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. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.

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. RESULTS: The models achieved an area under the receiver operating characteristic curve (AUROC) of 0.756 at six hours (any-AKI) and 0.721 at 12 h (moderate-to-severe AKI) prior. Prospectively, both models had high positive predictive values (0.796 and 0.546 for any-AKI and moderate-to-severe AKI models, respectively) and triggered more in patients who developed AKI vs. those who did not (median of 1.82 [IQR 0-4.71] vs. 0 [IQR 0-0.73] and 2.35 [IQR 0.14-4.96] vs. 0 [IQR 0-0.8] triggers per 8 h for any-AKI and moderate-to-severe AKI models, respectively). CONCLUSIONS: The two AKI prediction models have good discriminative performance using common features, which can aid in accurately and informatively monitoring AKI risk in ICU patients.

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. Primary exposure was the lowest cumulative value of each component; mean, systolic, diastolic, and pulse pressure, sustained for at least 120 min during ICU stay. Primary outcome was ICU mortality and secondary outcomes were composite outcomes of acute kidney injury or death and myocardial injury or death during ICU stay. Multivariable logistic regression spline and threshold regression adjusting for potential confounders were conducted to evaluate associations between exposures and outcomes. Sensitivity analysis was conducted in 4211 patients with septic shock. RESULTS: Lower values of all blood pressures components were associated with a higher risk of ICU mortality. Estimated change-points for the risk of ICU mortality were 69 mmHg for mean, 100 mmHg for systolic, 60 mmHg for diastolic, and 57 mmHg for pulse pressure. The strength of association between blood pressure components and ICU mortality as determined by slopes of threshold regression were mean (- 0.13), systolic (- 0.11), diastolic (- 0.09), and pulse pressure (- 0.05). Equivalent non-linear associations between blood pressure components and ICU mortality were confirmed in septic shock patients. We observed a similar relationship between blood pressure components and secondary outcomes. CONCLUSION: Blood pressure component association with ICU mortality is the strongest for mean followed by systolic, diastolic, and weakest for pulse pressure. Critical care teams should continue to follow MAP-based resuscitation, though exploratory analysis focusing on blood pressure components in different sepsis phenotypes in critically ill ICU patients is needed.

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. We extracted a stratified machine learning dataset of 36,782 healthcare-associated infection patients. The model leveraged vital signs, laboratory measurements and demographics to predict HAI before clinical suspicion, defined as the order of a microbiology test or administration of antibiotics. Results: Our best performing infection risk model achieves a cross-validated AUC of 0.88 at 1 h before clinical suspicion and maintains an AUC >0.85 for 48 h before suspicion by aggregating information across demographics and a set of 163 vital signs and laboratory measurements. A second model trained on a reduced feature space comprising demographics and the 36 most frequently measured vital signs and laboratory measurements can still achieve an AUC of 0.86 at 1 h before clinical suspicion. These results compare favorably against using temperature alone and clinical rules such as the quick sequential organ failure assessment (qSOFA) score. Along with the performance results, we also provide an analysis of model interpretability via feature importance rankings. Conclusion: The predictive model aggregates information from multiple physiological parameters such as vital signs and laboratory measurements to provide a continuous risk score of infection that can be deployed in hospitals to provide advance warning of patient deterioration.

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.S. Health System from April to August of 2020, totaling 10,079 encounters. Of these, 5172 resulted in admission as an inpatient within 72 h. Inpatient encounters (n = 6079) were also considered for patients with positive COVID-19 test results, of which 970 resulted in a transfer to the ICU or in-hospital mortality. Laboratory results, vital signs, symptoms, and comorbidities were investigated for each of these care transitions. Different top risk factors were found, but two factors common to hospital admission and ICU transfer were respiratory rate and the need for oxygen support. Comorbidities common to both settings were cerebrovascular disease and congestive heart failure. Regarding laboratory results, the neutrophil-to-lymphocyte ratio was associated with transitions to higher levels of care, along with the ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT).

