The BE-ALIVE score: assessing 30-day mortality risk in patients presenting with acute coronary syndromes

AIM: To create and validate a simple scoring system for predicting 30-day mortality in patients presenting with acute coronary syndromes (ACS) at their moment of admission. METHODS AND RESULTS: 2407 consecutive patients presenting to Harefield Hospital with measured arterial blood gases, from January 2011 to December 2020, were studied to build the training set. 30-day mortality in this group was 17.2%. A scoring algorithm that was built using binary logistic regression of variables available on admission was then converted to an additive risk score. The resultant scoring system is the BE-ALIVE score, which incorporates the following factors:Base Excess (1 point for <-2 mmol/L), Age (<65 years: 0 points, 65-74: 1 point, 75-84: 2 points, ≥85: 3 points), Lactate (<2 mmol/L: 0 points, 2-4.9: 1 point, 5-9.9: 3 points, ≥10: 6 points), Intubated (2 points), Left Ventricular function (mildly impaired or better: -1 point, moderately impaired: 1 point, severely impaired: 3 points) and External/out of hospital cardiac arrest 2 points).The scoring system was validated using a testing set of 515 patients presenting to Harefield Hospital in 2021. The validation metrics were excellent with a c-statistic of 0.9, Brier's score 0.06 vs a naïve classifier of 0.15, Spiegelhalter's z-statistic probability of 0.267 and a calibration slope of 1.08. CONCLUSION: The BE-ALIVE score is a simple and accurate scoring system to predict 30-day mortality in patients presenting with ACS. Appreciating this mortality risk can allow prompt involvement of appropriate care such as the shock team

Reliable Multimodal Heartbeat Classification using Deep Neural Networks

Copyright © 2023 Authors. Arrhythmias are deviations from the normal heart rhythm with impact on the cardiovascular health. Their prompt detection plays an important role in mitigating potential negative outcomes, particularly in patients in the intensive care units (ICU). Heartbeat detection has mainly been focused on electrocardiogram (ECG) signals. However, ICU patient mobility frequently leads to disconnection of certain ECG leads, potentially compromising the accurate heartbeat classification. Arterial line blood pressure (ABP) and central venous pressure (CVP) signals are routinely monitored in ICU patients. Changes in the ABP and CVP suggest alterations in the haemodynamic status and cardiac function of the patients. Thus, leveraging these signals for heartbeat classification, either independently or in conjunction with ECG data, present a viable approach to ensure that even in scenarios where ECG signals are unavailable, alarm systems alerting healthcare providers of arrhythmias remain functional. Moreover, while many researchers have successfully created methodologies to accurately classify heartbeats including paced beats, none were able to distinguish various sub-classes of paced heartbeats. A more comprehensive distinction is crucial as it not only aids in the identification of pacing settings but also facilitates the detection of inadequate pacing settings, a critical aspect in patient care. In this paper, we employ a hybrid model using long-short term memory networks (LSTM) and convolutional neural network (CNN), along with different residual CNN (ResNet) models for multimodal arrhythmia classification and for comprehensive paced heartbeats classification. When using all three channels, ResNet50 achieved the best accuracy of 99.58% on 5 different arrhythmia classes, whereas ResNet34 achieved an accuracy of 93.82% on 12 paced classes. The significant efficiency of utilizing ABP and CVP signals independently for classification, was also highlighted. ResNet50 was trained with ABP and CVP signals independently and correctly identified arrhythmias with an accuracy of 98.79% and 96.67%, respectively. For classifying 12 different paced heartbeats, ResNet34 achieved 74.04% accuracy with ABP signals and 74.38% with CVP signals. Moreover, the same ResNet50 model was trained on the MIT-BIH arrhythmia database, achieving an accuracy, sensitivity, and precision of 98.78%, 98.77% and 98.80%, which demonstrates the scalability of the proposed model.British Heart Foundation for sponsoring this project (No.FS/19/73/34690

How robust are recommended waiting times to pacing after cardiac surgery that are derived from observational data?

