Zhigang Pan

While point-of-care ultrasound (POCUS) has been integrated into daily practice by general practitioners (GPs) in some countries, there is a paucity of literature documenting its use by Chinese GPs. Additionally, artificial intelligence (AI)-assisted diagnosis is increasingly applied to imaging equipment. This study aims to explore the diagnostic performance of GPs in carotid plaque detection using AI-enhanced POCUS after systematic training. This diagnostic accuracy trial was conducted in Shanghai, where 7 GPs received systematic training then recruited patients with high atherosclerotic cardiovascular disease risk during outpatient visits for free carotid plaque screening. We comprehensively evaluated their diagnostic performance using 2 approaches: per-patient (ie, assessed whether patients had any carotid plaque) and per-vessel (ie, determined plaque presence in any carotid vessel). Evaluation metrics included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the κ statistic for agreement. A total of 169 patients (mean age 69.6 years [SD 5.76]; 37.9% male) were included in this study. At the per-patient level, sensitivity was 0.87 (95% CI, 0.80-0.95); specificity was 0.91 (95% CI, 0.86-0.97); PPV was 0.89 (95% CI, 0.82-0.96); NPV was 0.89 (95% CI, 0.83-0.96); and the κ value was 0.78 (95% CI, 0.69-0.88), indicating high agreement with the gold standard. The diagnostic performance evaluated at the per-vessel level was comparable to that at the per-patient level. Our findings indicate that, after systematic training, GPs can effectively perform carotid plaque screening using AI-enhanced POCUS within a clinical workflow in high-risk elderly populations.

General practitioners’ (GPs’), attitudes to and acceptance of GP-CDSS-2.0, and the influence of GP-CDSS-2.0, a clinical decision support system

Background: This study aimed to explore the impact of carotid ultrasound finding interpretation by general practitioners (GPs) on patients’ lipid management. Methods: This study is an observational cohort study involving elderly patients aged ≥ 60 years with elevated risk of atherosclerotic cardiovascular disease (ASCVD). Participants were divided into two groups: image interpretation group and routine care group, and then followed for 3 months. Our primary outcome was to explore the between‐group differences in low‐density lipoprotein (LDL) changes between the two groups at baseline and after 3 months of follow‐up. Secondary outcomes included between‐group differences in changes of total cholesterol (TC), triglyceride (TG), and high‐density lipoprotein (HDL). The Generalized Estimating Equation model was used to calculate interaction p values, β coefficients, and their 95% CIs. Results: This study enrolled 338 participants, with 169 in the image interpretation group (age 69.6 ± 5.76 years) and 169 in the routine care group (age 71.59 ± 5.26 years). The between‐group difference in LDL change from baseline to follow‐up was statistically significant (interaction p =  0.028), with a β coefficient of −0.38 (95% CI −0.71, −0.04), indicating that the image interpretation group had a 0.38 mmol/L greater LDL reduction than the routine care group. This remained significant after adjusting for confounders (interaction p =  0.025, β = −0.38, 95% CI −0.70, −0.05). There were no significant between‐group differences observed against changes in TC, HDL, and TG. Conclusion: This study indicated that adding image interpretation of carotid ultrasound results to routine care by GPs significantly improves lipid management in elderly patients with elevated ASCVD risk.

China’s primary care was required to improve the quality of atrial fibrillation (AF) management by government. The aim of the study was to investigate whether a clinical decision support system for non-valvular AF (GP-CDSS-2.0) affected appropriate anticoagulation of patients with AF in primary care. The study was a cluster randomized controlled study, carried out in 13 community health centers in Shanghai, China, as 1 community health center in the intervention group withdraw the study. Patients with AF who consented to take part in and finish the follow-up were enrolled. We allocated the 14 community health centers into the intervention and control groups using stratified randomization method at a ratio of 1 to 1. General practitioners in the intervention group treated patients using GP-CDSS-2.0, while those in the control group provided usual care. The proportion of appropriate anticoagulation was the primary outcome. The secondary outcomes and safety outcomes mainly included the transition rate of patients’ anticoagulation therapies and the incidences of bleeding and composite adverse outcomes. 405 patients were included in the analyses of primary outcome, with 182 in the intervention group and 223 in the control group. After 1 year’s follow-up, the proportion of appropriate anticoagulation in the intervention group was higher than that in the control group (55.2% in the intervention group and 35.9% in the control group; relative risk (RR) = 1.441, 95% CI, 1.429–1.453, p < 0.001). The proportion of appropriate anticoagulation in the intervention group and the control group increased by 20% and 0.9%, respectively, compared with those at baseline (35.2% in the intervention group and 35.0% in the control group). More subjects in the intervention group experienced a correct transition of their anticoagulation therapies during the study (RR = 2.528, 95% CI, 1.276–5.008, p = 0.008). The intervention group showed a tendency of less bleeding and composite adverse outcomes. The utilization of GP-CDSS-2.0 in primary care had the potential to raise the proportion of appropriate anticoagulation among patients with AF. Additionally, it could assist them in transitioning to proper anticoagulation treatments. Registration Number ChiCTR2100052307. Registration Date 22/11/2021.

