scholarly journals Evolution of cardiac function in COVID 19 patients in the intensive care unit: insights from machine learning

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
P Marti Castellote ◽  
F Loncaric ◽  
M Nogueira ◽  
M Sitges ◽  
B Stessel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pulmonary Hypertension ◽  
Intensive Care ◽  
Cardiac Function ◽  
Diastolic Dysfunction ◽  
Diastolic Function ◽  
Clinical Data ◽  
Cardiac Cycle ◽  
Doppler Velocity ◽  
Output Space

Abstract Funding Acknowledgements Type of funding sources: None. Background Repeated echocardiographic assessment of cardiac function is integral in management of intensive care units (ICU) patients. Machine learning (ML) can assist by integrating whole-cardiac cycle echo data derived from flow assessment and deformation imaging, and grouping patients on the basis of patterns of cardiac dysfunction and its evolution over time. Cardiac involvement has been suggested to be important in COVID-19 outcome and echo evaluation can inform on cardiac status. We use unsupervised ML to investigate and integrate longitudinal data from the COVID-HO study (NCT04371679) to determine the potential of tracking changes in cardiac function during ICU hospitalization.  Methods In a single-centre, COVID-19 patients (n = 38) were prospectively followed with echocardiography as part of ICU management. The endpoint was defined as death or ICU discharge. LV myocardial deformation, as well as aortic, mitral and pulmonary artery blood-pool Doppler velocity profiles were used as input for ML. Clinical data was used to validate the ML derived phenotypes. Echo data from the initial and final echo examination were used to create an output space where participants were positioned based on cardiac function blinded to outcome status. Regression was used to estimate the echo and clinical characteristics of different regions in the space. Patient trajectories in the output space were investigated for each patient. Results Endpoint was not reached in 24% (n = 9) at the time of analysis. The cohort was 68% male, aged 65 ± 12 years, and with an ICU mortality 21% (n = 8). The median spent in ICU was 10 (IQR 7-18) days. The ML analysis demonstrated a heterogeneous output space (Fig 1A) we could define a gradual change in the shape of the pulmonary outflow velocity profile, from a normal  towards pulmonary hypertension (Fig 1A, x axis). Jointly with differences in diastolic function (mitral inflow fusion and A wave accentuation) defined two regions: with signs of pulmonary hypertension (gray); and with normal pulmonary pressures but LV diastolic dysfunction (yellow). Investigation of patient trajectories (Fig 1B) demonstrated the feasibility of tracking changes during ICU hospitalization, showing a shift of a patient that died in the ICU, from initial diastolic dysfunction towards pulmonary hypertension (red), and a patient shifting from a region with normal diastolic function towards pulmonary hypertension, but with a positive outcome (blue). Echo data concurs with observed dynamics (Fig 1C and 1D). Conclusion ML can integrate complex, whole-cardiac cycle echo data to group heterogeneous patients based on similarity of cardiac function. Patient trajectories across the output space demonstrate the feasibility of ML for echo data-based follow-up of patients during ICU hospitalization. Further echo and clinical data integration can improve characterisation of the output space regions and better define changes in cardiac function during hospitalization. Abstract Figure 1

Abstract P428: A New Mouse Model Of Myocardial Lipids, Fibrosis, Arrhythmia And Diastolic Dysfunction Induced By Hyperlipidemia And Cardiac LDLR

2021 ◽  
Vol 129 (Suppl_1) ◽  
Author(s):  
Monique Williams ◽  
Camila Iansen Irion ◽  
Jose Manuel Condor Capcha ◽  
Guerline Lambert ◽  
Grace Seo ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Cardiac Function ◽  
Diastolic Dysfunction ◽  
Diastolic Function ◽  
Lipid Accumulation ◽  
Ldl Receptor ◽  
Density Lipoprotein ◽  
Poloxamer 407 ◽  
Whole Body ◽  
Subsequent Increase