AIMS: For bradycardic patients after cardiac surgery, it is unknown how long to wait before implanting a permanent pacemaker (PPM). Current recommendations vary and are based on observational studies. This study aims to examine why this variation may exist. METHODS AND RESULTS: We conducted first a study of patients in our institution and second a systematic review of studies examining conduction disturbance and pacing after cardiac surgery. Of 5849 operations over a 6-year period, 103 (1.8%) patients required PPM implantation. Only pacing dependence at implant and time from surgery to implant were associated with 30-day pacing dependence. The only predictor of regression of pacing dependence was time from surgery to implant. We then applied the conventional procedure of receiver operating characteristic (ROC) analysis, seeking an optimal time point for decision-making. This suggested the optimal waiting time was 12.5 days for predicting pacing dependence at 30 days for all patients (area under the ROC curve (AUC) 0.620, P = 0.031) and for predicting regression of pacing dependence in patients who were pacing-dependent at implant (AUC 0.769, P < 0.001). However, our systematic review showed that recommended optimal decision-making time points were strongly correlated with the average implant time point of those individual studies (R = 0.96, P < 0.001). We further conducted modelling which revealed that in any such study, the ROC method is strongly biased to indicate a value near to the median time to implant as optimal. CONCLUSION: When commonly used automated statistical methods are applied to observational data with the aim of defining the optimal time to pacing after cardiac surgery, the suggested answer is likely to be similar to the average time to pacing in that cohort

Observations of red-giant variable stars by Aboriginal Australians

Aboriginal Australians carefully observe the properties and positions of stars, including both overt and subtle changes in their brightness, for subsistence and social application. These observations are encoded in oral tradition. I examine two Aboriginal oral traditions from South Australia that describe the periodic changing brightness in three pulsating, red-giant variable stars: Betelgeuse (Alpha Orionis), Aldebaran (Alpha Tauri), and Antares (Alpha Scorpii). The Australian Aboriginal accounts stand as the only known descriptions of pulsating variable stars in any Indigenous oral tradition in the world. Researchers examining these oral traditions over the last century, including anthropologists and astronomers, missed the description of these stars as being variable in nature as the ethnographic record contained several misidentifications of stars and celestial objects. Arguably, ethnographers working on Indigenous Knowledge Systems should have academic training in both the natural and social sciences.Comment: The Australian Journal of Anthropology (2018

Complete revascularization is associated with higher mortality in patients with ST-elevation myocardial infarction, multi-vessel disease and shock defined by hyperlactataemia: results from the Harefield Shock Registry incorporating explainable machine learning

Aims Revascularization strategy for patients with ST-elevation myocardial infarction (STEMI) and multi-vessel disease varies according to the patient’s cardiogenic shock status, but assessing shock acutely can be difficult. This article examines the link between cardiogenic shock defined solely by a lactate of ≥2 mmol/L and mortality from complete vs. culprit-only revascularization in this cohort. Methods and results Patients presenting with STEMI, multi-vessel disease without severe left main stem stenosis and a lactate ≥2 mmol/L between 2011 and 2021 were included. The primary endpoint was mortality at 30 days by revascularization strategy for shocked patients. Secondary endpoints were mortality at 1 year and over a median follow-up of 30 months. Four hundred and eight patients presented in shock. Mortality in the shock cohort was 27.5% at 30 days. Complete revascularization (CR) was associated with higher mortality at 30 days [odds ratio (OR) 2.1 (1.02–4.2), P = 0.043], 1 year [OR 2.4 (1.2–4.9), P = 0.01], and over 30 months follow-up [hazard ratio (HR) 2.2 (1.4–3.4), P < 0.001] compared with culprit lesion-only percutaneous coronary intervention (CLOP). Mortality was again higher in the CR group after propensity matching (P = 0.018) and inverse probability treatment weighting [HR 2.0 (1.3–3.0), P = 0.001]. Furthermore, explainable machine learning demonstrated that CR was behind only blood gas parameters and creatinine levels in importance for predicting 30-day mortality. Conclusion In patients presenting with STEMI and multi-vessel disease in shock defined solely by a lactate of ≥2 mmol/L, CR is associated with higher mortality than CLOP.British Heart Foundation (FS/19/73/34690 to I.C.)

Different methods of providing automatic external defibrillators to out-of-hospital cardiac arrests to prevent sudden cardiac death

OBJECTIVES: This is a protocol for a Cochrane Review (intervention). The objectives are as follows:. To establish the effectiveness of different methods of early AED application (non-dispatched layperson, dispatched layperson, dispatched professional, drone delivery - all interventions) versus standard care (comparator) in adults who suffer a witnessed out-of-hospital cardiac arrest in a public setting (population) upon outcomes of survival and neurological function

Improving the Energy Performance Contracting Process using Building Performance Simulation: Lessons Learnt from Post Occupancy Investigation of a Case Study in the UK

There is a niche trend to use ‘Energy Performance Contracts’ (EPCs), for new buildings to ensure that minimum energy performance is achieved in practice. Building Performance Simulation (BPS) help to estimate performance and assess risks during design, construction and operational stages. This paper reports on an office building in the UK that has been procured under an EPC. The current performance shows that it will be challenging for the building to achieve the target. Being one of the first new buildings in the UK to be subjected to an EPC, analysis of the design, construction and operation process, provides insights into the specific issues related to building procurement and operation. It is suggested that scenario analysis, accounting for uncertainties and dynamic BPS should be used throughout the procurement process to quantify and manage the risks associated with performance targets. The paper also identifies that if performance targets are not defined comprehensively, there can be unintended consequences that lead to underperformance

Multimodal Arrhythmia Classification Using Deep Neural Networks

Arrhythmias are deviations from the normal heart rhythm with impact on the cardiovascular health. Their prompt detection plays an important role in mitigating potential negative outcomes, particularly in patients in the intensive care units (ICU). The detection of arrhythmias has mainly been focused on electrocardiogram (ECG) signals. However, ICU patient mobility frequently leads to disconnection of certain ECG leads, potentially compromising the accurate arrhythmia detection. Arterial line blood pressure (ABP) and central venous pressure (CVP) signals are routinely monitored in ICU patients. Changes in the ABP and CVP suggest alterations in the haemodynamic status and cardiac function of the patients. Thus, leveraging these signals for arrhythmia detection, either independently or in conjunction with ECG data, present a viable approach to ensure that even in scenarios where ECG signals are unavailable, alarm systems alerting healthcare providers of arrhythmias remain functional. In this paper, we employ a hybrid model using long-short term memory networks (LSTM) and convolutional neural network (CNN), along with different residual CNN (ResNet) models for multimodal arrhythmia classification. When using all three channels, ResNet50 achieved the best accuracy of 99.58% on five different arrhythmia classes. The significant efficiency of utilizing ABP and CVP signals independently for the classification of arrhythmias, was also highlighted. ResNet50 was trained with ABP and CVP signals independently and correctly identified arrhythmias with an accuracy of 98.79% and 96.67%, respectively. Moreover, the same ResNet50 model was trained on the MIT-BIH arrhythmia database, achieving an accuracy, sensitivity, and precision of 98.78%, 98.77% and 98.80%, which demonstrates the scalability of the proposed model.British Heart Foundation for sponsoring this project (No.FS/19/73/34690)

Development of a rapid semi-automated tool to measure total kidney volume in autosomal dominant polycystic kidney disease

Background Total kidney volume (TKV) is an approved early prognostic marker of progression in autosomal dominant polycystic kidney disease. The approval of tolvaptan for patients with rapid disease progression in Europe requires accurate patient stratification. Current methods of TKV measurement rely on manual segmentation which is time consuming, restricting its clinical use. To address this important clinical challenge we report the development and performance of a semi-automated method (Sheffield TKV tool) to measure TKV in patients with this disease. Methods 1.5T MRI scans were acquired (Siemens Avanto) in 61 adult patients with autosomal dominant polycystic kidney disease. Manual segmentation of the kidneys was performed on T2 true fast imaging with steady state precession MRI. Computational semi-automated segmentation methods were tested in a subgroup of ten patients and the optimum method used in all 61 cases to measure TKV (mL). Manual and semi-automated results were compared by Bland–Altman analyses. Processing time for manual and semi-automated methods were recorded. Findings Our cohort consisted of 29 men and 32 women (mean age 45 years, SD 14). Estimated GFR (eGFR) in patients within 1 month of the MRI ranged between 32 and 138 mL/min. TKV measured by manual segmentation ranged between 258 and 3680 mL. The Sheffield TKV tool performed optimally for calculating TKV, reporting accurate results in 80% of cases compared with manual TKV. Inaccuracies were associated with erroneous inclusion of blood vessels, the renal hilum, or leakage into neighbouring tissues, and overall were more frequent in smaller kidneys. Processing time for TKV with the Sheffield TKV tool was 2–5 min compared with 20–30 min for manual segmentation. Interpretation We describe a new rapid, semi-automated method for measuring TKV on MRI which should be a useful tool for evaluating patients with autosomal dominant polycystic kidney disease. We plan to optimise MRI acquisition sequences and extract the renal hilar volume to improve performance of the Sheffield TKV tool and validate it in another population with autosomal dominant polycystic kidney disease, with the ultimate aim of using it in clinical practice. Funding Insigneo (Institute for in silico medicine) bursary (from Sheffield Teaching Hospitals NHS Foundation Trust), National Institute for Health Research