scholarly journals Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients with Acute Heart Failure

2021 ◽  
Author(s):  
Faisal Rahman ◽  
Noam Finkelstein ◽  
Anton Alyakin ◽  
Nisha Gilotra ◽  
Jeff Trost ◽  
...  
Keyword(s):  
Heart Failure ◽  
High Risk ◽  
Cardiogenic Shock ◽  
Risk Model ◽  
Acute Decompensated Heart Failure ◽  
Clinical Care ◽  
Delayed Diagnosis ◽  
Low Risk ◽  
Cohort Identification ◽  
High Risk Cohort

Abstract Objective: Despite technological and treatment advancements over the past two decades, cardiogenic shock (CS) mortality has remained between 40-60%. A number of factors can lead to delayed diagnosis of CS, including gradual onset and nonspecific symptoms. Our objective was to develop an algorithm that can continuously monitor heart failure patients, and partition them into cohorts of high- and low-risk for CS.Methods: We retrospectively studied 24,461 patients hospitalized with acute decompensated heart failure, 265 of whom developed CS, in the Johns Hopkins Healthcare system. Our cohort identification approach is based on logistic regression, and makes use of vital signs, lab values, and medication administrations recorded during the normal course of care. Results: Our algorithm identified patients at high-risk of CS. Patients in the high-risk cohort had 10.2 times (95% confidence interval 6.1-17.2) higher prevalence of CS than those in the low-risk cohort. Patients who experienced cardiogenic shock while in the high-risk cohort were first deemed high-risk a median of 1.7 days (interquartile range 0.8 to 4.6) before cardiogenic shock diagnosis was made by their clinical team. Conclusions: This risk model was able to predict patients at higher risk of CS in a time frame that allowed a change in clinical care. Future studies need to evaluate if CS analysis of high-risk cohort identification may affect outcomes.

scholarly journals New risk model is able to identify patients with a low risk of progression in systemic sclerosis

RMD Open ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e001524
Author(s):  
Nina Marijn van Leeuwen ◽  
Marc Maurits ◽  
Sophie Liem ◽  
Jacopo Ciaffi ◽  
Nina Ajmone Marsan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
High Risk ◽  
Disease Progression ◽  
Risk Model ◽  
Immunosuppressive Drugs ◽  
Low Risk ◽  
Expert Assessment ◽  
Risk Of Progression

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.ResultsOf the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197–0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.ConclusionOur machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.

scholarly journals A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

2020 ◽  
Author(s):  
Yi Ding ◽  
Tian Li ◽  
Min Li ◽  
Tuersong Tayier ◽  
MeiLin Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Clinical Information ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Positive Regulation ◽  
Risk Patients ◽  
Cox Analysis

Abstract Background: Autophagy and long non-coding RNAs (lncRNAs) have been the focus of research on the pathogenesis of melanoma. However, the autophagy network of lncRNAs in melanoma has not been reported. The purpose of this study was to investigate the lncRNA prognostic markers related to melanoma autophagy and predict the prognosis of patients with melanoma.Methods: We downloaded RNA-sequencing data and clinical information of melanoma from The Cancer Genome Atlas. The co-expression of autophagy-related genes (ARGs) and lncRNAs was analyzed. The risk model of autophagy-related lncRNAs was established by univariate and multivariate COX regression analyses, and the best prognostic index was evaluated combined with clinical data. Finally, gene set enrichment analysis was performed on patients in the high- and low-risk groups.Results: According to the results of the univariate COX analysis, only the overexpression of LINC00520 was associated with poor overall survival, unlike HLA-DQB1-AS1, USP30-AS1, AL645929, AL365361, LINC00324, and AC055822. The results of the multivariate COX analysis showed that the overall survival of patients in the high-risk group was shorter than that recorded in the low-risk group (p<0.001). Moreover, in the receiver operating characteristic curve of the risk model we constructed, the area under the curve (AUC) was 0.734, while the AUC of T and N was 0.707 and 0.658, respectively. The Gene Ontology was mainly enriched with the positive regulation of autophagy and the activation of the immune system. The results of the Kyoto Encyclopedia of Genes and Genomes enrichment were mostly related to autophagy, immunity, and melanin metabolism.Conclusion: The positive regulation of autophagy may slow the transition from low-risk patients to high-risk patients in melanoma. Furthermore, compared with clinical information, the autophagy-related lncRNAs risk model may better predict the prognosis of patients with melanoma and provide new treatment ideas.

