Machine Learning Model for the Prediction of Hemorrhage in Intensive Care Units

A Combined Model of Vital Signs and Serum Biomarkers Outperforms Shock Index in the Prediction of Hemorrhage Control Interventions in Surgical Intensive Care Unit Patients

BackgroundDistinguishing surgical intensive care unit (ICU) patients with ongoing bleeding who require hemorrhage control interventions (HCI) can be challenging. Guidelines recommend risk-stratification with clinical variables and prediction tools, however supporting evidence remains mixed.MethodsThis retrospective study evaluated adult patients admitted to the surgical ICU with concern for ongoing hemorrhage under our institution's "Hemorrhage Watch" (HW) protocol and aimed to derive a clinical prediction model identifying those needing HCI with serial vital signs (VS) and serum biomarkers. The HW protocol included ICU admission followed by a 3-h observation period with VS monitoring every 15 min and hourly biomarkers. The primary outcome was the need for HCI (operative and endovascular interventions) within nine hours of ICU arrival. Secondary outcomes included in-hospital mortality, blood transfusions, and ICU and hospital length-of-stay. A clinical prediction model was developed by utilizing the variables most associated with HCI in a best subsets regression, which was subsequently internally validated using a Bootstrap algorithm.Results305 patients were identified for inclusion and 18 (5.9%) required HCI (3 operative, 15 endovascular). The median age was 70 years (IQR 54, 83), 60% had traumatic injuries, and 73% were enrolled from the emergency department. Blood product transfusion and mortality were similar between the HCI and no-HCI groups. Our analysis demonstrated that a model based on the minimum hemoglobin (9.9 vs 8.1 g/dL), minimum diastolic (57 vs 53 mm Hg) and systolic blood pressures (105 vs 90 mm Hg), and minimum respiratory rate (15 vs 18) could predict HCI with an area under the Receiver Operating Characteristics curve (AUROC) of 0.87, outperforming the Shock Index (SI) (AUROC = 0.64).ConclusionsIn this study of surgical ICU patients with concern for ongoing bleeding, a prediction model using serial VS and biomarkers outperformed the SI and may help identify those requiring HCI.

Intensive care unit nurses' experiences of nursing concerns, activities, and documentation on patient deterioration: A focus-group study

BACKGROUND

Although prognosis prediction models using nursing documentation have good predictive performance, the experiences of intensive care unit nurses related to nursing activities and documentation when a patient's condition deteriorates are yet to be explored.

OBJECTIVE

The aim of this study was to explore nurses' experiences of nursing activities and documentation in intensive care units when a patient's condition deteriorates.

METHODS

This was a descriptive qualitative study using focus-group interviews with intensive care unit nurses in tertiary or university-affiliated hospitals. In total, 19 registered nurses with at least 1 year of clinical experience in the adult intensive care unit were recruited using a purposive sampling method. Five focus-group interviews were conducted, and the data were analysed through a qualitative content analysis.

RESULTS

Intensive care unit nurses' experiences with patient deterioration were classified into four main categories-perceived patient deterioration; endeavours to verify nurses' concerns; nursing activities to improve a patient's condition; and optimising documentation practices-which comprised 12 subcategories. Intensive care unit nurses recognise patient deterioration through nursing activities and documentation, and the two processes influence each other. However, nursing activities related to nurses' concerns were mainly handed over verbally rather than documented due to the inflexibility of the available standardised forms and the potential uncertainty of those concerns.

CONCLUSIONS

The findings reveal how intensive care unit nurses perceive, intervene, and document the condition of a deteriorating patient. Nurses' concerns may be the first sign of a patient's deteriorating condition and are therefore crucial for minimising patient risk. Therefore, efforts to systematically document nurses' concerns may contribute to improving patient outcomes.

Prognosis of major bleeding based on residual variables and machine learning for critical patients with upper gastrointestinal bleeding: A multicenter study

BACKGROUND

Upper gastrointestinal bleeding (UGIB) is a significant cause of morbidity and mortality worldwide. This study investigates the use of residual variables and machine learning (ML) models for predicting major bleeding in patients with severe UGIB after their first intensive care unit (ICU) admission.

