Mortality Risk Assessment Research Articles

Comparison of performances between risk scores for predicting mortality at 30 days in patients with community acquired pneumonia

BackgroundRisk scores facilitate the assessment of mortality risk in patients with community-acquired pneumonia (CAP). Despite their utilities, there is a scarcity of evidence comparing the various RS simultaneously. This study aims to evaluate and compare multiple risk scores reported in the literature for predicting 30-day mortality in adult patients with CAP.MethodsA retrospective cohort study on patients diagnosed with CAP was conducted across two hospitals in Colombia. The areas under receiver operating characteristic curves (ROC-curves) were calculated for the outcome of survival or death at 30 days using the scores obtained for each of the analyzed questionnaires.ResultsA total of 7454 potentially eligible patients were included, with 4350 in the final analysis, of whom 15.2% (662/4350) died within 30 days. The average age was 65.4 years (SD: 21.31), and 59.5% (2563/4350) were male. Chronic kidney disease was 3.7% (9.2% vs. 5.5%; p < 0.001) (OR: 1.85) higher in subjects who died compared to those who survived. Among the patients who died, 33.2% (220/662) presented septic shock compared to 7.3% (271/3688) of the patients who survived (p < 0.001). The best performances at 30 days were shown by the following scores: PSI, SMART-COP and CURB 65 scores with the areas under ROC-curves of 0.83 (95% CI: 0.8–0.85), 0.75 (95% CI: 0.66–0.83), and 0.73 (95% CI: 0.71–0.76), respectively. The RS with the lowest performance was SIRS with the area under ROC-curve of 0.53 (95% CI: 0.51–0.56).ConclusionThe PSI, SMART-COP and CURB 65, demonstrated the best diagnostic performances for predicting 30-day mortality in patients diagnosed with CAP. The burden of comorbidities and complications associated with CAP was higher in patients who died.

Proenkephalin Improves Cardio-Renal Risk Prediction in Acute Coronary Syndromes: The KID-ACS Score.

Circulating proenkephalin (PENK) is a stable endogenous polypeptide with fast response to glomerular dysfunction and tubular damage. This study examined the predictive value of PENK for renal outcomes and mortality in patients with acute coronary syndromes (ACS). Proenkephalin was measured in plasma in a prospective multicentre ACS cohort from Switzerland (n=4787) and in validation cohorts from the UK (n=1141), Czechia (n=927), and Germany (n=220). A biomarker-enhanced risk score (KID-ACS score) for simultaneous prediction of in-hospital acute kidney injury (AKI) and 30-day mortality was derived and externally validated. On multivariable adjustment for established risk factors, circulating PENK remained associated with in-hospital AKI (per log2 increase: adjusted odds ratio [OR] 1.53, 95% confidence interval [CI] 1.13-2.09, P=0.007) and 30-day mortality (adjusted hazard ratio [HR] 2.73, 95% CI 1.85-4.02, P<0.001). The KID-ACS score integrates PENK and showed an area under the receiver operating characteristic curve (AUC) of 0.72 (95% CI 0.68-0.76) for in-hospital AKI, and of 0.91 (95% CI 0.87-0.95) for 30-day mortality in the derivation cohort. Upon external validation, KID-ACS achieved similarly high performance for in-hospital AKI (Zurich: AUC 0.73, 95% CI 0.70-0.77; Czechia: AUC 0.75, 95% CI 0.68-0.81; Germany: AUC 0.71, 95% CI 0.55-0.87) and 30-day mortality (UK: AUC 0.87, 95% CI 0.83-0.91; Czechia: AUC 0.91, 95% CI 0.87-0.94; Germany: AUC 0.96, 95% CI 0.92-1.00) outperforming the CA-AKI score and the GRACE 2.0 score, respectively. Circulating PENK offers incremental value for predicting in-hospital AKI and mortality in ACS. The simple 6-item KID-ACS risk score integrates PENK and provides a novel tool for simultaneous assessment of renal and mortality risk in patients with ACS.

