of 3135 hip arthroscopies were included, of which 339 had AR, 1876 did not, and 920 presented one of the two signs (PWS and ISS). There were no statistically significant differences 1 and 2 years after surgery (n.s.) between patients with and without AR in HAGOS domain scores, HSAS, VAS, or NRS. Both groups showed improvement at both follow-ups. The two groups did not differ in relation to intraoperative findings and the procedures they have had.

CONCLUSION

The outcome 1 and 2 years after arthroscopic treatment of FAIS is not different for patients with and without AR.

LEVEL OF EVIDENCE

III.

Association between patient factors and hospital completeness of a patient-reported outcome measures program in joint arthroplasty, a cohort study

Background

The collection of patient-reported outcome measures (PROMs) following arthroplasty is common. PROMs data collection programs seek to maximise completeness in order to minimise selection bias and optimise representativeness of the sample attained. We aimed to determine if patient factors influence variation in PROMs program completeness between-hospitals.

Methods

Using data from a national arthroplasty registry PROMs program, we tested for associations between patient characteristics (age, sex, body mass index [BMI] and American Society of Anaesthesiologists [ASA] class) and both potential completeness (registration completeness: the proportion of arthroplasty patients that were registered in the PROMs electronic system) and actual completeness (response completeness: the proportion of arthroplasty patients who provided PROMs data) using linear regression.

Results

When using all elective primary total hip, knee or shoulder arthroplasty procedures (N = 31,801) from 43 hospitals as the denominator, overall registration completeness was 52%, varying from 5 to 87% between hospitals. Overall pre-operative response completeness was 46%, varying from 5 to 82% between hospitals. There were no significant associations between hospital-level registration completeness or response completeness and age, sex, BMI or ASA score.

Conclusion

Completeness rates of a PROMs program in arthroplasty varied widely between hospitals but in the absence of a relationship between measured patient factors and completeness rates, the observed variation likely relates to local site factors such as access to patients, local resources and clinician engagement with the program. Efforts to improve the rates of completeness of arthroplasty PROMs programs at individual hospitals may not improve the representativeness of the sample.

Hospitals vary in how completely they include and follow up patients when measuring patient-reported health. It is important to know if the completeness of inclusion and follow up by hospitals is affected by patient characteristics. This study addresses that problem by using data from a national patient outcome survey involving over 30,000 patients from 43 hospitals to look at the correlation between certain patient characteristics (like age, sex, obesity and general health) and the rates of inclusion and follow up for each hospital.

Rates of inclusion (registering patients in the program) and follow up rates varied widely between hospitals (from 5 to 87%). The rates of inclusion and follow up were not correlated with patient age, sex, body mass index or their general health. These findings suggest that the variation between hospitals in the rates of inclusion of joint replacement patients in health surveys are not due to differences in patient characteristics. The differences may be due to local hospital factors, such as resources and clinician engagement.

Registry Studies Use Inconsistent Methods to Account for Patients Lost to Follow-up, and Rates of Patients LTFU Are High

PURPOSE

To determine methods described in the literature to account for patients lost to follow-up (LTFU) in registry studies and whether rates of patient LTFU are within acceptable margins.

METHODS

A scoping review, where a literature search is conducted for studies from 9 arthroscopy registries, was performed on EMBASE, MEDLINE, and the annual reports of each registry. Inclusion criteria included studies with information on patient-reported outcome measures and being based on 9 national registries identified. Exclusion criteria included review articles, conference abstracts, studies not based on registry data, and studies from regional, claims-based, or multicenter registries. Studies were then divided into categories based on method of LTFU analysis used.

RESULTS

Thirty-six articles were identified for the final analysis. Categories for LTFU analysis included dropout analyses (n = 10), referencing validation studies (n = 12), contacting nonresponders (n = 4), and sensitivity analyses (n = 1). Referencing validation studies was the most common method (n = 12). Majority (n = 35) of the studies exceeded the recommended maximum rates for LTFU.

CONCLUSIONS

Registry studies use inconsistent methods to account for patient LTFU, and rates of patients LTFU are unacceptably high.

CLINICAL RELEVANCE

The impact of patients LTFU in studies related to arthroscopic intervention is unknown. A universal method for accounting for patient follow-up is needed.

