BACKGROUND: Predicting long-term mortality is essential for understanding prognosis and guiding treatment decisions in patients with ischemic stroke. Therefore, this study aimed to develop and validate the method for predicting 1-year and 5-year mortality after ischemic stroke.
METHODS: We utilized data from the linked dataset comprising the administrative claims database of the Health Insurance Review and Assessment Service and the Clinical Research Center for Stroke registry data for patients with acute stroke within 7 days of onset. The outcome was all-cause mortality following ischemic stroke. Clinical variables linked to long-term mortality following ischemic stroke were determined. A nomogram was constructed based on the Cox\'s regression analysis. The performance of the risk prediction model was evaluated using the Harrell\'s C index.
RESULTS: This study included 42,207 ischemic stroke patients, with a mean age of 66.6 years and 59.2% being male. The patients were randomly divided into training (n=29,916) and validation (n=12,291) groups. Variables correlated with long-term mortality in patients with ischemic stroke, including age, sex, body mass index, stroke severity, stroke mechanisms, onset-to-door time, pre-stroke dependency, history of stroke, diabetes mellitus, hypertension, coronary artery disease, chronic kidney disease, cancer, smoking, fasting glucose level, previous statin therapy, thrombolytic therapy such as intravenous thrombolysis and endovascular recanalization therapy, medications, and discharge modified Rankiin Scale were identified as predictors. We developed a predictive system named Stroke Measures Analysis of pRognostic Testing - Mortality (SMART-M) by constructing a nomogram using the identified features. The C-statistics of the nomogram in the developing and validation groups were 0.806 (95% confidence interval [CI], 0.802-0.812) and 0.803 (95% CI, 0.795-0.811), respectively.
CONCLUSIONS: The SMART-M method demonstrated good performance in predicting long-term mortality in ischemic stroke patients. This method may help physicians and family members understand the long-term outcomes and guide the appropriate decision-making process.

OBJECTIVE: Although insulin resistance (IR) has been recognized to be a causal component in various diseases, current information on the relationship between IR and long-term mortality in the general population is limited and conclusions varied among different IR indicators and different populations. We aimed to assess associations between different measurements of IR with long-term all-cause mortality and cardiovascular mortality risk for the general population.
METHODS: We included 13,909 individuals from the Third National Health and Nutrition Examination Survey. Mortality was identified via National Death Index information until December 31, 2019. IR was measured using fasting insulin, homeostasis model assessment of IR (HOMA-IR), homeostasis model assessment of β-cell function, quantitative insulin sensitivity check index (QUICKI), insulin-to-glucose ratio (IGR), triglyceride glucose (TyG) index, TyG-body mass index (TyG-BMI), and hypertriglyceridemic-waist phenotype.
RESULTS: During median 25-year follow-up, 5,306 all-cause mortality events occurred. After multivariate adjustment, variables significantly associated with elevated all-cause mortality risk were (hazard ratio [95 % confidence interval]): higher insulin (1.07 [1.02;1.13]); HOMA-IR (1.08 [1.03;1.13]); IGR (1.05 [1.00;1.11]); TyG (1.07 [1.00;1.14]); TyG-BMI (1.24 [1.02;1.51]); lower QUICKI (0.91 [0.86-0.96]). After stratification by diabetes status, higher insulin, HOMA-IR, TyG-BMI and lower QUICKI were significantly associated with increased risk of all-cause mortality in both diabetes and non-diabetes populations (all P for interaction > 0.05). Higher TyG (adjusted HR 1.17 [1.09;1.26], P for interaction = 0.018) and hypertriglyceridemic-waist phenotype (adjusted HR 1.26 [1.08;1.46], P for interaction = 0.047) were significantly associated with increased risk of all-cause mortality in patients with diabetes, however, these associations could not be seen in people without diabetes. Similar results were observed between the above-mentioned IR indicators and cardiovascular death.
CONCLUSIONS: Fasting insulin, HOMA-IR, TyG-BMI, and QUICKI may indicate mortality risk in diabetes and non-diabetes populations, with TyG and the hypertriglyceridemic-waist phenotype showing particular relevance for individuals with diabetes. Further studies are needed to validate these findings and determine their broader applicability.

