Reassessing the management of uncomplicated urinary tract infection: A retrospective analysis using machine learning causal inference.

Background: Uncomplicated urinary tract infection (UTI) is a common indication for outpatient antimicrobial therapy. National guidelines for the management of uncomplicated UTI were published by the Infectious Diseases Society of America in 2011, however it is not fully known the extent to which they align with current practices, patient diversity, and pathogen biology, all of which have evolved significantly in the time since their publication.

Objective: We aimed to re-evaluate efficacy and adverse events for first-line antibiotics (nitrofurantoin, and trimethoprim-sulfamethoxazole), versus second-line antibiotics (fluoroquinolones) and versus alternative agents (oral β-lactams) for uncomplicated UTI in contemporary clinical practice by applying machine learning algorithms to a large claims database formatted into the Observational Medical Outcomes Partnership (OMOP) common data model.

Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma.

Multiple myeloma management requires a balance between maximizing survival, minimizing adverse events to therapy, and monitoring disease progression. While previous work has proposed data-driven models for individual tasks, these approaches fail to provide a holistic view of a patient's disease state, limiting their utility to assist physician decision-making. To address this limitation, we developed a transformer-based machine learning model that jointly (1) predicts progression-free survival (PFS), overall survival (OS), and adverse events (AE), (2) forecasts key disease biomarkers, and (3) assesses the effect of different treatment strategies, e.

Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

Objective: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients.

Materials And Methods: We trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment.

Attention-based neural networks for clinical prediction modelling on electronic health records.

Background: Deep learning models have had a lot of success in various fields. However, on structured data they have struggled. Here we apply four state-of-the-art supervised deep learning models using the attention mechanism and compare against logistic regression and XGBoost using discrimination, calibration and clinical utility.

Development and characterization of a fecal-induced peritonitis model of murine sepsis: results from a multi-laboratory study and iterative modification of experimental conditions.

Background: Preclinical sepsis models have been criticized for their inability to recapitulate human sepsis and suffer from methodological shortcomings that limit external validity and reproducibility. The National Preclinical Sepsis Platform (NPSP) is a consortium of basic science researchers, veterinarians, and stakeholders in Canada undertaking standardized multi-laboratory sepsis research to increase the efficacy and efficiency of bench-to-bedside translation. In this study, we aimed to develop and characterize a 72-h fecal-induced peritonitis (FIP) model of murine sepsis conducted in two independent laboratories.

Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes.

Objectives: A novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes.

Methods: Demographic, clinical and serological data from 805 patients with SLE obtained within 15 months of diagnosis and at 3-year and 5-year follow-up were included. For each visit, sera were assessed for 29 antinuclear antibodies (ANA) immunofluorescence patterns and 20 autoantibodies.

Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis.

Multiple myeloma is a plasma cell malignancy almost always preceded by precursor conditions, but low tumor burden of these early stages has hindered the study of their molecular programs through bulk sequencing technologies. Here, we generate and analyze single cell RNA-sequencing of plasma cells from 26 patients at varying disease stages and 9 healthy donors. In silico dissection and comparison of normal and transformed plasma cells from the same bone marrow biopsy enables discovery of patient-specific transcriptional changes.

Comparison of growth features and cancer stem cell prevalence in intrahepatic and extrahepatic cholangiocarcinoma cell lines.

Aim Of The Study: Intra- and extrahepatic cholangiocarcinoma (I-CCA and E-CCA respectively) exhibit different growth features that contribute to different clinical outcomes. Cancer stem cells (CSCs) influence tumor growth and thereby may be responsible for these differences. The aim of this study was to document and compare the growth features of human I-CCA and E-CCA cell lines and determine whether any differences observed could be explained by differences in the prevalence and/or stem cell surface marker (SCSM) expression profiles of CSCs within the tumor cell lines.

Assessment of a Crisis Standards of Care Scoring System for Resource Prioritization and Estimated Excess Mortality by Race, Ethnicity, and Socially Vulnerable Area During a Regional Surge in COVID-19.

Importance: Crisis standards of care (CSOC) scores designed to allocate scarce resources during the COVID-19 pandemic could exacerbate racial disparities in health care.

Objective: To analyze the association of a CSOC scoring system with resource prioritization and estimated excess mortality by race, ethnicity, and residence in a socially vulnerable area.

Design, Setting, And Participants: This retrospective cohort analysis included adult patients in the intensive care unit during a regional COVID-19 surge from April 13 to May 22, 2020, at 6 hospitals in a health care network in greater Boston, Massachusetts.

The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It.

As the use of machine learning algorithms in health care continues to expand, there are growing concerns about equity, fairness, and bias in the ways in which machine learning models are developed and used in clinical and business decisions. We present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. We suggest mechanisms for identifying and dealing with bias and discuss challenges and opportunities to increase fairness through analytics in the health insurance industry.

Fibroblast Growth Factor 19 Induced Changes in Non-malignant Cholangiocytes.

Background And Aims: Fibroblast growth factor (FGF)19 has been implicated in the pathogenesis of murine hepatocellular carcinoma. Whether it plays a role in the development or course of human cholangiocarcinoma remains to be determined. The aim of this study was to determine whether prolonged exposure to FGF19 results in the transformation of non-malignant human cholangiocytes into cells with malignant features.

Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies.

Reinforcement learning (RL) has the potential to significantly improve clinical decision making. However, treatment policies learned via RL from observational data are sensitive to subtle choices in study design. We highlight a simple approach, trajectory inspection, to bring clinicians into an iterative design process for model-based RL studies.

Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media.

The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data.

Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer.

Purpose: Key oncology end points are not routinely encoded into electronic medical records (EMRs). We assessed whether natural language processing (NLP) can abstract treatment discontinuation rationale from unstructured EMR notes to estimate toxicity incidence and progression-free survival (PFS).

Methods: We constructed a retrospective cohort of 6,115 patients with early-stage and 701 patients with metastatic breast cancer initiating care at Memorial Sloan Kettering Cancer Center from 2008 to 2019.

Ending Restraint of Incarcerated Individuals Giving Birth.

Advocates have long suggested making shackling incarcerated people during childbirth illegal. Yet exceptions would likely still allow prison personnel to implement restraint and leave clinicians no course for freeing a patient. This article argues that clinicians' assessments of laboring individuals' clinical needs must be prioritized, ethically and legally.

Predicting Response to Tocilizumab Monotherapy in Rheumatoid Arthritis: A Real-world Data Analysis Using Machine Learning.

Objective: Tocilizumab (TCZ) has shown similar efficacy when used as monotherapy as in combination with other treatments for rheumatoid arthritis (RA) in randomized controlled trials (RCTs). We derived a remission prediction score for TCZ monotherapy (TCZm) using RCT data and performed an external validation of the prediction score using real-world data (RWD).

Methods: We identified patients in the Corrona RA registry who used TCZm (n = 452), and matched the design and patients from 4 RCTs used in previous work (n = 853).

Pulse of the Pandemic: Iterative Topic Filtering for Clinical Information Extraction from Social Media.

The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data.

Veno-venous extracorporeal membrane oxygenation allocation in the COVID-19 pandemic.

Rapid global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resultant clinical illness, coronavirus disease 2019 (COVID-19), drove the World Health Organization to declare COVID-19 a pandemic. Veno-venous Extra-Corporeal Membrane Oxygenation (VV-ECMO) is an established therapy for management of patients demonstrating the most severe forms of hypoxemic respiratory failure from COVID-19. However, features of COVID-19 pathophysiology and necessary length of treatment present distinct challenges for utilization of VV-ECMO within the current healthcare emergency.

A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection.

Antibiotic resistance is a major cause of treatment failure and leads to increased use of broad-spectrum agents, which begets further resistance. This vicious cycle is epitomized by uncomplicated urinary tract infection (UTI), which affects one in two women during their life and is associated with increasing antibiotic resistance and high rates of prescription for broad-spectrum second-line agents. To address this, we developed machine learning models to predict antibiotic susceptibility using electronic health record data and built a decision algorithm for recommending the narrowest possible antibiotic to which a specimen is susceptible.

Predicting human health from biofluid-based metabolomics using machine learning.

Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified-where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal.

Alterations in chemokine receptor CCR5 activity influence tumor cell biology in human cholangiocarcinoma cell lines.

Introduction And Objectives: Intrahepatic (I-CCA) and extrahepatic (E-CCA) cholangiocarcinoma (CCA) have different growth patterns and risks for tumor metastasis. Inhibition and/or activation of the chemokine receptor CCR subclasses have been reported to alter tumor cell biology in non-CCA cancers. In this study we documented CCR expression profiles in representative human I-CCA and E-CCA cell lines and the in vitro effects of CCR antagonists and agonists on tumor cell biology.

Enhanced gemcitabine cytotoxicity with knockdown of multidrug resistance protein genes in human cholangiocarcinoma cell lines.

Background And Aim: Cholangiocarcinoma (CCA) is an often fatal primary cancer of the liver that tends to be resistant to chemotherapy. Multidrug resistance proteins (MRPs) contribute to the chemoresistance of these tumors. The objectives of the study were to document MRP expression profiles in two representative human intrahepatic and extrahepatic CCA cells lines (HuCCT1 and KMBC, respectively) and gemcitabine-induced cytotoxicity prior to and following MRP knockdown.

Predicting Remission Among Patients With Rheumatoid Arthritis Starting Tocilizumab Monotherapy: Model Derivation and Remission Score Development.

Objective: Most patients with rheumatoid arthritis (RA) strive to consolidate their treatment from methotrexate combinations. The objective of this analysis was to identify patients with RA most likely to achieve remission with tocilizumab (TCZ) monotherapy by developing and validating a prediction model and associated remission score.

Methods: We identified four TCZ monotherapy randomized controlled trials in RA and chose two for derivation and two for internal validation.

The effects of hyperthermia on human hepatocellular carcinoma stem and mature cancer cells.

Introduction And Objectives: Hepatocellular carcinoma (HCC) can recur following radiofrequency ablation and other hyperthermic treatment modalities. Cancer stem cells (CSCs) are a subpopulation of HCC cells that are difficult to eradicate and largely responsible for tumor recurrences. Thus, the principal objective of this study was to determine whether human HCC CSCs are relatively thermal-resistant compared to non-stem or mature cancer cells (MCCs).

Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph.

Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior research has demonstrated the ability to construct such a graph from over 270,000 emergency department patient visits.