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Using scores from the 4AT delirium detection tool as an indicator of possible dementia:a study of 75 221 older adult hospital admissions
No full textINTRODUCTION: Overall dementia diagnosis rates are substantially below true rates. Hospital admissions of older people involve cognitive and functional assessments relevant to dementia diagnosis. These assessments could be harnessed to contribute to identifying patients for further assessment. Yet relationships of inpatient cognitive tests with known dementia are unclear. The 4AT (www.the4AT.com) assesses for delirium (Scores 4-12) and also cognitive impairment via embedded cognitive tests (Scores 1-3). We investigated relationships between 4AT scores and clinical dementia diagnoses.METHODS: We included participants aged ≥65 years admitted as a medical emergency to three hospitals from 4 January 2016 to 4 January 2020, who had the 4AT performed on admission. Clinical dementia diagnosis was ascertained from linked primary care, hospital discharge and community prescribing data.RESULTS: Of 75 221 admissions, 62 188 (82.7%; 33 625 unique patients; mean age 80.2 years; 55.8% female) had a 4AT on admission. Of these, 9948 (16.0%) had a recorded clinical dementia diagnosis at the time of admission, with a further 1197 (1.9%) receiving a new diagnosis at discharge. Of admissions with dementia, 9669/11 145 (86.8%) had a 4AT score ≥1 on admission, compared to 14 994/51 043 (29.4%) without dementia.4AT ≥1 had a sensitivity of 0.87 (95% CI 0.86-0.87) and a specificity of 0.71 (0.70-0.71) in relation to clinical dementia diagnosis. 4AT ≥4 showed sensitivity of 0.50 (0.50-0.51) and a specificity of 0.88 (0.88-0.88).CONCLUSIONS: 4AT scores were associated with clinically diagnosed dementia. These results suggest that routinely collected 4AT scores could be leveraged in conjunction with other clinical indicators to identify patients with possible undiagnosed dementia who could undergo further inpatient diagnostic assessment and/or post-discharge specialist follow-up.</p
High-sensitivity point-of-care measurement of cardiac troponin: A scientific statement of the association for acute cardiovascular care of the ESC
No full textNew technologies enabling access to high-sensitivity cardiac troponin (hs-cTn) assays at the point of care (POC) are available for routine use. POC technology can accelerate cardiac troponin testing within the hospital setting and support testing in other health care environments. Pre-analytical and analytical issues unique to POC testing are discussed. The opportunities and the evidence needed to support routine use of hs-cTn POC assays in clinical care are outlined. Based on recent developments the potential impact of hs-cTn at the POC in multiple clinical settings is described and a roadmap on the steps required for successful implementation is provided
Pasolini and Stendhal, from Bologna to Rome:Stages of a novelistic crystallisation
No full textStarting from the analysis of an unpublished letter that Pasolini had addressed to Stendhal, and which he wanted to use as the preface-dedication of his first novel, this essay proposes a Stendhalian journey through the evolution of Pasolini's novels. This journey follows the stages of the crystallization of love, as Stendhal had represented them in the drawing of a journey from Bologna to Rome. For both writers, the departure from Bologna is the original scene of a romantic and romantic failure. The last stage of this journey, in Rome, is marked by the critical readings of Roland Barthes and Leonardo Sciascia
Tomorrow’s Cities Risk Agreement Approach: utilising the analytical, communication and convening power of science for inclusive, risk-sensitive urban planning
Get PDFGlobal disaster risk reduction in urban development frameworks call for people-centred, participatory, and integrated approaches to addressing urban risk and building resilience. This paper presents a methodology that engages communities at risk and policy actors to assess scientifically projected impacts of multiple hazards on locally defined future urban scenarios and co-develop measures to reduce future hazard impacts. The methodology enables stakeholders to identify barriers and strategies to support more people-centred, participatory, and risk-sensitive future urban development. Within a workshop, selected community groups are first introduced to an interactive dashboard that simplifies the communication of projected multi-hazard impacts (e.g., human displacement, casualties, loss of education capacity). Community groups identify and discuss impacts of different hazards, exposure, and vulnerability features along with projected impacts on community-led future urban scenarios. Such evidence-based and participatory discussions lead to a set of revisions of the urban scenarios. Finally, the groups discuss existing community, urban planning, and local decision-making challenges that could hinder the implementation of the urban scenarios. The proposed methodology is presented within the framework of the Tomorrow’s Cities Decision Support Environment (TCDSE) and illustrated through a deployment in Rapti, Nepal. Findings confirm the ability of the approach to facilitate a shared understanding of context-specific risk amongst diverse local and policy actors. The combination of scientific and local information improves awareness and gives agency to marginalised groups for improved communication with urban planners in disaster risk reduction decision-making
Molecular and Cellular Mechanisms Leading to Topical Steroid Withdrawal Syndrome Remain Unexplained and Warrant Further Research
Get PDFEvaluating Re-Identification Risks scores in Publicly Available Clinical Trial Datasets: Insights and Implications
