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Aims Concern about the cardiovascular safety of coronavirus disease 2019 (COVID-19) vaccines among individuals with cardiovascular disease (CVD) may lead to vaccine hesitancy. We sought to assess the association between two COVID-19 vaccines, BNT162b2 and CoronaVac, and the risk of major adverse cardiovascular events (MACE) in individuals with established CVD. Methods and results We identified individuals with a history of CVD before 23 February 2021 and a diagnosis of MACE between 23 February 2021 and 31 January 2022 in Hong Kong. MACE was defined as a composite of myocardial infarction, stroke, revascularization, and cardiovascular death. Electronic health records from the Hong Kong Hospital Authority were linked to vaccination records from the Department of Health. A self-controlled case-series method was used to evaluate the risk of MACE for 0–13 and 14–27 days after two doses of COVID-19 vaccine. We estimated incidence rate ratios (IRRs) to compare the risk of MACE between each risk period and the baseline period. A total of 229 235 individuals with CVD were identified, of which 1764 were vaccinated and had a diagnosis of MACE during the observation period (BNT162b2 = 662; CoronaVac = 1102). For BNT162b2, IRRs were 0.48 [95% confidence interval (CI) 0.23–1.02] for the first dose and 0.87 (95% CI 0.50–1.52) for the second dose during the 0–13 days risk period, 0.40 (95% CI 0.18–0.93) for the first dose and 1.13 (95% CI 0.70–1.84) for the second dose during the 14–27 days risk period. For CoronaVac, the IRRs were 0.43 (95% CI 0.24–0.75) for the first dose and, 0.73 (95% CI 0.46–1.16) for the second dose during the 0–13 days risk period, 0.54 (95% CI 0.33–0.90) for the first dose and 0.83 (95% CI 0.54–1.29) for the second dose during the 14–27 days risk period. Consistent results were found in subgroup analyses for different sexes, age groups and different underlying cardiovascular conditions. Conclusion Our findings showed no evidence of an increased risk of MACE after vaccination with BNT162b2 or CoronaVac in patients with CVD. Future research is required to monitor the risk after the third dose of each vaccine.

Key Points Question Are long-acting injectable antipsychotics (LAIAs) associated with a lower risk of disease relapse, health care use, and adverse events compared with oral antipsychotics among people in Hong Kong with schizophrenia? Findings In this 16-year, population-based, self-controlled case series study of 70 396 individuals with a diagnosis of schizophrenia, 23 719 were prescribed LAIAs and oral antipsychotics. There were 48% fewer psychiatric hospitalizations, 47% fewer hospitalizations for schizophrenia, 44% fewer suicide attempts, and 37% fewer all-cause hospitalizations during full treatment periods with LAIAs alone, without an increased risk of adverse events; this association was also observed when excluding the first 90 days of treatment. Meaning This study suggests that clinicians should more broadly consider the long-term use of LAIAs for people with schizophrenia.

BackgroundOrgan segmentation is an important step in computer-aided diagnosis and pathology detection. Accurate kidney segmentation in abdominal computed tomography (CT) sequences is an essential and crucial task for surgical planning and navigation in kidney tumor ablation. However, kidney segmentation in CT is a substantially challenging work because the intensity values of kidney parenchyma are similar to those of adjacent structures.ResultsIn this paper, a coarse-to-fine method was applied to segment kidney from CT images, which consists two stages including rough segmentation and refined segmentation. The rough segmentation is based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm and the refined segmentation is implemented with improved GrowCut (IGC) algorithm. The SKFCM algorithm introduces a kernel function and spatial constraint into fuzzy c-means clustering (FCM) algorithm. The IGC algorithm makes good use of the continuity of CT sequences in space which can automatically generate the seed labels and improve the efficiency of segmentation. The experimental results performed on the whole dataset of abdominal CT images have shown that the proposed method is accurate and efficient. The method provides a sensitivity of 95.46% with specificity of 99.82% and performs better than other related methods.ConclusionsOur method achieves high accuracy in kidney segmentation and considerably reduces the time and labor required for contour delineation. In addition, the method can be expanded to 3D segmentation directly without modification.

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