Background Cardiorespiratory fitness (CRF) is a key metric for risk stratification and clinical decision-making in cardiovascular disease (CVD). CRF assessment in Chinese patients with CVD currently relies on reference standards derived from other patients, which may lead to prognostic risk misclassification. Objectives This study aimed to establish CRF reference standards for Chinese patients and assess their utility in stratifying major adverse cardiovascular event (MACE). Methods We conducted a multicenter retrospective study including 56,495 patients with CVD from 20 hospitals across China from 2018 to 2024. Sex- and age-specific CRF reference values and percentiles were derived using 10-year age groups and compared with those of healthy Chinese adults. Prognostic performance was evaluated in a validation cohort of 6,317 patients (median follow-up: 24 months [IQR: 12-36]) using Kaplan-Meier and Cox regression analyses. Results The established CRF reference standards demonstrated significant differences across sex, age, and CVD subtype (P < 0.001). Compared with age- and sex-matched healthy adults, patients with CVD had lower peak oxygen uptake, with relative CRF levels of 73.3% to 78.7% in men and 75.4% to 84.0% in women. Compared with the high-CRF group, the adjusted MACE risk was higher in the middle-CRF (HR: 1.46; 95% CI: 1.16-1.83; P < 0.001) and low-CRF groups (HR: 2.29; 95% CI: 1.81-2.90; P < 0.001). Kaplan-Meier analysis revealed significantly lower event-free survival with decreasing CRF (log-rank P < 0.001). Conclusions This study establishes the first CRF reference standards specific to Chinese patients with CVD and demonstrates their prognostic utility for MACE risk stratification. These findings provide a data-driven framework for functional risk assessment, secondary prevention, and individualized rehabilitation strategies in clinical practice.

Background:Peak heart rate (HR peak) and first ventilatory threshold heart rate (HR VT1) guide exercise prescription formulation, but existing formulas lack accuracy in coronary heart disease (CHD) patients due to unaccounted pathophysiological differences. This study aimed to construct prediction equation for HR peak and HR VT1 in CHD patients. Methods:This was a multicenter retrospective study that included 14,465 cases of cardiopulmonary exercise test (CPET) data from CHD patients in 20 hospitals in China. Seventy percent of the cohort was divided into a development group (n = 10,125), and the remaining 30% served as a validation group (n = 4340). Stepwise multiple backward regression established HR peak and HR VT1 equations, with accuracy compared to traditional formulas. Results:Age, weight, resting heart rate (HR rest), CHD diagnostic category, and β-blockers were included in the equation. The mean absolute percentage error (MAPE) of China-CPET-HR peak is 9.04%, with an adjusted coefficient of determination (R 2) of 0.399. For the China-CPET-HR VT1 formula, the MAPE is 7.32% and the adjusted R 2 is 0.509. The %HR peaks of the FOX, TANAKA, KETEYIAN, and China-CPET-HR peak formulas are 82 ± 11%, 79 ± 11%, 105 ± 13%, and 100 ± 11%, respectively. Conclusion:Based on CPET data from CHD patients, we developed prediction equations for HR peak and HR VT1. The prediction accuracy of these equations is significantly higher than others, which helps to formulate accurate individualized exercise prescriptions and rehabilitation training guidance for CHD patients.

Objective To provide a basis for further optimizing the diagnosis and treatment strategies of severe and critical corona virus disease 2019 (COVID-19) by investigating and analyzing the epidemiological and clinical characteristics of the death cases. Methods The epidemiological and clinical characteristics of 47 death cases obtained from Huoshenshan Hospital in Whuhan, Hubei Province were retrospectively analyzed. Results All the patients developed initial symptoms in Wuhan. The time from onset to admission was (12.60±5.60) days. Most of them were male (68.09%) with non-nosocomial infection (91.49%), advanced age (>60 years, 89.36%). Over half of the cases (51.06%) reported a history of contact with suspected or confirmed patients, and comorbidity of chronic diseases (70.21%). Multiple organ dysfunction syndrome (MODS) occurred in 29 cases (61.70%) with heart failure (51.06%) and renal failure (36.17%). The main clinical symptoms included fever, fatigue, dyspnea and cough. At admission,most cases were severe (55.32%) or critical (42.55%), and the in-hospital survival was longer for the severe than for the critical (P=0.02). 76.59% of the patients received invasive mechanical ventilation, and they had a longer in-hospital survival than those with non-invasive mechanical ventilation (P<0.05). Conclusions This group of cases occurred during the peak of the COVID-19 outbreak in China, characterized by male, elder and history of chronic diseases. Acute respiratory distress syndrome (ARDS) caused by COVID-19 was responsible for patients' death, and MODS manifestated by heart and kidney failure also implicated in the process. Disease severity and invasive mechanical ventilation were related to in-hospital survival. DOI: 10.11855/j.issn.0577-7402.2020.05.02