Quantifying the Impact of Climate Change on Mortality Rates: A Stochastic Actuarial Approach in Uganda

Purpose: The aim of the study was to analyze the quantifying the impact of climate change on mortality rates: a stochastic actuarial approach in Uganda. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that climate change significantly increases mortality rates, particularly through the rise in extreme weather events and temperature-related illnesses. Using stochastic actuarial models, the research demonstrated a strong correlation between climate variability and higher mortality, especially among vulnerable populations such as the elderly and those with pre-existing health conditions. The findings underscore the urgent need for climate adaptation strategies in Uganda's public health planning to mitigate the adverse effects of climate change on mortality. Unique Contribution to Theory, Practice and Policy: Risk theory, ecological theory &amp; actuarial life table theory may be used to anchor future studies on analyze the quantifying the impact of climate change on mortality rates: a stochastic actuarial approach in Uganda. Practitioners should integrate climate data into actuarial models used for mortality forecasting and risk assessment. Policymakers should develop and enforce regulatory standards that require the inclusion of climate change considerations in actuarial assessments and reporting.

Total and regional fat-to-muscle mass ratio in relation to all-cause and cause-specific mortality in men and women.

Fat-to-muscle mass ratio (FMR) has been considered a potentially useful indicator for assessing disease risk. However, the association between FMR and risk of death remains inconclusive. To investigate the associations of FMR (the whole body, trunk, legs and arms) with all-cause and cause-specific mortality by sex. A total of 337951 participants from the UK Biobank were included. Fat mass and muscle mass were estimated using a bioelectrical impedance assessment device. Cox proportional hazard regression was applied to explore the associations. During a median follow-up of 12.58 years, we recorded 22391 (6.62 %) deaths, of which 4427 were from cardiovascular disease, 11740 from cancer, and 1458 from respiratory disease. We observed associations of different shapes (U-shaped for whole body and trunk, L-shaped for legs, and J-shaped for arms) between FMR and all-cause mortality (P for non-linearity <0.001). Compared with the lowest quintile of whole body and trunk FMR, the all-cause mortality risk was reduced by 12-14% and 7-15% in men and women in quintile 2-4, respectively. The hazard ratio (HR) associated with arm FMR in men and leg FMR in men and women (quintile 5 vs 1) were 1.14 (95% CI 1.05, 1.23), 0.76 (0.71, 0.82), and 0.78 (0.70, 0.88) for all-cause mortality, respectively. The associations between FMR and cause-specific mortality were mostly similar to those for all-cause mortality. Our study revealed significant associations of total and regional FMR with mortality, providing new evidence that FMR may be a potentially useful indicator for assessing mortality risk.

COVID-19 Vaccine: Predicting Vaccine Types and Assessing Mortality Risk Through Ensemble Learning Algorithms

Background There is no doubt that vaccination is crucial for preventing the spread of diseases; however, not every vaccine is perfect or will work for everyone. The main objective of this work is to predict which vaccine will be most effective for a candidate without causing severe adverse reactions and to categorize a patient as potentially at high risk of death from the COVID-19 vaccine. Methods A comprehensive analysis was conducted using a dataset on COVID-19 vaccine adverse reactions, exploring binary and multiclass classification scenarios. Ensemble models, including Random Forest, Decision Tree, Light Gradient Boosting, and extreme gradient boosting algorithm, were utilized to achieve accurate predictions. Class balancing techniques like SMOTE, TOMEK_LINK, and SMOTETOMEK were incorporated to enhance model performance. Results The study revealed that pre-existing conditions such as diabetes, hypertension, heart disease, history of allergies, prior vaccinations, other medications, age, and gender were crucial factors associated with poor outcomes. Moreover, using medical history, the ensemble learning classifiers achieved accuracy scores ranging from 75% to 87% in predicting the vaccine type and mortality possibility. The Random Forest model emerged as the best prediction model, while the implementation of the SMOTE and SMOTETOMEK methods generally improved model performance. Conclusion The random forest model emerges as the top recommendation for machine learning tasks that require high accuracy and resilience. Moreover, the findings highlight the critical role of medical history in optimizing vaccine outcomes and minimizing adverse reactions.

