Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness.

In anticipation of potential future pandemics, we examined the challenges and opportunities presented by the COVID-19 outbreak. This analysis highlights how artificial intelligence (AI) and predictive models can support both patients and clinicians in managing subsequent infectious diseases, and how legislators and policymakers could support these efforts, to bring learning healthcare system (LHS) from guidelines to real-world implementation. This report chronicles the trajectory of the COVID-19 pandemic, emphasizing the diverse data sets generated throughout its course. We propose strategies for harnessing this data via AI and predictive modelling to enhance the functioning of LHS. The challenges faced by patients and healthcare systems around the world during this unprecedented crisis could have been mitigated with an informed and timely adoption of the three pillars of the LHS: Knowledge, Data and Practice. By harnessing AI and predictive analytics, we can develop tools that not only detect potential pandemic-prone diseases early on but also assist in patient management, provide decision support, offer treatment recommendations, deliver patient outcome triage, predict post-recovery long-term disease impacts, monitor viral mutations and variant emergence, and assess vaccine and treatment efficacy in real-time. A patient-centric approach remains paramount, ensuring patients are both informed and actively involved in disease mitigation strategies.

Impact of Anatomical and Viability-guided Completeness of Revascularization on Clinical Outcomes in Ischemic Cardiomyopathy.

BACKGROUND: Complete revascularization of coronary disease has been linked to improved outcomes in patients with preserved left ventricular (LV) function. OBJECTIVES: To identify the impact of complete revascularization in patients with severe LV dysfunction. METHODS: Patients enrolled in the REVIVED-BCIS2 trial were eligible if baseline/procedural angiograms and viability studies were available for analysis by independent core laboratories. Anatomical and viability-guided completeness of revascularization were measured by the coronary and myocardial revascularization indices (RIcoro and RImyo) respectively, where RIcoro=[change in BCIS Jeopardy Score (BCIS-JS)] / [baseline BCIS-JS] and RImyo=[number of revascularized viable segments] / [ number of viable segments supplied by diseased vessels]. The PCI group was classified as having complete or incomplete revascularization by median RIcoro and RImyo. The primary outcome was death or hospitalization for heart failure. RESULTS: Of 700 randomized patients, 670 were included. The baseline BCIS-JS and SYNTAX scores were 8 (6 to 10) and 22 (15 to 29) respectively. In those assigned to PCI, median RIcoro and RImyo values were 67% and 85%. Compared to the group assigned to optimal medical therapy alone, there was no difference in the likelihood of the primary outcome in those receiving complete anatomical or viability-guided revascularization (HR 0.90, 95% CI 0.62-1.32 and HR 0.95, 95% CI 0.66-1.35 respectively). A sensitivity analysis by residual SYNTAX score showed no association with outcome. CONCLUSIONS: In patients with severe left ventricular dysfunction, neither complete anatomical nor viability-guided revascularization were associated with improved event-free survival compared to incomplete revascularization or treatment with medical therapy alone.

Design of ANCHOR-RA: a multi-national cross-sectional study on screening for interstitial lung disease in patients with rheumatoid arthritis.

BACKGROUND: Patients with rheumatoid arthritis (RA) are at risk of developing interstitial lung disease (ILD), which is associated with high mortality. Screening tools based on risk factors are needed to decide which patients with RA should be screened for ILD using high-resolution computed tomography (HRCT). The ANCHOR-RA study is a multi-national cross-sectional study that will develop a multivariable model for prediction of RA-ILD, which can be used to inform screening for RA-ILD in clinical practice. METHODS: Investigators will enrol consecutive patients with RA who have ≥ 2 of the following risk factors for RA-ILD: male; current or previous smoker; age ≥ 60 years at RA diagnosis; high-positive rheumatoid factor and/or anti-cyclic citrullinated peptide (titre > 3 x upper limit of normal); presence or history of certain extra-articular manifestations of RA (vasculitis, Felty's syndrome, secondary Sjögren's syndrome, cutaneous rheumatoid nodules, serositis, and/or scleritis/uveitis); high RA disease activity in the prior 12 months. Patients previously identified as having ILD, or who have had a CT scan in the prior 2 years, will not be eligible. Participants will undergo an HRCT scan at their local site, which will be assessed centrally by two expert radiologists. Data will be collected prospectively on demographic and RA-related characteristics, patient-reported outcomes, comorbidities and pulmonary function. The primary outcomes will be the development of a probability score for RA-ILD, based on a multivariable model incorporating potential risk factors commonly assessed in clinical practice, and an estimate of the prevalence of RA-ILD in the study population. It is planned that 1200 participants will be enrolled at approximately 30 sites in the USA, UK, Germany, France, Italy, Spain. DISCUSSION: Data from the ANCHOR-RA study will add to the body of evidence to support recommendations for screening for RA-ILD to improve detection of this important complication of RA and enable early intervention. TRIAL REGISTRATION: clinicaltrials.gov NCT05855109 (submission date: 3 May 2023).

