The Association of Preoperative Hematocrit With Adverse Events Following Exploratory Laparotomy in Septic Patients: A Retrospective Analysis

A U shaped association between the HCT-ALB and hospital mortality in patients with sepsis

The difference between hematocrit and serum albumin (HCT-ALB) demonstrates diagnostic significance in infectious diseases, yet the nonlinear relationship between HCT-ALB and hospital mortality in ICU patients with sepsis remains unexplored. This retrospective multicenter cohort study analyzed 7,546 ICU sepsis patients (mean age 66 ± 16 years) to elucidate the HCT-ALB-mortality relationship. Using Cox proportional hazards models with smooth curve fitting, we identified a U-shaped association: Threshold analysis revealed an inflection point at 6.1. Below this threshold, each unit HCT-ALB increase corresponded to reduced mortality risk (adjusted HR 0.986, 95%CI 0.972-0.999; P = 0.036). Conversely, values ≥ 6.1 predicted escalating risk (adjusted HR 1.048 per unit increase, 95%CI 1.037-1.060; P < 0.0001). Significant age interaction was observed (P for interaction < 0.05), with heightened mortality risk in elderly patients (≥ 65 years: HR 1.022, 95%CI 1.014-1.031). These findings establish HCT-ALB as a non-linear predictor of sepsis outcomes, emphasizing its critical threshold dynamics and age-dependent prognostic implications.

An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals

Clinical risk prediction with electronic health records (EHR) using machine learning has attracted lots of attentions in recent years, where one of the key challenges is how to protect data privacy. Federated learning (FL) provides a promising framework for building predictive models by leveraging the data from multiple institutions without sharing them. However, data distribution drift across different institutions greatly impacts the performance of FL. In this paper, an adaptive FL framework was proposed to address this challenge. Our framework separated the input features into stable, domain-specific, and conditional-irrelevant parts according to their relationships to clinical outcomes. We evaluate this framework on the tasks of predicting the onset risk of sepsis and acute kidney injury (AKI) for patients in the intensive care unit (ICU) from multiple clinical institutions. The results showed that our framework can achieve better prediction performance compared with existing FL baselines and provide reasonable feature interpretations.

Association between hematocrit and the 30-day mortality of patients with sepsis: A retrospective analysis based on the large-scale clinical database MIMIC-IV

This research sought to ascertain the relationship between hematocrit (HCT) and mortality in patients with sepsis. Methods: A retrospective analysis was conducted on the clinical data of septic patients who were hospitalized between 2008 and 2019 in an advanced academic medical center in Boston, Massachusetts, registered in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, We analyzed basic information including gender, age, race, and types of the first admission, laboratory indicators including HCT, platelets, white blood cells, albumin, bilirubin, hemoglobin, and serum creatinine, and 30-day mortality. A Cox proportional hazards regression model was utilized to analyze the relationship between HCT and 30-day mortality in patients with sepsis. Results: This research recruited 2057 patients who met the research requirements from 2008 to 2019. According to the HCT level, it was classified into the low HCT level, the regular HCT level, and the high HCT level. The 30-day mortality rate was 62.6%, 27.5%, and 9.9% for patients with the low HCT level, the regular HCT level, and the high HCT level, respectively (p < 0.05). The multivariate Cox proportional hazard regression model analysis displayed that compared with patients with the regular HCT level, the 30-day mortality of patients with the low HCT level increased by 58.9% (hazard ratio = 1.589, 95% confidence interval (CI) = 1.009–2.979, p < 0.05). Conclusion: The low HCT level is an independent risk factor for the increase of the 30-day mortality in patients with sepsis and can be used as a significant predictor of the clinical outcome of sepsis.