Assessment of the Respiratory Disease Mortality Risk from Single and Composite Exposures to PM2.5 and Ozone - Guangzhou City, Guangdong Province, China, 2018-2021.

Fine particulate matter (PM2.5) and ozone (O3) are prevalent pollutants in the atmosphere, which threaten human health, especially the respiratory system. Typically, people are exposed to a mixture of various pollutants in the environment. Thus, the single and combined effects of both pollutants need to be investigated. PM2.5 and O3 increase the risk of death from lung cancer, chronic obstructive pulmonary disease (COPD), and respiratory diseases, with their lagged and cumulative effects analyzed, indicating an acute effect. In addition, combined exposure to both pollutants can significantly affect disease deaths. This study provides further evidence of the single and combined effects of PM2.5 and O3 on respiratory diseases, emphasizing the need for sustained efforts in air pollution control, with greater attention to the synergistic management of air pollutants.

Association Between Persistent Hypothermia After Cardiopulmonary Bypass in Neonates and Odds of Serious Complications.

Persistent hypothermia after cardiopulmonary bypass (CPB) in neonates with congenital heart defects (CHD) has been historically considered benign despite lack of evidence on its prognostic significance. Examine associations between the magnitude and pattern of unintentional postoperative hypothermia and odds of complications in neonates with CHD undergoing CPB. Retrospective cohort study. Single northeastern U.S., urban pediatric quaternary care center with an established cardiac surgery program. Population-based sample of neonates greater than or equal to 34 weeks gestation undergoing their first CPB between 2015 and 2019. None. Hourly temperature measurements for the first 48 postoperative hours were extracted from inpatient medical records, and clinical characteristics and outcomes were accessed through the local patient registry. Group-based trajectory modeling (GBTM) identified latent temporal temperature trajectories. Associations of trajectories with outcomes were assessed using multivariable binary logistic regression. Outcomes (postoperative complications) were manually adjudicated by experts or were predefined by the patient registry. Four hundred fifty neonates met inclusion criteria. Their mean (sd) gestational age was 38 weeks (1.3), mean (sd) birth weight was 3.19 kilograms (0.55), median (interquartile range) surgical age was 4.7 days (3.3-7.0), 284 of 450 (63%) were male, and 272 of 450 (60%) were White. GBTM identified three distinct curvilinear temperature trajectories: persistent hypothermia (n = 38, 9%), resolving hypothermia (n = 233, 52%), and normothermia (n = 179, 40%). Compared with the normothermic group, those with persistent hypothermia had significantly higher odds of cardiac arrest, actionable arrhythmia, delayed first successful extubation, prolonged cardiac ICU length of stay, very poor weight gain, and 30-day hospital mortality. The persistent hypothermia group was characterized by greater odds of having a lower gestational age, more prevalent neurologic abnormalities, more unplanned reoperations, and a low surgical mortality risk assessment. Persistent postoperative hypothermia in neonates after CPB is independently associated with having greater odds of complications. Recovery patterns from postoperative hypothermia may be a clinically useful marker to identify patient instability in neonates. Additional research is needed for causal modeling and prospective validation before clinical adoption.

Artificial Intelligence-Based Models for Prediction of Mortality in ICU Patients: A Scoping Review.

