ISSN 1671-5411 CN 11-5329/R

2022 Vol. 19, No. 12

Angiography-derived radial wall strain predicts coronary lesion progression in non-culprit intermediate stenosis
Zhi-Qing WANG, Bo XU, Chun-Ming LI, Chang-Dong GUAN, Yue CHANG, Li-Hua XIE, Su ZHANG, Jia-Yue HUANG, Patrick W Serruys, William Wijns, Liang-Long CHEN, Sheng-Xian TU
2022, 19(12): 937-948. doi: 10.11909/j.issn.1671-5411.2022.12.004
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 BACKGROUND  Intermediate coronary lesions (ICLs) are highly prevalent but ported mixed prognosis. Radial strain has been associated with plaque vulnerability, yet its role in predicting lesion progression is largely unknown. The purpose of this study was to determine the predictive value of angiography-derived radial wall strain (RWS) for progression of untreated non-culprit ICLs.  METHODS  Post-hoc analysis was conducted in a study cohort including 603 consecutive patients with 808 ICLs identified at index procedure with angiographic follow-up of up to two years. RWS analysis was performed on selected angiographic frames with minimal foreshortening and vessel overlap. Lesion progression was defined as ≥ 20% increase in percent diameter stenosis.  RESULTS  Lesion progression occurred in 49 ICLs (6.1%) with a median follow-up period of 16.8 months. Maximal RWS (RWSmax), frequently located at the proximal and throat plaque regions, distinguished progressive ICLs from silent ones. The largest area under the curve value of 0.75 (95% CI: 0.67–0.82, P < 0.001) was reached at the optimal RWSmax cutoff value of > 12.6%. According to this threshold, 178 ICLs were classified as having a high strain pattern. Exposure to a high strain amplitude with RWSmax > 12.6% was independently associated with an increased risk of lesion progression (adjusted HR = 6.82, 95% CI: 3.67–12.66, P < 0.001).  CONCLUSIONS  Assessment of RWS from coronary angiography is feasible and provides independent prognostic value in patients with untreated ICLs.
Association of serum complement C1q with cardiovascular outcomes among patients with acute coronary syndrome undergoing percutaneous coronary intervention
Qiu-Xuan LI, Xiao-Teng MA, Qiao-Yu SHAO, Zhi-Qiang YANG, Jing LIANG, Li-Xia YANG, Dong-Mei SHI, Yu-Jie ZHOU, Zhi-Jian WANG
2022, 19(12): 949-959. doi: 10.11909/j.issn.1671-5411.2022.12.001
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 OBJECTIVE  To determine the association of serum complement C1q levels with cardiovascular outcomes among patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI), and evaluate the value of C1q modified by high-sensitivity C-reactive protein (hs-CRP) levels as an independent predictor.  METHODS  As a single-center prospective observational study, we analyzed 1701 patients who had received primary or elective PCI for ACS at Beijing Anzhen Hospital, Capital Medical University, Beijing, China between June 1, 2016 and November 30, 2017. The associations of C1q modified by hs-CRP with major adverse cardiovascular events (MACE) were determined in survival analysis.  RESULTS  Patients with the lowest C1q tertile had the highest cumulative risk of MACE (log-rank P = 0.007). In fully adjusted Cox regression models, stratifying the total population according to hs-CRP dichotomy, C1q was significantly associated with MACE in patients with hs-CRP levels less than 2 mg/L but not in those with 2 mg/L or more (Pinteraction = 0.02). In patients with hs-CRP levels less than 2 mg/L, with the lowest C1q tertile as reference, the risk of MACE was reduced by 40.0% in the middle C1q tertile [hazard ratio (HR) = 0.600, 95% CI: 0.423–0.852, P = 0.004] and by 43.9% in the highest C1q tertile (HR = 0.561, 95% CI: 0.375–0.840, P = 0.005).  CONCLUSIONS  Serum complement C1q is significantly associated with cardiovascular outcomes in patients with ACS undergoing PCI, only when hs-CRP levels are less than 2 mg/L. This finding implicates the usefulness of C1q for the risk stratification in ACS patients with reduced systemic inflammation.
