Risk Assessing Medical Inpatients for Hospital-Acquired Venous Thrombosis: The Medical Inpatients Thrombosis and Hemostasis (MITH) Study

Introduction: Multiple regulatory agencies and professional societies recommend risk assessment of hospitalized medical patients for hospital-acquired (HA) venous thromboembolism (VTE) and provision of pharmacologic prophylaxis to those at risk. Extant risk assessment models (RAMs) include risk factors not knowable or difficult to assess at admission and often do not include risk factors reflecting illness acuity (such as laboratory studies and vital signs at admission). We developed a RAM for HA-VTE that reports absolute VTE risk, as opposed to arbitrary risk categories, using only objective risk factors measured within the first 24 hours of admission. Methods: The study setting was a combined academic and community 540-bed teaching hospital in northwest Vermont (The University of Vermont Medical Center). Using validated electronic health record (EHR) derived phenotypes (computable phenotypes), we captured all medical admissions between 2010-2019 and examined patient demographics, past medical history, and presenting vital and laboratory measures as potential risk factors for HA-VTE. As risk assessment should happen within 24 hours of admission, we only assessed risk factors knowable within this timeframe. Individuals with VTE at admission were excluded. Key outcome and risk factor definitions were validated using chart review. Bayesian logistic regression with a least absolute shrinkage and selection operator (LASSO) prior probability distribution was used to select risk factors for the model. Variables with a t-statistic ≥1.5 or ≤-1.5 were included in the final model. Full or prophylactic anticoagulation use was adjusted for in the final model. Model performance was assessed using bootstrap resampling to estimate area under the receiver operating characteristic (AUC) curve and calibration slope with 95% confidence interval (CI). Results: There were 62,468 medical admissions in the study period with 219 HA-VTE events. Chart review demonstrated the positive predictive value of our HA-VTE computable phenotype to be 84% and the negative predictive value 99%. Mean age was 65 years and 51% were male. Comorbid conditions were common in this hospitalized population, including active cancer (29%), congestive heart failure (25%), diabetes (27%), hypertension (59%), and prior myocardial infarction (13%). Seven risk factors met the criteria for inclusion in the final model: prior history of VTE (OR 2.7; 95% CI 1.8, 3.8), red cell distribution width ≥14.7% (OR 1.6; 95% CI 1.2, 2.2), creatinine ≥2.0 mg/dL or on dialysis (OR 2.0; 95% CI 1.4, 2.8), serum sodium <136 MEq/L (OR 1.5; 95% CI 1.1, 2.1), active cancer (OR 1.4; 95% CI 1.1, 2.0), malnutrition based on prior reported weight loss (OR 2.1; 95% CI 1.3, 3.3), and low hemoglobin (<13.6 g/dL in men, <12.1 g/dL in women; OR 1.5; 95% CI 1.0, 2.1). The unadjusted AUC of the RAM was 0.73 with an unadjusted calibration slope 1.09 (Figure 1). The optimism-adjusted AUC was 0.68 (95% CI 0.64, 0.71) and the optimism-adjusted calibration slope was 0.87 (95% CI: 0.72, 1.03). Discussion: We developed and internally validated a RAM for HA-VTE during medical hospitalization which incorporates simple, objective risk factors knowable within the first 24 hours of admission. Unlike most prior RAMs, this model also incorporates risk factors reflecting illness severity such as laboratory results. The RAM has good fit and calibration and will be moved forward to external validation. Future applications include incorporating the RAM into hospital admission workflows and assessing VTE prophylaxis rates and the incidence of HA-VTE and HA-bleeding. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Development and presentation of an objective risk stratification tool for healthcare workers when dealing with the COVID-19 pandemic in the UK: risk modelling based on hospitalisation and mortality statistics compared with epidemiological data

ObjectivesHealthcare workers have greater exposure to SARS-CoV-2 and an estimated 2.5-fold increased risk of contracting COVID-19 than the general population. We wished to explore the predictive role of basic demographics to establish a simple tool that could help risk stratify healthcare workers.SettingWe undertook a review of the published literature (including multiple search strategies in MEDLINE with PubMed interface) and critically assessed early reports on preprint servers. We explored the relative risk of mortality from readily available demographics to identify the population at the highest risk.ResultsThe published studies specifically assessing the risk of healthcare workers had limited demographics available; therefore, we explored the general population in the literature. Clinician demographics: Mortality increased with increasing age from 50 years onwards. Male sex at birth, and people of black and minority ethnicity groups had higher susceptibility to both hospitalisation and mortality. Comorbid disease. Vascular disease, renal disease, diabetes and chronic pulmonary disease further increased risk. Risk stratification tool: A risk stratification tool was compiled using a white female aged <50 years with no comorbidities as a reference. A point allocated to risk factors was associated with an approximate doubling in risk. This tool provides numerical support for healthcare workers when determining which team members should be allocated to patient facing clinical duties compared with remote supportive roles.ConclusionsWe generated a tool that provides a framework for objective risk stratification of doctors and healthcare professionals during the COVID-19 pandemic, without requiring disclosure of information that an individual may not wish to share with their direct line manager during the risk assessment process. This tool has been made freely available through the British Medical Association website and is widely used in the National Health Service and other external organisations.

