An updated analysis on myocarditis and pericarditis cases reported following mRNA SARS-CoV-2 vaccination in Singapore.

INTRODUCTION: Messenger ribonucleic acid (mRNA) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines have been associated with myocarditis/pericarditis, especially in young males. We evaluated the risk of myocarditis/pericarditis following mRNA vaccines by brand, age, sex and dose number in Singapore. METHODS: Adverse event reports of myocarditis/pericarditis following mRNA vaccines received by the Health Sciences Authority from 30 December 2020 to 25 July 2022 were included, with a data lock on 30 September 2022. Case adjudication was done by an independent panel of cardiologists using the US Centers for Disease Control and Prevention case definition. Reporting rates were compared with expected rates using historical data from 2018 to 2020. RESULTS: Of the 152 adjudicated cases, males comprised 75.0%. The median age was 30 years. Most cases occurred after Dose 2 (49.3%). The median time to onset was 2 days. Reporting rates were highest in males aged 12-17 years for both primary series (11.5 [95% confidence interval [CI] 6.7-18.4] per 100,000 doses, post-Dose 2) and following booster doses (7.1 [95% CI 3.0-13.9] per 100,000 doses). In children aged 5-11 years, myocarditis remained very rare (0.2 per 100,000 doses). The reporting rates for Booster 1 were generally similar or lower than those for Dose 2. CONCLUSIONS: The risk of myocarditis/pericarditis with mRNA vaccines was highest in adolescent males following Dose 2, and this was higher than historically observed background rates. Most cases were clinically mild. The risk of myocarditis should be weighed against the benefits of receiving an mRNA vaccine, keeping in mind that SARS-CoV-2 infections carry substantial risks of myocarditis/pericarditis, as well as the evolving landscape of the disease.

Nationwide safety surveillance of COVID-19 mRNA vaccines following primary series and first booster vaccination in Singapore.

Background: The real-world safety profile of COVID-19 mRNA vaccines remains incompletely elucidated. Methods: We performed a nationwide post-market safety surveillance analysis in Singapore, on vacinees aged 5 years and older, through mid-September 2022. Observed-over-expected (O/E) analyses were performed to identify potential safety signals among eight shortlisted adverse events of special interest (AESIs): strokes, cerebral venous thrombosis (CVT), acute myocardial infarction, myocarditis/pericarditis, pulmonary embolism, immune thrombocytopenia, convulsions and appendicitis. Self-controlled case series analyses (SCCS) were performed to validate signals of concern, occurring within 42 days of vaccination. Findings: Elevated risks were observed on O/E analyses for the following AESIs: myocarditis/pericarditis, [rate ratio (RR): 3.66, 95 % confidence interval (95 % CI): 2.71 to 4.94], appendicitis [RR: 1.14 (1.02 to 1.27)] and CVT [RR: 2.11 (1.18 to 3.77)]. SCCS analyses generated corroborative findings: myocarditis/pericarditis, [relative incidence (RI): 6.96 (3.95 to 12.27) at 1 to 7 days post-dose 2], CVT [RI: 4.30 (1.30 to 14.20) at 22 to 42 days post-dose 1] and appendicitis [RI: 1.31 (1.03 to 1.67) at 1 to 7 days post-dose 1]. Booster dose 1 continued to be associated with higher rates of myocarditis/pericarditis on O/E analysis [RR: 2.30, (1.39 to 3.80) and 1.69, (1.11 to 2.59)] at 21- and 42-days post-booster dose 1, respectively. Males aged 12 to 17 exhibited highest risks of both myocarditis/pericarditis [RI: 6.31 (1.36 to 29.3)] and appendicitis [RI: 2.01 (1.12 to 3.64)] after primary vaccination. Similarly, CVT was also predominantly observed in males aged above 50 (11 out of 16 cases), within 42-days of vaccination. Interpretation: Our data suggest that myocarditis/pericarditis, appendicitis and CVT are associated with primary vaccination using COVID-19 mRNA vaccines. Males at specific ages exhibit higher risks for all three AEs identified. The risk of myocarditis/pericarditis continues to be elevated after booster dose 1.

A novel bidirectional LSTM deep learning approach for COVID-19 forecasting.

