Predicted Risk Of Mortality Research Articles

Validation of mortality risk scores after esophagectomy

PurposeOncological esophagectomy is the mainstay in esophageal cancer treatment, but perioperative mortality remains a significant concern. Various scoring systems exist to identify patients at high risk for postoperative complications and death. In the following, we aim to evaluate and compare these different scoring systems.MethodsWe analyzed data from 714 patients who underwent esophagectomy between 2002 and 2021. Each patient’s risk was calculated using three models: the International Esodata Study Group (IESG) 90-day mortality risk prediction, the Steyerberg 30-day mortality score, and the Fuchs et al. preoperative in-hospital mortality score (Fuchs score). The diagnostic performance of these models was assessed using the area under the receiver operating characteristic (ROC) curves.ResultsOf the 714 patients, the majority (87.67%) underwent abdomino-thoracic esophagectomy with intrathoracic anastomosis. The IESG score classified 52.1% as very low, 26.6% low, 17.5% middle, 2.8% high, and 1% as very high risk, while the Fuchs score identified 94.5% as low-risk and 5.5% as high-risk patients. Mortality rates were 6.9% at 90 days, 3.4% at 30 days, and 6.7% in-hospital. The area under the ROC curve was 0.634 (95%CI: 0.557–0.712) for the IESG model, 0.637 (95%CI: 0.526–0.747) for the Steyerberg score, and 0.686 (95%CI: 0.611–0.760) for the Fuchs score.ConclusionsExisting risk score systems provide a possibility for preoperative risk stratification, particularly for identifying high-risk patients. However, due to their limited predictive ability, improvements are needed to apply these strategies effectively in clinical practice.

Expiration-Triggered Sinus Arrhythmia Predicts Mortality Risk in the General Elderly Population

Reduced respiratory sinus arrhythmia, quantified as expiration-triggered sinus arrhythmia (ETA) from simultaneous electrocardiogram and respiration recordings, is a strong long-term mortality predictor in myocardial infarction survivors. Here, we investigated whether ETA also predicts mortality risk in the general elderly population. ETA was quantified from 30-min electrocardiogram and respiration recordings in 1788 general population subjects aged ≥60 years, who were then followed for a median of 4.0 years (median age 72 years, 58% female). Four-year all-cause mortality was 4.6%. Abnormal ETA using a predefined cutoff (≤0.19 ms) was associated with a 4-year mortality of 6.9%, compared to 3.7% in the remaining participants (p = 0.0022). ETA remained a significant mortality predictor in multivariable Cox analysis, also considering a modified Framingham score incorporating sex, age, smoking, cholesterol, blood pressure, antihypertensive medication, family history, diabetes and clinical atherosclerosis (multivariable hazard ratio 1.81; 95% confidence interval 1.17–2.81; p = 0.008). Combined risk prediction by ETA (using an optimized cutoff of ≤0.86 ms) and the Framingham score stratified patients into a low-risk (both parameters normal), an intermediate-risk (one parameter abnormal) and a high-risk group (both parameters abnormal), with 4-year mortality rates of 1.9%, 4.4% and 10.1%, respectively. We conclude that in elderly general population subjects, ETA is a mortality risk predictor that complements classical clinical risk stratification.

Analysis of clinical features and prognosis in cardiac amyloidosis patients from Spanish hospitals (2016-2021).

