Evaluation of an anaesthesia automated record keeping system : a human factors approach

Anaesthesia Information Management System (AIMS) is an automated record keeping system that imports and stores patient’s vital signs information from a physiological monitor in real-time. However, only a handful of studies have examined the effect of automated record keeping system on anaesthetists’ cognitive performance. Therefore, the current thesis aims to evaluate AIMS in terms of anaesthetists’ attitude (Study 1) and its effect on their cognitive performance (Study 2). Study 1, a questionnaire study examined anaesthetists’ trust and acceptance of AIMS. Forty-two anaesthetists at Tuen Mun Hospital (TMH) and Po Oi hospitals (POH) have completed a self-reported questionnaire. Results found that anaesthetists generally adopted a positive attitude toward AIMS. They exhibited a high level of trust and acceptance of AIMS. Also, they perceived AIMS as highly useful and relevant to their job. Study 2, a simulation study compared AIMS with manual record keeping on anaesthetists’ vigilance, situation awareness (SA) and mental workload. 20 anaesthetists at TMH were randomly assigned to two conditions: (1) AIMS and (2) Manual. Each participant received a 45-minute scenario in a full-scale simulation. Participants were asked to take over a case of general anaesthesia and perform record keeping. Results showed that AIMS did not impair anaesthetists’ vigilance and SA. In addition, it reduced anaesthetists’ mental workload and enabled them to spend less time on record keeping task. The current thesis provides an evaluation of AIMS by using a human factors approach. It contributes to the understanding on the effect of AIMS on anaesthetist’ in terms of attitude and cognitive performance. Based on the evaluation, we generate some recommendation for designers and hospitals to address the limitation of AIMS in interface designs and to increase anaesthetists’ acceptance of AIMS

Functional constituency members in Hong Kong's Legislative Council : a study of governance, legitimacy and accountability in action / y Cheung Hin Kei, Hankie, Ho Sing Yan, Simon, Li Man Pong, Charles, Tse Tsz Ying, Amy, Wong Man Ting, Mandy

published_or_final_versionPolitics and Public AdministrationMasterMaster of Public Administratio

Detection of microalbuminuria in non-insulin dependent diabetes mellitus (NIDDM) patients without overt proteinuria by a semiquantitative albumin-creatinine urine strips

AbstractMicroalbuminuria is the hallmark of the reversible stage of incipient diabetic nephropathy. A cost- effective and convenient bedside screening test is essential to detect this phase. We used Clinitek 50® which is a semiquantitative strip test to check spot urine sample from 81 patients with albustix one plus or less. The incidence of Clinitek 50® microalbuminuria was 17%, 18.2% and 75% in 47, 22 and 12 patients with albustix negative, trace or one plus respectively. Nineteen and 13 of the 21 Clinitek 50® positive patients were checked for spot urine DCA 2000® and two 12-hour urine collection for immunoassay respectively. Around 60% of these samples fell into the microalbuminuria range and 40% into the overt albuminuria range by either technique. There was no false positive of Clinitek 50®. The lowest range of microalbuminuria detected by Clinitek 50® was 27 μg/minute (38 mg/day). We concluded that Clinitek 50® is a useful screening test as it is nonexpensive, easily operated and has a sensitivity close to the lower range of microalbuminuria

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results

Incident heart failure and myocardial infarction in sodium-glucose cotransporter-2 vs. dipeptidyl peptidase-4 inhibitor users

Aims This study aimed to compare the rates of major cardiovascular adverse events in sodium-glucose cotransporter-2 inhibitors (SGLT2I) and dipeptidyl peptidase-4 inhibitors (DPP4I) users in a Chinese population. SGLT2I and DPP4I are increasingly prescribed for type 2 diabetes mellitus patients. However, few population-based studies are comparing their effects on incident heart failure or myocardial infarction. Methods and results This was a population-based retrospective cohort study using the electronic health record database in Hong Kong, including type 2 diabetes mellitus patients receiving either SGLT2I or DPP4I from 1 January 2015 to 31 December 2020. Propensity score matching was performed in a 1:1 ratio based on demographics, past comorbidities, and non-SGLT2I/DPP4I medications with nearest neighbour matching (caliper = 0.1). Univariable and multivariable Cox models were used to identify significant predictors for new-onset heart failure, new-onset myocardial infarction, cardiovascular mortality, and all-cause mortality. Sensitivity analyses with competing risk models and multiple propensity score matching approaches were conducted. A total of 41 994 patients (58.89% males, median admission age at 58 years old, interquartile range [IQR]: 51.2–65.3) were included with a median follow-up of 5.6 years (IQR: 5.32–5.82). In the matched cohort, SGLT2I use was significantly associated with lower risks of new-onset heart failure (hazard ratio [HR]: 0.73, 95% confidence interval [CI]: [0.66, 0.81], P < 0.0001), myocardial infarction (HR: 0.81, 95% CI: [0.73, 0.90], P < 0.0001), cardiovascular mortality (HR: 0.67, 95% CI: [0.53, 0.84], P < 0.001), and all-cause mortality (HR: 0.26, 95% CI: [0.24, 0.29], P < 0.0001) after adjusting for significant demographics, past comorbidities, and non-SGLT2I/DPP4I medications. Conclusions SGLT2 inhibitors are protective against adverse cardiovascular events including new-onset heart failure, myocardial infarction, cardiovascular mortality, and all-cause mortality. The prescription of SGLT2I is preferred when taken into consideration individual cardiovascular and metabolic risk profiles in addition to drug–drug interactions

