BackgroundMost patients undergoing Transcatheter aortic valve implantation (TAVR) are elderly with significant co-morbidities and there is limited information available regarding factors that influence length of stay (LOS) post-procedure. The aim of this study was to identify the patient, and procedural factors that affect post-TAVR LOS using a contemporary multinational registry.MethodsWe conducted a retrospective cohort study, with patients recruited from three high volume tertiary institutions. The primary outcome was the LOS post-TAVR procedure. We examined patient and procedural factors in a cause-specific Cox multivariable regression model to elucidate their effect on LOS, accounting for the competing risk of post-procedural death. Hazard ratios (HR) greater than 1 indicate a shorter LOS, while HRs less than 1 indicate a longer LOS.ResultsThe cohort consisted of 809 patients. Patient factors associated with longer LOS were older age, prior atrial fibrillation, and greater patient urgency. Patient factors associated with shorter LOS were lower NYHA class, higher ejection fraction and higher mean aortic valve gradients.Procedural characteristics associated with shorter LOS were conscious sedation (HR = 1.19, 95% CI 1.06–1.35, p = 0.004). Transapical access was associated with prolonged LOS (HR = 0.49, 95% CI 0.41–0.58, p < 0.001).ConclusionThis multicenter study identified potentially modifiable patient and procedural factors associated with a prolonged LOS. Future research is needed to determine if interventions focused on these factors will translate to a shorter LOS.Electronic supplementary materialThe online version of this article (doi:10.1186/s12872-017-0573-7) contains supplementary material, which is available to authorized users.
Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision‐making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension‐related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real‐time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.
Objective:Artificial intelligence and machine learning (AI/ML) are increasingly being applied to big clinical data to tackle research questions that cannot be answered with traditional statistical methods. The field is still in its nascent stages and there is a paucity of guidelines for conducting and reporting AI/ML research in hypertension. The objective was to apply the HUMANE checklist to survey the present landscape of AI/ML in hypertension to inform the development of hypertension-specific guidelines and recommendations.Design and method:The HUMANE checklist was developed by global clinical and AI/ML experts through the Delphi method. It assesses the quality of medical AI/ML articles based on whether they cover subjects expected in any peer-reviewed, clinical or AI/ML research publication. A cooping review was carried out to identify articles presenting original research in AI/ML and hypertension published in 2019–2021. Two independent reviewers applied the checklist to each article and in the case of discordance, the response was adjudicated by an AI/ML expert. Results were analysed to assess compliance with the survey (% of papers satisfying checklist requirements).Results:A total of 63 manuscripts was reviewed. A summary of results is shown in Figure 1. Highest compliance was seen for items relating to general article presentation, with compliance ranging from 68% to 98% (description of statistical analysis methods and background context, respectively). Lowest compliance was seen with checklist items relating to clinical research and AI/ML methods. 44% of reviewed articles described the demographics of their dataset and 48% stated their inclusion/exclusion criteria. Nonetheless, datasets were deemed appropriate for investigative aims in 93% of articles. 30% of manuscripts reported their calibration measures, while 73% stated their performance metrics. Internal validation was carried out in 75% of studies, but external validity was assessed in only 14% of cases. Algorithmic bias was addressed in 11% of papers.Conclusions:Application of AI/ML methods in hypertension research is growing, but the majority of current work has major shortfalls in reporting quality, model validation and algorithmic bias. Our study identifies areas of improvement to enable the full realisation of the potential of AI/ML in hypertension.
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