Screening for in vitro systematic reviews: a comparison of screening methods and training of a machine learning classifier

Objective: Existing strategies to identify relevant studies for systematic review may not perform equally well across research domains. We compare four approaches based on either human or automated screening of either title and abstract or full text, and report the training of a machine learning algorithm to identify in vitro studies from bibliographic records. Methods: We used a systematic review of oxygen-glucose deprivation (OGD) in PC-12 cells to compare approaches. For human screening, two reviewers independently screened studies based on title and abstract or full text, with disagreements reconciled by a third. For automated screening, we applied text mining to either title and abstract or full text. We trained a machine learning algorithm with decisions from 2000 randomly selected PubMed Central records enriched with a dataset of known in vitro studies. Results: Full-text approaches performed best, with human (sensitivity: 0.990, specificity: 1.000 and precision: 0.994) outperforming text mining (sensitivity: 0.972, specificity: 0.980 and precision: 0.764). For title and abstract, text mining (sensitivity: 0.890, specificity: 0.995 and precision: 0.922) outperformed human screening (sensitivity: 0.862, specificity: 0.998 and precision: 0.975). At our target sensitivity of 95% the algorithm performed with specificity of 0.850 and precision of 0.700. Conclusion: In this in vitro systematic review, human screening based on title and abstract erroneously excluded 14% of relevant studies, perhaps because title and abstract provide an incomplete description of methods used. Our algorithm might be used as a first selection phase in in vitro systematic reviews to limit the extent of full text screening required.</p

Motivational Interviewing Post-Stroke: An Analysis of Stroke Survivors' Concerns and Adjustment

Our earlier research demonstrated that participation in four sessions of motivational interviewing (MI) early post-stroke has a positive impact on stroke survivors' mood. However, the theoretical underpinnings of MI in supporting adjustment (rather than its traditional use in supporting behavior change) require clarification. This article describes a content analysis of MI transcripts for 10 participants in our previous study, to identify the focus of discussions (patient "concerns") and potential effective components of our MI approach. Patients' post-stroke concerns were shown in 16 categories, including frustration, family impact, and getting well. There was a pattern of change discourse across sessions: "Sustain talk" (reasons for not changing) reduced from Session 1 onward, "change talk" (intent to change) increased then reduced, and "change expressed" (changes achieved) increased from Sessions 1 to 4. MI facilitates healthy adjustment post-stroke in some patients, in turn affecting mood, but clarification of how this effect is achieved requires further exploration

INVESTIGATE-I (INVasive Evaluation before Surgical Treatment of Incontinence Gives Added Therapeutic Effect?): study protocol for a mixed methods study to assess the feasibility of a future randomised controlled trial of the clinical utility of invasive urodynamic testing

<p>Abstract</p> <p>Background</p> <p>Urinary incontinence is an important health problem to the individual sufferer and to health services. Stress and stress predominant mixed urinary incontinence are increasingly managed by surgery due to advances in surgical techniques. Despite the lack of evidence for its clinical utility, most clinicians undertake invasive urodynamic testing (IUT) to confirm a functional diagnosis of urodynamic stress incontinence before offering surgery for this condition. IUT is expensive, embarrassing and uncomfortable for women and carries a small risk. Recent systematic reviews have confirmed the lack of high quality evidence of effectiveness.</p> <p>The aim of this pilot study is to test the feasibility of a future definitive randomised control trial that would address whether IUT alters treatment decisions and treatment outcome in these women and would test its clinical and cost effectiveness.</p> <p>Methods/design</p> <p>This is a mixed methods pragmatic multicentre feasibility pilot study with four components:-</p> <p>(a) A multicentre, external pilot randomised trial comparing basic clinical assessment with non-invasive tests and IUT. The outcome measures are rates of recruitment, randomisation and data completion. Data will be used to estimate sample size necessary for the definitive trial.</p> <p>(b) Qualitative interviews of a purposively sampled sub-set of women eligible for the pilot trial will explore willingness to participate, be randomised and their overall trial experience.</p> <p>(c) A national survey of clinicians to determine their views of IUT in this context, the main outcome being their willingness to randomise patients into the definitive trial.</p> <p>(d) Qualitative interviews of a purposively sampled group of these clinicians will explore whether and how they use IUT to inform their decisions.</p> <p>Discussion</p> <p>The pilot trial will provide evidence of feasibility and acceptability and therefore inform the decision whether to proceed to the definitive trial. Results will inform the design and conduct of the definitive trial and ensure its effectiveness in achieving its research aim.</p> <p>Trial registration number</p> <p>Current Controlled Trials <a href="http://www.controlled-trials.com/ISRCTN71327395">ISRCTN71327395</a> assigned 7^thJune 2010.</p