Comparison of sodium-glucose cotransporter-2 inhibitor and dipeptidyl peptidase-4 inhibitor on the risks of new-onset atrial fibrillation, stroke and mortality in diabetic patients: A propensitysScore-matched study in Hong Kong

Objective To compare the effects of sodium-glucose cotransporter 2 inhibitors (SGLT2Is) and dipeptidyl peptidase-4 inhibitors (DPP4Is) on adverse outcomes in diabetic patients in Hong Kong. Methods This was a retrospective population-based cohort study of type 2 diabetes mellitus patients (n = 72,746) treated with SGLT2I or DPP4I between January 1, 2015, and December 31, 2020, in Hong Kong. Patients with exposure to both DPP4I and SGLT2I therapy, without complete demographics or mortality data, or who had prior atrial fibrillation (AF) were excluded. The study outcomes were new-onset AF, stroke/transient ischemic attack, cardiovascular mortality and all-cause mortality. Propensity score matching (1:1 ratio) between SGLT2I and DPP4I users was performed. Results The unmatched study cohort included 21,713 SGLT2I users and 39,510 DPP4I users (total: n = 61,233 patients; 55.37% males, median age: 62.7 years [interquartile range (IQR): 54.6–71.9 years]). Over a median follow-up of 2030 (IQR: 1912–2117) days, 2496 patients (incidence rate [IR]: 4.07%) developed new-onset AF, 2179 patients (IR: 3.55%) developed stroke/transient ischemic attack, 1963 (IR: 3.20%) died from cardiovascular causes and 6607 patients (IR: 10.79%) suffered from all-cause mortality. After propensity score matching (SGLT2I: n = 21,713; DPP4I: n = 21,713), SGLT2I users showed lower incidence of new-onset AF (1.96% vs. 2.78%, standardized mean difference [SMD] = 0.05), stroke (1.80% vs. 3.52%, SMD = 0.11), cardiovascular mortality (0.47% vs. 1.56%, SMD = 0.11) and all-cause mortality (2.59% vs. 7.47%, SMD = 0.22) compared to DPP4I users. Cox regression found that SGLT2I users showed lower risk of new-onset AF (hazard ratio [HR]: 0.68, 95% confidence interval [CI]: [0.56, 0.83], P = 0.0001), stroke (HR: 0.64, 95% CI: [0.53, 0.79], P < 0.0001), cardiovascular mortality (HR: 0.39, 95% CI: [0.27, 0.56], P < 0.0001) and all-cause mortality (HR: 0.44, 95% CI: [0.37, 0.51], P < 0.0001) after adjusting for significant demographics, past comorbidities, medications and laboratory tests. Conclusions Based on real-world data of type 2 diabetic patients in Hong Kong, SGLT2I use was associated with lower risk of incident AF, stroke/transient ischemic attack, and cardiovascular and all-cause mortality outcomes compared to DPP4I use

Clinical characteristics, risk factors and outcomes of cancer patients with COVID-19: A population-based study

