Background:Validated patient-reported outcome measures (PROMs) of hip pain and function at the time of arthroscopy could be predictors of the final outcome. Little is known about how patient factors or pathologic intra-articular findings relate to hip pain or function at the time of surgery for those presenting with femoroacetabular impingement (FAI).Purpose:To evaluate all patient and operative factors that contribute to hip pain and dysfunction in patients with FAI.Study Design:Cross-sectional study; Level of evidence, 3.Methods:A prospective cohort of patients undergoing hip arthroscopy for FAI were electronically enrolled between February 2015 and September 2016. Baseline PROMs were collected, including Hip disability and Osteoarthritis Outcome Score (HOOS) for pain, HOOS–Physical Function Shortform (HOOS-PS), Veterans RAND 12-Item Health Survey (VR-12), and University of California–Los Angeles (UCLA) Activity Score. Surgeons documented intra-articular operative findings and treatment. Multivariable linear regression models were created for continuous scores of HOOS pain, HOOS-PS, and VR-12 Physical Component Score as outcome measures. Risk factors included patient characteristics and intraoperative anatomic and pathologic findings.Results:During the study period, 396 patients underwent arthroscopic hip procedures, and 373 (94%) completed preoperative PROMs; 331 patients were undergoing arthroscopic surgery for FAI. The mean patient age was 32.91 ± 12.49 years, mean body mass index was 26.22 ± 4.92 kg/m2, and 71% were female. Multivariate analyses demonstrated female sex, lower education levels, smoking, lower mental health scores, and lower activity-level scores predicted HOOS pain preoperatively. According to multivariate analysis, patient factors associated with worse baseline HOOS-PS include smoking, additional years of education, lower mental health, and activity scores. Lower baseline VR-12 functional scores were predicted by female sex, elevated body mass index, smoking, and lower activity levels. For all baseline PROMs, there was no instance where an arthroscopic variable or pathologic finding proved statistically significant after the important patient covariates were controlled for.Conclusion:Patient factors, including mental health, activity level, sex, and smoking, are more predictive of baseline hip pain (as measured by HOOS) and function than are intra-articular findings (eg, status of the labrum or articular cartilage) during hip arthroscopy for FAI. Future studies evaluating patient outcomes after surgery for FAI should consider adjusting for these identified patient factors to accurately interpret the effect of treatment on patient-reported outcomes after surgery.

There was no clinically significant difference between the pediatric and adult IKDC form scores in adolescents aged 13 to 17 years. This result allows use of whichever form is most practical for long-term tracking of patients. A simple linear equation can convert one form into the other. If the adult questionnaire is used at this age, it can be consistently used during follow-up.

Aims The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Methods Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC. Results Within the imputed datasets, the LOS (RMSE 1.161) and PROMs models (RMSE 15.775, 11.056, 21.680 for KOOS pain, function, and QOL, respectively) demonstrated good accuracy. For all models, the accuracy of predicting outcomes in a new set of patients were consistent with the cross-validation accuracy overall. Upon validation with a new patient dataset, the LOS and readmission models demonstrated high accuracy (71.5% and 65.0%, respectively). Similarly, the one-year PROMs improvement models demonstrated high accuracy in predicting ten-point improvements in KOOS pain (72.1%), function (72.9%), and QOL (70.8%) scores. Conclusion The data-driven models developed in this study offer scalable predictive tools that can accurately estimate the likelihood of improved pain, function, and quality of life one year after knee arthroplasty as well as LOS and 90 day readmission. Cite this article: Bone Joint J 2020;102-B(9):1183–1193.

