Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset

Mapping the landscape of machine learning models used for predicting transfusions in surgical procedures: a scoping review

Massive transfusion of blood products poses challenges in determining the need for transfusion and the appropriate volume of blood products. This review explores the use of machine learning (ML) models to predict transfusion risk during surgical procedure, focusing on the methodology, variables, and software employed to predict transfusion. This scoping review investigates the development and current state of machine learning models for predicting transfusion risk during surgical procedure, aiming to inform physicians about the field's progress and potential directions.The review was conducted using the databases Cochrane, Embase, and PubMed. The search included keywords related to blood transfusion, statistical models, and surgical procedures. Peer-reviewed articles were included, while literature reviews, case reports, and non-human studies were excluded.A total of 40 studies met the inclusion criteria. The most frequently studied biological variables included haemoglobin, platelet count, international normalized ratio (INR), activated partial thromboplastin time (aPTT), fibrinogen, creatinine, white blood cells, and albumin. Clinical variables of importance included age, sex, surgery type, blood pressure, weight, surgery duration, american society of anesthesiology (ASA) status, blood loss, and body mass index (BMI). The software employed varied, with Python, R, SPSS, and SAS being the most commonly used. Logistic regression was the predominant methodology used in 20 studies.Our scoping review highlights the need for improved reporting and transparency in methodology, variables, and software used. Future research should focus on providing detailed descriptions and open access to codes of respective models, promoting reproducibility, and enhancing the clinical relevance of transfusion risk prediction models.

Development of machine learning models to predict perioperative blood transfusion in hip surgery

BACKGROUND

Allogeneic Blood transfusion is common in hip surgery but is associated with increased morbidity. Accurate prediction of transfusion risk is necessary for minimizing blood product waste and preoperative decision-making. The study aimed to develop machine learning models for predicting perioperative blood transfusion in hip surgery and identify significant risk factors.

METHODS

Data of patients undergoing hip surgery between January 2013 and October 2021 in the Peking Union Medical College Hospital were collected to train and test predictive models. The primary outcome was perioperative red blood cell (RBC) transfusion within 72 h of surgery. Fourteen machine learning algorithms were established to predict blood transfusion risk incorporating patient demographic characteristics, preoperative laboratory tests, and surgical information. Discrimination, calibration, and decision curve analysis were used to evaluate machine learning models. SHapley Additive exPlanations (SHAP) was performed to interpret models.

RESULTS

In this study, 2431 hip surgeries were included. The Ridge Classifier performed the best with an AUC = 0.85 (95% CI, 0.81 to 0.88) and a Brier score = 0.21. Patient-related risk factors included lower preoperative hemoglobin, American Society of Anesthesiologists (ASA) Physical Status > 2, anemia, lower preoperative fibrinogen, and lower preoperative albumin. Surgery-related risk factors included longer operation time, total hip arthroplasty, and autotransfusion.

CONCLUSIONS

The machine learning model developed in this study achieved high predictive performance using available variables for perioperative blood transfusion in hip surgery. The predictors identified could be helpful for risk stratification, preoperative optimization, and outcomes improvement.

Advances in pediatric perioperative care using artificial intelligence

PURPOSE OF THIS REVIEW

This article explores how artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care. It will also describe potential future applications of AI, such as models for airway device selection, controlling anesthetic depth and nociception during surgery, and contributing to the training of pediatric anesthesia providers.

RECENT FINDINGS

The use of AI in healthcare has increased in recent years, largely due to the accessibility of large datasets, such as those gathered from electronic health records. Although there has been less focus on pediatric anesthesia compared to adult anesthesia, research is on- going, especially for applications focused on risk factor identification for adverse perioperative events. Despite these advances, the lack of formal external validation or feasibility testing results in uncertainty surrounding the clinical applicability of these tools.

SUMMARY

The goal of using AI in pediatric anesthesia is to assist clinicians in providing safe and efficient care. Given that children are a vulnerable population, it is crucial to ensure that both clinicians and families have confidence in the clinical tools used to inform medical decision- making. While not yet a reality, the eventual incorporation of AI-based tools holds great potential to contribute to the safe and efficient care of our patients.

Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist

Although in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF by patients who could benefit from such treatment. In order for patients to choose to consider IVF treatment when appropriate, it is critical for them to be provided with an accurate, understandable IVF prognosis. Machine learning (ML) can meet the challenge of personalized prognostication based on data available prior to treatment. The development, validation, and deployment of ML prognostic models and related patient counseling report delivery require specialized human and platform expertise. This review article takes a pragmatic approach to review relevant reports of IVF prognostic models and draws from extensive experience meeting patients' and providers' needs with the development of data and model pipelines to implement validated ML models at scale, at the point-of-care. Requirements of using ML-based IVF prognostics at point-of-care will be considered alongside clinical ML implementation factors critical for success. Finally, we discuss health, social, and economic objectives that may be achieved by leveraging combined human expertise and ML prognostics to expand fertility care access and advance health and social good.

Informing the State of Process Modeling and Automation of Blood Banking and Transfusion Services Through a Systematic Mapping Study

Purpose

The current state of the art in process modeling of blood banking and transfusion services is not well grounded; methodological reviews are lacking to bridge the gap between such blood banking and transfusion processes (and their models) and their automation. This research aims to fill this gap with a methodological review.

Methods

A systematic mapping study was adopted, driven by five key research questions. Identified research studies were accepted based on fulfilling the following inclusion criteria: 1) research studies should focus on blood banking and transfusion process modeling since the late 1970s; and 2) research studies should focus on process automation in relation to workflow-based systems, with papers classified into categories in line with the analysis undertaken to answer each of the research questions.

Results

The search identified 22 papers related to modeling and automation of blood banking and transfusion, published in the period 1979-2022. The findings revealed that only four process modeling languages were reported to visualize process workflows. The preparation of blood components, serologic testing, blood distribution, apheresis, preparation for emergencies, maintaining blood banking and transfusion safety, and documentation have not been reported to have been modeled in the literature. This review revealed the lack of use of Business Process Modeling Notation (BPMN) as the industry standard process modeling language in the domain. The review also indicated a deficiency in modeling specialized processes in blood banking and transfusion, with the majority of reported processes being described as high level, but lacking elaboration. Automation was reported to improve transfusion safety, and to reduce cost, time cycle, and human errors.

Conclusion

The work highlights the non-existence of a developed process architectural framework for blood banking and transfusion processes, which is needed to lay the groundwork for identifying and modeling strategic, managerial, and operational processes to bridge the gap with their enactment in healthcare systems. This paves the way for the development of a data-harvesting platform for blood banking and transfusion services.

Forecasting imminent atrial fibrillation in long-term electrocardiogram recordings

BACKGROUND

Despite the morbidity associated with acute atrial fibrillation (AF), no models currently exist to forecast its imminent onset. We sought to evaluate the ability of deep learning to forecast the imminent onset of AF with sufficient lead time, which has important implications for inpatient care.

METHODS

We utilized the Physiobank Long-Term AF Database, which contains 24-h, labeled ECG recordings from patients with a history of AF. AF episodes were defined as ≥5 min of sustained AF. Three deep learning models incorporating convolutional and transformer layers were created for forecasting, with two models focusing on the predictive nature of sinus rhythm segments and AF epochs separately preceding an AF episode, and one model utilizing all preceding waveform as input. Cross-validated performance was evaluated using area under time-dependent receiver operating characteristic curves (AUC(t)) at 7.5-, 15-, 30-, and 60-min lead times, precision-recall curves, and imminent AF risk trajectories.

RESULTS

There were 367 AF episodes from 84 ECG recordings. All models showed average risk trajectory divergence of those with an AF episode from those without ∼15 min before the episode. Highest AUC was associated with the sinus rhythm model [AUC = 0.74; 7.5-min lead time], though the model using all preceding waveform data had similar performance and higher AUCs at longer lead times.

CONCLUSIONS

In this proof-of-concept study, we demonstrated the potential utility of neural networks to forecast the onset of AF in long-term ECG recordings with a clinically relevant lead time. External validation in larger cohorts is required before deploying these models clinically.

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects

Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. Machine vision-based techniques are already in clinical use in radiology, ophthalmology, and dermatology, where some applications currently equal or exceed the performance of specialty physicians in areas of image interpretation. While machine vision in anesthesia has many potential applications, its development remains in its infancy in our specialty. Early research for machine vision in anesthesia has focused on automated recognition of anatomical structures during ultrasound-guided regional anesthesia or line insertion; recognition of the glottic opening and vocal cords during video laryngoscopy; prediction of the difficult airway using facial images; and clinical alerts for endobronchial intubation detected on chest radiograph. Current machine vision applications measuring the distance between endotracheal tube tip and carina have demonstrated noninferior performance compared to board-certified physicians. The performance and potential uses of machine vision for anesthesia will only grow with the advancement of underlying machine vision algorithm technical performance developed outside of medicine, such as convolutional neural networks and transfer learning. This article summarizes recently published works of interest, provides a brief overview of techniques used to create machine vision applications, explains frequently used terms, and discusses challenges the specialty will encounter as we embrace the advantages that this technology may bring to future clinical practice and patient care. As machine vision emerges onto the clinical stage, it is critically important that anesthesiologists are prepared to confidently assess which of these devices are safe, appropriate, and bring added value to patient care.