Healthcare professionals may be able to anticipate more accurately a patient's timing of death and assess their possibility of recovery by implementing a real-time clinical decision support system. Using such a tool, the healthcare system can better understand a patient's condition and make more informed judgements about distributing limited resources. This scoping review aimed to analyze various death prediction AI (Artificial Intelligence) algorithms that have been used in ICU (Intensive Care Unit) patient populations. The search strategy of this study involved keyword combinations of outcome and patient setting such as mortality, survival, ICU, terminal care. These terms were used to perform database searches in MEDLINE, Embase, and PubMed up to July 2022. The variables, characteristics, and performance of the identified predictive models were summarized. The accuracy of the models was compared using their Area Under the Curve (AUC) values. Databases search yielded an initial pool of 8271 articles. A two-step screening process was then applied: first, titles and abstracts were reviewed for relevance, reducing the pool to 429 articles. Next, a full-text review was conducted, further narrowing down the selection to 400 key studies. Out of 400 studies on different tools or models for prediction of mortality in ICUs, 16 papers focused on AI-based models which were ultimately included in this study that have deployed different AI-based and machine learning models to make a prediction about negative patient outcome. The accuracy and performance of the different models varied depending on the patient populations and medical conditions. It was found that AI models compared with traditional tools like SAP3 or APACHE IV score were more accurate in death prediction, with some models achieving an AUC of up to 92.9%. The overall mortality rate ranged from 5% to more than 60% in different studies. We found that AI-based models exhibit varying performance across different patient populations. To enhance the accuracy of mortality prediction, we recommend customizing models for specific patient groups and medical contexts. By doing so, healthcare professionals may more effectively assess mortality risk and tailor treatments accordingly. Additionally, incorporating additional variables-such as genetic information-into new models can further improve their accuracy.

Shock Index and Modified Shock Index Are Predictors of Long-Term Mortality in STEMI and NSTEMI Patients

Background: Acute coronary syndromes (ACS), including STEMI and NSTEMI, are leading causes of mortality worldwide. The Shock Index (SI) and Modified Shock Index (MSI) are non-invasive markers traditionally used to assess hemodynamic status but their role in predicting long-term mortality in ACS patients is less understood. Objective: To evaluate the prognostic value of SI and MSI in predicting long-term mortality among patients with STEMI and NSTEMI. Methods: A prospective cohort study was conducted on 275 consecutive patients diagnosed with STEMI and NSTEMI at the Department of Cardiology, PIMS, Islamabad, between March 1, 2024, and July 31, 2024. Patients were divided into two groups: Group 1 (n=138) assessed using SI and Group 2 (n=137) using MSI. Follow-up for three years was conducted to record all-cause mortality. Data were analyzed using SPSS version 25.0, with Cox proportional hazards regression and Kaplan-Meier survival analysis employed to assess mortality risk. Results: During the three-year follow-up, mortality rates were 24.6% in the SI group and 26.3% in the MSI group. Higher SI and MSI values were significantly associated with increased mortality (SI HR=1.85, 95% CI: 1.35-2.54, p&lt;0.001; MSI HR=2.10, 95% CI: 1.50-2.93, p&lt;0.001). Conclusion: SI and MSI are significant predictors of long-term mortality in STEMI and NSTEMI patients and should be integrated into routine clinical assessments for better risk stratification.

Systematic review and meta-analysis to predict mortality in heart failure with preserved ejection fraction: Development and validation of the HF-DANAS score.

