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Background The impact of statin therapy on short‐term mortality among critically ill patients with hemorrhagic stroke or ischemic stroke remains uncertain. We investigated associations between statin use and ICU and hospital mortality in this patient population. Methods We conducted a retrospective cohort study using the MIMIC‐IV database, including 6918 patients (2960 HS and 3958 IS) after applying strict exclusion criteria. Statin use was assessed by type, dose (standard vs. high), and initiation timing (pre‐ICU vs. post‐ICU). Survival outcomes were evaluated using Kaplan–Meier analysis and multivariable Cox regression models, landmark analyses, and Fine–Gray competing‐risk models, with propensity score matching to adjust for confounding factors. Results Statin use significantly reduced ICU mortality at 30 days in HS (HR = 0.59, 95% CI: 0.41–0.87) and IS (HR = 0.45, 95% CI: 0.32–0.64) cohorts. Atorvastatin and simvastatin showed pronounced protective effects, independent of dose intensity. Post‐ICU initiation of statins conferred greater benefit compared with pre‐ICU initiation, especially in HS patients. Shorter statin treatment duration (≥ 3 days) sufficiently captured beneficial effects. Patients with hyperlipidemia demonstrated enhanced mortality benefit. Conclusion Statin use is associated with significantly lower short‐term mortality in critically ill stroke patients, supporting tailored strategies for optimal statin initiation and duration. Statin use in critically ill stroke patients is associated with reduced short‐term ICU and hospital mortality, particularly with post‐ICU initiation and at least 3 days of treatment. Atorvastatin and simvastatin showed the strongest protective effects. These findings support tailored statin strategies in acute stroke care.

Respiratory failure (RF) after trauma is one of the major causes of patients being admitted to the ICU and leads to a high mortality rate. However, we cannot predict mortality rates based on patients' various indicators. The aim of this study is to develop and validate a nomogram for predicting mortality in patients in the intensive care unit (ICU). A total of 377 patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database were included in the study. All participants were systematically divided into a development cohort for modelling and a validation cohort for internal validation at a ratio of 7:3. Following patient admission, a comprehensive collection of 30 clinical indicators was performed. The least absolute shrinkage and selection operator (LASSO) regression technique was employed to discern pivotal risk factors. A multivariate Cox regression model was established, and a receiver operating curve (ROC) was plotted, and the area under the curve (AUC) was calculated. Furthermore, the decision curve analysis (DCA) was performed, and the nomogram was compared with the acute physiology score III (APSIII) and Oxford acute severity of illness score (OASIS) scoring systems to assess the net clinical benefit. The indicators included in our model were age, OASIS score, SAPS III score, respiratory rate (RR), blood urea nitrogen (BUN) and hematocrit. The results demonstrated that our model yielded satisfied performance on the development cohort and on internal validation. The calibration curve underscored a robust concordance between predicted and actual outcomes. The DCA showed a superior clinical utility of our model in contrast to previously reported scoring systems. In summary, we devised a nomogram for predicting mortality during the ICU stay of RF patients following trauma and established a prediction model that facilitates clinical decision making. However, external validation is needed in the future.

Background Inflammation plays a key role in the initiation and progression of atrial fibrillation (AF). Lymphocyte‐to‐monocyte ratio (LMR) has been proved to be a reliable predictor of many inflammation‐associated diseases, but little data are available on the relationship between LMR and AF. We aimed to evaluate the predictive value of LMR in predicting all‐cause mortality among AF patients. Methods Data of patients diagnosed with AF were retrieved from the Medical Information Mart for Intensive Care‐III (MIMIC‐III) database. X‐tile analysis was used to calculate the optimal cutoff value for LMR. The Cox regression model was used to assess the association of LMR and 28‐day, 90‐day, and 1‐year mortality. Additionally, a propensity score matching (PSM) method was performed to minimize the impact of potential confounders. Results A total of 3567 patients hospitalized with AF were enrolled in this study. The X‐tile software indicated that the optimal cutoff value of LMR was 2.67. A total of 1127 pairs were generated, and all the covariates were well balanced after PSM. The Cox proportional‐hazards model showed that patients with the low LMR (≤2.67) had a higher 1‐year all‐cause mortality than those with the high LMR (>2.67) in the study cohort before PSM (HR = 1.640, 95% CI: 1.437–1.872, p < 0.001) and after PSM (HR = 1.279, 95% CI: 1.094–1.495, p = 0.002). The multivariable Cox regression analysis for 28‐day and 90‐day mortality yielded similar results. Conclusions The lower LMR (≤2.67) was associated with a higher risk of 28‐day, 90‐day, and 1‐year all‐cause mortality, which might serve as an independent predictor in AF patients. Our study was the first to investigate the association between admission lymphocyte‐to‐monocyte ratio (LMR) in the peripheral blood and risk of death among critically ill patients with atrial fibrillation with a 1‐year follow‐up. We analyzed LMR as a continuous variable and used restricted cubic spline regression analysis to estimate the association between LMR and its hazard ratios. In addition, we analyzed LMR as a categorical variable and performed the Cox proportional‐hazards regression model to assess the association of LMR and 28‐day, 90‐day, and 1‐year mortality. A propensity score matching method was performed to minimize the impact of potential confounders.