Get PDFBackgroundThe motivations to share anonymised datasets from clinical trials within the scientific community are increasing. Many anonymised datasets are now publicly available for secondary research. However, it is uncertain whether they pose a privacy risk to the involved participants.MethodsWe located a broad sample of publicly available, de-identified/anonymised randomised clinical trial datasets from human participants and contacted their owners to request access, following their local procedures. We classified personal data within these datasets, including unique direct identifiers such as date of birth and other personal data that, on their own, does not identify an individual but may do so when combined with each other, such as sex, age and race (indirect identifiers). Combining indirect identifiers forms strata, and adding more identifiers increases granularity by dividing the data into a larger number of smaller strata. The re-identification risk score equations evaluate membership in these strata in three ways: first, by measuring the proportions of participants in strata above predetermined risk threshold levels (Ra); second, by locating the smallest stratum (Rb); third, by estimating the average membership across all strata in a dataset (Rc). The risk scores range from 0 (lowest risk) to 1 (highest risk); they do not aim to re-identify individuals in the datasets and are used for routinely collected health records. If a dataset contained a direct identifier, it automatically scored 1 in all metrics. Conversely, if a dataset contained no direct or up to one indirect identifier, it automatically scored 0 in all metrics. Finally, we explored which characteristics of the datasets were associated with the risk scores and compared the risk scores and their usability.ResultsSeventy datasets from 14 data sources were analysed. Thirty-one datasets were shared with minimal restrictions (open access), while 39 were shared with varying levels of restrictions before access was granted (controlled access). Datasets had, on average, four identifiers and mean risk scores ranging from 0.47 to 0.91. The most common pieces of information present in the datasets that, when combined, may indirectly identify a participant were sex (80%) and age (72.9%).ConclusionsThis study confirms that clinical trial datasets are rich in personal details and that using re-identification risk scores as a measure of this richness is feasible. These scores could inform the anonymisation process of clinical trials datasets regarding their level of granularity prior to releasing them for secondary research. We propose a strategy for employing these scores in the decision-making process for releasing clinical trials datasets.<br/
Clinical predictors of readmission to psychiatric inpatient care:A 20-year follow up study of former adolescent inpatients
Get PDFBACKGROUND: Readmission is a poor outcome associated with significant economic and psychosocial burden, particularly among young people. This study aimed to investigate predictors of time to readmission, the number of readmissions, and the cumulative duration of all readmissions among adolescent psychiatric inpatients over a 20-year observation period.METHODS: A total of 508 adolescents participated in the original study. The length of index hospitalisation, previous inpatient admissions, the number of psychiatric diagnoses, and the severity of depression, anxiety, mania, psychosis, obsessive-compulsive disorder, eating disorder, conduct disorder, and alcohol and substance use were used to predict readmissions. Separate Bayesian regressions were conducted to examine the impact of these predictors on time to readmission, number of readmissions and the cumulative duration of readmissions.RESULTS: The severity of psychosis symptoms predicted all three outcomes. Once participants with existing schizophrenia spectrum diagnoses were removed from the sample, psychosis symptoms still predicted time to readmission and the number of readmissions. Previous inpatient admissions predicted more frequent admissions during the observation period. The severity of depression symptoms was associated with shorter time to first readmission.DISCUSSION: Looking at a range of patient and service level measures, psychosis symptoms predicted all three readmission outcomes in psychiatric inpatients. This finding suggests that psychosis symptoms may be a useful transdiagnostic marker of illness severity, predicting poor outcome into adulthood.</p
Dual probability approach for risk adjustment in patients with a low clinical likelihood of coronary artery disease
Get PDFAimsTo investigate whether the PROMISE Minimal Risk Score (PMRS) enables adjustment of the risk factor-weighted clinical likelihood of obstructive CAD.Methods and resultsTwo cohorts of stable patients with new-onset chest pain were established: a diagnosis cohort (n=4,298) and a prognosis cohort (n=14,013). Patients were stratified by the risk factor-weighted clinical likelihood model, and patients with low (>5-15%) clinical likelihood were further stratified by the PMRS using a ≥34% cut-off. For the diagnosis cohort, obstructive CAD was defined invasively by fractional flow reserve ≤0.80. For the prognosis cohort, the primary endpoint was non-fatal myocardial infarction or all-cause death.In the diagnosis cohort, 1,669 (39%) patients had low (>5-≤15%) clinical likelihood of whom 301/1,669 (18%) patients had a PMRS ≥34%. In these patients, the prevalence of obstructive CAD was 14/301 (4.7%), similar to patients with very-low (≤5%) clinical likelihood (64/1,667 (3.8%), p=0.21). In the prognosis cohort, 6,187 (44%) patients had low (>5-≤15%) clinical likelihood of whom 993/6,187 (16%) patients had a PMRS ≥34%. In these patients, event rates were similar to patients with very-low (≤5%) clinical likelihood (hazard ratio 0.91 (95% confidence interval 0.52-1.52), p=0.77). Compared to patients with low (>5-≤15%) clinical likelihood and a PMRS <34%, the prevalence of obstructive CAD and risk were lower in patients with low (>5-≤15%) clinical likelihood and a PMRS ≥34% (p<0.01 for both comparisons).ConclusionIn patients with low (>5-≤15%) clinical likelihood of obstructive CAD, the PMRS enables safe down-classification of 1 in 6 patients to a very-low (≤5%) clinical likelihood category.Clinicaltrials.gov identifiersNCT02264717, NCT03481712, NCT04707859, NCT01174550 and NCT01149590
Guided tensor lifting
Get PDFDomain-specific languages (DSLs) for machine learning are revolutionizing the speed and efficiency of machine learning workloads as they enable users easy access to high-performance compiler optimizations and accelerators. However, to take advantage of these capabilities, a user must first translate their legacy code from the language it is currently written in, into the new DSL. The process of automatically lifting code into these DSLs has been identified by several recent works, which propose program synthesis as a solution. However, synthesis is expensive and struggles to scale without carefully designed and hard-wired heuristics. In this paper, we present an approach for lifting that combines an enumerative synthesis approach with a Large Language Model used to automatically learn the domain-specific heuristics for program lifting, in the form of a probabilistic grammar. Our approach outperforms the state-of-the-art tools in this area, despite only using learned heuristics
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