scholarly journals Validation of the Zwolle score for selection of very low-risk STEMI patients treated with primary angioplasty

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Cordero ◽  
B Cid ◽  
P Monteiro ◽  
J.M Garcia-Acuna ◽  
M Rodriguez-Manero ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Risk Score ◽  
Mortality Risk ◽  
Area Under The Curve ◽  
Coronary Intervention ◽  
Low Risk ◽  
Individual Risk ◽  
Care Organization ◽  
Grace Score ◽  
Selection Of

Abstract Background The Zwolle risk score was designed to stratify the actual in-hospital mortality risk of ST-elevation myocardial infarction (STEMI) patients treated with primary percutaneous coronary intervention (p-PCI) but, also, for decision-making related to patients location in an intensive care unit or not. Since the GRACE score continues being the gold-standard for individual risk assessment in STEMI in most institutions we assessed the specificity of both scores for in-hospital mortality. Methods We assessed the accuracy of Zwolle risk score for in-hospital mortality estimation as compared to the GRACE score in all patients admitted for STEMI in 3 tertitary hospitals. Patients with Zwolle risk score <3 would qualify as “low risk”, 3–5 as “intermediate risk” and ≥6 as “high risk”. Patients with GRACE score <140 were classified as low-risk. Specificity, sensitivity and classification were assessed by ROC curves and the area under the curve (AUC). Results We included 4,446 patients, mean age 64.7 (13.6) years, 24% women and 39% with diabetes. Mean GRACE score was 157.3 (4.9) and Zwolle was 2.8 (3.3). In-hospital mortality was 10.6% (471 patients). Patients who died had higher GRACE score (218.4±4.9 vs. 149.6±37.5; p<0.001) and Zwolle score (7.6±4.3 vs. 2.3±2.18; p<0.001); a statistically significant increase of in-hospital mortality risk, adjusted adjusted by age, gender and revascularization, was observed with both scores (figure). A total of 1,629 patients (40.0%) were classified as low risk by the GRACE score and 2,962 (66.6%) by the Zwolle score; in-hospital mortality was 1.6% and 2.7%, respectively. Moreover, the was a significant increase of in-hospital mortality rate according to Zwolle categories (2.7%; 13.0%; 41.6%)The AUC of both score was the same (p=0.49) but the specificity of GRACE score <140 was 43.1% as compared to 72.6% obtained by Zwolle score <3; patients accurately classified was also lower with the GRACE score threshold (48.8% vs. 73.7%). Conclusions Selection of low-risk STEMI patients treated with p-PCI based on the Zwolle risk score has higher specificity than the GRACE score and might be useful for the care organization in clinical practice. Funding Acknowledgement Type of funding source: None

scholarly journals A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2808
Author(s):  
Tzong-Yun Tsai ◽  
Jeng-Fu You ◽  
Yu-Jen Hsu ◽  
Jing-Rong Jhuang ◽  
Yih-Jong Chern ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Prediction Error ◽  
Area Under The Curve ◽  
Low Risk ◽  
Brier Score ◽  
Individual Risk ◽  
T4 Colon Cancer ◽  
Testing Set

(1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were stratified into the high- or low-risk group by their risk score, with the cut-off points determined by a classification and regression tree (CART). (2) Results: The five candidate predictors were tumor location, preoperative carcinoembryonic antigen value, histologic type, T stage and nodal stage. Based on the CART, patients were categorized into the low-risk or high-risk groups. The model has high predictive accuracy (prediction error ≤5%) and good discrimination ability (area under the curve >0.7). (3) Conclusions: The prediction model quantifies individual risk and is feasible for selecting patients with pT4 colon cancer who are at high risk of developing mPC.