Background The frailty index based on laboratory tests (FI‐lab) can identify individuals at increased risk for adverse health outcomes. The association between the FI‐lab and all‐cause mortality in patients with heart failure (HF) in the intensive care unit (ICU) remains unknown. This study aimed to determine the correlation between FI‐lab and all‐cause mortality to evaluate the impact of FI‐lab on the prognosis of critically ill patients with HF. Methods This retrospective observational study utilized data extracted from the Medical Information Mart for Intensive Care IV database. The FI‐lab, which consists of 33 laboratory tests, was constructed. Patients were then grouped into quartiles (Q1–Q4) based on their FI‐lab scores. Kaplan–Meier analysis was used to compare all‐cause mortality among the four groups. A Cox proportional hazard analysis was conducted to examine the association between the FI‐lab score and all‐cause mortality. The incremental predictive value of adding FI‐lab to classical disease severity scores was assessed using Harrell's C statistic, integrated discrimination improvement (IDI) and net reclassification improvement (NRI). Results Among 3021 patients, 838 (27.74%) died within 28 days, and 1400 (46.34%) died within a 360 day follow‐up period. Kaplan–Meier analysis indicated that patients with higher FI‐lab scores had significantly higher risks of all‐cause mortality (log‐rank P < 0.001). Multivariable Cox regression suggested that FI‐lab, evaluated as a continuous variable (for each 0.01 increase), was associated with increased 28 day mortality [hazard ratio (HR) 1.02, 95% confidence interval (CI) (1.01–1.03), P < 0.001] and 360 day mortality [HR 1.02, 95% CI (1.01–1.02), P < 0.001]. When assessed in quartiles, the 28 day mortality risk [HR 1.66, 95% CI (1.28–2.15), P < 0.001] and 360 day mortality risk [HR 1.48, 95% CI (1.23–1.8), P < 0.001] were significantly higher for FI‐lab Q4 compared with FI‐lab Q1. FI‐lab significantly improved the predictive capability of classical disease severity scores for 28 and 360 day mortality. Conclusions In ICU patients diagnosed with HF, the FI‐lab is a potent predictor of short‐term and long‐term mortality in critically ill patients with HF. The active use of FI‐lab to identify high‐risk groups among critically ill HF patients and initiate timely interventions may have significant value in improving the prognosis of critically ill patients with HF.

Background The triglyceride–glucose (TyG) index has demonstrated correlations with adverse clinical outcomes in patients with ischaemic stroke, coronary heart disease and cardiac failure. However, its association with overall mortality in individuals concurrently experiencing heart failure (HF) and chronic kidney disease (CKD) remains inadequately explored. Methods Utilizing the Medical Information Mart for Intensive Care IV (Version 2.2) repository, subjects underwent quartile stratification based on the TyG index. The primary endpoint was all‐cause mortality during hospitalization. Cox proportional hazard models were employed to examine the correlation between TyG and all‐cause mortality in HF patients with CKD. Evaluation involved Kaplan–Meier (KM) analysis and restricted cubic splines (RCSs) to compare mortality rates during hospitalization and 1 year after admission across cohorts with varying TyG index levels. Results A cohort of 1537 HF and CKD patients participated. Cox regression analysis revealed elevated TyG levels as an independent risk factor for both in‐hospital and 1 year mortality. RCS analysis indicated a rising, non‐linear association between TyG levels and all‐cause mortality (P value for non‐linear <0.001). KM survival curves demonstrated a statistically significant reduction in survival rates within the high TyG index group compared with the low one (log‐rank P < 0.001). Conclusions The TyG index exhibited substantial independent prognostic value for elevated in‐hospital and 1 year all‐cause mortality among the cohort with HF and CKD. These findings suggest that assessing the TyG index could play a crucial role in developing novel therapeutic strategies to improve outcomes for this high‐risk demographic.