Parametric Modeling and Deep Learning for Enhancing Pain Assessment in Postanesthesia

OBJECTIVE

The problem of reliable and widely accepted measures of pain is still open. It follows the objective of this work as pain estimation through post-surgical trauma modeling and classification, to increase the needed reliability compared to measurements only.

METHODS

This article proposes (i) a recursive identification method to obtain the frequency response and parameterization using fractional-order impedance models (FOIM), and (ii) deep learning with convolutional neural networks (CNN) classification algorithms using time-frequency data and spectrograms. The skin impedance measurements were conducted on 12 patients throughout the postanesthesia care in a proof-of-concept clinical trial. Recursive least-squares system identification was performed using a genetic algorithm for initializing the parametric model. The online parameter estimates were compared to the self-reported level by the Numeric Rating Scale (NRS) for analysis and validation of the results. Alternatively, the inputs to CNNs were the spectrograms extracted from the time-frequency dataset, being pre-labeled in four intensities classes of pain during offline and online training with the NRS.

RESULTS

The tendency of nociception could be predicted by monitoring the changes in the FOIM parameters' values or by retraining online the network. Moreover, the tissue heterogeneity, assumed during nociception, could follow the NRS trends. The online predictions of retrained CNN have more specific trends to NRS than pain predicted by the offline population-trained CNN.

CONCLUSION

We propose tailored online identification and deep learning for artefact corrupted environment. The results indicate estimations with the potential to avoid over-dosing due to the objectivity of the information.

SIGNIFICANCE

Models and artificial intelligence (AI) allow objective and personalized nociception-antinociception prediction in the patient safety era for the design and evaluation of closed-loop analgesia controllers.

A Narrative Review of Multiinstitutional Data Registries of Pediatric Congenital Heart Disease in Pediatric Cardiac Anesthesia and Critical Care Medicine

Congenital heart disease (CHD) is one of the most common birth anomalies. While the care of children with CHD has improved over recent decades, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. Electronic health record systems have enabled institutions to combine data on the management and outcomes of children with CHD in multicenter registries. The application of descriptive analytics methods to these data can improve clinicians' understanding and care of children with CHD. This narrative review covers efforts to leverage multicenter data registries relevant to pediatric cardiac anesthesia and critical care to improve the care of children with CHD.

Use of artificial intelligence in paediatric anaesthesia: a systematic review

Objectives

Although the development of artificial intelligence (AI) technologies in medicine has been significant, their application to paediatric anaesthesia is not well characterised. As the paediatric operating room is a data-rich environment that requires critical clinical decision-making, this systematic review aims to characterise the current use of AI in paediatric anaesthesia and to identify barriers to the successful integration of such technologies.

Methods

This review was registered with PROSPERO (CRD42022304610), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in five electronic databases (Embase, Medline, Central, Scopus, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for paediatric anaesthesia (<18 yr old) within the perioperative setting.

Results

From 3313 records identified in the initial search, 40 were included in this review. Identified applications of AI were described for patient risk factor prediction (24 studies; 60%), anaesthetic depth estimation (2; 5%), anaesthetic medication/technique decision guidance (2; 5%), intubation assistance (1; 2.5%), airway device selection (3; 7.5%), physiological variable monitoring (6; 15%), and operating room scheduling (2; 5%). Multiple domains of AI were discussed including machine learning, computer vision, fuzzy logic, and natural language processing.

Conclusion

There is an emerging literature regarding applications of AI for paediatric anaesthesia, and their clinical integration holds potential for ultimately improving patient outcomes. However, multiple barriers to their clinical integration remain including a lack of high-quality input data, lack of external validation/evaluation, and unclear generalisability to diverse settings.

Systematic review protocol

CRD42022304610 (PROSPERO).