Abstract 13677: Usefulness of 2-year Iodine-123 Metaiodobenzylguanidine-based Risk Model for the Post-discharge Risk Stratification in Acute Decompensated Heart Failure Patients With Preserved Left Ventricular Ejection Fraction

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Shunsuke Tamaki ◽  
Takahisa Yamada ◽  
Tetsuya Watanabe ◽  
Takashi Morita ◽  
Yoshio Furukawa ◽  
...  
Keyword(s):  
Heart Failure ◽  
Risk Score ◽  
Mortality Risk ◽  
Risk Model ◽  
Acute Decompensated Heart Failure ◽  
Decompensated Heart Failure ◽  
Cardiac Mortality ◽  
Post Discharge ◽  
Patients With Heart Failure ◽  
Iodine 123

Background: A four-parameter risk model including cardiac iodine-123 metaiodobenzylguanidine (MIBG) imaging and readily available clinical parameters has been recently developed for the prediction of 2-year cardiac mortality risk in patients with chronic heart failure (CHF) using a Japanese CHF database consisting of 1322 patients. However, there is no information available on the usefulness of 2-year MIBG-based cardiac mortality risk score for the prediction of post-discharge prognosis in patients with heart failure with preserved LVEF (HFpEF) who are admitted with acute decompensated heart failure (ADHF). Methods and Results: Patients' data were extracted from The Prospective mUlticenteR obServational stUdy of patIenTs with Heart Failure with Preserved Ejection Fraction (PURSUIT-HFpEF) study, which is a prospective multicenter observational registry for ADHF patients with LVEF ≥50% in Osaka. We studied 239 patients who survived to discharge. Cardiac MIBG imaging was performed just before discharge. The 2-year cardiac mortality risk score was calculated using four parameters, including age, LVEF, NYHA functional class, and the cardiac MIBG heart-to-mediastinum ratio on delayed image. The patients were stratified into three groups based on the 2-year cardiac mortality risk score: low- (<4%), intermediate- (4-12%), and high-risk (>12%) groups. The endpoint was all-cause death. During a follow-up period of 1.6±0.8 years, 33 patients had all-cause death. Multivariate Cox analysis showed that 2-year MIBG-based cardiac mortality risk score was an independent predictor of all-cause death (p=0.0009). There was significant difference in the rate of all-cause death among the three groups stratified by 2-year cardiac mortality risk score (Figure). Conclusions: In this multicenter study, the 2-year MIBG-based cardiac mortality risk score was shown to be useful for the prediction of post-discharge clinical outcome in HFpEF patients admitted for ADHF.

scholarly journals Cost-Effectiveness of the International Late Effects of Childhood Cancer Guideline Harmonization Group Screening Guidelines to Prevent Heart Failure in Survivors of Childhood Cancer

2020 ◽  
Vol 38 (33) ◽  
pp. 3851-3862 ◽  
Author(s):  
Matthew J. Ehrhardt ◽  
Zachary J. Ward ◽  
Qi Liu ◽  
Aeysha Chaudhry ◽  
Anju Nohria ◽  
...  
Keyword(s):  
Heart Failure ◽  
Cost Effectiveness ◽  
High Risk ◽  
Childhood Cancer ◽  
Late Effects ◽  
Cost Effective ◽  
Risk Groups ◽  
Low Risk ◽  
Moderate Risk ◽  
Survivors Of Childhood Cancer