Background: Hyperlipidemia is a major risk factor for CVD. Patients with HF with preserved ejection fraction (HFpEF) have more myocardial lipid accumulation than patients with reduced EF (HFrEF). RNASeq data from cardiac biopsies showed downregulation of the gene for lipoprotein lipase (LPL) that degrades triglycerides, in HFpEF patients compared to healthy and HFrEF controls. Poloxamer-407 (p407) induces hyperlipidemia by blocking LPL and subsequent increase in plasma triglycerides and low-density lipoprotein (LDL) cholesterol. We hypothesized that mice treated with p407 and cardiac LDL-Receptor (LDLR) over-expression (OE) develop hyperlipidemia, myocardial lipid accumulation, and diastolic dysfunction resulting in HFpEF and arrhythmias. Methods: Baseline cardiac function was assessed by echo for male and female C57Bl6 mice (n=9) for 2 groups: 4wk biweekly i.p. p407-injections with (n=4) or without (n=3) single i.v. injection with AAV9-cTnT-LDLR. Cardiac function was assessed by echocardiography at 3 and 4 wks. Blood Pressure (BP) and Whole Body Plethysmography (WBP) were assessed during wk4. Ttest was used for statistics. PR and ORO staining and telemetry were performed at wk4. Results: At wk3, P407 and LDLR OE led to alterations in diastolic function (increased IVCT, IVRT, MV E/E’, MPI, and NFT) and increased LV wall thickness, p<0.05. At wk4, there was pulmonary hypertension (increased mean pulmonary arterial pressure, decreased pulmonary acceleration time p <.05).Histology showed excessive myocardial lipids and fibrosis, and telemetry showed incidents of second-degree and higher-degree AV block. The group injected solely with p407 show e d alterations in diastolic function (increased IVCT, IVRT, NFT, LVMPI, LVMPI NFT p<.05 ) and decreased EDV, ESV, EDLVM, ESLVM, p<.05 at wk4. All groups had preserved %EF and no abnormalities in BP or WBP. Conclusions: P407 and cardiac LDLR OE induce a drastic decline in cardiac diastolic function over a shorter period of time compared to p407 alone. Diastolic dysfunction was observed in wk3 followed by pulmonary hypertension, arrhythmia, myocardial lipid accumulation and fibrosis in wk4. This new model may allow for more rapid investigations of cardiac abnormalities seen in HFpEF patients.

scholarly journals Echocardiographic phenotyping of the continuum of myocardial functional remodelling in left ventricular hypertrophy - machine learning validated with cardiac magnetic resonance

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
F Loncaric ◽  
PM Marti Castellote ◽  
L Sanchiz ◽  
G Piella ◽  
A Garcia-Alvarez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Left Ventricular Hypertrophy ◽  
Cardiac Function ◽  
Ventricular Hypertrophy ◽  
Cardiac Cycle ◽  
Left Ventricular ◽  
Healthy Individuals ◽  
The Relationship ◽  
Output Space

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Horizon 2020 European Commission Project H2020-MSCA-ITN-2016 (764738) and the Clinical Research in Cardiology grant from the Spanish Cardiac Society Background Exploring phenotypes of left ventricular hypertrophy (LVH) and interpreting the relationship of genotype and phenotype are contemporary clinical challenges. Machine learning (ML) can help by integrating whole-cardiac cycle echo data and separating patients based on subtle differences of cardiac function. The aim is to investigate if an unsupervised ML approach has the potential to explore the LVH spectrum and recognize phenotypes related to distinct disease aetiologies and genotypes. Methods The cohort consisted of 342 participants: patients with hypertrophic cardiomyopathy (HCM)(n = 27), HCM relatives (n = 31), hypertensive patients (HTN) (n = 189), and healthy individuals (n = 95). All had echocardiography performed, whereas magnetic resonance (MR) and genetic testing were performed when clinically indicated. Myocardial deformation of the LV and left atrium, aortic and mitral blood-pool Doppler, as well as the septal mitral annular tissue Doppler velocity profiles were used as input for ML. Clinical data, including echo measurements, were not part of the learning, but used to validate the ML-derived phenotypes. An unsupervised ML algorithm was used to create an output space where participants were positioned based on cardiac function. Regression was used to estimate the echo and clinical characteristics of different regions in the space.  Results The ML analysis of HCM and relative data shows grouping of HCM patients in the right-most region of the output space (Fig 1B). This region was related to LV outflow tract obstruction, mitral inflow fusion, systolic impairment with septal involvement, as well as LA and LV strain impairment (Fig 1A). Clinical data concurred - showing reduced global longitudinal strain, elevated LV mass, and a pattern of systolic and diastolic impairment - defining a comprehensive phenotype of LV remodelling related to HCM. Exploration of the genotype/phenotype relationship revealed G + P- relatives grouping on the transition from the healthy to the remodelling region. Projection of the HTN and healthy individuals into the HCM space defined the LVH disease spectrum, with healthy individuals projecting in the existing healthy region and HTNs in the transition from health to extreme remodelling (Fig 1C). MR findings of late gadolinium enhancement correlated with the ML-derived functional remodelling phenotype (Fig 1C). Furthermore, 6 patients with a clinical need for septal myectomy were located in the extreme remodelling part of the output space (Fig 1C, red circles). Conclusion ML can integrate complex, whole-cardiac cycle echo data to group patients based on similarity of cardiac function. Using an interpretable ML approach, we can explore the spectrum of LV remodelling in different aetiologies and interpret the relationship between genotype and phenotype. The methodology can accommodate new patients by projecting them into the existing space to aid in clinical interpretation, risk assessment and patient management. Abstract Figure 1