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. The model was externally validated on the Medical Information Mart for Intensive Care III (MIMIC III) cohort in all ICU admissions from 2001 to 2012. The predicted baseline creatinine from the model was compared with measured serum creatinine levels. We compared the performance of our model with that of the backcalculated estimated serum creatinine from the Modification of Diet in Renal Disease (MDRD) equation. RESULTS: Following ascertainment of eligibility criteria, 44,370 patients from the Mayo Clinic and 6,112 individuals from the MIMIC III cohort were enrolled. Our model used 6 features from the Mayo Clinic and MIMIC III datasets, including the presence of chronic kidney disease, weight, height, and age. Our model had significantly lower error than the MDRD backcalculation (mean absolute error [MAE] of 0.248 vs. 0.374 in the Mayo Clinic test data; MAE of 0.387 vs. 0.465 in the MIMIC III cohort) and higher correlation (intraclass correlation coefficient [ICC] of 0.559 vs. 0.050 in the Mayo Clinic test data; ICC of 0.357 vs. 0.030 in the MIMIC III cohort). DISCUSSION/CONCLUSION: Using machine learning models, baseline serum creatinine could be estimated with higher accuracy than the backcalculated estimated serum creatinine level.

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. METHODS: We used data of 98 472 adult ICU admissions at Mayo Clinic between 1 January 2005 and 31 December 2017 and 51 801 encounters from Medical Information Mart for Intensive Care III (MIMIC-III) cohort. A gradient-boosting model was trained on 80% of the Mayo Clinic cohort using a set of features to predict AKI acquired in the ICU. RESULTS: AKI was identified in 39 307 (39.9%) encounters in the Mayo Clinic cohort. Patients who developed AKI in the ICU were older and had higher ICU and in-hospital mortality compared to patients without AKI. A 30-feature model yielded an area under the receiver operating curve of 0.690 [95% confidence interval (CI) 0.682-0.697] in the Mayo Clinic cohort set and 0.656 (95% CI 0.648-0.664) in the MIMIC-III cohort. CONCLUSIONS: Using machine learning, AKI among ICU patients can be predicted using information available prior to admission. This model is independent of ICU information, making it valuable for stratifying patients at 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 (AKICr) and might underperform when predicting urine-output-triggered AKI (AKIUO). We aimed to describe how admission AKICr prediction models perform in all AKI patients. MATERIALS AND METHODS: Three types of models were trained: 1) pAKIany, predicting AKI based on creatinine or urine output, 2) pAKIUO, predicting AKI based only on urine output, and 3) pAKICr, predicting AKI based only on creatinine. We compared model performance and predictive features. RESULTS: The pAKIany models had the best overall performance (AUROC 0.673-0.716) and the most consistent performance across three patient cohorts grouped by type of AKI trigger (min AUROC of 0.636). The pAKICr models had fair performance in predicting AKICr (AUROCs 0.702-0.748) but poor performance predicting AKIUO (AUROCs 0.581-0.695). The predictive features for the pAKICr models and pAKIUO models were distinct, while top features for the pAKIany models were consistently a combination of those for the pAKICr and pAKIUO models. CONCLUSION: Ignoring urine output in the outcome during model training resulted in models that are unlikely to predict AKIUO adequately and may miss a substantial proportion of patients in practice.

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. Specifically, we hypothesize that integration of AKI guidelines into CCIS will decrease the proportion of patients with Stage 1 AKI deteriorating into higher stages of AKI. METHODS: The study was conducted in two intensive care units (ICUs) at University Hospitals Bristol, UK, in a before (control) and after (intervention) format. The intervention consisted of the AKIN guidelines and AKI care bundle which included guidance for medication usage, AKI advisory and dashboard with AKI score. Clinical data and patient outcomes were collected from all patients admitted to the units. AKI stage was calculated using the Acute Kidney Injury Network (AKIN) guidelines. Maximum AKI stage per admission, change in AKI stage and other metrics were calculated for the cohort. Adherence to eGFR-based enoxaparin dosing guidelines was evaluated as a proxy for clinician awareness of AKI. RESULTS: Each phase of the study lasted a year, and a total of 5044 admissions were included for analysis with equal numbers of patients for the control and intervention stages. The proportion of patients worsening from Stage 1 AKI decreased from 42% (control) to 33.5% (intervention), p = 0.002. The proportion of incorrect enoxaparin doses decreased from 1.72% (control) to 0.6% (intervention), p < 0.001. The prevalence of any AKI decreased from 43.1% (control) to 37.5% (intervention), p < 0.05. CONCLUSIONS: This observational study demonstrated a significant reduction in AKI progression from Stage 1 and a reduction in overall development of AKI. In addition, a reduction in incorrect enoxaparin dosing was also observed, indicating increased clinical awareness. This study demonstrates that AKI guidelines coupled with a newly designed AKI care bundle integrated into CCIS can impact patient outcomes positively.

