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.

Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup.

Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes.

Association of systolic, diastolic, mean, and pulse pressure with morbidity and mortality in septic ICU patients: a nationwide observational study.

Background: Intensivists target different blood pressure component values to manage intensive care unit (ICU) patients with sepsis. We aimed to evaluate the relationship between individual blood pressure components and organ dysfunction in critically ill septic patients.

Methods: In this retrospective observational study, we evaluated 77,328 septic patients in 364 ICUs in the eICU Research Institute database.

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.

A Retrospective Cohort Study of Clinical Factors Associated with Transitions of Care among COVID-19 Patients.

Coronavirus Disease 2019 (COVID-19) is an international health crisis. In this article, we report on patient characteristics associated with care transitions of: 1) hospital admission from the emergency department (ED) and 2) escalation to the intensive care unit (ICU). Analysis of data from the electronic medical record (EMR) was performed for patients with COVID-19 seen in the ED of a large Western U.

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.

Impact of a computerized decision support tool deployed in two intensive care units on acute kidney injury progression and guideline compliance: a prospective observational study.

Background: Acute kidney injury (AKI) affects a large proportion of the critically ill and is associated with worse patient outcomes. Early identification of AKI can lead to earlier initiation of supportive therapy and better management. In this study, we evaluate the impact of computerized AKI decision support tool integrated with the critical care clinical information system (CCIS) on patient outcomes.

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.

Quantification of global diastolic function by kinematic modeling-based analysis of transmitral flow via the parametrized diastolic filling formalism.

Quantitative cardiac function assessment remains a challenge for physiologists and clinicians. Although historically invasive methods have comprised the only means available, the development of noninvasive imaging modalities (echocardiography, MRI, CT) having high temporal and spatial resolution provide a new window for quantitative diastolic function assessment. Echocardiography is the agreed upon standard for diastolic function assessment, but indexes in current clinical use merely utilize selected features of chamber dimension (M-mode) or blood/tissue motion (Doppler) waveforms without incorporating the physiologic causal determinants of the motion itself.

E-wave generated intraventricular diastolic vortex to L-wave relation: model-based prediction with in vivo validation.

The Doppler echocardiographic E-wave is generated when the left ventricle's suction pump attribute initiates transmitral flow. In some subjects E-waves are accompanied by L-waves, the occurrence of which has been correlated with diastolic dysfunction. The mechanisms for L-wave generation have not been fully elucidated.

The vortex formation time to diastolic function relation: assessment of pseudonormalized versus normal filling.

In early diastole, the suction pump feature of the left ventricle opens the mitral valve and aspirates atrial blood. The ventricle fills via a blunt profiled cylindrical jet of blood that forms an asymmetric toroidal vortex ring inside the ventricle whose growth has been quantified by the standard (dimensionless) expression for vortex formation time, VFTstandard = {transmitral velocity time integral}/{mitral orifice diameter}. It can differentiate between hearts having distinguishable early transmitral (Doppler E-wave) filling patterns.

The quest for load-independent left ventricular chamber properties: Exploring the normalized pressure phase plane.

The pressure phase plane (PPP), defined by dP(t)/dt versus P(t) coordinates has revealed novel physiologic relationships not readily obtainable from conventional, time domain analysis of left ventricular pressure (LVP). We extend the methodology by introducing the normalized pressure phase plane (nPPP), defined by 0 ≤ P ≤ 1 and -1 ≤ dP/dt ≤ +1. Normalization eliminates load-dependent effects facilitating comparison of conserved features of nPPP loops.

Low-sodium DASH diet improves diastolic function and ventricular-arterial coupling in hypertensive heart failure with preserved ejection fraction.

Background: Heart failure with preserved ejection fraction (HFPEF) involves failure of cardiovascular reserve in multiple domains. In HFPEF animal models, dietary sodium restriction improves ventricular and vascular stiffness and function. We hypothesized that the sodium-restricted dietary approaches to stop hypertension diet (DASH/SRD) would improve left ventricular diastolic function, arterial elastance, and ventricular-arterial coupling in hypertensive HFPEF.

Early left ventricular diastolic function quantitation using directional impedances.

Impedance has been used in vascular biology to characterize the frequency dependent opposition the circulatory system presents to blood flow in response to a pulsatile pressure gradient. It has also been used to characterize diastolic function (DF) via the early, diastolic left ventricular (LV) pressure-flow relation. In a normal LV, early filling volume is accommodated primarily by wall-thinning and ascent of the mitral annulus relative to the spatially fixed apex (longitudinal chamber expansion).

Quantitative assessment of left ventricular diastolic function via longitudinal and transverse flow impedances.

Flow impedance has been used to characterize the physical properties of the vascular system by assessing its phasic flow response to pulsatile pressure input in terms of resistance as a function of frequency. Impedance has also been used to characterize global diastolic left ventricular (LV) chamber properties. In early diastole the LV is a mechanical suction pump and accommodates filling by simultaneously expanding in two principal spatial directions: longitudinal (base-to-apex, long-axis) and transverse (radial, short-axis).

Spatio-temporal attributes of left ventricular pressure decay rate during isovolumic relaxation.

Global left ventricular (LV) isovolumic relaxation rate has been characterized: 1) via the time constant of isovolumic relaxation τ or 2) via the logistic time constant τ(L). An alternate kinematic method, characterizes isovolumic relaxation (IVR) in accordance with Newton's Second Law. The model's parameters, stiffness E(k), and damping/relaxation μ result from best fit of model-predicted pressure to in vivo data.

Vortex formation time-to-left ventricular early rapid filling relation: model-based prediction with echocardiographic validation.

During early rapid filling, blood aspirated by the left ventricle (LV) generates an asymmetric toroidal vortex whose development has been quantified using vortex formation time (VFT), a dimensionless index defined by the length-to-diameter ratio of the aspirated (equivalent cylindrical) fluid column. Since LV wall motion generates the atrioventricular pressure gradient resulting in the early transmitral flow (Doppler E-wave) and associated vortex formation, we hypothesized that the causal relation between VFT and diastolic function (DF), parametrized by stiffness, relaxation, and load, can be elucidated via kinematic modeling. Gharib et al.

Determination of early diastolic LV vortex formation time (T*) via the PDF formalism: a kinematic model of filling.

The filling (diastolic) function of the human left ventricle is most commonly assessed by echocardiography, a non-invasive imaging modality. To quantify diastolic function (DF) empiric indices are obtained from the features (height, duration, area) of transmitral flow velocity contour, obtained by echocardiography. The parameterized diastolic filling (PDF) formalism is a kinematic model developed by Kovács et.