Cardiac amyloidosis (CA) is a prevalent yet underdiagnosed heart condition characterized by the abnormal accumulation of amyloid fibres, frequently resulting in heart failure (HF), particularly in older people. Despite advancements in non-invasive diagnostic techniques and treatments, the epidemiology of CA patients remains inadequately understood. This nationwide retrospective observational study sought to comprehensively investigate CA patients' characteristics, mortality, and readmission patterns. A retrospective observational study encompassed all patients hospitalized with CA between 2016 and 2021 across Spanish hospitals. Standardized incidence rates were calculated using age and sex-adjusted methods, utilizing the Spanish population as the reference. The investigation delved into demographic variables, comorbidities, mortality during the index episode, and 30 and 365-day readmissions for circulatory system diseases. Predictors of readmission were also examined. A total of 5,739 index episodes were identified, with CA being the primary cause of admission in 14.1% of cases. The mean age was 81.4 ± 9.9 years, predominantly males (70.3%). The age and sex-standardized hospital attendance rate was 3.90 admissions per 100,000 population (95% CI: 3.82-3.98), higher in males. Common comorbidities included HF (96.4%), atrial fibrillation (46.3%), and renal failure (44.4%). The mortality rate during the index episode was 11.7%, with cardiogenic shock (OR: 9.03; 95% CI: 4.22-19.32) and major psychiatric disorders (OR: 3.36; 95% CI: 1.42-7.94) identified as predictors. Over the follow-up period, 13.1% were readmitted at 30 days and 36.6% at 365. Nephritis (IRR: 2.05; 95% CI: 1.42-2.96) and asthma (IRR: 1.52; 95% CI 1.11-2.07) were associated with increased 30-day readmissions, while renal failure (IRR: 1.43; 95% CI: 1.28-1.59) and chronic pulmonary disease (IRR: 1.40; 95% CI: 1.18-1.67) were linked to higher 365-day readmissions. Predictors of mortality risk in 365-day readmissions included advanced cancer (HR: 1.31; 95% CI: 1.00-1.71), cardiogenic shock in the index episode (HR: 2.72; 95% CI: 1.33-5.57), and a higher number of readmissions during that period (HR: 1.64; 95% CI: 1.56-1.73). This study contributes valuable insights into the significant prevalence of CA, which is often overlooked and underestimated. Afflicting predominantly elderly males with concomitant HF and multiple comorbidities, CA poses a significant clinical challenge. The findings underscore the need for enhanced awareness, early detection, and a multidisciplinary approach to manage this complex cardiac condition.

Evaluation of the predictive value of the glucose-to-potassium ratio in predicting in-hospital mortality of patients with sepsis and septic shock.

In this study, we aimed to evaluate the role of the glucose-to-potassium ratio in predicting in-hospital mortality and prognosis of patients diagnosed with sepsis and septic shock in an emergency department and admitted to an emergency critical intensive care unit (ICU). This study was aretrospective and observational evaluation of nontraumatic sepsis and septic shock patients > 18years of age who were admitted to the emergency department of atertiary training and research hospital and had available glucose and potassium values at the time of admission. The patients were evaluated over a24-month period. The primary goal of this study was to examine the relationship between the glucose-to-potassium ratio and in-hospital mortality in sepsis patients. Data derived from 175 patients were included in the statistical analysis. Blood urea nitrogen, creatinine, lactate dehydrogenase, direct bilirubin, C‑reactive protein, and lactate levels were found to be significantly higher in the nonsurvivor group than in the survivor group (p < 0.05). On the other hand, hemoglobin, hematocrit, albumin, pH, HCO3 and base excess levels were found to be statistically significantly higher in the survivor group than in the nonsurvivor group (p < 0.05). The glucose-to-potassium ratio was not significant in terms of predicting mortality in sepsis patients (p = 0.324). In the receiver-operating characteristic (ROC) analysis of various parameters' significance in terms of predicting mortality, APACHE2 scores had the highest area under the curve (AUC) value (0.729). According to the results of this study, the glucose-to-potassium ratio did not have asignificant value in predicting mortality risk in sepsis and septic shock patients admitted from the emergency department to the emergency critical ICU.

Development and assessment of a mortality risk prediction nomogram model for pneumocystis disease in ICU within 28 days