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. Acute kidney injury stage was calculated in real time hourly based on the guidelines and using these temporal stage scores calculated for the population; the prevalence and progression of acute kidney injury stage was compared between the two units. We hypothesized that the prevalence and progression of acute kidney injury stage between the two units may be different. RESULTS: More cardiac intensive care unit patients had no acute kidney injury (stage <1) during their intensive care unit stay but more cardiac intensive care unit patients developed acute kidney injury (stage >1), compared to the General Intensive Care Unit. Both at intensive care unit admission and discharge, more General Intensive Care Unit patients had acute kidney injury; however, the number of cardiac intensive care unit patients with acute kidney injury was three times higher at discharge than admission. Acute kidney injury developed in a different pattern in the two intensive care units over five days of intensive care unit stay. In the General Intensive Care Unit, acute kidney injury was most prevalent on second day of intensive care unit stay and in cardiac intensive care unit acute kidney injury was most prevalent on the third day of intensive care unit stay. We observed the biggest increase in new acute kidney injury in the first day of General Intensive Care Unit and second day of the cardiac intensive care unit stay. CONCLUSIONS: The study demonstrates the different trends of acute kidney injury pattern in general and cardiac intensive care unit patient populations highlighting the earlier development of acute kidney injury on General Intensive Care Unit and more prevalence of acute kidney injury on discharge from cardiac intensive care unit.

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. We used random forest classification to provide continuous AKI risk score. RESULTS: We included 4572 and 1958 patients in the training and validation mutually exclusive cohorts, respectively. Acute kidney injury occurred in 1355 patients (30%) in the training cohort and 580 (30%) in the validation cohort. We incorporated known AKI risk factors and routinely measured vital characteristics and laboratory results. The model was run throughout ICU admission every 15 minutes and achieved an area under the receiver operating characteristic curve of 0.88 on validation. It was 92% sensitive and 68% specific and detected 30% of AKI cases at least 6 hours before the criterion standard time (AKI stages 1-3). For discrimination of AKI stages 2 to 3, the model had 91% sensitivity, 71% specificity, and 53% detection of AKI cases at least 6 hours before AKI onset. CONCLUSION: We developed and validated an AKI prediction model using random forest for continuous monitoring of ICU patients. This model could be used to identify high-risk patients for preventive measures or identifying patients of prospective interventional trials.

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. In this article, we describe the actual measurement frequency in patients admitted to general ward in a community hospital in Arizona, US. We present the data in the form of 2 sets of graphs. The first set of graphs are histograms which show the distribution of the number of measurements in a 24 h period for 6 different vital signs. The second set of graphs show the proportion of the patient population who had a measurement of a vital sign for each hour of the last day of patient's general ward stay. The significance of this data on predicting deterioration is discussed in Ghosh et al. (2017) [1].

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. To validate EDI, we calculated EDI scores on an additional 2418 general ward stays and compared it to the Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS). EDI was trained using the most significant variables in predicting deterioration by leveraging the knowledge from a large dataset through data mining. It was implemented electronically for continuous automatic computation. The discriminative performance of EDI, MEWS, and NEWS was calculated before deterioration using the area under the receiver operating characteristic curve (AUROC). Additionally, the performance of the 3 scores for 24h prior to deterioration were computed. EDI was a better discriminator of deterioration than MEWS or NEWS; AUROC values for the validation dataset were: EDI - 0.7655, NEWS - 0.6569, MEWS - 0.6487. EDI also identified more patients likely to deteriorate for the same specificity as NEWS or MEWS. EDI had the best performance among the 3 scores for the last 24h of the patient stay. CONCLUSION: EDI detects more deteriorations for the same specificity as the other two scores. Our results show that EDI performs better at predicting deterioration than commonly used NEWS and MEWS.

