Differential spatial working memory-related functional network reconfiguration in young and older adults.

Functional brain networks have preserved architectures in rest and task; nevertheless, previous work consistently demonstrated task-related brain functional reorganization. Efficient rest-to-task functional network reconfiguration is associated with better cognition in young adults. However, aging and cognitive load effects, as well as contributions of intra- and internetwork reconfiguration, remain unclear. We assessed age-related and load-dependent effects on global and network-specific functional reconfiguration between rest and a spatial working memory (SWM) task in young and older adults, then investigated associations between functional reconfiguration and SWM across loads and age groups. Overall, global and network-level functional reconfiguration between rest and task increased with age and load. Importantly, more efficient functional reconfiguration associated with better performance across age groups. However, older adults relied more on internetwork reconfiguration of higher cognitive and task-relevant networks. These reflect the consistent importance of efficient network updating despite recruitment of additional functional networks to offset reduction in neural resources and a change in brain functional topology in older adults. Our findings generalize the association between efficient functional reconfiguration and cognition to aging and demonstrate distinct brain functional reconfiguration patterns associated with SWM in aging, highlighting the importance of combining rest and task measures to study aging cognition.

Brain networks identified by functional connectivity (FC) have preserved architectures from rest to task and across task demands. Higher similarity, implying more efficient network reconfiguration, was associated with better cognition and task performance in young adults. To examine how it may be influenced by aging, we compared whole-brain and network-level FC similarities between resting-state and spatial working memory fMRI in young and older adults. At whole-brain level and higher order cognitive networks, older adults evidenced less efficient network reconfiguration from rest to task than young adults. Importantly, more efficient reconfiguration was associated with better accuracy. This relationship relied more on internetwork connections in older adults. Despite reduced neural resources compared to young, maintaining efficient network updating still contributes to better cognition at older age.

Improved interpretable machine learning emergency department triage tool addressing class imbalance.

Objective: The Score for Emergency Risk Prediction (SERP) is a novel mortality risk prediction score which leverages machine learning in supporting triage decisions. In its derivation study, SERP-2d, SERP-7d and SERP-30d demonstrated good predictive performance for 2-day, 7-day and 30-day mortality. However, the dataset used had significant class imbalance. This study aimed to determine if addressing class imbalance can improve SERP's performance, ultimately improving triage accuracy. Methods: The Singapore General Hospital (SGH) emergency department (ED) dataset was used, which contains 1,833,908 ED records between 2008 and 2020. Records between 2008 and 2017 were randomly split into a training set (80%) and validation set (20%). The 2019 and 2020 records were used as test sets. To address class imbalance, we used random oversampling and random undersampling in the AutoScore-Imbalance framework to develop SERP+-2d, SERP+-7d, and SERP+-30d scores. The performance of SERP+, SERP, and the commonly used triage risk scores was compared. Results: The developed SERP+ scores had five to six variables. The AUC of SERP+ scores (0.874 to 0.905) was higher than that of the corresponding SERP scores (0.859 to 0.894) on both test sets. This superior performance was statistically significant for SERP+-7d (2019: Z = -5.843, p < 0.001, 2020: Z = -4.548, p < 0.001) and SERP+-30d (2019: Z = -3.063, p = 0.002, 2020: Z = -3.256, p = 0.001). SERP+ outperformed SERP marginally on sensitivity, specificity, balanced accuracy, and positive predictive value measures. Negative predictive value was the same for SERP+ and SERP. Additionally, SERP+ showed better performance compared to the commonly used triage risk scores. Conclusions: Accounting for class imbalance during training improved score performance for SERP+. Better stratification of even a small number of patients can be meaningful in the context of the ED triage. Our findings reiterate the potential of machine learning-based scores like SERP+ in supporting accurate, data-driven triage decisions at the ED.

Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.