To develop and assess a nomogram predictive model for evaluating the 28-day mortality risk in patients diagnosed with Pneumocystis who have been admitted to the intensive care unit (ICU). From 2008 to 2022, clinical data on patients with Pneumocystis were collected using the American Critical Care Medical Information Database IV (MIMIC-IV). Initially, 63 significant predictive indicators were included, with ICU admission as the time node and all-cause mortality within 28 days as the outcome. Using complete data modeling, the variable selection approach combines two methods: Lasso regression (glmnet package) and collinearity screening (car package). Use the bootstrap method 1000 times for internal validation. Calculate the AUC, mean sensitivity, and specificity of 1000 resamplings, as well as the 95% confidence interval, and then plot the ROC, calibration, and DCA curves. The patients were split into two groups based on their 28-day survival status: 83 cases (67.48%) in the survival group and 40 instances (32.52%) in the death group. Five variables—the history of malignant tumors, Lods scores, Oasis scores, and complications of shock and severe renal injury—were eventually included in the entire sample after screening. According to Receiver Operating Characteristic (ROC) analysis, the model’s sensitivity was 0.600 (95% CI 0.448–0.752), specificity was 0.904 (95% CI 0.840–0.967), and AUC was 0.814 (95% CI 0.732–0.897). The DCA curve indicates that the model application has high accuracy, which leads to a net benefit for population prediction, and the model calibration curve demonstrates good calibration accuracy. The bootstrap model’s 1000 internal validation results demonstrate that the model’s calibration performance is good and that its accuracy is highest when the probability of patient outcome events falls between 35 and 60%, which yields the highest net benefit for population prediction. This study developed a nomogram utilizing MIMIC-IV clinical big data to predict the 28-day mortality risk in patients with Pneumocystis disease. Incorporating five critical factors, the nomogram offers a user-friendly, visual method for calculating personalized risk scores based on patient-specific information, including medical history, laboratory results, and clinical scores. Demonstrating robust discrimination and calibration, this tool provides clinicians with a valuable resource for assessing prognosis and making evidence-based treatment decisions.

Evaluating the effectiveness of handheld ultrasound in primary blast lung injury: a comprehensive study

The incidence of blast injuries has been rising globally, particularly affecting the lungs due to their vulnerability. Primary blast lung injury (PBLI) is associated with high morbidity and mortality rates, while early diagnostic methods are limited. With advancements in medical technology, and portable handheld ultrasound devices, the efficacy of ultrasound in detecting occult lung injuries early remains unclear. This study evaluates the effectiveness of immediate lung ultrasound in diagnosing PBLI. The study involved 25 healthy male Bama mini-pigs subjected to BST-I-type biological shock wave tubes. The pigs were randomly assigned to non-injured and injured groups with driving pressures of 4.0 MPa, 4.5 MPa, and 4.8 MPa. Four PBLI models were created: no injury, minor, moderate, serious and severe. Immediate lung ultrasound following the BLUE-PLUS protocol and arterial blood gas analysis were conducted pre-injury and 0.5 h, 3 h, 6 h, 12 h, and 24 h post-injury, respectively. The study analyzed lung ultrasound score differences and their correlations with lung function parameters, using ROC analysis to determine early diagnostic standards and mortality prediction efficacy. The study found that in cases of moderate and severe PBLI, lung ultrasound scores and AaDO2 significantly increased at 0.5 h post-injury, while PaO2 decreased. There was good consistency between left and right lung ultrasound results at all times. Lung ultrasound scores were significantly correlated with PaO2 and AaDO2 but not with PaCO2. The scores accurately predicted injury severity at various time points within 24 h post-injury, and the 0.5 h lung ultrasound score predicted 24 h mortality with 95.8% efficiency. PBLI exhibits hidden severity, necessitating improved early diagnostics. Immediate lung ultrasound provides effective differentiation for moderate and severe PBLI at multiple time points within 24 h post-injury, is easy to implement, and offers effective mortality risk prediction as early as 0.5 h post-injury. These findings underscore lung ultrasound’s significant clinical application value in pre-hospital early treatment settings for PBLI.

Post-extrasystolic variation of ST segment and T wave as a mortality risk predictor after myocardial infarction

Post-extrasystolic variation of ST segment and T wave as a mortality risk predictor after myocardial infarction

Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients

South Africa was the most affected country in Africa by the coronavirus disease 2019 (COVID-19) pandemic, where over 4 million confirmed cases of COVID-19 and over 102,000 deaths have been recorded since 2019. Aside from clinical methods, artificial intelligence (AI)-based solutions such as machine learning (ML) models have been employed in treating COVID-19 cases. However, limited application of AI for COVID-19 in Africa has been reported in the literature. This study aimed to investigate the performance and interpretability of several ML algorithms, including deep multilayer perceptron (Deep MLP), support vector machine (SVM) and Extreme gradient boosting trees (XGBoost) for predicting COVID-19 mortality risk with an emphasis on the effect of cross-validation (CV) and principal component analysis (PCA) on the results. For this purpose, a dataset with 154 features from 490 COVID-19 patients admitted into the intensive care unit (ICU) of Tygerberg Hospital in Cape Town, South Africa, during the first wave of COVID-19 in 2020 was retrospectively analysed. Our results show that Deep MLP had the best overall performance (F1 = 0.92; area under the curve (AUC) = 0.94) when CV and the synthetic minority oversampling technique (SMOTE) were applied without PCA. By using the Shapley Additive exPlanations (SHAP) model to interpret the mortality risk predictions, we identified the Length of stay (LOS) in the hospital, LOS in the ICU, Time to ICU from admission, days discharged alive or death, D-dimer (blood clotting factor), and blood pH as the six most critical variables for mortality risk prediction. Also, Age at admission, Pf ratio (PaO2/FiO2 ratio), troponin T (TropT), ferritin, ventilation, C-reactive protein (CRP), and symptoms of acute respiratory distress syndrome (ARDS) were associated with the severity and fatality of COVID-19 cases. The study reveals how ML could assist medical practitioners in making informed decisions on handling critically ill COVID-19 patients with comorbidities. It also offers insight into the combined effect of CV, PCA, and SMOTE on the performance of ML models for COVID-19 mortality risk prediction, which has been little explored.

Opposite outcomes of triglyceride-glucose index and associated cardiovascular mortality risk in type 2 diabetes mellitus participants by different obesity criteria

We investigated the correlation between Triglyceride-glucose (TyG) index and cardiovascular disease (CVD) mortality in type 2 diabetes mellitus (T2DM) population across different obesity classications using a cohort study. We analyzed 7867 T2DM participants from the National Health and Nutrition Examination Survey 1999–2018, categorizing them into obese or non-obese group by body mass index (BMI) and waist circumference (WC). Cox regression models were used to estimate the correlation between TyG index and CVD mortality risk, comparing the results across the two obesity classifications. Over a 9.1-year follow-up, 691 CVD deaths occurred. Among non-obese T2DM participants (BMI-defined), the hazard ration for CVD mortality was 1.73 in the fourth quartile group of TyG index compared with the first quartile group. Conversely, among obese T2DM participants (WC-defined), the fourth quartile group of TyG index held a 1.51-fold risk of CVD mortality compared with the first quartile group. The association between obesity and higher CVD risk was observed in WC-defined obesity but not in BMI-defined obesity. A totally opposite relationship appeared between TyG index and CVD mortality based on how obesity was defined using BMI or WC in the T2DM participants, suggesting a reevaluation of BMI’s accuracy in predicting mortality risk.

Can a mechanical circulatory support comprehensive approach to cardiogenic shock at referral centers reduce 30-day mortality?

Although mortality risk prediction in cardiogenic shock (CS) is possible, assessing the impact of the multitude of therapeutic efforts on outcomes is not straightforward. We assessed whether a temporary mechanical circulatory support comprehensive approach to the treatment of CS may reduce 30-day mortality as compared to expected mortality predicted by the recently proposed Cardiogenic Shock Score (CSS). Consecutive CS patients supported by pVAD Impella (Abiomed, Danvers, MA) at two national referral centers were included. 170 patients were included: age was 65 ± 13 years, and 75.9% were male and acute myocardial infarction was the prevalent cause of shock (71.1%). Expected mortality according to CSS was higher than observed (51.8% vs. 41.5%, p &amp;lt; 0.001), this trend being particularly evident for CSS &amp;gt; 4. The AUC ROC curve confirmed poor diagnostic accuracy in this population (AUC 0.53 CI: 0.23–0.82, p = 0.83). The lower observed mortality compared to the expected mortality in critical cardiogenic shock population underscores the role of a comprehensive approach to acute cardiac care patients at referral centers, which should consider including temporary mechanical circulatory support.

Pubertal timing as a predictor of epigenetic aging and mortality risk in young adulthood.

