Learning to prescribe intravenous fluids: A scoping review.
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ABSTRACT:
SUBMITTER: McCrory RFR
PROVIDER: S-EPMC5732109 | biostudies-literature | 2017 Dec
REPOSITORIES: biostudies-literature
Publications
Learning to prescribe intravenous fluids: A scoping review.
McCrory Richard F R RFR Gormley Gerard Joseph GJ Maxwell Alexander Peter AP Dornan Tim T
Perspectives on medical education 20171201 6
<h4>Introduction</h4>Prescribing intravenous (IV) fluid therapy is a core skill expected of qualified doctors at the point of graduation, but medical graduates often feel ill-equipped to perform this task. This lack of preparedness contributes to treatment-related patient harm. This scoping review maps the current state of published evidence about how junior doctors prescribe IV fluid therapy and learn how to do it.<h4>Methods</h4>We searched five electronic databases and grey literature from 19 ...[more]
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Project description:BackgroundThe concept that upper extremities can be used as an infusion route during cardiopulmonary resuscitation in pregnant women is a reasonable recommendation considering the characteristic circulation of pregnant women; however, this method is not based on scientific evidence.Objective of the reviewWe conducted a scoping review to determine whether the infusion route should be established above the diaphragm during cardiopulmonary resuscitation in a pregnant woman.DiscussionWe included randomized controlled trials (RCTs) and non-RCTs on the infusion of fluids in pregnant women after 20 weeks of gestation requiring establishment of an infusion route due to cardiac arrest, massive bleeding, intra-abdominal bleeding, cesarean section, severe infection, or thrombosis. In total, 3150 articles from electronic database were extracted, respectively. After title and abstract review, 265 articles were extracted, and 116 articles were extracted by full-text screening, which were included in the final analysis. The 116 articles included 78 studies on infusion for pregnant women. The location of the intravenous infusion route could be confirmed in only 17 studies, all of which used the upper extremity to secure the venous route.ConclusionPregnant women undergo significant physiological changes that differ from those of normal adults, because of pressure and drainage of the inferior vena cava and pelvic veins by the enlarged uterus. Therefore, despite a lack of evidence, it seems logical to secure the infusion route above the diaphragm when resuscitating a pregnant woman.
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Project description:This study sought to determine the use of intravenous fluids in the early care of patients with acute decompensated heart failure (HF) who are treated with loop diuretics.Intravenous fluids are routinely provided to many hospitalized patients.We conducted a retrospective cohort study of patients admitted with HF to 346 hospitals from 2009 to 2010. We assessed the use of intravenous fluids during the first 2 days of hospitalization. We determined the frequency of adverse in-hospital outcomes. We assessed variation in the use of intravenous fluids across hospitals and patient groups.Among 131,430 hospitalizations for HF, 13,806 (11%) were in patients treated with intravenous fluids during the first 2 days. The median volume of administered fluid was 1,000 ml (interquartile range: 1,000 to 2,000 ml), and the most commonly used fluids were normal saline (80%) and half-normal saline (12%). Demographic characteristics and comorbidities were similar in hospitalizations in which patients did and did not receive fluids. Patients who were treated with intravenous fluids had higher rates of subsequent critical care admission (5.7% vs. 3.8%; p < 0.0001), intubation (1.4% vs. 1.0%; p = 0.0012), renal replacement therapy (0.6% vs. 0.3%; p < 0.0001), and hospital death (3.3% vs. 1.8%; p < 0.0001) compared with those who received only diuretics. The proportion of hospitalizations that used fluid treatment varied widely across hospitals (range: 0% to 71%; median: 12.5%).Many patients who are hospitalized with HF and receive diuretics also receive intravenous fluids during their early inpatient care, and the proportion varies among hospitals. Such practice is associated with worse outcomes and warrants further investigation.
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Project description:Intravenous fluids (IVFs) are the most commonly used drugs in hospitalized patients. Knowledge of the indications and pharmacokinetics of IVFs is critical for all medical disciplines. Isotonic saline (normal saline, 0.9% NS) is the most utilized intravenous solution. Isotonic saline effectively expands the intravascular compartment, as one-quarter of the infusate goes intravascularly, while the remaining three-quarters go into the interstitial space. The proper use of IVFs in different clinical scenarios is paramount. IVFs differ with regard to their half-life, intravascular volume expansion, preparation, and cost. Crystalloids are more commonly utilized due to their relatively low cost and availability. Colloids are very advantageous in cases of shock or hemorrhage, as they remain in the intravascular space, thus facilitating an increase in blood pressure (BP) prior to blood administration. Colloids are also advantageous in cases of burns and severe hypoglobulinemia. Human albumin (5%, 20%, and 25%) is the most used colloid solution. It remains intravascularly provided and there is no capillary leak as in systematic inflammation. The goal in hospitalized patients is timely and adequate intravenous fluid resuscitation. Utilization of a large volume of isotonic saline may lead to hypervolemia, hypernatremia, hyperchloremia, metabolic acidosis, and hypokalemia. The use of balanced intravenous solutions has been advocated to avoid these complications.
