Patient safety in dentistry: development of a candidate 'never event' list for primary care

Introduction The 'never event' concept is often used in secondary care and refers to an agreed list of patient safety incidents that 'should not happen if the necessary preventative measures are in place'. Such an intervention may raise awareness of patient safety issues and inform team learning and system improvements in primary care dentistry. Objective To identify and develop a candidate never event list for primary care dentistry. Methods A literature review, eight workshops with dental practitioners and a modified Delphi with 'expert' groups were used to identify and agree candidate never events. Results Two-hundred and fifty dental practitioners suggested 507 never events, reduced to 27 distinct possibilities grouped across seven themes. Most frequently occurring themes were: 'checking medical history and prescribing' (119, 23.5%) and 'infection control and decontamination' (71, 14%). 'Experts' endorsed nine candidate never event statements with one graded as 'extreme risk' (failure to check past medical history) and four as 'high risk' (for example, extracting wrong tooth). Conclusion Consensus on a preliminary list of never events was developed. This is the first known attempt to develop this approach and an important step in determining its value to patient safety. Further work is necessary to develop the utility of this method

Parents' responses to prognostic disclosure at diagnosis of a child with a high‐risk brain tumor: Analysis of clinician‐parent interactions and implications for clinical practice

Background: Previous studies have found that parents of children with cancer desire more prognostic information than is often given even when prognosis is poor. We explored in audio‐recorded consultations the kinds of information they seek. / Methods: Ethnographic study including observation and audio recording of consultations at diagnosis. Consultations were transcribed and analyzed using an interactionist perspective including tools drawn from conversation and discourse analysis. / Results: Enrolled 21 parents and 12 clinicians in 13 cases of children diagnosed with a high‐risk brain tumor (HRBT) over 20 months at a tertiary pediatric oncology center. Clinicians presented prognostic information in all cases. Through their questions, parents revealed what further information they desired. Clinicians made clear that no one could be absolutely certain what the future held for an individual child. Explicit communication about prognosis did not satisfy parents’ desire for information about their own child. Parents tried to personalize prognostic information and to apply it to their own situation. Parents moved beyond prognostic information presented and drew conclusions, which could change over time. Parents who were present in the same consultations could form different views of their child's prognosis. / Conclusion: Population level prognostic information left parents uncertain about their child's future. The need parents revealed was not for more such information but rather how to use the information given and how to apply it to their child in the face of such uncertainty. Further research is needed on how best to help parents deal with uncertainty and make prognostic information actionable

Triangle Counting in Dynamic Graph Streams

Estimating the number of triangles in graph streams using a limited amount of memory has become a popular topic in the last decade. Different variations of the problem have been studied, depending on whether the graph edges are provided in an arbitrary order or as incidence lists. However, with a few exceptions, the algorithms have considered {\em insert-only} streams. We present a new algorithm estimating the number of triangles in {\em dynamic} graph streams where edges can be both inserted and deleted. We show that our algorithm achieves better time and space complexity than previous solutions for various graph classes, for example sparse graphs with a relatively small number of triangles. Also, for graphs with constant transitivity coefficient, a common situation in real graphs, this is the first algorithm achieving constant processing time per edge. The result is achieved by a novel approach combining sampling of vertex triples and sparsification of the input graph. In the course of the analysis of the algorithm we present a lower bound on the number of pairwise independent 2-paths in general graphs which might be of independent interest. At the end of the paper we discuss lower bounds on the space complexity of triangle counting algorithms that make no assumptions on the structure of the graph.Comment: New version of a SWAT 2014 paper with improved result

Don't Let Primary Care Patients Slip through the Nets

Nearly 7 million patients might have been subject to a general practice error in the past year. With new models of care proposed, Sukhmeet Panesar and colleagues urge GPs to have a greater awareness of patient safet

Internal and external cooling methods and their effect on body temperature, thermal perception and dexterity

