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Objective: To evaluate the psychometric properties of the 12-item Hip disability and Osteoarthritis Outcome Score (HOOS-12) and Knee injury and Osteoarthritis Outcome Score (KOOS-12) for use in evaluating outcomes after joint replacement for osteoarthritis. Design: Patient-reported outcomes data collected by the Australian Orthopaedic Association National Joint Replacement Registry were used for this analysis. HOOS-12 and KOOS-12 domain (pain, function, quality of life) and summary impact data were available. The Oxford Hip Score (OHS), Oxford Knee Score (OKS) and EQ-5D-5L were used as comparators. Instruments were administered pre-operatively and at 6 months post-operatively. Internal consistency reliability, floor and ceiling effects, convergent validity, known groups validity, and responsiveness were evaluated using standard psychometric techniques. Results: Baseline HOOS-12 and KOOS-12 data were available for 3,023 patients undergoing primary total hip replacement and 4,010 patients undergoing primary total knee replacement. At baseline, high internal consistency was demonstrated for all domains and summary scores (Cronbach's alpha: HOOS-12 = 0.81–0.93; KOOS-12 = 0.82–0.92). Post-operative ceiling effects (>15% of patients scoring the best possible score) were identified for the HOOS-12 pain (46%), function (39%) and quality of life domains (26%) and summary score (17%), and for the KOOS-12 pain (21%) and function domains (18%). The HOOS-12 and KOOS-12 could differentiate between two known groups (lowest/highest OHS or OKS quartiles post-operatively; p < 0.001) and were highly responsive to change (effect sizes for HOOS-12: 2.20–2.83; KOOS-12: 1.82–2.35). Conclusion: The HOOS-12 and KOOS-12 have good psychometric properties for capturing joint replacement outcomes including excellent responsiveness, although ceiling effects may limit monitoring of post-operative improvement Refereed/Peer-reviewed

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.

Summary Objective To evaluate the structural validity of the 12-item Hip disability and Osteoarthritis Outcome Score (HOOS-12) and 12-item Knee injury and Osteoarthritis Outcome Score (KOOS-12) using Rasch analysis and consider psychometric implications for research and clinical use. Method Individual-level HOOS-12 and KOOS-12 data from the Australian Orthopaedic Association National Joint Replacement Registry, collected before and after primary total hip and knee replacement, were used for this analysis. Using the Rasch analytic approach, overall model fit and item fit were examined, together with potential reasons for misfit including response threshold ordering, differential item functioning, internal consistency, unidimensionality and item targeting. Results Overall misfit to the Rasch model was evident for both instruments. A degree of item misfit was also observed, although most items demonstrated logical sequencing of response options. Only two items (hip/knee pain frequency and awareness of hip/knee problems) displayed disordered response thresholds. The pain, function, and quality of life domains of the HOOS-12 and KOOS-12 demonstrated excellent internal consistency reliability (person separation index: 0.80–0.93) and unidimensionality. A mismatch between item difficulty and person ability scores at the highest end of the HOOS-12 and KOOS-12 scales contributed to post-operative ceiling effects (mean logit for HOOS-12: 3.57; KOOS-12: 2.58; ≈0 indicates well-targeted scale). Conclusion We found evidence to support the structural validity of the three HOOS-12 and KOOS-12 domains for evaluating joint replacement outcomes. However, there may be missing content in both instruments particularly for high-functioning patients. Minor refinement of some response options may be warranted to improve item performance.

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 SARS-CoV-2 positive cases and 564 controls, accounting for the time course of illness at point of assessment. Clinical differentiators of cases and controls were used to derive model-based risk scores. Significant symptoms included abdominal pain, cough, diarrhea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5°C and 37.9°C, and temperature above 38°C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, we firstly used leave-one-out cross-validation, which confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. Secondly, in a separate ongoing prospective study of 237 COVID-19 and 346 primary care patients presenting with symptoms of acute respiratory infection, the baseline model had a sensitivity of 0.57 and specificity of 0.89, and in retrospective notes review of 100 COVID-19 cases diagnosed in primary care, sensitivity was 0.56. A web-app based tool has been developed for easy implementation as an adjunct to laboratory testing to differentiate COVID-19 positive cases among patients presenting in outpatient settings.

Background The role of probiotics in allergy prevention remains uncertain but has been shown in some studies to have a possible protective effect on eczema. Objective We aimed to assess the effect of probiotic supplementation in the first 6 months of life on eczema and allergic sensitization at 1 year of age in Asian infants at risk of allergic disease. Methods A double-blind, placebo-controlled randomized clinical trial involving 253 infants with a family history of allergic disease was carried out. Infants received at least 60 mL of commercially available cow's milk formula with or without probiotic supplementation [Bifidobacterium longum (BL999) 1 x 10(7) colony forming unit (CFU)/g and Lactobacillus rhamnosus (LPR) 2 x 10(7) CFU/g] daily for the first 6 months. Clinical evaluation was performed at 1, 3, 6 and 12 months of age, with serum total IgE measurement and skin prick tests conducted at the 12-month visit. The primary and secondary end-points were eczema and allergen sensitization, respectively. Results The incidence of eczema in the probiotic (22%) group was similar to that in the placebo group (25%) (P=0.53). The median Scoring Atopic Dermatitis score at 12 months was 17.10 (9.74) in the probiotic group and 11.60 (8.40) in the placebo group (P=0.17). The prevalence of allergen sensitization showed no difference (probiotic=24% vs. placebo=19%, P=0.26). The total IgE geometric mean (95% confidence interval) was 18.76 (12.54-24.98) kU/L in the probiotic group and 23.13 (16.01-30.24) kU/L in the placebo group (P=0.15). Atopic eczema (with sensitization) in the probiotic (7.3%) group was comparable to the placebo group (5.8%) (P=0.86). Conclusion Early life administration of a cow's milk formula supplemented with probiotics showed no effect on prevention of eczema or allergen sensitization in the first year of life in Asian infants at risk of allergic disease. Further work is needed to determine whether timing of supplementation, dose and probiotic strain are important considerations.