UNASSIGNED: The association between fibrinogen-to-albumin ratio (FAR) and in-hospital mortality in patients with spontaneous intracerebral hemorrhage (ICH) has been established. However, the association with long-term mortality in spontaneous ICH remains unclear. This study aims to investigate the association between FAR and long-term mortality in these patients.
UNASSIGNED: Our retrospective study involved 3,538 patients who were diagnosed with ICH at West China Hospital, Sichuan University. All serum fibrinogen and serum albumin samples were collected within 24 h of admission and participants were divided into two groups according to the FAR. We conducted a Cox proportional hazard analysis to evaluate the association between FAR and long-term mortality.
UNASSIGNED: Out of a total of 3,538 patients, 364 individuals (10.3%) experienced in-hospital mortality, and 750 patients (21.2%) succumbed within one year. The adjusted hazard ratios (HR) showed significant associations with in-hospital mortality (HR 1.61, 95% CI 1.31-1.99), 1-year mortality (HR 1.45, 95% CI 1.25-1.67), and long-term mortality (HR 1.45, 95% CI 1.28-1.64). Notably, the HR for long-term mortality remained statistically significant at 1.47 (95% CI, 1.15-1.88) even after excluding patients with 1-year mortality.
UNASSIGNED: A high admission FAR was significantly correlated with an elevated HR for long-term mortality in patients with ICH. The combined assessment of the ICH score and FAR at admission showed higher predictive accuracy for long-term mortality than using the ICH score in isolation.

BACKGROUND: During the first COVID-19 pandemic wave (1st CoPW), nursing homes (NHs) experienced a high rate of COVID-19 infection and death. Residents who survived the COVID-19 infection may have become frailer. This study aimed to determine the predictive value of having a COVID-19 infection during the 1st CoPW for 2-year mortality in NH residents.
METHODS: This was a retrospective study conducted in three NHs. Residents who had survived the 1st CoPW (March to May 2020) were included. The diagnosis of COVID-19 was based on the results of a positive reverse transcriptase-polymerase chain reaction test. The collected data also included age, sex, length of residence in the NH, disability status, legal guardianship status, nutritional status, need for texture-modified food, hospitalization or Emergency Department visits during lockdown and SARS-COV2 vaccination status during the follow-up. Non-adjusted and adjusted Cox models were used to analyse factors associated with 2-year post-1st CoPW mortality.
RESULTS: Among the 315 CoPW1 survivors (72% female, mean age 88 years, 48% with severe disability), 35% presented with COVID-19. Having a history of COVID-19 was not associated with 2-year mortality: hazard ratio (HR) [95% confidence interval] = 0.96 [0.81-1.13], p = 0.62. The factors independently associated with 2-year mortality were older age (for each additional year, HR = 1.05 [1.03-1.08], p < 0.01), severe disability vs. moderate or no disability (HR = 1.35 [1.12-1.63], p < 0.01) and severe malnutrition vs. no malnutrition (HR = 1.29 [1.04-1.60], p = 0.02). Considering that vaccination campaign started during the follow-up, mortality was associated with severe malnutrition before and severe disability after the start of the campaign. Vaccination was independently associated with better survival (HR 0.71 [0.55-0.93], p = 0.02).
CONCLUSIONS: Having survived a COVID-19 infection during the 1st CoPW did not affect subsequent 2-year survival in older adults living in NHs. Severe malnutrition and disability remained strong predictor of mortality in this population, whereas vaccination was associated to better survival.