Abstract 137: Efficacy and Cost Savings Realized through Validation of a Tool to Identify Low Risk STEMI Patients not Requiring CCU Care

2012 ◽  
Vol 5 (suppl_1) ◽  
Author(s):  
William E Downey ◽  
Lara M Cassidy ◽  
Kerstin Liebner ◽  
Robyn Magyar ◽  
Angela D Humphrey ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Risk Score ◽  
New Technologies ◽  
Cost Savings ◽  
Low Risk ◽  
Hospital Death ◽  
Cardiac Care ◽  
Primary Pci ◽  
Risk Patients ◽  
Support Cost

Introduction In the early 1960s, the creation of Cardiac Care Units (CCUs) led to a 50% reduction in the in-hospital mortality of acute myocardial infarction (AMI). Prompt application of closed chest cardiac resuscitation and external defibrillation -- then new technologies -- served to reduce the consequences of the event. Over the ensuing four decades, therapeutic advances in the treatment of AMI (e.g. prompt reperfusion strategies) have favorably altered its natural history, potentially obviating the need for CCU care. Since such care is expensive, identification of a low risk cohort of patients in whom this care is not necessary could allow substantial improvements in the cost of cardiac care. Hypothesis Existing risk models can be used to accurately identify low risk STEMI patients who do not require CCU care after primary PCI. Methods We performed a retrospective chart review of all STEMI cases from 2010 at Carolinas Medical Center. We then assessed them using the TIMI STEMI risk score and a risk assessment algorithm for uncomplicated STEMI developed at Brigham and Women's Hospital (BWH). The BWH STEMI Care Redesign defines low risk STEMI patients as those who are promptly revascularized via successful single vessel PCI with (1) no evidence of ongoing ischemia, (2) EF>40%, (3) absence of CHF, hemodynamic or electrical instability, and (4) who are awake without need of respiratory support. Cost data (fixed and variable) from Quality Advisor™, a product by Premier, was abstracted for each STEMI case, examining specific resources used in CCU and non-CCU units. Results Among 310 consecutive STEMI patients, in-hospital mortality was 3.9%. The BWH risk score identified 46.4% of these patients as low-risk. Among these patients, in-hospital mortality was 0%. Only one of these 144 low-risk patients required subsequent CCU care. None required CPR or defibrillation after revascularization. The TIMI STEMI risk score <2 classified 26.1% of the patients as low-risk. Among these patients, in-hospital mortality was 0%. However, 3.7% of these "low-risk" patients had ventricular arrhythmias or respiratory decompensation during or shortly after PCI. None of the 3.7% were classified as "low-risk" by the BWH model. CCU care added $723 in fixed costs and $340 in variable costs per hospital day. Conclusion The BWH model, but not the TIMI STEMI risk score, accurately predicted a sizable cohort of STEMI patients at very low risk of in-hospital death and complications. These patients may be appropriate for admission to non-CCU level care immediately following primary PCI. Doing so would be projected to yield a cost savings of >$1000 per patient.

scholarly journals A Synergistic Effect of Lp(a) and GRACE Score on Cardiovascular Risk in Acute Coronary Syndrome Patients Undergoing Percutaneous Coronary Intervention: A Cohort Study From China

2021 ◽  
Vol 8 ◽  
Author(s):  
Chengping Hu ◽  
Jinxing Liu ◽  
Hongya Han ◽  
Yan Sun ◽  
Yujing Cheng ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Percutaneous Coronary Intervention ◽  
Cardiovascular Risk ◽  
Risk Score ◽  
Cardiovascular Events ◽  
Coronary Intervention ◽  
Grace Risk Score ◽  
Percutaneous Coronary ◽  
Grace Score ◽  
Fatal Myocardial Infarction

