Facilitate Risk Communication Research Articles

A novel machine learning prediction model for metastasis in breast cancer.

Breast cancer (BC) metastasis is the common cause of high mortality. Conventional prognostic criteria cannot accurately predict the BC metastasis risk. The machine learning technologies can overcome the disadvantage of conventional models. We developed a model to predict BC metastasis using the random survival forest (RSF) method. Based on demographic data and routine clinical data, we used RSF-recursive feature elimination to identify the predictive variables and developed a model to predict metastasis using RSF method. The area under the receiver operating characteristic curve (AUROC) and Kaplan-Meier survival (KM) analyses were plotted to validate the predictive effect when C-index was plotted to assess the discrimination and Brier scores was plotted to assess the calibration of the predictive model. We developed a metastasis prediction model comprising three variables (pathological stage, aspartate aminotransferase, and neutrophil count) selected by RSF-recursive feature elimination. The model was reliable and stable when assessed by the AUROC (0.932 in training set and 0.905 in validation set) and KM survival analyses (p < .0001). The C-indexes (0.959) and Brier score (0.097) also validated the good predictive ability of this model. This model relies on routine data and examination indicators in real-time clinical practice and exhibits an accurate prediction performance without increasing the cost for patients. Using this model, clinicians can facilitate risk communication and provide precise and efficient individualized therapy to patients with breast cancer.

Barriers and facilitators to using an objective risk communication tool during primary care dental consultations: A Theoretical Domains Framework (TDF) informed qualitative study

ObjectivesObjective risk communication tools can supplement clinical judgement and support the understanding of potential health risks. This study used the Theoretical Domains Framework (TDF) to identify barriers and facilitators to implementing a risk communication aid within primary care dental consultations. MethodsDentists (N = 13), recruited via a dental practice database and through professional contacts were interviewed using a TDF-informed semi-structured interview schedule. Data were analysed inductively and deductively coding the themes using the TDF. ResultsEight theoretical domains (environmental context and resources; beliefs about consequences; goals; memory, attention, and decision processes; optimism; reinforcement; social influences and behavioural regulation) and thirteen sub-themes were identified. Insufficient resources and patient factors were commonly encountered barriers and led to increasing pressure to prioritise other tasks. Whilst dentists had a favourable view towards a risk communication aid and acknowledged its benefits, some were sceptical about its ability to facilitate behaviour change. Self-monitoring strategies and colleague support facilitated tool usage. ConclusionsThis study identified six barriers and seven facilitators to implementing a risk communication tool within primary care dental settings. Dentists appreciated the value of using a risk communication tool during dental consultations, although some required further support to integrate the tool into practice. Clinical significanceOur findings provide a sound theoretical base for interventions aimed at facilitating patient behaviour change through the use of risk communication in dentistry. Further research should apply behavioural science to support the implementation of the tool in clinical practice.

Scenario-Based Messages on Social Media Motivate COVID-19 Information Seeking.

Communicating information about health risks empowers individuals to make informed decisions. To identify effective communication strategies, we manipulated the specificity, self-relevance, and emotional framing of messages designed to motivate information seeking about COVID-19 exposure risk. In Study 1 (N=221,829), we conducted a large-scale social media field study. Using Facebook advertisements, we targeted users by age and political attitudes. Episodic specificity drove engagement: Advertisements that contextualized risk in specific scenarios produced the highest click-through rates, across all demographic groups. In Study 2, we replicated and extended our findings in an online experiment (N=4,233). Message specificity (but not self-relevance or emotional valence) drove interest in learning about COVID-19 risks. Across both studies, we found that older adults and liberals were more interested in learning about COVID-19 risks. However, message specificity increased engagement across demographic groups. Overall, evoking specific scenarios motivated information seeking about COVID-19, facilitating risk communication to a broad audience.

Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Patients With Severe Acute Exacerbation.

