Predictor Of One-year Mortality Research Articles

Left and right ventricular function measured by strain analysis predicts one-year mortality in ventricular tachycardia ablation patients with structural heart disease

Abstract Introduction Radiofrequency catheter ablation has become increasingly important in the management of scar-related ventricular tachycardia (VT). Wide range of clinical and procedural predictors of VT recurrence and mortality are described by previous studies. However, currently known echocardiographic predictors are mainly limited to conventional echo parameters. Speckle tracking echocardiography (STE) is a novel and promising method to evaluate systolic and diastolic function. STE derived parameters, such as global longitudinal strain (GLS), are proven predictors of mortality and arrhythmic events in heart diseases. Aims The aim of the present study was to assess whether STE derived parameters could be considered predictors of VT recurrence and mortality after catheter ablation of ventricular tachycardia in patients with structural heart disease. Methods We measured pre-ablation strain and strain rate parameters with speckle tracking. Left ventricular four-chamber longitudinal strain (LV4CLS), early diastolic strain rate (e’-sr), right ventricular free wall longitudinal strain (RVFWLS) and right ventricular four-chamber longitudinal strain (RV4CLS) were assessed in 73 cases. Patients were followed up for one year. Endpoints were VT recurrence, which was defined as either ICD recording of a sustained VT episode requiring therapy or an episode of sustained VT recorded on 12-lead ECG or CIED, and all-cause mortality. Results Univariate analysis using the Cox proportional hazard model revealed that only lower LVEF (HR: 0.52 [0.31-0.88], p=0.02) was associated with higher VT recurrence in the first year among the echocardiographic parameters. High LVEDD (HR: 3.9 [1.31-11.65], p=0.02), low TAPSE (HR: 0.52 [0.28-0.98], p=0.04), low LV4CLS (HR: 0.4 [0.19-0.83], p=0.01) and low RV4CLS (HR: 0.31 [0.13-0.72], p<0.01) were the predictors of 1-year mortality. Conclusion Strain parameters, especially right ventricular four-chamber longitudinal strain may supplement conventional echocardiographic parameters in the prediction of one-year mortality after catheter ablation in patients with structural heart disease. Further prospective clinical studies are necessary to verify the significance of strain parameters.

Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients

BackgroundMachine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome. MethodsThis study was performed on 13,682 patients at Tehran Heart Center from 2015 to 2021. Patients were split into 70:30 for testing and training. Four ML models were designed: a traditional Logistic Regression (LR) model, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ada Boost models. The importance of features was calculated using the RF feature selector and SHAP based on the XGBoost model. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) for the prediction on the testing dataset was the main measure of the model's performance. ResultsFrom a total of 9,073 patients with >1-year follow-up, 340 participants died. Higher age and higher rates of comorbidities were observed in these patients. Body mass index and lipid profile demonstrated a U-shaped correlation with the outcome. Among the models, RF had the best discrimination (AUC 0.866), while the highest sensitivity (80.9%) and specificity (88.3%) were for LR and XGBoost models, respectively. All models had AUCs of >0.8. ConclusionML models can predict 1-year mortality after PCI with high performance. A classic LR statistical approach showed comparable results with other ML models. The individual-level assessment of inter-variable correlations provided new insights into the non-linear contribution of risk factors to post-PCI mortality.

Serum Copeptin Level as a Predictor of One-Year Mortality after Acute Decompensated Heart Failure Hospitalization