The morbidity and mortality of heart failure with preserved ejection fraction (HFpEF) continue to increase with the accelerating global aging process. During the past decade, the pathophysiology, diagnostic methods, and prognostic prediction of HFpEF have been revolutionized, resulting in new and effective management strategies. Dynamic prognostic assessment facilitates systematic clinical management of patients, and the aim of this study was to investigate the risk factors for mortality in patients with HFpEF and to develop a risk prediction assessment model. Data for the derivation cohort were obtained from three databases, PubMed, Embase, and Cochrane. The validation cohort was obtained from the Chinese Heart Failure Center database. The β-coefficient was calculated based on the risk ratio (RR) and 95% confidence intervals (CI) corresponding to each risk factor to construct a mortality risk assessment model. A total of 30 studies were included in the meta-analysis: 22 prospective cohort studies and 8 retrospective cohort studies, including 34196 HFpEF patients. Seven predictors of all-cause mortality in HFpEF patients were derived. Considering the need for feasibility in clinical practice, we performed subgroup and sensitivity analyses and determined the following cutoff values: age>75years (RR: 2.07, 95% CI: 1.83-2.35; P<0.001), male sex (RR: 1.36, 95% CI: 1.17-1.59; P<0.001), DM (RR: 1.23, 95% CI: 1.11-1.36; P<0.001), anaemia (RR: 1.53, 95% CI: 1.41-1.67; P<0.001), albumin concentration<3.2g/dL (RR: 1.29, 95% CI: 1.14-1.47; P<0.001), AF (RR: 1.27, 95% CI: 1.12-1.43; P<0.001), and NYHA class III/IV (RR: 1.63, 95% CI: 1.43-1.87; P<0.001). The area under the receiver operating characteristic (ROC) curve (AUC) for this model was 71.3% (95% CI: 0.696-0.736), with an optimal cut-off value of 10.75. The sensitivity and specificity were 0.778 and 0.566, respectively. According to this risk score, we divided patients into three risk classes (low, moderate, and high risk), the numbers of patients who died by the end of the 1-year follow-up were 23 (1.87%), 82 (5.62%), and 382 (15.52%) in these three groups, and the 5-year mortality rates were 9.82%, 20.68%, and 43.28%, respectively. This study developed an HF-DANAS scoring system for the HFpEF mortality risk containing seven predictors, providing clinicians with a simple assessment tool that can help improve clinical management.

How to assess survival prognosis in patients hospitalized for community-acquired pneumonia in 2024?

Community-acquired pneumonia (CAP) is increasingly recognized as a complex, multisystemic disease with the potential to cause both acute and long-term sequelae, significantly impacting patient mortality rates. In this manuscript, the authors review the current methodologies for assessing mortality risk among CAP patients. The most common prediction scores for ICU care and short-term mortality include Pneumonia Severity Index (PSI), CURB-65, SMART COP, SCAP, and ATS/IDSA criteria. These models have clinical utility in the prediction of short-term mortality, but they have significant limitations in addressing long-term mortality. For patients who are discharged alive from the hospital, we do not have scores to predict long term mortality. The development of an optimal prognostic tool for postacute sequelae of CAP is imperative. Such a tool should identify specific populations at increased risk. Moreover, accurately identifying at-risk populations is essential for their inclusion in clinical trials that evaluate potential therapies designed to improve short and long-term clinical outcomes in patients with CAP.

Risk assessment for perfluorooctanoic acid (PFOA) in air, blood serum and water: mortality from liver and kidney disease

BackgroundCancer and non-cancer associations have been observed with PFAS (perfluoroalkyl and polyfluoroalkyl) substances in the general population, in populations from locally contaminated environments and in exposed workers.MethodsA quantitative risk assessment...

Food insecurity and the risk of mortality among Hispanics with hypertension

Background and aimsHypertension continues to be a major public health problem affecting almost half of the adults in the US. The intersection of hypertension with food insecurity has not been well-examined specifically among minority populations. We aimed to examine the influence of food insecurity on mortality among adult Hispanics. Methods and resultsData on adult Hispanic (age≥ 20 years) respondents of the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010 were analyzed. Mortality was assessed by linking these data with the National Death Index through December 31, 2019. Using complex samples Cox regression analysis, the relationship between hypertension, food insecurity, and mortality was assessed. Sociodemographic (age, gender, poverty-income-ratio, marital status, and citizenship status) and health-related characteristics (COPD, diabetes, cardiovascular disease, chronic kidney disease) of the population were included as covariates in the regression analysis to assess mortality risk. The crude hazard ratio (HR) for overall mortality related to hypertension was 4.95 (95% confidence interval [CI] = 4.22–5.82, p < .001). The adjusted HR was elevated, 2.01 (95%CI = 1.50–2.70, p < .001), among individuals with both hypertension and food insecurity. However, among individuals with hypertension and no food insecurity, there was no statistically significant increase in the risk of mortality (HR = 1.09, 95%CI = 0.89–1.34, p > 0.05). ConclusionsIn adult Hispanics, food insecurity significantly increases the risk of mortality among those with hypertension compared to food-secure individuals. Clinicians should be sensitized to the need for food security among Hispanics with hypertension to effectively manage hypertension and reduce premature mortality.