Background: Patients with chronic hepatitis B (CHB) with severe acute exacerbation (SAE) are at a progression stage of acute-on-chronic liver failure (ACLF) but uniform models for predicting ACLF occurrence are lacking. We aimed to present a risk prediction model to early identify the patients at a high risk of ACLF and predict the survival of the patient.Methods: We selected the best variable combination using a novel recursive feature elimination algorithm to develop and validate a classification regression model and also an online application on a cloud server from the training cohort with a total of 342 patients with CHB with SAE and two external cohorts with a sample size of 96 and 65 patients, respectively.Findings: An excellent prediction model called the PATA model including four predictors, prothrombin time (PT), age, total bilirubin (Tbil), and alanine aminotransferase (ALT) could achieve an area under the receiver operating characteristic curve (AUC) of 0.959 (95% CI 0.941–0.977) in the development set, and AUC of 0.932 (95% CI 0.876–0.987) and 0.905 (95% CI 0.826–0.984) in the two external validation cohorts, respectively. The calibration curve for risk prediction probability of ACLF showed optimal agreement between prediction by PATA model and actual observation. After predictive stratification into different risk groups, the C-index of predictive 90-days mortality was 0.720 (0.675–0.765) for the PATA model, 0.549 (0.506–0.592) for the end-stage liver disease score model, and 0.648 (0.581–0.715) for Child–Turcotte–Pugh scoring system.Interpretation: The highlypredictive risk model and easy-to-use online application can accurately predict the risk of ACLF with a poor prognosis. They may facilitate risk communication and guidetherapeutic options.

Converting health risks into loss of life years - a paradigm shift in clinical risk communication.

For facilitating risk communication in clinical management, such a ratio-based measure becomes easier to understand if expressed as a loss of life expectancy. The cohort, consisting of 543,410 adults in Taiwan, was recruited between 1994 and 2008. Health risks included lifestyle, biomarkers, and chronic diseases. A total of 18,747 deaths were identified. The Chiang’s life table method was used to estimate a loss of life expectancy. We used Cox regression to calculate hazard ratios (HRs) for health risks. The increased mortality from cardio-metabolic risks such as high cholesterol (HR=1.10), hypertension (HR=1.48) or diabetes (HR=2.02) can be converted into a loss of 1.0, 4.4, and 8.9 years in life expectancy, respectively. The top 20 of the 30 risks were associated with a loss of 4 to 10 years of life expectancy, with 70% of the cohort having at least two such risk factors. Smoking, drinking, and physical inactivity each had 5-7 years loss. Individuals with diabetes or an elevated white count had a loss of 7-10 years, while prolonged sitting, the most prevalent risk factor, had a loss of 2-4 years. Those with diabetes (8.9 years) and proteinuria (9.1 years) present at the same time showed a loss of 16.2 years, a number close to the sum of each risk. Health risks, expressed as life expectancy loss, could facilitate risk communication. The paradigm shift in expressing risk intensity can help set public health priorities scientifically to promote a focus on the most important ones in primary care.

Social media response and crisis communications in active shootings during COVID-19 pandemic

Active shooting, a man-made hazard, remains an unsolved challenge as communities get threatened more frequently than ever before. As a low-cost alternative to the traditional approaches of responding to such crisis, data-driven approaches can help to identify more tailored response strategies and guide towards more informed decision-making. Recently, social media platforms helped researchers and practitioners with sufficient details and coverage to understand how communities respond to natural hazards through social media interactions. However, the empirical literature does not provide any comprehensive guidance on public reactions to active shootings as observed through social media interactions. This study adopted a holistic data analytics approach to collect large-scale social media data from Twitter (~252 K tweets, 04.17.20–05.20.20). The 2020 Nova Scotia Attacks were among the major shooting events observed during this period in addition to the unprecedented experiences people were having due to the COVID-19 pandemic. This study used several natural language processing and data mining approaches (such as temporal heatmaps, word bigrams, and topic mining) to cluster the social media crisis communication patterns of active shootings and create infographics of the diverse needs, concerns, and reactions observed in the aftermath of such events. Key interactions include bailing out of shooters, shooting investigation, police response, gun violence, lessons learned from the previous school (Sandy Hook) and mass shootings (El Paso), vehicle ramming (Toronto Van Attack), mobility issues, and health concerns during COVID-19 pandemic, changes in economy and education systems. This study would allow first responders and emergency management officials to enhance the capacity of social sharing platforms and facilitate risk communication in no-notice scenarios. Additionally, the infographics could serve as a data dictionary in future active shooting scenarios to maximize peer influence.