From Pixels to Prognosis: Unlocking the Potential of Deep Learning in Fibrotic Lung Disease Imaging Analysis.

The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis IPF has created an urgent need for reliable biomarkers to predict disease progression and treatment response. Some patients experience stable disease trajectories, while others deteriorate rapidly, making treatment decisions challenging. High-resolution chest CT has become crucial for diagnosis, but visual assessments by radiologists suffer from low reproducibility and high interobserver variability. To address these issues, computer-based image analysis, called quantitative CT, has emerged. However, many quantitative CT methods rely on human input for training, therefore potentially incorporating human error into computer training. Rapid advances in artificial intelligence, specifically deep learning, aim to overcome this limitation by enabling autonomous quantitative analysis. While promising, deep learning also presents challenges including the need to minimize algorithm biases, ensuring explainability, and addressing accessibility and ethical concerns. This review explores the development and application of deep learning in improving the imaging process for fibrotic lung disease.

Operational relevance of the Sydney Declaration: The example of the Australian Federal Police (AFP) Forensics Command.

In 2022, a group of eminent forensic scientists published The Sydney Declaration - Revisiting the essence of forensic science through its fundamental principles in Forensic Science International. The Sydney Declaration was delivered to revisit "the essence of forensic science, its purpose, and fundamental principles". At its heart, revisiting these foundational principles is hoped to "benefit forensic science as a whole to be more relevant, effective and reliable". But can these principles be translated operationally by a forensic services provider to achieve the benefits prescribed? How do we make the leap from a theoretical concept and begin to put it into practice to bring about the real and meaningful change that the declaration hopes to achieve? In this paper we will attempt to discuss how the Australian Federal Police (AFP) Forensics Command has reflected on the Sydney Declaration by relating reforms developed and implemented to our operating model with some selected principles. We hope to show that while the Sydney Declaration could be perceived as academic and disconnected from operations, it has the potential to impact and positively influence reforms and changes for forensic science providers. The AFP Forensics Command experience shows the operational relevance of The Sydney Declaration.

Progressive Pulmonary Fibrosis and Interstitial Lung Abnormalities: AJR Expert Panel Narrative Review.

Progressive pulmonary fibrosis (PPF) and interstitial lung abnormalities (ILA) are relatively new concepts in interstitial lung disease (ILD) imaging and clinical management. Recognition of signs of PPF, as well as identification and classification of ILA, are important tasks during chest high-resolution CT interpretation, to optimize management of patients with ILD and those at risk of developing ILD. However, following professional society guidance, the role of imaging surveillance remains unclear in stable patients with ILD, asymptomatic patients with ILA who are at risk of progression, and asymptomatic patients at risk of developing ILD without imaging abnormalities. In this AJR Expert Panel Narrative Review, we summarize the current knowledge regarding PPF and ILA and describe the range of clinical practice with respect to imaging patients with ILD, those with ILA, and those at risk of developing ILD. In addition, we offer suggestions to help guide surveillance imaging in areas with an absence of published guidelines, where such decisions are currently driven primarily by local pulmonologists' preference.

Towards the adoption of quantitative computed tomography in the management of interstitial lung disease.

The shortcomings of qualitative visual assessment have led to the development of computer-based tools to characterise and quantify disease on high-resolution computed tomography (HRCT) in patients with interstitial lung diseases (ILDs). Quantitative CT (QCT) software enables quantification of patterns on HRCT with results that are objective, reproducible, sensitive to change and predictive of disease progression. Applications developed to provide a diagnosis or pattern classification are mainly based on artificial intelligence. Deep learning, which identifies patterns in high-dimensional data and maps them to segmentations or outcomes, can be used to identify the imaging patterns that most accurately predict disease progression. Optimisation of QCT software will require the implementation of protocol standards to generate data of sufficient quality for use in computerised applications and the identification of diagnostic, imaging and physiological features that are robustly associated with mortality for use as anchors in the development of algorithms. Consortia such as the Open Source Imaging Consortium have a key role to play in the collation of imaging and clinical data that can be used to identify digital imaging biomarkers that inform diagnosis, prognosis and response to therapy.

