Tao, Cui

Background The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug‐eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. Methods and Results We developed and validated a new AI‐based pipeline using retrospective data of drug‐eluting stent‐treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de‐identified Clinformatics Data Mart Database (n=9978). The 36 months following drug‐eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI‐DAPT model. The AI‐DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%–92%] for ischemia and 84% [95% CI, 82%–87%] for bleeding predictions. Conclusions Our AI‐DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.

LINKED CONTENTThis article is linked to Wijarnpreecha et al and Huang & Nguyen papers. To view these articles, visit https://doi.org/10.1111/apt.16590 and https://doi.org/10.1111/apt.16576

LINKED CONTENTThis article is linked to Wijarnpreecha et al and Gillespie & Hayes papers. To view these articles, visit https://doi.org/10.1111/apt.16490 and https://doi.org/10.1111/apt.16512

SummaryBackgroundPrevious studies have demonstrated an association between nonselective beta‐blockers (NSBBs) and lower risk of hepatocellular carcinoma (HCC) in cirrhosis. However, there has been no population‐based study investigating the risk of HCC among cirrhotic patients treated using carvedilol.AimsTo determine the risk of HCC among cirrhotic patients with NSBBs including carvedilol.MethodsThis retrospective cohort study utilised the Cerner Health Facts database in the United States from 2000 to 2017. Kaplan‐Meier estimate, Cox proportional hazards regression, and propensity score matching (PSM) were used to test the HCC risk among the carvedilol, nadolol, and propranolol groups compared with no beta‐blocker group.ResultsThe final cohort comprised 107 428 eligible patients. The 100‐month cumulative HCC incidence of NSBBs was significantly lower than the no beta‐blocker group (carvedilol (11.24%) vs no beta‐blocker (15.69%), nadolol (27.55%) vs no beta‐blocker (32.11%), and propranolol (26.17%) vs no beta‐blocker (28.84%) (P values < 0.0001). NSBBs were associated with a significantly lower risk of HCC (Hazard ratio: carvedilol 0.61 (95% CI 0.51‐0.73), nadolol 0.74 (95% CI 0.63‐0.87), propranolol 0.75 (95% CI 0.66‐0.84) after PSM in the multivariate cox analysis. In subgroup analysis, NSBBs reduced the risk of HCC in cirrhosis with complications and non‐alcoholic cirrhosis.ConclusionsNSBBs, including carvedilol, were associated with a significantly decreased risk of HCC in patients with cirrhosis when compared with no beta‐blocker regardless of complications status. Future randomised‐controlled studies comparing the incidence of HCC among NSBBs should elucidate which NSBB would be the best option to prevent HCC in cirrhosis.

AbstractPurposeSevere adverse events (AEs), such as Guillain‐Barré syndrome (GBS) occur rarely after influenza vaccination. We identify highly associated AEs with GBS and develop prediction models for GBS using the US Vaccine Adverse Event Reporting System (VAERS) reports following trivalent influenza vaccination (FLU3).MethodsThis study analyzed 80 059 reports from the US VAERS between 1990 and 2017. Several AEs were identified as highly associated with GBS and were used to develop the prediction model. Some common and mild AEs that were suspected to be underreported when GBS occurred simultaneously were removed from the final model. The analyses were validated using European influenza vaccine AEs data from EudraVigilance.ResultsOf the 80 059 reports, 1185 (1.5%) were annotated as GBS related. Twenty‐four AEs were identified as having strong association with GBS. The full prediction model, using age, sex, and all 24 AEs achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 85.4% (90% CI: [83.8%, 86.9%]). After excluding the nine (e.g., pruritus, rash, injection site pain) likely underreported AEs, the final AUC became 77.5% (90% CI: [75.5%, 79.6%]). Two hundred and one (0.25%) reports were predicted as of high risk of GBS (predicted probability >25%) and 84 actually developed GBS.ConclusionThe prediction performance demonstrated the potential of developing risk‐prediction models utilizing the VAERS cohort. Excluding the likely underreported AEs sacrificed some prediction power but made the model more interpretable and feasible. The high absolute risk of even a small number of AE combinations suggests the promise of GBS prediction within the VAERS dataset.

