Enhancing automatic multilabel diagnosis of electrocardiogram signals: A masked transformer approach.

Background And Objective: Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. Although deep learning models have been widely applied to ECG classification tasks, their accuracy remains limited, especially in handling complex signal patterns in real-world clinical settings. This study explores the potential of Transformer models to improve ECG classification accuracy.

Deep Learning-Enhanced Noninvasive Detection of Pulmonary Hypertension and Subtypes via Chest Radiographs, Validated by Catheterization.

Background: Pulmonary hypertension (PH) is a complex, life-threatening condition requiring noninvasive, accessible, and accurate diagnostic tools, particularly in resource-limited settings. Early and precise identification of PH and its subtypes is critical for effective management and timely intervention.

Research Question: Can deep learning (DL) methods applied to chest radiography (CXR) accurately detect PH and its subtype, congenital heart disease-associated pulmonary arterial hypertension (CHD-PAH)?

Study Design And Methods: A retrospective cohort study was conducted with 4,576 patients, including 2,288 patients with PH, who underwent CXR followed by right heart catheterization (RHC) or transthoracic echocardiography.

Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension.

Transthoracic echocardiography (TTE), commonly used for initial screening of pulmonary hypertension (PH), often lacks sufficient accuracy. To address this gap, we developed and validated a multimodal fusion model for improved PH screening (MMF-PH). The study was registered in the ClinicalTrials.

Prevalence and Clinical Significance of Low QRS Voltages in Patients Presenting for Cardiovascular or Related Diseases.

Background: We investigated the presence of low QRS voltage (LQRSV) in a large sample population presenting for cardiovascular diseases. Further studies on LQRSV prevalence and clinical implications are warranted.

Methods: We conducted a cross-sectional study using ECG data from the National Center for Cardiovascular Diseases of China, collected from January 2015 to December 2023.

Design and rationale of the Comprehensive intelligent Hypertension managEment SyStem (CHESS) evaluation study: A cluster randomized controlled trial for hypertension management in primary care.

Background: Hypertension management in China is suboptimal with high prevalence and low control rate due to various barriers, including lack of self-management awareness of patients and inadequate capacity of physicians. Digital therapeutic interventions including mobile health and computational device algorithms such as clinical decision support systems (CDSS) are scalable with the potential to improve blood pressure (BP) management and strengthen the healthcare system in resource-constrained areas, yet their effectiveness remains to be tested. The aim of this report is to describe the protocol of the Comprehensive intelligent Hypertension managEment SyStem (CHESS) evaluation study assessing the effect of a multifaceted hypertension management system for supporting patients and physicians on BP lowering in primary care settings.

A Comprehensive Patient Blood Management Program During Cardiopulmonary Bypass in Patients Over 60 Years of Age.

Purpose: There is currently no consensus on the most appropriate blood transfusion strategy for older adults undergoing cardiovascular surgery. We aimed to investigate the potential benefits of the patient blood management (PBM) program specifically for advanced age patients, and to evaluate the relationship of age and PBM in cardiovascular surgery.

Patients And Methods: We collected data from patients over 60 years old who underwent on-pump cardiovascular surgery.

Needle-Perc-Assisted Endoscopic Surgery in Treatment with Renal Staghorn Stones: A Prospective Randomized Controlled Study from a Large-Volume Stone Center.

Introduction: The goal of this study was to evaluate the efficacy and safety of needle-perc-assisted endoscopic surgery (NAES) in the treatment of staghorn renal stones via a single-center prospective randomized controlled study.

Methods: A total of 219 patients with partial or complete staghorn renal stones were prospectively randomized into two groups between January 2020 and April 2022. In group A (n = 112), patients were treated with traditional standard access, multiple if necessary, and in group B (n = 107), only one standard access was made, and needle-perc was assisted to remove the residual stones in the same stage.

Needle-perc-assisted endoscopic surgery (NAES) for patients with complicated solitary kidney stones: a prospective randomized study from a single center.

Purpose: The goal of this study is to compare traditional percutaneous nephrolithotomy (PCNL) and needle-perc-assisted endoscopic surgery (NAES) in the treatment of complicated solitary kidney stones via a single-center randomized controlled prospective study.

Methods: A total of patients with complex (Guy's score II-IV) solitary kidney stones between July 2019 to June 2022 were enrolled in the study. Participants were stratified into two groups: needle-perc-assisted endoscopic surgery group (group A) and traditional PCNL group (group B).

A transfusion risk stratification score to facilitate quality management in cardiopulmonary bypass.

Background: Our previous showed that a blood management program in the cardiopulmonary bypass (CPB) department, reduced red blood cell (RBC) transfusion and complications, but assessing transfusion practice solely based on transfusion rates was insufficient. This study aimed to design a risk stratification score to predict perioperative RBC transfusion to guide targeted measures for on-pump cardiac surgery patients.

Study Design And Methods: We analyzed data from 42,435 adult cardiac patients.

The Status Quo of Internet Medical Services in China: A Nationwide Hospital Survey.

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Automated ICD coding for coronary heart diseases by a deep learning method.

Automated ICD coding via machine learning that focuses on some specific diseases has been a hot topic. As one of the leading causes of death, coronary heart diseases (CHD) have seldom been specifically studied by related research, probably due to lack of data concretely targeting at the diseases. Based on Fuwai-CHD and MIMIC-III-CHD, which are a private dataset from Fuwai Hospital and the CHD-related subset of a public dataset named MIMIC-III respectively, this study aimed at automated CHD coding by a deep learning method, which mainly consists of three modules.

Evaluation of the effectiveness and safety of a multi-faceted computerized antimicrobial stewardship intervention in surgical settings: A single-centre cluster-randomized controlled trial.