BACKGROUND: Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However, gaps in literature include incomplete reporting of model performance, inadequate alignment of model formulation with implementation use-case, and insufficient explainability hindering trust and adoption in clinical settings. Hence, we aim to develop an explainable machine learning EHR-based model that prompts palliative care processes by predicting for 365-day mortality risk among patients with advanced cancer within an outpatient setting. METHODS: Our cohort consisted of 5,926 adults diagnosed with Stage 3 or 4 solid organ cancer between July 1, 2017, and June 30, 2020 and receiving ambulatory cancer care within a tertiary center. The classification problem was modelled using Extreme Gradient Boosting (XGBoost) and aligned to our envisioned use-case: "Given a prediction point that corresponds to an outpatient cancer encounter, predict for mortality within 365-days from prediction point, using EHR data up to 365-days prior." The model was trained with 75% of the dataset (n = 39,416 outpatient encounters) and validated on a 25% hold-out dataset (n = 13,122 outpatient encounters). To explain model outputs, we used Shapley Additive Explanations (SHAP) values. Clinical characteristics, laboratory tests and treatment data were used to train the model. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), while model calibration was assessed using the Brier score. RESULTS: In total, 17,149 of the 52,538 prediction points (32.6%) had a mortality event within the 365-day prediction window. The model demonstrated an AUROC of 0.861 (95% CI 0.856-0.867) and AUPRC of 0.771. The Brier score was 0.147, indicating slight overestimations of mortality risk. Explanatory diagrams utilizing SHAP values allowed visualization of feature impacts on predictions at both the global and individual levels. CONCLUSION: Our machine learning model demonstrated good discrimination and precision-recall in predicting 365-day mortality risk among individuals with advanced cancer. It has the potential to provide personalized mortality predictions and facilitate earlier integration of palliative care.

Effect of Cangrelor on Infarct Size in ST-Segment Elevation Myocardial Infarction Treated By Primary Percutaneous Coronary Intervention: A Randomized Controlled Trial (The PITRI Trial).

Background: The administration of intravenous cangrelor at reperfusion achieves faster onset of platelet P2Y12 inhibition than oral ticagrelor and has been shown to reduce myocardial infarct (MI) size in the pre-clinical setting. We hypothesized that the administration of cangrelor at reperfusion will reduce MI size and prevent microvascular obstruction (MVO) in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). Methods: This was a Phase 2, multi-center, randomized, double-blind, placebo controlled clinical trial conducted between November 2017 to November 2021 in six cardiac centers in Singapore (NCT03102723). Patients were randomized to receive either cangrelor or placeboinitiated prior to the PPCI procedure on top of oral ticagrelor. The key exclusion criteria included: presenting <6 hours of symptom onset, prior MI and stroke or transient ischemic attack; on concomitant oral anticoagulants; and a contraindication for cardiovascular magnetic resonance (CMR). The primary efficacy endpoint was acute MI size by CMR within the first week expressed as percentage of the left ventricle mass ( %LVmass). MVO was identified as areas of dark core of hypoenhancement within areas of late gadolinium enhancement. The primary safety endpoint was Bleeding Academic Research Consortium (BARC)-defined major bleeding in the first 48 hours. Continuous variables were compared by Mann-Whitney U test [reported as median (1st quartile- 3rd quartile)] and categorical variables were compared by Fisher's exact test. A 2-sided P<0.05 was considered statistically significant. Results: Of 209 recruited patients, 164 patients (78% ) completed the acute CMR scan. There were no significant differences in acute MI size [placebo: 14.9 (7.3 - 22.6) %LVmass versus cangrelor: 16.3 (9.9 - 24.4)%LVmass, P=0.40] or the incidence [placebo: 48% versus cangrelor: 47%, P=0.99] and extent of MVO [placebo:1.63 (0.60 - 4.65)%LVmass versus cangrelor: 1.18 (0.53 - 3.37)%LVmass, P=0.46] between placebo and cangrelor despite a two-fold decrease in platelet reactivity with cangrelor. There were no BARC-defined major bleeding events in either group in the first 48 hours. Conclusions: Cangrelor administered at time of PPCI did not reduce acute MI size or prevent MVO in STEMI patients given oral ticagrelor despite a significant reduction of platelet reactivity during the PCI procedure.

Strategies to improve implementation of cascade testing in hereditary cancer syndromes: a systematic review.