Use of artificial neural networks in the prognosis of musculoskeletal diseases-a scoping review

To determine the current evidence on artificial neural network (ANN) in prognostic studies of musculoskeletal diseases (MSD) and to assess the accuracy of ANN in predicting the prognosis of patients with MSD. The scoping review was reported under the Preferred Items for Systematic Reviews and the Meta-Analyses extension for Scope Reviews (PRISMA-ScR). Cochrane Library, Embase, Pubmed, and Web of science core collection were searched from inception to January 2023. Studies were eligible if they used ANN to make predictions about MSD prognosis. Variables, model prediction accuracy, and disease type used in the ANN model were extracted and charted, then presented as a table along with narrative synthesis. Eighteen Studies were included in this scoping review, with 16 different types of musculoskeletal diseases. The accuracy of the ANN model predictions ranged from 0.542 to 0.947. ANN models were more accurate compared to traditional logistic regression models. This scoping review suggests that ANN can predict the prognosis of musculoskeletal diseases, which has the potential to be applied to different types of MSD.

Use of machine learning in pediatric surgical clinical prediction tools: A systematic review

PURPOSE

Clinical prediction tools (CPTs) are decision-making instruments utilizing patient data to predict specific clinical outcomes, risk-stratify patients, or suggest personalized diagnostic or therapeutic options. Recent advancements in artificial intelligence have resulted in a proliferation of CPTs created using machine learning (ML)-yet the clinical applicability of ML-based CPTs and their validation in clinical settings remain unclear. This systematic review aims to compare the validity and clinical efficacy of ML-based to traditional CPTs in pediatric surgery.

METHODS

Nine databases were searched from 2000 until July 9, 2021 to retrieve articles reporting on CPTs and ML for pediatric surgical conditions. PRISMA standards were followed, and screening was performed by two independent reviewers in Rayyan, with a third reviewer resolving conflicts. Risk of bias was assessed using the PROBAST.

RESULTS

Out of 8300 studies, 48 met the inclusion criteria. The most represented surgical specialties were pediatric general (14), neurosurgery (13) and cardiac surgery (12). Prognostic (26) CPTs were the most represented type of surgical pediatric CPTs followed by diagnostic (10), interventional (9), and risk stratifying (2). One study included a CPT for diagnostic, interventional and prognostic purposes. 81% of studies compared their CPT to ML-based CPTs, statistical CPTs, or the unaided clinician, but lacked external validation and/or evidence of clinical implementation.

CONCLUSIONS

While most studies claim significant potential improvements by incorporating ML-based CPTs in pediatric surgical decision-making, both external validation and clinical application remains limited. Further studies must focus on validating existing instruments or developing validated tools, and incorporating them in the clinical workflow.

TYPE OF STUDY

Systematic Review LEVEL OF EVIDENCE: Level III.

Prediction of red blood cell transfusion after orthopedic surgery using an interpretable machine learning framework

Objective

Postoperative red blood cell (RBC) transfusion is widely used during the perioperative period but is often associated with a high risk of infection and complications. However, prediction models for RBC transfusion in patients with orthopedic surgery have not yet been developed. We aimed to identify predictors and constructed prediction models for RBC transfusion after orthopedic surgery using interpretable machine learning algorithms.

Methods

This retrospective cohort study reviewed a total of 59,605 patients undergoing orthopedic surgery from June 2013 to January 2019 across 7 tertiary hospitals in China. Patients were randomly split into training (80%) and test subsets (20%). The feature selection method of recursive feature elimination (RFE) was used to identify an optimal feature subset from thirty preoperative variables, and six machine learning algorithms were applied to develop prediction models. The Shapley Additive exPlanations (SHAP) value was employed to evaluate the contribution of each predictor towards the prediction of postoperative RBC transfusion. For simplicity of the clinical utility, a risk score system was further established using the top risk factors identified by machine learning models.

Results

Of the 59,605 patients with orthopedic surgery, 19,921 (33.40%) underwent postoperative RBC transfusion. The CatBoost model exhibited an AUC of 0.831 (95% CI: 0.824-0.836) on the test subset, which significantly outperformed five other prediction models. The risk of RBC transfusion was associated with old age (>60 years) and low RBC count (<4.0 × 10¹²/L) with clear threshold effects. Extremes of BMI, low albumin, prolonged activated partial thromboplastin time, repair and plastic operations on joint structures were additional top predictors for RBC transfusion. The risk score system derived from six risk factors performed well with an AUC of 0.801 (95% CI: 0.794-0.807) on the test subset.

Conclusion

By applying an interpretable machine learning framework in a large-scale multicenter retrospective cohort, we identified novel modifiable risk factors and developed prediction models with good performance for postoperative RBC transfusion in patients undergoing orthopedic surgery. Our findings may allow more precise identification of high-risk patients for optimal control of risk factors and achieve personalized RBC transfusion for orthopedic patients.