Development and validation of a nomogram predicting one-year mortality in patients undergoing percutaneous coronary intervention
Jing-Jing SONG, Yu-Peng LIU, Wen-Yao WANG, Jie YANG, Jun WEN, Jing CHEN, Jun GAO, Chun-Li SHAO, Yi-Da TANG
2022, 19(12): 960-969. doi: 10.11909/j.issn.1671-5411.2022.12.003
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 OBJECTIVE  To formulate a nomogram to predict the risk of one-year mortality after percutaneous coronary intervention (PCI) based on a large-scale real-world Asian cohort.  METHODS  This study cohort included consecutive patients undergoing PCI in the National Center for Cardiovascular Diseases of China. The endpoint was all-cause mortality. Least absolute shrinkage and selection operator Cox regression and backward stepwise regression were used to select potential risk factors. A nomogram based on the predictors was accordingly constructed to predict one-year mortality. The performance of the nomogram was evaluated. Patients were stratified into low-, intermediate- and high-risk groups according to the tertile points in the nomogram and compared by the Kaplan-Meier analysis.  RESULTS  A total of 9603 individuals were included in this study and randomly divided into the derivation cohort (60%) and the validation cohort (40%). Six variables were selected to formulate the nomogram, including age, renal insufficiency, cardiac dysfunction, previous cerebrovascular disease, previous PCI, and TIMI 0–1 before PCI. The area under the curve of this nomogram regarding one-year mortality risks were 0.792 and 0.754 in the derivation cohort and validation cohort, respectively. Kaplan-Meier curve successfully stratified the patients according to three risk groups. This nomogram calibrated well and exhibited satisfactory clinical utility in the decision curve analysis.  CONCLUSIONS  This study developed and validated a simple-to-use nomogram predicting one-year mortality risk in Asian patients undergoing PCI and could help clinicians make risk-dependent decisions.
Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis
Xin-Mu LI, Xin-Yi GAO, Gary Tse, Shen-Da HONG, Kang-Yin CHEN, Guang-Ping LI, Tong LIU
2022, 19(12): 970-980. doi: 10.11909/j.issn.1671-5411.2022.12.002
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 BACKGROUND  The electrocardiogram (ECG) is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The application of artificial intelligence (AI) has contributed to clinical practice in terms of aiding diagnosis, prognosis, risk stratification and guiding clinical management. The aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG.  METHODS  We searched Embase, PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Random-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were plotted. Subgroup analysis was performed. Heterogeneity and the risk of bias were also assessed.  RESULTS  A total of 11 studies including 104,737 subjects were included. The area under the curve for HF diagnosis was 0.986, with a corresponding pooled sensitivity of 0.95 (95% CI: 0.86–0.98), specificity of 0.98 (95% CI: 0.95–0.99) and diagnostic odds ratio of 831.51 (95% CI: 127.85–5407.74). In the patient selection domain of QUADAS-2, eight studies were designated as high risk.  CONCLUSIONS  According to the available evidence, the incorporation of AI can aid the diagnosis of HF. However, there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.