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. The recognition that all left ventricles (LV) initiate filling by serving as mechanical suction pumps allows global diastolic function to be assessed based on laws of motion that apply to all chambers. What differentiates one heart from another are the parameters of the equation of motion that governs filling. Accordingly, development of the Parametrized Diastolic Filling (PDF) formalism has shown that the entire range of clinically observed early transmitral flow (Doppler E-wave) patterns are extremely well fit by the laws of damped oscillatory motion. This permits analysis of individual E-waves in accordance with a causal mechanism (recoil-initiated suction) that yields three (numerically) unique lumped parameters whose physiologic analogues are chamber stiffness (k), viscoelasticity/relaxation (c), and load (xo). The recording of transmitral flow (Doppler E-waves) is standard practice in clinical cardiology and, therefore, the echocardiographic recording method is only briefly reviewed. Our focus is on determination of the PDF parameters from routinely recorded E-wave data. As the highlighted results indicate, once the PDF parameters have been obtained from a suitable number of load varying E-waves, the investigator is free to use the parameters or construct indexes from the parameters (such as stored energy 1/2kxo(2), maximum A-V pressure gradient kxo, load independent index of diastolic function, etc.) and select the aspect of physiology or pathophysiology to be quantified.

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. We propose that the recirculating diastolic intraventricular vortex ring generates L-waves and based on this mechanism, we predict the presence of L-waves in the right ventricle (RV). We imaged intraventricular flow using Doppler echocardiography and phase-contrast magnetic resonance imaging (PC-MRI) in 10 healthy volunteers. L-waves were recorded in all subjects, with highest velocities measured typically 2 cm below the annulus. Fifty-five percent of cardiac cycles (189 of 345) had L-waves. Color M-mode images eliminated mid-diastolic transmitral flow as the cause of the observed L-waves. Three-dimensional intraventricular flow patterns were imaged via PC-MRI and independently validated our hypothesis. Additionally as predicted, L-waves were observed in the RV, by both echocardiography and PC-MRI. The re-entry of the E-wave-generated vortex ring flow through a suitably located echo sample volume can be imaged as the L-wave. These waves are a general feature and a direct consequence of LV and RV diastolic fluid mechanics.

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. Hence, insight into load-invariant systolic and diastolic chamber properties and their coupling to load can be obtained. To demonstrate utility, high-fidelity P(t) data from 14 subjects (4234 beats) was analyzed. PNR, the nPPP (dimensionless) pressure, where -dP/dtpeak occurs, was 0.61 and had limited variance (7%). The relative load independence of PNR was corroborated by comparison of PPP and nPPP features of normal sinus rhythm (NSR) and (ejecting and nonejecting) premature ventricular contraction (PVC) beats. PVCs had lower P(t)max and lower peak negative and positive dP(t)/dt values versus NSR beats. In the nPPP, +dP/dtpeak occurred at higher (dimensionless) P in PVC beats than in regular beats (0.44 in NSR vs. 0.48 in PVC). However, PNR for PVC versus NSR remained unaltered (PNR = 0.64; P > 0.05). Possible mechanistic explanation includes a (near) load-independent (constant) ratio of maximum cross-bridge uncoupling rate to instantaneous wall stress. Hence, nPPP analysis reveals LV properties obscured by load and by conventional temporal P(t) and dP(t)/dt analysis. nPPP identifies chamber properties deserving molecular and cellular physiologic explanation.

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. METHODS AND RESULTS: Thirteen patients with treated hypertension and compensated HFPEF consumed the DASH/SRD (target sodium, 50 mmol/2100 kcal) for 21 days. We measured baseline and post-DASH/SRD brachial and central blood pressure (via radial arterial tonometry) and cardiovascular function with echocardiographic measures (all previously invasively validated). Diastolic function was quantified via the parametrized diastolic filling formalism that yields relaxation/viscoelastic (c) and passive/stiffness (k) constants through the analysis of Doppler mitral inflow velocity (E-wave) contours. Effective arterial elastance (Ea) end-systolic elastance (Ees) and ventricular-arterial coupling (defined as the ratio Ees:Ea) were determined using previously published techniques. Wilcoxon matched-pairs signed-rank tests were used for pre-post comparisons. The DASH/SRD reduced clinic and 24-hour brachial systolic pressure (155 ± 35 to 138 ± 30 and 130 ± 16 to 123 ± 18 mm Hg; both P=0.02), and central end-systolic pressure trended lower (116 ± 18 to 111 ± 16 mm Hg; P=0.12). In conjunction, diastolic function improved (c=24.3 ± 5.3 to 22.7 ± 8.1 g/s; P=0.03; k=252 ± 115 to 170 ± 37 g/s(2); P=0.03), Ea decreased (2.0 ± 0.4 to 1.7 ± 0.4 mm Hg/mL; P=0.007), and ventricular-arterial coupling improved (Ees:Ea=1.5 ± 0.3 to 1.7 ± 0.4; P=0.04). CONCLUSIONS: In patients with hypertensive HFPEF, the sodium-restricted DASH diet was associated with favorable changes in ventricular diastolic function, arterial elastance, and ventricular-arterial coupling. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00939640.

