IMPORTANCE Even though osteoarthritis is a chronic and progressive disease, pharmacological agents are mainly studied over short-term periods, resulting in unclear recommendations for long-term disease management. OBJECTIVE To search, review, and analyze long-term (Ն12 months) outcomes (symptoms, joint structure) from randomized clinical trials (RCTs) of medications for knee osteoarthritis. DATA SOURCES AND STUDY SELECTION The databases of MEDLINE, Scopus, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials were searched until June 30, 2018 (MEDLINE alerts through August 31, 2018) for RCTs of patients with knee osteoarthritis that had treatment and follow-up lasting 1 year or longer. DATA EXTRACTION AND SYNTHESIS Data at baseline and at the longest available treatment and follow-up of 12 months' duration or longer (or the change from baseline) were extracted. A Bayesian random-effects network meta-analysis was performed. MAIN OUTCOMES AND MEASURES The primary outcome was the mean change from baseline in knee pain. Secondary outcomes were physical function and joint structure (the latter was measured radiologically as joint space narrowing). Standardized mean differences (SMDs) and mean differences with 95% credibility intervals (95% CrIs) were calculated. Findings were interpreted as associations when the 95% CrIs excluded the null value. RESULTS Forty-seven RCTs (22 037 patients; mean age range, mostly 55-70 years; and a higher mean proportion of women than men, around 70%) included the following medication categories: analgesics; antioxidants; bone-acting agents such as bisphosphonates and strontium ranelate; nonsteroidal anti-inflammatory drugs; intra-articular injection medications such as hyaluronic acid and corticosteroids; symptomatic slow-acting drugs in osteoarthritis such as glucosamine and chondroitin sulfate; and putative disease-modifying agents such as cindunistat and sprifermin. Thirty-one interventions were studied for pain, 13 for physical function, and 16 for joint structure. Trial duration ranged from 1 to 4 years. Associations with decreases in pain were found for the nonsteroidal anti-inflammatory drug celecoxib (SMD, −0.18 [95% CrI, −0.35 to −0.01]) and the symptomatic slow-acting drug in osteoarthritis glucosamine sulfate (SMD, −0.29 [95% CrI, −0.49 to −0.09]), but there was large uncertainty for all estimates vs placebo. The association with pain improvement remained significant only for glucosamine sulfate when data were analyzed using the mean difference on a scale from 0 to 100 and when trials at high risk of bias were excluded. Associations with improvement in joint space narrowing were found for glucosamine sulfate (SMD, −0.42 [95% CrI, −0.65 to −0.19]), chondroitin sulfate (SMD, −0.20 [95% CrI, −0.31 to −0.07]), and strontium ranelate (SMD, −0.20 [95% CrI, −0.36 to −0.05]). CONCLUSIONS AND RELEVANCE In this systematic review and network meta-analysis of studies of patients with knee osteoarthritis and at least 12 months of follow-up, there was uncertainty a...
The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.
Results may indicate a high degree of conservativeness in the UL of nicotinic acid, fixed at 35 mg/d in United States and 10 mg/d in Europe. Reconsideration of the UL of nicotinic acid for nutritional supplements, possibly differentiating between ULs in healthy and unhealthy individuals, may be warranted.
Background:Nursing and health care research are increasingly using e-questionnaires and e-forms for data collection and survey conduction. The main reason lies in costs, time and data-entry errors containment, increased flexibility, functionality and usability. In spite of this growing usage, no specifc and comprehensive guidelines for designing and submitting e-questionnaires have been produced so far.Objective:The aim of this review is to collect information on the current best practices, taking them from various fields of application. An evaluation of the efficacy of the single indication is provided.Method:A literature review of guidelines currently available on WebSM (Web Survey Methodology) about electronic questionnaire has been performed. Four search strings were used: “Electronic Questionnaire Design”, “Electronic Questionnaire”, “Online Questionnaire” and “Online survey”. Articles’ inclusion criteria were English language, relevant topic in relation to the aim of the research and the publication date from January 1998 to July 2014.Results:The review process led to identify 48 studies. The greater part of guidelines is reported for Web, and e-mail questionnaire, while a lack of indications emerges especially for app and e-questionnaires.Conclusion:Lack of guidelines on e-questionnaires has been found, especially in health care research, increasing the risk of use of ineffective and expensive instruments; more research in this field is needed.
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