Hereditary cancer syndromes constitute approximately 10% of all cancers. Cascade testing involves testing of at-risk relatives to determine if they carry the familial pathogenic variant. Despite growing efforts targeted at improving cascade testing uptake, current literature continues to reflect poor rates of uptake, typically below 30%. This study aims to systematically review current literature on intervention strategies to improve cascade testing, assess the quality of intervention descriptions and evaluate the implementation outcomes of listed interventions. We searched major databases using keywords and subject heading of "cascade testing". Interventions proposed in each study were classified according to the Effective Practice and Organization of Care (EPOC) taxonomy. Quality of intervention description was assessed using the TIDieR checklist, and evaluation of implementation outcomes was performed using Proctor's Implementation Outcomes Framework. Improvements in rates of genetic testing uptake was seen in interventions across the different EPOC taxonomy strategies. The average TIDieR score was 7.3 out of 12. Items least reported include modifications (18.5%), plans to assess fidelity/adherence (7.4%) and actual assessment of fidelity/adherence (7.4%). An average of 2.9 out of 8 aspects of implementation outcomes were examined. The most poorly reported outcomes were cost, fidelity and sustainability, with only 3.7% of studies reporting them. Most interventions have demonstrated success in improving cascade testing uptake. Uptake of cascade testing was highest with delivery arrangement (68%). However, the quality of description of interventions and assessment of implementation outcomes are often suboptimal, hindering their replication and implementation downstream. Therefore, further adoption of standardized guidelines in reporting of interventions and formal assessment of implementation outcomes may help promote translation of these interventions into routine practice.

Development and validation of a prehospital termination of resuscitation (TOR) rule for out - of hospital cardiac arrest (OHCA) cases using general purpose artificial intelligence (AI).

BACKGROUND: Prehospital identification of futile resuscitation efforts (defined as a predicted probability of survival lower than 1%) for out-of-hospital cardiac arrest (OHCA) may reduce unnecessary transport. Reliable prediction variables for OHCA 'termination of resuscitation' (TOR) rules are needed to guide treatment decisions. The Universal TOR rule uses only three variables (Absence of Prehospital ROSC, Event not witnessed by EMS and no shock delivered on the scene) has been externally validated and is used by many EMS systems. Deep learning, an artificial intelligence (AI) platform is an attractive model to guide the development of TOR rule for OHCA. The purpose of this study was to assess the feasibility of developing an AI-TOR rule for neurologically favorable outcomes using general purpose AI and compare its performance to the Universal TOR rule. METHODS: We identified OHCA cases of presumed cardiac etiology who were 18 years of age or older from 2016 to 2019 in the All-Japan Utstein Registry. We divided the dataset into 2 parts, the first half (2016-2017) was used as a training dataset for rule development and second half (2018-2019) for validation. The AI software (Prediction One®) created the model using the training dataset with internal cross-validation. It also evaluated the prediction accuracy and displayed the ranking of influencing variables. We performed validation using the second half cases and calculated the prediction model AUC. The top four of the 11 variables identified in the model were then selected as prognostic factors to be used in an AI-TOR rule, and sensitivity, specificity, positive predictive value, and negative predictive value were calculated from validation cohort. This was then compared to the performance of the Universal TOR rule using same dataset. RESULTS: There were 504,561 OHCA cases, 18 years of age or older, 302,799 cases were presumed cardiac origin. Of these, 149,425 cases were used for the training dataset and 153,374 cases for the validation dataset. The model developed by AI using 11 variables had an AUC of 0.969, and its AUC for the validation dataset was 0.965. The top four influencing variables for neurologically favorable outcome were Prehospital ROSC, witnessed by EMS, Age (68 years old and younger) and nonasystole. The AUC calculated using the 4 variables for the AI-TOR rule was 0.953, and its AUC for the validation dataset was 0.952 (95%CI 0.949 -0.954). Of 80,198 patients in the validation cohort that satisfied all four criteria for the AI-TOR rule, 58 (0.07%) had a neurologically favorable one-month survival. The specificity of AI-TOR rule was 0.990, and the PPV was 0.999 for predicting lack of neurologically favorable survival, both the specificity and PPV were higher than that achieved with the universal TOR (0.959, 0.998). CONCLUSIONS: The accuracy of prediction models using AI software to determine outcomes in OHCA was excellent and the AI-TOR rule's variables from prediction model performed better than the Universal TOR rule. External validation of our findings as well as further research into the utility of using AI platforms for TOR prediction in clinical practice is needed.

Using heart rate variability to develop a predictive model for post-operative cardiovascular complications following major abdominal surgery: A pilot study.

