Hematocrit drift and outcomes in surgical patients with aneurysmal subarachnoid hemorrhage.

BACKGROUND: There is a paucity of conclusive evidence regarding the impact of downward drift in hematocrit levels among patients who have undergone surgical clipping for aneurysmal subarachnoid hemorrhage (aSAH). This study endeavors to explore the potential association between hematocrit drift and mortality in this specific patient population. METHODS: A cohort study was conducted, encompassing adult patients diagnosed with aSAH at a university hospital. The primary endpoint was follow-up mortality. Propensity score matching was employed to align patients based on their baseline characteristics. Discrimination capacity across various models was assessed and compared using net reclassification improvement (NRI). RESULTS: Among the 671 patients with aSAH in the study period, 118 patients (17.6%) experienced an in-hospital hematocrit drift of more than 25%. Following adjustment with multivariate regression analysis, patients with elevated hematocrit drift demonstrated significantly increased odds of mortality (aOR: 2.12, 95% CI: 1.14 to 3.97; P = 0.019). Matching analysis yielded similar results (aOR: 2.07, 95% CI: 1.05 to 4.10; P = 0.036). The inclusion of hematocrit drift significantly improved the NRI (P < 0.0001) for mortality prediction. When in-hospital hematocrit drift was served as a continuous variable, each 10% increase in hematocrit drift corresponded to an adjusted odds ratio of 1.31 (95% CI 1.08-1.61; P = 0.008) for mortality. CONCLUSIONS: In conclusion, the findings from this comprehensive cohort study indicate that a downward hematocrit drift exceeding 25% independently predicts mortality in surgical patients with aSAH. These findings underscore the significance of monitoring hematocrit and managing anemia in this patient population.

Anticoagulation therapy in non-valvular atrial fibrillation after intracerebral hemorrhage: A propensity score-matched study.

BACKGROUND: The effect of antithrombotic therapy on patients with atrial fibrillation who sustained previous intracerebral hemorrhage (ICH) remains uncertain. Data regarding antithrombotic therapy use in these patients are limited. This study aims to compare the clinical and overall outcomes of antithrombotic therapy and usual care in patients with atrial fibrillation who sustained ICH. METHODS: We assembled consecutive patients with atrial fibrillation sustaining an ICH from our institution. Multivariable regression analysis and propensity-matched analysis were applied to assess associations of different antithrombotic therapies and outcomes. The primary outcome was mortality within the longest follow-up. Kaplan-Meier curves and log-rank tests of the time-to-event data were used to assess differences in survival. RESULTS: In total, 296 consecutive patients with atrial fibrillation who survived an ICH were included in this study. Our analysis demonstrated that antithrombotic therapy was associated with reduced mortality up to a 4-year duration of follow-up (OR, 0.49, 95 % CI 0.30-0.81). Similar results were obtained from the propensity-matched analysis (OR, 0.58, 95 % CI 0.34-0.98). Subgroup analysis showed that compared with usual care, direct oral anticoagulant (DOAC) with or without antiplatelet was associated with a lower risk of long-term mortality (OR, 0.34, 95 % CI 0.17-0.69). In addition, our analysis observed a significant interaction between cardiac insufficiency and treatment effect (P = 0.04). CONCLUSIONS: In patients with atrial fibrillation who have a history of ICH, administration of antithrombotic therapy, especially DOAC, was associated with lower mortality. Future randomized trials are warranted to test the positive net clinical benefit of DOAC therapy.

Association between antithrombotic therapy and mortality in patients hospitalized for COVID­19.

BACKGROUND: The prothrombotic state is a common abnormality in patients with coronavirus disease 2019 (COVID-19). However, there is controversy over the use of anticoagulants, especially oral anticoagulants (OAC) due to limited studies. We sought to evaluate the association between antithrombotic therapy on mortality and clinical outcomes in patients hospitalized for COVID-19 through propensity score matching (PSM) analysis. METHODS: A retrospective cohort study was performed to include adult patients with COVID-19 in a university hospital. The primary outcome was in-hospital mortality. Secondary outcomes included intensive care unit (ICU) admission, mechanical ventilation, and acute kidney injury (AKI) during hospitalization. PSM was used as a powerful tool for matching patients' baseline characteristics. Adjusted odds ratios (aOR) with 95% confidence intervals (CI) were calculated from the models. RESULTS: Of 4,881 COVID-19 patients during the study period, 690 (14.1%) patients received antithrombotic therapy and 4,191 (85.9%) patients were under no antithrombotic therapy. After adjustment with multivariate regression analysis, patients receiving OAC, compared with those who did not receive any antithrombotic therapy, had significantly lower odds for in-hospital mortality (aOR: 0.46. 95% CI: 0.24 to 0.87; P= 0.017). PSM analysis observed similar results (aOR: 0.35. 95% CI: 0.19 to 0.61; P< 0.001). Moreover, in critically ill patients who received mechanical ventilation, antithrombotic treatment (aOR: 0.54. 95% CI: 0.32 to 0.89; P= 0.022) was associated with reduced risk of mortality. CONCLUSIONS: The application OACs was associated with reduced hospital mortality and mechanical ventilation requirement in COVID-19 patients. Besides, antithrombotic treatment was associated with a reduction in in-hospital mortality among critically ill COVID-19 patients who required mechanical ventilation.