Early pubertal timing is associated with adverse health in adulthood. These effects may be mediated by DNA methylation changes associated with accelerated cellular aging and mortality risk, but few studies tested associations between pubertal timing and epigenetic markers in adulthood. Additionally, pubertal timing effects often vary by sex and are understudied in diverse youth. Thus, this longitudinal study examined links between pubertal timing and later epigenetic aging and mortality risk together with sex differences in predominantly Black youth. Participants included 350 individuals (58% female, 42% male; 80% Black, 19% non-Hispanic White). Perceived pubertal timing relative to peers and self-reported phenotypic pubertal timing based on age-adjusted Tanner scores were assessed during early adolescence (Mage = 13) whereas epigenetic aging (GrimAge, DunedinPace of Aging Calculated from the Epigenome, and PhenoAge) and mortality risk were measured during young adulthood (Mage = 27). After adjusting for covariates (smoking, body mass index, family income, early-life stress, race/ethnicity, sex, parenthood), early pubertal timing (both perceived and phenotypic) predicted higher epigenetic mortality risk, and early phenotypic pubertal timing predicted accelerated DunedinPace of Aging Calculated from the Epigenome. Both perceived and phenotypic early pubertal timing were correlated with accelerated GrimAge. Off-time phenotypic pubertal timing (i.e., early and late) was associated with accelerated PhenoAge in males only whereas perceived off-time pubertal timing was unexpectedly linked with lower PhenoAge acceleration. These findings extend prior research by linking two dimensions of early pubertal timing with epigenetic mortality risk and accelerated aging in racially diverse young adults and showing nonlinear effects on PhenoAge acceleration that differ across pubertal timing measures and show some sex differences. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Emergency department early mortality model for patients admitted after presenting to a tertiary medical center emergency department.

Identifying patients in the emergency department (ED) at higher risk for in-hospital mortality can inform shared decision making and goals-of-care discussions. Electronic health record systems allow for integrated multivariable logistic regression (LR) modeling, which can provide early predictions of mortality risk in time for crucial decision making during a patient's initial care. Many commonly used LR models require blood gas analysis values, which are not frequently obtained in the ED. The goal of this study was to develop an all-cause mortality prediction model, derived from commonly collected ED data, which can assess mortality risk early in ED care. Data were obtained for all patients, age 18 and older, admitted from the ED to Atrium Health Wake Forest Baptist from April 1, 2016, through March 31, 2020. Initial vital signs including heart rate, respiratory rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse oximetry, weight, body mass index, comprehensive metabolic panel, and a complete blood count were electronically retrieved for all patients. The prediction model was developed using LR. The ED early mortality (EDEM) model was compared with the rapid Emergency Medicine Score (REMS) for performance analysis. A total of 45,004 patients met inclusion criteria, comprising a total of 77,117 admissions. In this cohort, 52.8% of patients were male and 47.2% were female. The model used 35 variables and yielded an area under the receiver operating characteristic curve (AUC) of 0.889 (95% CI 0.874-0.905) with a sensitivity of 0.828 (95% CI 0.791-0.860), a specificity of 0.788 (95% CI 0.783-0.794), a negative predictive value of 0.995 (95% CI 0.994-0.996), and a positive predictive value of 0.084 (95% CI 0.076-0.092). This outperformed REMS in this data set, which yielded an AUC of 0.500 (95% CI 0.455-0.545). The EDEM model was predictive of in-hospital mortality and was superior to REMS.

Social Vulnerability and Biological Aging in New York City: An Electronic Health Records-Based Study.