Background: Post-stroke dysphagia (PSD) affects up to 76% of stroke patients and increases aspiration pneumonia (AP) risk, leading to higher mortality among older survivors. Current risk assessment tools for AP in PSD patients lack precision.Methods: We conducted a retrospective study of 7,134 stroke patients admitted to Jinshan Hospital from 2019 to 2023. We used multivariable logistic regression to identify AP predictors and constructed a nomogram model using these predictors. Model performance was evaluated using bootstrap resampling, calibration, and decision curve analysis. Internal validation was conducted on 30% of cases, and external validation was performed on 500 PSD patients from community health centers.Results: Among 2,663 PSD patients, 578 (21.7%) developed AP. Independent predictors included age, stroke severity, hyperlipidemia, hyperhomocysteinemia, heart failure, CRP, WBC, neutrophil ratio, Hb, FBG, prealbumin, BNP, and serum sodium. The nomogram model showed excellent discrimination (C-index: 0.885) and good agreement between predicted and observed AP probabilities. It provided net benefit across various threshold probabilities.Conclusion: Our study developed the first dedicated nomogram for AP risk prediction in PSD patients, incorporating novel predictor combinations and demonstrating robust validation across multi-center cohorts. This fills an important clinical need under community conditions by enabling early identification of high-risk PSD patients using routinely available clinical variables.

Introduction: The community-based management of type 2 diabetes in China remains suboptimal. Incorporating family support presents a promising approach to enhancing facilitators and addressing barriers to effective diabetes self-management, particularly in the Chinese context where familial relationships and collective family responsibility play a central role in health-related decision-making and daily life. We aimed to validate a family support-based self-management behavioral intervention program, Systematic evaluation, core education, self-directed planning, triple feedback, and develop a habit program (SeCe-STRIVE). The program comprises 5 modules: systematic evaluation, core education, self-directed planning, triple feedback, and habit development. Research Design and Methods: We conducted a community-based, single-center, parallel randomized controlled trial in the Xietu community of Xuhui District, Shanghai. Eligible patients were randomly assigned to either the intervention or control group. Participants in the intervention group received the SeCe-STRIVE health management program based on family support, while those in the control group received the community-based follow-up management program for type 2 diabetes. The primary outcome measured was the total self-management behavior score and its changes across various dimensions from baseline. Secondary outcomes included family support, family function, diabetes knowledge, quality of life, self-efficacy, and biochemical markers. Post-intervention changes in both primary and secondary outcomes relative to baseline were analyzed using analysis of covariance. Results: The study ultimately included 113 participants in the intervention group and 112 in the control group. The intervention group’s total self-management behavior score improved by 12.74 (95% CI = 10.07, 15.40, P < .001) points more than the control group. Across all dimensions of self-management behaviors, the intervention group showed greater improvement, including diet, exercise, blood glucose monitoring, foot care, and medication adherence. Participants with low family support exhibited greater improvements in self-management behaviors compared to those with high family support. The intervention group demonstrated significant improvements in family support, family function, diabetes knowledge, and self-efficacy. Although quality of life and biochemical indicators of glycated hemoglobin, fasting blood glucose, and blood lipids showed a decreasing trend, the differences were not statistically significant. Conclusions: Compared to the control group, patients with type 2 diabetes who received the SeCe-STRIVE program intervention showed significant improvements in self-management behaviors, accompanied by enhancements in family support, family functioning, and self-efficacy. Further large-scale, multi-center trials with longer follow-up periods are needed to confirm these results. Trial Registration: ChiCTR, ChiCTR2200060416. Registered 01 June 2022, https://www.chictr.org.cn/showproj.html?proj=167086