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Project description:BackgroundUnderstanding students' learning styles, and modifying teaching styles and material accordingly, is an essential to delivering quality education. Knowing more about the learning styles of physiotherapy learners will assist educators' planning and delivering of learning activities. The purpose of this scoping review was to explore what is published about physiotherapy learning styles.MethodsAn adapted Arksey and O'Malley framework was applied to undertake this systematic scoping review. Nine electronic databases (CINAHL, BIOMED CENTRAL, Cochrane, Web of Science, PROQUEST, PubMed, OTseeker, Scopus, ERIC) were searched using the keywords: 'learning styles' and 'physiotherapy'. English-language, primary research articles that investigated physiotherapy learners' learning styles were sought.ResultsOf 396 potentially-relevant articles, 15 were included in this review. The studies mostly reflected undergraduate students (910 undergraduates, 361 postgraduates, 23 professionals), in developed countries. Nine articles used the Kolb's experiential learning theory (ELT); one study applied Honey and Mumford's approach; two studies used the Gregorc model of cognition and three studies did not specify an underlying theory. Outcome measures included different versions of Kolb's Learning Style Inventory, the visual-aural-read/write-kinesthetic questionnaire, Gregorc style delineator, Felder Silverman's Index of Learning Survey, and Honey and Mumford's Learning Style Questionnaire. The preferred physiotherapy learning styles, according to the ELT, seem to be Converger (learns "hands-on" and applying previously attained knowledge) and Assimilator (gathers and organises information to make the most sense).ConclusionsBoth physiotherapy learners and physiotherapists have specific learning styles of active participation, underpinned with practical examples of theoretical concepts. More research is needed in developing countries, and on postgraduate and professional physiotherapy learners' learning styles. Also, further research should focus on defining and describing physiotherapy learning styles in a way to be used as an industry standard; and developing valid and reliable learning style outcome measures applicable across physiotherapy learners and settings.
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Project description:OBJECTIVE:To determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe which outcomes have been studied, the data sources most widely used and whether reporting of machine learning predictive models aligns with established reporting guidelines. DESIGN:A scoping review. DATA SOURCES:MEDLINE, EMBASE, CINAHL, ProQuest, Scopus, Web of Science, Cochrane Library, INSPEC and ACM Digital Library were searched on 18 July 2018. ELIGIBILITY CRITERIA:We included English articles published between 1980 and 2018 that used machine learning to predict population-health-related outcomes. We excluded studies that only used logistic regression or were restricted to a clinical context. DATA EXTRACTION AND SYNTHESIS:We summarised findings extracted from published reports, which included general study characteristics, aspects of model development, reporting of results and model discussion items. RESULTS:Of 22 618 articles found by our search, 231 were included in the review. The USA (n=71, 30.74%) and China (n=40, 17.32%) produced the most studies. Cardiovascular disease (n=22, 9.52%) was the most studied outcome. The median number of observations was 5414 (IQR=16?543.5) and the median number of features was 17 (IQR=31). Health records (n=126, 54.5%) and investigator-generated data (n=86, 37.2%) were the most common data sources. Many studies did not incorporate recommended guidelines on machine learning and predictive modelling. Predictive discrimination was commonly assessed using area under the receiver operator curve (n=98, 42.42%) and calibration was rarely assessed (n=22, 9.52%). CONCLUSIONS:Machine learning applications in population health have concentrated on regions and diseases well represented in traditional data sources, infrequently using big data. Important aspects of model development were under-reported. Greater use of big data and reporting guidelines for predictive modelling could improve machine learning applications in population health. REGISTRATION NUMBER:Registered on the Open Science Framework on 17 July 2018 (available at https://osf.io/rnqe6/).
| S-EPMC7592293 | biostudies-literature
Project description:Feedback is an effective pedagogy aimed to create cognitive dissonance and reinforce learning as a key component of clinical training programs. Pharmacy learners receive constant feedback. However, there is limited understanding of how feedback is utilized in pharmacy education. This scoping review sought to summarize the breadth and depth of the use of feedback within pharmacy education and identify areas for future research. PubMed, Embase, Scopus, and Web of Science were searched for English articles since January 2000 to identify studies related to feedback in pharmacy education. Sixty-four articles were included for analysis, stratified by moderate and major theory talk, where moderate theory talk explicitly included feedback into study design and major theory talk included feedback into both study design and analysis. Feedback was provided in Bachelor (14%), Master (15.6%), Doctor of Pharmacy (67.2%) and post-graduate programs (4.7%) on a variety of curricular objectives including communication and patient work up in didactic, objective structured clinical examination (OSCE), and experiential settings, and career/interview preparation in the co-curriculum. Feedback comments were mostly written in didactic courses, and both written and verbal in OSCE, experiential, and co-curricular settings. The pharmacy education feedback literature lacks depth beyond student perceptions, especially with respect to assessing the effectiveness and quality of feedback for learning. While feedback has been utilized throughout pharmacy education across myriad outcomes, several areas for inquiry exist which can inform the design of faculty and preceptor development programs, ensuring provision of effective, quality feedback to pharmacy learners.