Background: Acutely decompensated heart failure patients have variable clinical outcomes. Some predictors of mortality in acute heart failure as resting heart rate, blood pressure, Pro-BNP level, and others have been identified. This study addresses the question whether copeptin may add value in the one-year prognosis in this category of high-risk patients. Results: To assess the relationship of serum copeptin level with one year mortality, we evaluated serum copeptin at admission and after 72 hours in 106 consecutive patients hospitalized for acute decompensated heart failure between May 2021 and November 2021 in a tertiary care hospital. Twenty-four patients (23.3%) died during the one-year follow-up period. The mortality was significantly higher in patients with advancing age, male sex, smokers, NYHA class IV heart failure, lower ejection fractions, higher heart rate, higher burden of arrhythmic events, lower creatinine clearance, and higher levels of copeptin at baseline and at day 3. Copeptin level at day 1 with cutoff value of ˃25.4 pmol/l predicted mortality with sensitivity 87.5% and specificity 70.73% [hazard ratio (95% confidence interval): (0.725 - 0.898), P<0.001], while copeptin level at day 3 with cutoff value ˃ 26.1 pmol/l predicted mortality with sensitivity 100% and specificity 73.17% [hazard ratio (95% confidence interval): (0.876 - 0.981), P<0.001]. Finally, increase in copeptin level from bassline was independently associated with increased mortality. (p˂0.001). Conclusions: Elevated copeptin level at baseline and 72 hours post-admission is suggested to be a strong predictor of one year mortality in patients with acute decompensated heart failure.

One-year mortality prediction for patients with sepsis: a nomogram integrating lactic dehydrogenase and clinical characteristics

BackgroundTo explore the association between myocardial enzymes and one-year mortality, and establish a nomogram integrating myocardial enzymes and clinical characteristics to predict one-year mortality among sepsis patients.MethodsData of 1,983 sepsis patients were extracted from Medical Information Mart for Intensive Care III database in this retrospective cohort study. All participants were randomly split into the training set for the development of model and testing set for the internal validation at the ratio of 7:3. Univariate logistic regression was used to screen variables with statistical differences which were made for stepwise regression, obtaining the predictors associated with one-year mortality of sepsis patients. Adopted multivariate logistic regression to assess the relationship between myocardial enzymes and one-year mortality of sepsis patients. A nomogram was established in predicting the one-year survival status of sepsis patients, and the performance of developed model were compared with LDH alone, sequential organ failure assessment (SOFA), simplified acute physiology score II (SAPS II) by receiver operator characteristic, calibration, and decision curves analysis.ResultsThe result found that LDH was associated with one-year mortality of sepsis patients [odds ratio = 1.28, 95% confidence interval (CI): 1.18–1.52]. Independent predictors, including age, gender, ethnicity, potassium, calcium, albumin, hemoglobin, alkaline phosphatase, vasopressor, Elixhauser score, respiratory failure, and LDH were identified and used to establish the nomogram (LDH-model) for predicting one-year mortality for sepsis patients. The predicted performance [area under curve (AUC) = 0.773, 95%CI: 0.748–0.798] of this developed nomogram in the training and testing sets (AUC = 0.750, 95%CI: 0.711–0.789), which was superior to that of LDH alone, SOFA score, SAPS II score. Additionally, calibration curve indicated that LDH-model may have a good agreement between the predictive and actual outcomes, while decision curve analysis demonstrated clinical utility of the LDH-model.ConclusionLDH level was related to the risk of one-year mortality in sepsis patients. A prediction model based on LDH and clinical features was developed to predict one-year mortality risk of sepsis patients, surpassing the predictive ability of LDH alone as well as conventional SAPS II and SOFA scoring systems.

Predictors of one-year mortality following hip fracture surgery in elderly.

Understanding mortality risk factors is critical to reducing mortality among elderly hip fracture patients. To investigate the effects of admission and post-operative levels of distribution width of red blood cells (RDW), albumin, and RDW/albumin (RA) ratio on predicting 1-year mortality following hip fracture surgery. A retrospective study was conducted on 275 elderly patients who underwent hip fracture surgery in a tertiary hospital between January 2018 and January 2022. Deaths within one year of hip fracture were defined as the deceased group. The survivors were defined as those who survived for at least one year. The relationship between admission and post-operative levels of RDW, albumin, RA, and mortality within one year after hip surgery was assessed statistically, including binary logistic regression analysis. The study also assessed other factors related to mortality. One-year mortality was 34.7%. There was a 3.03-year (95% CI [1.32-4.75]) difference between the deceased (79.55 ± 8.36 years) and survivors (82.58 ± 7.41 years) (p<0.001). In the deceased group, the mean hemoglobin (HGB) values at admission (p=0.022) and post-operative (p=0.04) were significantly lower. RDW values at admission (p=0.001) and post-op (p=0.001) were significantly lower in the survivor group. The mean albumin values at admission (p<0.001) and post-operative (p<0.001) in the survivor group were significantly higher than in the deceased group. A significant difference was found between the survivor group and the deceased group in terms of mean RA ratio at admission and post-operative (p<0.001). Based on binary logistic regression analysis, presence of chronic obstructive pulmonary disease (COPD) (OR 3.73, 95% CI [1.8-7.76]), RDW (OR 1.78, 95% CI [1.48-2.14]), and albumin (OR 0.81, 95% CI [0.75-0.87]), values at admission were found to be independent predictors of 1-year mortality in elderly patients with hip fracture. Based on this study, presence of COPD, higher RDW, and lower albumin levels at admission were independent predictors of 1-year mortality following hip fracture surgery in the elderly.