BACKGROUND: Heart Rate Variability (HRV) is a dynamic reflection of heart rhythm regulation by various physiological inputs. HRV deviations have been found to correlate with clinical outcomes in patients under physiological stresses. Perioperative cardiovascular complications occur in up to 5% of adult patients undergoing abdominal surgery and are associated with significantly increased mortality. This pilot study aimed to develop a predictive model for post-operative cardiovascular complications using HRV parameters for early risk stratification and aid post-operative clinical decision-making. METHODS: Adult patients admitted to High Dependency Units after elective major abdominal surgery were recruited. The primary composite outcome was defined as cardiovascular complications within 7 days post-operatively. ECG monitoring for HRV parameters was conducted at three time points (pre-operative, immediately post-operative, and post-operative day 1) and analyzed based on outcome group and time interactions. Candidate HRV predictors were included in a multivariable logistic regression analysis incorporating a stepwise selection algorithm. RESULTS: 89 patients were included in the analysis, with 8 experiencing cardiovascular complications. Three HRV parameters, when measured immediately post-operatively and composited with patient age, provided the basis for a predictive model with AUC of 0.980 (95% CI: 0.953, 1.00). The negative predictive value was 1.00 at a statistically optimal predicted probability cut-off point of 0.16. CONCLUSION: Our model holds potential for accelerating clinical decision-making and aiding in patient triaging post-operatively, using easily acquired HRV parameters. Risk stratification with our model may enable safe early step-down care in patients assessed to have a low risk profile of post-operative cardiovascular complications.

Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution.

INTRODUCTION: Deep learning models can assess the quality of images and discriminate among abnormalities in small bowel capsule endoscopy (CE), reducing fatigue and the time needed for diagnosis. They serve as a decision support system, partially automating the diagnosis process by providing probability predictions for abnormalities. METHODS: We demonstrated the use of deep learning models in CE image analysis, specifically by piloting a bowel preparation model (BPM) and an abnormality detection model (ADM) to determine frame-level view quality and the presence of abnormal findings, respectively. We used convolutional neural network-based models pretrained on large-scale open-domain data to extract spatial features of CE images that were then used in a dense feed-forward neural network classifier. We then combined the open-source Kvasir-Capsule dataset (n = 43) and locally collected CE data (n = 29). RESULTS: Model performance was compared using averaged five-fold and two-fold cross-validation for BPMs and ADMs, respectively. The best BPM model based on a pre-trained ResNet50 architecture had an area under the receiver operating characteristic and precision-recall curves of 0.969±0.008 and 0.843±0.041, respectively. The best ADM model, also based on ResNet50, had top-1 and top-2 accuracies of 84.03±0.051 and 94.78±0.028, respectively. The models could process approximately 200-250 images per second and showed good discrimination on time-critical abnormalities such as bleeding. CONCLUSION: Our pilot models showed the potential to improve time to diagnosis in CE workflows. To our knowledge, our approach is unique to the Singapore context. The value of our work can be further evaluated in a pragmatic manner that is sensitive to existing clinician workflow and resource constraints.

Prehospital stroke care in Singapore.

ABSTRACT: Due to the narrow window of opportunity for stroke therapeutics to be employed, effectiveness of stroke care systems is predicated on the efficiency of prehospital stroke systems. A robust prehospital stroke system of care that provides a rapid and well-coordinated response maximises favourable poststroke outcomes, but achieving this presents a unique set of challenges dependent on demographic and geographical circumstances. Set in the context of a highly urbanised first-world nation with a rising burden of stroke, Singapore's prehospital stroke system has evolved to reflect the environment in which it operates. This review aims to characterise the current state of prehospital stroke care in Singapore, covering prehospital aspects of the stroke survival chain from symptom onset till arrival at the emergency department. We identify areas for improvement and innovation, as well as provide insights into the possible future of prehospital stroke care in Singapore.

Socioeconomic disadvantage and long-term survival duration in out-of-hospital cardiac arrest patients: A population-based cohort study.