UNASSIGNED: Abnormal glucose metabolism is present in most patients with coronary artery disease (CAD). Inflammation is considered to be a common risk factor for CAD and diabetes. Fibrinogen-to-albumin ratio (FAR), a novel inflammation biomarker, has been proposed as a predictor for cardiovascular disease. However, the relationship between the level of FAR and long-term mortality including all-cause, cardiovascular and cancer mortality, remains unknown in CAD patients, especially those with prediabetes.
UNASSIGNED: We enrolled 66,761 CAD patients from 2007 to 2020 from a multi-center registry cohort study. The primary outcomes were the all-cause, cardiovascular and cancer mortality. FAR was calculated using the following formula: Fibrinogen (g/L)/Albumin (g/L). Patients were divided into three groups by FAR tertile (low FAR (FAR-L), median FAR (FAR-M), high FAR (FAR-H)), and further categorized into 9 groups according to FAR and glucose metabolism status (normal glucose regulation (NGR), prediabetes mellitus (PreDM), diabetes mellitus (DM)). Cox regression models and competing risk models were used to examine the relationships between FAR and clinical outcomes.
UNASSIGNED: 66,761 patients (63.1 ± 11.0 years, 75.3% male) were enrolled. During the follow-up, 10,534 patients died, including 4991 cardiovascular deaths and 1092 cancer deaths. After adjusting for confounders, higher FAR was associated with increased risk of all-cause and cause-specific mortality in CAD patients with NGR, PreDM and DM. The risk of all-cause and cardiovascular mortality was highest in FAR-H with DM (HR (95% CI) = 1.71 (1.58-1.86), 2.11 (1.86-2.38), respectively; p < 0.001). FAR-H with PreDM was significantly associated with the highest risk of cancer mortality (HR (95% CI) = 2.27 (1.70-3.02), p < 0.001). Adding FAR to the original model significantly improved the prediction of long-term mortality.
UNASSIGNED: Increased FAR was significantly associated with higher risk of all-cause and cause-specific mortality in CAD patients with NGR, PreDM and DM. Abnormal glucose metabolism augments the relationship between FAR and mortality.
UNASSIGNED: ClinicalTrials.gov NCT05050877.

Aim: There is a lack of data about the association between admission serum albumin levels and long-term mortality in heart failure (HF) patients with cardiac resynchronization therapy defibrillators (CRT-D). We aim to investigate this connection in HF patients with CRT-D. Methods: The study population consisted of 477 HF patients with CRT-D. The cohort was divided into three groups according to albumin values, and the relationship between these groups and long-term mortality were evaluated. Results: Long-term all-cause mortality (HR: 3.32, 95% CI: 2.12-6.84), appropriate (HR: 4.44, 95% CI: 2.44-8.06) and inappropriate (HR: 2.95, 95% CI: 1.88-6.02) shocks were higher in the low albumin group. Conclusion: Low albumin levels are associated with the long-term mortality and appropriate shock treatment in HF patients with CRT-D.
[Box: see text].

UNASSIGNED: Epicardial fat is associated with cardiovascular risk factors and adverse outcomes. However, it is not clear if epicardial fat remains to be a mortality risk when coronary calcium score (CAC) is taken into account.
UNASSIGNED: We studied the 1005 participants from the St. Francis Heart Study who were apparently healthy with CAC scores at 80th percentile or higher for age and gender, randomly assigned to placebo or statin therapy. At baseline, lipid profiles and non-contrast CT images were obtained where the epicardial fat volume was analyzed. Likelihood ratio testing was used to assess the additional prognostic value of epicardial fat to CAC for the risk of all-cause mortality.
UNASSIGNED: Increased epicardial fat volume was associated with higher CAC. For each unit increase in lnCAC, the average epicardial fat volume increased by 3.34 mL/m2. After a mean follow-up period of 17 years, 179 (18%) participants died. Increased epicardial fat volume was associated with an adjusted hazard ratio of 1.11 (95% CI: 1.02 to 1.20) predicting all-cause mortality. In the stratified analysis testing strata of epicardial fat and CAC, those with increased epicardial fat and increased CAC had the highest risk of death. Compared with a model containing lnCAC and traditional risk factors, a model additionally containing epicardial fat volume yielded a better model fit (likelihood ratio test p < 0.001).
UNASSIGNED: Increased epicardial fat volume is associated with increased all-cause mortality risk. In addition, it portends incremental prognostic value to CAC score in mortality prediction.

