Predictor Of All-cause Mortality Research Articles

Characterization and Mortality Risk of Impaired Left Ventricular Filling in COPD.

In COPD, impaired left ventricular (LV) filling might be associated with coexisting HFpEF or due to reduced pulmonary venous return indicated by small LV size. We investigate the all-cause mortality associated with small LV or HFpEF and clinical features discriminating between both patterns of impaired LV filling. We performed transthoracic echocardiography (TTE) in patients with stable COPD from the COSYCONET cohort to define small LV as LVEDD below the normal range and HFpEF features according to recommendations of the European Society of Cardiology. We assessed the E/A and E/e' ratios, NT-pro-BNP, hs-Troponin I, FEV1, RV, DLCo, and discriminated patients with small LV from those with HFpEF features or no relevant cardiac dysfunction as per TTE (normalTTE). The primary outcome was all-cause mortality after four and a half year. In 1752 patients with COPD, the frequency of small LV, HFpEF-features, and normalTTE was 8%, 16%, and 45%, respectively. Patients with small LV or HFpEF features had higher all-cause mortality rates than patients with normalTTE, HR: 2.75 (95% CI: [1.54 - 4.89]) and 2.16 (95% CI: [1.30 - 3.61]), respectively. Small LV remained an independent predictor of all-cause mortality after adjusting for confounders including exacerbation frequency and measures of RV, DLCo, or FEV1. Compared to normalTTE, patients with small LV had reduced LV filling, as indicated by lowered E/A. Yet in contrast to patients with HFpEF-features, patients with small LV had normal LV filling pressure (E/e') and lower levels of NT-pro-BNP and hs-Troponin I. In COPD, both small LV and HFpEF-features are associated with increased all-cause mortality and represent two distinct patterns of impaired LV filling.

Brain natriuretic peptide is a long-term cardiovascular predictor in carotid endarterectomy

: Background In noncardiac surgery, several biomarkers are known to play a role in predicting long-term complications, such as major adverse cardiovascular events (MACE), myocardial infarction, or death. Carotid endarterectomy (CEA) is considered a low to medium-risk surgery for carotid stenosis aimed at preventing stroke events. Brain natriuretic peptide (BNP) is a biomarker with potential prognostic value regarding MACE. Since its role in patients undergoing CEA is unknown, this study aims to assess the potential role of BNP as a short and long-term predictor of all-cause mortality and MACE in patients undergoing CEA. Methods From a prospective database, patients who underwent CEA under regional anesthesia (RA) at a tertiary hospital center were enrolled, and a post hoc analysis was conducted. Patients on which BNP levels were measured up to fifteen days before surgery, and two groups based on the BNP threshold (200 pg/mL) were defined and compared. Kaplan Meier survival curves and adjusted hazard ratios (aHR) were assessed by multivariable Cox regression. The primary outcome was the incidence of long-term MACE and all-cause mortality. Secondary outcomes included the incidence of AMI and AHF. Results A total of 89 patients were evaluated. The mean age of the cohort was 71.2 ± 8.7 years, with 71 (79.8%) males, and presented a median follow-up of 30 [13.5-46.4] months. BNP > 200 pg/mL has demonstrated positive predictive value for MACE (aHR: 5.569, confidence interval (CI): 2.441-12.7, p < 0.001) and all-cause mortality (aHR: 3.469, CI: 1.315-9.150, p = 0.018). Conclusion BNP has been demonstrated to independently predict long-term all-cause mortality, MACE and AMI following CEA. It serves as a low-cost, ready-to-use biomarker, although further studies are necessary.

Nocturnal Hypoxemic Burden in Patients with Heart Failure: Emerging Prognostic Role of Its Non-Specific Component

BackgroundNocturnal hypoxemic burden has been shown to be a robust, independent predictor of all-cause mortality in patients with heart failure and reduced ejection fraction (HFrEF) and to occur in a severe form even in patients with low or negligible frequency of respiratory events (apneas/hypopneas). This suggests the existence of two components of hypoxemic burden: one unrelated to respiratory events and the other related. The aim of this study was to characterize these two components and to evaluate their prognostic value. MethodsNocturnal hypoxemic burden was assessed in a cohort of 280 patients with HFrEF by measuring the percentage of sleep with an oxygen saturation (SpO2) <90% (T90), and the area of the SpO2 curve below 90% (Area90). Both indices were also recalculated within the sleep segments associated with respiratory events (event-related component: T90Eve, Area90Eve) and outside these segments (non-specific component: T90Nspec, Area90Nspec). The outcome of the survival analysis (Cox regression) was all-cause mortality. ResultsDuring a median follow-up of 60 months, 87 patients died. T90, Area90, and their components were significant in univariate analysis (p<0.05 all). However, when these indices were adjusted for known risk factors, T90, T90Nspec, Area90, and Area90Nspec remained statistically significant (p=0.018, hazard ratio (HR)=1.12, 95%CI=(1.02, 1.23); p=0.007, HR=1.20, 95%CI=(1.05, 1.37); p=0.020, HR=1.05, 95%CI=(1.01, 1.10); p=0.0006, HR=1.15, 95%CI=(1.06, 1.25)), whereas T90Eve and Area90Eve did not (p=0.27, p=0.28). These results were internally validated using bootstrap resampling. ConclusionsBy demonstrating a significant independent association of non-specific hypoxemic burden with all-cause mortality, this study suggests that this component of total nocturnal hypoxemic burden may play an important prognostic role in patients with HFrEF.

