Effect of Common Medications on Longitudinal Pectoralis Muscle Area in Smokers.

Background: Cigarette smoke contributes to skeletal muscle wasting. While exercise and nutritional therapies are effective in improving skeletal muscle quantity and quality, the effect of medications on longitudinal muscle loss is unclear. We investigated whether long-term use of common medications affects longitudinal skeletal muscle changes in current and former smokers.

Factors related to depressive symptoms during the second year of COVID-19 pandemic in Brazil: A cross-sectional study with adolescents.

Purpose: This study aimed to identify factors associated with depressive symptoms in adolescents during the COVID-19 pandemic in Brazil.

Design And Methods: A cross-sectional study was conducted in 2021 with a sample of 1303 students (50.96% females; mean age = 16.

Proteomics and machine learning in the prediction and explanation of low pectoralis muscle area.

Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual's risk for developing low muscle mass using proteomics and machine learning. We identified eight biomarkers associated with low pectoralis muscle area (PMA).

Interdisciplinary Delphi study by PROSEC North America: Recommendations on single indirect restorations made from ceramic and nonmetallic biomaterials for posterior teeth.

Objective: This article puts forward consensus recommendations from PROSEC North America regarding single indirect restorations made from ceramic and nonmetallic biomaterials in posterior teeth.

Overview: The consensus process involved a multidisciplinary panel and three consensus workshops. A systematic literature review was conducted across five databases to gather evidence.

Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records.

Background: Risk prediction plays a crucial role in planning for prevention, monitoring, and treatment. Electronic Health Records (EHRs) offer an expansive repository of temporal medical data encompassing both risk factors and outcome indicators essential for effective risk prediction. However, challenges emerge due to the lack of readily available gold-standard outcomes and the complex effects of various risk factors.

Family history as the strongest predictor of aortic and peripheral aneurysms in patients with intracranial aneurysms.

Objective: Intracranial aneurysms (IA) and aortic aneurysms (AA) are both abnormal dilations of arteries with familial predisposition and have been proposed to share co-prevalence and pathophysiology. Associations of IA and non-aortic peripheral aneurysms are less well-studied. The goal of the study was to understand the patterns of aortic and peripheral (extracranial) aneurysms in patients with IA, and risk factors associated with the development of these aneurysms.

Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.

Objective: Postpartum depression (PPD) represents a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. Thus, we aimed to develop and estimate the performance of a generalizable risk stratification model for PPD in patients without a history of depression using information collected as part of routine clinical care.

Validation of an ICD-Code-Based Case Definition for Psychotic Illness Across Three Health Systems.

Background And Hypothesis: Psychosis-associated diagnostic codes are increasingly being utilized as case definitions for electronic health record (EHR)-based algorithms to predict and detect psychosis. However, data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis.

Association of Acute Respiratory Disease Events with Quantitative Interstitial Abnormality Progression at CT in Individuals with a History of Smoking.

Background Acute respiratory disease (ARD) events are often thought to be airway-disease related, but some may be related to quantitative interstitial abnormalities (QIAs), which are subtle parenchymal abnormalities on CT scans associated with morbidity and mortality in individuals with a smoking history. Purpose To determine whether QIA progression at CT is associated with ARD and severe ARD events in individuals with a history of smoking. Materials and Methods This secondary analysis of a prospective study included individuals with a 10 pack-years or greater smoking history recruited from multiple centers between November 2007 and July 2017.

Proteomics and Machine Learning in the Prediction and Explanation of Low Pectoralis Muscle Area.

Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual's risk for developing low muscle mass using proteomics and machine learning. We identified 8 biomarkers associated with low pectoralis muscle area (PMA).

Utility of peripheral protein biomarkers for the prediction of incident interstitial features: a multicentre retrospective cohort study.

Introduction/rationale: Protein biomarkers may help enable the prediction of incident interstitial features on chest CT.

Methods: We identified which protein biomarkers in a cohort of smokers (COPDGene) differed between those with and without objectively measured interstitial features at baseline using a univariate screen (t-test false discovery rate, FDR p<0.001), and which of those were associated with interstitial features longitudinally (multivariable mixed effects model FDR p<0.

Validation of an ICD-code-based case definition for psychotic illness across three health systems.

Background And Hypothesis: Early detection of psychosis is critical for improving outcomes. Algorithms to predict or detect psychosis using electronic health record (EHR) data depend on the validity of the case definitions used, typically based on diagnostic codes. Data on the validity of psychosis-related diagnostic codes is limited.

HistoriView: Implementation and Evaluation of a Novel Approach to Review a Patient Using a Scalable Space-Efficient Timeline without Zoom Interactions.

Background:  Timelines have been used for patient review. While maintaining a compact overview is important, merged event representations caused by the intricate and voluminous patient data bring event recognition, access ambiguity, and inefficient interaction problems. Handling large patient data efficiently is another challenge.

Fostering Digital Health in Universities: An Experience of the First Junior Scientific Committee of the Brazilian Congress of Health Informatics.

Objectives: Digital health (DH) is a revolution driven by digital technologies to improve health. Despite the importance of DH, curricular updates in healthcare university programs are scarce, and DH remains undervalued. Therefore, this report describes the first Junior Scientific Committee (JSC) focusing on DH at a nationwide congress, with the aim of affirming its importance for promoting DH in universities.

Offspring cardiometabolic outcomes and postnatal growth trajectories after exposure to maternal SARS-CoV-2 infection.

Objective: The objective of this study is to determine whether in utero exposure to SARS-CoV-2 is associated with increased risk for a cardiometabolic diagnosis by 18 months of age.

Methods: This retrospective electronic health record (EHR)-based cohort study included the live-born offspring of all individuals who delivered during the COVID-19 pandemic (April 1, 2020-December 31, 2021) at eight hospitals in Massachusetts. Offspring exposure was defined as a positive maternal SARS-CoV-2 polymerase chain reaction test during pregnancy.

LATTE: Label-efficient incident phenotyping from longitudinal electronic health records.

Electronic health record (EHR) data are increasingly used to support real-world evidence studies but are limited by the lack of precise timings of clinical events. Here, we propose a label-efficient incident phenotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data. By leveraging the pre-trained semantic embeddings, LATTE selects predictive features and compresses their information into longitudinal visit embeddings through visit attention learning.

Self-rated health among adolescents from vulnerable areas and their sociodemographic, lifestyle and contextual factors: A multilevel analysis.

Purpose: Understanding self-rated health in young people can help orient global health actions, especially in regions of social vulnerability. The present study analysed individual and contextual factors associated with self-rated health in a sample of Brazilian adolescents.

Design And Methods: Cross-sectional data from 1272 adolescents (aged 11-17; 48.

Sex-Specific Neurodevelopmental Outcomes Among Offspring of Mothers With SARS-CoV-2 Infection During Pregnancy.

Importance: Prior studies using large registries have suggested a modest increase in risk for neurodevelopmental diagnoses among children of mothers with immune activation during pregnancy, and such risk may be sex-specific.

Objective: To determine whether in utero exposure to SARS-CoV-2 is associated with sex-specific risk for neurodevelopmental disorders up to 18 months after birth, compared with unexposed offspring born during or prior to the COVID-19 pandemic period.

Design, Setting, And Participants: This retrospective cohort study included the live offspring of all mothers who delivered between January 1 and December 31, 2018 (born and followed up before the COVID-19 pandemic), between March 1 and December 31, 2019 (born before and followed up during the COVID-19 pandemic), and between March 1, 2020, and May 31, 2021 (born and followed up during the COVID-19 pandemic).

Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.

The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use.