Evaluating whether the proportional odds models to analyse ordinal outcomes in COVID-19 clinical trials is providing clinically interpretable treatment effects: A systematic review.

BACKGROUND: After an initial recommendation from the World Health Organisation, trials of patients hospitalised with COVID-19 often include an ordinal clinical status outcome, which comprises a series of ordered categorical variables, typically ranging from 'Alive and discharged from hospital' to 'Dead'. These ordinal outcomes are often analysed using a proportional odds model, which provides a common odds ratio as an overall measure of effect, which is generally interpreted as the odds ratio for being in a higher category. The common odds ratio relies on the assumption of proportional odds, which implies an identical odds ratio across all ordinal categories; however, there is generally no statistical or biological basis for which this assumption should hold; and when violated, the common odds ratio may be a biased representation of the odds ratios for particular categories within the ordinal outcome. In this study, we aimed to evaluate to what extent the common odds ratio in published COVID-19 trials differed to simple binary odds ratios for clinically important outcomes. METHODS: We conducted a systematic review of randomised trials evaluating interventions for patients hospitalised with COVID-19, which used a proportional odds model to analyse an ordinal clinical status outcome, published between January 2020 and May 2021. We assessed agreement between the common odds ratio and the odds ratio from a standard logistic regression model for three clinically important binary outcomes: 'Alive', 'Alive without mechanical ventilation', and 'Alive and discharged from hospital'. RESULTS: Sixteen randomised clinical trials, comprising 38 individual comparisons, were included in this study; of these, only 6 trials (38%) formally assessed the proportional odds assumption. The common odds ratio differed by more than 25% compared to the binary odds ratios in 55% of comparisons for the outcome 'Alive', 37% for 'Alive without mechanical ventilation', and 24% for 'Alive and discharged from hospital'. In addition, the common odds ratio systematically underestimated the odds ratio for the outcome 'Alive' by -16.8% (95% confidence interval: -28.7% to -2.9%, p = 0.02), though differences for the other outcomes were smaller and not statistically significant (-8.4% for 'Alive without mechanical ventilation' and 3.6% for 'Alive and discharged from hospital'). The common odds ratio was statistically significant for 18% of comparisons, while the binary odds ratio was significant in 5%, 16%, and 3% of comparisons for the outcomes 'Alive', 'Alive without mechanical ventilation', and 'Alive and discharged from hospital', respectively. CONCLUSION: The common odds ratio from proportional odds models often differs substantially to odds ratios from clinically important binary outcomes, and similar to composite outcomes, a beneficial common OR from a proportional odds model does not necessarily indicate a beneficial effect on the most important categories within the ordinal outcome.

Periradicular tissue fluid-derived biomarkers for apical periodontitis: An in vitro methodological and in vivo cross-sectional study.

BACKGROUND: Periradicular tissue fluid (PTF) offers a source of diagnostic, prognostic and predictive biomarkers for endodontic disease. AIMS: (1) To optimize basic parameters for PTF paper point sampling in vitro for subsequent in vivo application. (2) To compare proteomes of PTF from teeth with normal apical tissues (NAT) and asymptomatic apical periodontitis (AAP) using high-throughput panels. METHODOLOGY: (1) To assess volume absorbance, paper points (n = 20) of multiple brands, sizes and sampling durations were inserted into PBS/1%BSA at several depths. Wetted lengths (mm) were measured against standard curves to determine volume absorbance (µL). To assess analyte recovery, paper points (n = 6) loaded with 2 µL recombinant IL-1ß (15.6 ng/mL) were eluted into 250 µL: (i) PBS; (ii) PBS/1% BSA; (iii) PBS/0.1% Tween20; (iv) PBS/0.25 M NaCl. These then underwent: (i) vortexing; (ii) vortexing/centrifugation; (iii) centrifugation; (iv) incubation/vortexing/centrifugation. Sandwich-ELISAs determined analyte recovery (%) against positive controls. (2) Using optimized protocols, PTF was retrieved from permanent teeth with NAT or AAP after accessing root canals. Samples, normalized to total fluid volume (TFV), were analysed to determine proteomic profiles (pg/TFV) of NAT and AAP via O-link Target-48 panel. Correlations between AAP and diagnostic accuracy were explored using principal-component analysis (PCA) and area under receive-operating-characteristic curves (AUC [95% CI]), respectively. Statistical comparisons were made using Mann-Whitney U, anova and post hoc Bonferonni tests (α < .01). RESULTS: (1) UnoDent's 'Classic' points facilitated maximum volume absorbance (p < .05), with no significant differences after 60 s (1.6 µL [1.30-1.73]), 1 mm depth and up to 40/0.02 (2.2 µL [1.98-2.20]). For elution, vortexing (89.3%) and PBS/1% BSA (86.9%) yielded the largest IL-1ß recovery (p < .05). (2) 41 (NAT: 13; AAP: 31) PTF samples proceeded to analysis. The panel detected 18 analytes (CCL-2, -3, -4; CSF-1; CXCL-8, -9; HGF; IL-1ß, -6, -17A, -18; MMP-1, -12; OLR-1; OSM; TNFSF-10, -12; VEGF-A) in ≥75% of AAP samples at statistically higher concentrations (p < .01). CXCL-8, IL-1ß, OLR-1, OSM and TNFSF-12 were strongly correlated to AAP. 'Excellent' diagnostic performance was observed for TNFSF-12 (AUC: 0.94 [95% CI: 0.86-1.00]) and the PCA-derived cluster (AUC: 0.96 [95% CI: 0.89-1.00]). CONCLUSIONS: Optimized PTF sampling parameters were identified in this study. When applied clinically, high-throughput proteomic analyses revealed complex interconnected networks of potential biomarkers. TNFSF-12 discriminated periradicular disease from health the greatest; however, clustering analytes further improved diagnostic accuracy. Additional independent investigations are required to validate these findings.