Evaluation of the effect of diastolic function on prognosis of patient with septic shock

QJM ◽  
2021 ◽  
Vol 114 (Supplement_1) ◽  
Author(s):  
Aya Abdel Khalek El zawawy ◽  
Samia Abdel Mohsen Abdel Lateef ◽  
Karim Youssef Kamal Hakim ◽  
Sameh Ahmed Refaat
Keyword(s):  
Intensive Care Unit ◽  
Septic Shock ◽  
Severe Sepsis ◽  
Intensive Care ◽  
Diastolic Dysfunction ◽  
Sample Size ◽  
Diastolic Function ◽  
Cardiac Myocyte ◽  
Suspected Infection ◽  
Predictive Risk Factor

Abstract Background Septic shock remains the leading cause of death in the intensive care unit (ICU), with an increasing incidence and a current mortality rate of approximately 30 %. Sepsis was defined by the presence of at least two criteria of systemic inflammatory response syndrome associated with a clinically or microbiologically documented, or a highly suspected infection. Severe sepsis was defined as a sepsis associated with at least one organ failure different from that responsible for the infection. Septic shock was defined as a severe sepsis associated with low blood pressure despite adequate vascular filling which required a vasopressor support. Cardiac dysfunction in sepsis is driven primarily by release of cytokines, mitochondrial dysfunction, and tissue hypoxia that leads to cardiac myocyte injury and death. Aim of the Work The aim of this study was to evaluate the effect of diastolic function on prognosis of septic shock in patients admitted to an intensive care unit (ICU). Patient and Methods This study was conducted on (50) patients with septic shock admitted to an intensive care unit (ICU) from November 2017 to November 2018. Results These patients was divided according to cardiac echocardiography findings into two groups:-Group 1:- 25 patients with preserved diastolic function and diastolic dysfunction grade I; 12 Male and 13 Female were included in the study, the average age was 43.44±13.69.Group 2:- Another 25 patients with diastolic dysfunction grade II and grade III; 11 Males and 14 Females were included in the study, the average age was 47.28±15.7. Conclusion We recommend assessment of patients admitted to the ICU with septic shock via echocardiography to determine the grade of diastolic dysfunction and using diastolic dysfunction as a predictive risk factor in various score assessment of ICU patients. Our study was limited by decreased sample size and we recommend further studies with increased sample size..

scholarly journals Image and structured data analysis for prognostication of health outcomes in patients presenting to the Emergency Department during the COVID-19 pandemic

2021 ◽  
Author(s):  
Liam Butler ◽  
Ibrahim Karabayir ◽  
Mohammad Samie Tootooni ◽  
Majid Afshar ◽  
Ari Goldberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Intensive Care ◽  
Deep Learning ◽  
Clinical Data ◽  
Chest Radiographs ◽  
Gradient Boosting ◽  
Learning Models ◽  
Icu Admission ◽  
Light Gradient

