Seizure EEG Quality in Right Unilateral Ultrabrief ECT.

OBJECTIVE: The aims of this study were to compare threshold and suprathreshold ictal electroencephalograms (EEGs) in right unilateral (RUL) ultrabrief (UB) electroconvulsive therapy (ECT) and to identify the differences between these EEGs. METHODS: This study is a retrospective review of 125 pairs EEGs from titration and subsequent sessions across a 2-year period. All EEGs were independently rated for by 2 assessors using a scale based on qualities of an EEG used to guide ECT treatment dose adequacy, for example, midictal amplitude, regularity, interhemispheric coherence, seizure end point, and postictal suppression. The scores of threshold and suprathreshold EEGs were compared within and between groups based on 2 ECT types, that is, RUL UB ECT and RUL brief pulse (BP) ECT. RESULTS: Paired t tests showed a statistically significant difference in between threshold and suprathreshold EEG scores in RUL UB ECT. There were no statistically significant differences between corresponding scores for RUL UB ECT and RUL BP ECT threshold and suprathreshold EEGs. CONCLUSIONS: There is a significant difference between the quality of threshold and suprathreshold EEGs in RUL UB ECT when measured with an EEG rating scale. Visual rating of ictal EEGs is as reliable in discriminating between threshold and suprathreshold seizure in RUL UB ECT as it is in RUL BP ECT.

Placebo response in psoriatic arthritis clinical trials: a systematic review and meta-analysis.

OBJECTIVE: To determine the placebo response rate in PsA randomized clinical trials (RCTs), its contributing factors and impact on the effect size of active treatments. METHODS: We searched multiple databases, from inception to 20 December 2020, for placebo-controlled RCTs in PsA. We used a random-effects meta-analysis to pool the response rates for the ACR20 criteria in the placebo arm, determined the risk difference for treatment vs placebo, and used meta-regression to determine the factors associated with placebo response rates. The risk of bias was assessed in duplicate. The study protocol was registered with PROSPERO: CRD42021226000. RESULTS: We included 42 RCTs (5050 patients receiving placebo) published between 2000 and 2020. The risk of bias was low in 28 trials, high in four, and with some concerns in 10. The pooled placebo response rate was 20.3% (95% CI: 18.6%, 22.1%; predicted intervals, 11.7-29.0%), with significant between-trial heterogeneity (I2 = 56.8%, P < 0.005). The pooled risk difference for treatment vs placebo was 27% (95% CI: 24%, 31%). In the multivariable meta-regression, there was a 15% (95% CI: 2.9%, 29.8%) increase in the odds of achieving the placebo response for each 5-year increment in publication year (P = 0.016). In addition, the active treatment risk difference decreased for every 5-year increment in publication year (ß = -0.053, 95% CI: -0.099, -0.007; P = 0.024) but was not associated with the placebo response. CONCLUSION: Despite increasing over time, the placebo response for ACR20 in PsA RCTs was not associated with the active treatment effect size.

Use of Multiprognostic Index Domain Scores, Clinical Data, and Machine Learning to Improve 12-Month Mortality Risk Prediction in Older Hospitalized Patients: Prospective Cohort Study.

BACKGROUND: The Multidimensional Prognostic Index (MPI) is an aggregate, comprehensive, geriatric assessment scoring system derived from eight domains that predict adverse outcomes, including 12-month mortality. However, the prediction accuracy of using the three MPI categories (mild, moderate, and severe risk) was relatively poor in a study of older hospitalized Australian patients. Prediction modeling using the component domains of the MPI together with additional clinical features and machine learning (ML) algorithms might improve prediction accuracy. OBJECTIVE: This study aims to assess whether the accuracy of prediction for 12-month mortality using logistic regression with maximum likelihood estimation (LR-MLE) with the 3-category MPI together with age and gender (feature set 1) can be improved with the addition of 10 clinical features (sodium, hemoglobin, albumin, creatinine, urea, urea-to-creatinine ratio, estimated glomerular filtration rate, C-reactive protein, BMI, and anticholinergic risk score; feature set 2) and the replacement of the 3-category MPI in feature sets 1 and 2 with the eight separate MPI domains (feature sets 3 and 4, respectively), and to assess the prediction accuracy of the ML algorithms using the same feature sets. METHODS: MPI and clinical features were collected from patients aged 65 years and above who were admitted to either the general medical or acute care of the elderly wards of a South Australian hospital between September 2015 and February 2017. The diagnostic accuracy of LR-MLE was assessed together with nine ML algorithms: decision trees, random forests, extreme gradient boosting (XGBoost), support-vector machines, naïve Bayes, K-nearest neighbors, ridge regression, logistic regression without regularization, and neural networks. A 70:30 training set:test set split of the data and a grid search of hyper-parameters with 10-fold cross-validation-was used during model training. The area under the curve was used as the primary measure of accuracy. RESULTS: A total of 737 patients (female: 370/737, 50.2%; male: 367/737, 49.8%) with a median age of 80 (IQR 72-86) years had complete MPI data recorded on admission and had completed the 12-month follow-up. The area under the receiver operating curve for LR-MLE was 0.632, 0.688, 0.738, and 0.757 for feature sets 1 to 4, respectively. The best overall accuracy for the nine ML algorithms was obtained using the XGBoost algorithm (0.635, 0.706, 0.756, and 0.757 for feature sets 1 to 4, respectively). CONCLUSIONS: The use of MPI domains with LR-MLE considerably improved the prediction accuracy compared with that obtained using the traditional 3-category MPI. The XGBoost ML algorithm slightly improved accuracy compared with LR-MLE, and adding clinical data improved accuracy. These results build on previous work on the MPI and suggest that implementing risk scores based on MPI domains and clinical data by using ML prediction models can support clinical decision-making with respect to risk stratification for the follow-up care of older hospitalized patients.

Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs.

The current management of patients with multimorbidity is suboptimal, with either a single-disease approach to care or treatment guideline adaptations that result in poor adherence due to their complexity. Although this has resulted in calls for more holistic and personalized approaches to prescribing, progress toward these goals has remained slow. With the rapid advancement of machine learning (ML) methods, promising approaches now also exist to accelerate the advance of precision medicine in multimorbidity. These include analyzing disease comorbidity networks, using knowledge graphs that integrate knowledge from different medical domains, and applying network analysis and graph ML. Multimorbidity disease networks have been used to improve disease diagnosis, treatment recommendations, and patient prognosis. Knowledge graphs that combine different medical entities connected by multiple relationship types integrate data from different sources, allowing for complex interactions and creating a continuous flow of information. Network analysis and graph ML can then extract the topology and structure of networks and reveal hidden properties, including disease phenotypes, network hubs, and pathways; predict drugs for repurposing; and determine safe and more holistic treatments. In this article, we describe the basic concepts of creating bipartite and unipartite disease and patient networks and review the use of knowledge graphs, graph algorithms, graph embedding methods, and graph ML within the context of multimorbidity. Specifically, we provide an overview of the application of graph theory for studying multimorbidity, the methods employed to extract knowledge from graphs, and examples of the application of disease networks for determining the structure and pathways of multimorbidity, identifying disease phenotypes, predicting health outcomes, and selecting safe and effective treatments. In today's modern data-hungry, ML-focused world, such network-based techniques are likely to be at the forefront of developing robust clinical decision support tools for safer and more holistic approaches to treating older patients with multimorbidity.

C-reactive protein and 10-year cardiovascular risk in rheumatoid arthritis.

OBJECTIVES: To evaluate the association between C-reactive protein (CRP) and 10-year risk of cardiovascular (CV) events using the Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis (ERS-RA), based on conventional and RA-specific risk factors but not CRP, in RA patients without previous cardiovascular events. METHODS: ERS-RA was calculated in 1,251 "Cardiovascular Obesity and Rheumatic Disease Study (CORDIS)" database patients [(age 60.4(9.3) years; 78% female; disease duration, 11.6(8) years; CDAI, 9(9); CRP, 6.8(12) mg/L]. RESULTS: The mean (SD) 10-year risk of CV events was 12.9% (10). After adjusting for the use of DMARDs and biologics, CRP concentrations were significantly associated with 10-year risk of CV events (coefficient=0.005 for each 10 mg/L CRP increment; 95%CI 0.000-0.111; p = 0.047). In mediation analysis, the association between CRP and ERS-RA was not explained by disease activity. CONCLUSION: In a large cohort of RA patients without previous cardiovascular events, a 20 mg/L increase in CRP concentrations was associated with a 1% increase in 10-year risk of CV events. This suggests that actively targeting residual inflammatory risk beyond conventional and RA-specific risk factors might further reduce CV event rates in RA patients.

Efficacy, Safety, and Tolerability of Treatments for Systemic Sclerosis-Related Interstitial Lung Disease: A Systematic Review and Network Meta-Analysis.