Age differences in COVID-19 risk-taking, and the relationship with risk attitude and numerical ability

This study aimed to investigate age differences in risk-taking concerning the coronavirus pandemic, while disentangling the contribution of risk attitude, objective risk and numeracy. We tested (i) whether older and younger adults differed in taking coronavirus-related health risks, (ii) whether there are age differences in coronavirus risk, risk attitude and numerical ability and (iii) whether these age differences in coronavirus risk, attitude and numerical ability are related to coronavirus risk-taking. The study was observational, with measures presented to all participants in random order. A sample of 469 participants reported their coronavirus-related risk-taking behaviour, objective risk, risk attitude towards health and safety risks, numerical ability and risk perception. Our findings show that age was significantly related to coronavirus risk-taking, with younger adults taking more risk, and that this was partially mediated by higher numeracy, but not objective risk or risk attitude. Exploratory analyses suggest that risk perception for self and others partially mediated age differences in coronavirus risk-taking. The findings of this study may better our understanding of why age groups differ in their adoption of protective behaviours during a pandemic and contribute to the debate whether age differences in risk-taking occur due to decline in abilities or changes in risk attitude.

A Bibliometric Analysis of Objective and Subjective Risk

In relation to “objective risk” or “subjective risk”, a bibliometric analysis was performed using documents found in the Scopus database. A search for related documents was narrowed down to 192 documents and these were considered in this study. The results of this study suggest that the use of the ranking method and descriptive statistics is not sufficient in presenting a concise bibliometric analysis. To create a more in-depth bibliometric analysis, the results of this study have to be analyzed together with a visualization map using VOSviewer software. This way, researchers can easily locate a specific gap in the literature, understand the relation between the papers on the same subject, and cite the literature studies based on their effectiveness.

Educating Pharmacists on the Risks of Strong Opioids With Descriptive and Simulated Experience Risk Formats: A Randomized Controlled Trial

Objectives. High opioid prescription rates in the United States and Europe suggest miscalibrated risk perceptions among those who prescribe, dispense, and take opioids. Findings from cognitive decision science suggest that risk perceptions and behaviors can differ depending on whether people learn about risks by experience or description. This study investigated effects of a descriptive versus an experience-based risk education format on pharmacists’ risk perceptions and counseling behavior in the long-term administration of strong opioids to patients with chronic noncancer pain. Methods. In an exploratory, randomized controlled online trial, 300 German pharmacists were randomly assigned to either a descriptive format (fact box) or a simulated experience format (interactive simulation). Primary Outcome Measures. 1) Objective risk perception, 2) subjective risk perception, and 3) intended and 4) actual counseling behavior. Results. Both risk formats significantly improved pharmacists’ objective risk perception, but pharmacists exposed to the fact box estimated the benefit-harm ratio more accurately than those exposed to the simulation. Both formats proved equally effective in adjusting pharmacists’ subjective risk perception toward a better recognition of opioids’ harms; however, pharmacists receiving the simulation showed a greater change in their actual counseling behavior and higher consistency between their intended and actual counseling than pharmacists receiving the fact box. Conclusion. The simulated experience format was less effective than the descriptive format in improving pharmacists’ objective risk perception, equally effective in motivating pharmacists to counsel patients on less risky treatment alternatives and more effective in changing the reported actual counseling behavior. Implications. These exploratory findings provide important insights into the relevance of the description-experience gap for drug safety and raise questions for future research regarding the specific mechanisms at work.

Which Risk Factors Matter More for Psychological Distress during the COVID-19 Pandemic? An Application Approach of Gradient Boosting Decision Trees

Background: A growing body of scientific literature indicates that risk factors for COVID-19 contribute to a high level of psychological distress. However, there is no consensus on which factors contribute more to predicting psychological health. Objectives: The present study quantifies the importance of related risk factors on the level of psychological distress and further explores the threshold effect of each rick factor on the level of psychological distress. Both subjective and objective measures of risk factors are considered in the model. Methods: We sampled 937 individual items of data obtained from an online questionnaire between 20 January and 13 February 2020 in China. Objective risk factors were measured in terms of direct distance from respondents’ housing to the nearest COVID-19 hospital, direct distance from respondents’ housing to the nearest park, and the air quality index (AQI). Perceived risk factors were measured in regard to perceived distance to the nearest COVID-19 hospital, perceived air quality, and perceived environmental quality. Psychological distress was measured with the Kessler psychological distress scale K6 score. The following health risk factors and sociodemographic factors were considered: self-rated health level, physical health status, physical activity, current smoker or drinker, age, gender, marital status, educational attainment level, residence location, and household income level. A gradient boosting decision tree (GBDT) was used to analyse the data. Results: Health risk factors were the greatest contributors to predicting the level of psychological distress, with a relative importance of 42.32% among all influential factors. Objective risk factors had a stronger predictive power than perceived risk factors (23.49% vs. 16.26%). Furthermore, it was found that there was a dramatic rise in the moderate level of psychological distress regarding the threshold of AQI between 40 and 50, and 110 and 130, respectively. Gender-sensitive analysis revealed that women and men responded differently to psychological distress based on different risk factors. Conclusion: We found evidence that perceived indoor air quality played a more important role in predicting psychological distress compared to ambient air pollution during the COVID-19 pandemic.