A reliability and readability analysis of silicosis-related Italian websites: implications for occupational health.

BACKGROUND: Silicosis represents a "classical" occupational disease characterized by a renewed interest. New risk factors are emerging, such as sandblasting in the jeans industry or hydrofracking, leading to clusters of acute or massive cases. OBJECTIVES: Given that the Internet could represent a worker education and empowerment tool, and considering the increase in popularity of silicosis-related information, we aimed at systematically analyzing the reliability and readability of online silicosis-relevant information. METHODS: The search term "silicosi" was used to query 5 top search engines. The first 3 pages of results were screened using two validated readability tools: namely, the Gulpease and the ReadIt DyLanLab grade level scores. RESULTS: Seventy sites were analyzed. The Gulpease score differed among the types of websites: academic websites differed from institutional websites, as well as encyclopedia/dictionary pages from institutional sites. The Lexical Model - ReadIt DyLanLab grade level differed among the types of websites. Encyclopedia/dictionary pages differed from academic, commercial, health-related, institutional and news sites. Approximately, half of the websites were intended/designed for workers. Only the Global Model - Read-It DyLanLab grade level differed according to the intended/designed target. Only 1.4% of websites adhered to Health on the Net Foundation Code of Conduct. CONCLUSIONS: Our findings may have important practical implications for occupational physicians and health agencies/authorities. They should make efforts in strengthening their online presence, and producing appropriate material. This could lead to positive outcomes in term of occupational health promotion, potentially enabling workers to increase and to improve their work-related health and its determinants.

Machine learning-based algorithms applied to drug prescriptions and other healthcare services in the Sicilian claims database to identify acromegaly as a model for the earlier diagnosis of rare diseases.

Acromegaly is a rare disease characterized by a diagnostic delay ranging from 5 to 10 years from the symptoms' onset. The aim of this study was to develop and internally validate machine-learning algorithms to identify a combination of variables for the early diagnosis of acromegaly. This retrospective population-based study was conducted between 2011 and 2018 using data from the claims databases of Sicily Region, in Southern Italy. To identify combinations of potential predictors of acromegaly diagnosis, conditional and unconditional penalized multivariable logistic regression models and three machine learning algorithms (i.e., the Recursive Partitioning and Regression Tree, the Random Forest and the Support Vector Machine) were used, and their performance was evaluated. The random forest (RF) algorithm achieved the highest Area under the ROC Curve value of 0.83 (95% CI 0.79-0.87). The sensitivity in the test set, computed at the optimal threshold of predicted probabilities, ranged from 28% for the unconditional logistic regression model to 69% for the RF. Overall, the only diagnosis predictor selected by all five models and algorithms was the number of immunosuppressants-related pharmacy claims. The other predictors selected by at least two models were eventually combined in an unconditional logistic regression to develop a meta-score that achieved an acceptable discrimination accuracy (AUC = 0.71, 95% CI 0.66-0.75). Findings of this study showed that data-driven machine learning algorithms may play a role in supporting the early diagnosis of rare diseases such as acromegaly.

Discovering predictive temporal patterns for Acute Kidney Injury from critical care data.

Acute Kidney Injury is a severe clinical condition with a high risk of multi-organs complications and mortality. For this reason, early recognition is crucial. Our proposal based on a 3-window framework discovers all hidden regularities, called Approximate Predictive Functional Dependencies, with the aim to enable early recognition of high-risk patients during hospitalization in the Intensive Care Unit (ICU). We evaluated the different severity stages according to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines, building different pathological state patterns, from admission to the discharge from ICU. According to the clinical practice, for each patient, we examined various characteristics expressed as a temporal history of events that may predict a pathological state pattern. We evaluated our proposal exploiting the MIMIC-IV dataset, a collection of Electronic Medical Records from ICU. The obtained results showed promising possibilities to use this type of dependency to support clinical practice.

A manifesto on explainability for artificial intelligence in medicine.

The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI. This is followed by a series of desiderata for attaining explainability in AI, each of which touches upon a key domain in biomedicine.

Latent Class Trajectory Modeling of 2-Component Disease Activity Score in 28 Joints Identifies Multiple Rheumatoid Arthritis Phenotypes of Response to Biologic Disease-Modifying Antirheumatic Drugs.

