Machine learning-based clinical decision support for infection risk prediction.

Background: Healthcare-associated infection (HAI) remains a significant risk for hospitalized patients and a challenging burden for the healthcare system. This study presents a clinical decision support tool that can be used in clinical workflows to proactively engage secondary assessments of pre-symptomatic and at-risk infection patients, thereby enabling earlier diagnosis and treatment.

Methods: This study applies machine learning, specifically ensemble-based boosted decision trees, on large retrospective hospital datasets to develop an infection risk score that predicts infection before obvious symptoms present.

Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19.

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed.

Early prediction of hemodynamic interventions in the intensive care unit using machine learning.

Background: Timely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible hemodynamic instability occurring at the bedside and to prompt assessment for potential hemodynamic interventions.

Methods: We used an ensemble of decision trees to obtain a real-time risk score that predicts the initiation of hemodynamic interventions an hour into the future.

A rapidly deployable individualized system for augmenting ventilator capacity.

Strategies to split ventilators to support multiple patients requiring ventilatory support have been proposed and used in emergency cases in which shortages of ventilators cannot otherwise be remedied by production or procurement strategies. However, the current approaches to ventilator sharing lack the ability to individualize ventilation to each patient, measure pulmonary mechanics, and accommodate rebalancing of the airflow when one patient improves or deteriorates, posing safety concerns to patients. Potential cross-contamination, lack of alarms, insufficient monitoring, and inability to adapt to sudden changes in patient status have prevented widespread acceptance of ventilator sharing.

A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit.

Background: Early recognition and timely intervention are critical steps for the successful management of shock. The objective of this study was to develop a model to predict requirement for hemodynamic intervention in the pediatric intensive care unit (PICU); thus, clinicians can direct their care to patients likely to benefit from interventions to prevent further deterioration.

Methods: The model proposed in this study was trained on a retrospective cohort of all patients admitted to a tertiary PICU at a single center in the United States, and validated on another retrospective cohort of all patients admitted to the PICU at a single center in the United Kingdom.

A practical algorithm to reduce false critical ECG alarms using arterial blood pressure and/or photoplethysmogram waveforms.

There has been a high rate of false alarms for the critical electrocardiogram (ECG) arrhythmia events in intensive care units (ICUs), from which the 'crying-wolf' syndrome may be resulted and patient safety may be jeopardized. This article presents an algorithm to reduce false critical arrhythmia alarms using arterial blood pressure (ABP) and/or photoplethysmogram (PPG) waveform features. We established long duration reference alarm datasets which consist of 573 ICU waveform-alarm records (283 for development set and 290 for test set) with total length of 551 patent days.

Estimation of the patient monitor alarm rate for a quantitative analysis of new alarm settings.

In many critical care units, default patient monitor alarm settings are not fine-tuned to the vital signs of the patient population. As a consequence there are many alarms. A large fraction of the alarms are not clinically actionable, thus contributing to alarm fatigue.

Predicting respiratory instability in the ICU.

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) contribute to the morbidity and mortality of intensive care patients worldwide, and have large associated human and financial costs. We identified a reference data set of 624 mechanically-ventilated patients in the MIMIC-II intensive care database with and without low PaO(2)/FiO(2) ratios (termed respiratory instability), and developed prediction algorithms for distinguishing these patients prior to the critical event. In the end, we had four rule sets using mean airway pressure, plateau pressure, total respiratory rate and oxygen saturation (SpO(2)), where the specificity/sensitivity rates were either 80%/60% or 90%/50%.

Development and evaluation of predictive alerts for hemodynamic instability in ICU patients.

This paper describes an algorithm for identifying ICU patients that are likely to become hemodynamically unstable. The algorithm consists of a set of rules that trigger alerts. Unlike most existing ICU alert mechanisms, it uses data from multiple sources and is often able to identify unstable patients earlier and with more accuracy than alerts based on a single threshold.

Prioritizing the organization and management of intensive care services in the United States: the PrOMIS Conference.

Objective: Adult critical care services are a large, expensive part of U.S. health care.

CIS: where are we going and what should we demand from industry?

Clinical information systems designed for use in the critical care setting have been available for many years. Yet, despite significant evidence that these systems contribute to patient safety and efficiency of care, they have not achieved widespread use. This paper examines some of the factors responsible for the slow growth in use of clinical information systems in the intensive care unit.

Frequency of laboratory test utilization in the intensive care unit and its implications for large-scale data collection efforts.

Objective: Mapping local use names to standardized nomenclatures such as LOINC (Logical Observation Identifiers Names and Codes) is a time-consuming task when done retrospectively or during the configuration of new information systems. The author sought to identify a subset of intensive care unit (ICU) laboratory tests, which, because of their frequency of use, should be the focus of efforts to standardize test names in ICU information systems.

Design: The author reviewed the ordering practices in medical, surgical, and pediatric ICUs within a large university teaching hospital to identify the subset of laboratory tests that represented the majority of tests performed in these settings.

Frequency of laboratory test utilization in the intensive care unit and its implications for large scale data collection efforts.

Mapping of local use names to standardized naming schemas such as LOINC" micro is a time consuming and difficult task when done retrospectively or during the configuration of new information systems. We found that a relatively small number of tests and profiles (106 to 205) represent 99% of all testing done in 3 ICUs studied. In addition, all of the lab studies needed for the most commonly used ICU scoring systems fell into the top 23 lab studies and profiles performed in each ICU studied.