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AI-driven decision support systems and epistemic reliance: a qualitative study on obstetricians' and midwives' perspectives on integrating AI-driven CTG into clinical decision making.
Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper explores the conditions that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor. This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper. There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process. Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice.
Erratum: Autism-related dietary preferences mediate autism-gut microbiome associations (Cell (2021) 184(24) (5916–5931.e17), (S0092867421012319), (10.1016/j.cell.2021.10.015))
(Cell 184, 5916–5931; November 24, 2021) Our paper reported evidence that autism-related dietary preferences mediate autism-microbiome associations. Since publication, we have become aware of an error in our paper that we are now correcting. Specifically, in the code we wrote and used to transform the microbiome count matrices in our variance component analysis, we inadvertently missed a matrix transposition, which affected their centered-log-ratio (clr) transformation and affected variance estimates in Figure 2 and Table S1 (listed in detail below). By missing the matrix transposition, we incorrectly calculated the geometric mean per-taxa rather than per-individual. However, the error does not affect the conclusions of the paper because the per-taxa and per-individual geometric means are similar, and so the resulting clr transformed matrices are similar as well (note that the clr transform should take the quotient of a microbiome/taxa quantity by the geometric mean of microbiome quantities across the sample/individual). To show that this is the case, we compared the correctly (geometric mean calculated per-individual) and incorrectly (geometric mean calculated per-taxa) clr transformed matrices by taking the nth column of both matrices (representing each of 247 individuals’ microbiome data) and calculating the Pearson's correlation coefficient between them. The median Pearson's correlation coefficient ranged from 0.90–0.94 for the common species, rare species, common genes, and rare genes matrices. As the correctly and incorrectly transformed matrices are highly correlated, this error has negligible impact on the variance component analysis results and does not change the overall conclusions of our work. The code error did not affect which microbiome features were identified as being differentially abundant, as the method used for this analysis (ANCOMv2.1) takes un-transformed count data as input. However, the data visualization for this analysis was affected with respect to the x-axes of Figures 3A–3C, 3E, 3F, and S4, which reflect the degree and directionality of differential abundance. In the updated plots, all the significant or near-significant microbiome features have identical directions of effect to the original plots as well as similar magnitudes of effect. We can confirm that the other instances of clr transformation were performed correctly; namely, in generating the dietary PCs, CD4+ T cells, and the PCA plot (Figure 5A). We have updated the following: (1) Figure 2 has been amended with the updated data: • Under age, species_common has changed from 33 to 35, transporter(TCDB)_common from 42 to 36, pathway(MetaCyc)_common from 40 to 39, and food(AES) from 33 to 46.• Under BMI, species, transporter(TCDB)_common has changed from 1 to 4, pathway(MetaCyc)_common from 0 to 1, genes(Microba)_common from 7 to 10, and food(AES) from 12 to 22.• Under ASD, genes(Microba)_rare has changed from 7 to 9.• Under IQ_DQ, species_common has changed from 3 to 5, genes(Microba)_common from 7 to 14, and food(AES) from 3 to 14.• Under Sleep, species_common has changed from 10 to 11, and genes(Microba)_common has changed from 0 to 6.• Under rBSC, species_common has changed from 5 to 6, species_rare from 41 to 40, transporter(TCDB)_common from 3 to 4, and genes(Microba)_common from 49 to 50.• Under dietary_PC1, enzyme(ECL4)_common has changed from 48 to 46, pathway(MetaCyc)_common from 25 to 24, and genes(Microba)_common from 48 to 47.• Under dietary_PC2, species_rare has changed from 1 to 0, genes(Microba)_common from 7 to 8, and genes(Microba)_rare from 3 to 0.• Under dietary_PC3, species_common has changed from 4 to 6, species_rare from 1 to 0, transporter(TCDB)_common from 21 to 17, pathway(MetaCyc)_common from 11 to 10, and genes(Microba)_common from 27 to 28.