Showing papers by "Rafael Alfonso-Cristancho published in 2017"

Comparative effectiveness of biologics for the management of rheumatoid arthritis: systematic review and network meta-analysis

Abstract: Our aim was to establish the comparative effectiveness of rheumatoid arthritis (RA) biologics, using a systematic review and network meta-analysis. The systematic review used randomized controlled trials (RCTs) in adults with RA who failed treatment with conventional disease-modifying agents for rheumatoid disease (cDMARDs). We compared the effectiveness of abatacept, adalimumab, etanercept, infliximab, certolizumab pegol, golimumab, and rituximab to tocilizumab, a recent biologic with a different mechanism of action (anti-IL-6 receptor). A network meta-analysis (NMA) included the indirect and direct evidence previously selected. In total, 207 articles were included describing 68 RCTs. The NMA showed that tocilizumab monotherapy was superior to standard care (ACR20, OR 13.27, 95 % CrI [3.958, 43.98]; ACR50, 17.45 [10.18, 31.24]; ACR70, 37.77 [7.226, 216.3]; EULAR, 10.42 [1.963, 54.8]); and methotrexate (MTX; ACR50, OR 5.44 [4.142, 7.238]; ACR70, 7.364 [1.4, 30.83]; EULAR, 4.226 [1.184, 15.58]) at 26 weeks. Similarly, the combination of tocilizumab + MTX was significantly better than standard care/placebo and MTX alone for ACR20, ACR50, ACR70, and EULAR at 26 weeks (OR 18.63 [5.32, 66.81]; 24.27 [14.5, 41.91]; 46.13 [10.08, 277]; 14.23 [2.493, 84.02]; 4.169 [2.267, 7.871]; 5.44 [4.142, 7.238]; 8.731 [4.203, 19.29]; 7.306 [4.393, 13.04], respectively). At 52 weeks, compared to MTX alone, tocilizumab + MTX was significantly better for ACR20 and ACR50 response. Few significant differences were found between tocilizumab (alone or in combination) and any other biologics. Results must be considered in context with the limitations of the available evidence. This NMA suggests that tocilizumab was superior to cDMARDs and as effective as other biologics for RA.

Improved Risk Prediction Following Surgery Using Machine Learning Algorithms

Abstract: Background: Machine learning is used to analyze big data, often for the purposes of prediction. Analyzing a patient’s healthcare utilization pattern may provide more precise estimates of risk for adverse events (AE) or death. We sought to characterize healthcare utilization prior to surgery using machine learning for the purposes of risk prediction. Methods: Patients from MarketScan Commercial Claims and Encounters Database undergoing elective surgery from 2007-2012 with ≥1 comorbidity were included. All available healthcare claims occurring within six months prior to surgery were assessed. More than 300 predictors were defined by considering all combinations of conditions, encounter types, and timing along with sociodemographic factors. We used a supervised Naive Bayes algorithm to predict risk of AE or death within 90 days of surgery. We compared the model’s performance to the Charlson’s comorbidity index, a commonly used risk prediction tool. Results: Among 410,521 patients (mean age 52, 52 ± 9.4, 56% female), 4.7% had an AE and 0.01% died. The Charlson’s comorbidity index predicted 57% of AE’s and 59% of deaths. The Naive Bayes algorithm predicted 79% of AE’s and 78% of deaths. Claims for cancer, kidney disease, and peripheral vascular disease were the primary drivers of AE or death following surgery. Conclusions: The use of machine learning algorithms improves upon one commonly used risk estimator. Precisely quantifying the risk of an AE following surgery may better inform patient-centered decision-making and direct targeted quality improvement interventions while supporting activities of accountable care organizations that rely on accurate estimates of population risk.

Effect of inhaled corticosteroid particle size on asthma efficacy and safety outcomes: a systematic literature review and meta-analysis

Abstract: Inhaled corticosteroids (ICS) are the primary treatment for persistent asthma. Currently available ICS have differing particle size due to both formulation and propellant, and it has been postulated that this may impact patient outcomes. This structured literature review and meta-analysis compared the effect of small and standard particle size ICS on lung function, symptoms, rescue use (when available) and safety in patients with asthma as assessed in head-to-head randomized controlled trials (RCTs). A systematic literature search of MEDLINE was performed to identify RCTs (1998–2014) evaluating standard size (fluticasone propionate-containing medications) versus small particle size ICS medication in adults and children with asthma. Efficacy outcomes included forced expiratory volume in 1 s (FEV1), morning peak expiratory flow (PEF), symptom scores, % predicted forced expiratory flow between 25 and 75% of forced vital capacity (FEF25–75%), and rescue medication use. Safety outcomes were also evaluated when available. Twenty-three independent trials that met the eligibility criteria were identified. Benefit-risk plots did not demonstrate any clinically meaningful differences across the five efficacy endpoints considered and no appreciable differences were noted for most safety endpoints. Meta-analysis results, using a random-effects model, demonstrated no significant difference between standard and small size particle ICS medications in terms of effects on mean change from baseline FEV1 (L) (−0.011, 95% confidence interval [CI]: −0.037, 0.014 [N = 3524]), morning PEF (L/min) (medium/low doses: −3.874, 95% CI: −10.915, 3.166 [N = 1911]; high/high-medium doses: 5.551, 95% CI: −1.948, 13.049 [N = 749]) and FEF25–75% predicted (−2.418, 95% CI: −6.400; 1.564 [N = 115]). Based on the available literature, no clinically significant differences in efficacy or safety were observed comparing small and standard particle size ICS medications for the treatment of asthma. GSK Clinical Study Register No: 202012 .