Browsing by Author "Pang, Herbert H"

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    A multicenter retrospective cohort study on predicting the risk for amiodarone pulmonary toxicity.
    (BMC pulmonary medicine, 2022-04) Kwok, Wang Chun; Ma, Ting Fung; Chan, Johnny Wai Man; Pang, Herbert H; Ho, James Chung Man

    Background

    Amiodarone is one of the most commonly used anti-arrhythmic agents. Amiodarone pulmonary toxicity is a potentially fatal adverse effect associated with amiodarone use. Previous studies on the epidemiology and risk factors for amiodarone pulmonary toxicity showed diverse results.

    Methods

    A multicenter retrospective cohort study was conducted to identify clinic-epidemiologic markers associated with amiodarone pulmonary toxicity for development of a prediction rule. Patients taking amiodarone who were managed in 3 centres in Hong Kong from 2005 to 2015 were included in this study. Penalized logistic regression was used to model the outcome as it is rare.

    Results

    A total of 34 cases with amiodarone pulmonary toxicity were identified among 1786 patients taking amiodarone for at least 90 days from 2005 to 2015. The incidence of amiodarone pulmonary toxicity was estimated to be 1.9%. The risk factors for amiodarone pulmonary toxicity included advanced age (OR 1.047, 95% CI 1.010-1.085, p = 0.013), ventricular arrhythmia (OR 2.703, 95% CI 1.053-6.935, p = 0.039), underlying lung disease (OR 2.511, 95% CI 1.146-5.501, p = 0.021) and cumulative dose of amiodarone (OR 4.762, 95% CI 1.310-17.309 p = 0.018).

    Conclusions

    The incidence of amiodarone pulmonary toxicity in Chinese patients in Hong Kong is estimated to be 1.9% in this study. Age, underlying lung disease, ventricular arrhythmia and cumulative dose of amiodarone are associated with the development of amiodarone pulmonary toxicity. A prediction rule was developed to inform the risk of developing amiodarone pulmonary toxicity.
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    Radiomics analysis using stability selection supervised component analysis for right-censored survival data.
    (Computers in biology and medicine, 2020-09) Yan, Kang K; Wang, Xiaofei; Lam, Wendy WT; Vardhanabhuti, Varut; Lee, Anne WM; Pang, Herbert H
    Radiomics is a newly emerging field that involves the extraction of massive quantitative features from biomedical images by using data-characterization algorithms. Distinctive imaging features identified from biomedical images can be used for prognosis and therapeutic response prediction, and they can provide a noninvasive approach for personalized therapy. So far, many of the published radiomics studies utilize existing out of the box algorithms to identify the prognostic markers from biomedical images that are not specific to radiomics data. To better utilize biomedical images, we propose a novel machine learning approach, stability selection supervised principal component analysis (SSSuperPCA) that identifies stable features from radiomics big data coupled with dimension reduction for right-censored survival outcomes. The proposed approach allows us to identify a set of stable features that are highly associated with the survival outcomes in a simple yet meaningful manner, while controlling the per-family error rate. We evaluate the performance of SSSuperPCA using simulations and real data sets for non-small cell lung cancer and head and neck cancer, and compare it with other machine learning algorithms. The results demonstrate that our method has a competitive edge over other existing methods in identifying the prognostic markers from biomedical imaging data for the prediction of right-censored survival outcomes.
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    The impact of lowering the study design significance threshold to 0.005 on sample size in randomized cancer clinical trials.
    (Journal of clinical and translational science, 2024-01) Leung, Tiffany H; Ho, James C; Wang, Xiaofei; Lam, Wendy W; Pang, Herbert H
    The proposal of improving reproducibility by lowering the significance threshold to 0.005 has been discussed, but the impact on conducting clinical trials has yet to be examined from a study design perspective. The impact on sample size and study duration was investigated using design setups from 125 phase II studies published between 2015 and 2022. The impact was assessed using percent increase in sample size and additional years of accrual with the medians being 110.97% higher and 2.65 years longer respectively. The results indicated that this proposal causes additional financial burdens that reduce the efficiency of conducting clinical trials.