Objectives: Lipoprotein(a) [Lp(a)] has been thought as an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). The Global Registry of Acute Coronary Events (GRACE) score is used to predict the risk of death or death/non-fatal myocardial infarction in patients with acute coronary syndromes (ACS). It suggests that there may be a synergism between Lp(a) and the GRACE risk score on predicting cardiovascular events. Accordingly, this study aimed to test the hypothesis that Lp(a)-related cardiovascular risk could be significantly modulated by the GRACE risk score in patients with ACS undergoing percutaneous coronary intervention (PCI).Methods: Patients hospitalized with ACS undergoing PCI were enrolled and followed up for 18 months. The primary outcome was the composite of death, non-fatal myocardial infarction, non-fatal stroke, and unplanned repeat revascularization. A Cox proportional hazard regression model was used to determine the relationship between Lp(a) and cardiovascular events.Results: A total of 6,309 patients were included (age: 60.1 ± 10.06 years, male: 75.2%, BMI: 26.2 ± 10.57 kg/m2). A total of 310 (4.9%) cardiovascular events occurred. When the overall population was stratified by a GRACE score of 91 or less vs. more than 91 and by tertiles of Lp(a), higher Lp(a) was significantly associated with cardiovascular events only when the GRACE score was &lt;91(tertile 2 vs. tertile 1: HR 1.31, 95% CI: 0.86–1.98, P = 0.205; tertile 3 vs. tertile 1: HR 1.94, 95% CI: 1.32–2.84, P = 0.001; P = 0.002). However, no such significant correlation between cardiovascular events and Lp(a) emerged in the case of a GRACE score 91 or less, and there was a significant interaction for cardiovascular events between Lp(a) tertiles and dichotomized GRACE scores (P &lt; 0.001).Conclusions: In ACS patients undergoing PCI, there was a synergistic effect between the GRACE risk score and on-statins Lp(a) on predicting cardiovascular events. This finding could help us more accurately identify patients who would benefit most from Lp(a)-lowering treatment.

scholarly journals Development of an infant mortality risk score in Uruguay

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Alegretti ◽  
I Leon ◽  
A Aleman ◽  
F Cavallieri ◽  
W Callero
Keyword(s):  
High Risk ◽  
Infant Mortality ◽  
Live Birth ◽  
Risk Score ◽  
Mortality Risk ◽  
Home Visit ◽  
Birth Certificate ◽  
Area Under The Curve ◽  
Binary Logistic Regression Model ◽  
Roc Curve Analysis

Abstract Background The Ministry of Public Health of Uruguay incorporated a comprehensive home visit before 7 days of discharge to monitor children at risk of infant mortality. In this context, a precise risk stratification of newborns is needed to optimize the implementation of the home visit. Objective Implement a validated infant mortality risk score for Uruguay using the national electronic live birth certificate. Methods Electronic records of newborns from 2014 to 2017 were used to develop the score. The variables of the electronic live birth certificate were considered for the model and data of Infant mortality was obtained from the national mortality registry. A multivariate binary logistic regression model was estimated with a random sample of 80% of the cohort, the remaining 20% was the validation set. ROC curve analysis was performed. R software was used. Results The 2014-2017 birth cohort contains 187,388 records. 1307 children under one year died (IMR 6.97 per 1,000 births). The variables included in the final model were birth weight, APGAR score at 5 minutes, number of prenatal visits, maternal educational level and father living at home. The area under the curve (AUC) was 89%, CI 95% [87% - 91%]. Two cut-off points were defined: 0.4% and 2%. Less than 0.4% was considered low risk (IMR 1.4 per 1,000 births), between 0.4 and 2% was considered intermediate risk (IMR 7.1 per 1,000 births) and more than 2% was considered high risk (IMR 99.2 per 1,000 births). Conclusions The score identifies high-risk newborns at the time of entering the data in the electronic live birth certificate. This information could be used to plan and implement the home visit and other actions, according to the level of risk identified. Key messages In Uruguay, high-risk newborns can be identified using data collected routinely. The procedure could be applied in other countries with electronic birth certificate.

Abstract 341: A Simple Prognostic Tool for In-Hospital Death in Patients Presenting with Out-of-Hospital Cardiac Arrest and ST-Elevation Myocardial Infarction

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_4) ◽  
Author(s):  
Andy T Tran ◽  
Anthony J Hart ◽  
John Spertus ◽  
Philip Jones ◽  
Ali O Malik ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Cardiac Arrest ◽  
Coronary Angiography ◽  
Hospital Mortality ◽  
Risk Score ◽  
Coronary Intervention ◽  
St Elevation Myocardial Infarction ◽  
Hospital Death ◽  
St Elevation ◽  
Hospital Cardiac Arrest