Background: Coronavirus disease 2019 (COVID-19) patients with a larger ratio of pneumonia lesions are more likely to progress to acute respiratory distress syndrome and death. This study aimed to investigate the relationship of baseline parameters with pneumonia lesions on admission, as quantified by an artificial intelligence (AI) algorithm using computed tomography (CT) images. Methods: This retrospective study quantitatively assessed lung lesions on CT using an AI algorithm in 1630 consecutive patients confirmed with COVID-19 on admission and classified the patients into none (0%), mild (>0–25%), intermediate (>25–50%), and severe (>50%) groups, according to the lesion ratio of the whole lung. A multivariate linear regression model was established to explore the relationship between the lesion ratio and laboratory parameters. The baseline parameters associated with lung lesions, including demographics, initial symptoms, and comorbidities, were determined using a multivariate ordinal regression model. Results: The 1630 patients confirmed with COVID-19 had a median whole lung lesion ratio of 4.1%, and the right lower lung lobe had the most lesions among the five lung lobes based on the evaluation of CT using AI algorithm. The whole lung lesion ratio was associated with the levels of plasma fibrinogen (r=0.280, p<0.001), plasma D-dimer (r=0.248, p<0.001), serum α-hydroxybutyrate dehydrogenase (r=0.363, p<0.001), serum albumin (r=-0.300, p<0.001), and peripheral blood leukocyte count (r=0.194, p<0.001). Among the four patients groups categorised by whole lung lesion ratio, the highest frequency of cough (p<0.001) and shortness of breath (p<0.001) were found in the severe group, and the highest frequency of hypertension (p<0.001), diabetes (p<0.001) and anemia (p=0.039) were observed in the intermediate group. Based on baseline ordinal regression analysis, cough (p=0.009), shortness of breath (p<0.001), hypertension (p=0.002), diabetes (p=0.005), and anemia (p=0.006) were independent risk factors for more severe lung lesions. Conclusions: Based on AI-enabled CT quantitation , patients with initial symptoms of cough/shortness of breath, or with comorbidities of hypertension, diabetes, or anemia, had a higher risk for more severe lung lesions on admission in COVID-19 patients.

Background: Since December, 2019, the outbreak of COVID-19 caused by a novel betacoronavirus is still accelerating throughout the world. Majority of infected individuals suffered from mild pneumonia, while a proportion of patients would progress to severe pneumonia. Therefore, it is vital to identify the patients at high risk of disease progression. Methods: In this retrospective, multicentre cohort study, laboratory confirmed COVID-19 patients from Huoshenshan hospital and Tongji Taikang hospital (Wuhan, China) were included. Clinical features with significant difference between severe and nonsevere group were screened out by univariate analysis. Then, these features were used to generate predictive models by using machine learning. Two test sets from two hospitals were established to evaluate the predictive performance of the trained models, respectively. Moreover, a software was developed for prediction in clinical practice. Findings: A total of 455 patients were included in this study. Twenty-one features with significant difference between severe and nonsevere group were selected in training and validation set for modeling. The optimal subset with 11 features in KNN model obtained the highest area under curve (AUC) value (0.9484, 95%CI: 0.924-0.973) among the four models in the validation set. D-dimer, CRP, and age showed the top three important features in the optimal feature subsets selected by K-fold cross validation. The highest AUC value (0.9594, 95%CI: 0.920-0.999) was obtained by support vector machine (SVM) model in test set from Huoshenshan hospital. A software for predicting disease progression based on machine learning was developed for clinical practice.Interpretations: The predictive models were successfully established based on machine learning, and achieved satisfied predictive performance of disease progression with optimal feature subsets. The predictive models can be conveniently used in clinical practice.Funding Statement: This work was supported by the National Natural Science Foundation of China (81700483), Chongqing Research Program of Basic Research and frontier technology (cstc2017jcyjAX0302), and Army Medical University frontier technology Research Program (2019XLC3051). Declaration of Interests: The authors declare that there is no conflict of interest.Ethics Approval Statement: This study was approved by the ethics committee of Wuhan Huoshenshan hospital (epicenter Wuhan, China). As all subjects were anonymized in this retrospective study, the written informed consent was waived due to urgent need.

Combined with the existing literature and clinical guidelines, as well as our clinical experience, we stratified the pneumonia patients with novel coronavirus infection into light, common, heavy and critical types, who were given symptomatic treatment, anti-viral and anti-infection treatment, immunotherapy and timely treatment of complications. According to the dialectical treatment of traditional Chinese medicine theory, the disease is "wet and warm", which is caused by internal and external causes, including internal dampness, heat, blood stasis and phlegm, and external pathogen of epidemic disease. According to the clinical stages, corresponding traditional Chinese medicine treatment was carried out. For the discharged patients, corresponding treatment and follow-up were also performed according to their clinical symptoms and imaging results.