Predictor Of All-cause Mortality Research Articles

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.

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.

Number of Premature Ventricular Complexes Predicts Long-Term Outcomes in Patients with Persistent Atrial Fibrillation

Background: Premature ventricular complexes (PVCs) are common electrocardiographic abnormalities and may be a prognosticator in predicting mortality in patients with structurally normal hearts or chronic heart diseases. Whether PVC burden was associated with mortality in patients with chronic atrial fibrillation (AF) remained unknown. We investigated the prognostic value of PVC burden in patients with persistent AF. Methods: A retrospective analysis of 24 h Holter recordings of 1767 patients with persistent AF was conducted. Clinical characteristics, 24 h average heart rate (HR), and PVC measures, including 24 h PVC burden and the presence of consecutive PVCs (including any PVC couplet, triplet, or non-sustained ventricular tachycardia) were examined for the prediction of all-cause and cardiovascular mortality using the Cox proportional hazards model. Results: After a median follow-up time of 30 months, 286 (16%) patients died and 1481 (84%) patients survived. Multivariate analysis revealed that age, heart failure, stroke, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, beta-blocker, digoxin, oral anticoagulant use, and estimated glomerular filtration rate were significant baseline predictors of all-cause mortality and cardiovascular mortality. Twenty-four-hour PVC burden and the presence of consecutive PVCs were significantly associated with all-cause and cardiovascular mortality after adjusting for significant clinical factors. When compared to the first quartile of PVC burden (&lt;0.003%/day), the highest quartile (&gt;0.3%/day) was significantly associated with an increased risk of all-cause mortality (hazard ratio, 2.46; 95% CI, 1.77–3.42) and cardiovascular mortality (hazard ratio: 2.67; 95% CI, 1.76–4.06). Conclusions: Twenty-four-hour PVC burden is independently associated with all-cause and cardiovascular mortality in patients with persistent AF.

#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.

Prognostic value of pulmonary diffusing capacity for carbon monoxide and ventilation-perfusion SPECT findings in pulmonary arterial hypertension.

Reduced pulmonary diffusing capacity for carbon monoxide (DLCO) can be observed in pulmonary arterial hypertension (PAH) and associates with increased mortality. However, the prognostic value of DLCO when corrected for haemoglobin (DLCOc), an independent modifier of DLCO, remains understudied. Additionally, the prognostic role of ventilation (V)-perfusion (Q) emission computed tomography (V/Q SPECT) findings in patients with PAH, which may concurrently be performed to rule out chronic thromboembolic pulmonary hypertension, is uncertain. A retrospective cohort study was conducted on 152 patients with PAH referred to a tertiary hospital for evaluation from January 2011 to January 2020. Lung function tests, clinical data and V/Q SPECT were ascertained. Cox regression analysis was performed to evaluate the association between DLCOc, DLCO and V/Q SPECT defects at referral with all-cause mortality. In equally adjusted Cox regression analysis, each percentage increase in DLCOc % predicted (%pred) (hazard ratio (HR) 0.97; 95% CI: 0.94-0.99) and DLCO%pred (HR 0.97; 95% CI: 0.94-0.99) was similarly associated with all-cause mortality. There was no detectable difference in area under the curve for prediction of all-cause mortality by DLCOc%pred and DLCO%pred (C-index 0.71 and 0.72, respectively, P=0.85 for difference). None of the defects noted on V/Q SPECT were significantly associated with mortality, but mismatched defects were associated with lower values of DLCOc%pred and DLCO%pred. DLCOc%pred and DLCO%pred perform equally as prognostic markers in PAH, supporting the use of either metric when available for prognostic stratification.