Background: Patients admitted to the emergency department (ED) with COVID-19 symptoms are routinely required to have chest radiographs and computed tomography (CT) scans. COVID-19 infection has been directly related to development of acute respiratory distress syndrome (ARDS) and severe infections lead to admission to intensive care and can also lead to death. The use of clinical data in machine learning models available at time of admission to ED can be used to assess possible risk of ARDS, need for intensive care unit (ICU) admission as well as risk of mortality. In addition, chest radiographs can be inputted into a deep learning model to further assess these risks. Purpose: This research aimed to develop machine and deep learning models using both structured clinical data and image data from the electronic health record (EHR) to adverse outcomes following ED admission. Materials and Methods: Light Gradient Boosting Machines (LightGBM) was used as the main machine learning algorithm using all clinical data including 42 variables. Compact models were also developed using 15 the most important variables to increase applicability of the models in clinical settings. To predict risk of the aforementioned health outcome events, transfer learning from the CheXNet model was implemented on our data as well. This research utilized clinical data and chest radiographs of 3571 patients 18 years and older admitted to the emergency department between 9th March 2020 and 29th October 2020 at Loyola University Medical Center. Main Findings: Our research results show that we can detect COVID-19 infection (AUC = 0.790 (0.746-0.835)) and predict the risk of developing ARDS (AUC = 0.781 (0.690-0.872), ICU admission (AUC = 0.675 (0.620-0.713)), and mortality (AUC = 0.759 (0.678-0.840)) at moderate accuracy from both chest X-ray images and clinical data. Principal Conclusions: The results can help in clinical decision making, especially when addressing ARDS and mortality, during the assessment of patients admitted to the ED with or without COVID-19 symptoms.

scholarly journals Clinical Predictive Models for COVID-19: Systematic Study (Preprint)

2020 ◽  
Author(s):  
Patrick Schwab ◽  
August DuMont Schütte ◽  
Benedikt Dietz ◽  
Stefan Bauer
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Intensive Care ◽  
Clinical Data ◽  
Predictive Models ◽  
Health Care Systems ◽  
Gradient Boosting ◽  
Support Vector ◽  
Care Systems ◽  
To Receive

BACKGROUND COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU. OBJECTIVE The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care. METHODS Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations. RESULTS Our experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00). CONCLUSIONS Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.

Assessing the Validity of Echocardiographic Criteria for Left Ventricular Diastolic Dysfunction in Patients with Pulmonary Hypertension

Cardiology ◽  
2020 ◽  
Vol 145 (11) ◽  
pp. 703-709
Author(s):  
John David Allison ◽  
Carl Zehner ◽  
Xiaoming Jia ◽  
Ihab Rafic Hamzeh ◽  
Mahboob Alam ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Diastolic Dysfunction ◽  
Diastolic Function ◽  
Diastolic Pressure ◽  
Left Ventricular ◽  
Heart Catheterization ◽  
Mitral Inflow ◽  
Mitral Annular Velocity ◽  
Lv Diastolic Dysfunction ◽  
Lv Diastolic Function

<b><i>Background:</i></b> In patients with pulmonary hypertension (PHT), the assessment of left ventricular (LV) diastolic function by echocardiography may not be reliable. PHT can affect Doppler parameters of LV diastolic function such as mitral inflow velocities and mitral annular velocities. The current guidelines for the assessment of LV diastolic function do not recommend specific adjustments for patients with PHT. <b><i>Methods:</i></b> We analyzed 36 patients from the PHT clinic that had an echocardiogram and right heart catheterization performed within 6 months of each other. Early mitral inflow velocity (E), lateral mitral annular velocity (lateral e’), septal mitral annular velocity (septal e’), tricuspid free wall annular velocity (RV e’) were measured and compared to the invasively measured intracardiac pressures including pulmonary capillary wedge pressure (PCWP), mean pulmonary artery pressure, and right ventricular end-diastolic pressure. <b><i>Results:</i></b> Among patients with PHT, the specificity of the septal e’ for LV diastolic dysfunction was 0.19, and the positive predictive value was 0.13 (lower than the lateral e’ or E/average e’). By receiver-operating characteristic curve analysis, the area under the curve (AUC) of lateral and septal e’ was just 0.64 (<i>p</i> = 0.9) and 0.53 (<i>p</i> = 0.6), respectively, while the AUC of average E/e’ was 0.94 (<i>p</i> &#x3c; 0.001). The septal e’ was paradoxically lower at 6.5 ± 1.9 cm/s for normal PCWP compared to 6.9 ± 1.7 cm/s for elevated PCWP (<i>p</i> = 0.04). 81 versus 40% (<i>p</i> = 0.017) of patients with normal versus elevated PCWP had an abnormal septal e’ &#x3c;7 cm/s. By linear regression, there was no correlation between the Doppler parameters of LV diastolic function and the PCWP. <b><i>Conclusion:</i></b> Our study suggests E/average e’ may be the only reliable tissue Doppler parameter of LV diastolic dysfunction in patients with PHT, and that septal e’ is paradoxically decreased in patients with PHT and normal left-sided filling pressures.