OBJECTIVE: The development of predictive models for patients treated by emergency medical services (EMS) is on the rise in the emergency field. However, how these models evolve over time has not been studied. The objective of the present work is to compare the characteristics of patients who present mortality in the short, medium and long term, and to derive and validate a predictive model for each mortality time.
METHODS: A prospective multicenter study was conducted, which included adult patients with unselected acute illness who were treated by EMS. The primary outcome was noncumulative mortality from all causes by time windows including 30-day mortality, 31- to 180-day mortality, and 181- to 365-day mortality. Prehospital predictors included demographic variables, standard vital signs, prehospital laboratory tests, and comorbidities.
RESULTS: A total of 4830 patients were enrolled. The noncumulative mortalities at 30, 180, and 365 days were 10.8%, 6.6%, and 3.5%, respectively. The best predictive value was shown for 30-day mortality (AUC = 0.930; 95% CI: 0.919-0.940), followed by 180-day (AUC = 0.852; 95% CI: 0.832-0.871) and 365-day (AUC = 0.806; 95% CI: 0.778-0.833) mortality.
CONCLUSIONS: Rapid characterization of patients at risk of short-, medium-, or long-term mortality could help EMS to improve the treatment of patients suffering from acute illnesses.

OBJECTIVE: This report aimed to describe mortality at 18 months in older survivors of the first wave of COVID-19.
METHODS: Observational cohort study.
METHODS: Patients aged ≥65 years hospitalized for COVID-19 in the acute geriatric wards of 2 centers.
METHODS: Characteristics of deceased and survivors were compared by Fisher exact, Mann-Whitney U, or 2-tailed t tests. Survival rates were analysed by Cox proportional hazards regression models.
RESULTS: Of a total of 323 patients admitted during the first wave, 196 survived the acute phase, with 34 patients who died in the 18 months after hospital discharge (17.3%). Higher mortality was observed in patients living in nursing homes (P = .033) and in those who were hospitalized after discharge during the follow-up period (97.1% vs 72.8%, P = .001). There was no difference in survival curves according to age, sex, presence of dyspnea, and dementia. Living in a nursing home significantly increased the mortality rates in the multivariate model adjusted for age and sex (hazard ratio 3.07, 95% CI 1.47-6.40; P = .007).
CONCLUSIONS: No excess mortality was observed during 18 months in older survivors of COVID-19. Living in a nursing home was associated with decreased survival rates.

Hypoalbuminemia associates with poor acute ischemic stroke (AIS) outcomes. We hypothesised a non-linear relationship and aimed to systematically assess this association using prospective stroke data from the Norfolk and Norwich Stroke and TIA Register. Consecutive AIS patients aged ≥40 years admitted December 2003-December 2016 were included. Outcomes: In-hospital mortality, poor discharge, functional outcome (modified Rankin score 3-6), prolonged length of stay (PLoS) > 4 days, and long-term mortality. Restricted cubic spline regressions investigated the albumin-outcome relationship. We updated a systematic review (PubMed, Scopus, and Embase databases, January 2020-June 2023) and undertook a meta-analysis. A total of 9979 patients were included; mean age (standard deviation) = 78.3 (11.2) years; mean serum albumin 36.69 g/L (5.38). Compared to the cohort median, albumin < 37 g/L associated with up to two-fold higher long-term mortality (HRmax; 95% CI = 2.01; 1.61-2.49) and in-hospital mortality (RRmax; 95% CI = 1.48; 1.21-1.80). Albumin > 44 g/L associated with up to 12% higher long-term mortality (HRmax1.12; 1.06-1.19). Nine studies met our inclusion criteria totalling 23,597 patients. Low albumin associated with increased risk of long-term mortality (two studies; relative risk 1.57 (95% CI 1.11-2.22; I2 = 81.28)), as did low-normal albumin (RR 1.10 (95% CI 1.01-1.20; I2 = 0.00)). Strong evidence indicates increased long-term mortality in AIS patients with low or low-normal albumin on admission.