Chronological age is not an accurate predictor of morbidity and mortality risk, as individuals' aging processes are diverse. Phenotypic age acceleration (PhenoAgeAccel) is a validated biological age measure incorporating chronological age and biomarkers from blood samples commonly used in clinical practice that can better reflect aging-related morbidity and mortality risk. The heterogeneity of age-related decline is not random, as environmental exposures can promote or impede healthy aging. Social Vulnerability Index (SVI) is a composite index accounting for different facets of the social, economic, and demographic environment grouped into four themes: socioeconomic status, household composition and disability, minority status and language, and housing and transportation. We aim to assess the concurrent and combined associations of the four SVI themes on PhenoAgeAccel and the differential effects on disadvantaged groups. We use electronic health records data from 31,913 patients from the Mount Sinai Health System (116,952 person-years) and calculate PhenoAge for years with available laboratory results (2011-2022). PhenoAge is calculated as a weighted linear combination of lab results, and PhenoAgeAccel is the differential between PhenoAge and chronological age. A decile increase in the mixture of SVI dimensions was associated with an increase of 0.23years (95% CI 0.21, 0.25) in PhenoAgeAccel. The socioeconomic status dimension was the main driver of the association, accounting for 61% of the weight. Interaction models revealed a more substantial detrimental association for women and racial and ethnic minorities with differences in leading SVI themes. These findings suggest that neighborhood-level social vulnerability increases the biological age of its residents, increasing morbidity and mortality risks. Socioeconomic status has the larger detrimental role among the different facets of social environment.

Supplemental Material for Pubertal Timing as a Predictor of Epigenetic Aging and Mortality Risk in Young Adulthood

Supplemental Material for Pubertal Timing as a Predictor of Epigenetic Aging and Mortality Risk in Young Adulthood

Predicting Mortality in Patients Hospitalized With Acute Myocardial Infarction: From the National Cardiovascular Data Registry.

In-hospital mortality risk prediction is an important tool for benchmarking quality and patient prognostication. Given changes in patient characteristics and treatments over time, a contemporary risk model for patients with acute myocardial infarction (MI) is needed. Data from 313 825 acute MI hospitalizations between January 2019 and December 2020 for adults aged ≥18 years at 784 sites in the National Cardiovascular Data Registry Chest Pain-MI Registry were used to develop a risk-standardized model to predict in-hospital mortality. The sample was randomly divided into 70% development (n=220 014) and 30% validation (n=93 811) samples, and 23 separate registry-based patient characteristics at presentation were considered for model inclusion using stepwise logistic regression with 1000 bootstrapped samples. A simplified risk score was also developed for individual risk stratification. The mean age of the study cohort was 65.3 (SD 13.1) years, and 33.6% were women. The overall in-hospital mortality rate was 5.0% (n=15 822 deaths). The final model included 14 variables, with out-of-hospital cardiac arrest, cardiogenic shock, and ST-segment elevation MI as the strongest independent predictors of mortality. The model also included age, comorbidities (dyslipidemia, diabetes, prior percutaneous coronary intervention, cerebrovascular disease, and peripheral artery disease), heart failure on admission, heart rate, systolic blood pressure, glomerular filtration rate, and hemoglobin. The model demonstrated excellent discrimination (C-statistic, 0.868 [95% CI 0.865-0.871]) and good calibration, with similar performance across subgroups based on MI type, periods before and during the COVID-19 pandemic, and hospital volume. The simplified risk score included values from 0 to 25, with mortality risk ranging from 0.3% with a score of 0 to 1 up to 49.4% with a score >11. This contemporary risk model accurately predicts in-hospital mortality for patients with acute MI and can be used for risk standardization across hospitals and at the bedside for patient prognostication.

Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning

The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the development and external validation cohorts. Variables were screened using the Recursive Feature Elimination method. Five machine learning algorithms were used to build predictive models. Models were evaluated through nested cross-validation to select the best one. The model was interpreted using Shapley Additive Explanations. We selected the optimal model to generate the web calculator. A total of 23 predictive features were selected. The Light Gradient Boosting Machine (LightGBM) model had an area under the receiver operating characteristic curve (AUC) of 0.842 (95% CI: 0.757–0.927), with an external 5-fold cross-validation average AUC of 0.842 ± 0.038, which was superior to the other models. External validation results also demonstrated good performance by the LightGBM model with an AUC of 0.856 (95% CI: 0.792–0.921). Based on this, we generated a web calculator by combining five high importance predictive factors. The LightGBM model was confirmed to be efficient and stable in predicting the mortality risk of patients with SCAP admitted to the ICU. The web calculator based on the LightGBM model can provide clinicians with a prognostic evaluation tool.

Association between Activity Quotient and cause-specific mortality - A prospective cohort study of 0.5 million participants in Asia.