scholarly journals Impact of Left Ventricular Diastolic Dysfunction and Biomarkers on Pulmonary Hypertension in Patients with Severe Aortic Stenosis

Medicina ◽  
2018 ◽  
Vol 54 (4) ◽  
pp. 63
Author(s):  
Birutė Gumauskienė ◽  
Aušra Krivickienė ◽  
Regina Jonkaitienė ◽  
Jolanta Vaškelytė ◽  
Adakrius Siudikas ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Aortic Valve ◽  
Aortic Stenosis ◽  
Diastolic Dysfunction ◽  
Diastolic Function ◽  
Severe Aortic Stenosis ◽  
Left Ventricular Diastolic Dysfunction ◽  
Left Ventricular ◽  
Ventricular Diastolic Dysfunction ◽  
Lv Diastolic Dysfunction

Background: Severe aortic stenosis (AS) complicated by pulmonary hypertension (PH) is associated with poor outcomes after surgical aortic valve replacement (AVR). There is still scarce information about predictors of secondary PH in this group of patients. Objectives: The aim of this study was to investigate the prognostic impact of biomarkers together with conventional Doppler echocardiographic parameters of left ventricular diastolic function on elevated pulmonary systolic pressure (PSP) in severe AS patients before surgical AVR. Methods: Sixty patients with severe isolated AS (aortic valve area <1 cm2) underwent echocardiography, N-terminal pro B-type natriuretic peptide (NT-proBNP) and growth differentiation factor-15 (GDF-15) measurements before AVR. PSP, left ventricular ejection fraction (LV EF), parameters of LV diastolic function (E/E’ ratio, mitral valve deceleration time (MV DT) and left atrial (LA) volume) were evaluated. PH was defined as an estimated PSP ≥ 45 mmHg. Results: Of the 60 patients, 21.7% with severe isolated AS had PH with PSP ≥ 45 mmHg (58.5 ± 11.2 mmHg). LV EF did not differ between groups and was not related to an elevated PSP (50 ± 8 vs. 49 ± 8%, p = 0.58). Parameters of LV diastolic dysfunction (E/E’ ratio > 14 (OR 6.00; 95% CI, 1.41–25.48; p = 0.009), MV DT ≤ 177.5 ms (OR 9.31; 95% CI, 2.06–41.14; p = 0.001), LA volume > 100 mL (OR 9.70; 95% CI, 1.92–49.03; p = 0.002)) and biomarkers (NT-proBNP > 4060 ng/L (OR 12.54; 95% CI, 2.80–55.99; p < 0.001) and GDF-15 > 3393 pg/mL (OR 18.33; 95% CI, 2.39–140.39; p = 0.001)) were significantly associated with elevated PSP in severe AS. Conclusions: Left ventricular diastolic dysfunction and elevated biomarkers levels could predict the development of pulmonary hypertension in patients with severe aortic stenosis. Elevation of biomarkers paired with worsening of LV diastolic dysfunction could help to stratify patients for earlier surgical treatment before the development of pulmonary hypertension.

scholarly journals Machine learning techniques to predict different levels of hospital care of CoVid-19

2021 ◽  
Author(s):  
Elena Hernández-Pereira ◽  
Oscar Fontenla-Romero ◽  
Verónica Bolón-Canedo ◽  
Brais Cancela-Barizo ◽  
Bertha Guijarro-Berdiñas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Clinical Data ◽  
Hospital Care ◽  
Data Set ◽  
Age And Gender ◽  
And Gender ◽  
Prediction Problems ◽  
Different Levels