A Century of Technology in Anesthesia & Analgesia

Technological innovation has been closely intertwined with the growth of modern anesthesiology as a medical and scientific discipline. Anesthesia & Analgesia, the longest-running physician anesthesiology journal in the world, has documented key technological developments in the specialty over the past 100 years. What began as a focus on the fundamental tools needed for effective anesthetic delivery has evolved over the century into an increasing emphasis on automation, portability, and machine intelligence to improve the quality, safety, and efficiency of patient care.

Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders

BACKGROUND

Accurate estimation of surgical transfusion risk is essential for efficient allocation of blood bank resources and for other aspects of anesthetic planning. This study hypothesized that a machine learning model incorporating both surgery- and patient-specific variables would outperform the traditional approach that uses only procedure-specific information, allowing for more efficient allocation of preoperative type and screen orders.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program Participant Use File was used to train four machine learning models to predict the likelihood of red cell transfusion using surgery-specific and patient-specific variables. A baseline model using only procedure-specific information was created for comparison. The models were trained on surgical encounters that occurred at 722 hospitals in 2016 through 2018. The models were internally validated on surgical cases that occurred at 719 hospitals in 2019. Generalizability of the best-performing model was assessed by external validation on surgical cases occurring at a single institution in 2020.

RESULTS

Transfusion prevalence was 2.4% (73,313 of 3,049,617), 2.2% (23,205 of 1,076,441), and 6.7% (1,104 of 16,053) across the training, internal validation, and external validation cohorts, respectively. The gradient boosting machine outperformed the baseline model and was the best- performing model. At a fixed 96% sensitivity, this model had a positive predictive value of 0.06 and 0.21 and recommended type and screens for 36% and 30% of the patients in internal and external validation, respectively. By comparison, the baseline model at the same sensitivity had a positive predictive value of 0.04 and 0.144 and recommended type and screens for 57% and 45% of the patients in internal and external validation, respectively. The most important predictor variables were overall procedure-specific transfusion rate and preoperative hematocrit.

CONCLUSIONS

A personalized transfusion risk prediction model was created using both surgery- and patient-specific variables to guide preoperative type and screen orders and showed better performance compared to the traditional procedure-centric approach.

EDITOR’S PERSPECTIVE

Machine Learning Model for Predicting Acute Respiratory Failure in Individuals With Moderate-to-Severe Traumatic Brain Injury

Background: There is a high incidence of acute respiratory failure (ARF) in moderate or severe traumatic brain injury (M-STBI), worsening outcomes. This study aimed to design a predictive model for ARF.

Methods: Adult patients with M-STBI [3 ≤ Glasgow Coma Scale (GCS) ≤ 12] with a definite history of brain trauma and abnormal head on CT images, obtained from September 2015 to May 2017, were included. Patients with age >80 years or <18 years, multiple injuries with TBI upon admission, or pregnancy (in women) were excluded. Two models based on machine learning extreme gradient boosting (XGBoost) or logistic regression, respectively, were developed for predicting ARF within 48 h upon admission. These models were evaluated by out-of-sample validation. The samples were assigned to the training and test sets at a ratio of 3:1.

Results: In total, 312 patients were analyzed including 132 (42.3%) patients who had ARF. The GCS and the Marshall CT score, procalcitonin (PCT), and C-reactive protein (CRP) on admission significantly predicted ARF. The novel machine learning XGBoost model was superior to logistic regression model in predicting ARF [area under the receiver operating characteristic (AUROC) = 0.903, 95% CI, 0.834–0.966 vs. AUROC = 0.798, 95% CI, 0.697–0.899; p < 0.05].

Conclusion: The XGBoost model could better predict ARF in comparison with logistic regression-based model. Therefore, machine learning methods could help to develop and validate novel predictive models.

Development of a preoperative risk score predicting allogeneic red blood cell transfusion in children undergoing spinal fusion

BACKGROUND

Children undergoing spinal fusion often receive blood products. The goal of this study was to develop a preoperative score to help physicians identify those who are at risk of allogeneic red blood cell (RBC) transfusion.

STUDY DESIGN AND METHODS

This retrospective study of children undergoing spinal fusion in the ACS-NSQIP Pediatric database (2016-2019) aimed at identifying risk factors associated with allogeneic RBC transfusion. Univariable logistic regression and multivariable logistic regression were performed using preoperative patient characteristics and aided in the creation of a simplified scoring system.