PURPOSE Survivors of childhood cancer treated with anthracyclines and/or chest-directed radiation are at increased risk for heart failure (HF). The International Late Effects of Childhood Cancer Guideline Harmonization Group (IGHG) recommends risk-based screening echocardiograms, but evidence supporting its frequency and cost-effectiveness is limited. PATIENTS AND METHODS Using the Childhood Cancer Survivor Study and St Jude Lifetime Cohort, we developed a microsimulation model of the clinical course of HF. We estimated long-term health outcomes and economic impact of screening according to IGHG-defined risk groups (low [doxorubicin-equivalent anthracycline dose of 1-99 mg/m2 and/or radiotherapy < 15 Gy], moderate [100 to < 250 mg/m2 or 15 to < 35 Gy], or high [≥ 250 mg/m2 or ≥ 35 Gy or both ≥ 100 mg/m2 and ≥ 15 Gy]). We compared 1-, 2-, 5-, and 10-year interval-based screening with no screening. Screening performance and treatment effectiveness were estimated based on published studies. Costs and quality-of-life weights were based on national averages and published reports. Outcomes included lifetime HF risk, quality-adjusted life-years (QALYs), lifetime costs, and incremental cost-effectiveness ratios (ICERs). Strategies with ICERs < $100,000 per QALY gained were considered cost-effective. RESULTS Among the IGHG risk groups, cumulative lifetime risks of HF without screening were 36.7% (high risk), 24.7% (moderate risk), and 16.9% (low risk). Routine screening reduced this risk by 4% to 11%, depending on frequency. Screening every 2, 5, and 10 years was cost-effective for high-risk survivors, and every 5 and 10 years for moderate-risk survivors. In contrast, ICERs were > $175,000 per QALY gained for all strategies for low-risk survivors, representing approximately 40% of those for whom screening is currently recommended. CONCLUSION Our findings suggest that refinement of recommended screening strategies for IGHG high- and low-risk survivors is needed, including careful reconsideration of discontinuing asymptomatic left ventricular dysfunction and HF screening in low-risk survivors.

Real-Time Risk Tool for Pharmacy Interventions

2020 ◽  
pp. 001857872097388
Author(s):  
Hanh L. Nguyen ◽  
Kristin S. Alvarez ◽  
Boryana Manz ◽  
Arun Nethi ◽  
Varun Sharma ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Models ◽  
Risk Model ◽  
Hospital Readmissions ◽  
Group Versus ◽  
Safety Net ◽  
Low Risk ◽  
Day Hospital ◽  
Significant Difference ◽  
Safety Net Hospital

Background: Adverse drug events (ADEs) result in excess hospitalizations. Thorough admission medication histories (AMHs) may prevent ADEs; however, the resources required oftentimes outweigh what is available in large hospital settings. Previous risk prediction models embedded into the Electronic Medical Record (EMR) have been used at hospitals to aid in targeting delivery of scarce resources. Objective: To determine if an AMH scoring tool used to allocate resources can decrease 30-day hospital readmissions. Design, Setting, and Participants: Propensity-matched cohort study, Medicine/Surgery patients in large academic safety-net hospital. Intervention or Exposure: Pharmacy-conducted AMHs identified by risk model versus standard of care AMH. Main Outcomes and Measures: A total of 30-day hospital readmissions and inpatient ADE prevention. Results: The model screened 87 240 hospitalizations between June 2017 and June 2019 and 4027 patients per group were included. There were significantly less 30 day readmissions among high-risk identified patients that received a pharmacy-conducted AMH compared to controls (11% vs 15%; P = 0.004) and no significant difference in readmission rates for low-risk patients. While there was significantly higher documentation of major ADE prevention in the pharmacy-led AMH group versus control (1656 vs 12; P < 0.001), there was no difference in electronically-detected inpatient ADEs between groups. Conclusions: A risk tool embedded into the EMR can be used to identify patients whom pharmacy teams can easily target for AMHs. This study showed significant reductions in readmissions for patients identified as high-risk. However, the same benefit in readmissions was not seen in those identified at low-risk, which supports allocating resources to those that will benefit the most.