AbstractIn this study, we analyze the capability of several state of the art machine learning methods to predict whether patients diagnosed with CoVid-19 (CoronaVirus disease 2019) will need different levels of hospital care assistance (regular hospital admission or intensive care unit admission), during the course of their illness, using only demographic and clinical data. For this research, a data set of 10,454 patients from 14 hospitals in Galicia (Spain) was used. Each patient is characterized by 833 variables, two of which are age and gender and the other are records of diseases or conditions in their medical history. In addition, for each patient, his/her history of hospital or intensive care unit (ICU) admissions due to CoVid-19 is available. This clinical history will serve to label each patient and thus being able to assess the predictions of the model. Our aim is to identify which model delivers the best accuracies for both hospital and ICU admissions only using demographic variables and some structured clinical data, as well as identifying which of those are more relevant in both cases. The results obtained in the experimental study show that the best models are those based on oversampling as a preprocessing phase to balance the distribution of classes. Using these models and all the available features, we achieved an area under the curve (AUC) of 76.1% and 80.4% for predicting the need of hospital and ICU admissions, respectively. Furthermore, feature selection and oversampling techniques were applied and it has been experimentally verified that the relevant variables for the classification are age and gender, since only using these two features the performance of the models is not degraded for the two mentioned prediction problems.

scholarly journals Validation of the 2016 ASE/EACVI Guideline for Diastolic Dysfunction in Patients With Unexplained Dyspnea and a Preserved Left Ventricular Ejection Fraction

2021 ◽  
Author(s):  
Arno A. van de Bovenkamp ◽  
Vidya Enait ◽  
Frances S. de Man ◽  
Frank T. P. Oosterveer ◽  
Harm Jan Bogaard ◽  
...  
Keyword(s):  
Heart Failure ◽  
Pulmonary Hypertension ◽  
Ejection Fraction ◽  
Diastolic Dysfunction ◽  
Diastolic Function ◽  
Diagnostic Performance ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Preserved Ejection Fraction ◽  
Ventricular Ejection Fraction

Background Echocardiography is considered the cornerstone of the diagnostic workup of heart failure with preserved ejection fraction. Thus far, validation of the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) echo‐algorithm for evaluation of diastolic (dys)function in a patient suspected of heart failure with preserved ejection fraction has been limited. Methods and Results The diagnostic performance of the 2016 ASE/EACVI algorithm was assessed in 204 patients evaluated for unexplained dyspnea or pulmonary hypertension with echocardiogram and right heart catheterization. Invasively measured pulmonary capillary wedge pressure (PCWP) was used as the gold standard. In addition, the diagnostic performance of H 2 FPEF score and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) were evaluated. There was a poor correlation between indexed left atrial volume, E/e′ (septal and average) or early mitral inflow (E), and PCWP ( r =0.25–0.30, P values all <0.01). No correlation was found in our cohort between e′ (septal or lateral) or tricuspid valve regurgitation and PCWP. The correlation between diastolic function grades of the ASE/EACVI algorithm and PCWP was poor ( r =0.17, P <0.05). The ASE/EACVI algorithm had a sensitivity and specificity of 35% and 87%, respectively; an accuracy of 67% and an area under the curve of 0.56. Moreover, in 30% of cases the algorithm was not applicable or indeterminate. H 2 FPEF score had a modest correlation with PCWP ( r =0.44, P <0.0001), and accuracy was 73%; NT‐proBNP correlated weakly with PCWP ( r =0.24, P <0.001), and accuracy was 57%. Conclusions The 2016 ASE/EACVI algorithm for the assessment of diastolic function has a limited diagnostic accuracy in patients evaluated for unexplained dyspnea and/or pulmonary hypertension, and especially sensitivity to detect diastolic dysfunction was low.

scholarly journals Clinical Predictive Models for COVID-19: Systematic Study

10.2196/21439 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e21439 ◽  
Author(s):  
Patrick Schwab ◽  
August DuMont Schütte ◽  
Benedikt Dietz ◽  
Stefan Bauer
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Intensive Care ◽  
Clinical Data ◽  
Predictive Models ◽  
Health Care Systems ◽  
Gradient Boosting ◽  
Support Vector ◽  
Care Systems ◽  
To Receive

Background COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU. Objective The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care. Methods Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations. Results Our experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00). Conclusions Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.

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