The promise of a “people-centred” approach to floods: Types of participation in the global literature of citizen science and community-based flood risk reduction in the context of the Sendai Framework

Floods are expected to become more intense and increasingly frequent over the coming years. Over the last few decades, scholars, policy makers and risk managers have been gradually acknowledging that community-based initiatives can represent a promising alternative for addressing the hazard of floods at the local scale. In the context of the Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR), this article presents a systematic literature review of the recent global body of literature on the topic. This work analyses 40 articles published over the last five years to identify the types of engagement allowed in the projects and to discuss the emerging debates in the field since the establishment of the SFDRR. The literature review interrogates where these approaches are being developed and how community-based approaches have been supporting the achievement of the SFDRR targets. The review shows that a growing body of literature has been applying community-based disaster risk reduction (CBDRR) and citizen science methods in order to respond to the call for a more people-centred approach towards floods. This call, outlined by the SFDRR, is particularly relevant in the context of vulnerable communities such as the residents of informal settlements, which have historically been disproportionately affected by floods. The article then provides an original contribution to the field by documenting and reflecting on the firsthand findings of a long-term community-based program assessing flood risks conducted within the Revitalising Informal settlements and Their Environments (RISE) program. The article outlines the implementation, operation and initial findings of the project, which involved community-members in the documentation of flood-levels in informal settlements in Suva, Fiji and Makassar, Indonesia between 2018 and 2020. The findings from the case study suggest that approaches involving communities in flood monitoring can, beyond facilitating flood documentation, unlock additional risk reduction benefits such as enhancing social capital and facilitating risk communication. The conclusions highlight that, similar to RISE’s flood monitoring project, several other community-based initiatives have been developed all over the world. While these initiatives vary significantly in the degrees of community participation and their methods, most of the literature agrees that these emerging methods are considered particularly promising in terms of improving disaster knowledge and awareness when community members participate in disaster risk reduction. The review of this body of literature, however, indicates that more research is needed to examine how social capital as well as cultural and political aspects can be harnessed and strengthened to play important roles in the response to floods.

Derivation of a Coronary Age Calculator Using Traditional Risk Factors and Coronary Artery Calcium: The Multi-Ethnic Study of Atherosclerosis.

BackgroundThe optimal method for communicating coronary heart disease (CHD) risk to individual patients is not yet clear. Recent research supports the concept of "coronary age" for more effective risk communication. We defined an individual's coronary age as the age at which an average healthy individual would have an equivalent estimated CHD risk as that calculated for the index individual, building on our previously validated MESA (Multi‐Ethnic Study of Atherosclerosis) 10‐year CHD Risk Score equations with and without coronary artery calcium (CAC).Methods and ResultsWe derived a coronary age by (1) calculating the MESA 10‐year CHD risk; (2) mathematically setting this equal to an equation describing risk of an average healthy MESA participant, as a function of age; and (3) solving for age. The risk discrimination of the resultant coronary age was compared with that of chronological age, the MESA CHD Risk Score, and CAC alone. Approximately 95% of coronary age values ranged from 30 years less to 30 years higher than chronological age. Although the mean chronological age of individuals experiencing CHD events compared with those free of events was 67.4 versus 61.8 years, the difference in coronary age including CAC was larger (80.6 versus 62.8 years). Coronary age with CAC had identical predictive ability to that of MESA CHD Risk Score and outperformed chronological age and CAC alone.ConclusionsThe newly derived coronary age is a convenient transformation of MESA CHD Risk, retaining very good risk discrimination. This easy‐to‐communicate tool will be available for patients and clinicians, potentially facilitating risk communication in routine care.

Quantitative Protection Factors for Common Masks and Face Coverings.

The performance of masks, whether intended to protect the community from exhaled infectious aerosol or to protect the wearer from inhaled infectious aerosol, depends on factors such as filtration efficiency, particle size distribution, leakage, and ventilation rate. These factors depend on the activities and facial features of the mask wearer so that the mask performance for real-world applications is difficult to predict. The present work shows how protection factor, a quantity often used to describe mask performance, can be estimated without involving human volunteers. By constraining these factors to known values, mask protection factors can be compared fairly and efficiently following a series of filtration efficiency measurements performed in the laboratory. Protection factors and mask emissions for exhalation and inhalation were evaluated for masks of seven types currently in use around the world and for a hypothetical mask with 99% efficiency on all particles. The performance of reusable masks made from cotton fabric was limited by the size of the native cotton fibers. Masks that utilized finer fibers, particularly electret fibers with relatively small diameters, showed excellent performance with moderate flow resistance. Results from this work, in addition to simple guidance for mask fit and usage, can facilitate risk communication and decision-making efforts during the COVID-19 pandemic.