Accurate prediction of all-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease using electronic health records

Introduction and ObjectivesDespite the huge clinical burden of MASLD, validated tools for early risk stratification are lacking, and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty. We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model. Patients and Methodsn=940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction. Patient timelines, spanning 12 years, were fully-annotated with demographic/clinical characteristics, ICD-9 and -10 codes, blood test results, prescribing data, and secondary care activity. A Transformer neural network (TNN) was trained to output concomitant probabilities of 12-, 24-, and 36-month all-cause mortality. In-sample performance was assessed using 5-fold cross-validation. Out-of-sample performance was assessed in an independent set of n=528 MASLD patients. ResultsIn-sample model performance achieved AUROC curve 0.74-0.90 (95% CI: 0.72-0.94), sensitivity 64%-82%, specificity 75%-92% and Positive Predictive Value (PPV) 94%-98%. Out-of-sample model validation had AUROC 0.70-0.86 (95% CI: 0.67-0.90), sensitivity 69%-70%, specificity 96%-97% and PPV 75%-77%. Key predictive factors, identified using coefficients of determination, were age, presence of type 2 diabetes, and history of hospital admissions with length of stay >14 days. ConclusionsA TNN, applied to routinely-collected longitudinal electronic health records, achieved good performance in prediction of 12-, 24-, and 36-month all-cause mortality in patients with MASLD. Extrapolation of our technique to population-level data will enable scalable and accurate risk stratification to identify people most likely to benefit from anticipatory health care and personalized interventions.

External validation of a 2-year all-cause mortality prediction tool developed using machine learning in patients with stage 4-5 chronic kidney disease.

Chronic kidney disease (CKD) is associated with increased mortality. Individual mortality prediction could be of interest to improve individual clinical outcomes. Using an independent regional dataset, the aim of the present study was to externally validate the recently published 2-year all-cause mortality prediction tool developed using machine learning. A validation dataset of stage 4 or 5 CKD outpatients was used. External validation performance of the prediction tool at the optimal cutoff-point was assessed by the area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and specificity. A survival analysis was then performed using the Kaplan-Meier method. Data of 527 outpatients with stage 4 or 5 CKD were analyzed. During the 2years of follow-up, 91 patients died and 436 survived. Compared to the learning dataset, patients in the validation dataset were significantly younger, and the ratio of deceased patients in the validation dataset was significantly lower. The performance of the prediction tool at the optimal cutoff-point was: AUC-ROC = 0.72, accuracy = 63.6%, sensitivity = 72.5%, and specificity = 61.7%. The survival curves of the predicted survived and the predicted deceased groups were significantly different (p < 0.001). The 2-year all-cause mortality prediction tool for patients with stage 4 or 5 CKD showed satisfactory discriminatory capacity with emphasis on sensitivity. The proposed prediction tool appears to be of clinical interest for further development.

Coronary Artery Disease Is A Stronger Predictor of All-Cause Mortality Than Left Ventricular Ejection Fraction Among Patients With Newly Diagnosed Heart Failure: Insights From the WDHR.

In patients with newly diagnosed heart failure (HF) and left ventricular ejection fraction (LVEF) <50%, little is known whether LVEF per se or presence of coronary artery disease (CAD) provides independent prognostic information on all-cause mortality. Using the WDHR (Western Denmark Heart Registry), we identified 3620 patients with newly diagnosed HF and LVEF 10% to 49% referred for first-time coronary angiography as part of general workup of HF. Patients were stratified by LVEF (10%-35% versus 36%-49%) and presence of CAD. We estimated 10-year all-cause mortality risk and calculated hazard ratios adjusted for relevant comorbidities and risk factors (aHRs). CAD was present in 1592 (44%) patients. Lower LVEF was associated with a relative 15% increased 10-year mortality: 37% for LVEF 36% to 49% versus 42% for LVEF 10% to 35% (aHR, 1.15 [95% CI, 0.99-1.34]). This result did not change when stratified into those with CAD (52% versus 56%; aHR, 1.11 [95% CI, 0.91-1.35]) and those without CAD (27% versus 33%; aHR, 1.24 [95% CI, 0.97-1.57]). In comparison, presence and extent of CAD were associated with a relative 43% increased 10-year mortality (CAD versus no CAD, 55.0% versus 31.5%; aHR, 1.43 [95% CI, 1.25-1.64]). Compared with a matched general population, excess mortality risk was higher for patients with HF and CAD (54.7% versus 26.3%; aHR, 2.10 [95% CI, 1.85-2.39]) versus those with HF and no CAD (31.4% versus 17.2%; aHR, 1.76 [95% CI, 1.52-2.02]). Among newly diagnosed patients with HF and LVEF <50%, presence and extent of CAD are associated with substantial higher all-cause mortality risk than lower LVEF.

Predictors of overall mortality after endovascular abdominal aortic repair - A single centre study.