Association between the triglyceride glucose index and in-hospital and 1-year mortality in patients with chronic kidney disease and coronary artery disease in the intensive care unit

ObjectiveThis study aimed to explore the association between the triglyceride glucose index (TyG) and the risk of in-hospital and one-year mortality in patients with chronic kidney disease (CKD) and cardiovascular disease (CAD) admitted to the intensive care unit (ICU).MethodsThe data for the study were taken from the Medical Information Mart for Intensive Care-IV database which contained over 50,000 ICU admissions from 2008 to 2019. The Boruta algorithm was used for feature selection. The study used univariable and multivariable logistic regression analysis, Cox regression analysis, and 3-knotted multivariate restricted cubic spline regression to evaluate the association between the TyG index and mortality risk.ResultsAfter applying inclusion and exclusion criteria, 639 CKD patients with CAD were included in the study with a median TyG index of 9.1 [8.6,9.5]. The TyG index was nonlinearly associated with in-hospital and one-year mortality risk in populations within the specified range.ConclusionThis study shows that TyG is a predictor of one-year mortality and in-hospital mortality in ICU patients with CAD and CKD and inform the development of new interventions to improve outcomes. In the high-risk group, TyG might be a valuable tool for risk categorization and management. Further research is required to confirm these results and identify the mechanisms behind the link between TyG and mortality in CAD and CKD patients.

Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials.

To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration). The developed algorithms distinguished between patients at high and low risk for 90-day and one-year mortality. The penalized logistic regression algorithm had the best performance metrics for both 90-day (c-statistic 0.80, calibration slope 0.95, calibration intercept -0.06, and Brier score 0.039) and one-year (c-statistic 0.76, calibration slope 0.86, calibration intercept -0.20, and Brier score 0.074) mortality prediction in the hold-out set. Using high-quality data, the ML-based prediction models accurately predicted 90-day and one-year mortality in patients aged 50 years or older with a FNF. The final models must be externally validated to assess generalizability to other populations, and prospectively evaluated in the process of shared decision-making.

One-year mortality after hospitalization for acute heart failure: Predicting factors (PRECIC study subanalysis)