Modeling the Factors Influencing the Adoption of a Map-Based Disaster Application in Indonesia: A Case of SIKK Magelang

Indonesia has high exposure to natural hazards, frequent disaster events, and a highly vulnerable population. Hence, risk communication and disaster information dissemination are crucial to reducing risks. Numerous disaster mobile applications (apps) with location-based services or maps already exist for facilitating risk communication to the public; however, availability does not automatically translate into use or adoption. Therefore, to understand the factors influencing the adoption of a map-based disaster app, the factors that affect the adoption of a local government-initiated map-based disaster app in Indonesia, namely Sistem Informasi Kebencanaan Kabupaten Magelang, or Disaster Information System of Magelang Regency (SIKK Magelang), were examined through the theoretical lenses of a combined information system (IS) success model. Partial Least Squared – Structural Equation Modelling (PLS-SEM), with the help of Smart-PLS, was used to examine the hypothesized relationships among the proposed constructs (latent variables). Nine exogenous latent variables were proposed as the antecedents of intention to use a map-based disaster application (the endogenous latent variable), including information quality, system quality, familiarity with online maps, perceived usefulness, user satisfaction, facilitating conditions, risk perceptions, information seeking behavior, and perceived quality of the Internet connection. Information-seeking behavior was also predicted to be influenced by risk perception. A questionnaire survey with an app trial was conducted in Magelang Regency for data collection. The study results confirmed the applicability of the combined IS success model frameworks in predicting the adoption of this map-based disaster app by the public. Although new constructs representing disaster-related online maps and familiarity with those maps were initially proposed in the contextual model, the model validation results showed that intention to use SIKK Magelang was significantly influenced only by perceived usefulness and user satisfaction. Practically, this study’s results offer guidance to the local emergency managers in Indonesia, especially Magelang Regency, regarding how to develop strategies to increase the app’s adoption.

Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP).

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

Cadmium and lead in geophagic clay consumed in Southern Nigeria: health risk from such traditional nutraceutical.

Geophagy is a cultural behavior, based on the recurrent intentional eating of clay soil, that is raising increasing concern as it implies multidimensional (space, time) potential risk of serious adverse health effects. This study investigated the level of toxic metals (Cd and Pb) in 20 Nigerian geophagic clays intended for both local consumption and distribution to the West Africa market. After sampling in 4 open markets in southern Nigeria (Akwa Ibom, Abia, Rivers, Imo), samples were subjected to digestion, ashing and analysis. The Pb levels in all samples exceeded the WHO/FAO maximum permissible limit of 0.1mgkg-1 whereas 16% exceeded the Cd limit of 0.3mgkg-1. The estimated daily intake of Pb for all samples ranged from 0.0032-0.0286mg kgbw-1day-1 to 0.0024-0.0215mg kgbw-1day-1 for children and adults, respectively. The estimated daily intakes for Cd ranged from bdl (below detection limit)-0.0010mg kgbw-1day-1 to bdl-0.0028mg kgbw-1day-1 for children and adults, respectively. In both cases, the WHO/FAO provisional Tolerable Weekly Intake is exceeded through the ingestion of these soils. Our results confirm health risks related to the geophagic practices, its role in exceeding health guidelines when considering aggregate exposure in the Nigerian scenario and body burden in developing organisms, young women, women at fertile age, and pregnant women. We discuss how geophagists consider clays as traditional nutraceuticals and how clarifying the nutraceutical role of geophagy could facilitate risk communication. Geophagic products are implicitly or explicitly marketed as dietary supplements, and as such they should be regulated (1) by labeling, and prohibition of scientifically unfounded health claims and (2) by safety standards before marketing. This is particularly critical when clays originate from countries living rapid, unplanned and uncontrolled development and dumped, like Nigeria.

Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP).

Risk assessment have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP).

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

LO64: The HEART score in predicting major adverse cardiac events in patients presenting to the emergency department with possible acute coronary syndrome: a systematic review and meta-analysis