© 2018 The Authors. Published by PLOS. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1371/journal.pone.0191416© 2018 Maley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Objective The present study aimed to compare a range of cooling methods possibly utilised by occupational workers, focusing on their effect on body temperature, perception and manual dexterity. Methods Ten male participants completed eight trials involving 30 min of seated rest followed by 30 min of cooling or control of no cooling (CON) (34C, 58% relative humidity). The cooling methods utilised were: ice cooling vest (CV0), phase change cooling vest melting at 14C (CV14), evaporative cooling vest (CVEV), arm immersion in 10C water (AI), portable water-perfused suit (WPS), heliox inhalation (HE) and ice slushy ingestion (SL). Immediately before and after cooling, participants were assessed for fine (Purdue pegboard task) and gross (grip and pinch strength) manual dexterity. Rectal and skin temperature, as well as thermal sensation and comfort, were monitored throughout. Results Compared with CON, SL was the only method to reduce rectal temperature (P = 0.012). All externally applied cooling methods reduced skin temperature (P0.05). Conclusion The present study observed that ice ingestion or ice applied to the skin produced the greatest effect on rectal and skin temperature, respectively. AI should not be utilised if workers require subsequent fine manual dexterity. These results will help inform future studies investigating appropriate pre-cooling methods for the occupational worker.This project is financially supported by the US Government through the Technical Support Working Group within the Combating Terrorism Technical Support Office.Published versio

Emergent complex neural dynamics

A large repertoire of spatiotemporal activity patterns in the brain is the basis for adaptive behaviour. Understanding the mechanism by which the brain's hundred billion neurons and hundred trillion synapses manage to produce such a range of cortical configurations in a flexible manner remains a fundamental problem in neuroscience. One plausible solution is the involvement of universal mechanisms of emergent complex phenomena evident in dynamical systems poised near a critical point of a second-order phase transition. We review recent theoretical and empirical results supporting the notion that the brain is naturally poised near criticality, as well as its implications for better understanding of the brain

The prevalence of anemia and its association with 90-day mortality in hospitalized community-acquired pneumonia

<p>Abstract</p> <p>Background</p> <p>The prevalence of anemia in the intensive care unit is well-described. Less is known, however, of the prevalence of anemia in hospitalized patients with lesser illness severity or without organ dysfunction. Community-acquired pneumonia (CAP) is one of the most frequent reasons for hospitalization in the United States (US), affecting both healthy patients and those with comorbid illness, and is typically not associated with acute blood loss. Our objective was to examine the development and progression of anemia and its association with 90d mortality in 1893 subjects with CAP presenting to the emergency departments of 28 US academic and community hospitals.</p> <p>Methods</p> <p>We utilized hemoglobin values obtained for clinical purposes, classifying subjects into categories consisting of no anemia (hemoglobin >13 g/dL), at least borderline (≤ 13 g/dL), at least mild (≤ 12 g/dL), at least moderate (≤ 10 g/dL), and severe (≤ 8 g/dL) anemia. We stratified our results by gender, comorbidity, ICU admission, and development of severe sepsis. We used multivariable logistic regression to determine factors independently associated with the development of moderate to severe anemia and to examine the relationship between anemia and 90d mortality.</p> <p>Results</p> <p>A total of 8240 daily hemoglobin values were measured in 1893 subjects. Mean (SD) number of hemoglobin values per patient was 4.4 (4.0). One in three subjects (33.9%) had at least mild anemia at presentation, 3 in 5 (62.1%) were anemic at some point during their hospital stay, and 1 in 2 (54.5%) survivors were discharged from the hospital anemic. Anemia increased with illness severity and was more common in those with comorbid illnesses, female gender, and poor outcomes. Yet, even among men and in those with no comorbidity or only mild illness, anemia during hospitalization was common (~55% of subjects). When anemia was moderate to severe (≤ 10 g/dL), its development was independently associated with increased 90d mortality, even among hospital survivors.</p> <p>Conclusions</p> <p>Anemia was common in hospitalized CAP and independently associated with 90d mortality when hemoglobin values were 10 g/dL or less. Whether prevention or treatment of CAP-associated anemia would improve clinical outcomes remains to be seen.</p

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND: The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS: We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS: The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION: Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms