Predictor Of All-cause Mortality Research Articles

Early detection of hepatobiliary involvement in cystic fibrosis: Biomarkers, radiologic methods, and genetic influences.

Cystic fibrosis-related hepatobiliary involvement (CFHBI) is a term used to describe a spectrum of hepatobiliary involvement ranging from a transient elevation of transaminase levels to advanced cystic fibrosis-associated liver disease (aCFLD). While CFHBI is common among people with cystic fibrosis (PwCF), aCFLD is rare impacting only approximately 5%-10% of the CF population. After respiratory/cardiorespiratory issues and transplant-related complications, aCFLD is now the 4th leading cause of mortality among PwCF. Additionally, aCFLD is an independent predictor of all-cause mortality and is associated with significant morbidity. Despite this recognition, our ability to predict those patients at greatest risk for aCFLD, identify early aCFLD, and monitor the incremental progression of CFHBI is lacking. Here, we review the strengths and weaknesses of the common biomarkers and imaging modalities used in the evaluation and monitoring of CFHBI, as well as the current understanding of genetic modifiers related to aCFLD.

Impact of the triglyceride-glucose index on 28-day mortality in non-diabetic critically Ill patients with sepsis: a retrospective cohort analysis

IntroductionSepsis is a life-threatening condition that poses a globally high mortality rate. Identifying risk factors is crucial. Insulin resistance and the TYG index, associated with metabolic disorders, may play a role. This study explores their correlation with mortality in non-diabetic septic patients.MethodsThis retrospective cohort study used data from the MIMIC-IV (version 2.1) database, which includes over 50,000 ICU admissions from 2008 to 2019 at Beth Israel Deaconess Medical Center in Boston. We included adult patients with sepsis who were admitted to the intensive care unit in the study. The primary outcome was to evaluate the ability of TYG to predict death at 28-day of hospital admission in patients with sepsis.ResultsThe study included 2213 patients with sepsis, among whom 549 (24.8%) died within 28 days of hospital admission. We observed a non-linear association between TYG and the risk of mortality. Compared to the reference group (lower TYG subgroup), the 28-day mortality increased in the higher TYG subgroup, with a fully adjusted hazard ratio of 2.68 (95% CI: 2.14 to 3.36). The area under the curve (AUC) for TYG was 67.7%, higher than for triglycerides alone (AUC = 64.1%), blood glucose (AUC = 62.4%), and GCS (AUC = 63.6%), and comparable to SOFA (AUC = 69.3%). The final subgroup analysis showed no significant interaction between TYG and each subgroup except for the COPD subgroup (interaction P-values: 0.076–0.548).ConclusionIn our study, TYG can be used as an independent predictor for all-cause mortality due to sepsis within 28 days of hospitalization.

Psychosocial Factors Linked to Uncontrolled Infection and Mortality among People Living with HIV Who Use Substances: A Latent Class Analysis.

To determine whether endorsement patterns of psychosocial symptoms revealed distinct subgroups, or latent classes, of people living with HIV who use substances (PLWH-SU), and to assess whether these classes demonstrated differential health outcomes over time. This study uses data from 801 PLWH-SU initially enrolled across 11 US hospitals during 2012-2014 and followed up in 2017. Latent class analysis included 28 psychosocial items. Regression analysis examined class membership as a predictor of viral suppression. Survival analysis examined class as a predictor of all-cause mortality. The selected model identified five unique classes. Individuals in classes characterized by more severe and more numerous psychosocial symptoms at baseline had lower likelihoods of viral suppression and survival. The study demonstrated the importance of considering patterns of overlapping psychosocial symptoms to identify subgroups of PLWH-SU and reveal their risks for adverse outcomes. Integration of primary, mental health, and substance use care is essential to address the needs of this population.

Prognostic impact of peripheral artery disease-related parameters in patients with acute coronary syndrome.

Lower extremity arterial disease (LEAD) and increased aortic stiffness are associated with higher mortality in patients with chronic coronary syndrome, while their prognostic significance after an acute coronary syndrome (ACS) is less known. We analyzed prevalence, clinical phenotypes and association of LEAD - assessed by the ankle-brachial index (ABI) - and increased aortic stiffness - assessed by the aortic pulse wave velocity (PWV) - with all-cause mortality and major adverse cardiovascular events (MACE) in patients admitted with an ACS. Among 270 patients admitted for ACS (mean age 67 years, 80% males), 41 (15%) had an ABI ≤0.9, with 14 of them (34%) presenting with intermittent claudication (symptomatic LEAD). Patients with symptomatic LEAD, compared with those with asymptomatic LEAD or without LEAD, had higher prevalence of cardiovascular risk factors, lower estimated glomerular filtration rate and higher high-sensitivity C-reactive protein. Patients with LEAD, either symptomatic or asymptomatic, more frequently presented with non-ST-elevation myocardial infarction and more frequently had multivessel coronary artery disease. Both symptomatic and asymptomatic LEAD were significantly associated with all-cause mortality after adjustment for confounders, including multivessel disease or carotid artery disease (hazard ratio 4.03, 95% confidence interval 1.61-10.08, P < 0.01), whereas PWV was not associated with the outcome in the univariable model. LEAD and PWV were not associated with a higher risk of MACE (myocardial infarction or unstable angina, stroke, or transient ischemic attack). LEAD, either clinical or subclinical, but not increased aortic stiffness, is an independent predictor of all-cause mortality in patients admitted for ACS.

Follistatin-respiratory connection predicting all-cause mortality among community-dwelling middle-to-old age individuals: Results from the I-Lan Longitudinal Study

ObjectivesThe link between aging and pulmonary function decline is well-established, but the underlying mechanisms have yet to be fully revealed. Serum follistatin, a myokine implicated in muscle degeneration, may play a role in age-related pulmonary changes. This study aims to investigate the relationship between serum follistatin levels and pulmonary function decline in community-dwelling older adults, and evaluate their combined association with all-cause mortality. Research design and methodsThis longitudinal cohort study utilized data from 751 participants aged ≥50 years in the I-Lan Longitudinal Aging Study between 2018−2019. Serum follistatin levels, spirometry results, demographic and clinical data were retrieved. Participants were stratified based on their follistatin levels. Survival curves and group comparisons based on follistatin levels and decline in peak expiratory flow (PEF) using Kaplan-Meier analysis and log-rank tests. Multivariate Cox proportional hazards models were further used to identify independent predictors of all-cause mortality during the 52-month follow-up. ResultsElevated follistatin levels significantly correlated with worse pulmonary function, particularly decreased PEF (p = 0.030). Kaplan–Meier analysis revealed the combination of elevated follistatin levels and decreased PEF was associated with increased risk of all-cause mortality (Log-rank p = 0.023). Cox proportional hazards models further identified that concurrent presence of higher follistatin levels and decreased PEF predicted higher risk of all-cause mortality (adjusted HR 3.58, 95% CI: 1.22–10.53, p = 0.020). ConclusionHigher serum follistatin levels correlate with decreased pulmonary function, specifically PEF decline, in community-dwelling older adults. Furthermore, the coexistence of elevated follistatin levels and decreased PEF was associated with risk of all-cause mortality. Follistatin may serve as a biomarker for pulmonary aging and related adverse outcomes.

Artificial Intelligence for the Prediction of All-Cause Mortality and Readmission in Heart Failure: A Meta-Analysis of 558,233 Patients

Artificial Intelligence for the Prediction of All-Cause Mortality and Readmission in Heart Failure: A Meta-Analysis of 558,233 Patients

Machine Learning for Prediction of All-Cause Mortality in Acute Coronary Syndrome

Machine Learning for Prediction of All-Cause Mortality in Acute Coronary Syndrome

The association of inflammatory biomarkers with clinical outcomes in diabetic retinopathy participants: data from NHANES 2009–2018

ObjectiveThe aim of this study was to assess the association of neutrophil lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), and system inflammation response index (SIRI) with the all-cause mortality and diabetes-cardiovascular mortality in participants with diabetic retinopathy (DR).MethodsA total of 572 participants with DR from NHANES were included, and divided into survival group (n = 440) and all-cause death group (n = 132). NLR = neutrophil count/lymphocyte count, MLR = monocyte count/lymphocyte count, SIRI = (neutrophil count × monocyte count)/lymphocyte count. We utilized the NHANES Public-Use Linked Mortality File through April 26, 2022, to determine mortality status. Diabetes-cardiovascular death was defined as death resulting from heart disease, cerebrovascular disease, or diabetes mellitus. The Spearson Correlation Analysis, Kaplan-Meier curves, Cox proportional hazards regression models, Restricted cubic spline plots and Decision Curve Analysis were used.ResultsThe all-cause mortality and diabetes-cardiovascular mortality were significantly higher in NLR ≥ 1.516, MLR ≥ 0.309, SIRI ≥ 0.756, and NLR + MLR + SIRI subgroups than NLR < 1.516, MLR < 0.309, SIRI < 0.756 subgroups, and other participants except NLR + MLR + SIRI (all P < 0.05). The HR of NLR, MLR, SIRI, NLR + MLR + SIRI for all-cause mortality were 1.979(1.13–3.468), 1.850(1.279–2.676), 1.821(1.096–3.025), 1.871(1.296–2.703), respectively. The hazard ratio of NLR, MLR, SIRI, NLR + MLR + SIRI for diabetes-cardiovascular mortality were 2.602(1.028–6.591), 2.673(1.483–4.818), 2.001(0.898–4.459), 2.554(1.426–4.575), respectively. In the restricted cubic spline plots, the relationship between NLR, MLR, SIRI and HR of all-cause mortality and diabetes-cardiovascular mortality was overall as “J” shaped. In both age < 60 and age > 60 years participants, the all-cause mortality and diabetes-cardiovascular mortality were significantly higher in NLR ≥ 1.516, MLR ≥ 0.309, SIRI ≥ 0.756, and NLR + MLR + SIRI subgroups than NLR < 1.516, MLR < 0.309, SIRI < 0.756 subgroups, and other participants except NLR + MLR + SIRI (all P < 0.05).ConclusionNLR, MLR, and SIRI may be three independent prognostic predictors for all-cause mortality and diabetes-cardiovascular mortality among individuals with DR. In practical clinical applications, combining NLR, MLR, and SIRI may enhance the prediction of all-cause mortality and diabetes-cardiovascular mortality in DR.

Geriatric nutrition risk index in the prediction of all-cause and cardiovascular mortality in older adults with hyperlipidemia: NHANES 1999–2018

BackgroundMalnutrition is linked to a higher risk of unfavorable outcomes in various illnesses. The present investigation explored the correlation between inadequate nutritional condition and outcomes in older individuals diagnosed with hyperlipidemia.MethodsThe geriatric nutritional risk index (GNRI) was used to evaluate the nutritional status. All patients were divided into two groups according to GNRI. A Kaplan-Meier analysis was used to assess the survival rates of different groups at risk of malnutrition. In addition, GNRI was used in COX proportional risk regression models to evaluate its predictive effect on both overall mortality and cardiovascular mortality among patients with hyperlipidemia. Furthermore, the study employed restricted cubic splines (RCS) to examine the nonlinear correlation between GNRI and mortality.ResultsThe study included 4,532 elderly individuals diagnosed with hyperlipidemia. During a median follow-up duration of 139 months, a total of 1498 deaths from all causes and 410 deaths from cardiovascular causes occurred. The Kaplan-Meier analysis demonstrated significantly poorer survival among individuals at risk of malnutrition, as indicated by the GNRI. In the malnutrition risk group, the modified COX proportional hazards model revealed that a decrease in GNRI was associated with a higher risk of all-cause mortality (HR=1.686, 95% CI 1.212-2.347) and cardiovascular mortality (HR=3.041, 95% CI 1.797-5.147). Furthermore, the restricted cubic splines revealed a non-linear association between GNRI and both all-cause mortality and cardiovascular mortality (p-value for non-linearity = 0.0039, p-value for non-linearity=0.0386).ConclusionsIn older patients with hyperlipidemia, lower levels of GNRI are associated with mortality. The GNRI could potentially be used to predict all-cause mortality and cardiovascular mortality.

Controlling nutritional status score in the prediction of cardiovascular disease prevalence, all-cause and cardiovascular mortality in chronic obstructive pulmonary disease population: NHANES 1999–2018

BackgroundMalnutrition is prevalent in chronic obstructive pulmonary disease (COPD) and associated with adverse outcomes, while COPD is intricately linked to cardiovascular disease (CVD), sharing common risk factors. The controlling nutritional status (CONUT) score, a promising tool for assessing malnutrition, warrants investigation into its predictive ability for cardiovascular disease prevalence and mortality in COPD patients.MethodsBased on the National Health and Nutrition Examination Survey (NHANES), this study analyzed 1501 adult COPD patients from 1999 to 2018. The endpoints were CVD prevalence, mortality related to CVD, and overall mortality. We evaluated the correlation of the CONUT score with each outcome using logistic regression and Cox regression models. The prognostic evaluation of patients was conducted using Kaplan-Meier curves in accordance with the CONUT score. We formed the receiver operating characteristic (ROC) curves for evaluating the CONUT score's discriminative capability.ResultsThe prevalence of malnutrition was 21.31% in COPD populations. Logistic analyses suggested a distinct connection between the CONUT score and CVD prevalence (OR:1.86, 95%CI:1.28-2.70) in individuals with COPD. The CONUT score demonstrated a significant correlation with a heightened risk of CVD mortality (HR: 1.86, 95%CI: 1.27-2.74) and overall mortality (HR: 1.50, 95%CI: 1.18-1.91). The prognostic outcomes might be effectively discriminated by the CONUT score, as seen by the Kaplan-Meier curves.ConclusionsIn summary, the CONUT score provides an uncomplicated and readily attainable marker for forecasting CVD prevalence, total mortality, and mortality from CVD among COPD patients.

Long-term follow-up in outpatients with mildly elevated pulmonary artery systolic pressure on echocardiography: a single-centre retrospective cohort study in Shanghai, China

ObjectiveTo investigate the correlation between mildly elevated pulmonary artery systolic pressure (PASP) on echocardiography and mortality, as well as long-term changes in PASP.DesignRetrospective cohort study.SettingShanghai, China, a single centre.ParticipantsA total...

The U-shape relationship between insulin resistance-related indexes and chronic kidney disease: a retrospective cohort study from National Health and Nutrition Examination Survey 2007–2016

BackgroundThere is ongoing debate on the correlation between chronic kidney disease (CKD) and insulin resistance (IR)-related indices. Our objective was to explore the prognostic ability of IR-related indexes for the prevalence of CKD, as well as the mortality from all causes and cardiovascular disease (CVD) in CKD patients.MethodsThe data used in this study came from the National Health and Nutrition Examination Survey (NHANES). Binary logistic regression analysis, Cox proportional hazards model, and restricted cubic spline (RCS) were used to analyze the relationship between IR-related indexes, including metabolic score of IR (METS-IR), homeostatic model assessment for IR (HOMA-IR), triglyceride glucose index (TyG), triglyceride glucose-waist-to-height ratio (TyG-WHtR), triglyceride glucose-body mass index (TyG-BMI), with CKD and its all-cause mortality and CVD mortality. Subgroup analysis was performed to test the stability of the results. Finally, the predictive power of IR-related indexes for CKD was tested by the receiver operating characteristic (ROC) curve.ResultsAmong the recruited 10,660 participants, 15.42% were CKD patients. All IR-related indexes were found to be nonlinearly correlated to the prevalence of CKD in the study. When the TyG index was higher than 9.05, it was positively associated with CKD (OR: 1.77, 95% CI 1.44–2.18). Moreover, increased TyG-WHtR level was correlated with a greater prevalence of CKD when it was higher than 4.3 (OR: 1.31, 95% CI 1.19–1.45). Other IR-related indexes (METS-IR, HOMA-IR, and TyG-BMI) showed fewer notable correlations with CKD. The association of IR-related indexes and the prevalence of CKD remained consistent in most subgroups (P for interactions > 0.05). TyG-WHtR was also the predictor of all-cause mortality in CKD patients (HR: 1.34, 95% CI 1.14–1.58), while other IR-related indexes were not correlated with the all-cause mortality or CVD mortality in CKD patients (P > 0.05). Otherwise, ROC curves showed that TyG-WHtR had more robust diagnostic efficacy than other IR-related indexes (METS-IR, HOMA-IR, TyG, and TyG-BMI) in predicting CKD (area under the curve: 0.630, 95% CI 0.615–0.644).ConclusionsIR-related biomarkers (METS-IR, HOMA-IR, TyG, and TyG-BMI) were positively correlated with the prevalence of CKD. Moreover, TyG-WHtR enhanced CKD and its all-cause mortality prediction. In patients with elevated levels of IR-related indexes, the early detection and intervention of IR may reduce the occurrence of CKD and the prognosis of CKD patients.

