Stroke Factors Associated with Thrombolysis Use in Hospitals in Singapore and US: A Cross-Registry Comparative Study

Background and Objectives: This paper aims to describe and compare the characteristics of 2 stroke populations in Singapore and in St. Louis, USA, and to document thrombolysis rates and contrast factors associated with its uptake in both populations. Methods: The stroke populations described were from the Singapore Stroke Registry (SSR) in -Singapore and the Cognitive Rehabilitation Research Group Stroke Registry (CRRGSR) in St. Louis, MO, USA. The registries were compared in terms of demographics and stroke risk factor history. Logistic regression was used to determine factors associated with thrombolysis uptake. Results: A total of 39,323 and 8,106 episodes were recorded in SSR and CRRGSR, respectively, from 2005 to 2012. Compared to CRRGSR, patients in SSR were older, male, and from the ethnic majority. Thrombolysis rates in SSR and CRRGSR were 2.5 and 8.2%, respectively, for the study period. History of ischemic heart disease or atrial fibrillation was associated with increased uptake in both populations, while history of stroke was associated with lower uptake. For SSR, younger age and males were associated with increased uptake, while having a history of smoking or diabetes was associated with decreased uptake. For CRRGSR, ethnic minority status was associated with decreased uptake. Conclusions: The comparison of stroke populations in Singapore and St Louis revealed distinct differences in clinicodemographics of the 2 groups. Thrombolysis uptake was driven by nonethnicity demographics in Singapore. Ethnicity was the only demographic driver of uptake in the CRRGSR population, highlighting the need to target ethnic minorities in increasing access to thrombolysis

Variation in coded frailty syndromes in secondary care administrative data: an international retrospective exploratory study

10.1136/bmjopen-2021-052735BMJ Open121e052735-e05273

Semi-automating abstract screening with a natural language model pretrained on biomedical literature

Abstract We demonstrate the performance and workload impact of incorporating a natural language model, pretrained on citations of biomedical literature, on a workflow of abstract screening for studies on prognostic factors in end-stage lung disease. The model was optimized on one-third of the abstracts, and model performance on the remaining abstracts was reported. Performance of the model, in terms of sensitivity, precision, F1 and inter-rater agreement, was moderate in comparison with other published models. However, incorporating it into the screening workflow, with the second reviewer screening only abstracts with conflicting decisions, translated into a 65% reduction in the number of abstracts screened by the second reviewer. Subsequent work will look at incorporating the pre-trained BERT model into screening workflows for other studies prospectively, as well as improving model performance

Additional file 1 of Semi-automating abstract screening with a natural language model pretrained on biomedical literature

Additional file 1. Search criteria and strategy. Appendix 1. Study eligibility criteria. Appendix 2. Search strategy

Predicting mortality in patients diagnosed with advanced dementia presenting at an acute care hospital: the PROgnostic Model for Advanced DEmentia (PRO-MADE)

Abstract Background Challenges in prognosticating patients diagnosed with advanced dementia (AD) hinders timely referrals to palliative care. We aim to develop and validate a prognostic model to predict one-year all-cause mortality (ACM) in patients with AD presenting at an acute care hospital. Methods This retrospective cohort study utilised administrative and clinical data from Tan Tock Seng Hospital (TTSH). Patients admitted to TTSH between 1st July 2016 and 31st October 2017 and identified to have AD were included. The primary outcome was ACM within one-year of AD diagnosis. Multivariable logistic regression was used. The PROgnostic Model for Advanced Dementia (PRO-MADE) was internally validated using a bootstrap resampling of 1000 replications and externally validated on a more recent cohort of AD patients. The model was evaluated for overall predictive accuracy (Nagelkerke’s R2 and Brier score), discriminative [area-under-the-curve (AUC)], and calibration [calibration slope and calibration-in-the-large (CITL)] properties. Results A total of 1,077 patients with a mean age of 85 (SD: 7.7) years old were included, and 318 (29.5%) patients died within one-year of AD diagnosis. Predictors of one-year ACM were age > 85 years (OR:1.87; 95%CI:1.36 to 2.56), male gender (OR:1.62; 95%CI:1.18 to 2.22), presence of pneumonia (OR:1.75; 95%CI:1.25 to 2.45), pressure ulcers (OR:2.60; 95%CI:1.57 to 4.31), dysphagia (OR:1.53; 95%CI:1.11 to 2.11), Charlson Comorbidity Index ≥ 8 (OR:1.39; 95%CI:1.01 to 1.90), functional dependency in ≥ 4 activities of daily living (OR: 1.82; 95%CI:1.32 to 2.53), abnormal urea (OR:2.16; 95%CI:1.58 to 2.95) and abnormal albumin (OR:3.68; 95%CI:2.07 to 6.54) values. Internal validation results for optimism-adjusted Nagelkerke’s R2, Brier score, AUC, calibration slope and CITL were 0.25 (95%CI:0.25 to 0.26), 0.17 (95%CI:0.17 to 0.17), 0.76 (95%CI:0.76 to 0.76), 0.95 (95% CI:0.95 to 0.96) and 0 (95%CI:-0.0001 to 0.001) respectively. When externally validated, the model demonstrated an AUC of 0.70 (95%CI:0.69 to 0.71), calibration slope of 0.64 (95%CI:0.63 to 0.66) and CITL of -0.27 (95%CI:-0.28 to -0.26). Conclusion The PRO-MADE attained good discrimination and calibration properties. Used synergistically with a clinician’s judgement, this model can identify AD patients who are at high-risk of one-year ACM to facilitate timely referrals to palliative care

Role of Fat and Bone Biomarkers in the Relationship Between Ethnicity and Bone Mineral Density in Older Men

10.1007/s00223-017-0342-8Calcified Tissue International102164-7

Estimating transmission parameters for COVID-19 clusters by using symptom onset data, Singapore, January-April 2020

10.3201/eid2702.203018Emerging Infectious Diseases272582-58

Estimating transmission parameters for COVID-19 clusters by using symptom onset data, Singapore, January-April 2020

10.3201/eid2702.203018Emerging Infectious Diseases272582-58

Additional file 1 of Risk factors for short-term all-cause mortality in patients with end stage renal disease: a scoping review

Supplementary Material