Clinical Value of Computational Angiography-derived Fractional Flow Reserve in Stable Coronary Artery Disease.

The utilization of FFR remains low. Our study evaluated the per-vessel prognostic value of computational pressure-flow dynamics-derived FFR (caFFR) among patients with stable coronary artery disease. A total of 3329 vessels from 1308 patients were included and analysed. They were stratified into ischaemic (caFFR ≤ 0.8) and non-ischaemic (caFFR > 0.8) cohorts, and the associations between PCI and outcomes were evaluated. The third cohort comprised all included vessels, and the associations between treatment adherent-to-caFFR (PCI in vessels with caFFR ≤ 0.8 and no PCI in vessels with caFFR > 0.8) and outcomes were evaluated. The primary outcome was VOCE, defined as a composite of vessel-related cardiovascular mortality, non-fatal myocardial infarction, and repeat revascularization. PCI was associated with a lower 3-year risk of VOCE in the ischaemic cohort (HR, 0.44; 95% CI, 0.26-0.74; P = 0.002) but not in the non-ischaemic cohort. The risk of VOCE was lower in the adherent-to-caFFR group (n = 2649) (HR, 0.69; 95% CI, 0.48-0.98; P = 0.039). A novel index that uses coronary angiography images to estimate FFR may have substantial clinical value in guiding management among patients with stable coronary artery disease.

A Multimodal Deep Learning Approach to Predicting Systemic Diseases from Oral Conditions.

Background: It is known that oral diseases such as periodontal (gum) disease are closely linked to various systemic diseases and disorders. Deep learning advances have the potential to make major contributions to healthcare, particularly in the domains that rely on medical imaging. Incorporating non-imaging information based on clinical and laboratory data may allow clinicians to make more comprehensive and accurate decisions. Methods: Here, we developed a multimodal deep learning method to predict systemic diseases and disorders from oral health conditions. A dual-loss autoencoder was used in the first phase to extract periodontal disease-related features from 1188 panoramic radiographs. Then, in the second phase, we fused the image features with the demographic data and clinical information taken from electronic health records (EHR) to predict systemic diseases. We used receiver operation characteristics (ROC) and accuracy to evaluate our model. The model was further validated by an unseen test dataset. Findings: According to our findings, the top three most accurately predicted chapters, in order, are the Chapters III, VI and IX. The results indicated that the proposed model could predict systemic diseases belonging to Chapters III, VI and IX, with AUC values of 0.92 (95% CI, 0.90-94), 0.87 (95% CI, 0.84-89) and 0.78 (95% CI, 0.75-81), respectively. To assess the robustness of the models, we performed the evaluation on the unseen test dataset for these chapters and the results showed an accuracy of 0.88, 0.82 and 0.72 for Chapters III, VI and IX, respectively. Interpretation: The present study shows that the combination of panoramic radiograph and clinical oral features could be considered to train a fusion deep learning model for predicting systemic diseases and disorders.

Risk of sepsis and pneumonia in patients initiated on SGLT2 inhibitors and DPP-4 inhibitors.

AIM: The organ protective effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors may be beneficial against infectious complications. This real-world study aims to compare the risk of pneumonia and sepsis between SGLT2 inhibitors and dipeptidyl peptidase 4 (DPP-4) inhibitors in patients with type 2 diabetes. METHODS: Using a territory-wide clinical registry in Hong Kong (Clinical Data Analysis and Reporting System [CDARS]), we included patients initiated on SGLT2 inhibitors or DPP-4 inhibitors between January 01, 2015 and December 31, 2019 through 1:2 propensity score matching. The primary outcomes were incident events of pneumonia, sepsis and the related mortality. Cox proportional hazards analysis was used to compare the risk of incident pneumonia and sepsis for SGLT2 inhibitors versus DPP-4 inhibitors. RESULTS: After propensity score matching, 10,706 new users of SGLT2 inhibitors and 18,281 new users of DPP-4 inhibitors were included. The mean age of all eligible subjects were 60 years (SD 11.07) and 61.1% were male. There were 309 pneumonia events [incidence rate per 1000 person-years (IR) = 11.38] among SGLT2 inhibitors users and 961 events (IR = 20.45) among DPP-4 inhibitors users, with lower risk of pneumonia among SGLT2 inhibitors users (adjusted HR 0.63 [95%CI 0.55-0.72], p<0.001). Similarly, SGLT2 inhibitors users had lower incidence of sepsis [164 (IR=6.00) vs. 610 (IR=12.88) events] as well as associated risk of incident sepsis (HR 0.52 [95% CI 0.44-0.62], p<0.001), compared to DPP-4 inhibitors users. Outcome analyses showed that SGLT2 inhibitors were associated with lower risk of pneumonia-related death (HR 0.41 [95%CI 0.29-0.58], p<0.001), sepsis-related death (HR 0.39 [95%CI 0.18-0.84], p<0.05), and infection-related death (HR 0.43 [95%CI 0.32-0.57], p<0.001), compared to DPP-4 inhibitors users. Results were consistent when stratified by age, sex, pre-existing cardiovascular disease, and type of SGLT2 inhibitors. CONCLUSION: We provide real-world evidence that irrespective of age, sex, prior-existing cardiovascular disease, or type of SGLT2 inhibitors used, patients with type 2 diabetes initiated on SGLT2 inhibitors have lower incidence of pneumonia and sepsis as well as mortality risk associated with pneumonia, sepsis, and infectious diseases, compared with those initiated on DPP-4 inhibitors.

