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IntroductionClinically important upper gastrointestinal bleeding is conventionally defined as bleeding accompanied by haemodynamic changes, requiring red blood cell transfusions or other invasive interventions. However, it is unclear if this clinical definition reflects patient values and preferences. This protocol describes a study to elicit views from patients and families regarding features, tests, and treatments for upper gastrointestinal bleeding that are important to them.Methods and analysisThis is a sequential mixed-methods qualitative-dominant multi-centre study with an instrument-building aim. We developed orientation tools and educational materials in partnership with patients and family members, including a slide deck and executive summary. We will invite intensive care unit (ICU) survivors and family members of former ICU patients to participate. Following a virtual interactive presentation, participants will share their perspectives in an interview or focus group. Qualitative data will be analysed using inductive qualitative content analysis, wherein codes will be derived directly from the data rather than using preconceived categories. Concurrent data collection and analysis will occur. Quantitative data will include self-reported demographic characteristics. This study will synthesise the values and perspectives of patients and family members to create a new trial outcome for a randomised trial of stress ulcer prophylaxis. This study is planned for May 2022 to August 2023. The pilot work was completed in Spring 2021.Ethics and disseminationThis study has ethics approval from McMaster University and the University of Calgary. Findings will be disseminated via manuscript and through incorporation as a secondary trial outcome on stress ulcer prophylaxis.Trial registration numberNCT05506150.

To categorize, quantify and interpret findings documented in feedback letters of monitoring or auditing visits for an investigator-initiated, peer-review funded multicenter randomized trial testing probiotics for critically ill patients.In 37 Canadian centers, monitoring and auditing visits were performed by 3 trained individuals; findings were reported in feedback letters. At trial termination, we performed duplicate content analysis on letters, categorizing observations first into unique findings, followed by 10 pre-determined trial quality management domains. We further classified each observation into a) missing operational records, b) errors in process, and potential threats to c) data integrity, d) patient privacy or e) safety.Across 37 monitoring or auditing visits, 75 unique findings were categorized into 10 domains. Most frequently, observations were in domains of training documentation (180/566 [32%]) and the informed consent process (133/566 [23%]). Most observations were missing operational records (438/566 [77%]) rather than errors in process (128/566 [23%]). Of 75 findings, 13 (62/566 observations [11%]) posed a potential threat to data integrity, 1 (1/566 observation [0.18%]) to patient privacy, and 9 (49/566 observations [8.7%]) to patient safety.Monitoring and auditing findings predominantly concerned missing documentation with minimal threats to data integrity, patient privacy or safety.PROSPECT (Probiotics: Prevention of Severe Pneumonia and Endotracheal Colonization Trial): NCT02462590.

IntroductionOveruse of cardiovascular healthcare services, defined as the provision of low-value (ineffective, harmful, cost-ineffective) tests, medications and procedures, may be common and associated with increased patient harm and health system inefficiencies and costs. We seek to systematically review the evidence for overuse of different cardiovascular healthcare services in high-income countries.Methods and analysisWe will search MEDLINE, EMBASE and Evidence-Based Medicine Reviews from 2010 onwards. Two investigators will independently review titles and abstracts and full-text studies. We will include published English-language studies conducted in high-income countries that enrolled adults (mean/median age ≥18 years) and reported the incidence or prevalence of overuse of cardiovascular tests, medications or procedures; adjusted risk factors for overuse; or adjusted associations between overuse and outcomes (reported estimates of morbidity, mortality, costs or lengths of hospital stay). Acceptable methods of defining low-value care will include literature review and multidisciplinary iterative panel processes, healthcare services with reproducible evidence of a lack of benefit or harm, or clinical practice guideline or Choosing Wisely recommendations. Two investigators will independently extract data and evaluate study risk of bias in duplicate. We will calculate summary estimates of the incidence and prevalence of overuse of different cardiovascular healthcare services across studies unstratified and stratified by country; method of defining low-value care; the percentage of included females, different races, and those with low and high socioeconomic status or cardiovascular risk; and study risks of bias using random-effects models. We will also calculate pooled estimates of adjusted risk factors for overuse and adjusted associations between overuse and outcomes overall and stratified by country using random-effects models. We will use the Grading of Recommendations, Assessment, Development and Evaluation to determine certainty in estimates.Ethics and disseminationNo ethics approval is required for this study as it deals with published data. Results will be presented at meetings and published in a peer-reviewed journal.PROSPERO registration numberCRD42021257490.