Prognostic impact of beta-blocker use by N-terminal pro-brain natriuretic peptide level in acute heart failure patients.

Both patients with heart failure (HF) with reduced ejection fraction (HFrEF) and those with HF with preserved ejection fraction (HFpEF) present with elevated N-terminal pro-brain natriuretic peptide (NT-proBNP) and have multiple comorbidities; consequently, the prognostic effect of NT-proBNP according to beta-blocker (BB) use is unknown. This retrospective study evaluated patients admitted for acute HF between January 2012 and December 2017 at Ulsan University Hospital. Clinical, echocardiographic, laboratory and drug prescription data, including BB data, were collected from the hospital database. Information on mortality was collected by reviewing medical records or using national death data. Of the 472 patients evaluated, 216 (45.8%) and 256 (54.2%) patients were and were not prescribed BB at discharge, respectively. A total of 224 (47.5%) patients died within a median follow-up duration of 44months. The Kaplan-Meier analysis showed reduced all-cause mortality with BB in HFrEF (ejection fraction≤40%) but not in HFpEF (ejection fraction>40%). In the multivariate Cox regression analysis, transmitral to tissue Doppler imaging, early diastolic velocity ratio (E/E'), NT-proBNP and BB use were independent predictors of all-cause mortality in HFrEF. Meanwhile, haemoglobin and NT-proBNP levels were independent predictors of HFpEF. The NT-proBNP cut-off value for determining all-cause mortality was set to 4800pg/mL. Among HFrEF patients with NT-proBNP<4800pg/mL, the survival rate was higher for patients with BB use than those with no BB use (log-rank P<0.001). However, in the HFpEF group, the survival rate associated with BB use did not differ according to the NT-proBNP levels. Both HFrEF and HFpEF patients with NT-proBNP levels of ≥4800pg/mL presented with multiple comorbidities, including lower body mass index and haemoglobin levels and higher creatinine levels, NT-proBNP levels and E/E'. In patients with acute HF, BB use is associated with reduced all-cause mortality in those with HFrEF but not in those with HFpEF. HFrEF patients with NT-proBNP levels of <4800pg/mL treated with BB have a higher survival rate than those not treated with BB. However, this benefit is not seen in HFrEF patients with NT-proBNP levels of ≥4800pg/mL or in all HFpEF patients, regardless of the NT-proBNP level. NT-proBNP levels are elevated in multiple comorbid conditions, and these comorbidities may contribute to the attenuated effects of BB on all-cause mortality.