Non-Pharmacological Therapies for Post-Viral Syndromes, Including Long COVID: A Systematic Review.

BACKGROUND: Post-viral syndromes (PVS), including Long COVID, are symptoms sustained from weeks to years following an acute viral infection. Non-pharmacological treatments for these symptoms are poorly understood. This review summarises the evidence for the effectiveness of non-pharmacological treatments for PVS. METHODS: We conducted a systematic review to evaluate the effectiveness of non-pharmacological interventions for PVS, as compared to either standard care, alternative non-pharmacological therapy, or placebo. The outcomes of interest were changes in symptoms, exercise capacity, quality of life (including mental health and wellbeing), and work capability. We searched five databases (Embase, MEDLINE, PsycINFO, CINAHL, MedRxiv) for randomised controlled trials (RCTs) published between 1 January 2001 to 29 October 2021. The relevant outcome data were extracted, the study quality was appraised using the Cochrane risk-of-bias tool, and the findings were synthesised narratively. FINDINGS: Overall, five studies of five different interventions (Pilates, music therapy, telerehabilitation, resistance exercise, neuromodulation) met the inclusion criteria. Aside from music-based intervention, all other selected interventions demonstrated some support in the management of PVS in some patients. INTERPRETATION: In this study, we observed a lack of robust evidence evaluating the non-pharmacological treatments for PVS, including Long COVID. Considering the prevalence of prolonged symptoms following acute viral infections, there is an urgent need for clinical trials evaluating the effectiveness and cost-effectiveness of non-pharmacological treatments for patients with PVS. REGISTRATION: The study protocol was registered with PROSPERO [CRD42021282074] in October 2021 and published in BMJ Open in 2022.

Systematic comparison of machine learning algorithms to develop and validate predictive models for periodontitis.

AIM: The aim of this study was to compare the validity of different machine learning algorithms to develop and validate predictive models for periodontitis. MATERIALS AND METHODS: Using national survey data from Taiwan (n = 3453) and the United States (n = 3685), predictors of periodontitis were extracted from the datasets and pre-processed, and then 10 machine learning algorithms were trained to develop predictive models. The models were validated both internally (bootstrap sampling) and externally (alternative country's dataset). The algorithms were compared across six performance metrics ([i] area under the curve for the receiver operating characteristic [AUC], [ii] accuracy, [iii] sensitivity, [iv] specificity, [v] positive predictive value, and [vi] negative predictive value) and two methods of data pre-processing ([i] machine-learning-based feature selection and [ii] dimensionality reduction into principal components). RESULTS: Many algorithms showed extremely strong performance during internal validation (AUC > 0.95, accuracy > 95%). However, this was not replicated in external validation, where predictive performance of all algorithms dropped off drastically. Furthermore, predictive performance differed according to data pre-processing methodology and the cohort on which they were trained. CONCLUSIONS: Larger sample sizes and more complex predictors of periodontitis are required before machine learning can be leveraged to its full potential.

Trends in the prevalence of dental caries in the US pediatric population 2011-2020.

OBJECTIVE: The aim of this study was to provide statistics on the trends and changes in the distribution of dental caries in the United States (US) pediatric population for the 10-year period 2011 through 2020. STUDY DESIGN: Using data from the 2011 to 2020 National Health and Nutrition Examination Surveys, estimates were derived for the prevalence of decayed and filled teeth in the US population aged between 2 to 11 years. Analyses were stratified by primary and permanent dentition, and prevalence was assessed amongst the various sociodemographic and body mass index (BMI) subgroups. RESULTS: Over the 10-year period, the following changes were observed: the prevalence of decayed primary teeth has decreased from 14.1% to 12.2%, the prevalence of filled primary teeth has decreased from 29.8% to 26.1%, the prevalence of decayed permanent teeth has decreased from 5.2% to 2.7%, and the prevalence of filled permanent teeth has decreased from 16.1% to 12.3%. Despite these decreases in prevalence, there remains substantial inequality in how the disease is distributed, with those from ethnic minorities, poorer households, and with a non-normal BMI carrying the majority of disease burden. CONCLUSIONS: The prevalence of dental caries has decreased over the past 10 years, but there is still inequality in disease distribution.