Background: Socioeconomic status (SES) is a well-established determinant of cardiovascular health. However, the relationship between SES and clinical outcomes in long-term out-of-hospital cardiac arrest (OHCA) is less well-understood. The Singapore Housing Index (SHI) is a validated building-level SES indicator. We investigated whether SES as measured by SHI is associated with long-term OHCA survival in Singapore. Methods: We conducted an open cohort study with linked data from the Singapore Pan-Asian Resuscitation Outcomes Study (PAROS), and the Singapore Registry of Births and Deaths (SRBD) from 2010 to 2020. We fitted generalized structural equation models, calculating hazard ratios (HRs) using a Weibull model. We constructed Kaplan-Meier survival curves and calculated the predicted marginal probability for each SHI category. Results: We included 659 cases. In both univariable and multivariable analyses, SHI did not have a significant association with survival. Indirect pathways of SHI mediated through covariates such as Emergency Medical Services (EMS) response time (HR of low-medium, high-medium and high SHI when compared to low SHI: 0.98 (0.88-1.10), 1.01 (0.93-1.11), 1.02 (0.93-1.12) respectively), and age of arrest (HR of low-medium, high-medium and high SHI when compared to low SHI: 1.02 (0.75-1.38), 1.08 (0.84-1.38), 1.18 (0.91-1.54) respectively) had no significant association with OHCA survival. There was no clear trend in the predicted marginal probability of survival among the different SHI categories. Conclusions: We did not find a significant association between SES and OHCA survival outcomes in residential areas in Singapore. Among other reasons, this could be due to affordable healthcare across different socioeconomic classes.

Corrigendum to "Development and Asian-wide validation of the Grade for Interpretable Field Triage (GIFT) for predicting mortality in pre-hospital patients using the Pan-Asian Trauma Outcomes Study (PATOS)" [The Lancet Regional Health - Western Pacific 34 (2023) 100733].

[This corrects the article DOI: 10.1016/j.lanwpc.2023.100733.].

Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model.

Emergency departments (ED) are complex, triage is a main task in the ED to prioritize patient with limited medical resources who need them most. Machine learning (ML) based ED triage tool, Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable ML framework with single center. We aimed to develop SERP with 3 Korean multicenter cohorts based on common data model (CDM) without data sharing and compare performance with inter-hospital validation design. This retrospective cohort study included all adult emergency visit patients of 3 hospitals in Korea from 2016 to 2017. We adopted CDM for the standardized multicenter research. The outcome of interest was 2-day mortality after the patients' ED visit. We developed each hospital SERP using interpretable ML framework and validated inter-hospital wisely. We accessed the performance of each hospital's score based on some metrics considering data imbalance strategy. The study population for each hospital included 87,670, 83,363 and 54,423 ED visits from 2016 to 2017. The 2-day mortality rate were 0.51%, 0.56% and 0.65%. Validation results showed accurate for inter hospital validation which has at least AUROC of 0.899 (0.858-0.940). We developed multicenter based Interpretable ML model using CDM for 2-day mortality prediction and executed Inter-hospital external validation which showed enough high accuracy.

Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS.

Background: Shock-refractory ventricular fibrillation (VF) or ventricular tachycardia (VT) is a treatment challenge in out-of-hospital cardiac arrest (OHCA). This study aimed to develop and validate machine learning models that could be implemented by emergency medical services (EMS) to predict refractory VF/VT in OHCA patients. Methods: This was a retrospective study examining adult non-traumatic OHCA patients brought into the emergency department by Singapore EMS from the Pan-Asian Resuscitation Outcomes Study (PAROS) registry. Data from April 2010 to March 2020 were extracted for this study. Refractory VF/VT was defined as VF/VT persisting or recurring after at least one shock. Features were selected based on expert clinical opinion and availability to dispatch prior to arrival at scene. Multivariable logistic regression (MVR), LASSO and random forest (RF) models were investigated. Model performance was evaluated using receiver operator characteristic (ROC) area under curve (AUC) analysis and calibration plots. Results: 20,713 patients were included in this study, of which 860 (4.1%) fulfilled the criteria for refractory VF/VT. All models performed comparably and were moderately well-calibrated. ROC-AUC were 0.732 (95% CI, 0.695 - 0.769) for MVR, 0.738 (95% CI, 0.701 - 0.774) for LASSO, and 0.731 (95% CI, 0.690 - 0.773) for RF. The shared important predictors across all models included male gender and public location. Conclusion: The machine learning models developed have potential clinical utility to improve outcomes in cases of refractory VF/VT OHCA. Prediction of refractory VF/VT prior to arrival at patient's side may allow for increased options for intervention both by EMS and tertiary care centres.

Towards advancing Out-of-Hospital cardiac arrest (OHCA) registries globally: Considerations from the global OHCA registry (GOHCAR) collaborative.