Frailty and all-cause and cancer-related mortality in cancer patients: A prospective cohort study

PurposeTo evaluate the associations between frailty and all-cause and cancer-related mortality. Additionally, the objective is to compare the magnitude of these associations between older adults and younger adults. MethodsWe gathered baseline data from NHANES (1999–2018) and developed a cumulative index consisting of 39 items to evaluate frailty. The National Death Index database was utilized to track the survival status of individuals. The Cox regression model was employed to estimate the associations between frailty status and all-cause and cancer-related mortality. ResultsUltimately, 3398 cancer patients were included in the analysis, comprising 910 younger adults and 2488 older adults. Compared to non-frail patients, the elevated all-cause and cancer-related mortality among pre-frail patients was not statistically significant (HRs = 1.312, 95%CI: 0.956–1.800, P = 0.092; HRs = 1.462, 0.811–2.635, P = 0.207). However, a significant elevation of both all-cause and cancer-related mortality risk was observed among frail patients (HRs = 2.213, 1.617–3.030, P < 0.001; HRs = 2.463, 95%CI = 1.370–4.429, P = 0.003). Frailty individuals demonstrated a more pronounced association with the prediction of all-cause mortality in younger (HRs = 2.230, 1.073–4.634, P = 0.032) than in older adults (HRs = 2.090, 1.475–2.960, P < 0.001). Sensitivity analysis consistently revealed robust results. RCS plots suggested a progressively escalating dose-response correlation between frailty and both all-cause and cancer-related mortality risk. ConclusionsPre-frailty did not result in an increase in mortality risks compared to non-frailty. However, frailty caused a higher all-cause and cancer-related mortality risk than non-frailty. Identifying those at risk and implementing targeted interventions may contribute to extending healthy life expectancy, regardless of age.

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.

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.

Multinational evaluation of anthropometric age (AnthropoAge) as a measure of biological age in the USA, England, Mexico, Costa Rica, and China: a population-based longitudinal study.

To validate AnthropoAge, a new metric of biological age (BA), for prediction of all-cause mortality and age-related outcomes and characterize population-specific aging patterns using multinational longitudinal cohorts. We analyzed harmonized multinational data from the Gateway to Global Aging, including studies from the US, England, Mexico, Costa Rica, and China. We used body mass index and waist-to-height ratio to estimate AnthropoAge and AnthropoAgeAccel in participants aged 50-90 years old as proxies of BA and age acceleration, respectively. We compared the predictive capacity for all-cause mortality of AnthropoAge and chronological age (CA) using Cox models, described aging trends in all countries and explored the utility of longitudinal assessments of AnthropoAgeAccel to predict new-onset functional decline and age-related diseases using generalized estimating equations (GEE). Using data from 55,628 participants, we found AnthropoAge (c-statistic 0.772) outperformed CA (0.76) for prediction of mortality independently of comorbidities, sex, race/ethnicity, education, and lifestyle; this result was replicated in most countries individually except for Mexico. Individuals with accelerated aging had a ∼39% higher risk of death, and AnthropoAge also identified trends of faster biological aging per year. In longitudinal analyses, higher AnthropoAgeAccel values were independently predictive of self-reported health deterioration and new-onset deficits in basic/instrumental activities of daily living (ADL/IADL), diabetes, hypertension, cancer, chronic lung disease, myocardial infarction, and stroke. AnthropoAge is a robust and reproducible BA metric associated with age-related outcomes. Its implementation could facilitate modeling trends of biological aging acceleration in different populations, although recalibration may enhance its utility in underrepresented populations such as individuals from Latin America.

Nocturnal hypoxemic burden in patients with heart failure: Emerging prognostic role of its nonspecific 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 (nonspecific 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 < .05 all). However, when these indices were adjusted for known risk factors, T90, T90Nspec, Area90, and Area90Nspec remained statistically significant (P = .018, hazard ratio (HR)=1.12, 95%CI=(1.02, 1.23); P = .007, HR=1.20, 95% CI = [1.05, 1.37]; P = .020, HR = 1.05, 95% CI = [1.01, 1.10]; P = .0006, HR = 1.15, 95% CI = [1.06, 1.25]), whereas T90Eve and Area90Eve did not (P = .27, P = .28). These results were internally validated using bootstrap resampling. ConclusionsBy demonstrating a significant independent association of nonspecific 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.