Yuen Lung Wong’s research while affiliated with Chinese University of Hong Kong and other places

Background Prognostication of survival among patients with advanced cancer is essential for palliative care (PC) planning. The implementation of a clinical point-of-care prognostic model may inform clinicians and facilitate decision-making. While early PC referral yields better clinical outcomes, actual referral time differs by clinical contexts and accessible. To summarize the various prognostic models that may cater to these needs, we conducted a systematic review and meta-analysis. Methods A systematic literature search was conducted in Ovid Medline, Embase, CINAHL Ultimate, and Scopus to identify eligible studies focusing on incurable solid tumors, validation of prognostic models, and measurement of predictive performances. Model characteristics and performances were summarized in tables. Prediction model study Risk Of Bias Assessment Tool (PROBAST) was adopted for risk of bias assessment. Meta-analysis of individual models, where appropriate, was performed by pooling C-index. Results 35 studies covering 35 types of prognostic models were included. Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP), and Objective Prognostic Score (OPS) were most frequently identified models. The pooled C-statistic of PPI for 30-day survival prediction was 0.68 (95% CI: 0.62–0.73, n = 6). The pooled C-statistic of PaP for 30-day survival prediction was 0.76 (95% CI: 0.70–0.80, n = 11), while that for 21-day survival prediction was 0.80 (0.71–0.86, n = 4). The pooled C-statistic of OPS for 30-days survival prediction was 0.69 (95% CI: 0.65–0.72, n = 3). All included studies had high risk of bias. Conclusion PaP appears to perform better but further validation and implementation studies were needed for confirmation.

Cannabis is the most extensively abused drug, leading to multiple health burdens such as traffic accidents and psychosis. There is a global wave of legalization of recreational and medical cannabis. This study aimed to understand future healthcare workers' intention to use cannabis through extended Theory of Planned Behavior (TPB). An online cross-sectional survey on cannabis, including validated survey tools and questions on demographics, knowledge, and constructs of the TPB was designed, and distributed during virtual classes in late 2020. Responses were obtained from the Faculty of Medicine of a local university. Nine hundred ninety-six responses were collected, of which 629 were complete and analysed. Age was the only demographic variable associated with cannabis use intention (p = 0.029). Respondents with intention had better knowledge of cannabis. All TPB and additional constructs, including perceived behavioral control (COR = 3.44, 95% CI 2.72–4.35, p < 0.001), descriptive norm (COR = 2.24, 95% CI 1.81–2.77, p < 0.001), injunctive norm (COR = 0.51, 95% CI 0.42–0.61, p < 0.001), attitude (COR = 1.23, 95% CI 1.18–1.28, p < 0.001), knowledge (COR = 1.08, 95% CI 1.03–1.14), and perceived availability (COR = 2.75, 95% CI 2.22–3.40, p < 0.001) were individually associated with intention. In the final multiple logistic regression model adjusted for age, only attitude (AOR = 1.19, 95% CI 1.13–1.25, p < 0.001) and perceived availability (p = 0.004) showed statistically significant associations with intention. Descriptive norm (standardized coefficient = 0.570) had better explanatory power than the injunctive norm (standardized coefficient = −0.143) in the model. Perceived behavioral control was associated with intention among respondents with negative to neutral attitudes towards cannabis (AOR = 2.48, 95% CI 1.63–3.77, p < 0.001), but not among those with positive attitudes. All TPB constructs positively correlated with the intention to use cannabis. Changing the attitudes and perceived control on cannabis use may be useful in preventing cannabis use.

e24091 Background: Prognostication by predicting the likelihood of surviving at certain time points is an important aspect for clinician-patient communication, inform medical decisions and facilitate advance care planning. Understanding disease trajectory has also been reported to improve patient satisfaction in medical encounters and reduce anxiety and depression of carer due to unprepared clinical deterioration. The main problem of applying currently available tools is that the included prognostic factors were designed to look at the more-ill patients in ward settings and sometimes needs specialized lab-tests, and is difficult to apply to out-patients and in real-world public care setting. Methods: 240 consecutive patients attending our oncology palliative care new-case assessment clinic from 1st January 2016 to 31st December 2018 were included. Information regarding underlying cancer, symptoms collected on baseline survey and laboratory results including blood counts, renal and liver function were collected. Survival outcomes were dichotomized at 60 days. Significant prognostic factors were identified by univariate analysis. Multivariable logistic regression were used for model building. Model fitness was assessed by Hosmer-Lemeshow test and R squared statistics, predictive properties assessed by area-under curve (AUC), sensitivity and specificity. The robustness of the prediction model was confirmed by bootstrapping. Results: 240 patients were included. The median KPS is 70. Prevalence of symptoms including tiredness (n = 135, 55.6%), anorexia (n = 106, 43.6%), shortness of breath on exertion (n = 76, 31.3%), peripheral edema (n = 58, 23.9%) and nausea (n = 35, 14.4%); prevalence of laboratory abnormalities including elevated bilirubin (n = 94, 38.7%), low albumin (n = 94, 38.7%), leucocytosis (n = 68, 28.0%), thrombocytosis (n = 61, 25.1%) and lymphopenia (n = 53, 21.8%). Hospital depressive screening scale, PHQ-9 score (cutoff = 8) was elevated in 81 patients (33.3%). At 60 days, there were 70 death events. On univariate analyses, all the above factors were significant predictors. On multivariate analysis and prognostic model development, the most significant prognostic factors were self-reported presence of poor appetite and edema, laboratory test of elevated WCC above normal, lymphocyte below normal and bilirubin above normal. A prognostic model built upon these five factors showed high sensitivity of 73% and specificity of 55% in predicting survival at 60 days. The significance and prediction property were maintained after bootstrapping operation. Compared to the parameters employed in palliative care prognostic index (PPI), the AUC of current predictive model is superior (0.782 vs 0.692). Conclusions: We hope to come up with a tailored prognostic tool for real-world ambulatory palliative care setting. This study is supported by KCC research grant.