Abstract Objective The goal of this study is to develop a robust Time Event Ontology (TEO), which can formally represent and reason both structured and unstructured temporal information. Materials and Methods Using our previous Clinical Narrative Temporal Relation Ontology 1.0 and 2.0 as a starting point, we redesigned concept primitives (clinical events and temporal expressions) and enriched temporal relations. Specifically, 2 sets of temporal relations (Allen’s interval algebra and a novel suite of basic time relations) were used to specify qualitative temporal order relations, and a Temporal Relation Statement was designed to formalize quantitative temporal relations. Moreover, a variety of data properties were defined to represent diversified temporal expressions in clinical narratives. Results TEO has a rich set of classes and properties (object, data, and annotation). When evaluated with real electronic health record data from the Mayo Clinic, it could faithfully represent more than 95% of the temporal expressions. Its reasoning ability was further demonstrated on a sample drug adverse event report annotated with respect to TEO. The results showed that our Java-based TEO reasoner could answer a set of frequently asked time-related queries, demonstrating that TEO has a strong capability of reasoning complex temporal relations. Conclusion TEO can support flexible temporal relation representation and reasoning. Our next step will be to apply TEO to the natural language processing field to facilitate automated temporal information annotation, extraction, and timeline reasoning to better support time-based clinical decision-making.

Abstract Objective In recent years, electronic health record systems have been widely implemented in China, making clinical data available electronically. However, little effort has been devoted to making drug information exchangeable among these systems. This study aimed to build a Normalized Chinese Clinical Drug (NCCD) knowledge base, by applying and extending the information model of RxNorm to Chinese clinical drugs. Methods Chinese drugs were collected from 4 major resources—China Food and Drug Administration, China Health Insurance Systems, Hospital Pharmacy Systems, and China Pharmacopoeia—for integration and normalization in NCCD. Chemical drugs were normalized using the information model in RxNorm without much change. Chinese patent drugs (i.e., Chinese herbal extracts), however, were represented using an expanded RxNorm model to incorporate the unique characteristics of these drugs. A hybrid approach combining automated natural language processing technologies and manual review by domain experts was then applied to drug attribute extraction, normalization, and further generation of drug names at different specification levels. Lastly, we reported the statistics of NCCD, as well as the evaluation results using several sets of randomly selected Chinese drugs. Results The current version of NCCD contains 16 976 chemical drugs and 2663 Chinese patent medicines, resulting in 19 639 clinical drugs, 250 267 unique concepts, and 2 602 760 relations. By manual review of 1700 chemical drugs and 250 Chinese patent drugs randomly selected from NCCD (about 10%), we showed that the hybrid approach could achieve an accuracy of 98.60% for drug name extraction and normalization. Using a collection of 500 chemical drugs and 500 Chinese patent drugs from other resources, we showed that NCCD achieved coverages of 97.0% and 90.0% for chemical drugs and Chinese patent drugs, respectively. Conclusion Evaluation results demonstrated the potential to improve interoperability across various electronic drug systems in China.

Abstract Introduction The semantic interoperability of electronic healthcare records (EHRs) systems is a major challenge in the medical informatics area. International initiatives pursue the use of semantically interoperable clinical models, and ontologies have frequently been used in semantic interoperability efforts. The objective of this paper is to propose a generic, ontology-based, flexible approach for supporting the automatic transformation of clinical models, which is illustrated for the transformation of Clinical Element Models (CEMs) into openEHR archetypes. Methods Our transformation method exploits the fact that the information models of the most relevant EHR specifications are available in the Web Ontology Language (OWL). The transformation approach is based on defining mappings between those ontological structures. We propose a way in which CEM entities can be transformed into openEHR by using transformation templates and OWL as common representation formalism. The transformation architecture exploits the reasoning and inferencing capabilities of OWL technologies. Results We have devised a generic, flexible approach for the transformation of clinical models, implemented for the unidirectional transformation from CEM to openEHR, a series of reusable transformation templates, a proof-of-concept implementation, and a set of openEHR archetypes that validate the methodological approach. Conclusions We have been able to transform CEM into archetypes in an automatic, flexible, reusable transformation approach that could be extended to other clinical model specifications. We exploit the potential of OWL technologies for supporting the transformation process. We believe that our approach could be useful for international efforts in the area of semantic interoperability of EHR systems.