Machine Learning for Benchmarking Critical Care Outcomes.

Objectives: Enhancing critical care efficacy involves evaluating and improving system functioning. Benchmarking, a retrospective comparison of results against standards, aids risk-adjusted assessment and helps healthcare providers identify areas for improvement based on observed and predicted outcomes. The last two decades have seen the development of several models using machine learning (ML) for clinical outcome prediction.

Improving ICU Risk Predictive Models Through Automation Designed for Resiliency Against Documentation Bias.

Objectives: Electronic health records enable automated data capture for risk models but may introduce bias. We present the Philips Critical Care Outcome Prediction Model (CCOPM) focused on addressing model features sensitive to data drift to improve benchmarking ICUs on mortality performance.

Design: Retrospective, multicenter study of ICU patients randomized in 3:2 fashion into development and validation cohorts.

Evaluation of ICU Risk Models Adapted for Use as Continuous Markers of Severity of Illness Throughout the ICU Stay.

Objectives: Evaluate the accuracy of different ICU risk models repurposed as continuous markers of severity of illness.

Design: Nonintervention cohort study.

Setting: eICU Research Institute ICUs using tele-ICU software calculating continuous ICU Discharge Readiness Scores between January 2013 and March 2016.

Evaluation of the dyspneic patient in the office.

Dyspnea is a nonspecific symptom of any disease involving the respiratory system. Although diseases of the lungs, chest wall, pleura, diaphragm, upper airway, and heart are most common, diseases of many other organ systems (eg, neuromuscular, skeletal, renal, endocrine, rheumatologic, hematologic, and psychiatric) may involve the respiratory system and present with dyspnea. Dyspnea should be evaluated systematically, and a thorough history and physical examination and baseline tests of heart and lung function are necessary to establish a complete database.

Identification of polymorphisms in the human glucocorticoid receptor gene (NR3C1) in a multi-racial asthma case and control screening panel.

The glucocorticoid receptor (GR) gene (NR3C1) maps to 5q31, a region genetically linked to asthma. In this study, NR3C1 exons 1A, 1B, and exons 1C to 9 (alpha and beta) were sequenced in a screening panel of asthmatics and unaffected controls from US Caucasian, African American, US Hispanic, and Dutch Caucasian populations to identify polymorphisms for genetic association studies. Eight polymorphisms were identified in exon 1A, but none were located in putative transcription regulatory sites.