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Background: Improving outcomes after surgical procedures and determining the value of health care can be facilitated by a scientifically valid, cost-effective, and scalable data outcome collection system. We hypothesized that such a system could be constructed in orthopaedic surgery to (1) capture >95% of baseline validated patient-reported outcome measures (PROMs) for patients undergoing elective surgery, (2) capture >95% of surgeon-entered data on disease severity and treatment, and (3) be implemented as standard clinical care in daily practice. Methods: A modified Research Electronic Data Capture (REDCap) system was developed and was implemented at the time of surgery in a prospective cohort to collect demographic data, general health PROMs, joint-specific PROMs, and disease severity and treatments from patients and surgeons. All elective knee, hip, and shoulder orthopaedic surgical procedures performed in the Cleveland Clinic system at 7 hospitals were included. Results: Of 16,021 consecutive eligible patients (February 18, 2015, to July 31, 2017), 2% (320) were excluded because of language or physical barriers, and 0.6% (91) of the remaining 15,701 patients refused to participate. Of the remaining 15,610 patients, 97.4% (15,202) completed PROMs, and surgeons provided details on the disease severity and treatment for 99.9% (15,592) of the 15,610 patients. Overall, 97.3% (15,185) of the 15,610 patients had complete patient-reported and surgeon-reported baseline enrollment. The median completion time was 11.5 minutes for the patients and 1.6 minutes for the surgeons. The overall complete 1-year follow-up rate was 72.5% (9,354 of 12,896). Conclusions: A data collection system with validated measures with >97% baseline completion of PROMs and surgeon forms regarding disease severity and treatments, across elective knee, hip, and shoulder orthopaedic surgical procedures, was successfully implemented at 7 hospitals. The system is potentially scalable to the entire orthopaedic community and could serve as a template for all procedural-based specialties during routine patient care.

Purpose-This study tested validity and efficiency of Orthopaedic Minimal Data Set (OrthoMiDaS) Episode of Care (OME).Methods-100 isolated rotator cuff repair cases in the OME database were analyzed. Surgeons completed a traditional operative note and OME report. A blinded reviewer extracted data from operative notes and implant logs in electronic medical records by manual chart review. OME and EMR data were compared with data counts and agreement between 40 variables of rotator cuff pathology and repair procedures. Data counts were assessed using raw percentages and *

Background: The length of most patient-reported outcome measures creates significant response burden, which hampers follow-up rates. The Patient Acceptable Symptom State (PASS) is a single-item, patient-reported outcome measure that asks patients to consider all aspects of life to determine whether the state of their joint is satisfactory; this measure may be viable for tracking outcomes on a large scale. Hypothesis: The PASS question would identify clinically successful anterior cruciate ligament reconstruction (ACLR) at 1-year follow-up with high sensitivity and moderate specificity. We defined “clinically successful” ACLR as changes in preoperative to postoperative scores on the Knee injury and Osteoarthritis Outcome Score (KOOS) pain subscale and the KOOS knee-related quality of life subscale in excess of minimal clinically important difference or final KOOS pain or knee-related quality of life subscale scores in excess of previously defined PASS thresholds. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Patients enrolled in a prospective longitudinal cohort completed patient-reported outcome measures immediately before primary ACLR. At 1-year follow-up, patients completed the same patient-reported outcome measures and answered the PASS question: “Taking into account all the activity you have during your daily life, your level of pain, and also your activity limitations and participation restrictions, do you consider the current state of your knee satisfactory?” Results: A total of 555 patients enrolled in our cohort; 464 were eligible for this study. Of these, 300 patients (64.7%) completed 1-year follow-up, of whom 83.3% reported satisfaction with their knee after surgery. The PASS question demonstrated high sensitivity to identify clinically successful ACLR (92.6%; 95% CI, 88.4%-95.6%). The specificity of the question was 47.1% (95% CI, 35.1%-59.5%). The overall agreement between the PASS and our KOOS-based criteria for clinically successful intervention was 81.9%, and the kappa value indicated moderate agreement between the two methods (κ = 0.44). Conclusion: The PASS question identifies individuals who have experienced clinically successful ACLR with high sensitivity. The limitation of the PASS is its low specificity, which we calculated to be 47.1%. Answering “no” to the PASS question meant that a patient neither improved after surgery nor achieved an acceptable final state of knee health. The brevity, interpretability, and correlation of the PASS question with significant improvements on various KOOS subscales make it a viable option in tracking ACLR outcomes on a national or global scale.