METHODS

The Medical Information Mart for Intensive Care IV and eICU databases were used. Conventional ML and long short-term memory models were constructed using pre-ICU and ICU admission day data to predict the recurrence of major gastrointestinal bleeding. In the models, residual data were utilized by subtracting the normal range from the test result. The models included eight algorithms. Shapley additive explanations and saliency maps were used for feature interpretability.

RESULTS

Twenty-five ML models were developed using data from 2604 patients. The light gradient-boosting machine algorithm model using pre-ICU admission residual data outperformed other models that used test results directly, with an AUC of 0.96. The key factors included aspartate aminotransferase, blood urea nitrogen, albumin, length of ICU admission, and respiratory rate.

CONCLUSIONS

ML models using residuals improved the accuracy and interpretability in predicting major bleeding during ICU admission in patients with UGIB. These interpretable features may facilitate the early identification and management of high-risk patients, thereby improving hemodynamic stability and outcomes.

Enhancing Trauma Care: A Machine Learning Approach with XGBoost for Predicting Urgent Hemorrhage Interventions Using NTDB Data

OBJECTIVE

Trauma is a leading cause of death worldwide, with many incidents resulting in hemorrhage before the patient reaches the hospital. Despite advances in trauma care, the majority of deaths occur within the first three hours of hospital admission, offering a very limited window for effective intervention. Unfortunately, a significant increase in mortality from hemorrhagic trauma is primarily due to delays in hemorrhage control. Therefore, we propose a machine learning model to predict the need for urgent hemorrhage intervention.

METHODS

This study developed and validated an XGBoost-based machine learning model using data from the National Trauma Data Bank (NTDB) from 2017 to 2019. It focuses on demographic and clinical data from the initial hours following trauma for model training and validation, aiming to predict whether trauma patients require urgent hemorrhage intervention.

RESULTS

The XGBoost model demonstrated superior performance across multiple datasets, achieving an AUROC of 0.872 on the training set, 0.869 on the internal validation set, and 0.875 on the external validation set. The model also showed high sensitivity (77.8% on the external validation set) and specificity (82.1% on the external validation set), with an accuracy exceeding 81% across all datasets, highlighting its high reliability for clinical applications.

CONCLUSIONS

Our study shows that the XGBoost model effectively predicts urgent hemorrhage interventions using data from the National Trauma Data Bank (NTDB). It outperforms other machine learning algorithms in accuracy and robustness across various datasets. These results highlight machine learning's potential to improve emergency responses and decision-making in trauma care.

A precise blood transfusion evaluation model for aortic surgery: a single-center retrospective study

Cardiac aortic surgery is an extremely complicated procedure that often requires large volume blood transfusions during the operation. Currently, it is not possible to accurately estimate the intraoperative blood transfusion volume before surgery. Therefore, in this study, to determine the clinically precise usage of blood for intraoperative blood transfusions during aortic surgery, we established a predictive model based on machine learning algorithms. We performed a retrospective analysis on 4,285 patients who received aortic surgery in Beijing Anzhen Hospital between January 2018 and September 2022. Ultimately, 3,654 patients were included in the study, including 2,557 in the training set and 1,097 in the testing set. By utilizing 13 current mainstream models and a large-scale cardiac aortic surgery dataset, we built a novel machine learning model for accurately predicting intraoperative red blood cell transfusion volume. Based on the transfusion-related risk factors that the model identified, we also established the relevant variables that affected the results. The results revealed that decision tree models were the most suitable for predicting the blood transfusion volume during aortic surgery. In particular, the mean absolute error for the best-performing extremely randomized forest model was 1.17 U, while the R2 value was 0.50. Further exploration into intraoperative blood transfusion during aortic surgery identified erythrocytes, estimated operation duration, body weight, sex, red blood cell count, and D-dimer as the six most significant risk factors. These factors were subsequently analyzed for their influence on intraoperative blood transfusion volume in relevant patients, as well as the protective threshold for prediction. The novel intraoperative blood transfusion prediction model for cardiac aorta surgery in this study effectively assists clinicians in accurately calculating blood transfusion volumes and achieving effective utilization of blood resources. Furthermore, we utilize interpretability technology to reveal the influence of critical risk factors on intraoperative blood transfusion volume, which provides an important reference for physicians to provide timely and effective interventions. It also enables personalized and precise intraoperative blood usage.