Level I, diagnostic study.

Background Establishing score points that reflect meaningful change from the patient perspective is important for interpreting patient-reported outcomes. This study estimated the minimum clinically important difference (MCID) values of 2 Patient-Reported Outcomes Measurement Information System (PROMIS) instruments and the Foot and Ankle Ability Measure (FAAM) Sports subscale within a foot and ankle orthopedic population. Methods Patients seen for foot and ankle conditions at an orthopedic clinic were administered the PROMIS Physical Function (PF) v1.2, the PROMIS Pain Interference (PI) v1.1, and the FAAM Sports at baseline and all follow-up visits. MCID estimation was conducted using anchor-based and distribution-based methods. Results A total of 3069 patients, mean age of 51 years (range = 18–94), were included. The MCIDs for the PROMIS PF ranged from approximately 3 to 30 points (median = 11.3) depending on the methods being used. The MCIDs ranged from 3 to 25 points (median = 8.9) for the PROMIS PI, and from 9 to 77 points (median = 32.5) for the FAAM Sports. Conclusions This study established a range of MCIDs in the PROMIS PF, PROMIS PI, and FAAM Sports indicating meaningful change in patient condition. MCID values were consistent across follow-up periods, but were different across methods. Values below the 25th percentile of MCIDs may be useful for low-risk clinical decisions. Midrange values (eg, near the median) should be used for high stakes decisions in clinical practice (ie, surgery referrals). The MCID values within the interquartile range should be utilized for most decision making. Level of Evidence Level I, diagnostic study, testing of previously developed diagnostic measure on consecutive patients with reference standard applied.

Objective This study sought to utilise machine learning methods in artificial intelligence to select the most relevant variables in classifying the presence and absence of root caries and to evaluate the model performance. Background Dental caries is one of the most prevalent oral health problems. Artificial intelligence can be used to develop models for identification of root caries risk and to gain valuable insights, but it has not been applied in dentistry. Accurately identifying root caries may guide treatment decisions, leading to better oral health outcomes. Methods Data were obtained from the 2015‐2016 National Health and Nutrition Examination Survey and were randomly divided into training and test sets. Several supervised machine learning methods were applied to construct a tool that was capable of classifying variables into the presence and absence of root caries. Accuracy, sensitivity, specificity and area under the receiver operating curve were computed. Results Of the machine learning algorithms developed, support vector machine demonstrated the best performance with an accuracy of 97.1%, precision of 95.1%, sensitivity of 99.6% and specificity of 94.3% for identifying root caries. The area under the curve was 0.997. Age was the feature most strongly associated with root caries. Conclusion The machine learning algorithms developed in this study perform well and allow for clinical implementation and utilisation by dental and nondental professionals. Clinicians are encouraged to adopt the algorithms from this study for early intervention and treatment of root caries for the ageing population of the United States, and for attaining precision dental medicine.

Study Design The Oswestry Disability Index v2.0 (ODI), SF36 Physical Function Domain (SF-36 PFD) and PROMIS Physical Function CAT v1.2 (PF CAT) questionnaires were prospectively collected from 1,607 patients complaining of back or leg pain, visiting a university-based spine clinic. All questionnaires were collected electronically, using a tablet computer. Objective To compare the psychometric properties of the PROMIS PF CAT to the ODI and SF36 Physical Function Domain in the same patient population. Summary of Background Data Evidence-based decision-making is improved by using high quality patient reported outcomes measures. Prior studies have revealed the shortcomings of the ODI and SF36, commonly used in spine patients. The PROMIS Network has developed measures with excellent psychometric properties. The Physical Function domain, delivered by Computerized Adaptive Testing (PF CAT), performs well in the spine patient population, though to-date direct comparisons with common measures have not been performed. Methods Standard Rasch analysis was performed to directly compare the psychometrics of the PF CAT, ODI, and SF36 PFD. Spearman correlations were computed to examine the correlations of the three instruments. Time required for administration was also recorded. Results 1,607 patients were administered all assessments. The time required to answer all items in the PF CAT, ODI and SF-36 PFD was 44, 169, and 99 seconds. The ceiling and floor effects were excellent for the PF CAT (0.81%, 3.86%), while the ceiling effects were marginal and floor effects quite poor for the ODI (6.91% and 44.24%) and SF-36 PFD (5.97% and 23.65%). All instruments significantly correlated with each other. Conclusions The PROMIS PF CAT outperforms the ODI and SF-36 PFD in the spine patient population and is highly correlated. It has better coverage, while taking less time to administer with fewer questions to answer.

AIMTo establish minimum clinically important difference (MCID) for measurements in an orthopaedic patient population with joint disorders.METHODSAdult patients aged 18 years and older seeking care for joint conditions at an orthopaedic clinic took the Patient-Reported Outcomes Measurement Information System Physical Function (PROMIS® PF) computerized adaptive test (CAT), hip disability and osteoarthritis outcome score for joint reconstruction (HOOS JR), and the knee injury and osteoarthritis outcome score for joint reconstruction (KOOS JR) from February 2014 to April 2017. MCIDs were calculated using anchor-based and distribution-based methods. Patient reports of meaningful change in function since their first clinic encounter were used as an anchor.RESULTSThere were 2226 patients who participated with a mean age of 61.16 (SD = 12.84) years, 41.6% male, and 89.7% Caucasian. Mean change ranged from 7.29 to 8.41 for the PROMIS® PF CAT, from 14.81 to 19.68 for the HOOS JR, and from 14.51 to 18.85 for the KOOS JR. ROC cut-offs ranged from 1.97-8.18 for the PF CAT, 6.33-43.36 for the HOOS JR, and 2.21-8.16 for the KOOS JR. Distribution-based methods estimated MCID values ranging from 2.45 to 21.55 for the PROMIS® PF CAT; from 3.90 to 43.61 for the HOOS JR, and from 3.98 to 40.67 for the KOOS JR. The median MCID value in the range was similar to the mean change score for each measure and was 7.9 for the PF CAT, 18.0 for the HOOS JR, and 15.1 for the KOOS JR.CONCLUSIONThis is the first comprehensive study providing a wide range of MCIDs for the PROMIS® PF, HOOS JR, and KOOS JR in orthopaedic patients with joint ailments.

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The HOOS JR, KOOS JR, and PROMIS PF are useful clinical instruments for assessing treatment change and may be selected as relevant to the specific needs and conditions of the adult joint reconstruction patient population. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1178-1184, 2018.