A current and ongoing challenge is to reduce patient mortality after endovascular abdominal aortic repair (EVAR). This study aimed to assess the predictors of all-cause mortality after EVAR. Data regarding the demographic characteristics, comorbidities, laboratory values, selected anatomical factors, post-EVAR treatment, surveillance and complications of patients who underwent elective EVAR for non-ruptured abdominal aortic aneurysm (AAA) between January 2010 and January 2021 were evaluated. Mortality was assessed until 10 October 2023. Multivariate analyses were performed after adjusting for age, hypertension, diabetes mellitus, dyslipidaemia, sex, smoking, number of lumbar arteries, patency of inferior mesenteric artery (IMA), IMA diameter and reinterventions. This study included 196 patients (183 men and 13 women) with a mean age of 72.4 ± 7.67 years. The overall mortality rate during a mean follow-up period of 5.75 ± 3.1 years was 50.0% (N = 98). The 2-, 5- and 10-year mortality rates were 9.7%, 32.0% and 66.6%, respectively. The mortality rates decreased by 59% in patients with reinterventions (hazard ratio [HR]: 0.41; 95% confidence interval [CI]: 0.23-0.73; p = .002) and by 59% in patients treated with angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARBs) (HR: 0.41; 95% CI: 0.26-0.66; p < .001). Chronic anticoagulation was associated with 2.09-fold higher mortality (HR: 2.09; 95% CI: 1.19-3.67; p = .010), and coronary artery disease (CAD) was associated with 1.74-fold higher mortality (HR: 1.74; 95% CI: 1.09-2.78; p = .021). Pre-EVAR AAA diameter and 1-year post-EVAR sac diameter were positively associated with mortality (HR: 1.05; 95% CI: 1.03-1.08; p < .001, and HR: 1.05; 95% CI: 1.03-1.07; p < .001, respectively), that is, an increase of pre-EVAR and/or 1-year post-EVAR AAA diameter by 1mm was associated with a 5% higher risk of all-cause mortality. Reinterventions and treatment with ACE inhibitors or ARBs may be associated with decreased post-EVAR mortality. A greater pre-EVAR, a post-EVAR AAA diameter, CAD and chronic anticoagulation were associated with higher all-cause mortality post-EVAR.

Right Ventriculoarterial Coupling Surrogates and Long-Term Survival in LVAD Recipients: Results of the ASSIST-ICD Multicentric Registry

Prediction of outcomes remains an unmet need in candidates for LVADs. The development of right-heart failure portends an excess in mortality rates, but imaging parameters of right ventricular systolic function have failed to demonstrate a prognostic role. By integrating pulmonary pressure, right ventriculoarterial coupling could fill this gap. The ASSIST-ICD registry was used to test right ventriculoarterial coupling as a surrogate parameter at implantation for the prediction of all-cause mortality. The ratio of the tricuspid annular-plane systolic excursion over the estimated systolic pulmonary pressure (TAPSE/sPAP) was not associated with long-term survival in univariate analysis (P = 0.89), nor was the pulmonary artery pulsatility index (PAPi) (P = 0.13). Conversely, the ratio of the right atrial pressure over the pulmonary capillary wedge pressure (RAP/PCWP) was associated with all-cause mortality (P < 0.01). After taking tricuspid regurgitation severity, LVAD indication, LVAD model, age, blood urea nitrogen levels, and pulmonary vascular resistance into account, RAP/PCWP remained associated with survival (HR 1.35 [1.10 - 1.65]; P < 0.01). Among pre-implant RVAC surrogates, only RAP/PCWP was associated with long-term all-cause mortality in LVAD recipients. This association was independent of established risk factors.

Modified NUTRIC Score as a Predictor of All-cause Mortality in Critically Ill Patients: A Systematic Review and Meta-analysis.

The purpose of our meta-analysis was to look at the impact of modified nutrition risk in the critically ill (mNUTRIC) on mortality in patients with critical illness. Literature relevant to this meta-analysis was searched in PubMed, Web of Science, and Cochrane Library till 26 August 2023. Prospective or retrospective studies, patients >18 years of age, studies that reported on mortality and mNUTRIC (mNUTRIC cut-off score) were included. The QUIPS tool was used to evaluate the risk for bias in prognostic factors. A total of 31 studies on mNUTRIC score, involving 13,271 patients were included. The summary area under the curve (sAUC) of 0.80 (95% CI: 0.76-0.83) illustrates the mNUTRIC score's strong discrimination. The pooled sensitivity was 0.79 (95% CI: 0.74-0.84) and pooled specificity was 0.68 (95% CI: 0.63-0.73). We found no discernible variation in the mNUTRIC's prediction accuracy among cut-off values of <5 and >5 in our subgroup analysis and sAUC values were 0.82 (95% CI: 0.78-0.85) and 0.78 (95% CI: 0.74-0.81), respectively. We observed that mNUTRIC can discriminate between critically ill individuals and predict their mortality. CRD42023460292. Prakash J, Verma S, Shrivastava P, Saran K, Kumari A, Raj K, et al. Modified NUTRIC Score as a Predictor of All-cause Mortality in Critically Ill Patients: A Systematic Review and Meta-analysis. Indian J Crit Care Med 2024;28(5):495-503.

Echocardiographic Predictive Factors of Worsening Outcome in Type 1 Cardiorenal Syndrome.