Background: In the emergent setting of ST-Elevation Myocardial Infarction (STEMI) complicating out-of-hospital cardiac arrest (OHCA), decisions for immediate coronary angiography are made when the likelihood of hospital survival is unknown. Estimating the risk of mortality at the time of hospital arrival might inform decisions for primary percutaneous coronary intervention. Methods: From the Cardiac Arrest Registry to Enhance Survival (CARES), we included adult OHCA patients from 2013-2018 presenting to hospitals with a STEMI. We developed a predictive model for in-hospital mortality using multivariable logistic regression to derive a scoring tool that was internally validated with bootstrap methods. Results: Of 7120 patients with OHCA and STEMI admitted at a hospital (mean age 62±13.2 years, 27% female), 3159 (44.4%) died during hospitalization. Higher age, unwitnessed arrest, non-shockable cardiac arrest rhythm, no sustained return of spontaneous circulation (ROSC) at the time of hospital admission, and resuscitation time on scene were most predictive of mortality (C-index, 0.82). Using the model β coefficients, we developed an integer risk score ranging from 0 to 10 points, corresponding to observed mortality rates of 5% to 100% (Figure 1). The odds of in-hospital mortality doubled for each 1-unit score increase (odds ratio, 2.01; 95% CI, 1.94-2.09; p<0.0001), and a score of ≥6, involving ~15% of patients, was associated with ≥85% in-hospital mortality risk. Conclusions: This risk score, based on simple prehospital characteristics, stratifies the range of in-hospital mortality from 5% to nearly 100% in OHCA patients with STEMI at the time of hospital presentation. The benefits of such a model in decision-making for immediate coronary angiography should be prospectively studied.

scholarly journals Impact of Admission Blood Glucose Added on GRACE Risk Score for All-Cause In-Hospital Mortality in Patients with Acute Coronary Syndrome

2018 ◽  
Vol 33 (2) ◽  
pp. 94-99
Author(s):  
Md Mesbahul Islam ◽  
Mohsin Ahmed ◽  
Mohammad Ali ◽  
Abdul Wadud Chowdhury ◽  
Khandakar Abu Rubayat
Keyword(s):  
Acute Coronary Syndrome ◽  
Blood Glucose ◽  
Hospital Mortality ◽  
Blood Sugar ◽  
Risk Score ◽  
Hospital Death ◽  
Predictive Values ◽  
Coronary Syndrome ◽  
Grace Risk Score ◽  
Grace Score

Background: Abnormal glucose metabolism is a predictor of worse outcome after acute coronary syndrome (ACS). However, this parameter is not included in risk prediction scores, including GRACE risk score. We sought to evaluate whether the inclusion of blood glucose at admission in a model with GRACE risk score improves risk stratification. Objectives: To assess whether inclusion of admission blood glucose in a model with GRACE risk score improves risk stratification of ACS patients admitted in a tertiary hospital of Bangladesh. Methods: This cross sectional comparative study was carried out in the department of cardiology, Dhaka Medical College Hospital (DMCH), Dhaka between May 2016 to April 2017. Data were collected from ACS patients admitted at CCU, DMCH who fulfilled inclusion and exclusion criteria. GRACE score was calculated for each patient. The predictive value of death by GRACE score was compared with the predictive value of combined GRACE score + admission blood sugar. Comparison between these results in two groups were done by unpaired t-test, analysis was conducted SPSS-22.0 for windows software. The significance of the results was determined in 95.0% confidence interval and a value of p <0.05 was considered to be statistically significant. Results: A total of 249 cases of ACS patients were selected. Most of the patients belonged to 5th and 6th decades 25.3% vs 37.3% and the mean age was 55.7±11.7 years. Most of the patients were male. High GRACE risk score (≥155) and elevated admission blood sugar (≥11) was found significantly higher in-hospital death whereas only high GRACE risk score (≥155) and normal admission blood sugar (<11) was found non significant regarding in-hospital death. Test of validity showed sensitivity of GRACE risk score regarding in-hospital death was 85.29%, specificity 57.7%, accuracy 61.4%, positive and negative predictive values were 24.2% and 96.1% respectively. The sensitivity of GRACE risk score + admission blood sugar regarding in-hospital death was 85.29%, specificity 62.33%, accuracy 65.46%, positive and negative predictive values were 26.36% and 96.4% respectively. Receiver-operator characteristic (ROC) were constructed using GRACE score and GRACE score + admission blood sugar of the patients with in-hospital death, which showed the sensitivity and specificity of GRACE score for predicting in-hospital death were found to be 79.4% and 58.1%, respectively. Whereas after adding admission blood sugar value to GRACE score both the sensitivity and specificity increased to 82.4% and 58.6% respectively in this new model. Logistic regression analysis of in-hospital mortality with independent risk factors showed GRACE score (≥155) + admission blood sugar (≥11.0 mmol/l) was more significantly associated with in-hospital mortality (P =0.001, OR = 6.675, 95% CI 2.366-13.610). Conclusion: In patients with the whole spectrum of acute coronary syndrome admission blood glucose can add prognostic information to the established risk factors with the GRACE risk score. Bangladesh Heart Journal 2018; 33(2) : 94-99

