Handling missing continuous outcome data in a Bayesian network meta-analysis

Background: A Bayesian network meta-analysis (NMA) model is a statistical method aimed at estimating the relative effects of multiple interventions against the same disease. The method has recently gained prominence, leading to the synthesis of the evidence regarding rank probabilities for each treatment. In several cases, an NMA is performed excluding incomplete data of studies retrieved through a systematic review, resulting in a loss of precision and power.  Methods: There are several methods for handling missing or incomplete data in an NMA framework, especially for continuous outcomes. In certain cases, only baseline and follow-up measurements are available; in this framework, to obtain data regarding mean changes, it is necessary to consider the pre-post study correlation. In this context, in a Bayesian setting, several authors suggest imputation strategies for pre-post correlation. In other cases, a variability measure associated with a mean change score might be unavailable. Different imputation methods have been suggested, such as those based on maximum standard deviation imputation. The purpose of this study is to verify the robustness of Bayesian NMA models concerning different imputation strategies through simulations.  Results: Simulation results show that the bias is notably small for every scenario, confirming that rankings provided by models are robust concerning different imputation methods in several heterogeneity-correlation settings.  Conclusions: This NMA method seems to be more robust to missing data imputation when data reported in different studies are generated in a low-heterogeneity scenario. The NMA method seems to be more robust to missing value imputation if the expectation of the prior distribution, defined on the heterogeneity parameter, approaches the true value of the variability across studies.&nbsp

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Electronic questionnaires design and implementation

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: \u201cElectronic Questionnaire Design\u201d, \u201cElectronic Questionnaire\u201d, \u201cOnline Questionnaire\u201d and \u201cOnline survey\u201d. Articles\u2019 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. \ua9 2017 Minto et al

Definition of a tolerable upper intake level of niacin: a systematic review and meta-analysis of the dose-dependent effects of nicotinamide and nicotinic acid supplementation

Context: Nicotinic acid and nicotinamide are soluble compounds of the vitamin B group, widely used to regulate the lipid profile in hyperlipidemic individuals. Higher doses of nicotinic acid are associated with adverse effects, especially flushing. A unique tolerable upper intake level (UL) of nicotinic acid has not been defined. Objective: This meta-analysis aims to evaluate adverse effects and their incidence after supplementation with different doses of nicotinic acid and nicotinamide, comparing results with current ULs in Europe and the United States. Data Sources: PubMed was searched for articles providing detailed information about nicotinic acid or nicotinamide supplementation and related outcomes. Study Selection: A total of 2670 citations were selected for screening. Two primary outcomes were considered: occurrence of adverse effects following nicotinic acid or nicotinamide supplementation, and dose at which adverse effects occurred. Data extraction: Details on study population, type and duration of treatment, dosage of vitamins, association with lipid-influencing drugs, length of follow-up, and incidence and type of adverse events were extracted. Results: After screening, 47 articles involving 11\u2005741 individuals were included. Meta-analysis was based on estimation of benchmark doses for the probability of adverse effects after supplementation. In individuals with dyslipidemia or cardiovascular disease, nicotinic acid monotherapy seems to be protective against any adverse effects considered, as adverse events occurred at doses above those used with other treatments. In healthy individuals treated with nicotinic acid alone, major adverse effects occurred at doses below 1000\u2009mg/d. Conclusions: Results may indicate a high degree of conservativeness in the UL of nicotinic acid, fixed at 35\u2009mg/d in United States and 10\u2009mg/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. \ua9 The Author(s) 2017. Published by Oxford University Press on behalf of the International Life Sciences Institute. All rights reserved. For Permissions, please e-mail: [email protected]

Wearable Devices for Caloric Intake Assessment: State of Art and Future Developments

Background: The self-monitoring of caloric intake is becoming necessary as the number of pathologies related to eating increases. New wearable devices may help people to automatically record energy assumed in their meals. Objective: The present review collects the released articles about wearable devices or method for automatic caloric assessments. Method: A literature research has been performed with PubMed, Google Scholar, Scopus and ClinicalTrials.gov search engines, considering released articles regarding applications of wearable devices in eating environment, from 2005 onwards. Results: Several tools allow caloric assessment and food registration: wearable devices counting the number of bites ingested by the user, instruments detecting swallows and chewings, methods that analyse food with digital photography. All of them still require more validation and improvement. Conclusion: Automatic recording of caloric intake through wearable devices is a promising method to monitor body weight and eating habits in clinical and non-clinical settings, and the research is still going on. \ua9 2017 Magrini et al

Screening PubMed abstracts: is class imbalance always a challenge to machine learning?

The growing number of medical literature and textual data in online repositories led to an exponential increase in the workload of researchers involved in citation screening for systematic reviews. This work aims to combine machine learning techniques and data preprocessing for class imbalance to identify the outperforming strategy to screen articles in PubMed for inclusion in systematic reviews

Handling missing continuous outcome data in a Bayesian network meta-analysis

Background: A Bayesian network meta-analysis (NMA) model is a statistical method aimed at estimating the relative effects of multiple interventions against the same disease. The method has recently gained prominence, leading to the synthesis of the evidence regarding rank probabilities for each treatment. In several cases, an NMA is performed excluding incomplete data of studies retrieved through a systematic review, resulting in a loss of precision and power. Methods: There are several methods for handling missing or incomplete data in an NMA framework, especially for continuous outcomes. In certain cases, only baseline and follow-up measurements are available; in this framework, to obtain data regarding mean changes, it is necessary to consider the pre-post study correlation. In this context, in a Bayesian setting, several authors suggest imputation strategies for pre-post correlation. In other cases, a variability measure associated with a mean change score might be unavailable. Different imputation methods have been suggested, such as those based on maximum standard deviation imputation. The purpose of this study is to verify the robustness of Bayesian NMA models concerning different imputation strategies through simulations. Results: Simulation results show that the bias is notably small for every scenario, confirming that rankings provided by models are robust concerning different imputation methods in several heterogeneity-correlation settings. Conclusions: This NMA method seems to be more robust to missing data imputation when data reported in different studies are generated in a low-heterogeneity scenario. The NMA method seems to be more robust to missing value imputation if the expectation of the prior distribution, defined on the heterogeneity parameter, approaches the true value of the variability across studies

Extending PubMed searches to ClinicalTrials.gov through a machine learning approach for systematic reviews

Objectives: Despite their essential role in collecting and organizing published medical literature, indexed search engines are unable to cover all relevant knowledge. Hence, current literature recommends the inclusion of clinical trial registries in systematic reviews (SRs). This study aims to provide an automated approach to extend a search on PubMed to the ClinicalTrials.gov database, relying on text mining and machine learning techniques. Study Design and Setting: The procedure starts from a literature search on PubMed. Next, it considers the training of a classifier that can identify documents with a comparable word characterization in the ClinicalTrials.gov clinical trial repository. Fourteen SRs, covering a broad range of health conditions, are used as case studies for external validation. A cross-validated support-vector machine (SVM) model was used as the classifier. Results: The sensitivity was 100% in all SRs except one (87.5%), and the specificity ranged from 97.2% to 99.9%. The ability of the instrument to distinguish on-topic from off-topic articles ranged from an area under the receiver operator characteristic curve of 93.4% to 99.9%. Conclusion: The proposed machine learning instrument has the potential to help researchers identify relevant studies in the SR process by reducing workload, without losing sensitivity and at a small price in terms of specificity