• Under diet_diversity, species_rare has changed from 20 to 14, transporter(TCDB)_common from 26 to 23, and genes(Microba)_common from 26 to 23.• We have also taken this opportunity to switch the y-axis order for “species_rare” and “enzyme(ECL4)_common” to better separate the taxonomic and functional datasets.(2) Table S1, which contains the raw data presented in Figure 2, has been amended with the updated OREML results.(3) In the main text, the third to fifth paragraphs of the section titled “Negligible variance in ASD diagnostic status is associated with the microbiome compared to age, stool and dietary traits” has been amended: • The age common species b2 estimate and standard error has changed from 33% (SE = 8%) to 35% (SE = 7%).• The p value for the BMI common species analysis has changed from p = 3.5e-2 (not FDR significant) to 1.8e-2 (FDR-significant).• With reference to the age gene-level ORM analyses, the range of standard errors has been changed from 13%–17% to 14%–17%.• The BMI rare genes b2 estimate has changed from 46% to 47%, and the p value has changed from 8.4e-3 to 1.1e-2.• The ASD rare genes b2 estimate has changed from 7% to 9%, and the p value has changed from 0.33 to 0.29.• The IQ-DQ common species b2 estimate has changed from 7% (SE = 13%, p = 0.39) to 5% (SE = 6%, p = 0.20).• The sleep problems common species b2 estimate has changed from 10% to 11%, and the p value has changed from 0.17 to 8.2e-2.• The stool consistency rare species b2 estimate has changed from 41% to 40%, and the p value has changed from 8.7e-6 to 2.8e-5.• The stool consistency rare genes standard error has changed from 20% to 21%, and the p value has changed from 2.5e-5 to 5.8e-5.• We have corrected an error where the dietary PC1 common genes b2 estimate (b2 = 48%, SE = 15%, p = 3.8e-4) was mislabeled as the rare genes analysis. We have also updated the common genes b2 estimate from 48% to 47% and updated the p value from 3.8e-4 to 4.5e-5.(4) Figure S1, which visualizes the diagonals and off-diagonals of the omics relationship matrix (ORM; which, in turn, is based on the centered-log-ratio transformed microbiome matrices) has been amended with the updated OREML results.(5) Figure S2, which draws upon ORMs using rare microbiome features to compare the effects of prior clr transformation versus binary coding as a sensitivity analysis, has been amended with the updated OREML results.(6) Figure S3, which provides a variety of OREML estimates to support Figure 2 (including the impact of estimating b2 with a combination of multiple ORMs and collapsing taxonomic microbiome data into higher levels of hierarchy), has been amended with the updated OREML results.(7) Methods S1, which provides results from extensive sensitivity analyses to support the main results, has also been amended with the updated OREML results. We have also updated the section “Estimating the upper limit of predictivity using non-additive models,” for which we used adaboost as a sensitivity analysis for a method that does not assume additivity. In this analysis, the mean prediction accuracy for ASD changed from 53% (SD = 7%) to 53% (SD = 8%), and the prediction accuracy for age changed from 62% (SD = 7%) to 63% (SD = 9%).(8) Figures 3A–3C, 3E, and 3F, which visualize differentially abundant microbiome features, now have updated x-axes.(9) Figure S4, which supports Figure 3 by providing results from sensitivity analyses for differential abundance, also has updated x-axes.(10) Tables S2.1, S2.3, S2.8, S2.13, and S2.14, which provide data (including x-axis coordinates) for Figures 3A–3C, 3E, and 3F, have been updated.(11) Unrelated to the clr transformation error, we have also updated the heading of the upper plot in Figure 4I to read “Diet ∼ Sensory score” rather than “Taxa ∼ Sensory score.”(12) The accompanying Zenodo code has been updated, and the link has been changed from https://zenodo.org/records/5558047 to https://zenodo.org/records/5558046. The specific code updates can be viewed on the linked GitHub page.These errors have now been corrected in the online version of the paper. We apologize for any inconvenience that this may have caused the readers.[Formula presented][Formula presented][Formula presented][Formula presented][Formula presented][Formula presented][Formula presented][Formula presented][Formula presented][Formula presented][Formula presented][Formula presented][Formula presented][Formula presented]
Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins.