scholarly journals P0184RISK PREDICTION FOR DEATH AND END-STAGE RENAL DISEASE DOES NOT PARALLEL REAL-LIFE TRAJECTORY OF OLDER PATIENTS WITH ADVANCED CHRONIC KIDNEY DISEASE - A ROMANIAN CENTER EXPERIENCE

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Andra Nastasa ◽  
Mugurel Apetrii ◽  
Mihai Onofriescu ◽  
Ionut Nistor ◽  
Hani Hussien ◽  
...  
Keyword(s):  
High Risk ◽  
Mortality Risk ◽  
Kidney Failure ◽  
Older Patients ◽  
Real Life ◽  
Low Risk ◽  
Individual Risk ◽  
Failure Risk ◽  
Risk Of Mortality ◽  
Life Trajectory

Abstract Background and Aims In Europe, the share of the elderly (≥65 years of age) in the total population is estimated to increase from 19.2% in 2016 to 29.1% by 2080. In 2016, European Renal Best Practice (ERBP) group published a clinical practice guideline on management of older patients with CKD stage3b or higher (eGFR&lt;45ml/min/1.73 m2). Two risk stratifications scores were emphasized: Bansal score for prognosticating risk of death in medium term, and Kidney Failure Risk Equation (KFRE) for estimating progression of CKD stage 3b or 4 to ESRD. Our group, as part of the ERBP team, aimed to evaluate and apply the framework proposed by the guideline, consisting of risk prediction for both mortality and progression to ESRD in a cohort of elderly patients with advanced CKD. After dividing the population in groups of risk, we described their real-life trajectory in terms of either reaching ESRD/death. Method In this retrospective cohort study we included patients aged ≥65 years with CKD stage 3b-4, evaluated at the Outpatient Nephrology Department of Dr. C. I. Parhon Hospital from Iași, Romania, between October 2016 – October 2018. Individual risk for mortality was predicted using Bansal score, a nine-variable equation model. A total score of 7 (associated with a mortality risk of 53.82%) was established as cut-off value to differentiate between 2 groups: high risk of mortality (Bansal ≥ 7) and low risk of mortality (Bansal &lt; 7), given the fact that the ERBP guidelines don’t define a threshold for high risk in respect to mortality outcome. According to the algorithm proposed by the guideline, individual risk for progression to ESRD at 5 years was calculated in the low mortality risk group, using the 4-variable Kidney Failure Risk Equation (KFRE). Results The final cohort included 958 patients, with a mean age of 74 years (SD: 7), and with similar gender distribution (50.6% female vs. 49.4% male). Predicted trajectory in terms of reaching ESRD / death: When we applied Bansal score for mortality, the total study population (N=958) was divided in two groups: N1 with high risk of mortality, which comprised more than half of the cohort (548 patients, 57.2%) and N2 with low risk of mortality (410 patients, 42.8%). Individual risk of progression to ESRD was then estimated in N2 group, using 4-variable KFRE. Nearly ¾ of this group (75.4%, 309 subjects) presented a low-risk of progression and ¼ (24.6%, 101 subjects) had high-risk. Real-life trajectory in terms of reaching ESRD / death: From the entire cohort, 31 patients started renal replacement therapy (RRT) and 164 patients died as their first clinical event. The RRT initiation rate was 3.6% of N1 group (20 subjects) versus 2.7% of N2 group (11 subjects). The mortality rate was 15.5% of N1 group (85 deaths) versus 19.3% of N2 group (79 deaths). Figure 1 depicts the real-life trajectory of the population groups in terms of reaching ESRD / death. Conclusion In a large population from Eastern Europe, the application of the algorithm from the Clinical Practice Guideline on management of older patients with advanced CKD showed that risk prediction for death and end-stage renal disease does not parallel the real-life trajectory of the population. More than half of the subjects had a high risk of mortality, however we found similar death rates in the 2 groups (high versus low risk of mortality). Also, the RRT initiation rates were similar, irrespective of predicted mortality risk or kidney failure risk, suggesting that implementing the guideline in real-life settings is still a challenge.