BACKGROUND: There is a paucity of head-to-head comparisons of the efficacy and harms of pharmacological treatments for systemic sclerosis-related interstitial lung disease (SSc-ILD). METHODS: We conducted a network meta-analysis (NMA) in order to compare the effects of different treatments with the placebo on change in forced vital capacity (FVC), change in diffusion lung capacity for CO (DLCO), serious adverse events (SAEs), discontinuation for adverse events and mortality in SSc-ILD. Standardized mean difference (SMD) and log odds ratio were estimated using NMA with fixed effects. RESULTS: Nine randomized clinical trials (926 participants) comparing eight interventions and the placebo for an average follow-up of one year were included. Compared to the placebo, only rituximab significantly reduced FVC decline (SMD (95% CI) = 1.00 (0.39 to 1.61)). Suitable data on FVC outcome for nintedanib were not available for the analysis. No treatments influenced DLCO. Safety and mortality were also not different across treatments and the placebo, although there were few reported events. Cyclophosphamide and pomalidomide were less tolerated than the placebo, mycophenolate, and nintedanib. CONCLUSION: Only rituximab significantly reduced lung function decline compared to the placebo. However, direct head-to-head comparison studies are required to confirm these findings and to better determine the safety profile of various treatments.

Changes in knowledge, beliefs, and behaviors related to fruit and vegetable consumption among Western Australian adults from 1995 to 2004.

OBJECTIVES: We monitored changes in self-reported knowledge, attitudes, and behaviors regarding fruit and vegetable consumption in Western Australia prior to and after a healthful-eating campaign. METHODS: We obtained telephone survey data from 2854 adults in Perth from Nutrition Monitoring Surveys conducted in 1995, 1998, 2001, and 2004. The "Go for 2&5" fruit and vegetable campaign was implemented from 2002 to 2005. RESULTS: We observed changes in knowledge, attitudes, and behaviors regarding fruit and vegetable intake. In 2004, respondents were more likely than in 1995 to report 2 servings of fruit (odds ratio [OR] = 3.66; 95% confidence interval [CI] = 2.85, 4.70) and 5 servings of vegetables (OR = 4.50; 95% CI = 3.49, 5.80) per day as optimal. Despite this, vegetable consumption in 2004 was less than in 1995 (rate ratio = 0.88; 95% CI = 0.82, 0.96; P = .003). Perceived adequacy of vegetable (59.3%) or fruit (34.5%) intake and insufficient time for vegetable preparation (14.3%) were the main barriers. CONCLUSIONS: Knowledge of the recommended fruit and vegetable intake increased following the Go for 2&5 campaign. Perceptions of the adequacy of current intake and time scarcity should be considered when designing nutrition interventions.

Comparison of a sports-hydration drink containing high amylose starch with usual hydration practice in Australian rules footballers during intense summer training.

BACKGROUND: Fluid deficits exceeding 1.6% can lead to physical and cognitive impairment in athletes. Sport drinks used by athletes are often hyper-osmolar but this is known to be suboptimal for rehydration in medical settings and does not utilize colonic absorptive capacity. Colonic absorption can be enhanced by fermentative production of short chain fatty acids (SCFA) from substrates such as high amylose maize starch (HAMS). This study therefore compared, in elite Australian Football League (AFL) players at the height of outdoor summer training, a novel dual-action sports oral rehydration strategy that contained HAMS as well as glucose, to their usual rehydration practices (Control). The primary outcome markers of hydration were hematocrit and body weight. METHODS: A randomized single-blind crossover study was undertaken in thirty-one AFL players; twenty-seven completed the study which was conducted on four days (two days in the Intervention arm and two in Control arm). The Intervention arm was comprised a 50-100 g evening preload of an acetylated HAMS (Ingredion Pty Ltd) followed by consumption of a specially formulated sports oral rehydration solution (SpORS) drink during intense training and recovery. Players followed their usual hydration routine in the Control arm. Quantitative assessments of body weight, hematocrit and urine specific gravity were made at three time-points on each day of training: pre-training, post-training (90 min), and at end of recovery (30-60 min later). GPS tracking monitored player exertion. RESULTS: Across the three time-points, hematocrit was significantly lower and body weight significantly higher in Intervention compared to Control arms (p < 0.02 and p = 0.001 respectively, mixed effects model). Weights were significantly heavier at all three assessment points for Intervention compared to Control arms (Δ = 0.30 ± 0.13, p = 0.02 pre-training; Δ = 0.43 ± 0.14, p = 0.002 post training; and Δ = 0.68 ± 0.14, p < 0.001 for recovery). Between the pre-training and end-of-recovery assessments, the Control arm lost 0.80 kg overall compared with 0.12 kg in the Intervention arm, an 85% lower reduction of bodyweight across the assessment period. CONCLUSION: The combination of the significantly lower hematocrit and increased body weight in the Intervention arm represents better hydration not only at the end of training as well as following a recovery period but also at its commencement. The magnitude of the benefit seems sufficient to have an impact on performance and further studies to test this possibility are now indicated. TRIAL REGISTRATION: Trial is listed on the Australian New Zealand Clinical Trials Registry ( ACTRN 12613001373763 ).