Introduction and objectivesOne-year mortality after hospitalization for heart failure (HF) is high. This study aims to identify predictive factors of one-year mortality. MethodsThis is a retrospective, single-center and observational study. All patients hospitalized for acute HF during one year were enrolled. ResultsA total of 429 patients were enrolled, mean age of 79 years. The in-hospital and one-year all-cause mortality rates were 7.9% and 34.3%, respectively. In the univariable analysis, the factors significantly associated with higher one-year mortality risk were: age ≥80 years (odds ratio (OR)=2.05, 95% confidence interval (CI) 1.35–3.11, p=0.001); active cancer (OR=2.93, 95% CI 1.36–6.32, p=0.008); dementia (OR=2.84, 95% CI 1.81–4.47, p<0.001); functional dependency (OR=2.63, 95% CI 1.65–4.19, p<0.001); atrial fibrillation (OR=1.86, 95% CI 1.24–2.80, p=0.004); higher creatinine (OR=2.03, 95% CI 1.29–3.21, p=0.002), urea (OR=2.92, 95% CI 1.95–4.36, p<0.001) and red cell distribution width (RDW; 4thQ OR=5.59, 95% CI 3.03–10.32, p=0.001); and lower hematocrit (OR=0.94, 95% CI 0.91–0.97, p<0.001), hemoglobin (OR=0.83, 95% CI 0.75–0.92, p<0.001) and platelet distribution width (PDW; OR=0.89, 95% CI 0.82–0.97, p=0.005). In the multivariable analysis, the independent predictors of higher one-year mortality risk were: age ≥80 years (OR=2.05, 95% CI 1.21–3.48); active cancer (OR=2.70, 95% CI 1.03–7.01); dementia (OR=2.69, 95% CI 1.53–4.74); higher urea (OR=2.97, 95% CI 1.84–4.80) and RDW (4thQ OR=5.24, 95% CI 2.55–10.76); and lower PDW (OR=0.88, 95% CI 0.80–0.97). ConclusionsActive cancer, dementia, and high values for urea and RDW at admission are predictors of one-year mortality in patients hospitalized for HF. These variables are readily available at admission and can support the clinical management of HF patients.

Clinical application of artificial intelligence algorithm for prediction of one-year mortality in heart failure patients.

Risk prediction for heart failure (HF) using machine learning methods (MLM) has not yet been established at practical application levels in clinical settings. This study aimed to create a new risk prediction model for HF with a minimum number of predictor variables using MLM. We used two datasets of hospitalized HF patients: retrospective data for creating the model and prospectively registered data for model validation. Critical clinical events (CCEs) were defined as death or LV assist device implantation within 1year from the discharge date. We randomly divided the retrospective data into training and testing datasets and created a risk prediction model based on the training dataset (MLM-risk model). The prediction model was validated using both the testing dataset and the prospectively registered data. Finally, we compared predictive power with published conventional risk models. In the patients with HF (n = 987), CCEs occurred in 142 patients. In the testing dataset, the substantial predictive power of the MLM-risk model was obtained (AUC = 0.87). We generated the model using 15 variables. Our MLM-risk model showed superior predictive power in the prospective study compared to conventional risk models such as the Seattle Heart Failure Model (c-statistics: 0.86 vs. 0.68, p < 0.05). Notably, the model with an input variable number (n = 5) has comparable predictive power for CCE with the model (variable number = 15). This study developed and validated a model with minimized variables to predict mortality more accurately in patients with HF, using a MLM, than the existing risk scores.

Influence of Body Mass Index in Donor-Recipient Size Mismatch in Pediatric Heart Transplantation.

Body weight is the traditional metric for matching donor and recipient size for pediatric heart transplantation (pHT). We hypothesized that mismatch in body mass index (BMI) or body surface area (BSA) rather than weight is better associated with outcomes of transplantation and therefore should be used for donor-recipient size matching. Analysis of the United Network for Organ Sharing database limited to pHT recipients was performed. Donor and recipient mismatch groups were created for weight, BMI, and BSA ratios. Differences in recipient characteristics between each cohort and the impact of mismatch on outcomes were statistically analyzed. A total of 4,465 patients were included in the analysis of which 43% had congenital heart disease (CHD). There were significant differences in patient characteristics by matching, independent of the matching parameter. Multivariable regression analysis showed that a low donor-recipient BMI ratio (compared to normal) (CHD OR 1.70; non-CHD 2.78) was a predictor of one-year mortality (all P < .001) in both CHD and non-CHD cohorts. Low BMI ratio was also associated with worse long-term survival in non-CHD groups, but not in the CHD cohort. Weight and BSA ratio did not predict one year or long-term survival. The use of low BMI donors compared to recipient may predict poor early and long-term survival and therefore should be avoided in pHT. The use of BMI matching may improve donor-recipient matching in pHT.

Improving prediction of one-year mortality of acute myocardial infarction using machine learning techniques

Improving prediction of one-year mortality of acute myocardial infarction using machine learning techniques

Combination of the Barthel Index at Discharge with GRACE Leads to Improved One-Year Mortality Prediction in Older Patients with Acute Myocardial Infarction.

Many older patients with acute myocardial infarction (AMI) have impaired ability for activities of daily living (ADL). Impaired ADL leads to poor prognosis in elderly patients. The Global Registry of Acute Coronary Events (GRACE) score is widely used for risk stratification in AMI patients but does not consider physical performance, which is an important prognosis predictor for older adults. This study assessed whether the Barthel Index (BI) score combine the GRACE score would achieve improved one-year mortality prediction in older AMI patients. This single-center retrospective study included 688 AMI patients aged ≥65 years who were divided into an impaired ADL group (BI ≤60, n = 102) and a normal ADL group (BI >60, n = 586) based on BI scores at discharge. The participants were followed up for one year. Cox survival models were constructed for BI score, GRACE score, and BI score combined GRACE score for one-year mortality prediction. Patients had a mean age of 76.29 ± 7.42 years, and 399 were men (58%). A lower BI score was associated with more years of hypertension and diabetes, less revascularization, longer hospital stays, and higher one-year mortality after discharge. Multivariable Cox regression analysis identified BI as a significant risk factor for one-year mortality in older AMI patients (HR 0.977, 95% CI, 0.963-0.992, P = 0.002). BI (0.774, 95% CI: 0.731-0.818) and GRACE (0.758, 95% CI: 0.704-0.812) scores had similar predictive power, but their combination outperformed either score alone (0.810, 95% CI: 0.770-0.851). BI at discharge is a significant risk factor for one-year mortality in older AMI patients, which can be better predicted by the combination of BI and GRACE scores.

Outcomes and predictors of one-year mortality in patients hospitalized with Acute Heart Failure.

Registries of patients hospitalized with acute heart failure (AHF) provided useful description of characteristics and outcomes. However, a contemporary registry which provides sufficient evidence on outcomes after discharge is needed. The study aims to identify 1-year clinical outcomes and prognostic predictors of patients hospitalized with AHF. This is a retrospective registry which enrolled patients who were hospitalized due to a principal diagnosis of AHF in a tertiary care center in Thailand between July 2017 and June 2019. Baseline characteristics and hospital courses between the deceased patients and the survivors at 1year were compared. Prognostic predictors for 1-year mortality were analyzed using Cox regression model. A total of 759 patients were enrolled (mean age of 68.9±15years, 49.8% men, mean ejection fraction of 47.1±19.2%, 55.7% heart failure reduced ejection fraction (HFrEF)). Among these, 40.7% had no history of heart failure. The in-hospital and 1-year mortality was 5.8% and 21.5%, respectively. Patients with HFrEF had lower 1-year mortality compared to those without (HR=0.57, p=0.04). Age≥70years, the history of heart failure, prior heart failure hospitalization, cerebrovascular accident (CVA), reactive airway disease, cancer, length of stay>10days and NT-proBNP≥10,000pg/mL were associated with higher 1-year mortality (p<0.05). The multivariate analysis showed age, CVA and NT-proBNP were independent predictors. Patients with AHF had high mortality after discharge. Patients with poor prognostic predictors, such as elderly, may benefit from continuous care. The study is the most recent registry of patients with AHF in Thailand.

Abstract 9403: Sex Differences in Clinical Features, Utilization of Oral Anticoagulant Agents and One Year Outcome in the Middle East. the Jordan Atrial Fibrillation Study