A growing number out-of-hospital cardiac arrest (OHCA) registries have been developed across the globe. A few of these are national, while others cover larger geographical regions. These registries have common objectives; continuous quality improvement, epidemiological research and providing infrastructure for clinical trials. OHCA registries make performance comparison across Emergency Medical Services systems possible for benchmarking, hypothesis generation and further research. Changes in OHCA incidence and outcomes provide insights about the effects of secular trends or health services interventions. These registries, therefore, have become a mainstay of OHCA management and research. However, developing and maintaining these registries is challenging. Coordination of different service providers to support data collection, sustainable resourcing, data quality and data security are the key challenges faced by these registries. Despite all these challenges, noteworthy progress has been made and further standardization and co-ordination across registries can result in great international benefit. In this paper we present a 'why' and 'how to' model for setting up OHCA registries, and suggestions for better international co-ordination through a Global OHCA Registries Collaborative (GOHCAR). We draw together the knowledge of a cohort of international researchers, with experience and expertise in OHCA registry development, management, and data synthesis.

Sex Differences in Receipt of Bystander Cardiopulmonary Resuscitation Considering Neighborhood Racial and Ethnic Composition.

BACKGROUND: Bystander cardiopulmonary resuscitation (B-CPR) and defibrillation for out-of-hospital cardiac arrest (OHCA) vary by sex, with women being less likely to receive these interventions in public. It is unknown whether sex differences persist when considering neighborhood racial and ethnic composition. We examined the odds of receiving B-CPR stratified by location and neighborhood. We hypothesized that women in predominantly Black neighborhoods will have a lower odds of receiving B-CPR. METHODS AND RESULTS: We conducted a retrospective study using the Cardiac Arrest Registry to Enhance Survival (CARES). Neighborhoods were classified by census tract. We modeled the odds of receipt of B-CPR (primary outcome), automatic external defibrillation application, and survival to hospital discharge (secondary outcomes) by sex. CARES collected 457 621 arrests (2013-2019); after appropriate exclusion, 309 662 were included. Women who had public OHCA had a 14% lower odds of receiving B-CPR (odds ratio [OR], 0.86 [95% CI, 0.82-0.89]), but effect modification was not seen by neighborhood (P=not significant). In predominantly Black neighborhoods, women who had public OHCA had a 13% lower odds of receiving B-CPR (adjusted OR, 0.87 [95% CI, 0.76-0.98]) and 12% lower odds of receiving automatic external defibrillation application (adjusted OR, 0.88 [95% CI, 0.78-0.99]). In predominantly Hispanic neighborhoods, women who had public OHCA were less likely to receive B-CPR (adjusted OR, 0.83 [95% CI, 0.73-0.96]) and less likely to receive automatic external defibrillation application (adjusted OR, 0.74 [95% CI, 0.64-0.87]). CONCLUSIONS: Women with public OHCA have a decreased likelihood of receiving B-CPR and automatic external defibrillation application. Findings did not differ significantly according to neighborhood composition. Despite this, our work has implications for considering strategies to reduce disparities around bystander response.

A machine-learning exploration of the exposome from preconception in early childhood atopic eczema, rhinitis and wheeze development.

BACKGROUND: Most previous research on the environmental epidemiology of childhood atopic eczema, rhinitis and wheeze is limited in the scope of risk factors studied. Our study adopted a machine learning approach to explore the role of the exposome starting already in the preconception phase. METHODS: We performed a combined analysis of two multi-ethnic Asian birth cohorts, the Growing Up in Singapore Towards healthy Outcomes (GUSTO) and the Singapore PREconception Study of long Term maternal and child Outcomes (S-PRESTO) cohorts. Interviewer-administered questionnaires were used to collect information on demography, lifestyle and childhood atopic eczema, rhinitis and wheeze development. Data training was performed using XGBoost, genetic algorithm and logistic regression models, and the top variables with the highest importance were identified. Additive explanation values were identified and inputted into a final multiple logistic regression model. Generalised structural equation modelling with maternal and child blood micronutrients, metabolites and cytokines was performed to explain possible mechanisms. RESULTS: The final study population included 1151 mother-child pairs. Our findings suggest that these childhood diseases are likely programmed in utero by the preconception and pregnancy exposomes through inflammatory pathways. We identified preconception alcohol consumption and maternal depressive symptoms during pregnancy as key modifiable maternal environmental exposures that increased eczema and rhinitis risk. Our mechanistic model suggested that higher maternal blood neopterin and child blood dimethylglycine protected against early childhood wheeze. After birth, early infection was a key driver of atopic eczema and rhinitis development. CONCLUSION: Preconception and antenatal exposomes can programme atopic eczema, rhinitis and wheeze development in utero. Reducing maternal alcohol consumption during preconception and supporting maternal mental health during pregnancy may prevent atopic eczema and rhinitis by promoting an optimal antenatal environment. Our findings suggest a need to include preconception environmental exposures in future research to counter the earliest precursors of disease development in children.

Assertive community treatment for high-utilizing alcohol misuse patients: a before-and-after cohort study protocol.

BACKGROUND: The challenge posed by Alcohol-Related Frequent Attenders (ARFAs) in Emergency Departments (EDs) is growing in Singapore, marked by limited engagement with conventional addiction treatment pathways. Recognizing this gap, this study aims to explore the potential benefits of Assertive Community Treatment (ACT) - an innovative, community-centered, harm-reduction strategy-in mitigating the frequency of ED visits, curbing Emergency Medical Services (EMS) calls, and uplifting health outcomes across a quartet of Singaporean healthcare institutions. METHODS: Employing a prospective before-and-after cohort design, this investigation targeted ARFAs aged 21 years and above, fluent in English or Mandarin. Eligibility was determined by a history of at least five ED visits in the preceding year, with no fewer than two due to alcohol-related issues. The study contrasted health outcomes of patients integrated into the ACT care model versus their experiences under the exclusive provision of standard emergency care across Hospitals A, B, C and D. Following participants for half a year post-initial assessment, the evaluation metrics encompassed socio-demographic factors, ED, and EMS engagement frequencies, along with validated health assessment tools, namely Christo Inventory for Substance-misuse Services (CISS) scores, University of California, Los Angeles (UCLA) Loneliness scores, and Centre for Epidemiologic Studies Depression Scale Revised (CESD-R-10) scores. DISCUSSION: Confronted with intricate socio-economic and medical challenges, the ARFA cohort often grapples with heightened vulnerabilities in relation to alcohol misuse. Pioneering the exploration of ACT's efficacy with ARFAs in a Singaporean context, our research is anchored in a patient-centered approach, designed to comprehensively address these multifaceted clinical profiles. While challenges, like potential high attrition rates and sporadic data collection, are anticipated, the model's prospective contribution towards enhancing patient well-being and driving healthcare efficiencies in Singapore is substantial. Our findings have the potential to reshape healthcare strategies and policy recommendations. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04447079. Initiated on 25 June 2020.

Extracorporeal cardiopulmonary resuscitation versus conventional CPR in cardiac arrest: an updated meta-analysis and trial sequential analysis.

BACKGROUND: Extracorporeal cardiopulmonary resuscitation (ECPR) may reduce mortality and improve neurological outcomes in patients with cardiac arrest. We updated our existing meta-analysis and trial sequential analysis to further evaluate ECPR compared to conventional CPR (CCPR). METHODS: We searched three international databases from 1 January 2000 through 1 November 2023, for randomised controlled trials or propensity score matched studies (PSMs) comparing ECPR to CCPR in both out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA). We conducted an updated random-effects meta-analysis, with the primary outcome being in-hospital mortality. Secondary outcomes included short- and long-term favourable neurological outcome and survival (30 days-1 year). We also conducted a trial sequential analysis to evaluate the required information size in the meta-analysis to detect a clinically relevant reduction in mortality. RESULTS: We included 13 studies with 14 pairwise comparisons (6336 ECPR and 7712 CCPR) in our updated meta-analysis. ECPR was associated with greater precision in reducing overall in-hospital mortality (OR 0.63, 95% CI 0.50-0.79, high certainty), to which the trial sequential analysis was concordant. The addition of recent studies revealed a newly significant decrease in mortality in OHCA (OR 0.62, 95% CI 0.45-0.84). Re-analysis of relevant secondary outcomes reaffirmed our initial findings of favourable short-term neurological outcomes and survival up to 30 days. Estimates for long-term neurological outcome and 90-day-1-year survival remained unchanged. CONCLUSIONS: We found that ECPR reduces in-hospital mortality, improves neurological outcome, and 30-day survival. We additionally found a newly significant benefit in OHCA, suggesting that ECPR may be considered in both IHCA and OHCA.