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Project description:BackgroundMachine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely, given the rapid technological developments in recent years.ObjectiveThis study aims to synthesize the literature on ML and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD).MethodsWe used a scoping review methodology using the Arksey and O'Malley framework to rapidly map research activity in ML for predicting PPD. Two independent researchers searched PsycINFO, PubMed, IEEE Xplore, and the ACM Digital Library in September 2020 to identify relevant publications in the past 12 years. Data were extracted from the articles' ML model, data type, and study results.ResultsA total of 14 studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine and random forest were the most commonly used algorithms in addition to Naive Bayes, regression, artificial neural network, decision trees, and XGBoost (Extreme Gradient Boosting). There was considerable heterogeneity in the best-performing ML algorithm across the selected studies. The area under the receiver operating characteristic curve values reported for different algorithms were support vector machine (range 0.78-0.86), random forest method (0.88), XGBoost (0.80), and logistic regression (0.93).ConclusionsML algorithms can analyze larger data sets and perform more advanced computations, which can significantly improve the detection of PPD at an early stage. Further clinical research collaborations are required to fine-tune ML algorithms for prediction and treatment. ML might become part of evidence-based practice in addition to clinical knowledge and existing research evidence.
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Project description:Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.
| S-EPMC9640667 | biostudies-literature
Project description:IntroductionFederated Learning (FL) is a decentralised approach to training statistical models, where training is performed across multiple clients, producing one global model. Since the training data remains with each local client and is not shared or exchanged with other clients the use of FL may reduce privacy and security risks (compared to methods where multiple data sources are pooled) and can also address data access and heterogeneity problems. Synthetic data is artificially generated data that has the same structure and statistical properties as the original but that does not contain any of the original data records, therefore minimising disclosure risk. Using FL to produce synthetic data (which we refer to as "federated synthesis") has the potential to combine data from multiple clients without compromising privacy, allowing access to data that may otherwise be inaccessible in its raw format.ObjectivesThe objective was to review current research and practices for using FL to generate synthetic data and determine the extent to which research has been undertaken, the methods and evaluation practices used, and any research gaps.MethodsA scoping review was conducted to systematically map and describe the published literature on the use of FL to generate synthetic data. Relevant studies were identified through online databases and the findings are described, grouped, and summarised. Information extracted included article characteristics, documenting the type of data that is synthesised, the model architecture and the methods (if any) used to evaluate utility and privacy risk.ResultsA total of 69 articles were included in the scoping review; all were published between 2018 and 2023 with two thirds (46) in 2022. 30% (21) were focussed on synthetic data generation as the main model output (with 6 of these generating tabular data), whereas 59% (41) focussed on data augmentation. Of the 21 performing federated synthesis, all used deep learning methods (predominantly Generative Adversarial Networks) to generate the synthetic data.ConclusionsFederated synthesis is in its early days but shows promise as a method that can construct a global synthetic dataset without sharing any of the local client data. As a field in its infancy there are areas to explore in terms of the privacy risk associated with the various methods proposed, and more generally in how we measure those risks.
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Project description:Abstract Background Recent literature and guidelines support routine use of isotonic intravenous (IV) fluids for maintenance therapy in hospitalized infants and children. Current prescribing practices are unknown. Objective To elicit paediatric residents’ choice of maintenance IV fluids, particularly with regard to tonicity, in a variety of clinical scenarios and patient ages. We hypothesized that residents would choose isotonic fluids in most cases, but there would be substantially more variability in fluid choice in the neonatal age group. Methods An Internet-based survey was e-mailed to trainees in the 17 paediatric residency programs across Canada, via the Canadian Paediatric Program Directors. The survey instrument included questions related to training, followed by a series of questions eliciting choice of IV fluid in a variety of clinical situations. Results A total of 147 survey responses were submitted (22% response rate). Isotonic solutions were selected by >75% across all clinical scenarios involving infants and children. Very hypotonic fluids were seldom chosen. There was more variability in fluid choice in neonates, with evidence of significant differences in fluid tonicity based on senior versus junior resident status and geographical location. Conclusions Results imply a predominance of isotonic fluid use in infants and children, suggesting that clinical practice has changed in response to risk of hyponatremia with hypotonic IV fluids. As hypothesized, there was more variability with respect to choice of maintenance fluids in neonates. This likely reflects a paucity of guidance in an age group with unique physiologic factors affecting fluid and electrolyte status.
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