Introduction: Atrial fibrillation (AF) is the most common sustained arrhythmia in women and men on a global level. Sex-specific differences in the baseline clinical and demographic features, management strategies and prognosis in patients with AF have been studied extensively but have received less attention in the Middle East Hypothesis: To compare the clinical characteristics, therapeutic strategies and one-year incidence of major cardiovascular events between women and men with AF Methods: The Jordan AF Study enrolled consecutive patients with AF in 29 hospitals and cardiology clinics in Jordan from May 2019 through December 2020. Patients were followed up for one year. We used the study data to compare clinical features, use of pharmaceutical agents and outcome in women compared with men Results: Of 2020 AF patients enrolled, 1095 (54.2%) were women. Women were older than men (mean age±SD 69.4±11.1 vs. 66.0±14.7 years, p&lt;0.001), and had higher prevalence of hypertension, diabetes, and obesity. Compared with men, women with nonvalvular AF had higher mean CH2DS2-VASc score(4.1±1.7 vs. 3.0±1.8, p&lt;0.001) and HAS-BLED score (1.7±1.1 vs. 1.6±1.2, p=0.05). High-risk CHA2DS2-VASc score was present in 83.9% of women and 78.9% of men (p=0.07), and oral anticoagulant agents were prescribed for 84.7% of women and 81.7% of men (p=0.14).At one year, women and men did not differ significantly in rates of all-cause death (12.0% vs. 13.1%, p=0.51), stroke/systemic embolism (4.1% vs. 4.8%, p=0.44), major bleeding events (2.0% vs. 3.2%, p=0.13), and intracranial bleeding (0.3% vs. 0.8%, p=0.24). Multiple logistic regression analysis showed no sex differences in one-year all-cause death. Moreover, age &gt; 70 years, hypertension, diabetes, heart failure, ischemic heart disease were associated with higher incidence of all-cause death Conclusions: Despite the fact that Middle Eastern women with AF had worse baseline clinical profile and risk scores, they did not differ from men in one-year outcome. Female sex was not a predictor of one-year mortality in this cohort.

Prognostic significance of HDL-C on long-term mortality in patients with COVID-19 pneumonia in the Turkish population: A potential mechanism for population differences.

Coronavirus disease 2019 (COVID-19) is diagnosed by the evidence of the presence of multiple phenotypes, including thrombosis, inflammation, and alveolar and myocardial damage, which can cause severe illness and mortality. High-density lipoprotein cholesterol (HDL-C) has pleiotropic properties, including anti-inflammatory, anti-infectious, antithrombotic, and endothelial cell protective effects. The aim of this study was to investigate the HDL-C levels and one-year mortality after the first wave of patients with COVID-19 were hospitalized. Data from 101 patients with COVID-19 were collected for this single-center retrospective study. Lipid parameters were collected on the admission. The relationship between lipid parameters and long-term mortality was investigated. The mean age of the non-survivor group (n = 38) was 68.8 ± 14.1 years, and 55% were male. The HDL-C levels were significantly lower in the non-survivors group compared with the survivors (26.9 ± 9.5 vs 36.8 ± 12.8 mg/dl, respectively p < 0.001). Multivariate regression analysis determined that age, C-reactive protein, D-dimer, hypertension, and HDL-C as independent predictors for the development of COVID-19 mortality. HDL-C levels <30.5 mg/dl had 71% sensitivity and 68% specificity to predict one-year mortality after COVID-19. The findings of this study showed that HDL-C is a predictor of one-year mortality in Turkish patients with COVID-19. COVID-19 is associated with decreased lipid levels, and it is an indicator of the inflammatory burden and increased mortality rate. The consequences of long-term metabolic dysregulations in patients that have recovered from COVID-19 still need to be understood.

Interpretability Analysis of One-Year Mortality Prediction for Stroke Patients Based on Deep Neural Network.

Clinically, physicians collect the benchmark medical data to establish archives for a stroke patient and then add the follow up data regularly. It has great significance on prognosis prediction for stroke patients. In this paper, we present an interpretable deep learning model to predict the one-year mortality risk on stroke. We design sub-modules to reconstruct features from original clinical data that highlight the dissimilarity and temporality of different variables. The model consists of Bidirectional Long Short-Term Memory (Bi-LSTM), in which a novel correlation attention module is proposed that takes the correlation of variables into consideration. In experiments, datasets are collected clinically from the department of neurology in a local AAA hospital. It consists of 2,275 stroke patients hospitalized in the department of neurology from 2014 to 2016. Our model achieves a precision of 0.9414, a recall of 0.9502 and an F1-score of 0.9415. In addition, we provide the analysis of the interpretability by visualizations with reference to clinical professional guidelines.