scholarly journals Exploring Triaging and Short-Term Outcomes of Early Invasive Strategy in Non-ST Segment Elevation Acute Coronary Syndrome: A Report from Japanese Multicenter Registry

2020 ◽  
Vol 9 (4) ◽  
pp. 1106
Author(s):  
Nobuhiro Ikemura ◽  
Yasuyuki Shiraishi ◽  
Mitsuaki Sawano ◽  
Ikuko Ueda ◽  
Yohei Numasawa ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Hospital Mortality ◽  
Risk Score ◽  
Mortality Risk ◽  
Adverse Outcomes ◽  
Invasive Strategy ◽  
Coronary Syndrome ◽  
Increased Risk ◽  
Elevation Acute Coronary Syndrome ◽  
Early Invasive Strategy

This observational study aimed to examine the extent of early invasive strategy (EIS) utilization in patients with non-ST elevation acute coronary syndrome (NSTE-ACS) according to the National Cardiovascular Data Registry (NCDR) CathPCI risk score, and its association with clinical outcomes. Using a prospective multicenter Japanese registry, 2968 patients with NSTE-ACS undergoing percutaneous coronary intervention within 72 hours of hospital arrival were analyzed. Multivariable logistic regression analyses were performed to determine predictors of EIS utilization. Additionally, adverse outcomes were compared between patients treated with and without EIS. Overall, 82.1% of the cohort (n = 2436) were treated with EIS, and the median NCDR CathPCI risk score was 22 (interquartile range: 14–32) with an expected 0.3–0.6% in-hospital mortality. Advanced age, peripheral artery disease, chronic kidney disease or patients without elevation of cardiac biomarkers were less likely to be treated with EIS. EIS utilization was not associated with a risk of in-hospital mortality; yet, it was associated with an increased risk of acute kidney injury (AKI) (adjusted odds ratio: 1.42; 95% confidence interval: 1.02–2.01) regardless of patients’ in-hospital mortality risk. Broader use of EIS utilization comes at the cost of increased AKI development risk; thus, the pre-procedural risk-benefit profile of EIS should be reassessed appropriately in patients with lower mortality risk.

scholarly journals Prediction of HF-Related Mortality Risk Using Genetic Risk Score Alone and in Combination With Traditional Risk Factors

2021 ◽  
Vol 8 ◽  
Author(s):  
Dong Hu ◽  
Lei Xiao ◽  
Shiyang Li ◽  
Senlin Hu ◽  
Yang Sun ◽  
...  
Keyword(s):  
Risk Score ◽  
Mortality Risk ◽  
Genetic Risk Score ◽  
Low Risk ◽  
C Statistic ◽  
Whole Exome ◽  
Composite Risk ◽  
Traditional Risk Factors ◽  
Discriminant Power ◽  
Related Mortality