Mild haemoglobin drop and clinical outcomes in acute coronary syndrome patients: finding from the BleeMACS registry
Ze-Kun ZHANG, Yan YAN, Si-Yi LI, Sergio Raposeiras-Roubín, Emad Abu-Assi, José P. Henriques, Fabrizio D’Ascenzo, Jorge Saucedo, Wei GONG, Shao-Ping NIE
2022, 19(12): 981-989. doi: 10.11909/j.issn.1671-5411.2022.12.005
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 BACKGROUND  Haemoglobin drop is common in acute coronary syndrome (ACS) patients and correlates with poor prognosis. However, the association between mild haemoglobin drop and adverse clinical outcome remains insufficiently investigated. This study aimed to examine the association between in-hospital haemoglobin drop and risk for adverse clinical outcomes in ACS patients, especially those with mild drop.  METHODS  Included patients from the BleeMACS (Bleeding complications in a Multicenter registry of patients discharged after an Acute Coronary Syndrome) registry were categorized into three groups by the presence and amount of in-hospital haemoglobin drop (non-drop, mild drop and severe drop). The cut-off point between mild drop and severe drop is ≥ 3 g/dL. Multivariate Cox regression was used to assess the association between haemoglobin drop and major adverse cardiac endpoints (MACE). Patients taking potent P2Y12 inhibitors were selected for the additional analysis. Propensity score matching was used to avoid selective bias in the additional analysis.  RESULTS  Of 6911 patients, 4949 patients (71.6%) experienced in-hospital haemoglobin drop. Compare with non-drop group, patients with haemoglobin drop had higher risk of MACE [adjusted hazard ratio (HR) = 1.36, 95% CI: 1.03–1.80 for mild drop group; adjusted HR = 1.70, 95% CI: 1.07–2.68 for severe drop group]. Patients in mild drop group were less likely to receive potent P2Y12 inhibitors at discharge (mild drop group vs. severe drop group vs. non-drop group: 10.9% vs. 10.7% vs. 23.8%). After propensity score matching adjustment among patients with potent P2Y12 inhibitors, patients in mild drop group were not associated with an increased risk of MACE than those in non-drop group (adjusted HR = 1.52, 95% CI: 0.49–4.72).  CONCLUSIONS  In-hospital haemoglobin drop was common in ACS patients and associated with a higher risk for adverse events. Reduced prescription for potent P2Y12 inhibitors may be responsible for poor prognoses among patients with mild haemoglobin drop.
Aortic valve leaflet disruption techniques in transcatheter aortic valve replacement
Brian Shin, David Li, Hong LIU
2022, 19(12): 990-994. doi: 10.11909/j.issn.1671-5411.2022.12.006
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With continued technological advancement and technical improvement of transcatheter aortic valve replacement (TAVR), it has become a desirable treatment option for aortic valve stenosis. Its minimally invasive approach compared to surgical aortic valve replacement offers the treatment to a broader patient population, mainly non-surgical candidates. A feared complication of TAVR is the occlusion of coronary artery ostium by the native aortic valve leaflet due to its displacement by the expanded transcatheter valve. Bioprosthetic or native aortic scallop intentional laceration to prevent iatrogenic coronary artery obstruction (BASILICA) is a technique developed to mitigate this risk by creating a lengthwise laceration of the left and/or right aortic valve leaflets prior to TAVR. Patient outcomes following TAVR with BASILICA have been promising. Meticulous preoperative examination, patient selection, and hemodynamic management are imperative. With continued refinement, BASILICA may further expand the application of TAVR to patients at high risk for coronary occlusion associated with the procedure.
Fragmented QRS complex with an additional R-wave attenuated by short RR interval in a patient with acute pulmonary embolism and cardiogenic shock
Koji Takahashi, Hiromasa Nakahara, Eiji Arimitsu, Satoshi Imamine, Yoshiyasu Obata, Kimio Nakanishi, Yoshiyasu Taniguchi, Maiko Amano, Chika Omori, Takafumi Okura
2022, 19(12): 995-1000. doi: 10.11909/j.issn.1671-5411.2022.12.008
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Point-of-care ultrasonography in geriatric medicine: usefulness for approaching infectious endocarditis diagnosis
Pablo Solla, Patricia Cancelo, Eva López, Jesús de la Hera, César Morís, José Gutiérrez
2022, 19(12): 1001-1002. doi: 10.11909/j.issn.1671-5411.2022.12.007
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Unplanned J-valve implantation during open heart surgery for severe valvular annulus and ventricle calcification
Tian-Ge LUO, Bo LI, Jie HAN, Hai-Bo ZHANG
2022, 19(12): 1003-1006. doi: 10.11909/j.issn.1671-5411.2022.12.009
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