Type 1 cardiorenal syndrome (CRS) is defined as acute decompensated heart failure (AHF) leading to secondary acute kidney injury. Few studies have evaluated the reliability of transthoracic echocardiography (TTE) in assessing outcomes in patients with type 1 CRS. We sought to identify echocardiographic predictors of outcomes (death and rehospitalization) in patients with type 1 CRS. This was a prospective longitudinal monocentric study, conducted from December 2020 to December 2022 in the cardiology department of the Internal Security Forces Hospital in Marsa, Tunisia. 68 patients with type 1 CRS were included prospectively. Physical, biological, and echocardiographic data were collected during the index hospitalization and at 3 and 6 months of follow-up. The mean age was 69 ± 10.1 years with a male predominance (72.0%). The mortality rate during initial hospitalization for AHF was 11.7%. The all-cause mortality rate at six months was 22.0%. The rehospitalization rate was 38.0%. Severe tricuspid regurgitation (p = 0.031), the subaortic velocity time integral (LVOT-VTI) with a cut-off value of 16, a sensitivity (Se) of 65%, and a specificity (Sp) of 85% (Area under the curve (AUC) = 0.818, p < 0.001), the right ventricular fractional area change (RV-FAC) with a cut-off value of 16, a Se of 60% and a Sp of 81% (AUC = 0.775, p < 0.001) were independent predictors of the cumulative rates of rehospitalization and mortality at six months. Left ventricular ejection fraction (LVEF) < 35% (HR = 0.828, 95% CI: 0.689-0.995, p = 0.044) and the RV-FAC (HR = 0.564, 95% CI: 0.361-0.881, p = 0.012) were independent predictors of all-cause mortality. LVOT-VTI (AUC = 0.766, p < 0.001) was a significantly independent predictor of rehospitalization. This study confirmed that type 1 CRS is associated with a poor prognosis. LVEF, LVOT-VTI, and RV-FAC are simple, reproducible, and sensitive ultrasound parameters for predicting outcomes in patients with type 1 CRS.

Machine learning for development of ECG-based prediction models for atrial fibrillation, thromboembolism and mortality in patients with heart failure

Abstract Background Heart failure (HF) is a worldwide epidemic affecting more than 64 million people. Given its massive burden both socially and economically, early identification of individuals at risk for complications is important. Purpose To test the predictive performance of non-invasive models from 12-lead digitalized electrocardiography (ECG) and cycle ergometer for adverse outcome in individuals with heart failure. Methods 598 Patients scheduled for right heart catheterization due to unexplained dyspnea were screened. All ECG recorded at admission were further analysed using the software "ECG-Precision-Analysis V-1-2023" to measure the duration of amplified p-wave (APWD at 175mm/sec, 80mm/mV) and to confirm the diagnosis of inter-atrial block. Ergometer was performed in a supine position with increasing intensity adapted to individual capacity (25 or 50W). The maximum physical capacity (Watt-max) was determined. Follow-up (FU) was scheduled at 12-,36-, 60- and 120-month after discharge with the primary endpoint as MACCE (a composite of new onset of AF, ischemic stroke and peripheral embolism) in at baseline event-free patients and the secondary endpoint as all-cause mortality in the total cohort. Three machine learning (ML) algorithms (COX regression, BSR and LASSO regression) were used to select candidate variables and establish predictive models for MACCE and all-cause mortality. Model evaluation was performed in terms of discriminatory power using C-statistics for each endpoint. Results Prediction models developed using machine learning (ML) algorithms (COX regression, BSR and LASSO regression) identified Watt-max and/or APWD as core components for MACCE and mortality prediction . APWD and Watt-max were key components of ML models for prediction of thromboembolic risk and AF (MACCE) and reached accurate diagnostic performances (C-statistics up to 0.74), moreover, another model consisted of APWD and aIAB (advanced inter-atrial block) was established to serve as a reference to ML models with moderate performance for MACCE (mean C-statistics &amp;lt;0.70). All ML models for prediction of all-cause mortality included Watt-max and achieved C-statistics &amp;gt; 0.75. Conclusions In patients with heart failure, the combination of APWD, aIAB and maximum excercice capacity allow to identify patients at risk for AF and stroke, whereas mortality is predominantly predicted by maximum exercise capacity but not APWD.Kaplan-Meier curves for MACCE/MortalitytimeROC curves for MACCE/Mortality

#1458 Adverse muscle composition predicts all-cause mortality in CKD

Abstract Background and Aims Sarcopenia is common in CKD and associates with morbidity and mortality. A rapid and standardized MRI (Magnetic Resonance Imaging) scan protocol allows an accurate muscle composition analysis by quantification of skeletal muscle volume and muscle fat infiltration (MFI). The combined observation of low muscle volume and high muscle fat infiltration, i.e. adverse muscle composition (AMC), is a prevalent muscle composition phenotype within CKD and associates to poor function, high prevalence of comorbidity, and an increased risk for coronary heart disease (CHD). It has also been shown that AMC is an independent predictor of all-cause mortality in patients with non-alcoholic fatty liver disease and in general population. In this study we aimed to investigate muscle composition as predictor of all-cause mortality within participants with CKD in the UK Biobank imaging study (UKB). Method This is a prospective study including UKB participants with CKD defined as having an eGFR cystatin C &amp;lt; 60 ml/min/1, 73 m2. A control group was created by matching each participant with CKD for sex, age, and BMI to four participants with normal eGFR. Cystatin C was analyzed based on blood samples at time of study enrolment 7-9 years prior to the MRI. Fat-tissue free thigh muscle volume and MFI were quantified using a rapid neck-to-knee water and fat separated MRI protocol and automated image analysis (AMRA® Researcher). For each participant, a personalized muscle volume z-score (sex- and body size-specific) was calculated and combined with MFI to divide the participants into four muscle composition phenotype groups; 1) normal muscle composition, 2) only low muscle volume, 3) only high MFI and 4) AMC, using previously published thresholds based on the whole UKB cohort. The thresholds were the 25th percentile (&amp;lt; -0.68 SD) for muscle volume z-score and the 75th percentile (men &amp;gt;7.69%; women &amp;gt;8.82%) for MFI. The mortality data were obtained through the UKB´s linkage to national death registries. All-cause mortality was investigated using Kaplan-Meier curves and Cox regression. Models within participants with CKD were crude and subsequently adjusted for sex, age, BMI, low hand grip strength (&amp;lt;16/27 kg for females/males), physical activity, smoking, alcohol, and previous diagnosis of cancer, prevalent CHD and type 2 diabetes, all assessed at the time of imaging. Results Conclusion AMC, determined by standardized MRI, was a strong predictor of all-cause mortality in CKD. The study results indicate that CKD patients with poor muscle health, identified as having both low muscle volume z-score and high MFI, have a significantly higher mortality risk. This vulnerable group would potentially benefit from targeted interventions and is also of specific interest when evaluating new treatments for CKD.