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). Simultaneously, the endocardial (transverse or short axis) dimension also increases while epicardial (transverse) external dimension remains essentially constant. To quantify these directional filling attributes, we compute longitudinal (Z(L)) and transverse (Z(T)) impedances during early rapid-filling (Doppler E-wave). Z(L) and Z(T) were calculated from 578 cardiac cycles of simultaneous transmitral flow and high fidelity LV pressure data in 17 subjects with normal LV function. Average Z(L) was 0.7 ± 0.4 mmHg s/cm(4) and average Z(T) was 238 ± 316 mmHg s/cm(2). Direct comparison, in the same units is achieved by computing Z(T) over the ≈10 cm(2) cross-sectional area of LV (denoted ZT) revealing that Z(L) is ≈34 times smaller than Z(T). This quantifies the physiologic preference for longitudinal LV volume accommodation. Lowest Z(L) and Z(T) values occurred in the first harmonic with monotonically increasing values with higher harmonics. We conclude that Z(L) and Z(T) characterize longitudinal and transverse chamber properties of DF and therefore, diastolic dysfunction can be viewed as a state of impedance mismatch.

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. An alternative validated expression, VFTkinematic reexpresses VFTstandard by incorporating left heart, near "constant-volume pump" physiology thereby revealing VFTkinematic's explicit dependence on maximum rate of longitudinal chamber expansion (E'). In this work, we show that VFTkinematic can differentiate between hearts having indistinguishable E-wave patterns, such as pseudonormal (PN; 0.75 < E/A < 1.5 and E/E' > 8) versus normal. Thirteen age-matched normal and 12 PN data sets (738 total cardiac cycles), all having normal LVEF, were selected from our Cardiovascular Biophysics Laboratory database. Doppler E-, lateral annular E'-waves, and M-mode data (mitral leaflet separation, chamber dimension) was used to compute VFTstandard and VFTkinematic. VFTstandard did not differentiate between groups (normal [3.58 ± 1.06] vs. PN [4.18 ± 0.79], P = 0.13). In comparison, VFTkinematic for normal (3.15 ± 1.28) versus PN (4.75 ± 1.35) yielded P = 0.006. Hence, the applicability of VFTkinematic for diastolic function quantitation has been broadened to include analysis of PN filling patterns in age-matched groups.

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. All three models (exponential, logistic, and kinematic) characterize global relaxation in terms of pressure decay rates. However, IVR is inhomogeneous and anisotropic. Apical and basal LV wall segments untwist at different times and rates, and transmural strain and strain rates differ due to the helically variable pitch of myocytes and sheets. Accordingly, we hypothesized that the exponential model (τ) or kinematic model (µ and E(k)) parameters will elucidate the spatiotemporal variation of IVR rate. Left ventricular pressures in 20 subjects were recorded using a high-fidelity, multipressure transducer (3 cm apart) catheter. Simultaneous, dual-channel pressure data was plotted in the pressure phase-plane (dP/dt vs. P) and τ, µ, and E(k) were computed in 1631 beats (average: 82 beats per subject). Tau differed significantly between the two channels (P < 0.05) in 16 of 20 subjects, whereas µ and E(k) differed significantly (P < 0.05) in all 20 subjects. These results show that quantifying the relaxation rate from data recorded at a single location has limitations. Moreover, kinematic model based analysis allows characterization of restoring (recoil) forces and resistive (crossbridge uncoupling) forces during IVR and their spatio-temporal dependence, thereby elucidating the relative roles of stiffness vs. relaxation as IVR rate determinants.