Introduction: Acute coronary syndrome (ACS) is a common, sometimes difficult to diagnose spectrum of diseases. Given the diagnostic challenge, it is sensible for emergency physicians to have an approach to prognosticate patients with possible ACS. The objective of this review was to investigate the ability of the HEART score to predict major adverse cardiac events (MACE) in patients presenting to the ED with possible ACS. Methods: Eleven databases and other sources identified 468 potentially relevant studies. Sixty-seven studies underwent full text review with 25 studies meeting eligibility criteria. Main outcome measures were pooled prevalence, risk ratio (RR), and absolute risk reduction (ARR) for MACE within six weeks of ED evaluation, comparing HEART score 0–3 versus 4–10. Model discrimination (sensitivity, specificity, concordance statistic) and calibration (observed to expected events ratio) were also evaluated. Results: Data from 25 studies including 41,397 patients were combined in the meta-analysis. In total, 4815 patients (11.6%) developed MACE. Among 18,866 patients with HEART score 0–3, 396 (2.1%) developed MACE (RR 0.08; ARR 0.20). Outcome measures were consistent across planned subgroup and sensitivity analyses. Among studies with secondary outcome data for patients with HEART score 0–3, 5 of 6461 (0.1%) died and 75 of 7556 (1.0%) had a myocardial infarction. Conclusion: The HEART score provides a reliable quantitative risk assessment of MACE in ED patients with possible ACS. Emergency clinicians should consider using the HEART score to facilitate risk communication and shared decision making with patients and other care providers.

Using the Short Graph Literacy Scale to Predict Precursors of Health Behavior Change.

Visual displays can facilitate risk communication and promote better health choices. Their effectiveness in improving risk comprehension is influenced by graph literacy. However, the construct of graph literacy is still insufficiently understood, partially because existing objective measures of graph literacy are either too difficult or too long. We constructed a new 4-item Short Graph Literacy (SGL) scale and examined how SGL scores relate to key cognitive, affective, and conative precursors of health behavior change described in common health behavior theories. We performed secondary analyses to adapt the SGL scale from an existing 13-item scale. The initial construction was based on data collected in a laboratory setting in Germany ( n = 51). The scale was then validated using data from nationally representative samples in Germany ( n = 495) and the United States ( n = 492). To examine how SGL scores relate to precursors of health behavior change, we performed secondary analyses of a third study involving a nationwide US sample with 47% participants belonging to racial/ethnic minorities and 46% with limited formal education ( n = 835). Graph literacy was significantly associated with cognitive precursors in theoretically expected ways (e.g., positive associations with risk comprehension and response efficacy and a negative association with cognitive risk perception). Patterns for affective precursors generally mirrored those for cognitive precursors, although numeracy was a stronger predictor than graph literacy for some affective factors (e.g., feelings of risk). Graph literacy had predictive value for most cognitive and affective precursors beyond numeracy. In addition, graph literacy (but not numeracy) predicted key conative precursors such as defensive processing. Our data suggest that the SGL scale is a fast and psychometrically valid method for measuring objective graph literacy. Our findings also highlight the theoretical and practical relevance of graph literacy.

Nomograms for incident risk of post-partum type 2 diabetes in Chinese women with prior gestational diabetes mellitus.

Counselling patients with gestational diabetes mellitus (GDM) on their individual risk of post-partum type 2 diabetes (T2D) is challenging. This study aimed to develop nomograms for predicting incident risk of post-partum T2D in women with GDM diagnosed by WHO 1998 criteria. We performed a retrospective cohort study in 1263 Chinese women with GDM, of whom 83 were diagnosed as T2D at 2.3years post-partum. Multivariate Cox proportional hazards models were used to investigate the independent predictors for post-partum T2D. The results of multivariate analyses were used to formulate nomograms for predicting incident risk of post-partum T2D. The predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUROC). On multivariate analysis, independent predictors of post-partum T2DM in women with GDM included family history of diabetes [hazard ratio (HR) and its 95% confidential interval (95% CI): 2.06 (95% CI: 1.32-3.22)], history of pregnancy-induced hypertension [3.11 (95% CI: 1.86-5.21)], pre-pregnancy BMI [1.00, 1.90 (95% CI: 1.14-3.16), and 3.67 (95% CI: 2.03-6.63) for BMI <24, 24-28, and ≥28kg/m2 ], and 2-hour glucose at 26-30 gestational weeks [1.00, 2.84 (95% CI: 1.42-5.69), and 9.42 (95% CI: 4.46-19.90) for 2-hour glucose at 7.8 ~ <8.5, 8.5 ~ <11.1, and ≥11.1mmol/L). The overall AUROC of nomogram was 82.8% (95% CI: 78.1%-87.5%), with AUROCs of 85.9% (95% CI: 79.7%-92.1%) and 83.2% (95% CI: 77.9%-88.6%) for post-partum 2-year and 3-year risk of T2D, respectively. This easy-to-use nomogram, with non-invasive clinical characteristics, can accurately predict the risk of post-partum T2D in women with GDM. It may facilitate risk communication between patients and clinicians.

