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.

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).

Diagnosis and monitoring of systemic sclerosis-associated interstitial lung disease using high-resolution computed tomography.

Patients with systemic sclerosis are at high risk of developing systemic sclerosis-associated interstitial lung disease. Symptoms and outcomes of systemic sclerosis-associated interstitial lung disease range from subclinical lung involvement to respiratory failure and death. Early and accurate diagnosis of systemic sclerosis-associated interstitial lung disease is therefore important to enable appropriate intervention. The most sensitive and specific way to diagnose systemic sclerosis-associated interstitial lung disease is by high-resolution computed tomography, and experts recommend that high-resolution computed tomography should be performed in all patients with systemic sclerosis at the time of initial diagnosis. In addition to being an important screening and diagnostic tool, high-resolution computed tomography can be used to evaluate disease extent in systemic sclerosis-associated interstitial lung disease and may be helpful in assessing prognosis in some patients. Currently, there is no consensus with regards to frequency and scanning intervals in patients at risk of interstitial lung disease development and/or progression. However, expert guidance does suggest that frequency of screening using high-resolution computed tomography should be guided by risk of developing interstitial lung disease. Most experienced clinicians would not repeat high-resolution computed tomography more than once a year or every other year for the first few years unless symptoms arose. Several computed tomography techniques have been developed in recent years that are suitable for regular monitoring, including low-radiation protocols, which, together with other technologies, such as lung ultrasound and magnetic resonance imaging, may further assist in the evaluation and monitoring of patients with systemic sclerosis-associated interstitial lung disease. A video abstract to accompany this article is available at: https://www.globalmedcomms.com/respiratory/Khanna/HRCTinSScILD.

The evolution of computer-based analysis of high-resolution CT of the chest in patients with IPF.

In patients with idiopathic pulmonary fibrosis (IPF), there is an urgent need of biomarkers which can predict disease behaviour or response to treatment. Most published studies report results based on continuous data which can be difficult to apply to individual patients in clinical practice. Having antifibrotic therapies makes it even more important that we can accurately diagnose and prognosticate in IPF patients. Advances in computer technology over the past decade have provided computer-based methods for objectively quantifying fibrotic lung disease on high-resolution CT of the chest with greater strength than visual CT analysis scores. These computer-based methods and, more recently, the arrival of deep learning-based image analysis might provide a response to these unsolved problems. The purpose of this commentary is to provide insights into the problems associated with visual interpretation of HRCT, describe of the current technologies used to provide quantification of disease on HRCT and prognostication in IPF patients, discuss challenges to the implementation of this technology and future directions.

Understanding the burden of interstitial lung disease post-COVID-19: the UK Interstitial Lung Disease-Long COVID Study (UKILD-Long COVID).

INTRODUCTION: The COVID-19 pandemic has led to over 100 million cases worldwide. The UK has had over 4 million cases, 400 000 hospital admissions and 100 000 deaths. Many patients with COVID-19 suffer long-term symptoms, predominantly breathlessness and fatigue whether hospitalised or not. Early data suggest potentially severe long-term consequence of COVID-19 is development of long COVID-19-related interstitial lung disease (LC-ILD). METHODS AND ANALYSIS: The UK Interstitial Lung Disease Consortium (UKILD) will undertake longitudinal observational studies of patients with suspected ILD following COVID-19. The primary objective is to determine ILD prevalence at 12 months following infection and whether clinically severe infection correlates with severity of ILD. Secondary objectives will determine the clinical, genetic, epigenetic and biochemical factors that determine the trajectory of recovery or progression of ILD. Data will be obtained through linkage to the Post-Hospitalisation COVID platform study and community studies. Additional substudies will conduct deep phenotyping. The Xenon MRI investigation of Alveolar dysfunction Substudy will conduct longitudinal xenon alveolar gas transfer and proton perfusion MRI. The POST COVID-19 interstitial lung DiseasE substudy will conduct clinically indicated bronchoalveolar lavage with matched whole blood sampling. Assessments include exploratory single cell RNA and lung microbiomics analysis, gene expression and epigenetic assessment. ETHICS AND DISSEMINATION: All contributing studies have been granted appropriate ethical approvals. Results from this study will be disseminated through peer-reviewed journals. CONCLUSION: This study will ensure the extent and consequences of LC-ILD are established and enable strategies to mitigate progression of LC-ILD.