Background: Common variants may contribute to the variation of prognosis of heart failure (HF) among individual patients, but no systematical analysis was conducted using transcriptomic and whole exome sequencing (WES) data. We aimed to construct a genetic risk score (GRS) and estimate its potential as a predictive tool for HF-related mortality risk alone and in combination with traditional risk factors (TRFs).Methods and Results: We reanalyzed the transcriptomic data of 177 failing hearts and 136 healthy donors. Differentially expressed genes (fold change &gt;1.5 or &lt;0.68 and adjusted P &lt; 0.05) were selected for prognosis analysis using our whole exome sequencing and follow-up data with 998 HF patients. Statistically significant variants in these genes were prepared for GRS construction. Traditional risk variables were in combination with GRS for the construct of the composite risk score. Kaplan–Meier curves and receiver operating characteristic (ROC) analysis were used to assess the effect of GRS and the composite risk score on the prognosis of HF and discriminant power, respectively. We found 157 upregulated and 173 downregulated genes. In these genes, 31 variants that were associated with the prognosis of HF were finally identified to develop GRS. Compared with individuals with low risk score, patients with medium- and high-risk score showed 2.78 (95%CI = 1.82–4.24, P = 2 × 10−6) and 6.54 (95%CI = 4.42–9.71, P = 6 × 10−21) -fold mortality risk, respectively. The composite risk score combining GRS and TRF predicted mortality risk with an HR = 5.41 (95% CI = 2.72–10.64, P = 1 × 10−6) for medium vs. low risk and HR = 22.72 (95% CI = 11.9–43.48, P = 5 × 10−21) for high vs. low risk. The discriminant power of GRS is excellent with a C statistic of 0.739, which is comparable to that of TRF (C statistic = 0.791). The combination of GRS and TRF could significantly increase the predictive ability (C statistic = 0.853).Conclusions: The 31-SNP GRS could well distinguish those HF patients with poor prognosis from those with better prognosis and provide clinician with reference for the intensive therapy, especially when combined with TRF.Clinical Trial Registration:https://www.clinicaltrials.gov/, identifier: NCT03461107.

scholarly journals Association of NT-proBNP and Subsequent Major Adverse Cardiovascular and Cerebral Events in Patients with Successful PCI for ACS and Normal LVEF

2020 ◽  
Author(s):  
Jia-Li Wang ◽  
Shu-Mei Zhao ◽  
Hui Chen ◽  
Chun-Yan Guo ◽  
Xue-Qiao Zhao ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Risk Score ◽  
Area Under The Curve ◽  
Coronary Intervention ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Coronary Syndrome ◽  
Increased Risk ◽  
Normal Lvef

Abstract Background: Does N-Terminal pro-brain natriuretic peptide (NT-proBNP) predict subsequent major adverse cardiovascular and cerebral event (MACCE) in patients received successful percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS) and had normal left ventricular ejection fraction (LVEF)? Methods: 3986 ACS patients were divided into 4 groups based on the quartile (Q) values of peak NT-proBNP measured during hospitalization. All patients were followed for MACCE, a composite of all-cause death, non-fatal myocardial infarction (MI) or stroke, and heart failure requiring hospitalization (HFRH), during a median of 35 months. The incidence of MACCE was compared among Q1-Q4. Receiver operation characteristic curves (ROC) were generated to compare the area under the curve (AUC) for MACCE, cardiovascular (CV) death and HFRH by adding NT-proBNP to the TIMI (thrombolysis in myocardial infarction) risk score.Results: The incidences of MACCE (5.6%, 9.1%, 13.0%, 20.1%, P <0.001), all-cause death (1.0%, 2.5%, 4.1%, 8.4%, P <0.001), non-fatal MI (2.0%,3.4%,4.8%,6.2%, P <0.001) and HFRH (1.5%, 2.3%, 4.1%, 5.9%, P <0.001) were significantly increased from Q1 to Q4, but, not stroke (1.4%, 1.4%, 1.3%, 2.1%, P =0.438). Each median level (337pg/ml) increase in NT-proBNP was significantly and independently associated with increased risk of MACCE (HR 1.02, 95%CI, 1.01-1.03; P <0.001). Compared with TIMI (thrombolysis in myocardial infarction) risk score alone, TIMI+NT-proBNP showed improved AUCs: CV death (0.76 vs. 0.72, P =0.0008), and HFRH (0.68 vs. 0.66, P =0.0017), MACCE (0.70 vs. 0.69, P =0.0012), respectively. Conclusion: NT-proBNP was significantly and independently associated with increased risk of subsequent MACCE in 3 years in ACS patients who received successful PCI and had normal LVEF, and improved the prognosis of major adverse events in addition to the TIMI risk score.

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