#1770 Clinical characteristics and outcomes in patients with ANCA-associated vasculitis (AAV) and kidney involvement—a Swedish single-center cohort

Abstract Background and Aims Poor patient outcomes in ANCA-associated vasculitis (AAV) with kidney involvement is attributed to multiple factors such as delay in diagnosis, inadequate efficacy of treatments and long-term complications due to the disease as well as the toxic effects of immunosuppressive therapy. The aim of the present study was to evaluate the clinical characteristics of a Swedish cohort of patients with AAV (granulomatosis with polyangiitis; GPA and microscopic polyangiitis; MPA) and kidney involvement to study causes of death and predictors of all-cause mortality. Method This retrospective single-centre cohort study included 93 patients from the Uppsala region in mid-Sweden, diagnosed with AAV and kidney involvement between 1993 and 2023. Clinical and laboratory variables were obtained from review of electronic medical records as well as patient follow-up visits. Differences between groups were assessed using independent t-test and Chi-square test. Multivariable logistic regression analyses were performed to evaluate the influence of risk factors of interest on all-cause mortality. Results 93 patients, 71 (76%) were clinically diagnosed with MPA, and 22 (24%) with GPA. The median age at diagnosis was 66.5 ± 13 years. eGFR at diagnosis was 29 ± 23.5 ml/min/1.73 m2 and 15% of patients were dialysis-dependent AAV at diagnosis. Patients with MPA and GPA received comparable induction therapy except for PLEX which was significantly more frequent in patients with GPA (52% vs 26% respectively, p = 0.041). 56 (60%) of patients died during follow up and survival time was 7.1 ± 6.9 years. Baseline clinical and biochemical characteristics are shown in Table 1. A multivariate logistic regression showed that older age at diagnosis (OR 95% CI 1.012-1.110; p = 0.013) and being dialysis-dependent at diagnosis (OR 95% CI 1.343-16.079; p = 0.015) significantly predicted all-cause mortality (Table 2). Causes of death are presented in Fig. 1. Infections was the most common cause of death (30%). Conclusion Long-term prognosis and patient survival were significantly associated with age and dialysis-dependency at diagnosis in this cohort of patients diagnosed with AAV and kidney involvement. Infections was the most common cause of death.

#2099 Long term outcomes in chronic kidney disease patients with heart failure with preserved ejection fraction