Examining the Relationship Between Chronic Pain and Mortality in U.S. Adults

Chronic pain (CP) significantly impacts quality of life and increases noncommunicable disease risk, with recent U.S. data showing a 6.3% incidence rate, surpassing diabetes, depression, and hypertension. International studies suggest higher mortality in CP populations, yet prior U.S. data are inconclusive. To investigate CP's mortality risk, we analyzed National Health Interview Survey and National Death Index data. We hypothesized that individuals with CP and high-impact CP (HICP [≥1 activity limitation]) would exhibit higher mortality rates. National Health Interview Survey provided demographics, pain reporting, lifestyle, and psychosocial data matched with National Death Index mortality records. Chi-square analyses explored the relationships between CP/HICP and demographics, lifestyle factors, psychosocial variables, and mortality. Cox proportional hazards models assessed mortality risk between groups. The weighted sample was 245,899,776; 20% reported CP and 8% HICP, both groups exhibiting higher mortality rates than pain-free individuals (CP: 5.55%, HICP: 8.79%, total: 2.82%). Hazard ratios indicated nearly double the mortality risk for CP and 2.5 times higher risk for HICP compared to those without these conditions. Adjusting for lifestyle and psychosocial factors reduced mortality risk but remained elevated compared with non-CP individuals. Heart disease, malignant neoplasms, and chronic lower respiratory diseases accounted for a higher percentage of deaths in CP cases. CP individuals showed higher rates of smoking, alcohol consumption, obesity, inactivity, depression, anxiety, emotional problems, and sleep disturbances. CP and HICP significantly influence mortality outcomes, leading to excess deaths compared with pain-free individuals. Given the relationship between pain, lifestyle, psychosocial variables, and mortality, further investigations are needed into CP causation and prevention strategies. PerspectiveThis article presents evidence regarding the relationship between CP, HICP, and mortality. Additional findings are discussed regarding the impact of demographics, lifestyle, and psychosocial variables on mortality in those with versus without CP and HICP. These findings are crucial for informing future research, prevention, and healthcare management strategies.

Mortality Risk Assessment Using the REVEAL 2.0 Score in Pulmonary Hypertension Secondary to Left Heart Disease.

Pulmonary hypertension (PH) frequently complicates the course of patients with left heart disease (PH-LHD) and is associated with worse clinical outcomes. Mortality calculators for PH-LHD are lacking, and it is unclear whether any risk prediction tools originally derived from other forms of PH can accurately predict outcomes in patients with PH-LHD. We retrospectively analyzed data from 161 patients diagnosed with PH-LHD referred to our pulmonary hypertension center from 2016 to 2022. We calculated the Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL 2.0) risk score and categorized patients as low, intermediate, or high-risk. We assessed survival at 1 and 3 years using Kaplan-Meier and Cox proportional hazards, as well as classification performance using a concordance index. At the first outpatient visit, 15% of patients were stratified as low-risk, 27% as intermediate, and 57% as high-risk. Cumulative 1-year survival rates were 100%, 94%, and 91% for the low, intermediate, and high-risk strata, respectively. Cumulative 3-year survival rates were 96%, 89%, and 70% for the low, intermediate, and high-risk strata, respectively. We found no difference in outcomes at 1 year between risk groups. High-risk patients had an increased risk of death at 3 years using REVEAL 2.0 (HR 5.32, p < 0.001). However, while REVEAL 2.0 accurately discriminated high-risk patients, the hazard ratio was not statistically different between patients classified as intermediate-risk compared to low-risk. REVEAL 2.0 accurately predicted 3-year survival in PH-LHD patients with high-risk features. However, the mortality risk between patients classified as intermediate-risk was not different from the low-risk stratum, suggesting inaccurate classification for this group of patients.

A study on the monitoring of heatwaves and bivariate frequency analysis based on mortality risk assessment in Wuhan, China.