OBJECTIVE: To determine whether using a reweighted disease activity score that better reflects joint synovitis, i.e., the 2-component Disease Activity Score in 28 joints (DAS28) (based on swollen joint count and C-reactive protein level), produces more clinically relevant treatment outcome trajectories compared to the standard 4-component DAS28. METHODS: Latent class mixed modeling of response to biologic treatment was applied to 2,991 rheumatoid arthritis (RA) patients in whom treatment with a biologic disease-modifying antirheumatic drug was being initiated within the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate cohort, using both 4-component and 2-component DAS28 scores as outcome measures. Patient groups with similar trajectories were compared in terms of pretreatment baseline characteristics (including disability and comorbidities) and follow-up characteristics (including antidrug antibody events, adherence to treatments, and blood drug levels). We compared the trajectories obtained using the 4- and 2-component scores to determine which characteristics were better captured by each. RESULTS: Using the 4-component DAS28, we identified 3 trajectory groups, which is consistent with previous findings. We showed that the 4-component DAS28 captures information relating to depression. Using the 2-component DAS28, 7 trajectory groups were identified; among them, distinct groups of nonresponders had a higher incidence of respiratory comorbidities and a higher proportion of antidrug antibody events. We also identified a group of patients for whom the 2-component DAS28 scores remained relatively low; this group included a high percentage of patients who were nonadherent to treatment. This highlights the utility of both the 4- and 2-component DAS28 for monitoring different components of disease activity. CONCLUSION: Here we show that the 2-component modified DAS28 defines important biologic and clinical phenotypes associated with treatment outcome in RA and characterizes important underlying response mechanisms to biologic drugs.

A Dashboard for Latent Class Trajectory Modeling: Application in Rheumatoid Arthritis.

A key trend in current medical research is a shift from a one-size-fit-all to precision treatment strategies, where the focus is on identifying narrow subgroups of the population that would benefit from a given intervention. Precision medicine will greatly benefit from accessible tools that clinicians can use to identify such subgroups, and to generate novel inferences about the patient population they are treating. We present a novel dashboard app that enables clinician users to explore patient subgroups with varying longitudinal treatment response, using latent class mixed modeling. The dashboard was developed in R Shiny. We present results of our approach applied to an observational study of patients with moderate to severe rheumatoid arthritis (RA) on first-line biologic treatment.

Protocol of a scoping review assessing injury rates and their determinants among healthcare workers in western countries.

INTRODUCTION: Healthcare workers (HCWs) are exposed to various risk factors and risky behaviours that may seriously affect their health and ability to work. The aim of this protocol is to detail the steps to follow in order to carry out a scoping review to assess the prevalence/incidence of injuries among HCWs. METHODS AND ANALYSIS: The study will be carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Protocols guidelines. Studies will be selected according to the following criteria: P (HCWs), E (exposure to injuries), C (different types of exposure and different categories of HCWs) and O (prevalence/incidence and determinants of injuries). A time filter has been set (literature between 2000 and 2018) to enable updated, direct comparison between the findings and the epidemiological data available at national and local 'Istituto Nazionale per l'Assicurazione contro gli Infortuni sul Lavoro' (National Institute for Insurance Against Accidents at Work) centres in Italy. No language restriction will be applied. ETHICS AND DISSEMINATION: Formal ethical approval is not required; primary data will not be collected, as they have already been published. The results will be disseminated through peer-reviewed publication(s), conference presentation(s) and the press.

Susceptibility to vaccine-preventable diseases and vaccination adherence among healthcare workers in Italy: A cross-sectional survey at a regional acute-care university hospital and a systematic review.

Healthcare Workers (HCWs) have an increased risk both to acquire and to spread vaccine preventable diseases (VPDs) both to their colleagues and, especially, to vulnerable patients. The prevention of occupational hazards among HCWs is based on proper adoption of the standard and additional precautions, immunizations, and secondary preventive measures, such as post-exposure prophylaxis. Moreover, HCWs are often referred to as the most trusted source of vaccine-related information for their patients. In the present article, we report the findings of a cross-sectional study investigating the compliance to vaccinations among HCWs employed at the Obstetric Unit of a regional acute-care University Hospital in Northern Italy. Furthermore, a systematic review of the literature for some VPDs (i.e., HBV, measles, rubella, varicella and influenza) was performed, over a 17-year period, in order to update the socio-demographic and professional characteristics, the susceptibility status and the vaccination rates among HCWs in Italy.