scholarly journals Intravenous versus Oral Acetaminophen for Pain: Systematic Review of Current Evidence to Support Clinical Decision-Making

Author(s):  
Farah Jibril ◽  
Sherif Sharaby ◽  
Ahmed Mohamed ◽  
Kyle J Wilby
2022 ◽  
Vol 14 (1) ◽  
Author(s):  
Micah L. A. Heldeweg ◽  
Lian Vermue ◽  
Max Kant ◽  
Michelle Brouwer ◽  
Armand R. J. Girbes ◽  
...  

Abstract Background Lung ultrasound has established itself as an accurate diagnostic tool in different clinical settings. However, its effects on clinical-decision making are insufficiently described. This systematic review aims to investigate the impact of lung ultrasound, exclusively or as part of an integrated thoracic ultrasound examination, on clinical-decision making in different departments, especially the emergency department (ED), intensive care unit (ICU), and general ward (GW). Methods This systematic review was registered at PROSPERO (CRD42021242977). PubMed, EMBASE, and Web of Science were searched for original studies reporting changes in clinical-decision making (e.g. diagnosis, management, or therapy) after using lung ultrasound. Inclusion criteria were a recorded change of management (in percentage of cases) and with a clinical presentation to the ED, ICU, or GW. Studies were excluded if examinations were beyond the scope of thoracic ultrasound or to guide procedures. Mean changes with range (%) in clinical-decision making were reported. Methodological data on lung ultrasound were also collected. Study quality was scored using the Newcastle–Ottawa scale. Results A total of 13 studies were included: five studies on the ED (546 patients), five studies on the ICU (504 patients), two studies on the GW (1150 patients), and one study across all three wards (41 patients). Lung ultrasound changed the diagnosis in mean 33% (15–44%) and 44% (34–58%) of patients in the ED and ICU, respectively. Lung ultrasound changed the management in mean 48% (20–80%), 42% (30–68%) and 48% (48–48%) of patients in the ED, in the ICU and in the GW, respectively. Changes in management were non-invasive in 92% and 51% of patients in the ED and ICU, respectively. Lung ultrasound methodology was heterogeneous across studies. Risk of bias was moderate to high in all studies. Conclusions Lung ultrasound, exclusively or as a part of thoracic ultrasound, has substantial impact on clinical-decision making by changing diagnosis and management in the EDs, ICUs, and GWs. The current evidence level and methodological heterogeneity underline the necessity for well-designed trials and standardization of methodology.


2020 ◽  
Vol 28 (4) ◽  
pp. 122-131
Author(s):  
Kyle N. Kunze ◽  
Matthew R. Cohn ◽  
Brady T. Williams ◽  
Grant Garrigues ◽  
Jorge Chahla

Anaesthesia ◽  
2016 ◽  
Vol 71 (9) ◽  
pp. 1091-1100 ◽  
Author(s):  
J. Heiberg ◽  
D. El-Ansary ◽  
D. J. Canty ◽  
A. G. Royse ◽  
C. F. Royse

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Suneel Kumar Garg ◽  
Omender Singh ◽  
Deven Juneja ◽  
Niraj Tyagi ◽  
Amandeep Singh Khurana ◽  
...  

Polymyxin B has resurged in recent years as a last resort therapy for Gram-negative multidrug-resistant (MDR) and extremely drug resistant (XDR) infections. Understanding newer evidence on polymyxin B is necessary to guide clinical decision making. Here, we present a literature review of polymyxin B in Gram-negative infections with update on its pharmacology.


2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i18-i18
Author(s):  
N Hassan ◽  
R Slight ◽  
D Weiand ◽  
A Vellinga ◽  
G Morgan ◽  
...  

Abstract Introduction Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients who may be at risk of developing infection and subsequent sepsis and assist clinicians with their care management. Aim To identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis and inform clinical decision making. Methods This systematic review was registered in PROSPERO database (CRD42020158685). We searched 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase, using appropriate search terms. We included quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adult population (>18 years) in all care settings, which included data on predictors to develop machine learning algorithms. The timeframe of the search was 1st January 2000 till the 25th November 2019. Data extraction was performed using a data extraction sheet, and a narrative synthesis of eligible studies was undertaken. Narrative analysis was used to arrange the data into key areas, and compare and contrast between the content of included studies. Quality assessment was performed using Newcastle-Ottawa Quality Assessment scale, which was used to evaluate the quality of non-randomized studies. Bias was not assessed due to the non-randomised nature of the included studies. Results Fifteen articles met our inclusion criteria (Figure 1). We identified 194 predictors that were used to train machine learning algorithms to predict infection and subsequent sepsis, with 13 predictors used on average across all included studies. The most significant predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR<60ml/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit < 30%. These predictors were used for the development of all the algorithms in the fifteen articles. All included studies used artificial intelligence techniques to predict the likelihood of sepsis, with average sensitivity 77.5±19.27, and average specificity 69.45±21.25. Conclusion The type of predictors used were found to influence the predictive power and predictive timeframe of the developed machine learning algorithm. Two strengths of our review were that we included studies published since the first definition of sepsis was published in 2001, and identified factors that can improve the predictive ability of algorithms. However, we note that the included studies had some limitations, with three studies not validating the models that they developed, and many tools limited by either their reduced specificity or sensitivity or both. This work has important implications for practice, as predicting the likelihood of sepsis can help inform the management of patients and concentrate finite resources to those patients who are most at risk. Producing a set of predictors can also guide future studies in developing more sensitive and specific algorithms with increased predictive time window to allow for preventive clinical measures.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4736-4736
Author(s):  
Joseph Shatzel ◽  
Derrick Tao ◽  
Sven R Olson ◽  
Edward Kim ◽  
Molly Daughety ◽  
...  

Abstract INTRODUCTION There are many interventions in the disciplines of hemostasis and thrombosis that have been shown to be effective by high quality evidence, leading to the development of evidence-based guidelines by several professional groups. The extent to which providers and medical trainees make use of these guidelines in real-time clinical decision making is not known. Current hemostasis and thrombosis guidelines also lack an easy to navigate algorithmic design such as what is used by the National Comprehensive Cancer Network (NCCN) which may limit their utilization. Using several evidence based guidelines and consensus expert opinion we created an algorithmic tool designed to easily answer clinical questions in thrombosis and hemostasis, and conducted a prospective study assessing provider understanding of current evidence based recommendations and the effects of the algorithmic tool on clinical decision making. METHODS We implemented a prospective survey study of health care providers and medical students from the Oregon Health & Science University during July of 2016. Practitioners who care for patients with thrombotic or hemostatic issues were eligible; including internists, hematologist and oncologists, family medicine practitioners, nurse practitioners & physician assistants, hematology and oncology fellows, internal medicine and family medicine residents, and medical students. The survey included demographic questions, 11 clinical vignettes with multiple-choice questions asking participants for the most evidence-based treatment decision and to rate their confidence in the answer, and post-assessment feedback. Participants were encouraged to use the resources they would typically use in a clinical setting to make these decisions. Included subjects were randomly assigned access to our evidence-based algorithmic tool, (available online at http://tinyurl.com/Hemostasis-ThrombosisGuideline) available as downloadable PDF. The 11 clinical questions were scored, and an unpaired t-test was performed to determine if any significant difference existed in scores between participants with and without the evidence-based algorithmic tool. RESULTS During the study period, 101 individuals participated: 48 medical students, 23 medicine residents, 17 attending physicians, 9 fellows, and 4 NP/PAs. Across all participants, those with access to the algorithms on average answered 3.84 (34%) more questions correctly (95% CI 3.08 - 4.60, P < 0.0001) (Table 1). Participants randomized to receive the algorithm were significantly more confident in their treatment decisions than participants without the algorithm (P < 0.0001). Significantly higher scores were found among individual groups including medical students, (mean difference 4.73, 95% CI 3.64 - 5.82, P < 0.0001), attending physicians (mean difference 2.58, 95% CI 0.63 - 4.53, P = 0.0131), and residents & fellows (mean difference 3.81, 95% CI 2.66 - 4.96, P < 0.0001). There was insufficient data to find a difference in score among NP/PAs who did and did not receive the algorithm. Participant reported confidence in their answers was significantly higher in those who were randomized to receive the algorithm (mean difference of0.95 on a 5-point confidence scale, 95% CI0.50 to 1.39, P < 0.0001). CONCLUSION Our study found that at baseline, there were limitations in provider and trainee understanding of the current evidence based management of clinical issues relevant to hemostasis and thrombosis, and that the use of an easy to navigate algorithmic tool significantly altered treatment decisions in commonly encountered clinical vignettes. Our findings suggest that utilization and decision-making may benefit from a more streamlined, algorithmic display of guidelines. Future prospective studies are needed to determine if such a tool improves management and outcomes in practice. Disclosures No relevant conflicts of interest to declare.


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