The increasingly frequent occurrence of urban heatwaves has become a significant threat to human health. To quantitatively analyze changes in heatwave characteristics and to investigate the return periods of future heatwaves in Wuhan City, China, this study extracted 9 heatwave definitions and divided them into 3 mortality risk levels to identify and analyze historical observations and future projections of heatwaves. The copula functions were employed to derive the joint distribution of heatwave severity and duration and to analyze the co-occurrence return periods. The results demonstrate the following. (1) As the concentration of greenhouse gas emissions increases, the severity of heatwaves intensifies, and the occurrence of heatwaves increases significantly; moreover, a longer duration of heatwaves correlated with higher risk levels in each emission scenario. (2) Increasing concentrations of greenhouse gas emissions result in significantly shorter heatwave co-occurrence return periods at each level of risk. (3) In the 3 risk levels under each emission scenario, the co-occurrence return periods for heatwaves become longer as heatwave severity intensifies and duration increases. Under the influence of climate change, regional-specific early warning systems for heatwaves are necessary and crucial for policymakers to reduce heat-related mortality risks in the population, especially among vulnerable groups.

LINAS-Score: prognostic model for mortality assessment in patients with cirrhotic liver and infected ascites.

Patients with liver cirrhosis often face a grave threat from infected ascites (IA). However, a well-established prognostic model for this complication has not been established in routine clinical practice. Therefore, we aimed to assess mortality risk in patients with liver cirrhosis and IA. We conducted a retrospective study across three tertiary hospitals, enrolling 534 adult patients with cirrhotic liver and IA, comprising 465 with spontaneous bacterial peritonitis (SBP), 34 with bacterascites (BA), and 35 with secondary peritonitis (SP). To determine the attributable mortality risk linked to IA, these patients were matched with 122 patients with hydropic decompensated liver cirrhosis but without IA. Clinical, laboratory, and microbiological parameters were assessed for their relation to mortality using univariable analyses and a multivariable random forest model (RFM). Least absolute shrinkage and selection operator (Lasso) regression model was used to establish an easy-to-use mortality prediction score. The in-hospital mortality risk was highest for SP (39.0%), followed by SBP (26.0%) and BA (25.0%). Besides illness severity markers, microbiological parameters, such as Candida spp., were identified as the most significant indicators for mortality. The Lasso model determined 15 parameters with corresponding scores, yielding good discriminatory power (area under the receiver operating characteristics curve=0.89). Counting from 0 to 83, scores of 20, 40, 60, and 80 corresponded to in-hospital mortalities of 3.3%, 30.8%, 85.2%, and 98.7%, respectively. We developed a promising mortality prediction score for IA, highlighting the importance of microbiological parameters in conjunction with illness severity for assessing patient outcomes.

Clinical characteristics and independent prognostic factors of patients with mantle cell lymphoma over last two decades with a focus on the interaction between tumor stage and age.

e19065 Background: Mantle Cell Lymphoma (MCL) poses challenges in clinical management due to its rarity and aggressiveness. Understanding its epidemiology and outcomes is crucial. This study examines demographic and clinicopathologic profiles of 746 MCL patients in the USA from 2000-2015, focusing on the interplay between age and tumor stage. Methods: Using SEER data, this retrospective study analyzes demographic and clinical variables to assess mortality risks. Univariate and multivariate Cox regression models evaluate overall mortality (OM) and MCL-specific mortality (CSM), identifying independent prognostic factors (HR &gt;1). The study explores age-tumor stage interaction. Results: Multivariate Cox proportional hazard regression analysis revealed age as pivotal, with those ≥80 facing higher risks for overall mortality (HR=3.93) and MCL-related mortality (HR=4.02) . Advanced Ann Arbor staging (II, III, IV) predicts heightened risks for both mortality types. Widowed status significantly influences outcomes ,with widowed individuals facing notably higher risks of overall mortality (HR=1.85) and MCL-related mortality (HR=1.81).Stage II and age groups 60-79 and ≥80 show substantial mortality risks, with similar trends in Stage IV combined with these age groups. Intriguingly, Stage III lacks significant age interactions, suggesting heterogeneous clinical profiles and treatment responses compared to patients in Stages II and IV. Therefore, the influence of age on mortality outcomes may be obscured by the complexities inherent in Stage III disease presentation and management. Conclusions: This study underscores age, staging, and marital status as key factors influencing MCL mortality. Widowed status emerges as notable, emphasizing social support's importance. Integrating these insights into clinical practice can enhance risk stratification and guide tailored interventions for improved outcomes. Our findings, particularly the lack of significant interactions between Stage III and age groups, highlight the intricate interplay between age and disease stage in MCL patients. This observation prompts us to advocate for further research aimed at elucidating the underlying mechanisms driving these complex interactions. Understanding the biological and clinical factors that influence the relationship between age and disease stage could provide valuable insights into MCL pathogenesis and progression. Further research in this area is warranted to inform personalized therapeutic approaches and ultimately enhance patient care and outcomes.

