The association between culture positivity and long-term mortality in critically ill surgical patients

Wu, Yi–Chang; Wong, Li-Ting; Wu, Chieh‐Liang; Chao, Wen‐Cheng

doi:10.1186/s40560-021-00576-2

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Hayasaka et al ( 61) designed an AI model using a CNN to classify difficult intubations based on the patient's facial image. Machine learning methods and statistical techniques were also used to investigate the relationship between positive cultures during hospitalization and long-term outcomes in critically ill surgical patients (62), the relationship between red cell distribution width (RDW) and prognosis in patients with sepsis-associated thrombocytopenia (SAT) (63), and the relationship between primary brain magnetic resonance imaging (MRI) data and functional outcomes of patients with severe herpes simplex encephalitis (HSE) 90 days after ICU admission (64). Moreover, a study (65) explored parametric and non-parametric methods for predicting cerebral performance category (CPC) using longitudinal data after cardiac arrest.…”

Section: Subcategory D2: Postoperativementioning

confidence: 99%

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

Hashemi,

Yousefzadeh,

Abin

et al. 2024

J Cell Mol Anesth

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: Anesthesia is the process of inducing and experiencing various conditions, such as painlessness, immobility, and amnesia, to facilitate surgeries and other medical procedures. During the administration of anesthesia, anesthesiologists face critical decision-making moments, considering the significance of the procedure and potential complications resulting from anesthesia-related choices. In recent years, artificial intelligence (AI) has emerged as a supportive tool for anesthesia decisions, given its potential to assist with control and management tasks. This study aims to conduct a comprehensive review of articles on the intersection of AI and anesthesia. A review was conducted by searching PubMed for peer-reviewed articles published between 2020 and early 2022, using keywords related to anesthesia and AI. The articles were categorized into nine distinct groups: "Depth of anesthesia", "Control of anesthesia delivery", "Control of mechanical ventilation and weaning", "Event prediction", "Ultrasound guidance", "Pain management", "Operating room logistic", "Monitoring", and "Neuro-critical care". Four reviewers meticulously examined the selected articles to extract relevant information. The studies within each category were reviewed by considering items such as the purpose and type of anesthesia, AI algorithms, dataset, data accessibility, and evaluation criteria. To enhance clarity, each category was analyzed with a higher resolution than previous review articles, providing readers with key points, limitations, and potential areas for future research to facilitate a better understanding of each concept. The advancements in AI techniques hold promise in significantly enhancing anesthesia practices and improving the overall experience for anesthesiologists.

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Section: Subcategory D2: Postoperativementioning

confidence: 99%

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

Hashemi,

Yousefzadeh,

Abin

et al. 2024

J Cell Mol Anesth

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“…e TCVGH critical care data warehouse was used to retrieve data with respects to demographic data, Charlson comorbidity index (CCI), Acute Physiology and Chronic Health Evaluation (APACHE) II score, presence of shock, receiving mechanical ventilation, underwent renal replacement, management including blood transfusion, and laboratory data [10]. Previous studies, including our studies, have shown the mortality association of early (day 1-3) overall fluid balance status and culture positivity of microbial culture during ICU admission, we hence included these two variables as covariates in the present study [11][12][13].…”

Section: Covariatesmentioning

confidence: 99%

Week-One Anaemia was Associated with Increased One-Year Mortality in Critically Ill Surgical Patients

Wong

et al. 2022

International Journal of Clinical Practice

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Background. Anaemia has a deleterious effect on surgical patients, but the long-term impact of anaemia in critically ill surgical patients remains unclear. Methods. We enrolled consecutive patients who were admitted to surgical intensive care units (ICUs) at a tertiary referral centre in central Taiwan between 2015 and 2020. We used both Cox proportional hazards analysis and propensity score-based analyses, including propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and covariate balancing propensity score (CBPS) to determine hazard ratios (HRs) and 95% confidence intervals (CIs) for one-year mortality. Results. A total of 7,623 critically ill surgical patients were enrolled, and 29.9% (2,280/7,623) of them had week-one anaemia (haemoglobin <10 g/dL). We found that anaemia was independently associated with an increased risk of one-year mortality after adjustment for relevant covariates (aHR, 1.170; 95% CI, 1.045–1.310). We further identified a consistent strength of association between anaemia and one-year mortality in propensity score-based analyses, with the adjusted HRs in the PSM, IPTW, and CBPS were 1.164 (95% CI 1.025–1.322), 1.179 (95% CI 1.030–1.348), and 1.181 (1.034–1.349), respectively. Conclusions. We identified the impact on one-year mortality of anaemia in critically ill surgical patients, and more studies are needed to validate our findings.