Objective Lung is often implicated in sepsis, resulting in acute respiratory distress syndrome (ARDS). The alveolar–arterial oxygen gradient [D(A‐a)O2] reflects lung diffusing capacity, which is usually compromised in ARDS. But whether D(A‐a)O2 impacts the prognosis of patients with sepsis remains to be explored. Our study aims to investigate the association between D(A‐a)O2 and 28‐day mortality in patients with sepsis using a large sample, multicenter Medical Information Mart for Intensive Care (MIMIC)‐IV database. Methods We extracted a data of 35 010 patients with sepsis from the retrospective cohort MIMIC‐IV database, by which the independent effects of D(A‐a)O2 on 28‐day death risk was investigated, with D(A‐a)O2 as being the exposure variable and 28‐day fatality being the outcome variable. Binary logistic regression and a two‐piecewise linear model were employed to explore the relationship between D(A‐a)O2 and the 28‐day death risk after confounding factors were optimized including demographic indicators, Charlson comorbidity index (CCI), Sequential Organ Failure Assessment (SOFA) score, drug administration, and vital signs. Results A total of 18 933 patients were finally included in our analysis. The patients' average age was 66.67 ± 16.01 years, and the mortality at 28 days was 19.23% (3640/18933). Multivariate analysis demonstrated that each 10‐mmHg rise of D(A‐a)O2 was linked with a 3% increase in the probability of death at 28 days either in the unadjusted model or in adjustment for demographic variables (Odds ratio [OR]: 1.03, 95% CI: 1.02 to 1.03). But, each 10 mmHg increase in D(A‐a)O2 was associated with a 3% increase of death (OR: 1.03, 95% CI: 1.023 to 1.033) in the case of adjustment for all covariants. Through smoothed curve fitting and generalized summation models, we found that non‐linear relationship existed between D(A‐a)O2 and the death at 28‐day, which demonstrated that D(A‐a)O2 had no any impacts on the prognosis of patients with sepsis when D(A‐a)O2 was less than or equal to 300 mmHg, but once D(A‐a)O2 exceeded 300 mmHg, however, every 10 mmHg elevation of D(A‐a)O2 is accompanied by a 5% increase of the 28‐day death (OR: 1.05; 95% CI:1.04 to 1.05, p < 0.0001). Conclusion Our findings suggests that D(A‐a)O2 is a valuable indicator for the management of sepsis patient, and it is recommended that D(A‐a)O2 be maintained less than 300 mmHg as far as possible during sepsis process. D(A‐a)O2 is a valuable indicator for the management of sepsis patient. When D(A‐a)O2 was less than or equal to 300 mmHg, it has no any association with the prognosis of patient, but the mortality rate increases once D(A‐a)O2 became greater than 300 mmHg. The safe range of D(A‐a)O2 for patients with sepsis in the hospital and ICU is less than 300 mmHg.

Objective The objective of the present study was to explore the correlation between the advanced lung cancer inflammation index (ALI) and in‐hospital mortality among patients diagnosed with community‐acquired pneumonia (CAP). Methods Data from the Medical Information Mart for Intensive Care‐IV database were adopted to analyze the in‐hospital mortality of ICU patients with CAP. Upon admission to the ICU, fundamental data including vital signs, critical illness scores, comorbidities, and laboratory results, were collected. The in‐hospital mortality of all CAP patients was documented. Multivariate logistic regression (MLR) models and restricted cubic spline (RCS) analysis together with subgroup analyses were conducted. Results This study includes 311 CAP individuals, involving 218 survivors as well as 93 nonsurvivors. The participants had an average age of 63.57 years, and the females accounted for approximately 45.33%. The in‐hospital mortality was documented to be 29.90%. MLR analysis found that ALI was identified as an independent predictor for in‐hospital mortality among patients with CAP solely in the Q1 group with ALI ≤ 39.38 (HR: 2.227, 95% CI: 1.026–4.831, P = 0.043). RCS analysis showed a nonlinear relationship between the ALI and in‐hospital mortality, with a turning point at 81, and on the left side of the inflection point, a negative correlation was observed between ALI and in‐hospital mortality (HR: 0.984, 95% CI: 0.975–0.994, P = 0.002). The subgroup with high blood pressure showed significant interaction with the ALI. Conclusion The present study demonstrated a nonlinear correlation of the ALI with in‐hospital mortality among individuals with CAP. Additional confirmation of these findings requires conducting larger prospective investigations. RCS analysis showed a nonlinear relationship between the ALI and in‐hospital mortality. ALI showed an inverse relationship with in‐hospital mortality up to a certain threshold, beyond which there was no significant association between ALI and in‐hospital mortality.