Risk Factors of Mortality and Long-Term Survival in Burn Patients With/Without Extracorporeal Membrane Oxygenation: A 16 Year Real-World Study.

Burn patients face cardiopulmonary failure risks, with recent observational studies suggesting promising outcomes for extracorporeal membrane oxygenation (ECMO). However, the effectiveness and long-term survival remain unclear. Our study aims to assess mortality risk factors and long-term survival in burn patients with and without ECMO. This study used Taiwan's National Health Insurance Research Database and designed a case-control with onefold propensity score matching across variables including sex, age, total body surface area (TBSA) burned, and index date. We analyzed mortality and survival risk factors in each stratified group with/without ECMO. Finally, we analyze the mortality according to ECMO and TBSA burned, and the cause of death and long-term survival. From 2000 to 2015, 4,556 burn patients with ECMO compared to an equivalent number without ECMO. Primary mortality include male, age >65, TBSA ≥30%, escharotomy, hemodialysis, and bacteremia. The ECMO group showed lower survival across all stratified risk factors, with the primary cause of death being burn-related issues, followed by respiratory and heart failure. The overall mortality rate was 54.41% with ECMO and 40.94% without ECMO (p < 0.001). Additionally, long-term survival is lower in the group with ECMO. This research provides a valuable real-world gross report about ECMO efficacy and long-term survival among burn patients with/without ECMO.

Respiratory muscle strength can improve the prognostic assessment in COPD

Impaired lung function, respiratory muscle weakness and exercise intolerance are present in COPD and contribute to poor prognosis. However, the contribution of the combination of these manifestations to define prognosis in COPD is still unknown. This study aimed to define cut-off points for both inspiratory and expiratory muscle strength (MIP and MEP, respectively) for mortality prediction over 42-months in patients with COPD, and to investigate its combination with other noninvasive established prognostic measures (FEV1, V̇O2peak and 6MWD) to improve risk identification. Patients with COPD performed pulmonary function, respiratory muscle strength, six-minute walk and cardiopulmonary exercise tests, and were followed over 42 months to analyze all-cause mortality. A total of 79 patients were included. The sample was mostly (91.1%) comprised of severe (n = 37) and very severe (n = 34) COPD, and 43 (54%) patients died during the follow-up period. Cut-points of ≤ 55 and ≤ 80 cmH2O for MIP and MEP, respectively, were associated with increased risk of death (log-rank p = 0.0001 for both MIP and MEP) in 42 months. Furthermore, MIP and MEP substantially improved the mortality risk assessment when combined with FEV1 (log-ranks p = 0.006 for MIP and p < 0.001 for MEP), V̇O2peak (log-rank: p < 0.001 for both MIP and MEP) and 6MWD (log-ranks: p = 0.005 for MIP; p = 0.015 for MEP). Thus, patients severely affected by COPD presenting MIP ≤ 55 and/or MEP ≤ 80 cmH2O are at increased risk of mortality. Furthermore, MIP and MEP substantially improve the mortality risk assessment when combined with FEV1, V̇O2peak and 6MWD in patients with COPD.