Background: Inappropriate antimicrobial use is common among patients undergoing surgery. It remains unclear whether a multi-faceted computerized antimicrobial stewardship programme is effective and safe in reducing inappropriate antimicrobial use in surgical settings.

Methods: A multi-faceted computerized antimicrobial stewardship intervention system was developed, and an open-label, cluster-randomized, controlled trial was conducted among 18 surgical teams that enrolled 2470 patients for open chest cardiovascular surgery.

Dose-dependent influence of red blood cell transfusion volume on adverse outcomes in cardiac surgery.

Introduction: Red blood cell (RBC) transfusion is associated with adverse outcomes, but there are few studies on the RBC volume. This study aimed to evaluate the relationship between intraoperative RBC volume and postoperative adverse outcomes for on-pump cardiac surgery.

Methods: Adult patients undergoing on-pump cardiac surgery from 1 January 2017 to 31 December 2018 were included.

Establishment and Validation of a Nomogram to Predict Hospital-Acquired Infection in Elderly Patients After Cardiac Surgery.

Background: Hospital-acquired infection (HAI) after cardiac surgery is a common clinical concern associated with adverse prognosis and mortality. The objective of this study is to determine the prevalence of HAI and its associated risk factors in elderly patients following cardiac surgery and to build a nomogram as a predictive model.

Methods: We developed and internally validated a predictive model from a retrospective cohort of 6405 patients aged ≥70 years, who were admitted to our hospital and underwent cardiac surgery.

Comparison of different feature extraction methods for applicable automated ICD coding.

Background: Automated ICD coding on medical texts via machine learning has been a hot topic. Related studies from medical field heavily relies on conventional bag-of-words (BoW) as the feature extraction method, and do not commonly use more complicated methods, such as word2vec (W2V) and large pretrained models like BERT. This study aimed at uncovering the most effective feature extraction methods for coding models by comparing BoW, W2V and BERT variants.

Prediction of acute kidney injury after cardiac surgery from preoperative N-terminal pro-B-type natriuretic peptide.

Background: Acute kidney injury (AKI) is common after cardiac surgery and is difficult to predict. N-terminal pro-B-type natriuretic peptide (NT-proBNP) is highly predictive for perioperative cardiovascular complications and may also predict renal injury. We therefore tested the hypothesis that preoperative NT-proBNP concentration is associated with renal injury after major cardiac surgery.

Quality Management of a Comprehensive Blood Conservation Program During Cardiopulmonary Bypass.

Background: Red blood cell transfusion is common and associated with adverse outcomes for cardiac surgery, while present blood conservation guidelines have not been fully implemented until now. This study evaluated our comprehensive blood conservation program after quality management and explored its impact on blood transfusion and outcomes in patients undergoing cardiopulmonary bypass (CPB).

Methods: We retrospectively compared blood transfusions and outcomes of patients from 2 different periods, before and after initiation of the quality management of the comprehensive blood conservation program.

Automated ICD coding for primary diagnosis via clinically interpretable machine learning.

Background: Computer-assisted clinical coding (CAC) based on automated coding algorithms has been expected to improve the International Classification of Disease, tenth version (ICD-10) coding quality and productivity, whereas studies oriented to primary diagnosis auto-coding are limited in the Chinese context.

Objective: This study aims at developing a machine learning (ML) model for automated primary diagnosis ICD-10 coding.

Methods: A total of 71,709 admissions in Fuwai hospital were included to carry out this study, corresponding to 168 primary diagnosis ICD-10 codes.

Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China.

Background: With the development and application of medical information system, semantic interoperability is essential for accurate and advanced health-related computing and electronic health record (EHR) information sharing. The openEHR approach can improve semantic interoperability. One key improvement of openEHR is that it allows for the use of existing archetypes.

Trends of Coronary Artery Bypass Grafting Performance in a Cohort of Hospitals in China Between 2013 and 2018.

Background: China has witnessed a rapid increase in the volume of coronary artery bypass grafting (CABG) but substantial gaps in the performance for CABG across the nation. The present study aimed to investigate the change in CABG performance after years of quality improvement measures in a national registry in China.

Methods: The study included 66 971 patients who underwent isolated CABG in a cohort of 74 tertiary hospitals in China between January 2013 and December 2018.

Prediction Scores for Any-Stage and Stage-3 Acute Kidney Injury After Adult Cardiac Surgery in a Chinese Population.

Objectives: This study was performed to internally derive and then validate risk score systems using preoperative and intraoperative variables to predict the occurrence of any-stage (stage 1, 2, 3) and stage-3 acute kidney injury (AKI) within seven days of cardiac surgery.

Design: Single-center, retrospective, observational study.

Setting: Single, large, tertiary care center.

Prediction of adverse drug reactions based on knowledge graph embedding.

Background: Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data.

An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records.

Background: Secondary hypertension is a kind of hypertension with a definite etiology and may be cured. Patients with suspected secondary hypertension can benefit from timely detection and treatment and, conversely, will have a higher risk of morbidity and mortality than those with primary hypertension.

Objective: The aim of this study was to develop and validate machine learning (ML) prediction models of common etiologies in patients with suspected secondary hypertension.

Open-label, single-centre, cluster-randomised controlled trial to Evaluate the Potential Impact of Computerisedantimicrobial stewardship (EPIC) on the antimicrobial use after cardiovascular surgeries: EPIC trial study original protocol.

Introduction: Inappropriate antimicrobial use increases the prevalence of antimicrobial-resistant bacteria. Surgeons are reluctant to implement recommendations of guidelines in clinical practice. Antimicrobial stewardship (AMS) is effective in antimicrobial management, but it remains labour intensive.