Abstract Background and Aims Heart failure (HF) is frequent in chronic kidney disease (CKD) and leads to high morbidity and mortality in this patient category. The aim of our study is to assess the clinical and biological profile, and outcomes in CKD patients with preserved left ventricular ejection fraction (LVEF). Method We prospectively enrolled CKD patients (pts) in pre-dialysis, stratified in two groups by the presence of heart failure symptoms: pts with NYHA class 1-2 (asymptomatic HF) versus pts with NYHA class 3-4 (symptomatic HF). Demographics, clinical history, co-morbidities, physical exam, ambulatory blood pressure monitoring, extensive blood chemistry, systemic inflammation and mineral bone disease parameters were recorded. Cardiac structure and systolic and diastolic function were assessed by echocardiography. Carotid Doppler ultrasound assessed intima media thickness (IMT). Al patients were followed until death of loss from records; patients with less than 6 months of surveillance were excluded. Primary endpoint was all-cause mortality. Secondary endpoints were cardiovascular mortality, a composite cardiac endpoint composite cardiac endpoint comprising coronary heart disease, heart failure hospitalization and new-onset arrhythmia, and reaching the end stage of renal disease (ESRD). Results We included 76 CKD patients (2 pts CKD stage 1, 9 pts CKD stage 2, 19 pts CKD stage 3a, 28 pts CKD stage 3b, 18 pts CKD stage 4) with LVEF ≥ 50%. Mean age 64.7 ± 11.4 years (yrs), men = 56 (73.7%). Mean follow up period was 86 ± 46.4 months (range 6-156 months). Patients with symptomatic HF (54 pts, 71.1%) were older (68 ± 9 vs 56 ± 12 yrs), hypertensive (98.1% vs 86.4%, p &amp;lt; 0.05), more frequent coronary artery disease (66.7% vs 27.3%, p &amp;lt; 0.05), had worse baseline biological profile: lower eGFR (36.9 ± 12.9 vs 55.5 ± 22.7 ml/min/1.73 m2, p &amp;lt; 0.05), lower serum albumin (4.1 ± 0.5 vs 4.4 ± 0.4 g/dl, p &amp;lt; 0.05), as well as higher levels of osteoprotegerin and fetuin A (p &amp;lt; 0.05). Also, symptomatic HF pts had more severe endothelial disease (higher IMT 0.81 ± 0.14 vs 0.63 ± 0.11 mm, p &amp;lt; 0.05), more severe subclinical LV dysfunction revealed by less negative global longitudinal strain GLS (−16.7 ± 2.66 vs −19.2 ± 2.95, p &amp;lt; 0.05) and higher ratio of mitral velocity to early diastolic velocity of the mitral annulus E/E` (12.64 ± 3.63 vs 9.68 ± 2.73, p &amp;lt; 0.05), as well as higher LV mass index (121.5 ± 31 vs 104.9 ± 22.7 g/m2, p &amp;lt; 0.05) and higher left atrial volume index LAVI (42.8 ± 16.8 vs 31.8 ± 11.3 ml/m2, p &amp;lt; 0.05) All-cause mortality was 17.1% (13 events), while cardiovascular mortality was 6.6% (5 events). All deaths were recorded in the symptomatic HF group. Also, a higher percent of pts from symptomatic HF group reached the composite cardiac endpoint (44.4% vs 22.7%) and ESRD (33.3% vs 18.2%) compared to asymptomatic HF group. Kaplan-Meier survival analysis showed significantly higher all-cause mortality (log rang = 0.008) and lower ESRD-free survival (log rank= 0.045) in symptomatic HF group. All-cause mortality was significantly correlated with NYHA class (p &amp;lt; 0.0001), etiology of CKD (p = 0.038), eGFR (p &amp;lt; 0.0001), proteinuria (p = 0.001), intact PTH (p = 0.008), osteocalcin (p = 0.012), osteopontin (p = 0.029), FGF23 (p = 0.044), IL6 (p = 0.037), GLS (p = 0.023), E/E` (p = 0.009). Cardiovascular mortality was significantly correlated with NYHA class (p = 0.019), etiology of CKD (p = 0.008), eGFR (p = 0.011), proteinuria (p = 0.001), intact PTH (p = 0.008), osteocalcin (p = 0.011), osteopontin (p = 0.006), FGF23 (p = 0.006). Cox regression analysis identified NYHA class (p = 0.004), FGF23 (p = 0.009) and IL6 (p = 0.025) as predictors for all-cause mortality, while E/E` (p = 0.032) and intact PTH (p-0.042) were predictors for cardiovascular mortality. Conclusion In our study, CKD pts with symptomatic HF carry a significant burden of morbidity and mortality, even in the case of preserved LV ejection fraction. This emphasizes the need for more comprehensive regular assessments of subclinical cardiac dysfunction in CKD pts. This approach might support targeted interventions, better symptom control, and improved long term outcomes.

#1469 Predicting mortality risk in incident dialysis patients using clinical predictors and patient-reported predictors

Abstract Background and Aims Prognostic prediction models can be used to identify dialysis patients that are at high risk of dying. By doing so, vulnerable patients can be monitored more closely and treatment can be intensified where needed. In addition, individualized predictions can help inform patients about their prognosis. Traditionally, prognostic models primarily rely on known clinical predictors such as age, comorbidities and laboratory markers. Although it is still unchartered territory in the field of nephrology, studies in other fields have shown that patient-reported predictors, like pain and fatigue, can be of use as predictors of survival. Predictive performance of current prognostic models for mortality risk amongst dialysis patients is subpar and we hypothesized that the addition of patient-reported predictors may improve predictions. Therefore, we aimed to develop a novel prognostic model for the prediction of two-year mortality in patients starting dialysis using both clinical predictors and patient-reported predictors. Method Data from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD) were used to develop two prognostic models for the prediction of 2-year all-cause mortality in incident dialysis patients: a base model consisting of clinical predictors and an updated model containing additional patient-reported predictors. Missing data were handled using a tenfold multiple imputation with fully conditional specification. The imputation model included all candidate predictors as well as the outcome variable of 2-year mortality. Predictors for the base model were selected based on existing mortality models and clinical expertise. Patient-reported predictors to add to the base model were chosen based on availability in NECOSAD, clinical expertise and literature describing patient-reported predictors. The models were developed using multivariate logistic regression and their discriminative abilities were assessed by calculating the area under the curve (AUC). Results A total of 1956 patients from NECOSAD were used for the analyses. Of these, 447 (22.9%) died within two years after baseline (3 months after dialysis initiation). The base model contained the following predictors: age, sex, serum albumin, C-reactive protein, diabetes mellitus, hemoglobin, residual kidney function (GFR at 3 months), body mass index, cardiovascular disease, dialysis modality, malignancy and primary kidney disease. The discriminative abilities of the base model were moderate with a AUC of 0.79 (95% CI: 0.77-0.82). The base model was updated using the following patient-reported predictors: mental component score, physical component score, general health perception, number of symptoms, symptom burden (measured with the KDQOL instrument) depressive symptoms, fatigue and pain. The addition of the patient-reported predictors marginally improved the predictive performance of the model achieving an AUC of 0.81 (95% CI: 0.79-0.84). Conclusion The base model including clinical predictors was enhanced slightly by the addition of patient-reported predictors.