Are responders to patient health surveys representative of those invited to participate? An analysis of the Patient-Reported Outcome Measures Pilot from the Australian Orthopaedic Association National Joint Replacement Registry

Background

Patient-reported outcome measures (PROMs) are commonly used to evaluate surgical outcome in patients undergoing joint replacement surgery, however routine collection from the target population is often incomplete. Representative samples are required to allow inference from the sample to the population. Although higher capture rates are desired, the extent to which this improves the representativeness of the sample is not known. We aimed to measure the representativeness of data collected using an electronic PROMs capture system with or without telephone call follow up, and any differences in PROMS reporting between electronic and telephone call follow up.

Methods

Data from a pilot PROMs program within a large national joint replacement registry were examined. Telephone call follow up was used for people that failed to respond electronically. Data were collected pre-operatively and at 6 months post-operatively. Responding groups (either electronic only or electronic plus telephone call follow up) were compared to non-responders based on patient characteristics (joint replaced, bilaterality, age, sex, American Society of Anesthesiologist (ASA) score and Body Mass Index (BMI)) using chi squared test or ANOVA, and PROMs for the two responder groups were compared using generalised linear models adjusted for age and sex. The analysis was restricted to those undergoing primary elective hip, knee or shoulder replacement for osteoarthritis.

Results

Pre-operatively, 73.2% of patients responded electronically and telephone follow-up of non-responders increased this to 91.4%. Pre-operatively, patients responding electronically, compared to all others, were on average younger, more likely to be female, and healthier (lower ASA score). Similar differences were found when telephone follow up was included in the responding group. There were little (if any) differences in the post-operative comparisons, where electronic responders were on average one year younger and were more likely to have a lower ASA score compared to those not responding electronically, but there was no significant difference in sex or BMI. PROMs were similar between those reporting electronically and those reporting by telephone.

Conclusion

Patients undergoing total joint replacement who provide direct electronic PROMs data are younger, healthier and more likely to be female than non-responders, but these differences are small, particularly for post-operative data collection. The addition of telephone call follow up to electronic contact does not provide a more representative sample. Electronic-only follow up of patients undergoing joint replacement provides a satisfactory representation of the population invited to participate.

Threshold Values for Success After Hip Arthroscopy Using the Patient-Reported Outcomes Measurement Information System Assessment: Determining the Minimum Clinically Important Difference and Patient Acceptable Symptomatic State

BACKGROUND

Threshold values for patient-reported outcome measures, such as the minimum clinically important difference (MCID) and patient acceptable symptomatic state (PASS), are important for relating postoperative outcomes to meaningful functional improvement.

PURPOSE

To determine the PASS and MCID after hip arthroscopy for femoroacetabular impingement using the Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaire.

STUDY DESIGN

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

METHODS

A consecutive series of patients undergoing primary hip arthroscopy for femoroacetabular impingement were administered preoperative and minimum 1-year postoperative PROMIS surveys focusing on physical function (PF) and pain interference (PI). External anchor questions for the MCID and PASS were given with the postoperative PROMIS survey. Receiver operator curves were constructed to determine the threshold values for the MCID and PASS. Curves were generated for the study population as well as separate cohorts segregated by median baseline PF or PI scores and preoperative athletic participation. A multivariate post hoc analysis was then constructed to evaluate factors associated with achieving the PASS or MCID.

RESULTS

There were 113 patients (35% male; mean ± SD age, 32.8 ± 12.5 years; body mass index, 25.8 ± 4.8 kg/m²), with 60 (53%) reporting preoperative athletic participation. Survey time averaged 77.5 ± 49.2 seconds. Anchor-based MCID values were 5.1 and 10.9 for the PF and PI domains, respectively. PASS thresholds were 51.8 and 51.9 for the PF and PI, respectively. PASS values were not affected by baseline scores, but athletic patients had a higher PASS threshold than did those not participating in a sport (53.1 vs 44.7). MCID values were affected by preoperative baseline scores but were largely independent of sports participation. A post hoc analysis found that 94 (83%) patients attained the MCID PF while 66 (58%) attained the PASS PF. A multivariate nominal logistic regression found that younger patients (P = .01) and athletic patients (P = .003) were more likely to attain the PASS.

CONCLUSION

The PROMIS survey is an efficient metric to evaluate preoperative disability and postoperative function after primary hip arthroscopy for femoroacetabular impingement. The MCID and PASS provide surgeons with threshold values to help determine PROMIS scores that are clinically meaningful to patients, and they can assist with therapeutic decision making as well as expectation setting.