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“…Critically ill surgical patients are typically admitted to the intensive care unit (ICU) perioperatively attributable to extensive procedures [ 1 ], massive hemorrhage [ 2 ], systemic inflammatory response syndrome (SIRS) [ 3 ], or severe comorbidities [ 4 ]. Many of these patients experience hemodynamic instability leading to serious hypoxia and metabolic acidosis [ 5 ], which are known to impact patient survival.…”

Section: Introductionmentioning

confidence: 99%

Association between anion gap and all-cause mortality of critically ill surgical patients: a retrospective cohort study

Sun

Weng

et al. 2023

BMC Surg

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Background There are few widely accepted and operationally feasible models for predicting the mortality risk of patients in surgical intensive care unit (SICU). Although serum anion gap (AG) is known to be correlated with severe metabolic acidosis, no investigations have been reported about the association between AG level and the outcome during hospitalization in SICU. This study aimed to explore the predictive power of AG for 90-day all-cause mortality in SICU. Methods Data of the eligible patients in SICU from 2008 to 2019 was obtained from the Medical Information Mart for Intensive Care IV version 2.0 (MIMIC-IV v2.0) database. Baseline clinical data of the selected patients was compared in different groups stratified by the outcome during their admission via univariate analysis. Restricted cubic spline (RCS) was drawn to confirm the relationship of AG and the short-term mortality. Kaplan-Meier survival curve was plotted in different AG level groups. Univariate and multivariate Cox analyses were performed, and Cox proportional-hazards models were built to investigate an independent role of AG to predict 90-day all-cause mortality risk in SICU. Receiver operating characteristics (ROC) curves analysis was performed to evaluate the predictive value of AG on the 90-day prognosis of patients. Results A total of 6,395 patients were enrolled in this study and the 90-day all-cause mortality rate was 18.17%. Univariate analysis showed that elevated serum AG was associated with higher mortality (P < 0.001). RCS analysis indicated a positively linear relationship between serum AG and the risk of 90-day all-cause mortality in SICU (χ2 = 4.730, P = 0.193). Kaplan-Meier survival analysis demonstrated that low-AG group (with a cutoff value of 14.10 mmol/L) had a significantly higher cumulative survival rate than the counterpart of high-AG group (χ2 = 96.370, P < 0.001). Cox proportional-hazards models were constructed and confirmed the independent predictive role of AG in 90-day all-cause mortality risk in SICU after adjusting for 23 confounding factors gradually (HR 1.423, 1.246–1.625, P < 0.001). In the further subgroup analyses, a significant interaction was confirmed between AG and sepsis as well as surgery on the risk for the 90-day mortality. The ROC curve showed that the optimal cut-off value of AG for predicting 90-day mortality was 14.89 with sensitivity of 60.7% and specificity of 54.8%. The area under curve (AUC) was 0.602. When combined with SOFA score, the AUC of AG for predicting 90-day prognosis was 0.710, with a sensitivity and specificity of 70% and 62.5% respectively. Conclusions Elevated AG (≥ 14.10 mmol/L) is an independent risk factor for predicting severe conditions and poor prognosis of critical ill surgical patients.

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The association between culture positivity and long-term mortality in critically ill surgical patients

Cited by 6 publications

References 36 publications

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

Week-One Anaemia was Associated with Increased One-Year Mortality in Critically Ill Surgical Patients

Association between anion gap and all-cause mortality of critically ill surgical patients: a retrospective cohort study

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