Predictors of 1‑year mortality in ambulatory patients with advanced heart failure awaiting heart transplant.

Heart failure (HF) is a complex syndrome involving diverse pathways and pathological processes that can manifest themselves in circulation as abnormal levels of various biomarkers. The aim of the study was to assess the factors associated with a worse prognosis in patients with advanced HF awaiting heart transplant during a 1‑year follow‑up. We prospectively assessed the data of 203 adult patients with advanced HF, who were hospitalized at our institution between 2016 and 2018. The study end point was all‑cause death during a 1‑year follow‑up. The median age of patients was 57 years (range, 52-60); 87.7% of patients were male. During follow‑up, 62 patients (30.5%) died. Serum levels of procalcitonin (hazard ratio [HR], 1.027; 95% CI, 1.020-1.034; P <0.001; per 10‑unit increase), high‑sensitivity C‑reactive protein (hs‑CRP; HR, 1.099; 95% CI, 1.016-1.883; P = 0.02; per 1‑unit increase), sodium (HR, 1.171; 95% CI, 1.076-1.272; P <0.001; per 1 ‑unit increase), and N ‑terminal pro-B ‑type natriuretic peptide (NT ‑proBNP; HR, 1.068; 95% CI, 1.033-1.105; P <0.001; per 1000‑unit increase) were independent risk factors for mortality. Procalcitonin generated the largest area under the curve (0.780; 95% CI, 0.712-0.848). Our study showed that higher serum hs ‑CRP, NT‑proBNP, and procalcitonin levels and lower serum sodium levels were independent risk factors for death during a 1‑year follow‑up in patients with advanced HF. Procalcitonin showed the strongest predictive power, sensitivity, and specificity, allowing for an effective identification of 1‑year survivors and nonsurvivors awaiting heart transplant.

Abstract 12426: Influence of Body Mass Index in Donor Recipient Size Mismatch in Pediatric Heart Transplantation

Introduction: Body weight has been the traditional metric for matching donor and recipient size for pediatric heart transplantation (pHT). However, the impact of such mismatch is not well established. We hypothesized that mismatch in compound measures such as body mass index (BMI) or body surface area (BSA) rather than weight, may impact outcomes of pHT and therefore should be used for donor-recipient size matching. Methods: Analysis of the United Network for Organ Sharing database limited to pHT recipients was performed. Donor and recipient mismatch tertiles were created for weight, BMI, and BSA ratios. Differences in recipient characteristics between each tertile were studied using univariate analysis. Multivariable analysis was used to assess the impact of each mismatch on one-year survival. Kaplan Meier analysis was used to assess overall survival. Results: A total of 4,465 pHT patients were available and included in the analysis of which 43% had congenital heart disease (CHD). Overall, those with low donor-recipient ratio for BMI were more likely to be younger recipients, less likely to be Caucasian and be on ECMO (all p&lt;0.01). In both CHD and non-CHD cohorts, multivariable regression analysis showed that use of ECMO (CHD OR 3.97; non-CHD OR 3.24), presence of liver dysfunction (CHD OR 1.10; non-CHD OR 1.11), and low donor to recipient BMI ratio (compared to normal) (CHD OR 1.70; non-CHD OR 2.28) were the only predictors of one-year mortality (all p &lt;0.001). Low BMI ratio was also associated with worse long-term survival in non-CHD groups, but not in the CHD cohort. This finding probably reflects inferior overall outcomes in the CHD cohort. Weight and BSA ratio did not predict one year or long-term survival. Conclusions: Traditional matching practice using weight may be inadequate and use of BMI matching may provide improved donor-recipient matching. Use of low BMI donors compared to recipients may predict poor early and long-term survival and therefore should be avoided in pediatric heart transplantation. Further refinement of matching may be needed in specific recipient populations.