Clinical and radiological features of extra-pulmonary sarcoidosis: a pictorial essay.

UNLABELLED: The aim of this manuscript is to describe radiological findings of extra-pulmonary sarcoidosis. Sarcoidosis is an immune-mediated systemic disease of unknown origin, characterized by non-caseating epitheliod granulomas. Ninety percent of patients show granulomas located in the lungs or in the related lymph nodes. However, lesions can affect any organ. Typical imaging features of liver and spleen sarcoidosis include visceromegaly, with multiple nodules hypodense on CT images and hypointense on T2-weighted MRI acquisitions. Main clinical and radiological manifestations of renal sarcoidosis are nephrolithiasis, nephrocalcinosis, and acute interstitial nephritis. Brain sarcoidosis shows multiple or solitary parenchymal nodules on MRI that enhance with a ring-like appearance after gadolinium. In spinal cord localization, MRI demonstrates enlargement and hyperintensity of spinal cord, with hypointense lesions on T2-weighted images. Skeletal involvement is mostly located in small bone, showing many lytic lesions; less frequently, bone lesions have a sclerotic appearance. Ocular involvement includes uveitis, conjunctivitis, optical nerve disease, chorioretinis. Erythema nodosum and lupus pernio represent the most common cutaneous manifestations encountered. Sarcoidosis in various organs can be very insidious for radiologists, showing different imaging features, often non-specific. Awareness of these imaging features helps radiologists to obtain the correct diagnosis. TEACHING POINTS: • Systemic sarcoidosis can exhibit abdominal, neural, skeletal, ocular, and cutaneous manifestations. • T2 signal intensity of hepatosplenic nodules may reflect the disease activity. • Heerfordt's syndrome includes facial nerve palsy, fever, parotid swelling, and uveitis. • In the vertebrae, osteolytic and/or diffuse sclerotic lesions can be found. • Erythema nodosum and lupus pernio represent the most common cutaneous manifestations.

An integrated clinicoradiological staging system for pulmonary sarcoidosis: a case-cohort study.

BACKGROUND: Mortality in pulmonary sarcoidosis is highly variable and a reliable prognostic algorithm for disease staging and for guiding management decisions is needed. The objective of this study is to derive and test a staging system for determining prognosis in pulmonary sarcoidosis. METHODS: We identified the prognostic value of high-resolution computed tomography (HRCT) patterns and pulmonary function tests, including the composite physiological index (CPI) in patients with pulmonary sarcoidosis. We integrated prognostic physiological and HRCT variables to form a clinical staging algorithm predictive of mortality in a test cohort. The staging system was externally validated in a separate cohort by the same methods of discrimination used in the primary analysis and tested for clinical applicability by four test observers. FINDINGS: The test cohort included 251 patients with pulmonary sarcoidosis in the study referred to the Sarcoidosis clinic at the Royal Brompton Hospital, UK, between Jan 1, 2000, and June 30, 2010. The CPI was the strongest predictor of mortality (HR 1·04, 95% CI 1·02-1·06, p<0·0001) in the test cohort. An optimal CPI threshold of 40 units was identified (HR 4·24, 2·84-6·33, p<0·0001). The CPI40, main pulmonary artery diameter to ascending aorta diameter ratio (MPAD/AAD), and an extent of fibrosis threshold of 20% were combined to form a staging algorithm. When assessed in the validation cohort (n=252), this staging system was strikingly more predictive of mortality than any individual variable alone (HR 5·89, 2·68-10·08, p<0·0001). The staging system was successfully applied to the test and validation cohorts combined, by two radiologists and two physicians. INTERPRETATION: A clear prognostic separation of patients with pulmonary sarcoidosis is provided by a simple staging system integrating the CPI and two HRCT variables.