The HEART score in predicting major adverse cardiac events in patients presenting to the emergency department with possible acute coronary syndrome: protocol for a systematic review and meta-analysis

BackgroundAcute coronary syndrome (ACS) is a common, sometimes difficult to diagnose spectrum of diseases occurring after abrupt reduction in blood flow through a coronary artery. Given the diagnostic challenge, it is sensible for emergency physicians to have an approach to prognosticate patients with possible ACS. Multiple prediction models have been developed to help identify patients at increased risk of adverse outcomes. The HEART score is the first model to be derived, validated, and undergo clinical impact studies in emergency department (ED) patients with possible ACS.ObjectiveTo develop a protocol for a prognostic systematic review of the literature evaluating the HEART score as a predictor of major adverse cardiac events (MACE) in patients presenting to the ED with possible ACS.Methods/designThis protocol is reported according to the PRISMA-P statement and is registered on PROSPERO. All methodological tools to be used are endorsed by the Cochrane Prognosis Methods Group. Pre-defined eligibility criteria are provided. Multiple strategies will be used to identify potentially relevant studies. Studies will be selected and data extracted using standardised forms based on the CHARMS checklist. The QUIPS tool will be used to assess the risk of bias within individual studies. Outcome measures will include prevalence, risk ratio, and absolute risk reduction for MACE within 6 weeks of ED evaluation, comparing HEART scores 0–3 versus 4–10. HEART score prognostic performance will be evaluated with the concordance (C) statistic (model discrimination), observed to expected events ratio (model calibration), and a decision curve analysis. Reporting biases and methodological, clinical, and statistical heterogeneity will be scrutinised. Unless deemed inappropriate, a meta-analysis and pre-defined subgroup and sensitivity analyses will be performed. Overall judgements about evidence quality and strength of recommendations will be summarised using the GRADE approach.DiscussionThis review will identify, select, and appraise studies evaluating the prognostic performance of the HEART score, producing results of interest to emergency physicians. These results may encourage shared clinical decision-making in the ED by facilitating risk communication with patients and health care providers.Systematic review registrationPROSPERO 2017 CRD42017084400.

Application of Industrial Risk Management Practices to Control Natural Hazards, Facilitating Risk Communication

Establishing a comprehensive management framework to manage the risk from natural hazards is challenging because of the extensive affected areas, uncertainty in predictions of natural disasters, and the involvement of various stakeholders. Applying risk management practices proven in the industrial sector can assist systematic hazard identification and quantitative risk assessment for natural hazards, thereby promoting interactive risk communication to the public. The objective of this study is to introduce methods of studying risk commonly used in the process industry, and to suggest how such methods can be applied to manage natural disasters. In particular, the application of Hazard and Operability (HAZOP), Safety Integrated Level (SIL), and Quantitative Risk Analysis (QRA) was investigated, as these methods are used to conduct key studies in industry. We present case studies of the application of HAZOP to identify climate-related natural hazards, and of SIL and QRA studies that were performed to provide quantitative risk indices for landslide risk management. The analyses presented in this study can provide a useful framework for improving the risk management of natural hazards through establishing a more systematic context and facilitating risk communication.

Do visual aids influenced patients’ risk perceptions for rare and very rare risks?

ObjectiveTo examine whether visual aids (a graphic representation and/or conceptual illustrations) influence patients’ risk perceptions for rare and very rare risks. MethodsParticipants were randomized to a scenario which varied by probability of infection (2% or 0.2%) and visual aid: numbers only, numbers + graphic representation, numbers + conceptual illustrations, or numbers + graphic representation + conceptual illustrations. Risk perceptions and likelihood of starting the medication were examined across the four formats, separately, in participants with and without a college education. ResultsAdding a graphic representation and/or conceptual illustrations did not affect risk perceptions among those with a higher level of education. Adding both a graphic representation + conceptual illustrations decreased risk perceptions and increased likelihood of starting the medication among participants randomized to the 2%, but not 0.2% scenario, among participants with lower levels of education. ConclusionAdding a graphic representation + conceptual illustrations to numbers may influence perceptions for rare, but not very rare, risks among patients with lower education.Practice Implications: Clinicians should be aware of the differential effects of visual aids developed to facilitate risk communication. Patients with higher levels of education may be less responsive to visual aids than those with lower education.