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Daredevil. Unusual suspects
Dazzling Daredevil stories from creators without fear! When the staff that once belonged to his mentor Stick is stolen, Matt Murdock is determined to get it back - even if that means joining an ancient ninja battle between the Seven and The Hand! When villains from Gladiator to Stilt-Man target the Kingpin, DD and his old friend Spider-Man must stand in the way! When a rich client hires Matt to sue Daredevil, it's time for everyone's favourite attorney to play to the camera - but where does the Jester fit in? And Echo returns, courtesy of her co-creator, David Mack! In a bid to pick up the pieces of her shattered life, Maya Lopez embarks on a Native American vision quest...but she never expected to encounter Wolverine! COLLECTING: DAREDEVIL: NINJA 1-3; DAREDEVIL/SPIDER-MAN 1-4; DAREDEVIL (1998) 20-25, 51-55; SPIDER-MAN/DAREDEVIL 1.
Book
Patient and Healthcare Provider Barriers to Hypertension Awareness, Treatment and Follow Up: A Systematic Review and Meta-Analysis of Qualitative and Quantitative Studies
Although the importance of detecting, treating, and controlling hypertension has been recognized for decades, the majority of patients with hypertension remain uncontrolled. The path from evidence to practice contains many potential barriers, but their role has not been reviewed systematically. This review aimed to synthesize and identify important barriers to hypertension control as reported by patients and healthcare providers.
Electronic databases MEDLINE, EMBASE and Global Health were searched systematically up to February 2013. Two reviewers independently selected eligible studies. Two reviewers categorized barriers based on a theoretical framework of behavior change. The theoretical framework suggests that a change in behavior requires a strong commitment to change [intention], the necessary skills and abilities to adopt the behavior [capability], and an absence of health system and support constraints.
Twenty-five qualitative studies and 44 quantitative studies met the inclusion criteria. In qualitative studies, health system barriers were most commonly discussed in studies of patients and health care providers. Quantitative studies identified disagreement with clinical recommendations as the most common barrier among health care providers. Quantitative studies of patients yielded different results: lack of knowledge was the most common barrier to hypertension awareness. Stress, anxiety and depression were most commonly reported as barriers that hindered or delayed adoption of a healthier lifestyle. In terms of hypertension treatment adherence, patients mostly reported forgetting to take their medication. Finally, priority setting barriers were most commonly reported by patients in terms of following up with their health care providers.
This review identified a wide range of barriers facing patients and health care providers pursuing hypertension control, indicating the need for targeted multi-faceted interventions. More methodologically rigorous studies that encompass the range of barriers and that include low- and middle-income countries are required in order to inform policies to improve hypertension control.
Journal Article
A Systematic Review of Medication Adherence Thresholds Dependent of Clinical Outcomes
In pharmacotherapy, the achievement of a target clinical outcome requires a certain level of medication intake or adherence. Based on Haynes's early empirical definition of sufficient adherence to antihypertensive medications as taking ≥80% of medication, many researchers used this threshold to distinguish adherent from non-adherent patients. However, we propose that different diseases, medications and patient's characteristics influence the cut-off point of the adherence rate above which the clinical outcome is satisfactory (thereafter medication adherence threshold). Moreover, the assessment of adherence and clinical outcomes may differ greatly and should be taken into consideration. To our knowledge, very few studies have defined adherence rates linked to clinical outcomes. We aimed at investigating medication adherence thresholds in relation to clinical outcomes.
We searched for studies that determined the relationship between adherence rates and clinical outcomes in the databases PubMed, Embase
and Web of Science™ until December 2017, limited to English-language. Our outcome measure was any threshold value of adherence. The inclusion criteria of the retrieved studies were (1) any measurement of medication adherence, (2) any assessment of clinical outcomes, and (3) any method to define medication adherence thresholds in relation to clinical outcomes. We excluded articles considered as a tutorial. Two authors (PB and IA) independently screened titles and abstracts for relevance, reviewed full-texts, and extracted items. The results of the included studies are presented qualitatively.
We analyzed 6 articles that assessed clinical outcomes linked to adherence rates in 7 chronic disease states. Medication adherence was measured with Medication Possession Ratio (MPR,
= 3), Proportion of Days Covered (PDC,
= 1), both (
= 1), or Medication Event Monitoring System (MEMS). Clinical outcomes were event free episodes, hospitalization, cortisone use, reported symptoms and reduction of lipid levels. To find the relationship between the targeted clinical outcome and adherence rates, three studies applied logistic regression and three used survival analysis. Five studies defined adherence thresholds between 46 and 92%. One study confirmed the 80% threshold as valid to distinguish adherent from non-adherent patients.
The analyzed studies were highly heterogeneous, predominantly concerning methods of calculating adherence. We could not compare studies quantitatively, mostly because adherence rates could not be standardized. Therefore, we cannot reject or confirm the validity of the historical 80% threshold. Nevertheless, the 80% threshold was clearly questioned as a general standard.
Journal Article
A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study
A major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic.
To investigate a deep learning approach to retrieve scientifically sound treatment studies from the biomedical literature.
We trained a Convolutional Neural Network using a noisy dataset of 403,216 PubMed citations with title and abstract as features. The deep learning model was compared with state-of-the-art search filters, such as PubMed's Clinical Query Broad treatment filter, McMaster's textword search strategy (no Medical Subject Heading, MeSH, terms), and Clinical Query Balanced treatment filter. A previously annotated dataset (Clinical Hedges) was used as the gold standard.
The deep learning model obtained significantly lower recall than the Clinical Queries Broad treatment filter (96.9% vs 98.4%; P<.001); and equivalent recall to McMaster's textword search (96.9% vs 97.1%; P=.57) and Clinical Queries Balanced filter (96.9% vs 97.0%; P=.63). Deep learning obtained significantly higher precision than the Clinical Queries Broad filter (34.6% vs 22.4%; P<.001) and McMaster's textword search (34.6% vs 11.8%; P<.001), but was significantly lower than the Clinical Queries Balanced filter (34.6% vs 40.9%; P<.001).
Deep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed. Unlike previous machine learning approaches, the proposed deep learning model does not require feature engineering, or time-sensitive or proprietary features, such as MeSH terms and bibliometrics. Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research. Further work is needed to optimize the deep learning model and to assess generalizability to other areas, such as diagnosis, etiology, and prognosis.
Journal Article
Efficacy of Hospital at Home in Patients with Heart Failure: A Systematic Review and Meta-Analysis
Heart failure (HF) is the commonest cause of hospitalization in older adults. Compared to routine hospitalization (RH), hospital at home (HaH)--substitutive hospital-level care in the patient's home--improves outcomes and reduces costs in patients with general medical conditions. The efficacy of HaH in HF is unknown.
We searched MEDLINE, Embase, CINAHL, and CENTRAL, for publications from January 1990 to October 2014. We included prospective studies comparing substitutive models of hospitalization to RH in HF. At least 2 reviewers independently selected studies, abstracted data, and assessed quality. We meta-analyzed results from 3 RCTs (n = 203) and narratively synthesized results from 3 observational studies (n = 329). Study quality was modest. In RCTs, HaH increased time to first readmission (mean difference (MD) 14.13 days [95% CI 10.36 to 17.91]), and improved health-related quality of life (HrQOL) at both, 6 months (standardized MD (SMD) -0.31 [-0.45 to -0.18]) and 12 months (SMD -0.17 [-0.31 to -0.02]). In RCTs, HaH demonstrated a trend to decreased readmissions (risk ratio (RR) 0.68 [0.42 to 1.09]), and had no effect on all-cause mortality (RR 0.94 [0.67 to 1.32]). HaH decreased costs of index hospitalization in all RCTs. HaH reduced readmissions and emergency department visits per patient in all 3 observational studies.
In the context of a limited number of modest-quality studies, HaH appears to increase time to readmission, reduce index costs, and improve HrQOL among patients requiring hospital-level care for HF. Larger RCTs are necessary to assess the effect of HaH on readmissions, mortality, and long-term costs.
Journal Article
Computerized clinical decision support systems for drug prescribing and management: A decision-maker-researcher partnership systematic review
Background
Computerized clinical decision support systems (CCDSSs) for drug therapy management are designed to promote safe and effective medication use. Evidence documenting the effectiveness of CCDSSs for improving drug therapy is necessary for informed adoption decisions. The objective of this review was to systematically review randomized controlled trials assessing the effects of CCDSSs for drug therapy management on process of care and patient outcomes. We also sought to identify system and study characteristics that predicted benefit.
Methods
We conducted a decision-maker-researcher partnership systematic review. We updated our earlier reviews (1998, 2005) by searching MEDLINE, EMBASE, EBM Reviews, Inspec, and other databases, and consulting reference lists through January 2010. Authors of 82% of included studies confirmed or supplemented extracted data. We included only randomized controlled trials that evaluated the effect on process of care or patient outcomes of a CCDSS for drug therapy management compared to care provided without a CCDSS. A study was considered to have a positive effect (
i.e.
, CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive.
Results
Sixty-five studies met our inclusion criteria, including 41 new studies since our previous review. Methodological quality was generally high and unchanged with time. CCDSSs improved process of care performance in 37 of the 59 studies assessing this type of outcome (64%, 57% of all studies). Twenty-nine trials assessed patient outcomes, of which six trials (21%, 9% of all trials) reported improvements.