#1358 The association between TNFRSF1B rs1061624 polymorphism and hypertension in end-stage kidney disease patients on dialysis

Abstract Background and Aims In chronic kidney disease (CKD), hypertension (HT) is commonly found during its development and is a leading cause for its progression. HT, dyslipidemia and visceral obesity are among the components of metabolic syndrome (MetS) and seem to associate with progression of CKD to end-stage kidney disease (ESKD). Enhanced levels of soluble tumor necrosis factor receptor 2 (sTNFR2) are known to associate with progressive CKD; and this biomarker was reported as an independent predictor of all-cause mortality in ESKD patients under dialysis. Concerning the TNFRSF1B gene, the polymorphism rs1061624 (A &amp;gt; G), located in the 3′-untranslated region (3′-UTR), was associated with the development of HT in men and with increased risk of MetS. The aim of this study was to determine the genotype frequencies of TNFRSF1B rs1061624 in ESKD patients with and without HT, and to evaluate its relationship with the circulating levels of some MetS biomarkers. Method We studied 277 ESKD patients on dialysis (recruited between 2016-2019), 170 diagnosed with HT and 107 without HT (no-HT group); age, sex, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP) and statin therapy were recorded. Real time PCR TaqMan SNP genotyping assay was used to assess genotype frequencies of TNFRSF1B rs1061624. We also evaluated the conventional lipid profile and the levels of oxidized low-density lipoprotein, adiponectin and leptin. Results The genotype frequencies in patients with and without HT were different (P = 0.002); the AA genotype was less frequent in HT than in no-HT group, while GG and AG genotypes were more frequent. Considering the AA genotype carriers, HT ESKD patients (n = 23), compared to the no-HT group (n = 33), presented lower adiponectin levels (P = 0.025); for the AG genotype carriers, HT group (n = 89) presented a trend towards lower adiponectin concentrations (P = 0.055), and were younger than no-HT patients (n = 48; P&amp;lt;0.001); in both comparisons, groups were matched for sex, BMI and number of patients under statins treatment, and in the first case also for age. Considering GG genotype, HT patients (n = 58) only differed from no-HT subjects (n = 26) in SBP, that was higher (P&amp;lt;0.001). Among HT patients, AG genotype carriers presented higher DBP (P = 0.004), lower values of leptin (P = 0.012) and leptin/adiponectin ratio (P = 0.022) and were younger (P&amp;lt;0.001) than GG genotype patients; compared to AA carriers, cholesterol levels were lower (P = 0.012). For the no-HT group, AG genotype patients presented lower DBP (P = 0.008) than AA genotype carriers. Conclusion The genotype frequencies were different in ESKD patients with and without HT, being G allele more frequent in HT. The HT ESKD patients with the AA or the AG genotypes seem to present lower adiponectin levels than the ESKD no-HT patients with the same genotypes. Acknowledgements This work was financed by CESPU, through the project SNPsCKD-GI2-CESPU-2022; FCT, in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences—UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy—i4HB.

A Critique and Examination of the Polysocial Risk Score Approach: Predicting Cognition in the Health and Retirement Study.

Polysocial risk scores were recently proposed as a strategy to improve clinical relevance of knowledge about social determinants of health. The objective of this paper was to assess if the polysocial risk score model improves prediction of cognition and all-cause mortality in middle-aged and older adults beyond simpler models including a smaller set of key social determinants of health. We used a sample of 13,773 individuals aged 50+ at baseline from the 2006 to 2018 waves of the Health and Retirement Study, a US population-based longitudinal cohort. Four linear mixed models were compared: two simple models including a priori selected covariates and two polysocial risk score models which used LASSO regularization to select covariates among 9 or 21 candidate social predictors. All models included age. Predictive accuracy was assessed via R-squared and root mean-squared prediction error (RMSPE) using training/test split and cross-validation. For predicting cognition, the simple model including age, race, gender, and education had an R-squared of 0.31 and an RMSPE of 0.880. Compared with this, the most complex polysocial risk score selected 12 predictors (R-squared=0.35 and RMSPE=0.858; 2.2% improvement). For all-cause mortality, the simple model including age, race, gender, and education had an AUROC of 0.747, while the most complex polysocial risk score did not demonstrate improved performance (AUROC = 0.745). Models built on a smaller set of key social determinants performed comparably to models built on a more complex set of social "risk factors".

Triglyceride-glucose index predicts all-cause mortality, but not cardiovascular mortality, in rural Northeast Chinese patients with metabolic syndrome: a community-based retrospective cohort study