Post-COVID highlights: Challenges and solutions of artificial intelligence techniques for swift identification of COVID-19.

Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rapid spread of the virus, and enhance intervention outcomes, all in response to this unprecedented global crisis. As we transition into a post-COVID era, we retrospectively evaluate these proposed studies and offer a review of the techniques employed in AI diagnostic models, with a focus on the solutions proposed for different challenges. This review endeavors to provide insights into the diverse solutions designed to address the multifaceted challenges that arose during the pandemic. By doing so, we aim to prepare the AI community for the development of AI tools tailored to address public health emergencies effectively.

Zinpentraxin Alfa for Idiopathic Pulmonary Fibrosis: The Randomized Phase III STARSCAPE Trial.

Rationale: A phase II trial reported clinical benefit over 28 weeks in patients with idiopathic pulmonary fibrosis (IPF) who received zinpentraxin alfa. Objectives: To investigate the efficacy and safety of zinpentraxin alfa in patients with IPF in a phase III trial. Methods: This 52-week phase III, double-blind, placebo-controlled, pivotal trial was conducted at 275 sites in 29 countries. Patients with IPF were randomized 1:1 to intravenous placebo or zinpentraxin alfa 10 mg/kg every 4 weeks. The primary endpoint was absolute change from baseline to Week 52 in FVC. Secondary endpoints included absolute change from baseline to Week 52 in percent predicted FVC and 6-minute walk distance. Safety was monitored via adverse events. Post hoc analysis of the phase II and phase III data explored changes in FVC and their impact on the efficacy results. Measurements and Main Results: Of 664 randomized patients, 333 were assigned to placebo and 331 to zinpentraxin alfa. Four of the 664 randomized patients were never administered study drug. The trial was terminated early after a prespecified futility analysis that demonstrated no treatment benefit of zinpentraxin alfa over placebo. In the final analysis, absolute change from baseline to Week 52 in FVC was similar between placebo and zinpentraxin alfa (-214.89 ml and -235.72 ml; P = 0.5420); there were no apparent treatment effects on secondary endpoints. Overall, 72.3% and 74.6% of patients receiving placebo and zinpentraxin alfa, respectively, experienced one or more adverse events. Post hoc analysis revealed that extreme FVC decline in two placebo-treated patients resulted in the clinical benefit of zinpentraxin alfa reported by phase II. Conclusions: Zinpentraxin alfa treatment did not benefit patients with IPF over placebo. Learnings from this program may help improve decision making around trials in IPF. Clinical trial registered with www.clinicaltrials.gov (NCT04552899).

High-resolution CT phenotypes in pulmonary sarcoidosis: a multinational Delphi consensus study.

One view of sarcoidosis is that the term covers many different diseases. However, no classification framework exists for the future exploration of pathogenetic pathways, genetic or trigger predilections, patterns of lung function impairment, or treatment separations, or for the development of diagnostic algorithms or relevant outcome measures. We aimed to establish agreement on high-resolution CT (HRCT) phenotypic separations in sarcoidosis to anchor future CT research through a multinational two-round Delphi consensus process. Delphi participants included members of the Fleischner Society and the World Association of Sarcoidosis and other Granulomatous Disorders, as well as members' nominees. 146 individuals (98 chest physicians, 48 thoracic radiologists) from 28 countries took part, 144 of whom completed both Delphi rounds. After rating of 35 Delphi statements on a five-point Likert scale, consensus was achieved for 22 (63%) statements. There was 97% agreement on the existence of distinct HRCT phenotypes, with seven HRCT phenotypes that were categorised by participants as non-fibrotic or likely to be fibrotic. The international consensus reached in this Delphi exercise justifies the formulation of a CT classification as a basis for the possible definition of separate diseases. Further refinement of phenotypes with rapidly achievable CT studies is now needed to underpin the development of a formal classification of sarcoidosis.

The everolimus eluting Synergy MegatronTM drug-eluting stent platform: Early outcomes from the European Synergy MegatronTM Implanters' Registry.