Background Redundancy in laboratory blood tests is common in intensive care units (ICUs), affecting patients’ health and increasing health care expenses. Medical communities have made recommendations to order laboratory tests more judiciously. Wise selection can rely on modern data-driven approaches that have been shown to help identify low-yield laboratory blood tests in ICUs. However, although conditional entropy and conditional probability distribution have shown the potential to measure the uncertainty of yielding an abnormal test, no previous studies have adapted these techniques to include them in machine learning models for predicting abnormal laboratory test results. Objective This study aimed to address the limitations of previous reports by adapting conditional entropy and conditional probability to extract features for predicting abnormal laboratory blood test results. Methods We used an ICU data set collected across Alberta, Canada, which included 55,689 ICU admissions from 48,672 patients. We investigated the features of conditional entropy and conditional probability by comparing the performances of 2 machine learning approaches for predicting normal and abnormal results for 18 blood laboratory tests. Approach 1 used patients’ vitals, age, sex, and admission diagnosis as features. Approach 2 used the same features plus the new conditional entropy–based and conditional probability–based features. Both approaches used 4 different machine learning models (fuzzy model, logistic regression, random forest, and gradient boosting trees) and 10 metrics (sensitivity, specificity, accuracy, precision, negative predictive value [NPV], F1 score, area under the curve [AUC], precision-recall AUC, mean G, and index balanced accuracy) to assess the performance of the approaches. Results Approach 1 achieved an average AUC of 0.86 for all 18 laboratory tests across the 4 models (sensitivity 78%, specificity 84%, precision 82%, NPV 75%, F1 score 79%, and mean G 81%), whereas approach 2 achieved an average AUC of 0.89 (sensitivity 84%, specificity 84%, precision 83%, NPV 81%, F1 score 83%, and mean G 84%). We found that the inclusion of the new features resulted in significant differences for most of the metrics in favor of approach 2. Sensitivity significantly improved for 8 and 15 laboratory tests across the different classifiers (minimum P Conclusions The findings suggest that conditional entropy–based features and pretest probability improve the capacity to discriminate between normal and abnormal laboratory test results. Detecting the next laboratory test result is an intermediate step toward developing guidelines for reducing overtesting in the ICU.

IntroductionAntipsychotic medications are commonly prescribed off-label in acutely ill patients for non-psychiatric clinical indications such as delirium or insomnia. New prescription initiation of antipsychotics in acute care settings increases the proportion of patients discharged home on antipsychotics without approved clinical indication. Long-term use of antipsychotics is associated with increased risk of sudden cardiac death, falls and cognitive impairment. An understanding of acute care off-label antipsychotic prescribing practices and healthcare professional, patient and family perceptions related to antipsychotic prescribing and deprescribing is necessary to facilitate in-hospital deprescribing initiatives.Methods and analysisWe present the protocol for a scoping review following the methodology proposed by Arksey and O’Malley and the Scoping Review Methods Manual by the Joanna Briggs Institute. We will search five databases including MEDLINE, EMBASE, CINAHL, PsycINFO and Web of Science from inception to 3 July 2021 (ie, planned search date). We will include both peer-reviewed and non-peer-reviewed qualitative and quantitative studies to identify antipsychotic prescribing practices, and to describe healthcare professional, patient and family perceptions towards antipsychotic prescribing and deprescribing in the acute care setting. Protocols, systematic and scoping reviews will be excluded. Two reviewers will calibrate and perform study screening and data abstraction for quantitative and qualitative outcomes of eligible studies. Quantitative outcomes will include study identifiers, demographics and descriptive statistics of antipsychotic prescribing practices. Qualitative synthesis describing perceptions on antipsychotic prescribing practices will include deductive thematic analysis with mapping of themes to the domains of the Theoretical Domains Framework, a 14-domain behaviour and behaviour change framework.Ethics and disseminationNo ethical approval will be required for this study as only data from published studies in which informed consent was obtained by primary investigators will be retrieved and analysed. The results of this scoping review will inform integrated knowledge translation initiatives aimed at in-hospital antipsychotic medication deprescribing.