Regular physical activity (PA) is important for reducing the risk of chronic diseases and improving overall health. Activity Quotient (AQ) is a novel metric that translates heart rate during PA into a weekly score, providing an objective measure of an individual's PA. We prospectively examined the association of AQ with cancer and cardiovascular (CVD) mortality outcomes, the two major causes of death, in a Taiwanese population. A cohort of 515,608 healthy adults (52% women) enrolled in a standard medical screening program was followed for mortality outcomes. The weekly AQ score of each participant was estimated based on self-reported PA intensity and weekly duration, and placed into six categories (0, ≤50, 51-99,100-149, 150-199, or≥200 AQ per week). We used multivariable Cox proportional hazard models adjusted for potential confounders to estimate the hazard ratios (HR) and 95% confidence intervals (CI). Higher weekly AQ scores were associated with lower risks of CVD, cancer, and all-cause mortalities. Compared with inactive individuals, HRs (CI) for the association of AQ scores of ≤50, 50-99, 100-149, 150-199, and≥200 were 0.93 (0.89-0.97), 0.91 (0.85-0.96), 0.84 (0.77-0.91), 0.84 (0.74-0.96), and 0.81 (0.73-0.90) with cancer mortality; and 0.88 (0.83-0.93), 0.86 (0.80-0.93), 0.81 (0.73-0.90), 0.71 (0.60-0.85), and 0.73 (0.64-0.84) with CVD mortality, respectively. Subgroup analyses showed that meeting 50 AQ a week was associated with lower risk of disease specific mortality risk across age groups and among individuals with known risk factors. Higher weekly AQ scores were also associated with longer life expectancy, with the highest gains observed among those achieving 150-199 weekly AQ. Our findings show that AQ may be an objective tool for assessing and tracking PA and predicting mortality risks. Encouraging individuals to achieve ≥50 AQ a week could have substantial public health benefits, including lower mortality from major chronic diseases as well as prolonged health- and life expectancy.

A novel nomogram for predicting mortality risk in young and middle-aged patients undergoing maintenance hemodialysis: a retrospective study.

The annual growth in the population of maintenance hemodialysis (MHD) patients is accompanied by a trend towards younger age groups among new cases. Despite the escalating mortality risk observed in MHD patients, there remains a dearth of research focused on young and middle-aged individuals in this cohort, leading to a deficiency in specialized predictive instruments for this demographic. This research seeks to explore the critical determinants impacting mortality risk in young and middle-aged MHD patients and to construct a prediction model accordingly. This study involved 127 young and middle-aged patients undergoing MHD in the Blood Purification Center of Chaohu Hospital of Anhui Medical University from January 2019 to January 2022. The follow-up period for each patient ended either at the time of death or on January 31, 2024. Participants were monitored to determine their survival status and categorized into two groups: those who survived (98 patients) and those who deceased (29 patients). Clinical data were gathered for analysis. Logistic regression was utilized to pinpoint independent risk factors for mortality among these patients. Subsequently, a nomogram was established to predict mortality risk. The efficacy of this model was assessed through the area under the receiver operating characteristic curve (AUC-ROC), alongside a calibration curve and the Hosmer-Lemeshow test to examine its fit. Additionally, decision curve analysis (DCA) was conducted to ascertain the clinical relevance of the predictive model. The study incorporated 127 young and middle-aged patients undergoing MHD, with a mortality rate recorded at 22.83% (29 cases). A logistic regression analysis revealed that age, hemoglobin (HB), serum magnesium (Mg), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-albumin ratio (PAR) were significant independent predictors of mortality among these patients. Utilizing these variables, a nomogram was developed to predict mortality risk, achieving an AUC of 0.899 (95% CI: 0.833-0.966). The model exhibited a specificity of 83.67% and a sensitivity of 82.76%, demonstrating substantial discriminative ability. The model's robustness was confirmed through internal validation with 1,000 bootstrap samples, yielding an AUC of 0.894 (95% CI: 0.806-0.949). The calibration curve closely aligned with the ideal curve, and the Hosmer-Lemeshow goodness-of-fit test yielded a χ 2 value of 6.312 with a p-value of 0.612, verifying the model's high calibration accuracy. Additionally, the DCA indicated that the model provides a net benefit across a wide range of decision thresholds from 0 to 0.99, underscoring its clinical utility. The nomogram developed from variables including age, HB levels, serum Mg, NLR, and PAR exhibits high levels of discrimination and calibration. This model effectively predicts mortality risk among young and middle-aged patients undergoing MHD, proving its clinical relevance.