COVID-19 has resulted in significant morbidity and mortality globally. We develop a model that uses data from thirty days before a fixed time point to forecast the daily number of new COVID-19 cases fourteen days later in the early stages of the pandemic. Various time-dependent factors including the number of daily confirmed cases, reproduction number, policy measures, mobility and flight numbers were collected. A deep-learning model using Bidirectional Long-Short Term Memory (Bi-LSTM) architecture was trained on data from 22nd Jan 2020 to 8 Jan 2021 to forecast the new daily number of COVID-19 cases 14 days in advance across 190 countries, from 9 to 31 Jan 2021. A second model with fewer variables but similar architecture was developed. Results were summarised by mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and total absolute percentage error and compared against results from a classical ARIMA model. Median MAE was 157 daily cases (IQR: 26-666) under the first model, and 150 (IQR: 26-716) under the second. Countries with more accurate forecasts had more daily cases and experienced more waves of COVID-19 infections. Among countries with over 10,000 cases over the prediction period, median total absolute percentage error was 33% (IQR: 18-59%) and 34% (IQR: 16-66%) for the first and second models respectively. Both models had comparable median total absolute percentage errors but lower maximum total absolute percentage errors as compared to the classical ARIMA model. A deep-learning approach using Bi-LSTM architecture and open-source data was validated on 190 countries to forecast the daily number of cases in the early stages of the COVID-19 outbreak. Fewer variables could potentially be used without impacting prediction accuracy.

Combining Machine Learning with a Rule-Based Algorithm to Detect and Identify Related Entities of Documented Adverse Drug Reactions on Hospital Discharge Summaries.

INTRODUCTION: Discharge summaries contain valuable information about adverse drug reactions, but their unstructured nature makes them challenging to analyse and use as a signal source for pharmacovigilance. Machine learning has shown promise in identifying discharge summaries that contain related drug-adverse event pairs but has fared relatively poorer in entity extraction. METHODS: A hybrid model is developed combining rule-based and machine learning algorithms using discharge summaries with the aim of maximising capture of related drug-adverse event pairs. The rule first identifies segments containing adverse event entities within a 100-character distance from a drug term; machine learning subsequently estimates the relatedness of the drug and adverse event entities contained. The approach is validated on four independent datasets that are temporally and geographically separated from model development data. The impact of restricted drug-adverse event pair detection on recall is evaluated by using two of the four validation datasets that do not impose rule-based restrictions to annotations. RESULTS: The hybrid model achieves a recall of 0.80 (fivefold cross validation), 0.80 (temporal) and 0.76 (geographical) on validation using datasets containing only pre-identified target text segments that fulfil the rule-based algorithm criteria. When tested on datasets that additionally contained drug-adverse event pairs not restricted by the rule-based criteria, recall of the model declines to 0.68 and 0.62 on temporally and geographically separated datasets, respectively. CONCLUSIONS: The proposed hybrid model demonstrates reasonable generalisability on external validation. Rule-based restriction of the detection space results in an approximately 12-14% reduction in recall but improves identification of the related drug and adverse event terms.

Applying the OMOP Common Data Model to Facilitate Benefit-Risk Assessments of Medicinal Products Using Real-World Data from Singapore and South Korea.

OBJECTIVES: The aim of this study was to characterize the benefits of converting Electronic Medical Records (EMRs) to a common data model (CDM) and to assess the potential of CDM-converted data to rapidly generate insights for benefit-risk assessments in post-market regulatory evaluation and decisions. METHODS: EMRs from January 2013 to December 2016 were mapped onto the Observational Medical Outcomes Partnership-CDM (OMOP-CDM) schema. Vocabulary mappings were applied to convert source data values into OMOP-CDM-endorsed terminologies. Existing analytic codes used in a prior OMOP-CDM drug utilization study were modified to conduct an illustrative analysis of oral anticoagulants used for atrial fibrillation in Singapore and South Korea, resembling a typical benefit-risk assessment. A novel visualization is proposed to represent the comparative effectiveness, safety and utilization of the drugs. RESULTS: Over 90% of records were mapped onto the OMOP-CDM. The CDM data structures and analytic code templates simplified the querying of data for the analysis. In total, 2,419 patients from Singapore and South Korea fulfilled the study criteria, the majority of whom were warfarin users. After 3 months of follow-up, differences in cumulative incidence of bleeding and thromboembolic events were observable via the proposed visualization, surfacing insights as to the agent of preference in a given clinical setting, which may meaningfully inform regulatory decision-making. CONCLUSIONS: While the structure of the OMOP-CDM and its accessory tools facilitate real-world data analysis, extending them to fulfil regulatory analytic purposes in the post-market setting, such as benefit-risk assessments, may require layering on additional analytic tools and visualization techniques.

Analysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort.

Importance: More than 1 billion adults have hypertension globally, of whom 70% cannot achieve their hypertension control goal with monotherapy alone. Data are lacking on clinical use patterns of dual combination therapies prescribed to patients who escalate from monotherapy. Objective: To investigate the most common dual combinations prescribed for treatment escalation in different countries and how treatment use varies by age, sex, and history of cardiovascular disease. Design, Setting, and Participants: This cohort study used data from 11 electronic health record databases that cover 118 million patients across 8 countries and regions between January 2000 and December 2019. Included participants were adult patients (ages ≥18 years) who newly initiated antihypertensive dual combination therapy after escalating from monotherapy. There were 2 databases included for 3 countries: the Iqvia Longitudinal Patient Database (LPD) Australia and Electronic Practice-based Research Network 2019 linked data set from South Western Sydney Local Health District (ePBRN SWSLHD) from Australia, Ajou University School of Medicine (AUSOM) and Kyung Hee University Hospital (KHMC) databases from South Korea, and Khoo Teck Puat Hospital (KTPH) and National University Hospital (NUH) databases from Singapore. Data were analyzed from June 2020 through August 2021. Exposures: Treatment with dual combinations of the 4 most commonly used antihypertensive drug classes (angiotensin-converting enzyme inhibitor [ACEI] or angiotensin receptor blocker [ARB]; calcium channel blocker [CCB]; ß-blocker; and thiazide or thiazide-like diuretic). Main Outcomes and Measures: The proportion of patients receiving each dual combination regimen, overall and by country and demographic subgroup. Results: Among 970 335 patients with hypertension who newly initiated dual combination therapy included in the final analysis, there were 11 494 patients from Australia (including 9291 patients in Australia LPD and 2203 patients in ePBRN SWSLHD), 6980 patients from South Korea (including 6029 patients in Ajou University and 951 patients in KHMC), 2096 patients from Singapore (including 842 patients in KTPH and 1254 patients in NUH), 7008 patients from China, 8544 patients from Taiwan, 103 994 patients from France, 76 082 patients from Italy, and 754 137 patients from the US. The mean (SD) age ranged from 57.6 (14.8) years in China to 67.7 (15.9) years in the Singapore KTPH database, and the proportion of patients by sex ranged from 24 358 (36.9%) women in Italy to 408 964 (54.3%) women in the US. Among 12 dual combinations of antihypertensive drug classes commonly used, there were significant variations in use across country and patient subgroup. For example starting an ACEI or ARB monotherapy followed by a CCB (ie, ACEI or ARB + CCB) was the most commonly prescribed combination in Australia (698 patients in ePBRN SWSLHD [31.7%] and 3842 patients in Australia LPD [41.4%]) and Singapore (216 patients in KTPH [25.7%] and 439 patients in NUH [35.0%]), while in South Korea, CCB + ACEI or ARB (191 patients in KHMC [20.1%] and 1487 patients in Ajou University [24.7%]), CCB + ß-blocker (814 patients in Ajou University [13.5%] and 217 patients in KHMC [22.8%]), and ACEI or ARB + CCB (147 patients in KHMC [15.5%] and 1216 patients in Ajou University [20.2%]) were the 3 most commonly prescribed combinations. The distribution of 12 dual combination therapies were significantly different by age and sex in almost all databases. For example, use of ACEI or ARB + CCB varied from 873 of 3737 patients ages 18 to 64 years (23.4%) to 343 of 2292 patients ages 65 years or older (15.0%) in South Korea's Ajou University database (P for database distribution by age < .001), while use of ACEI or ARB + CCB varied from 2121 of 4718 (44.8%) men to 1721 of 4549 (37.7%) women in Australian LPD (P for drug combination distributions by sex < .001). Conclusions and Relevance: In this study, large variation in the transition between monotherapy and dual combination therapy for hypertension was observed across countries and by demographic group. These findings suggest that future research may be needed to investigate what dual combinations are associated with best outcomes for which patients.

Time series classification using a modified LSTM approach from accelerometer-based data: A comparative study for gait cycle detection.

