Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling

Warfarin-A natural anticoagulant: A review of research trends for precision medication

BACKGROUND

Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability.

PURPOSE

The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage.

METHODS

A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature.

RESULTS

Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database.

CONCLUSION

Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

BACKGROUND

 Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

METHODS

 Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.

RESULTS

 Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.

CONCLUSION

 ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

Clinical effect of short-term spinal cord stimulation in the treatment of patients with primary brainstem hemorrhage-induced disorders of consciousness

Objective

Recently, short-term spinal cord stimulation (st-SCS) has been used in neurorehabilitation and consciousness recovery. However, little is known about its effects on primary brainstem hemorrhage (PBSH)-induced disorders of consciousness (DOC). In this study, we examined the therapeutic effects of st-SCS in patients with PBSH-induced DOC.

Methods

Fourteen patients received a 2-week st-SCS therapy. Each patient's state of consciousness was evaluated using the Coma Recovery Scale-Revised (CRS-R). CRS-R evaluation scores were recorded at the baseline (before SCS implantation) and 14 days later.

Results

Over 70% (10/14) of the patients (CRS-R score increased to ≥2 points) responded to the SCS stimulation after 14 days of st-SCS treatment. All items included in the CRS-R exhibited a significant increase post-treatment compared with pretreatment. After 2 weeks of st-SCS treatment, seven patients showed diagnostic improvement, resulting in a 50% (7/14) overall effective rate. Approximately 75% (3/4) of patients with minimally conscious state plus (MCS+) improved to emergence from MCS (eMCS), and 50% (1/2) of patients with vegetative state or unresponsive wakefulness syndrome (VS/UWS) improved to MCS+.

Conclusion

In PBSH-induced DOC, st-SCS is a safe and effective treatment. The clinical behavior of the patients improved significantly following the st-SCS intervention, and their CRS-R scores markedly increased. This was most effective for MCS+.

Optimizing the dynamic treatment regime of in-hospital warfarin anticoagulation in patients after surgical valve replacement using reinforcement learning

OBJECTIVE

Warfarin anticoagulation management requires sequential decision-making to adjust dosages based on patients' evolving states continuously. We aimed to leverage reinforcement learning (RL) to optimize the dynamic in-hospital warfarin dosing in patients after surgical valve replacement (SVR).

MATERIALS AND METHODS

10 408 SVR cases with warfarin dosage-response data were retrospectively collected to develop and test an RL algorithm that can continuously recommend daily warfarin doses based on patients' evolving multidimensional states. The RL algorithm was compared with clinicians' actual practice and other machine learning and clinical decision rule-based algorithms. The primary outcome was the ratio of patients without in-hospital INRs >3.0 and the INR at discharge within the target range (1.8-2.5) (excellent responders). The secondary outcomes were the safety responder ratio (no INRs >3.0) and the target responder ratio (the discharge INR within 1.8-2.5).

RESULTS

In the test set (n = 1260), the excellent responder ratio under clinicians' guidance was significantly lower than the RL algorithm: 41.6% versus 80.8% (relative risk [RR], 0.51; 95% confidence interval [CI], 0.48-0.55), also the safety responder ratio: 83.1% versus 99.5% (RR, 0.83; 95% CI, 0.81-0.86), and the target responder ratio: 49.7% versus 81.1% (RR, 0.61; 95% CI, 0.58-0.65). The RL algorithms performed significantly better than all the other algorithms. Compared with clinicians' actual practice, the RL-optimized INR trajectory reached and maintained within the target range significantly faster and longer.

DISCUSSION

RL could offer interactive, practical clinical decision support for sequential decision-making tasks and is potentially adaptable for varied clinical scenarios. Prospective validation is needed.

CONCLUSION

An RL algorithm significantly optimized the post-operation warfarin anticoagulation quality compared with clinicians' actual practice, suggesting its potential for challenging sequential decision-making tasks.