Introduction Cancer patients may be susceptible to poorer outcomes in COVID-19 infection owing to the immunosuppressant effect of chemotherapy/radiotherapy and cancer growth, along with the potential for nosocomial transmission due to frequent hospital admissions. Methods This was a population-based retrospective cohort study of COVID-19 patients who presented to Hong Kong public hospitals between 1 January 2020 and 8 December 2020. The primary outcome was a composite endpoint of requirement for intubation, ICU admission and 30-day mortality. Results The following study consisted of 6089 COVID-19 patients (median age 45.9 [27.8.1–62.7] years; 50% male), of which 142 were cancer subjects. COVID-19 cancer patients were older at baseline and tended to present with a higher frequency of comorbidities, including diabetes mellitus, hypertension, chronic obstructive pulmonary disease, ischemic heart disease, ventricular tachycardia/fibrillation and gastrointestinal bleeding (p < 0.05). These subjects also likewise tended to present with higher serum levels of inflammatory markers, including D-dimer, lactate dehydrogenase, high sensitivity troponin-I and C-reactive protein. Multivariate Cox regression showed that any type of cancer presented with an almost four-fold increased risk of the primary outcome (HR: 3.77; 95% CI: 1.63–8.72; p < 0.002) after adjusting for significant demographics, Charlson comorbidity index, number of comorbidities, past comorbidities and medication history. This association remained significant when assessing those with colorectal (HR: 5.07; 95% CI: 1.50–17.17; p < 0.009) and gastrointestinal malignancies (HR: 3.79; 95% CI: 1.12–12.88; p < 0.03), but not with lung, genitourinary, or breast malignancies, relative to their respective cancer-free COVID-19 counterparts. Conclusions COVID-19 cancer patients are associated with a significantly higher risk of intubation, ICU admission and/or mortality

Sulfonylurea is associated with higher risks of ventricular arrhythmia or sudden cardiac death compared with metformin: A population-based cohort study

Background Commonly prescribed diabetic medications such as metformin and sulfonylurea may be associated with different arrhythmogenic risks. This study compared the risk of ventricular arrhythmia or sudden cardiac death between metformin and sulfonylurea users in patients with type 2 diabetes. Methods and Results Patients aged ≥40 years who were diagnosed with type 2 diabetes or prescribed antidiabetic agents in Hong Kong between January 1, 2009, and December 31, 2009, were included and followed up until December 31, 2019. Patients prescribed with both metformin and sulfonylurea or had prior myocardial infarction were excluded. The study outcome was a composite of ventricular arrhythmia or sudden cardiac death. Metformin users and sulfonylurea users were matched at a 1:1 ratio by propensity score matching. The matched cohort consisted of 16 596 metformin users (47.70% men; age, 68±11 years; mean follow‐up, 4.92±2.55 years) and 16 596 sulfonylurea users (49.80% men; age, 70±11 years; mean follow‐up, 4.93±2.55 years). Sulfonylurea was associated with higher risk of ventricular arrhythmia or sudden cardiac death than metformin hazard ratio (HR, 1.90 [95% CI, 1.73–2.08]). Such difference was consistently observed in subgroup analyses stratifying for insulin usage or known coronary heart disease. Conclusions Sulfonylurea use is associated with higher risk of ventricular arrhythmia or sudden cardiac death than metformin in patients with type 2 diabetes

A territory-wide Study of arrhythmogenic right ventricular cardiomyopathy patients from Hong Kong

Background: Arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) is a hereditary disease characterized by fibrofatty infiltration of the right ventricular myocardium that predisposes affected patients to malignant ventricular arrhythmias, dual-chamber cardiac failure and sudden cardiac death (SCD). The present study aims to investigate the risk of detrimental cardiovascular events in an Asian population of ARVC/D patients, including the incidence of malignant ventricular arrhythmias, new-onset heart failure with reduced ejection fraction (HFrEF), as well as long-term mortality. Methods and Results: This was a territory-wide retrospective cohort study of patients diagnosed with ARVC/D between 1997 and 2019 in Hong Kong. This study consisted of 109 ARVC/D patients (median age: 61 [46–71] years; 58% male). Of these, 51 and 24 patients developed incident VT/VF and new-onset HFrEF, respectively. Five patients underwent cardiac transplantation, and 14 died during follow-up. Multivariate Cox regression identified prolonged QRS duration as a predictor of VT/VF (p <0.05). Female gender, prolonged QTc duration, the presence of epsilon waves and T-wave inversion (TWI) in any lead except aVR/V1 predicted new-onset HFrEF (p <0.05). The presence of epsilon waves, in addition to the parameters of prolonged QRS duration and worsening ejection fraction predicted all-cause mortality (p <0.05). Clinical scores were developed to predict incident VT/VF, new-onset HFrEF and all-cause mortality, and all were significantly improved by machine learning techniques. Conclusions: Clinical and electrocardiographic parameters are important for assessing prognosis in ARVC/D patients and should in turn be used in tandem to aid risk stratification in the hospital setting

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results