Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study

Objectives: To use national, pre- and post-pandemic electronic health records (EHR) to develop and validate a scenario-based model incorporating baseline mortality risk, infection rate (IR) and relative risk (RR) of death for prediction of excess deaths. Design: An EHR-based, retrospective cohort study. Setting: Linked EHR in Clinical Practice Research Datalink (CPRD); and linked EHR and COVID-19 data in England provided in NHS Digital Trusted Research Environment (TRE). Participants: In the development (CPRD) and validation (TRE) cohorts, we included 3.8 million and 35.1 million individuals aged ≥30 years, respectively. Main outcome measures: One-year all-cause excess deaths related to COVID-19 from March 2020 to March 2021. Results: From 1 March 2020 to 1 March 2021, there were 127,020 observed excess deaths. Observed RR was 4.34% (95% CI, 4.31–4.38) and IR was 6.27% (95% CI, 6.26–6.28). In the validation cohort, predicted one-year excess deaths were 100,338 compared with the observed 127,020 deaths with a ratio of predicted to observed excess deaths of 0.79. Conclusions: We show that a simple, parsimonious model incorporating baseline mortality risk, one-year IR and RR of the pandemic can be used for scenario-based prediction of excess deaths in the early stages of a pandemic. Our analyses show that EHR could inform pandemic planning and surveillance, despite limited use in emergency preparedness to date. Although infection dynamics are important in the prediction of mortality, future models should take greater account of underlying conditions

Divulging the Hidden Capacity and Sodiation Kinetics of Na_<i>x</i>C₆Cl₄O₂: A High Voltage Organic Cathode for Sodium Rechargeable Batteries

In the current emerging sustainable organic battery field, quinones are seen as one of the prime candidates for application in rechargeable battery electrodes. Recently, C₆Cl₄O₂, a modified quinone, has been proposed as a high voltage organic cathode. However, the sodium insertion mechanism behind the cell reaction remained unclear due to the nescience of the right crystal structure. Here, the framework of the density functional theory (DFT) together with an evolutionary algorithm was employed to elucidate the crystal structures of the compounds Na_<i>x</i>C₆Cl₄O₂ (<i>x</i> = 0.5, 1.0, 1.5 and 2). Along with the usefulness of PBE functional to reflect the experimental potential, also the importance of the hybrid functional to divulge the hidden theoretical capacity is evaluated. We showed that the experimentally observed lower specific capacity is a result of the great stabilization of the intermediate phase Na_1.5C₆Cl₄O₂. The calculated activation barriers for the ionic hops are 0.68, 0.40, and 0.31 eV, respectively, for NaC₆Cl₄O₂, Na_1.5C₆Cl₄O₂, and Na₂C₆Cl₄O₂. These results indicate that the kinetic process must not be a limiting factor upon Na insertion. Finally, the correct prediction of the specific capacity has confirmed that the theoretical strategy used, employing evolutionary simulations together with the hybrid functional framework, can rightly model the thermodynamic process in organic electrode compounds

Scrupulous Probing of Bifunctional Catalytic Activity of Borophene Monolayer: Mapping Reaction Coordinate with Charge Transfer

We have envisaged the hydrogen evolution and oxygen evolution reactions (HER and OER) on two-dimensional (2D) noble metal free borophene monolayer based on first-principles electronic structure calculations. We have investigated the effect of Ti functionalization on borophene monolayer from the perspective of HER and OER activities enhancement. We have probed the activities based on the reaction coordinate, which is conceptually related to the adsorption free energies of the intermediates of HER and OER, as well as from the vibrational frequency analysis with the corresponding charge transfer mechanism between the surface and the adsorbate. Ti-functionalized borophene has emerged as a promising material for HER and OER mechanisms. We believe that our probing method, based on reaction coordinate coupled with vibrational analysis that has been validated by the charge transfer mechanism, would certainly become as a robust prediction route for HER and OER mechanisms in coming days

Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation

<div><p>Background</p><p>Risk prediction is crucial in many areas of medical practice, such as cardiac transplantation, but existing clinical risk-scoring methods have suboptimal performance. We develop a novel risk prediction algorithm and test its performance on the database of all patients who were registered for cardiac transplantation in the United States during 1985-2015.</p><p>Methods and findings</p><p>We develop a new, interpretable, methodology (ToPs: Trees of Predictors) built on the principle that specific predictive (survival) models should be used for specific clusters within the patient population. ToPs <i>discovers</i> these specific clusters and the specific predictive model that performs best for each cluster. In comparison with existing clinical risk scoring methods and state-of-the-art machine learning methods, our method provides significant improvements in survival predictions, both post- and pre-cardiac transplantation. For instance: in terms of 3-month survival post-transplantation, our method achieves AUC of 0.660; the best clinical risk scoring method (RSS) achieves 0.587. In terms of 3-year survival/mortality predictions post-transplantation (in comparison to RSS), holding specificity at 80.0%, our algorithm correctly predicts survival for 2,442 (14.0%) more patients (of 17,441 who actually survived); holding sensitivity at 80.0%, our algorithm correctly predicts mortality for 694 (13.0%) more patients (of 5,339 who did not survive). ToPs achieves similar improvements for other time horizons and for predictions pre-transplantation. ToPs discovers the most relevant features (covariates), uses available features to best advantage, and can adapt to changes in clinical practice.</p><p>Conclusions</p><p>We show that, in comparison with existing clinical risk-scoring methods and other machine learning methods, ToPs significantly improves survival predictions both post- and pre-cardiac transplantation. ToPs provides a more accurate, personalized approach to survival prediction that can benefit patients, clinicians, and policymakers in making clinical decisions and setting clinical policy. Because survival prediction is widely used in clinical decision-making across diseases and clinical specialties, the implications of our methods are far-reaching.</p></div

Venn diagram of clinical features used for each clinical risk score.

<p>(a) Post-transplantation, (b) Pre-transplantation.</p

Significant reduction in chronic kidney disease progression with sodium glucose co-transporter-2 inhibitors compared to dipeptidyl peptidase-4 inhibitors in adults with type 2 diabetes in UK clinical setting: An observational outcomes study based on international guidelines for kidney disease.

Aims To confirm the reno-protective effects of sodium-glucose cotransporter-2 (SGLT2) inhibitors compared with dipeptidyl peptidase-4 (DPP-4) inhibitors on the onset and progression of chronic kidney disease (CKD) in routine clinical practice. Materials and Methods We conducted a retrospective cohort study using the Clinical Practice Research Datalink Aurum database linked to Hospital Episode Statistics. The primary outcome was risk of the composite CKD endpoint based on the recent consensus guidelines for kidney disease: >40% decline in estimated glomerular filtration rate (eGFR), kidney death or end-stage kidney disease (ESKD; a composite of kidney transplantation, maintenance of dialysis, sustained low eGFR Results A total of 131 824 people with type 2 diabetes (T2D) were identified; 79.0% had no known history of CKD. During a median follow-up of 2.1 years, SGLT2 inhibitor initiation was associated with lower risk of progression to composite kidney endpoints than DPP-4 inhibitor initiation (7.48 vs. 11.77 events per 1000 patient-years, respectively). Compared with DPP-4 inhibitor initiation, SGLT2 inhibitor initiation was associated with reductions in the primary composite CKD endpoint (hazard ratio [HR] 0.64, 95% confidence interval [CI] 0.56-0.74), all-cause mortality (HR 0.74, 95% CI 0.64-0.86) and ESKD (HR 0.37, 95% CI 0.25-0.55), reduced the rate of sustained low eGFR (HR 0.33, 95% CI 0.19-0.57), and reduced diagnoses of ESKD in primary care (HR 0.04, 95% CI 0.01-0.18). Results were consistent across subgroup and sensitivity analyses. Conclusions In adults with T2D, initiation of an SGLT2 inhibitor was associated with a significantly reduced risk of CKD progression and death compared with initiation of a DPP-4 inhibitor.</p

Comparisons among ToPs/R, existing clinical risk scores, regression methods, and machine learning benchmarks for post-transplantation survival prediction using C-index and AUC (at horizons of 3-months, 1-year, and 3-years).

<p>Train on 2005-2009; predict on 2010-2015.</p

Comparisons among ToPs/R, regression methods, and machine learning benchmarks for pre-transplantation survival prediction using C-index and AUC (at horizons of 3-months, 1-year, 3-years, and 10-years).

<p>Comparisons among ToPs/R, regression methods, and machine learning benchmarks for pre-transplantation survival prediction using C-index and AUC (at horizons of 3-months, 1-year, 3-years, and 10-years).</p

Kaplan-Meier survival curves for different periods in post- and pre- transplantation.

<p>Kaplan-Meier survival curves for different periods in post- and pre- transplantation.</p

Calibration graphs for pre-transplantation (<i>ρ</i> = Root mean square error).

<p>Calibration graphs for pre-transplantation (<i>ρ</i> = Root mean square error).</p