A Validated Risk Model for Prediction of Early Readmission in Patients with Hepatic Encephalopathy

2018 ◽  
Vol 17 (5) ◽  
pp. 0-10
Author(s):  
Andrew J. Kruger ◽  
Fasika Aberra ◽  
Sylvester M. Black ◽  
Alice Hinton ◽  
James Hanje ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Hepatic Encephalopathy ◽  
High Risk ◽  
Risk Model ◽  
Cox Regression ◽  
Low Risk ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Early Readmission ◽  
The Impact

Introduction and aim. Hepatic encephalopathy (HE) is a common complication in cirrhotics and is associated with an increased healthcare burden. Our aim was to study independent predictors of 30-day readmission and develop a readmission risk model in patients with HE. Secondary aims included studying readmission rates, cost, and the impact of readmission on mortality. Material and methods. We utilized the 2013 Nationwide Readmission Database (NRD) for hospitalized patients with HE. A risk assessment model based on index hospitalization variables for predicting 30-day readmission was developed using multivariate logistic regression and validated with the 2014 NRD. Patients were stratified into Low Risk and High Risk groups. Cox regression models were fit to identify predictors of calendar-year mortality. Results. Of 24,473 cirrhosis patients hospitalized with HE, 32.4% were readmitted within 30-days. Predictors of readmission included presence of ascites (OR: 1.19; 95% CI: 1.06-1.33), receiving paracentesis (OR: 1.43; 95% CI: 1.26-1.62) and acute kidney injury (OR: 1.11; 95% CI: 1.00-1.22). Our validated model stratified patients into Low Risk and High Risk of 30-day readmissions (29% and 40%, respectively). The cost of the first readmission was higher than index admission in the 30-day readmission cohort ($14,198 vs. $10,386; p-value < 0.001). Thirty-day readmission was the strongest predictor of calendar-year mortality (HR: 4.03; 95% CI: 3.49-4.65). Conclusions. Nearly one-third of patients with HE were readmitted within 30-days, and early readmission adversely impacted healthcare utilization and calendar-year mortality. With our proposed simple risk assessment model, patients at high risk for early readmissions can be identified to potentially avert poor outcomes.

scholarly journals Evaluation of a Warning System for Early-Season Control of Grapevine Powdery Mildew

Plant Disease ◽  
2012 ◽  
Vol 96 (1) ◽  
pp. 104-110 ◽  
Author(s):  
Tito Caffi ◽  
Sara E. Legler ◽  
Vittorio Rossi ◽  
Riccardo Bugiani
Keyword(s):  
High Risk ◽  
Powdery Mildew ◽  
Risk Model ◽  
Warning System ◽  
Northern Italy ◽  
Low Risk ◽  
Short Message ◽  
Early Season ◽  
Weather Forecasts ◽  
Model Driven

In several grape-growing areas of the world, including northern Italy, powdery mildew epidemics, caused by Erysiphe necator, are mainly triggered by the ascospores produced in overwintered chasmothecia. Growers in northern Italy usually control the disease with fixed-interval fungicide applications. A warning system was developed for early-season powdery mildew control based on (i) short-term weather forecasts, (ii) a model that simulates the severity of each E. necator ascosporic infection, and (iii) a mobile phone short-message system. This warning system was evaluated in six vineyards in northern Italy from 2006 to 2008, between bud break of vines and early berry development; an unsprayed control was compared with “low-risk” and “high-risk” model-driven sprays and a calendar-based “grower” spray program. Use of the warning system reduced disease severity on leaves and bunches compared with the unsprayed control and resulted in the same level of control of powdery mildew as the grower's spray program, with reduced fungicide applications and costs. On average, 5.7 sprays were applied following the grower's spray program (with an average cost of 221 €/ha/year); use of the warning system reduced fungicide applications by 36% (low-risk program, saving of 56 €/ha/year) or 75% (high-risk program, saving of 161 €/ha/year).