Novel risk model for one-year mortality and guidance of management strategy in isolated severe secondary tricuspid regurgitation

Abstract Background Isolated severe tricuspid regurgitation (TR) is a challenging valve lesion to manage because of adverse prognosis, and surgery remains controversial especially in secondary etiologies. We aimed to devise a risk model to predict one-year mortality in isolated secondary TR patients, and evaluated whether risk category influenced the association between tricuspid valve surgery and survival. Methods Consecutive patients with isolated severe secondary TR by echocardiography during 2004–2018 were retrospectively studied. A multivariable model was developed for one-year all-cause mortality from two thirds of study patients (randomly-selected derivation cohort), and then assessed in the remaining patients (validation cohort). Associations between tricuspid valve surgery and survival were analyzed overall and in risk categories in multivariable analyses. Results Amongst 8575 isolated TR patients with secondary etiologies, mean age was 71.3±14.5 years and 5153 (60.1%) were female. Tricuspid valve surgery was performed in 487 (5.7%) patients, and was independently associated with improved survival during follow-up with hazards ratio 0.57 (95% confidence interval 0.41–0.81) in multivariable analysis. One-year mortality occurred in 2108 (24.6%) patients, and an additive risk score out of 14 based on independent predictors of one-year mortality is shown in the Table 1. The c-statistic for the derivation cohort was 0.712, and for the validation cohort 0.729. By risk score category, one-year mortality was 5.7% for 0–1 (low), 10.1% for 2–3 (mildly elevated), 24.3% for 4–7 (moderately elevated) and 50.9% for &amp;gt;7 (severely elevated), as shown in Figure 1. Surgery was associated with improved survival in the three higher score categories of isolated TR patients (P&amp;lt;0.001 to P=0.037), but did not improve survival in lower risk patients with scores of 0–1 (P=0.140). Conclusion We devised and validated a novel risk model which moderately discriminated one-year mortality for isolated severe secondary TR. The score may help guide decision-making for management strategy in those with elevated scores, when the under-utilized tricuspid valve surgery is associated with improved survival, and further research is necessary to apply this score for those considering for transcatheter therapies. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): National Heart Foundation of New Zealand Table 1Figure 1

Hypoalbuminaemia and One-Year Mortality in Haemodialysis Patients with Heart Failure: A Cohort Analysis.

Introduction: The prevalence of chronic kidney disease (CKD) and heart failure (HF) has been rising over the past decade, with a prevalence close to 40%. Cardiovascular disease and malnutrition are common comorbidities and known risk factors for mortality in haemodialysis (HD) patients. We aimed to evaluate the one-year mortality rate after dialysis induction, and the impact of serum albumin levels on survival outcomes, in patients with CKD and HF. Methods: This was a retrospective analysis of patients with CKD and HF who underwent chronic HD between January 2016 and December 2019 in a tertiary-care Portuguese hospital. Variables were submitted to univariate and multivariate analysis to determine factors predictive of one-mortality after HD start. Results: In total, 204 patients were analysed (mean age 75.1 ± 10.3 years). Within the first year of HD start, 28.7% of patients died. These patients were significantly older [79.8 ± 7.2 versus 72.9 ± 10.9 years, p < 0.001; OR 1.08 (1.04–1.13), p < 0.001] and had a higher mean Charlson Index [9.0 ± 1.8 versus 8.3 ± 2.0, p = 0.015; OR 1.22 (1.04–1.44), p = 0.017], lower serum creatinine [5.1 ± 1.6 mg/dL versus 5.8 ± 2.0 mg/dL; p = 0.021; OR 0.80 (0.65–0.97), p = 0.022], lower albumin levels [3.1 ± 0.6 g/dL versus 3.4 ± 0.6 g/dL, p < 0.001; OR 0.38 (0.22–0.66), p = 0.001] and started haemodialysis with a central venous catheter more frequently [80.4% versus 66.2%, p = 0.050]. Multivariate analysis identified older age [aOR 1.07 (1.03–1.12), p = 0.002], lower serum creatinine [aOR 0.80 (0.64–0.99), p = 0.049] and lower serum albumin [aOR 0.41 (0.22–0.75), p = 0.004] as predictors of one-year mortality. Conclusion: In our cohort, older age, lower serum creatinine and lower serum albumin were independent risk factors for one-year mortality, highlighting the prognostic importance of malnutrition in patients starting chronic HD.