Application of the IDEAL framework in hepatopancreatobiliary surgery: a review of the literature.

PURPOSE: To evaluate every stage of surgical innovation and generate high-quality research evidence, the IDEAL (Idea, Development, Exploration, Assessment, Long-term study) framework was developed. This study aimed to explore the application of the IDEAL framework in hepatopancreatobiliary surgery and identify factors limiting its dissemination. METHODS: We conducted a citation search of 8 core IDEAL framework articles in PubMed, Embase, Web of Science, and Scopus databases from 2009 to 2022. Two independent reviewers screened and selected articles related to hepatopancreatobiliary surgery. RESULTS: A total of 1621 articles were identified through citation search. Following screening, 132 articles were finally retained, including 75 original studies (57%) and 57 secondary studies (43%). Of the original studies, only 10 articles (13%) accurately applied the IDEAL framework in methodology, distributed as follows: 1 in pre-IDEAL stage (0), 2 in Idea stage (1), 7 in Development stage (2a), 1 in Exploration stage (2b), and no articles in Assessment and Long-term study stages (3, 4). In the secondary studies, 36 articles (63%) mentioned and discussed the IDEAL framework, and all supported its application. CONCLUSIONS: The application of the IDEAL framework in hepatopancreatobiliary surgery is increasingly widespread, as evidenced by its substantial citation in numerous articles. However, the utilization of the IDEAL framework remains predominantly confined to the early stages of innovation in hepatopancreatobiliary surgery, coupled with instances of misapplication stemming from insufficient comprehension of the framework. Further efforts are necessary to extend the impact of the IDEAL framework and provide surgeons with comprehensive guidance for its judicious implementation.

Machine learning model to estimate probability of remission in patients with idiopathic membranous nephropathy.

BACKGROUND: Idiopathic membranous nephropathy (IMN) is a type of nephrotic syndrome and the leading cause of chronic kidney disease. As far as we know, no predictive model for assessing the prognosis of IMN is currently available. This study aims to establish a nomogram to predict remission probability in patients with IMN and assists clinicians to make treatment decisions. METHODS: A total of 266 patients with histopathology-proven IMN were included in this study. Least absolute shrinkage and selection operator regression was utilized to identify the most important variables. Subsequently, multivariate Cox regression analysis was conducted to construct a nomogram, and bootstrap resampling was employed for internal validation. Receiver operating characteristic and calibration curves and decision curve analysis (DCA) were utilized to assess the performance and clinical utility of the developed model. RESULTS: A prognostic nomogram was established, which incorporated creatinine, glomerular_basement_membrane_thickening, gender, IgG_deposition, low-density lipoprotein cholesterol, and fibrinogen. The areas under the curves of the 3-, 12-, 24-month were 0.751, 0.725, and 0.830 in the training set, and 0.729, 0.730, and 0.948 in the validation set respectively. These results and calibration curves demonstrated the good discrimination and calibration of the nomogram in the training and validation sets. Additionally, DCA indicated that the nomogram was useful for remission prediction in clinical settings. CONCLUSION: The nomogram was useful for clinicians to evaluate the prognosis of patients with IMN in early stage.

Ethical Considerations of Using ChatGPT in Health Care.

ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.

A gigabyte interpreted seismic dataset for automatic fault recognition.

The lack of large-scale open-source expert-labelled seismic datasets is one of the barriers to applying today's AI techniques to automatic fault recognition tasks. The dataset present in this article consists of a large number of processed seismic images and their corresponding fault annotations. The processed seismic images, which are originally from a seismic survey called Thebe Gas Field in the Exmouth Plateau of the Carnarvan Basin on the NW shelf of Australia, are represented in Python Numpy format, which can be easily adopted by various AI models and will facilitate cooperation with researchers in the field of computer science. The corresponding fault annotations were firstly manually labelled by expert interpreters of faults from seismic data in order to investigate the structural style and associated evolution of the basin. Then the fault interpretation and seismic survey are processed and collected using Petrel software and Python programs separately. This dataset can help to train, validate, and evaluate the performance of different automatic fault recognition workflow.