Healthcare Big Data in Hong Kong: Development and Implementation of Artificial Intelligence-Enhanced Predictive Models for Risk Stratification.

Routinely collected electronic health records (EHRs) data contain a vast amount of valuable information for conducting epidemiological studies. With the right tools, we can gain insights into disease processes and development, identify the best treatment and develop accurate models for predicting outcomes. Our recent systematic review has found that the number of big data studies from Hong Kong has rapidly increased since 2015, with an increasingly common application of artificial intelligence (AI). The advantages of big data are that i) the models developed are highly generalisable to the population, ii) multiple outcomes can be determined simultaneously, iii) ease of cross-validation by for model training, development and calibration, iv) huge numbers of useful variables can be analyzed, v) static and dynamic variables can be analyzed, vi) non-linear and latent interactions between variables can be captured, vii) artificial intelligence approaches can enhance the performance of prediction models. In this paper, we will provide several examples (cardiovascular disease, diabetes mellitus, Brugada syndrome, long QT syndrome) to illustrate efforts from a multi-disciplinary team to identify data from different modalities to develop models using territory-wide datasets, with the possibility of real-time risk updates by using new data captured from patients. The benefit is that only routinely collected data are required for developing highly accurate and high-performance models. AI-driven models outperform traditional models in terms of sensitivity, specificity, accuracy, area under the receiver operating characteristic and precision-recall curve, and F1 score. Web and/or mobile versions of the risk models allow clinicians to risk stratify patients quickly in clinical settings, thereby enabling clinical decision-making. Efforts are required to identify the best ways of implementing AI algorithms on the web and mobile apps.

Frailty scoring in vascular and endovascular surgery: A systematic review.

One in 10 independently living adults aged 65 years old and older is considered frail, and frailty is associated with poor postoperative outcomes. This systematic review aimed to examine the association between frailty assessments and postoperative outcomes in patients with vascular disease. Electronic databases - MEDLINE, Embase, and the Cochrane Library - were searched from inception until January 2022, resulting in 648 articles reviewed for potential inclusion and 16 studies selected. Demographic data, surgery type, frailty measure, and postoperative outcomes predicted by frailty were extracted from the selected studies. The risk of bias was assessed using the Newcastle-Ottawa Scale. The selected studies (mean age: 56.1-76.3 years) had low-to-moderate risk of bias and included 16 vascular (elective and nonelective) surgeries and eight frailty measures. Significant associations (p < 0.05) were established between mortality (30-day, 90-day, 1-year, 5-year), 30-day morbidity, nonhome discharge, adverse events, failure to rescue, patient requiring care after discharge, and amputation following critical limb ischaemia. The strongest evidence was found between 30-day mortality and frailty. Composite 30-day morbidity and mortality, functional status at discharge, length of stay, spinal cord deficit, and access site complications were found to be nonsignificantly associated with frailty. With frailty being significantly associated with several adverse postoperative outcomes, preoperative frailty assessments can potentially be clinically useful in helping practitioners predict and guide the pre-, peri-, and postoperative management of frail with vascular disease.