BACKGROUND: Gait event detection (GED) is an important aspect in identifying and interpret a user's gait to assess gait abnormalities and design intelligent assistive devices. RESEARCH QUESTION: There is a need to develop robust GED models that can accurately detect various gait instances in different scenarios and environments. METHODS: This paper presents a novel method of detecting heel strikes (HS) and toe offs (TO) during the user's gait cycle using a modified Long Short-Term Memory (LSTM) networks approach. The method was tested on a database from Movement Analysis in Real-world Environments using Accelerometers (MAREA) (n = 20 healthy subjects) that consisted of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. Modifications include oversampling, composite accelerations and optimizing the LSTM network architecture were made. RESULTS: Performance of our modified model was found to be better than six state-of-the-art GED algorithms, with a median F1 score of 0.98 for Heel Strikes and 0.98 for Toe Offs in the scenario of steady walking in an indoor environment, and a median F1 score of 0.94 for Heel Strikes and 0.68 for Toe-offs in the scenario of walking and running in an outdoor environment. SIGNIFICANCE: This paper highlights the potential of the single proposed model to be an alternative to the six GED models in gait detection under various conditions.

Mapping the Shah-modified Barthel Index to the Health Utility Index Mark III by the Mean Rank Method.

PURPOSE: To map the Shah-modified Barthel Index (SBI) to the Health Utility Index Mark III (HUI-3) in stroke patients, and to compare the performance of a recently developed method called the Mean Rank Method (MRM) against a popular method, the Ordinary Least Squares (OLS) method. METHODS: A cohort of 473 patients who had their first clinical stroke diagnosis and hospital admission and were assessed using the SBI and HUI-3 at 3 months and/or 12 months post-admission. Observations were split to form a training dataset (N = 473) and a validation dataset (N = 245). RESULTS: In the training dataset, the MRM using SBI total score as the predictor produced a mapped utility distribution that closely resembled the observed utility distribution. It had almost no shrinkage of the standard deviation (P = 0.542), whereas the OLS using SBI total score and SBI item scores under-estimated the standard deviation by 28% and 26%, respectively (each P < 0.001). The MRM mapping gave better fit in terms of smaller mean absolute error and larger intra-class correlation than the two versions of OLS mapping, whereas the OLS gave smaller mean-squared errors than the MRM. Multivariate regression analysis showed that the use of OLS-mapped utilities tended to under-estimate both the mean utility of people who had no comorbidity and the utility-comorbidity association as compared to the observed utility-comorbidity pattern although the differences did not reach statistical significance (each P > 0.05). The MRM-mapped utility showed utility-comorbidity pattern more similar to the observed. Similar findings were obtained from the validation dataset. CONCLUSIONS: The MRM performed well. Mapping functions are available to map the SBI to the HUI-3 Utility Index.

Mapping the Alzheimer's Disease Cooperative Study-Activities of Daily Living Inventory to the Health Utility Index Mark III.

PURPOSE: To map the Alzheimer's Disease Cooperative Study-Activities of Daily Living Inventory (ADCS-ADL) to the Health Utility Index Mark III (HUI3) in people living with dementia (PWD) and to compare the performance of five methods for mapping. METHODS: A cross-sectional study of 346 dyads of community-dwelling PWD and family caregiver was carried out in Singapore. ADCS-ADL and HUI3 were rated by the family caregivers. Disease severity ratings and Mini Mental State Examination (MMSE) results were retrieved from medical records. A recently proposed mapping method called the Mean Rank Method (MRM) was described and applied, and the results were compared with regression-based mapping, including ordinary least squares, censored least absolute deviation (CLAD), Tobit and response mapping. RESULTS: The MRM produced a mapped utility distribution that closely resembled the observed utility distribution. The standard deviations (SDs) of the observed and MRM-mapped utility were both 0.340, whereas the SDs of the other mapped utilities ranged from 0.243 (response mapping) to 0.283 (CLAD). Regressing the MRM- and CLAD-mapped and observed utility values upon disease severity and MMSE gave similar regression lines (each P > 0.05). Regressing the other mapped utility values upon the covariates under- (over-) estimated the utility of good (poor) clinical states. However, regression-based mapping methods gave a better fit at the individual level, as measured by root mean square error, mean absolute error and R2. K fold cross-validation gave similar results. CONCLUSIONS: The MRM is accurate at the group level. The regression-based mapping methods are more accurate for making individual-level prediction. In addition, CLAD also performed reasonably well at the group level.