Comparison of Outcomes Between Low-Risk Aortic Valve Replacement Trials and a Surgical Registry

It remains unknown whether outcomes of the Placement of Aortic Transcatheter Valves 3 (PARTNER 3) and Evolut Low Risk trials are comparable with surgical outcomes in nontrial settings, considering the added risk of concomitant cardiac operations. To compare 30-day mortality and stroke incidences of patients in the surgical aortic valve replacement (SAVR) arm of low-risk trials with those of similar patients in the US Society of Thoracic Surgeons Adult Cardiac Surgery Database (STS ACSD). A cross-sectional sampling study was conducted of adults in the STS ACSD with severe aortic stenosis at low surgical risk for AVR who underwent SAVR during the years low-risk AVR trials (PARTNER 3 and Evolut Low Risk) were enrolling (calendar years 2016-2018). After trial exclusion criteria were applied, 1000 samples were created, each including 1000 patients selected from the STS ACSD according to the likelihood-based selection probabilities to achieve similar STS predicted risk of mortality (PROM) distribution to the trial participants. The distribution of 30-day mortality and stroke rates among these samples were compared with the results from the trials. The analysis was conducted between October 2, 2023, and May 27, 2024. The main outcomes were 30-day postoperative mortality and stroke. Among 25 811 patients at low risk undergoing SAVR, the mean (SD) age was 71 (7) years, including 650 220 men (65%). The probability-based sampling yielded a trial-like sample with STS PROM (mean [SD], 1.9% [0.6%]) and concomitant coronary artery bypass graft surgery frequencies of 13%, comparable with the trials. Among the sampled cohorts, the mean (SD) 30-day mortality rate was 1.39% (0.38%) which was not significantly different from the mortality rate in PARTNER 3 (1.1%) (P = .83) and Evolut Low Risk (1.3%) (P = .65). The stroke rate was 1.25% (0.36%), significantly lower than PARTNER 3 (2.4%) (P = .002) and Evolut Low Risk (3.4%) (P < .001). In this cross-sectional study, the national samples of low-risk trial-like patients undergoing SAVR during the trial enrollment period had similar 30-day mortality but a lower incidence of stroke compared with the SAVR arm of both low-risk trials. These findings overall suggest that the low-risk trial findings may be generalizable to the broader national SAVR cohort.

Serum Lactate Dehydrogenase Levels: The Grim Reaper Sign in Acute Pancreatitis?

Acute pancreatitis (AP) frequently presents in emergency departments and poses challenges in predicting severity and mortality. Established scoring systems like Ranson criteria, Acute Physiology And Chronic Health Evaluation II (APACHE) II, and Bedside Index of Severity in Acute Pancreatitis (BISAP) have varying effectiveness. Lactate dehydrogenase (LDH), an enzyme released during tissue damage, shows promise as a marker for organ injury in AP. This study aimed to evaluate LDH's potential to predict mortality risk and hospital stay duration in AP patients. A retrospective cohort study analyzed AP cases at HCA Healthcare facilities from January 2011 to January 2021. Among 514 patients with LDH data at admission, groups were categorized based on LDH levels. Outcomes included hospital and ICU stay lengths, mortality rates, and factors such as age, gender, race, BMI, and medical history. Patients were stratified into three groups: Group 1 (<300 IU/L), Group 2 (300-600 IU/L), and Group 3 (>600 IU/L) based onLDHlevels. Patients withLDH>600 IU/L experienced an average hospital stay extension of 4.5 days,were 3.2 times more likely to require ICU admission, and faced a 12.1 times higher mortality risk compared to those with LDH <300 IU/L. This study highlights LDH as a potentially valuable predictor of hospital stay duration, ICU requirements, and mortality rates in AP patients. Its cost-effectiveness and accessibility suggest LDH testing could aid clinical decision-making in AP management. Future prospective studies should further explore LDH's role in optimizing AP patient care.