Epidemiology and age-related mortality in critically ill patients with intra-abdominal infection or sepsis: an international cohort study

Arvaniti, Kostoula; Dimοpoulos, George; Antonelli, Massimo; Blot, Koen; Creagh‐Brown, Ben; Deschepper, Mieke; Lange, Dylan W. de; Waele, Jan J. De; Dikmen, Yalım; Eckmann, Christian; Einav, Sharon; François, Guy; Fjeldsoee-Nielsen, Hans; Girardis, Massimo; Jovanovic, Bojan; Lindner, Matthias; Koulenti, Despoina; Labeau, Sonia; Lipman, Jeffrey; Lipovestky, Fernando; Makikado, Luis Daniel Umezawa; Maseda, Emilio; Mikstacki, Adam; Montravers, Philippe; Paiva, José Artur; Pereyra, Cecilia; Rello, Jordi; Timsit, Jean‐François; Tomescu, Dana; Vogelaers, Dirk; Blot, Stijn

doi:10.1016/j.ijantimicag.2022.106591

Cited by 25 publications

(13 citation statements)

References 40 publications

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“…A lower GCS score indicates more severely impaired consciousness, which may lead to a poor outcome if timely medical intervention is not conducted (42). Age was also demonstrated to have a strong relationship with in-hospital mortality in the ICU by previous studies (43,44), with a higher mortality rate occurring among elderly patients. These patients generally have reduced immunity, underlying chronic diseases, and worse recovery ability, which may complicate their health status and result in adverse outcomes (45,46).…”

Section: P-valuementioning

confidence: 64%

Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients

et al. 2022

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BackgroundIn-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes.Materials and methodsA retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms.ResultsA total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission.ConclusionThe prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.

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Section: P-valuementioning

confidence: 64%

Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients

et al. 2022

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“…8 Sepsis severity is associated with higher mortality from intra-abdominal infections, potentially due in part to a high number of pathogenic organisms that result in a pro-inflammatory state. 3,6–9…”

Section: Introductionmentioning

confidence: 99%

Proteomic changes associated with racial background and sepsis survival outcomes

et al. 2022

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“…In a hospital setting, it often requires the removal of vascular devices and debridement of surgical site infections [18,102]. Failure to achieve source control, especially in severe IAI, has been associated with increased mortality in several studies [10,23,30]. The optimal timing of intervention is unknown, but efficacy is time-dependent, especially in severe IAI [103][104][105].…”

Section: Source Controlmentioning

confidence: 99%

“…Overall mortality for IAIs in critically ill patients ranges from 29 to 42%, being higher for late-onset hospital-acquired IAIs, especially when septic shock is present [ 23 , 28 , 29 ]. Other independent factors that have an impact on mortality are older age, failure of source control, diffuse peritonitis, and infection with resistant strains, with the latter being the most important, associated higher in-hospital mortality (56%) and longer hospital stay compared to patients without [ 25 , 30 ].…”

Section: Clinical Manifestationsmentioning

confidence: 99%

Severe infections caused by difficult-to-treat Gram-negative bacteria

Dettori,

Portunato,

Vena

et al. 2023

Current Opinion in Critical Care

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Purpose of review Antimicrobial resistance (AMR) in Gram-negative bacteria (GNB) poses a significant global health concern, contributing to increased infections, mortality rates, and healthcare costs. This review discusses the main clinical manifestations, therapeutic options, and recent findings in managing antibiotic-resistant GNB, with a focus on difficult-to-treat infections. Recent findings Difficult-to-treat resistance (DTR) is a novel classification that identifies GNB exhibiting intermediate or resistant phenotypes to first-line agents in the carbapenem, beta-lactam, and fluoroquinolone categories. The main pathogens implicated in severe infections include DTR Enterobacterales, DTR Pseudomonas aeruginosa, and DTR Acinetobacter baumannii. Although the clinical implications of DTR strains are still under investigation, certain studies have linked them to prolonged hospital stays and poor patient outcomes. Summary Severe infections caused by DTR-GNB pose a formidable challenge for healthcare providers and represent a growing global health issue. The proper administration and optimization of novel antibiotics at our disposal are of paramount importance for combating bacterial resistance and improving patient prognosis.

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Epidemiology and age-related mortality in critically ill patients with intra-abdominal infection or sepsis: an international cohort study

Cited by 25 publications

References 40 publications

Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients

Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients

Proteomic changes associated with racial background and sepsis survival outcomes

Severe infections caused by difficult-to-treat Gram-negative bacteria

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