BackgroundMetabolic syndrome (MetS) includes a group of metabolic irregularities, including insulin resistance (IR), atherogenic dyslipidemia, central obesity, and hypertension. Consistent evidence supports IR and ongoing low-grade inflammation as the main contributors to MetS pathogenesis. However, the association between the triglyceride-glucose (TyG) index and mortality in people with MetS remains uncertain. The objective of this study was to examine the correlation between the baseline TyG index and all-cause and cardiovascular (CV) mortality in rural Northeast Chinese individuals with MetS.MethodsFor the Northeast China Rural Cardiovascular Health Study, 3918 participants (mean age, 55 ± 10; 62.4% women) with MetS at baseline were enrolled in 2012–2013 and followed up from 2015 to 2017. The TyG index was calculated using the equation TyG index = ln [fasting TG (mg/dL) × fasting glucose (mg/dL)/2] and subdivided into tertiles [Q1(< 8.92); Q2 (8.92–9.36); Q3 (≥ 9.36)]. Multivariate Cox proportional hazards models were developed to examine the correlations between mortality and the baseline TyG index.ResultsDuring a median of 4.66 years of follow-up, 196 (5.0%) all-cause deaths and 108 (2.8%) CV disease-related deaths occurred. The incidence of all-cause mortality was significantly different among TyG index tertiles of the overall population (P = 0.045). Kaplan–Meier analysis demonstrated a significantly increased risk of all-cause mortality in rural Chinese patients with a higher TyG index (log-rank P < 0.05). After adjusting for possible confounders, Cox proportional hazard analysis revealed that the TyG index could effectively predict all-cause mortality (HR for the third vs. first tertile of TyG was 1.441 [95% confidence interval, 1.009–2.059]), but not CV mortality, in rural Chinese patients with MetS.ConclusionsThe TyG index is an effective predictor of all-cause mortality in rural Chinese patients with MetS. This indicates that the TyG index may be useful for identifying rural Chinese individuals with MetS at a high risk of death.

Native myocardial T1 and right ventricular size by CMR predict outcome in systemic sclerosis-associated pulmonary hypertension.

Measures of right heart size and function are prognostic in systemic sclerosis-associated pulmonary hypertension (SSc-PH), but the importance of myocardial tissue characterisation remains unclear. We aimed to investigate the predictive potential and interaction of cardiovascular magnetic resonance (CMR) myocardial tissue characterisation and right heart size and function in SSc-PH. A retrospective, single-centre, observational study of 148 SSc-PH patients confirmed by right heart catheterization who underwent clinically-indicated CMR including native myocardial T1 and T2 mapping from 2016 to 2023 was performed. Sixty-six (45%) patients died during follow-up (median 3.5 years, range 0.1-7.3). Patients who died were older (65 vs 60 years, p= 0.035) with more dilated (RVEDVi and RVESVi, p< 0.001), hypertrophied (RVMi, p= 0.013) and impaired (RVEF, p< 0.001) right ventricles, more dilated right atria (RAi, p= 0.043) and higher native myocardial T1 (p< 0.001).After adjustment for age, RVESVi (p = 0.0023) and native T1 (p = 0.0024) were independent predictors of all-cause mortality. Both RVESVi and native T1 remained independently predictive after adjusting for age and PH subtype (RVESVi p < 0.001, T1 p = 0.0056). Optimal prognostic thresholds for RVESVi and native T1 were ≤38 mL/m2 and ≤1119 ms, respectively (p < 0.001). Patients with RVESVi ≤ 38 mL/m2 and native T1 ≤ 1119 ms had significantly better outcomes than all other combinations (p < 0.001). Furthermore, patients with RVESVi > 38mL/m2 and native T1 ≤ 1119 ms had significantly better survival than patients with RVESVi > 38mL/m2 and native T1 > 1119ms (p = 0.017). We identified prognostically relevant CMR metrics and thresholds for patients with SSc-PH. Assessing myocardial tissue characterisation alongside RV function confers added value in SSc-PH and may represent an additional treatment target.

Dialysis parameters associated with SARS-CoV-2 infection and prognosis in end-stage kidney disease

Background The Covid-19 pandemic has affected patients with end-stage kidney disease (ESKD). Whether dialysis parameters have a prognostic value in ESKD patients with Covid-19 remains unclear. Materials and Methods We retrospectively evaluated clinical characteristics, blood pressure (BP) and dialysis parameters in ESKD patients undergoing maintenance outpatient hemodialysis, with (Covid-ESKD) and without (No-Covid-ESKD) Covid-19, at four Brazilian hemodialysis facilities. The Covid-ESKD (n = 107; 54% females; 60.8 ± 17.7 years) and No-Covid-ESKD (n = 107; 62% females; 58.4 ± 14.6 years) groups were matched by calendar time. The average BP and dialysis parameters were calculated during the pre-infection, acute infection, and post-infection periods. The main outcomes were Covid-19 hospitalization and all-cause mortality. Results Covid-ESKD patients had greater intradialytic and postdialysis systolic BP and lower predialysis weight, postdialysis weight, ultrafiltration rate, and interdialytic weight gain during acute-illness compared to 1-week-before-illness, while these changes were not observed in No-Covid-ESKD patients. After 286 days of follow-up (range, 276–591), there were 18 Covid-19-related hospitalizations and 28 deaths among Covid-ESKD patients. Multivariable logistic regression analysis showed that increases in predialysis systolic BP from 1-week-before-illness to acute-illness (OR, 95%CI = 1.06, 1.02–1.10; p = .004) and Covid-19 vaccination (OR, 95%CI = 0.16, 0.04–0.69; p = .014) were associated with hospitalization in Covid-ESKD patients. Multivariable Cox-regression analysis showed that Covid-19-related hospitalization (HR, 95%CI = 5.17, 2.07–12.96; p < .001) and age (HR, 95%CI = 1.05, 1.01–1.08; p = .008) were independent predictors of all-cause mortality in Covid-ESKD patients. Conclusion Acute Covid-19 illness is associated with variations in dialysis parameters of volume status in patients with ESKD. Furthermore, increases in predialysis BP during acute Covid-19 illness are associated with an adverse prognosis in Covid-ESKD patients.