Climatic predictors of species distributions neglect biophysiologically meaningful variables

This is the final version. Available on open access from Wiley via the DOI in this record.Aim: Species distribution models (SDMs) have played a pivotal role in predicting how species might respond to climate change. To generate reliable and realistic predictions from these models requires the use of climate variables that adequately capture physiological responses of species to climate and therefore provide a proximal link between climate and their distributions. Here, we examine whether the climate variables used in plant SDMs are different from those known to influence directly plant physiology. Location: Global. Methods: We carry out an extensive, systematic review of the climate variables used to model the distributions of plant species and provide comparison to the climate variables identified as important in the plant physiology literature. We calculate the top ten SDM and physiology variables at 2.5 degree spatial resolution for the globe and use principal component analyses and multiple regression to assess similarity between the climatic variation described by both variable sets. Results: We find that the most commonly used SDM variables do not reflect the most important physiological variables and differ in two main ways: (i) SDM variables rely on seasonal or annual rainfall as simple proxies of water available to plants and neglect more direct measures such as soil water content; and (ii) SDM variables are typically averaged across seasons or years and overlook the importance of climatic events within the critical growth period of plants. We identify notable differences in their spatial gradients globally and show where distal variables may be less reliable proxies for the variables to which species are known to respond. Main conclusions: There is a growing need for the development of accessible, fine-resolution global climate surfaces of physiological variables. This would provide a means to improve the reliability of future range predictions from SDMs and support efforts to conserve biodiversity in a changing climate

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Procalcitonin Is Not a Reliable Biomarker of Bacterial Coinfection in People With Coronavirus Disease 2019 Undergoing Microbiological Investigation at the Time of Hospital Admission

Abstract Admission procalcitonin measurements and microbiology results were available for 1040 hospitalized adults with coronavirus disease 2019 (from 48 902 included in the International Severe Acute Respiratory and Emerging Infections Consortium World Health Organization Clinical Characterisation Protocol UK study). Although procalcitonin was higher in bacterial coinfection, this was neither clinically significant (median [IQR], 0.33 [0.11–1.70] ng/mL vs 0.24 [0.10–0.90] ng/mL) nor diagnostically useful (area under the receiver operating characteristic curve, 0.56 [95% confidence interval, .51–.60]).</jats:p

Implementation of corticosteroids in treating COVID-19 in the ISARIC WHO Clinical Characterisation Protocol UK:prospective observational cohort study

BACKGROUND: Dexamethasone was the first intervention proven to reduce mortality in patients with COVID-19 being treated in hospital. We aimed to evaluate the adoption of corticosteroids in the treatment of COVID-19 in the UK after the RECOVERY trial publication on June 16, 2020, and to identify discrepancies in care. METHODS: We did an audit of clinical implementation of corticosteroids in a prospective, observational, cohort study in 237 UK acute care hospitals between March 16, 2020, and April 14, 2021, restricted to patients aged 18 years or older with proven or high likelihood of COVID-19, who received supplementary oxygen. The primary outcome was administration of dexamethasone, prednisolone, hydrocortisone, or methylprednisolone. This study is registered with ISRCTN, ISRCTN66726260. FINDINGS: Between June 17, 2020, and April 14, 2021, 47 795 (75·2%) of 63 525 of patients on supplementary oxygen received corticosteroids, higher among patients requiring critical care than in those who received ward care (11 185 [86·6%] of 12 909 vs 36 415 [72·4%] of 50 278). Patients 50 years or older were significantly less likely to receive corticosteroids than those younger than 50 years (adjusted odds ratio 0·79 [95% CI 0·70–0·89], p=0·0001, for 70–79 years; 0·52 [0·46–0·58], p80 years), independent of patient demographics and illness severity. 84 (54·2%) of 155 pregnant women received corticosteroids. Rates of corticosteroid administration increased from 27·5% in the week before June 16, 2020, to 75–80% in January, 2021. INTERPRETATION: Implementation of corticosteroids into clinical practice in the UK for patients with COVID-19 has been successful, but not universal. Patients older than 70 years, independent of illness severity, chronic neurological disease, and dementia, were less likely to receive corticosteroids than those who were younger, as were pregnant women. This could reflect appropriate clinical decision making, but the possibility of inequitable access to life-saving care should be considered. FUNDING: UK National Institute for Health Research and UK Medical Research Council