Background This study aimed to investigate the clinical value and prognostic significance of the alanine aspartate aminotransferase‐to‐lymphocyte ratio index (ALRI) in patients diagnosed with acute myocardial infarction (AMI). Methods Clinical indices of patients with AMI were collected from the Medical Information Mark for Intensive Care (MIMIC) III database and Wuhan Sixth Hospital. Cox regression analysis was used to explore whether ALRI was a risk factor for a worse prognosis in patients with AMI, and a nomogram including ALRI was created to estimate its predictive performance for 28‐day mortality. Results Based on clinical data from the MIMIC‐III database, we found that a high ALRI was closely associated with a variety of clinical parameters. It was an important risk factor for 28‐day survival in patients with AMI (HR = 5.816). ALRI had a high predictive power for worse 28‐day survival in patients with AMI (area under the curve [AUC] = 0.754). Additionally, we used clinical data from the Wuhan Sixth Hospital to verify the predictive power of ALRI in patients with AMI, and a high level of ALRI remained an independent risk factor for worse survival in patients with AMI (HR = 4.969). The AMI nomogram, including ALRI, displayed a good predictive performance for 28‐day mortality in both the MIMIC‐III (AUC = 0.826) and Wuhan Sixth Hospital cohorts (AUC = 0.795). Conclusion The ALRI is closely related to the survival outcomes of patients with newly diagnosed AMI, indicating that it could serve as a novel biomarker for risk stratification such patients. We aimed to explore the correlation between aminotransferase‐to‐lymphocyte ratio index (ALRI) and clinical features of acute myocardial infarction patients, and investigate the prognostic significance of ALRI using two data sets.

Background New-onset atrial fibrillation (NOAF) is the most common arrhythmia in critically ill patients admitted to intensive care and is associated with poor prognosis and disease burden. Identifying high-risk individuals early is crucial. This study aims to create and validate a NOAF prediction model for critically ill patients using machine learning (ML). Methods The data came from two non-overlapping datasets from the Medical Information Mart for Intensive Care (MIMIC), with MIMIC-IV used for training and subset of MIMIC-III used as external validation. LASSO regression was used for feature selection. Eight ML algorithms were employed to construct the prediction model. Model performance was evaluated based on identification, calibration, and clinical application. The SHapley Additive exPlanations (SHAP) method was used for visualizing model characteristics and individual case predictions. Results Among 16,528 MIMIC-IV patients, 1520 (9.2%) developed AF post-ICU admission. A model with 23 variables was built, with XGBoost performing best, achieving an AUC of 0.891 (0.873–0.888) in validation and 0.769 (0.756–0.782) in external validation. Key predictors included age, mechanical ventilation, urine output, sepsis, blood urea nitrogen, percutaneous arterial oxygen saturation, continuous renal replacement therapy and weight. A risk probability greater than 0.6 was defined as high risk. A friendly user interface had been developed for clinician use. Conclusion We developed a ML model to predict the risk of NOAF in critically ill patients without cardiac surgery and validated its potential as a clinically reliable tool. SHAP improves the interpretability of the model, enables clinicians to better understand the causes of NOAF, helps clinicians to prevent it in advance and improves patient outcomes.

Objectives This study aims to investigate the factors associated with systemic inflammatory response syndrome (SIRS) negativity during the early stage of sepsis in deceased septic patients. Methods Adult septic patients were included from the Medical Information Mart for Intensive Care IV (MIMIC‐IV) database between 2008 and 2019. Patients who did not survive after 28 days were assigned to the SIRS‐negative or SIRS‐positive group according to whether the SIRS score was less than two points within 24 h of intensive care unit admission. Logistic regression and a restricted cubic spline model were used to analyze factors and dose–response relationships. Results A total of 53,150 patients were screened in the MIMIC‐IV database, and 2706 sepsis nonsurvivors were ultimately included, 101 of whom were negative for SIRS. There were significant differences in sequential organ failure assessment (SOFA) scores between groups (8.18 ± 3.58 vs. 9.75 ± 4.28, p < 0.001). Logistic regression analysis indicated that lactate (odds ratio [OR] = 0.75 [95% CI = 0.62–0.90], p = 0.002), SOFA score (OR = 0.93 [95% CI = 0.87–1.00], p = 0.046), and age (OR = 1.04 [95% CI = 0.88–1.15], p = 0.012) were independent factors related to SIRS negativity in septic patients. Analysis with a restricted cubic spline model showed that the OR of SIRS negativity continued to increase with age, particularly for those over 80 years old (p for nonlinearity = 0.024). The OR of SIRS negativity was more than 1 when the SOFA score was <4 (p for nonlinearity = 0.149) and when the lactate was <1 (p for nonlinearity = 0.014). Conclusions For sepsis patients with poor prognoses, elderly individuals are more likely to be SIRS negative when they have mild organ dysfunction damage or mild tissue hypoperfusion in the early stage of sepsis. This warranted an opportunity to provide early diagnosis for elderly population with negative SIRS score in order to prevent poor outcomes.