BackgroundResults of previous randomised trials have shown that interventions that lower LDL cholesterol concentrations can significantly reduce the incidence of coronary heart disease (CHD) and other major vascular events in a wide range of individuals. But each separate trial has limited power to assess particular outcomes or particular categories of participant.MethodsA prospective meta-analysis of data from 90,056 individuals in 14 randomised trials of statins was done. Weighted estimates were obtained of effects on different clinical outcomes per 1.0 mmol/L reduction in LDL cholesterol.FindingsDuring a mean of 5 years, there were 8186 deaths, 14,348 individuals had major vascular events, and 5103 developed cancer. Mean LDL cholesterol differences at 1 year ranged from 0.35 mmol/L to 1.77 mmol/L (mean 1.09) in these trials. There was a 12% proportional reduction in all-cause mortality per mmol/L reduction in LDL cholesterol (rate ratio [RR] 0.88, 95% CI 0.84-0.91; p<0.0001). This reflected a 19% reduction in coronary mortality (0.81, 0.76-0.85; p<0.0001), and non-significant reductions in non-coronary vascular mortality (0.93, 0.83-1.03; p=0.2) and non-vascular mortality (0.95, 0.90-1.01; p=0.1). There were corresponding reductions in myocardial infarction or coronary death (0.77, 0.74-0.80; p<0.0001), in the need for coronary revascularisation (0.76, 0.73-0.80; p<0.0001), in fatal or non-fatal stroke (0.83, 0.78-0.88; p<0.0001), and, combining these, of 21% in any such major vascular event (0.79, 0.77-0.81; p<0.0001). The proportional reduction in major vascular events differed significantly (p<0.0001) according to the absolute reduction in LDL cholesterol achieved, but not otherwise. These benefits were significant within the first year, but were greater in subsequent years. Taking all years together, the overall reduction of about one fifth per mmol/L LDL cholesterol reduction translated into 48 (95% CI 39-57) fewer participants having major vascular events per 1000 among those with pre-existing CHD at baseline, compared with 25 (19-31) per 1000 among participants with no such history. There was no evidence that statins increased the incidence of cancer overall (1.00, 0.95-1.06; p=0.9) or at any particular site.InterpretationStatin therapy can safely reduce the 5-year incidence of major coronary events, coronary revascularisation, and stroke by about one fifth per mmol/L reduction in LDL cholesterol, largely irrespective of the initial lipid profile or other presenting characteristics. The absolute benefit relates chiefly to an individual's absolute risk of such events and to the absolute reduction in LDL cholesterol achieved. These findings reinforce the need to consider prolonged statin treatment with substantial LDL cholesterol reductions in all patients at high risk of any type of major vascular event.
Efficacy of cholesterol-lowering therapy in 18,686 people with diabetes in 14 randomised trials of statins: a meta-analysis.