scholarly journals Implementation of a Cardiogenic Shock Team and Clinical Outcomes (INOVA-SHOCK Registry): Observational and Retrospective Study (Preprint)

2018 ◽  
Author(s):  
Behnam Tehrani ◽  
Alexander Truesdell ◽  
Ramesh Singh ◽  
Charles Murphy ◽  
Patricia Saulino
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Acute Myocardial Infarction ◽  
Cardiogenic Shock ◽  
Clinical Outcomes ◽  
Mechanical Circulatory Support ◽  
Acute Decompensated Heart Failure ◽  
Survival Rates ◽  
Decompensated Heart Failure ◽  
Circulatory Support

BACKGROUND The development and implementation of a Cardiogenic Shock initiative focused on increased disease awareness, early multidisciplinary team activation, rapid initiation of mechanical circulatory support, and hemodynamic-guided management and improvement of outcomes in cardiogenic shock. OBJECTIVE The objectives of this study are (1) to collect retrospective clinical outcomes for acute decompensated heart failure cardiogenic shock and acute myocardial infarction cardiogenic shock, and compare current versus historical survival rates and clinical outcomes; (2) to evaluate Inova Heart and Vascular Institute site specific outcomes before and after initiation of the Cardiogenic Shock team on January 1, 2017; (3) to compare outcomes related to early implementation of mechanical circulatory support and hemodynamic-guided management versus historical controls; (4) to assess survival to discharge rate in patients receiving intervention from the designated shock team and (5) create a clinical archive of Cardiogenic Shock patient characteristics for future analysis and the support of translational research studies. METHODS This is an observational, retrospective, single center study. Retrospective and prospective data will be collected in patients treated at the Inova Heart and Vascular Institute with documented cardiogenic shock as a result of acute decompensated heart failure or acute myocardial infarction. This registry will include data from patients prior to and after the initiation of the multidisciplinary Cardiogenic Shock team on January 1, 2017. Clinical outcomes associated with early multidisciplinary team intervention will be analyzed. In the study group, all patients evaluated for documented cardiogenic shock (acute decompensated heart failure cardiogenic shock, acute myocardial infarction cardiogenic shock) treated at the Inova Heart and Vascular Institute by the Cardiogenic Shock team will be included. An additional historical Inova Heart and Vascular Institute control group will be analyzed as a comparator. Means with standard deviations will be reported for outcomes. For categorical variables, frequencies and percentages will be presented. For continuous variables, the number of subjects, mean, standard deviation, minimum, 25th percentile, median, 75th percentile and maximum will be reported. Reported differences will include standard errors and 95% CI. RESULTS Preliminary data analysis for the year 2017 has been completed. Compared to a baseline 2016 survival rate of 47.0%, from 2017 to 2018, CS survival rates were increased to 57.9% (58/110) and 81.3% (81/140), respectively (P=.01 for both). Study data will continue to be collected until December 31, 2018. CONCLUSIONS The preliminary results of this study demonstrate that the INOVA SHOCK team approach to the treatment of Cardiogenic Shock with early team activation, rapid initiation of mechanical circulatory support, hemodynamic-guided management, and strict protocol adherence is associated with superior clinical outcomes: survival to discharge and overall survival when compared to 2015 and 2016 outcomes prior to Shock team initiation. What may limit the generalization of these results of this study to other populations are site specific; expertise of the team, strict algorithm adherence based on the INOVA SHOCK protocol, and staff commitment to timely team activation. Retrospective clinical outcomes (acute decompensated heart failure cardiogenic shock, acute myocardial infarction cardiogenic shock) demonstrated an increase in current survival rates when compared to pre-Cardiogenic Shock team initiation, rapid team activation and diagnosis and timely utilization of mechanical circulatory support. CLINICALTRIAL ClinicalTrials.gov NCT03378739; https://clinicaltrials.gov/ct2/show/NCT03378739 (Archived by WebCite at http://www.webcitation.org/701vstDGd)

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