Accurate and interpretable prediction of ICU-acquired AKI.

Purpose: We developed and validated two parsimonious algorithms to predict the time of diagnosis of any stage of acute kidney injury (any-AKI) or moderate-to-severe AKI in clinically actionable prediction windows.

Materials And Methods: In this retrospective single-center cohort of adult ICU admissions, we trained two gradient-boosting models: 1) any-AKI model, predicting the risk of any-AKI at least 6 h before diagnosis (50,342 admissions), and 2) moderate-to-severe AKI model, predicting the risk of moderate-to-severe AKI at least 12 h before diagnosis (39,087 admissions). Performance was assessed before disease diagnosis and validated prospectively.

Estimation of Baseline Serum Creatinine with Machine Learning.

Introduction: Comparing current to baseline serum creatinine is important in detecting acute kidney injury. In this study, we report a regression-based machine learning model to predict baseline serum creatinine.

Methods: We developed and internally validated a gradient boosting model on patients admitted in Mayo Clinic intensive care units from 2005 to 2017 to predict baseline creatinine.

Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning.

Background: Acute kidney injury (AKI) carries a poor prognosis. Its incidence is increasing in the intensive care unit (ICU). Our purpose in this study is to develop and externally validate a model for predicting AKI in the ICU using patient data present prior to ICU admission.

Including urinary output to define AKI enhances the performance of machine learning models to predict AKI at admission.

Purpose: Acute kidney injury (AKI) is a prevalent and detrimental condition in intensive care unit patients. Most AKI predictive models only predict creatinine-triggered AKI (AKI) and might underperform when predicting urine-output-triggered AKI (AKI). We aimed to describe how admission AKI prediction models perform in all AKI patients.

Descriptive study of differences in acute kidney injury progression patterns in General and Cardiac Intensive Care Units.

Background: Acute kidney injury is common in critically ill patients with detrimental effects on mortality, length of stay and post-discharge outcomes. The Acute Kidney Injury Network developed guidelines based on urine output and serum creatinine to classify patients into stages of acute kidney injury.

Methods: In this analysis we utilize the Acute Kidney Injury Network guidelines to evaluate the acute kidney injury stage in patients admitted to general and cardiac intensive care units over a period of 18 months.

Automated Continuous Acute Kidney Injury Prediction and Surveillance: A Random Forest Model.

Objective: To develop and validate a prediction model of acute kidney injury (AKI) of any severity that could be used for AKI surveillance and management to improve clinical outcomes.

Patients And Methods: This retrospective cohort study was conducted in medical, surgical, and mixed intensive care units (ICUs) at Mayo Clinic in Rochester, Minnesota, including adult (≥18 years of age) ICU-unique patients admitted between October 1, 2004, and April 30, 2011. Our primary objective was prediction of AKI using extant clinical data following ICU admission.

Description of vital signs data measurement frequency in a medical/surgical unit at a community hospital in United States.

Vitals signs are measured at scheduled intervals by nurses in typical general wards. Vital signs may be measured more frequently if the patient condition deteriorates. In many units, the vital signs measurement frequency for some patients is different from the scheduled frequency due to various reasons such as staffing, patient acuity etc.

Early Deterioration Indicator: Data-driven approach to detecting deterioration in general ward.

Introduction: Early detection of deterioration could facilitate more timely interventions which are instrumental in reducing transfer to higher levels of care such as Intensive Care Unit (ICU) and mortality [1,2].

Methods And Results: We developed the Early Deterioration Indicator (EDI) which uses log likelihood risk of vital signs to calculate continuous risk scores. EDI was developed using data from 11,864 general ward admissions.

Heuristics to determine ventilation times of ICU patients from the MIMIC-II database.

Mechanical ventilation is an important life support tool for patients in intensive care units (ICU). For various research purposes related to patient hemodynamic and cardiopulmonary monitoring, it is important to know when a patient is on a ventilator. Unfortunately, the widely used MIMIC-II database contains results from user charted data, where the user did not always store ventilation on and off times explicitly and accurately.

Predicting ICU hemodynamic instability using continuous multiparameter trends.

Background: Identifying hemodynamically unstable patients in a timely fashion in intensive care units (ICUs) is crucial because it can lead to earlier interventions and thus to potentially better patient outcomes. Current alert algorithms are typically limited to detecting dangerous conditions only after they have occurred and suffer from high false alert rates. Our objective was to predict hemodynamic instability at least two hours before a major clinical intervention (e.

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.

Morphograms: exploiting correlation patterns to efficiently identify clinically significant events in intensive care units.

In this paper, we present a simple technique that utilizes the cross correlations between ECG signals and an arterial blood pressure (ABP) signal for the purpose of assessing signal quality and detecting artifacts in the ABP signal. The technique was tested using cases from a physician-annotated patient monitoring signal database from Beth Israel/Harvard-MIT University data bank. The results were encouraging: 45% of the manually annotated artifacts were correctly classified and 98% of the manually annotated true events were correctly classified.