BackgroundAlthough statin therapy reduces the risk of occlusive vascular events in people with diabetes mellitus, there is uncertainty about the effects on particular outcomes and whether such effects depend on the type of diabetes, lipid profile, or other factors. We undertook a prospective meta-analysis to help resolve these uncertainties.MethodsWe analysed data from 18 686 individuals with diabetes (1466 with type 1 and 17,220 with type 2) in the context of a further 71,370 without diabetes in 14 randomised trials of statin therapy. Weighted estimates were obtained of effects on clinical outcomes per 1.0 mmol/L reduction in LDL cholesterol.FindingsDuring a mean follow-up of 4.3 years, there were 3247 major vascular events in people with diabetes. There was a 9% proportional reduction in all-cause mortality per mmol/L reduction in LDL cholesterol in participants with diabetes (rate ratio [RR] 0.91, 99% CI 0.82-1.01; p=0.02), which was similar to the 13% reduction in those without diabetes (0.87, 0.82-0.92; p<0.0001). This finding reflected a significant reduction in vascular mortality (0.87, 0.76-1.00; p=0.008) and no effect on non-vascular mortality (0.97, 0.82-1.16; p=0.7) in participants with diabetes. There was a significant 21% proportional reduction in major vascular events per mmol/L reduction in LDL cholesterol in people with diabetes (0.79, 0.72-0.86; p<0.0001), which was similar to the effect observed in those without diabetes (0.79, 0.76-0.82; p<0.0001). In diabetic participants there were reductions in myocardial infarction or coronary death (0.78, 0.69-0.87; p<0.0001), coronary revascularisation (0.75, 0.64-0.88; p<0.0001), and stroke (0.79, 0.67-0.93; p=0.0002). Among people with diabetes the proportional effects of statin therapy were similar irrespective of whether there was a prior history of vascular disease and irrespective of other baseline characteristics. After 5 years, 42 (95% CI 30-55) fewer people with diabetes had major vascular events per 1000 allocated statin therapy.InterpretationStatin therapy should be considered for all diabetic individuals who are at sufficiently high risk of vascular events.
Germline and somatic genetic variants in the p53 pathway interact to affect cancer risk, progression and drug response
Insights into oncogenesis derived from cancer susceptibility loci could facilitate better cancer management and treatment through precision oncology. However, therapeutic applications have thus far been limited by our current lack of understanding regarding both their interactions with somatic cancer driver mutations and their influence on tumorigenesis. Here, by integrating germline datasets relating to cancer susceptibility with tumour data capturing somatically-acquired genetic variation, we provide evidence that single nucleotide polymorphism (SNPs) and somatic mutations in the p53 tumor suppressor pathway can interact to influence cancer development, progression and treatment response. We go on to provide human genetic evidence of a tumor-promoting role for the pro-survival activities of p53, which supports the development of more effective therapy combinations through their inhibition in cancers retaining wild-type p53. Significance We describe significant interactions between heritable and somatic genetic variants in the p53 pathway that affect cancer susceptibility, progression and treatment response. Our results offer evidence of how cancer susceptibility SNPs can interact with cancer driver genes to affect cancer progression and identify novel therapeutic targets.
Heritable genetic variants in key cancer genes link cancer risk with anthropometric traits.
BackgroundHeight and other anthropometric measures are consistently found to associate with differential cancer risk. However, both genetic and mechanistic insights into these epidemiological associations are notably lacking. Conversely, inherited genetic variants in tumour suppressors and oncogenes increase cancer risk, but little is known about their influence on anthropometric traits.MethodsBy integrating inherited and somatic cancer genetic data from the Genome-Wide Association Study Catalog, expression Quantitative Trait Loci databases and the Cancer Gene Census, we identify SNPs that associate with different cancer types and differential gene expression in at least one tissue type, and explore the potential pleiotropic associations of these SNPs with anthropometric traits through SNP-wise association in a cohort of 500,000 individuals.ResultsWe identify three regulatory SNPs for three important cancer genes, FANCA, MAP3K1 and TP53 that associate with both anthropometric traits and cancer risk. Of particular interest, we identify a previously unrecognised strong association between the rs78378222[C] SNP in the 3' untranslated region (3'-UTR) of TP53 and both increased risk for developing non-melanomatous skin cancer (OR=1.36 (95% 1.31 to 1.41), adjusted p=7.62E-63), brain malignancy (OR=3.12 (2.22 to 4.37), adjusted p=1.43E-12) and increased standing height (adjusted p=2.18E-24, beta=0.073±0.007), lean body mass (adjusted p=8.34E-37, beta=0.073±0.005) and basal metabolic rate (adjusted p=1.13E-31, beta=0.076±0.006), thus offering a novel genetic link between these anthropometric traits and cancer risk.ConclusionOur results clearly demonstrate that heritable variants in key cancer genes can associate with both differential cancer risk and anthropometric traits in the general population, thereby lending support for a genetic basis for linking these human phenotypes.