Background: This study investigates the obesity paradox, where obesity is linked to lower mortality in certain patient groups, focusing on its impact on long-term mortality in chronic critically ill (CCI) patients. Methods: We retrospectively analyzed CCI patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database’s Intensive Care Unit, categorizing them into six groups based on Body Mass Index (BMI). Using stepwise multivariable Cox regression and restricted cubic spline models, we examined the association between BMI and 90 day mortality, accounting for confounding variables through subgroup analyses. Results: The study included 1996 CCI patients, revealing a 90 day mortality of 34.12%. Overweight and obese patients exhibited significantly lower mortality compared to normal-weight individuals. Adjusted analysis showed lower mortality risks in overweight and obese groups (HRs 0.60 to 0.72, p < 0.001). The cubic spline model indicated a negative correlation between BMI and 90 day mortality, with subgroup analyses highlighting interactions with age. Conclusion: Our findings confirm the obesity paradox in CCI patients, especially among the elderly (65–85 years) and very elderly (≥ 85 years). The results suggest a beneficial association of higher BMI in older CCI patients, though caution is advised for those under 45.

Background: The short-term prognosis of stroke patients is mainly influenced by the severity of the primary disease at admission and the trend of disease development during the acute phase (1–7 days after admission). Objective: The aim of this study is to explore the relationship between the bioelectrical impedance analysis (BIA) parameter trajectories during the acute phase of stroke patients and their short-term prognosis, and to investigate the predictive value of the prediction model constructed using BIA parameter trajectories and clinical indicators at admission for short-term prognosis in stroke patients. Methods: A total of 162 stroke patients were prospectively enrolled, and their clinical indicators at admission and BIA parameters during the first 1–7 days of admission were collected. A Group-Based Trajectory Model (GBTM) was employed to identify different subgroups of longitudinal trajectories of BIA parameters during the first 1–7 days of admission in stroke patients. The random forest algorithm was applied to screen BIA parameter trajectories and clinical indicators with predictive value, construct prediction models, and perform model comparisons. The outcome measure was the Modified Rankin Scale (mRS) score at discharge. Results: PA in BIA parameters can be divided into four separate trajectory groups. The incidence of poor prognosis (mRS: 4–6) at discharge was significantly higher in the “Low PA Rapid Decline Group” (85.0%) than in the “High PA Stable Group “ (33.3%) and in the “Medium PA Slow Decline Group “(29.5%) (all P < 0.05). In-hospital mortality was the highest in the “Low PA Rapid Decline Group” (60%) compared with the remaining trajectory groups (P < 0.05). Compared with the prediction model with only clinical indicators (Model 1), the prediction model with PA trajectories (Model 2) demonstrated higher predictive accuracy and efficacy. The area under the receiver operating characteristic curve (AUC) of Model 2 was 0.909 [95% CI 0.863, 0.956], integrated discrimination improvement index (IDI), 0.035 (P < 0.001), and net reclassification improvement (NRI), 0.175 (P = 0.031). Conclusion: PA trajectories during the first 1–7 days of admission are associated with the short-term prognosis of stroke patients. PA trajectories have additional value in predicting the short-term prognosis of stroke patients.

The purpose of this study was to investigate early stage dynamic changes in relevant indicators in neurocritical patients to identify biomarkers that can predict a poor prognosis at an early stage (1–4 days after admission). This study retrospectively collected clinical data, inflammatory indicators, and nutritional indicators from 77 patients at the neurology intensive care unit. The 3-month modified Rankin scale score was used as the outcome indicator. A linear mixed model was used to analyze changes in inflammatory indicators and nutritional indicators in neurocritical patients over time from 1–4 days after admission. Logistic regression was used to determine the independent risk factors for a poor prognosis in neurocritical patients and to construct a predictive model. The predictive efficacy of the model was verified using leave-one-out cross-validation and decision curve analysis methods. The analysis results showed that 1–4 days after admission, the inflammatory indicators of white blood cell and absolute monocyte counts and the nutritional indicators of body cell mass(BCM), fat-free mass, body cell mass/phase angle (BCM/PA), intracellular water, extracellular water, and skeletal muscle index increased overall, while the nutritional indicators of albumin and visceral fat area decreased overall. The logistic multivariate regression model showed that the Charlson comorbidity index (CCI) (odds ratio (OR) = 2.526, 95% CI [1.202, 5.308]), hemoglobin (Hb)(on admission)-Hb(min) (OR = 1.049, 95% CI [1.015, 1.083), BCM(on admission) (OR = 0.794, 95% CI [0.662, 0.952]), and the change in BCM/PA 1–4 days after admission (OR = 1.157, 95% CI [1.070, 1.252]) were independent risk factors for a poor prognosis in neurocritical patients. The predictive analysis showed that the predictive power of Model 1 with BCM/PA (area under the curve (AUC) = 0.95, 95% CI (0.90, 0.99)) was 93%, 65%, 141%, and 133% higher than that of Model 2 without BCM/PA, the CCI, the APACHE II score, and the NRS2002 score (all P < 0.05), respectively. The CCI, Hb(on admission)-Hb(min), BCM(on admission), and an increase in BCM/PA 1–4 days after admission were independently associated with a poor prognosis in neurocritical patients. Of these variables, BCM/PA may be a valid indicator for early stage prediction of a poor prognosis in neurocritical patients.

New-onset atrial fibrillation (NOAF) is a common cardiovascular complication in critically ill patients and is consistently associated with adverse outcomes. However, substantial heterogeneity exists in its clinical presentation and prognosis. This study aimed to identify distinct clinical subtypes of NOAF and evaluate their prognostic and management-related implications. Adult NOAF patients were extracted from the MIMIC-IV database. Demographic and laboratory data within 24 h of ICU admission were analyzed. Consensus k-means clustering was used for identifying subtypes. Survival differences were compared using Kaplan-Meier and log-rank tests, and multivariable Cox models assessed mortality risk and pharmacologic treatment associations. Key variables identified by SHAP analysis were incorporated into a simplified six-variable model, validated externally in MIMIC-III. Among 8472 NOAF patients, four distinct subtypes were identified from the MIMIC-IV cohort (n = 5554), showing progressively increased severity and mortality. Subtype A (30.28%) included mainly post-cardiac surgery patients with preserved homeostasis and the lowest 28-day mortality (4.9%). Subtype B (34.52%) was characterized by marked hypomagnesemia and a moderate burden of comorbid malignancy (28-day mortality 15.5%). Subtype C (19.70%) featured anemia, hypoxemia, and inflammation (28-day mortality 30.2%). Subtype D (15.50%) presented with organ failure and the highest 28-day mortality (42.7%). 28-day mortality risk increased stepwise across subtypes (HR 4.24-5.98; all P < 0.001). Pharmacologic responses, including heart rate control, sedation, and electrolyte therapy, varied across different subtypes. The simplified six-variable model demonstrated high predictive performance (AUC 0.89-0.96) in external validation. Unsupervised clustering revealed four distinct NOAF subtypes in ICU patients, characterized by heterogeneous clinical trajectories. The simplified six-variable model enabled practical bedside classification, supporting precision risk assessment and potentially informing phenotype-oriented management of NOAF in the ICU.