RESULTS

Out of 13,929 total patients, 2990 (21.5%) were transfused. We created a risk score based on 10 independent predictors of transfusion: age, sex, race, weight < 3rd percentile, American Society of Anesthesiologists physical status classification, cardiac risk factors, hematologic disease, preoperative anemia, deformity type, and number of spinal levels to be fused. Patients in both the training and testing cohorts were assigned a score ranging from 0 (lowest risk) to 21 (highest risk). The developed transfusion risk score showed 77% accuracy in distinguishing patients who did not receive a transfusion during or soon after surgery (AUROC 0.7736 [95% CI, 0.7619-0.7852]) in the training cohort and 77% accuracy (AUROC 0.7732 [95% CI, 0.7554-0.7910]) in the testing cohort.

DISCUSSION

Our score, based on routinely available preoperative data, accurately estimates the risk of allogeneic RBC transfusion in pediatric patients undergoing spinal fusion. Future studies will inform whether patient blood management interventions targeted to high-risk patients can help reduce the need for transfusion and improve outcomes.

Thromboelastography Changes of Whole Blood Compared to Blood Component Transfusion in Infant Craniosynostosis Surgery

ABSTRACT

Surgical treatment of craniosynostosis with cranial vault reconstruction in infants is associated with significant blood loss. The optimal blood management approach is an area of active investigation. Thromboelastography (TEG) was used to examine changes in coagulation after surgical blood loss that was managed by transfusion with either whole blood or blood components. Transfusion type was determined by availability of whole blood from the blood bank.This retrospective study examined differences in posttransfusion TEG maximum amplitude (MA), a measure of the maximum clot strength, for patients transfused with whole blood or blood components. We included all patients less than 24 months old who underwent cranial vault remodeling, received intraoperative transfusions with whole blood or blood components, and had baseline and posttransfusion TEG measured. Whole blood was requested for all patients and was preferentially used when it was available from the American Red Cross.Of 48 eligible patients, 30 received whole blood and 18 received blood components. All patients received an intraoperative antifibrinolytic agent. The posttransfusion MA in the whole blood group was 61.8 mm (IQR 59.1, 64.1) compared to 57.9 mm (IQR 50.5, 60.9) in the blood components group (P = 0.010). There was a greater posttransfusion decrease in MA for patients transfused with blood components (median decrease of 7.7 mm [IQR -3.4, 6.3]) compared with whole blood (median decrease of 2.1 mm [IQR -9.6, 7.5] P < 0.001).Transfusion with blood components was associated with a greater decrease in MA that was likely related to decreased postoperative fibrinogen in this group. Patients who received whole blood had higher postoperative fibrinogen levels.

The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review

Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field of surgery. This article aims to provide a comprehensive literature review of artificial intelligence as it applies to surgery and discuss practical examples, current applications, and challenges to the adoption of this technology. Furthermore, we elaborate on the utility of natural language processing and computer vision in improving surgical outcomes, research, and patient care.

Machine Learning for the Prediction of Red Blood Cell Transfusion in Patients During or After Liver Transplantation Surgery

Aim: This study aimed to use machine learning algorithms to identify critical preoperative variables and predict the red blood cell (RBC) transfusion during or after liver transplantation surgery. Study Design and Methods: A total of 1,193 patients undergoing liver transplantation in three large tertiary hospitals in China were examined. Twenty-four preoperative variables were collected, including essential population characteristics, diagnosis, symptoms, and laboratory parameters. The cohort was randomly split into a train set (70%) and a validation set (30%). The Recursive Feature Elimination and eXtreme Gradient Boosting algorithms (XGBOOST) were used to select variables and build machine learning prediction models, respectively. Besides, seven other machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC) was used to compare the prediction performance of different models. The SHapley Additive exPlanations package was applied to interpret the XGBOOST model. Data from 31 patients at one of the hospitals were prospectively collected for model validation. Results: In this study, 72.1% of patients in the training set and 73.2% in the validation set underwent RBC transfusion during or after the surgery. Nine vital preoperative variables were finally selected, including the presence of portal hypertension, age, hemoglobin, diagnosis, direct bilirubin, activated partial thromboplastin time, globulin, aspartate aminotransferase, and alanine aminotransferase. The XGBOOST model presented significantly better predictive performance (AUROC: 0.813) than other models and also performed well in the prospective dataset (accuracy: 76.9%). Discussion: A model for predicting RBC transfusion during or after liver transplantation was successfully developed using a machine learning algorithm based on nine preoperative variables, which could guide high-risk patients to take appropriate preventive measures.