Conclusions
CCDSSs inconsistently improved process of care measures and seldomly improved patient outcomes. Lack of clear patient benefit and lack of data on harms and costs preclude a recommendation to adopt CCDSSs for drug therapy management.
Journal Article
Preliminary comparison of the performance of the National Library of Medicine’s systematic review publication type and the sensitive clinical queries filter for systematic reviews in PubMed
Objective: The National Library of Medicine (NLM) inaugurated a “publication type” concept to facilitate searches for systematic reviews (SRs). On the other hand, clinical queries (CQs) are validated search strategies designed to retrieve scientifically sound, clinically relevant original and review articles from biomedical literature databases. We compared the retrieval performance of the SR publication type (SR[pt]) against the most sensitive CQ for systematic review articles (CQrs) in PubMed. Methods: We ran date-limited searches of SR[pt] and CQrs to compare the relative yield of articles and SRs, focusing on the differences in retrieval of SRs by SR[pt] but not CQrs (SR[pt] NOT CQrs) and CQrs NOT SR[pt]. Random samples of articles retrieved in each of these comparisons were examined for SRs until a consistent pattern became evident. Results: For SR[pt] NOT CQrs, the yield was relatively low in quantity but rich in quality, with 79% of the articles being SRs. For CQrs NOT SR[pt], the yield was high in quantity but low in quality, with only 8% being SRs. For CQrs AND SR[pt], the quality was highest, with 92% being SRs. Conclusions: We found that SR[pt] had high precision and specificity for SRs but low recall (sensitivity), whereas CQrs had much higher recall. SR[pt] OR CQrs added valid SRs to the CQrs yield at low cost (i.e., added few non-SRs). For searches that are intended to be exhaustive for SRs, SR[pt] can be added to existing sensitive search filters.
Journal Article
Computerized clinical decision support systems for acute care management: A decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes
Background
Acute medical care often demands timely, accurate decisions in complex situations. Computerized clinical decision support systems (CCDSSs) have many features that could help. However, as for any medical intervention, claims that CCDSSs improve care processes and patient outcomes need to be rigorously assessed. The objective of this review was to systematically review the effects of CCDSSs on process of care and patient outcomes for acute medical care.
Methods
We conducted a decision-maker-researcher partnership systematic review. MEDLINE, EMBASE, Evidence-Based Medicine Reviews databases (Cochrane Database of Systematic Reviews, DARE, ACP Journal Club, and others), and the Inspec bibliographic database were searched to January 2010, in all languages, for randomized controlled trials (RCTs) of CCDSSs in all clinical areas. We included RCTs that evaluated the effect on process of care or patient outcomes of a CCDSS used for acute medical care compared with care provided without a CCDSS. A study was considered to have a positive effect (
i.e.
, CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive.
Results
Thirty-six studies met our inclusion criteria for acute medical care. The CCDSS improved process of care in 63% (22/35) of studies, including 64% (9/14) of medication dosing assistants, 82% (9/11) of management assistants using alerts/reminders, 38% (3/8) of management assistants using guidelines/algorithms, and 67% (2/3) of diagnostic assistants. Twenty studies evaluated patient outcomes, of which three (15%) reported improvements, all of which were medication dosing assistants.
Conclusion
The majority of CCDSSs demonstrated improvements in process of care, but patient outcomes were less likely to be evaluated and far less likely to show positive results.
Journal Article
The McMaster Health Information Research Unit: Over a Quarter-Century of Health Informatics Supporting Evidence-Based Medicine
Evidence-based medicine (EBM) emerged from McMaster University in the 1980-1990s, which emphasizes the integration of the best research evidence with clinical expertise and patient values. The Health Information Research Unit (HiRU) was created at McMaster University in 1985 to support EBM. Early on, digital health informatics took the form of teaching clinicians how to search MEDLINE with modems and phone lines. Searching and retrieval of published articles were transformed as electronic platforms provided greater access to clinically relevant studies, systematic reviews, and clinical practice guidelines, with PubMed playing a pivotal role. In the early 2000s, the HiRU introduced Clinical Queries—validated search filters derived from the curated, gold-standard, human-appraised Hedges dataset—to enhance the precision of searches, allowing clinicians to hone their queries based on study design, population, and outcomes. Currently, almost 1 million articles are added to PubMed annually. To filter through this volume of heterogenous publications for clinically important articles, the HiRU team and other researchers have been applying classical machine learning, deep learning, and, increasingly, large language models (LLMs). These approaches are built upon the foundation of gold-standard annotated datasets and humans in the loop for active machine learning. In this viewpoint, we explore the evolution of health informatics in supporting evidence search and retrieval processes over the past 25+ years within the HiRU, including the evolving roles of LLMs and responsible artificial intelligence, as we continue to facilitate the dissemination of knowledge, enabling clinicians to integrate the best available evidence into their clinical practice.
Journal Article