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations.Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits.Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01).Conclusion: Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objectives: This study aimed to evaluate the screening performance of COPD-PS questionnaire, COPD-SQ questionnaire, peak expiratory flow (PEF), COPD-PS questionnaire combined with PEF, and COPD-SQ questionnaire combined with PEF for chronic obstructive pulmonary disease (COPD).Methods: This was a cross-sectional study. We distributed self-designed surveys and COPD screening scales (COPD-PS questionnaire and COPD-SQ questionnaire) to residents who underwent physical examination in five community health centers in Haicang District, Xiamen City, from February 2023 to May 2023, and measured their lung function and PEF with a portable device. We used logistic regression to obtain the coefficients of COPD-PS questionnaire, COPD-SQ questionnaire, and PEF, and plotted the receiver operating characteristic curves of each tool for diagnosing COPD and moderate-to-severe COPD. We evaluated and compared the optimal cut-off points and scores of sensitivity, specificity, Youden index, and area under the curve (AUC) values, and assessed the screening efficiency of different methods.Results: Of the 3,537 residents who completed the COPD-SQ questionnaire, COPD-PS questionnaire, and spirometry, 840 were diagnosed with COPD. We obtained the coefficients of COPD-PS questionnaire combined with peak expiratory flow (PEF), and COPD-SQ questionnaire combined with PEF, by logistic regression as −0.479-0.358 × PEF +0.321 × COPD-PS score and − 1.286-0.315 × PEF +0.125 × COPD-SQ score, respectively. The sensitivity of diagnosing COPD by COPD-SQ questionnaire, COPD-PS questionnaire, PEF, COPD-PS questionnaire combined with PEF, and COPD-SQ questionnaire combined with PEF were 0.439, 0.586, 0.519, 0.586, 0.612 respectively, and the specificity were 0.725, 0.621, 0.688, 0.689, 0.663 respectively, with ROC values of 0.606 (95%CI: 0.586–0.626), 0.640 (0.619–0.661), 0.641 (0.619–0.663), 0.678 (0.657–0.699), 0.685 (0.664–0.706) respectively. The sensitivity of diagnosing GOLD II and above by COPD-SQ questionnaire, COPD-PS questionnaire, PEF, COPD-PS questionnaire combined with PEF, and COPD-SQ questionnaire combined with PEF were 0.489, 0.620, 0.665, 0.630, 0.781 respectively, and the specificity were 0.714, 0.603, 0.700, 0.811, 0.629 respectively, with ROC values of 0.631 (95%CI: 0.606–0.655), 0.653 (0.626–0.679), 0.753 (0.730–0.777), 0.784 (0.762–0.806), 0.766 (0.744–0.789) respectively.Conclusion: Our study found that the accuracy of COPD screening by COPD-SQ questionnaire and COPD-PS questionnaire can be improved by combining the results of PEF. The screening performance of COPD-SQ questionnaire combined with PEF is relatively better. In future research, further studies are needed to optimize the performance of screening tools and understand whether their use will affect clinical outcomes.

The burden of type 2 diabetes mellitus (T2DM) in China continues to escalate. To investigate the impact of family support system on the self-management behaviour of patients with T2DM. In this cross-sectional study, 1,042 participants were sampled using a multistage stratified probability-proportionate-to-size method. On-site interviews were conducted using a structured questionnaire administered by 122 family doctors from 13 community health service centres in 8 administrative districts. A structural equation model was established to investigate the impact of individual factors and family support system on the self-management behaviour of T2DM patients. Statistical analysis was performed using the SPSS 25.0 software. The self-management behaviour pass rate among individuals with T2DM was 40.9%. In terms of individual factors, those with a high school education level or above demonstrated higher self-management behaviour scores than those with a junior high school education level or lower. The structural equation model revealed a path of interactions among individual factors, family support system-related factors, and the self-management behaviour of patients with T2DM: Family function → Family support → Patients’ self-management behaviour → Quality of life. The coefficient of the direct path from family function to family support was 0.74 (P < 0.001), while the coefficient of the direct path from family support to self-management behaviour was 0.68 (P = 0.001). There is significant room for improvement in the self-management behaviour of T2DM patients in China. Family function can enhance the self-management behaviour of T2DM patients by promoting family support.

Background: To investigate whether anemia is associated with incident cardiovascular events and all-cause death among participants who received intensive blood pressure (BP) treatment in the Systolic Blood Pressure Intervention Trial (SPRINT). Methods: A total of 4394 participants who received intensive BP control (systolic BP <120 mmHg) in SPRINT were included. Anemia status was self-reported. Our primary outcome was a composite of cardiovascular events, and the secondary outcome was all-cause death. Cox regression was used to compare the incidence of outcomes between participants with anemia and non-anemia. In order to balance the baseline characteristics between the 2 groups, inverse probability of treatment weighting (IPTW) was applied. Hazard ratios (HRs), along with 95% confidence intervals (CIs), were then calculated. Results: There were 4394 participants who received intensive BP control (537 participants with anemia). Participants with anemia were older (mean age 68.86 versus 67.75, p = 0.01) and more likely to be female (64.8% versus 31.8%, p < 0.001). The presence of anemia was strongly associated with composite cardiovascular events after adjusting for potential confounders (HR 1.66, 95% CI 1.18–2.34, p = 0.004). The association remained statistically significant even in the population after IPTW (HR 1.55, 95% CI 1.06–2.27, p = 0.024). The secondary outcome revealed that participants with anemia had a higher rate of all-cause death compared to those without anemia. The HR of all-cause death for participants with anemia was 1.61 (95% CI 1.00–2.57, p = 0.049) in the population after IPTW. Conclusions: Anemia appears to be an independent risk factor for composite cardiovascular events and all-cause death among participants who received intensive BP control in SPRINT. Clinical Trial Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01206062. All SPRINT anonymized data can be found at the National Heart, Lung and Blood Institute (NHLBI) Biologic Specimen and Data Repository (https://biolincc.nhlbi.nih.gov/home/).