Periodontitis links to concurrent systemic comorbidities among 'self-perceived health' individuals.

BACKGROUND AND OBJECTIVE: Our recent work shows that periodontitis experience reflects host susceptibility to the onset of multiple systemic diseases and conditions. This cross-sectional study further investigated whether and to what extent the existing periodontitis could reflect the concurrent presence of inflammatory comorbidities among 'self-perceived health' individuals. MATERIALS AND METHODS: There were 115 'self-perceived health' adults who completed a questionnaire on demographic characteristics and lifestyles. Twenty medical diagnostic tests were then performed to detect eight common systemic diseases and conditions. Meanwhile, full-mouth periodontal examination was undertaken, and the subjects were classified as two subgroups with or without Generalized Severe Periodontitis (Stages III/IV, generalized). The interlink of periodontal status and concurrent systemic comorbidities was assessed. RESULTS: 98.3% (113/115) of the subjects exhibited at least one undiagnosed systemic disease/disorder. Of them, 52.2% (59/113) and 47.8% (54/113) concurrently presented with 1-5 or ≥6 abnormal test results, respectively. Overall, 96.5% (111/115) had periodontitis. Generalized Severe Periodontitis was present in 43.2% (48/111) of the periodontitis patients, and it was significantly associated with the profiles of abnormal test results after adjusting potential confounders (abnormal test results 1-5 vs ≥6; OR: 3.23, p = .012). CONCLUSIONS: The present study shows that existing severe periodontitis could well reflect the concurrent presence of multiple inflammatory comorbidities. Oral and medical professionals can play proactive roles in enhancing health awareness and healthcare, through strong collaboration and teamwork.

Association between adipocyte fatty acid-binding protein with left ventricular remodelling and diastolic function in type 2 diabetes: a prospective echocardiography study.

BACKGROUND: The relationship between adipocyte fatty acid-binding protein (AFABP) and cardiac remodelling has been reported in cross-sectional studies, although with conflicting results. Type 2 diabetes mellitus (T2DM) is associated with left ventricular (LV) hypertrophy and diastolic dysfunction, as well as elevated circulating AFABP levels. Here we investigated prospectively the association between AFABP with the longitudinal changes of cardiac remodelling and diastolic dysfunction in T2DM. METHODS: Circulating AFABP levels were measured in 176 T2DM patients without cardiovascular diseases (CVD) at baseline. All participants received detailed transthoracic echocardiography both at baseline and after 1 year. Multivariable linear and Cox regression analyses were used to evaluate the associations of circulating AFABP levels with changes in echocardiography parameters and incident major adverse cardiovascular events (MACE), respectively. RESULTS: The median duration between baseline and follow-up echocardiography assessments was 28 months. Higher sex-specific AFABP quartiles at baseline were associated with increase in LV mass and worsening of average E/e' (all P < 0.01). Multivariable linear regression demonstrated that AFABP in the highest quartile was independently associated with both increase in LV mass (ß = 0.89, P < 0.01) and worsening of average E/e' (ß = 0.57, P < 0.05). Moreover, multivariable Cox regression analysis showed that elevated baseline circulating AFABP level independently predicted incident MACE (HR 2.65, 95% CI 1.16-6.05, P < 0.05) after adjustments for age, sex, body mass index, glycated haemoglobin, hypertension, dyslipidemia and presence of chronic kidney disease. CONCLUSION: Circulating AFABP level at baseline predicted the development of LV hypertrophy, diastolic dysfunction and MACE in T2DM patients without CVD.