Urinary non-albumin protein-creatinine ratio is an independent predictor of mortality in patients with type 2 diabetes: a retrospective cohort study

Albuminuria is a well-known predictor of chronic kidney disease in patients with type 2 diabetes mellitus (DM). However, proteinuria is associated with chronic complications in patients without albuminuria. In this retrospective cohort study, we explored whether non-albumin proteinuria is associated with all-cause mortality and compared the effects of non-albumin proteinuria on all-cause mortality between patients with and without albuminuria. We retrospectively collected data from patients with type 2 DM for whom we had obtained measurements of both urinary albumin-to-creatinine ratio (UACR) and urinary protein-to-creatinine ratio (UPCR) from the same spot urine specimen. Urinary non-albumin protein-creatinine ratio (UNAPCR) was defined as UPCR–UACR. Of the 1809 enrolled subjects, 695 (38.4%) patients died over a median follow-up of 6.4 years. The cohort was separated into four subgroups according to UACR (30 mg/g) and UNAPCR (120 mg/g) to examine whether these indices are associated with all-cause mortality. Compared with the low UACR and low UNAPCR subgroup as the reference group, multivariable Cox regression analyses indicated no significant difference in mortality in the high UACR and low UNAPCR subgroup (hazard ratio [HR] 1.189, 95% confidence interval [CI] 0.889–1.589, P = 0.243), but mortality risks were significantly higher in the low UACR and high UNAPCR subgroup (HR 2.204, 95% CI 1.448–3.356, P < 0.001) and in the high UACR with high UNAPCR subgroup (HR 1.796, 95% CI 1.451–2.221, P < 0.001). In the multivariable Cox regression model with inclusion of both UACR and UNAPCR, UNAPCR ≥ 120 mg/g was significantly associated with an increased mortality risk (HR 1.655, 95% CI 1.324–2.070, P < 0.001), but UACR ≥ 30 mg/g was not significantly associated with mortality risk (HR 1.046, 95% CI 0.820–1.334, P = 0.717). In conclusion, UNAPCR is an independent predictor of all-cause mortality in patients with type 2 DM.

Comparison of mini nutritional assessment tool and geriatric nutrition risk index in predicting 12-year mortality among community-dwelling older persons

BackgroundMalnutrition is a common and dangerous condition in older adults, which has been associated with an increased risk of mortality. ObjectivesThis study aims to evaluate and compare the abilities of Mini Nutritional Assessment short form (MNA-SF), MNA full form (MNA-FF), and geriatric nutritional risk index (GNRI) to predict all-cause and expanded cardiovascular disease (CVD)-related mortality in community-dwelling older adults. MethodsThis research is an observational cohort study conducted in a community-setting, with a 12-year follow-up involving 1,001 community-living older adults aged ≥65 years who are enrolled in 2009 and followed up until 2021. Nutritional status assessment was carried out in 2009 using MNA-SF, MNA-FF, and GNRI. Multivariate Cox proportional hazards regression was applied to determine adjusted hazard ratios of mortality with 95% confidence intervals. ResultsA total of 368 deaths (36.76%) and 122 expanded CVD-related deaths (12.19%) were observed after a median follow-up of 12 years. Compared with normal nutritional status, poor nutritional status assessed by the MNA-SF, MNA-FF, and GNRI was found to be associated with an increased all-cause mortality in older persons. MNA-SF and MNA-FF, but not GNRI, were associated with expanded CVD-related mortality. The MNA-FF showed better discriminatory accuracy for all-cause and expanded CVD-related mortality (C-statistics = 0.77, 95% CI = 0.63–0.79; 0.79, 0.70–0.83) than MNA-SF (0.76, 0.73–0.79; 0.76, 0.72–0.81) and GNRI (0.75, 0.73–0.79; 0.76, 0.72–0.80). ConclusionsOur findings indicate that MNA-SF, MNA-FF, and GNRI were all independent predictors of all-cause mortality. In particular, the MNA-FF may be the best nutritional assessment tool for predicting all-cause and CVD-related mortality among older persons residing in community, compared with MNA-SF and GNRI.