Effects of Different Anesthetics on Perioperative Organ Protection and Postoperative Cognitive Function in Patients Undergoing Cardiac Valve Replacement with Cardiopulmonary Bypass

In this paper, we have carried out an experimental study to investigate the effects of different anesthetics on perioperative organ protection and postoperative cognitive function in patients undergoing cardiac valve replacement with cardiopulmonary bypass. To realize this idea, a total of 90 patients with single valve replacement under general anesthesia and hypothermic cardiopulmonary bypass from January 2020 to October 2021 were enrolled. These patients were assigned into three groups, with 30 cases in each group by the digital table method. Group A was anesthetized with sufentanil combined with dexmedetomidine. Group B was anesthetized with sufentanil combined with etomidate. Group C was anesthetized with sufentanil combined with propofol. Perioperative organ protection and postoperative cognitive function of the three groups were compared. At T 0 time point, there was no significant difference in blood WBC, blood N, and CRP among groups A, B, and C (P > 0.05); At T 4 and T 5 time points, the indexes of blood WBC, blood N, and CRP in groups A, B, and C were higher compared to the T 0 time point. At T 4 and T 5 time points, the indexes of blood WBC, blood N, and CRP in group A were significantly lower compared to group B and group C. Before treatment, there was no significant difference in ALT and AST among groups A, B, and C (P > 0.05). After treatment, the indexes of ALT and AST in group A were significantly lower compared to group B and group C at T 4 and T 5 time points (P < 0.05). Before treatment, there was no significant difference in urea and creatinine among groups A, B, and C (P > 0.05). After treatment, the urea and creatinine indexes of group A were significantly lower compared to group B and group C at T 4 and T 5 time points (P < 0.05). Before treatment, there was no significant difference in CK-MB and CTnl among groups A, B, and C (P > 0.05); After treatment, the indexes of CK-MB and CTnl in group A were significantly lower compared to group B and group C at T 4 and T 5 time points (P  < 0.05). Before treatment, there was no significant difference in MOCA scores among groups A, B, and C (P > 0.05). After treatment, the MOCA scores of group A were significantly higher compared to group B and group C at T 5 and T 6 time points (P  < 0.05). Sufentanil combined with dexmedetomidine for heart valve replacement under cardiopulmonary bypass can reduce the dosage of anesthetics during the operation and have a certain perioperative protective effect on important organs such as the heart, lung, liver, and kidney, which may be related to reducing intraoperative hemodynamic fluctuations and inhibiting inflammatory stress response.

Nonlinear Machine Learning in Warfarin Dose Prediction: Insights from Contemporary Modelling Studies

Objective: This study aimed to systematically assess the characteristics and risk of bias of previous studies that have investigated nonlinear machine learning algorithms for warfarin dose prediction. Methods: We systematically searched PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), China Science and Technology Journal Database (VIP), and Wanfang Database up to March 2022. We assessed the general characteristics of the included studies with respect to the participants, predictors, model development, and model evaluation. The methodological quality of the studies was determined, and the risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). Results: From a total of 8996 studies, 23 were assessed in this study, of which 23 (100%) were retrospective, and 11 studies focused on the Asian population. The most common demographic and clinical predictors were age (21/23, 91%), weight (17/23, 74%), height (12/23, 52%), and amiodarone combination (11/23, 48%), while CYP2C9 (14/23, 61%), VKORC1 (14/23, 61%), and CYP4F2 (5/23, 22%) were the most common genetic predictors. Of the included studies, the MAE ranged from 1.47 to 10.86 mg/week in model development studies, from 2.42 to 5.18 mg/week in model development with external validation (same data) studies, from 12.07 to 17.59 mg/week in model development with external validation (another data) studies, and from 4.40 to 4.84 mg/week in model external validation studies. All studies were evaluated as having a high risk of bias. Factors contributing to the risk of bias include inappropriate exclusion of participants (10/23, 43%), small sample size (15/23, 65%), poor handling of missing data (20/23, 87%), and incorrect method of selecting predictors (8/23, 35%). Conclusions: Most studies on nonlinear-machine-learning-based warfarin prediction models show poor methodological quality and have a high risk of bias. The analysis domain is the major contributor to the overall high risk of bias. External validity and model reproducibility are lacking in most studies. Future studies should focus on external validity, diminish risk of bias, and enhance real-world clinical relevance.