BACKGROUND: The Synergy MegatronTM is an everolimus-drug eluting stent that may offer advantages in the treatment of aorto-ostial disease and large proximal vessels. AIMS: To report the short- to medium-term clinical outcomes from the European Synergy MegatronTM Implanters' Registry. METHODS: This registry was an investigator-initiated study conducted at 14 European centers. The primary outcome was target lesion failure (TLF), defined as the composite of cardiovascular death, target vessel myocardial infarction (MI), and target lesion revascularisation. RESULTS: Five hundred seventy-five patients underwent PCI with MegatronTM between 2019 and 2021. Patients were 69 ± 12 years old, 26% had diabetes mellitus, 24% had moderate-severe left ventricular impairment and 59% presented with an acute coronary syndrome. 15% were deemed prohibitively high risk for surgical revascularisation. The target vessel involved the left main stem in 55%, the ostium of the RCA in 13% and was a true bifurcation (Medina 1,1,1) in 50%.  At 1 year, TLF was observed in 40 patients, with 26 (65%) occurring within the first 30 days. The cumulative incidence of TLF was 4.5% at 30 days and 8.6% (95% CI 6.3-11.7) at 1 year. The incidence of stent thrombosis was 0.5% with no late stent thromboses. By multivariate analysis, the strongest independent predictors of TLF were severe left ventricular impairment (HR 3.43, 95% CI: 1.67-6.76, p < 0.001) and a target vessel involving the left main (HR 4.00 95% CI 1.81-10.15 p = 0.001). CONCLUSIONS: Use of the Synergy MegatronTM everolimus eluting stent in a 'real-world' setting shows favorable outcomes at 30 days and 1 year.

Combination of BAL and Computed Tomography Differentiates Progressive and Non-progressive Fibrotic Lung Diseases.

Rationale: Identifying patients with pulmonary fibrosis (PF) at risk of progression can guide management. Objectives: To explore the utility of combining baseline BAL and computed tomography (CT) in differentiating progressive and nonprogressive PF. Methods: The derivation cohort consisted of incident cases of PF for which BAL was performed as part of a diagnostic workup. A validation cohort was prospectively recruited with identical inclusion criteria. Baseline thoracic CT scans were scored for the extent of fibrosis and usual interstitial pneumonia (UIP) pattern. The BAL lymphocyte proportion was recorded. Annualized FVC decrease of >10% or death within 1 year was used to define disease progression. Multivariable logistic regression identified the determinants of the outcome. The optimum binary thresholds (maximal Wilcoxon rank statistic) at which the extent of fibrosis on CT and the BAL lymphocyte proportion could distinguish disease progression were identified. Measurements and Main Results: BAL lymphocyte proportion, UIP pattern, and fibrosis extent were significantly and independently associated with disease progression in the derivation cohort (n = 240). Binary thresholds for increased BAL lymphocyte proportion and extensive fibrosis were identified as 25% and 20%, respectively. An increased BAL lymphocyte proportion was rare in patients with a UIP pattern (8 of 135; 5.9%) or with extensive fibrosis (7 of 144; 4.9%). In the validation cohort (n = 290), an increased BAL lymphocyte proportion was associated with a significantly lower probability of disease progression in patients with nonextensive fibrosis or a non-UIP pattern. Conclusions: BAL lymphocytosis is rare in patients with extensive fibrosis or a UIP pattern on CT. In patients without a UIP pattern or with limited fibrosis, a BAL lymphocyte proportion of ⩾25% was associated with a lower likelihood of progression.

Imaging Features of Autoimmune Disease-Related Interstitial Lung Diseases.

Interstitial lung diseases (ILDs) associated with autoimmune diseases show characteristic signs of imaging. Radiologic signs are also used in the identification of ILDs with features suggestive of autoimmune disease that do not meet the criteria for a specific autoimmune disease. Radiologists play a key role in identifying these signs and assessing their relevance as part of multidisciplinary team discussions. A radiologist may be the first health care professional to pick up signs of autoimmune disease in a patient referred for assessment of ILD or with suspicion for ILD. Multidisciplinary team discussion of imaging findings observed during follow-up may inform a change in diagnosis or identify progression, with implications for a patient's treatment regimen. This article describes the imaging features of autoimmune disease-related ILDs and the role of radiologists in assessing their relevance.

Multi-site, Multi-domain Airway Tree Modeling.

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).