Nomogram to Predict 90-Day All-Cause Mortality in Acute Ischemic Stroke Patients after Endovascular Thrombectomy.

Although Endovascular Thrombectomy (EVT) significantly improves the prognosis of Acute Ischemic Stroke (AIS) patients with large vessel occlusion, the mortality rate remains higher. This study aimed to construct and validate a nomogram for predicting 90-day all-cause mortality in AIS patients with large vessel occlusion and who have undergone EVT. AIS patients with large vessel occlusion in the anterior circulation who underwent EVT from May 2017 to December 2022 were included. 430 patients were randomly split into a training group (N=302) and a test group (N=128) for the construction and validation of our nomogram. In the training group, multivariate logistic regression analysis was performed to determine the predictors of 90-day all-cause mortality. The C-index, calibration plots, and decision curve analysis were applied to evaluate the nomogram performance. Multivariate logistic regression analysis revealed neurological deterioration during hospitalization, age, baseline National Institutes of Health Stroke Scale (NIHSS) score, occlusive vessel location, malignant brain edema, and Neutrophil-to-lymphocyte Ratio (NLR) as the independent predictors of 90-day all-cause mortality (all p ≤ 0.039). The C-index of the training and test groups was 0.891 (95%CI 0.848-0.934) and 0.916 (95% CI: 0.865-0.937), respectively, showing the nomogram to be well distinguished. The Hosmer-Lemeshow goodness-of-fit test revealed the p-values for both the internal and external verification datasets to be greater than 0.5. Our nomogram has incorporated relevant clinical and imaging features, including neurological deterioration, age, baseline NIHSS score, occlusive vessel location, malignant brain edema, and NLR ratio, to provide an accurate and reliable prediction of 90-day all-cause mortality in AIS patients undergoing EVT.

The utility of serum glucose potassium ratio as a predictive factor for haemorrhagic transformation, stroke recurrence, and mortality among ischemic stroke patients

Background and ObjectiveGlucose-Potassium Ratio (GPR) has emerged as a biomarker in several pathophysiological conditions. However, the association between GPR and long-term outcomes in stroke patients has not been investigated. Our study evaluated the applicability of baseline GPR as a predictive prognostic tool for clinical outcomes in ischemic stroke patients. MethodsThe multicenter retrospective cohort study included acute-subacute adult ischemic stroke patients who had their baseline serum GPR levels measured. Eligible patients were categorized into two sub-cohorts based on the baseline GPR levels (<1.67 vs. ≥ 1.67). The primary outcome was the incidence of 30-day hemorrhagic transformation, while stroke recurrence, and all-cause mortality within twelve months, were considered secondary. ResultsAmong 4083 patients screened, 1047 were included in the current study. In comparison with GPR < 1.67 group, patients with ≥ 1.67 GPR had a significantly higher ratio of all-cause mortality within twelve months (aHR 2.07 [95 % CI 1.21–3.75] p = 0.01), and higher ratio of 30-day hemorrhagic transformation but failed to reach the statistical significance (aHR 1.60 [95 % CI 0.95–2.79], p = 0.08). ConclusionOverall, baseline GPR serum is an independent predictor of all-cause mortality within twelve months in patients with acute and subacute ischemic stroke. Further clinical studies are necessary to validate these findings.

Lactate-to-albumin ratio: A promising predictor of 28-day all-cause mortality in critically Ill patients with acute ischemic stroke

IntroductionNumerous diseases have been found to be associated with the lactate-to-albumin ratio (LAR), as confirmed by existing research. This study aims to investigate the relationship between LAR within 24 hours of admission and a 28-day mortality rate in patients manifesting ischemic stroke. MethodsThis retrospective cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.1) database. We included adult patients with acute ischemic stroke (AIS) who were admitted to the intensive care unit. The primary outcome entailed evaluating the ability of LAR to predict death at 28-day of hospital admission in patients with AIS. ResultsA total of 502 patients with ischemic stroke were enrolled in the study, of which 185 (36.9 %) died within 28 days after hospital admission. We identified a linear association between LAR and mortality risk. Compared with the reference group (first LAR tertile), the 28-day mortality was increased in the highest tertile; the fully adjusted HR value was 1.21 (1.08 to 1.40). the Area Under the Curve (AUC) value for LAR was 58.26 % (95 % CI: 53.05 % - 63.46 %), which was higher than that for arterial blood lactate (AUC = 56.88 %) and serum albumin (AUC = 55.29 %) alone. It was not inferior even when compared to SOFA (AUC = 56.28 %). The final subgroup analysis exhibited no significant interaction of LAR with each subgroup (P for interaction: 0.079 - 0.848). ConclusionIn our study, LAR emerged as a promising predictor of all-cause mortality in acute ischemic stroke patients within 28 days of admission.

Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer.

Breast cancer chemotherapy/immunotherapy can be associated with treatment-limiting cardiotoxicity. Radiomics techniques applied to ultrasound, known as ultrasomics, can be used in cardio-oncology to leverage echocardiography for added prognostic value. To utilize ultrasomics features collected prior to antineoplastic therapy to enhance prediction of mortality and heart failure (HF) in patients with breast cancer. Patients were retrospectively recruited in a study at the West Virginia University Cancer Institute. The final inclusion criteria were met by a total of 134 patients identified for the study. Patients were imaged using echocardiography in the parasternal long axis prior to receiving chemotherapy. All-cause mortality and HF, developed during treatment, were the primary outcomes. 269 features were assessed, grouped into four major classes: demographics (n = 21), heart function (n = 7), antineoplastic medication (n = 17), and ultrasomics (n = 224). Data was split into an internal training (60%, n = 81) and testing (40%, n = 53) set. Ultrasomics features augmented classification of mortality (area under the curve (AUC) 0.89 vs. 0.65, P = 0.003), when compared to demographic variables. When developing a risk prediction score for each feature category, ultrasomics features were significantly associated with both mortality (P = 0.031, log-rank test) and HF (P = 0.002, log-rank test). Further, only ultrasomics features provided significant improvement over demographic variables when predicting mortality (C-Index: 0.78 vs. 0.65, P = 0.044) and HF (C-Index: 0.77 vs. 0.60, P = 0.017), respectively. With further investigation, a clinical decision support tool could be developed utilizing routinely obtained patient data alongside ultrasomics variables to augment treatment regimens.

External validation of the 2-year mortality prediction tool in hemodialysis patients developed using a Bayesian network

Abstract Background and hypothesis In recent years, a number of predictive models have appeared to predict the risk of medium-term mortality in haemodialysis patients, but only one, limited to patients aged over 70, has undergone sufficiently powerful external validation. Recently, using a national learning database and an innovative approach based on Bayesian networks and 14 carefully selected predictors, we have developed a clinical prediction tool to predict all-cause mortality at 2 years in all incident haemodialysis patients. In order to generalise the results of this tool and propose its use in routine clinical practice, we carried out an external validation using an independent external validation database. Methods A regional, multicenter, observational, retrospective cohort study was conducted to externally validate the tool for predicting 2-year all-cause mortality in incident and prevalent hemodialysis patients. This study recruited a total of 142 incident and 697 prevalent adult hemodialysis patients followed up in one of the 8 AURAL Alsace dialysis centers. Results In Incident patients, the 2-year all-cause mortality prediction tool had an area under the receiver curve of 0.73, an accuracy of 65%, a sensitivity of 71%, a specificity of 63%. In prevalent patients, the performance for the external validation were similar in terms of AUC-ROC, accuracy, and specificity but was lower in term of sensitivity. Conclusion The tool for predicting all-cause mortality at 2 years, developed using a Bayesian network and 14 routinely available explanatory variables, obtained satisfactory external validation in incident patients, but sensitivity was insufficient in prevalent patients.