Medicine and meteorology: Cloud, connectivity, and care

10q26 - The enigma in age-related macular degeneration

Despite comprehensive research efforts over the last decades, the pathomechanisms of age-related macular degeneration (AMD) remain far from being understood. Large-scale genome wide association studies (GWAS) were able to provide a defined set of genetic aberrations which contribute to disease risk, with the strongest contributors mapping to distinct regions on chromosome 1 and 10. While the chromosome 1 locus comprises factors of the complement system with well-known functions, the role of the 10q26-locus in AMD-pathophysiology remains enigmatic. 10q26 harbors a cluster of three functional genes, namely PLEKHA1, ARMS2 and HTRA1, with most of the AMD-associated genetic variants mapping to the latter two genes. High linkage disequilibrium between ARMS2 and HTRA1 has kept association studies from reliably defining the risk-causing gene for long and only very recently the genetic risk region has been narrowed to ARMS2, suggesting that this is the true AMD gene at this locus. However, genetic associations alone do not suffice to prove causality and one or more of the 14 SNPs on this haplotype may be involved in long-range control of gene expression, leaving HTRA1 and PLEKHA1 still suspects in the pathogenic pathway. Both, ARMS2 and HTRA1 have been linked to extracellular matrix homeostasis, yet their exact molecular function as well as their role in AMD pathogenesis remains to be uncovered. The transcriptional regulation of the 10q26 locus adds an additional level of complexity, given, that gene-regulatory as well as epigenetic alterations may influence expression levels from 10q26 in diseased individuals. Here, we provide a comprehensive overview on the 10q26 locus and its three gene products on various levels of biological complexity and discuss current and future research strategies to shed light on one of the remaining enigmatic spots in the AMD landscape.

Publication trends of artificial intelligence in retina in 10 years: Where do we stand?

PURPOSE

Artificial intelligence (AI) has been applied in the field of retina. The purpose of this study was to analyze the study trends within AI in retina by reporting on publication trends, to identify journals, countries, authors, international collaborations, and keywords involved in AI in retina.

MATERIALS AND METHODS

A cross-sectional study. Bibliometric methods were used to evaluate global production and development trends in AI in retina since 2012 using Web of Science Core Collection.

RESULTS

A total of 599 publications were retrieved ultimately. We found that AI in retina is a very attractive topic in scientific and medical community. No journal was found to specialize in AI in retina. The USA, China, and India were the three most productive countries. Authors from Austria, Singapore, and England also had worldwide academic influence. China has shown the greatest rapid increase in publication numbers. International collaboration could increase influence in this field. Keywords revealed that diabetic retinopathy, optical coherence tomography on multiple diseases, algorithm were three popular topics in the field. Most of top journals and top publication on AI in retina were mainly focused on engineering and computing, rather than medicine.

CONCLUSION

These results helped clarify the current status and future trends in researches of AI in retina. This study may be useful for clinicians and scientists to have a general overview of this field, and better understand the main actors in this field (including authors, journals, and countries). Researches are supposed to focus on more retinal diseases, multiple modal imaging, and performance of AI models in real-world clinical application. Collaboration among countries and institutions is common in current research of AI in retina.

Automated deep learning in ophthalmology: AI that can build AI

PURPOSE OF REVIEW

The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demonstrating the effectiveness of this technique and discuss current challenges and future directions of automated deep learning.

RECENT FINDINGS

There are several commercially available automated deep learning platforms. Although specific features differ between platforms, they utilise the common approach of supervised learning. Ophthalmology is an exemplar speciality in the area, with a number of recent proof-of-concept studies exploring classification of retinal fundus photographs, optical coherence tomography images and indocyanine green angiography images. Automated deep learning has also demonstrated impressive results in other specialities such as dermatology, radiology and histopathology.

SUMMARY

Automated deep learning allows users without coding expertise to develop deep learning algorithms. It is rapidly establishing itself as a valuable tool for those with limited technical experience. Despite residual challenges, it offers considerable potential in the future of patient management, clinical research and medical education.

VIDEO ABSTRACT

http://links.lww.com/COOP/A44.

Machine learning model for predicting out-of-hospital cardiac arrests using meteorological and chronological data

OBJECTIVES

To evaluate a predictive model for robust estimation of daily out-of-hospital cardiac arrest (OHCA) incidence using a suite of machine learning (ML) approaches and high-resolution meteorological and chronological data.

METHODS

In this population-based study, we combined an OHCA nationwide registry and high-resolution meteorological and chronological datasets from Japan. We developed a model to predict daily OHCA incidence with a training dataset for 2005-2013 using the eXtreme Gradient Boosting algorithm. A dataset for 2014-2015 was used to test the predictive model. The main outcome was the accuracy of the predictive model for the number of daily OHCA events, based on mean absolute error (MAE) and mean absolute percentage error (MAPE). In general, a model with MAPE less than 10% is considered highly accurate.

RESULTS

Among the 1 299 784 OHCA cases, 661 052 OHCA cases of cardiac origin (525 374 cases in the training dataset on which fourfold cross-validation was performed and 135 678 cases in the testing dataset) were included in the analysis. Compared with the ML models using meteorological or chronological variables alone, the ML model with combined meteorological and chronological variables had the highest predictive accuracy in the training (MAE 1.314 and MAPE 7.007%) and testing datasets (MAE 1.547 and MAPE 7.788%). Sunday, Monday, holiday, winter, low ambient temperature and large interday or intraday temperature difference were more strongly associated with OHCA incidence than other the meteorological and chronological variables.

CONCLUSIONS

A ML predictive model using comprehensive daily meteorological and chronological data allows for highly precise estimates of OHCA incidence.

Investigating the challenges and opportunities for medicines management in an NHS field hospital during the COVID-19 pandemic

INTRODUCTION

Hospital admissions from COVID-19 initially increased rapidly within the UK. National Health Service (NHS) field hospitals are part of a capacity building response built at great scale and speed to respond to the anticipated increased demand the NHS faces during this time. NHS Nightingale Hospital Birmingham (NHB) is modelled to treat mild to moderate (non-critical care) COVID-19 disease, to provide step-down capacity for patients in recovery, or for palliating patients in the dying phase of their disease in the Midlands. Opportunities and challenges presented for optimal medicines management (MM) during the development of the NHB are investigated, and a framework developed to support future NHS field hospitals of this model.

METHODS

A team, comprised of an associate medical director, trust chief pharmacist and senior pharmacists iteratively developed a framework to convert the large non-hospital setting into a functioning NHS field hospital with standardised MM processes adjusted appropriately to cope with operational constraints in the pandemic situation. NHB has, because of its repurposing, both challenges and advantages affecting MM that influence development of the framework. Throughout implementation, a 7-week period between announcement and opening, there was continuous evaluation, external stakeholder validation and peer review.

RESULTS

The PESTLE model, a mechanism of analysis to identify elements of a project environment (Political, Environmental, Social, Technological, Legal and Economic), was applied to identify influencing factors and support detailed project planning. Compliance with medicines legislation was at the forefront of all MM process development for the NHB field hospital. Internal factors were identified by the core MM team, resulting in a workforce, education & training and clinical pharmacy MM plan.

DISCUSSION

MM processes are extensive and integral to NHS field hospitals. The presented framework of influencing factors may support future NHS field hospital development. It is pertinent to have a broad team working approach to any large-scale project such as outlined here, and suggest the identified factors be used as a core framework for development of any future MM processes in NHS field hospitals.