Objective: The aim of this meta-analysis was to summarize the current available evidence on nonoperative management (NOM) with antibiotics for uncomplicated appendicitis, both in adults and children. Summary Background Data: Although earlier meta-analyses demonstrated that NOM with antibiotics may be an acceptable treatment strategy for patients with uncomplicated appendicitis, evidence is limited by conflicting results. Methods: Systematic literature search was performed using MEDLINE, the Cochrane Central Register of Controlled Trials, and EMBASE databases for randomized and nonrandomized studies comparing antibiotic therapy (AT) and surgical therapy-appendectomy (ST) for uncomplicated appendicitis. Literature search was completed in August 2018. Results: Twenty studies comparing AT and ST qualified for inclusion in the quantitative synthesis. In total, 3618 patients were allocated to AT (n = 1743) or ST (n = 1875). Higher complication-free treatment success rate (82.3% vs 67.2%; P < 0.00001) and treatment efficacy based on 1-year follow-up rate (93.1% vs 72.6%; P < 0.00001) were reported for ST. Index admission antibiotic treatment failure and rate of recurrence at 1-year follow-up were reported in 8.5% and 19.2% of patients treated with antibiotics, respectively. Rates of complicated appendicitis with peritonitis identified at the time of surgical operation (AT: 21.7% vs ST: 12.8%; P = 0.07) and surgical complications (AT: 12.8% vs ST: 13.6%; P = 0.66) were equivalent. Conclusions: Antibiotic therapy could represent a feasible treatment option for image-proven uncomplicated appendicitis, although complication-free treatment success rates are higher with ST. There is also evidence that NOM for uncomplicated appendicitis does not statistically increase the perforation rate in adult and pediatric patients receiving antibiotic treatment. NOM with antibiotics may fail during the primary hospitalization in about 8% of cases, and an additional 20% of patients might need a second hospitalization for recurrent appendicitis.

The ultimate goal of glaucoma management is the preservation of patients’ visual function and quality of life (QoL). The disease itself as well as the medical or surgical treatment can have an enormous impact on a patient’s QoL. Even the mere diagnosis of a chronic, irreversible, potentially blinding disorder can adversely affect the patient’s sense of well-being and QoL by eliciting significant anxiety. Patients with primary open-angle glaucoma rarely present with visual symptoms, at least early in the course of the disease. A better understanding of patient-reported QoL can improve patient–physician interaction and enhance treatment adherence by customizing treatment options based on individual patient profile, thus optimizing long-term prognosis. These aspects are summarized and critically appraised in this article.

Using the best quality of clinical research evidence is essential for choosing the right treatment for patients. How to identify the best research evidence is, however, difficult. In this narrative review we summarise these threats and describe how to minimise them. Pertinent literature was considered through literature searches combined with personal files. Treatments should generally not be chosen based only on evidence from observational studies or single randomised clinical trials. Systematic reviews with meta-analysis of all identifiable randomised clinical trials with Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessment represent the highest level of evidence. Even though systematic reviews are trust worthier than other types of evidence, all levels of the evidence hierarchy are under threats from systematic errors (bias); design errors (abuse of surrogate outcomes, composite outcomes, etc.); and random errors (play of chance). Clinical research infrastructures may help in providing larger and better conducted trials. Trial Sequential Analysis may help in deciding when there is sufficient evidence in meta-analyses. If threats to the validity of clinical research are carefully considered and minimised, research results will be more valid and this will benefit patients and heath care systems.

Objective To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects. Design Scoping review. Methods We searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests. Results Overall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation. Conclusions While most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies.