Meta-analysis of [18F]FDG-PET/CT in pulmonary sarcoidosis.

BACKGROUND: 18F-Fluorodeoxyglucose (FDG) PET/CT is emerging as a tool in the diagnosis and evaluation of pulmonary sarcoidosis, however, there is limited consensus regarding its diagnostic performance and prognostic value. METHOD: A meta-analysis was conducted with PubMed, Science Direct, MEDLINE, Scopus, and CENTRAL databases searched up to and including September 2023. 1355 studies were screened, with seventeen (n = 708 patients) suitable based on their assessment of the diagnostic performance or prognostic value of FDG-PET/CT. Study quality was assessed using the QUADAS-2 tool. Forest plots of pooled sensitivity and specificity were generated to assess diagnostic performance. Pooled changes in SUVmax were correlated with changes in pulmonary function tests (PFT). RESULTS: FDG-PET/CT in diagnosing suspected pulmonary sarcoidosis (six studies, n = 400) had a pooled sensitivity of 0.971 (95%CI 0.909-1.000, p = < 0.001) and specificity of 0.873 (95%CI 0.845-0.920)(one study, n = 169). Eleven studies for prognostic analysis (n = 308) indicated a pooled reduction in pulmonary SUVmax of 4.538 (95%CI 5.653-3.453, p = < 0.001) post-treatment. PFTs displayed improvement post-treatment with a percentage increase in predicted forced vital capacity (FVC) and diffusion capacity of the lung for carbon monoxide (DLCO) of 7.346% (95%CI 2.257-12.436, p = 0.005) and 3.464% (95%CI -0.205-7.132, p = 0.064), respectively. Reduction in SUVmax correlated significantly with FVC (r = 0.644, p < 0.001) and DLCO (r = 0.582, p < 0.001) improvement. CONCLUSION: In cases of suspected pulmonary sarcoidosis, FDG-PET/CT demonstrated good diagnostic performance and correlated with functional health scores. FDG-PET/CT may help to guide immunosuppression in cases of complex sarcoidosis or where treatment rationalisation is needed. CLINICAL RELEVANCE STATEMENT: FDG-PET/CT has demonstrated a high diagnostic performance in the evaluation of suspected pulmonary sarcoidosis with radiologically assessed disease activity correlating strongly with clinically derived pulmonary function tests. KEY POINTS: In diagnosing pulmonary sarcoidosis, FDG-PET/CT had a sensitivity and specificity of 0.971 and 0.873, respectively. Disease activity, as determined by SUVmax, reduced following treatment in all the included studies. Reduction in SUVmax correlated with an improvement in functional vital capacity, Diffusion Capacity of the Lungs for Carbon Monoxide, and subjective health scoring systems.

Developing, Implementing, Evaluating Electronic Apparent Cause Analysis Across a Health Care System.

An interdisciplinary team developed, implemented, and evaluated a standardized structure and process for an electronic apparent cause analysis (eACA) tool that includes principles of high reliability, human factors engineering, and Just Culture. Steps include assembling a team, describing what happened, determining why the event happened, determining how defects might be fixed, and deciding which defects will be fixed. The eACA is an intuitive tool for identifying defects, apparent causes of those defects, and the strongest corrective actions. Moreover, the eACA facilitates system learning by aggregating apparent causes and corrective action trends to prioritize and implement system change(s).

A common data model for the standardization of intensive care unit medication features.

Objective: Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. Materials and Methods: A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Results: Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n = 43) and clinical practice-related (n = 63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. Conclusion: The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

INTRODUCTION: Intravenous (IV) medications are a fundamental cause of fluid overload (FO) in the intensive care unit (ICU); however, the association between IV medication use (including volume), administration timing, and FO occurrence remains unclear. METHODS: This retrospective cohort study included consecutive adults admitted to an ICU ≥72 hours with available fluid balance data. FO was defined as a positive fluid balance ≥7% of admission body weight within 72 hours of ICU admission. After reviewing medication administration record (MAR) data in three-hour periods, IV medication exposure was categorized into clusters using principal component analysis (PCA) and Restricted Boltzmann Machine (RBM). Medication regimens of patients with and without FO were compared within clusters to assess for temporal clusters associated with FO using the Wilcoxon rank sum test. Exploratory analyses of the medication cluster most associated with FO for medications frequently appearing and used in the first 24 hours was conducted. RESULTS: FO occurred in 127/927 (13.7%) of the patients enrolled. Patients received a median (IQR) of 31 (13-65) discrete IV medication administrations over the 72-hour period. Across all 47,803 IV medication administrations, ten unique IV medication clusters were identified with 121-130 medications in each cluster. Among the ten clusters, cluster 7 had the greatest association with FO; the mean number of cluster 7 medications received was significantly greater in patients in the FO cohort compared to patients who did not experience FO (25.6 vs.10.9. p<0.0001). 51 of the 127 medications in cluster 7 (40.2%) appeared in > 5 separate 3-hour periods during the 72-hour study window. The most common cluster 7 medications included continuous infusions, antibiotics, and sedatives/analgesics. Addition of cluster 7 medications to a prediction model with APACHE II score and receipt of diuretics improved the ability for the model to predict fluid overload (AUROC 5.65, p =0.0004). CONCLUSIONS: Using ML approaches, a unique IV medication cluster was strongly associated with FO. Incorporation of this cluster improved the ability to predict development of fluid overload in ICU patients compared with traditional prediction models. This method may be further developed into real-time clinical applications to improve early detection of adverse outcomes.

Approach to Pulmonary Nodules in Connective Tissue Disease.

The assessment of pulmonary nodules is a common and often challenging clinical scenario. This evaluation becomes even more complex in patients with connective tissue diseases (CTDs), as a range of disease-related factors must also be taken into account. These diseases are characterized by immune-mediated chronic inflammation, leading to tissue damage, collagen deposition, and subsequent organ dysfunction. A thorough examination of nodule features in these patients is required, incorporating anatomic and functional information, along with patient demographics, clinical factors, and disease-specific knowledge. This integrated approach is vital for effective risk stratification and precise diagnosis. This review article addresses specific CTD-related factors that should be taken into account when evaluating pulmonary nodules in this patient group.

Continuous Positive Airway Pressure but Not GLP1-mediated Weight Loss Improves Early Cardiovascular Disease in Obstructive Sleep Apnea: A Randomized Proof-of-Concept Study.

Rationale: Obstructive sleep apnea (OSA) is an independent risk factor for cardiovascular (CV) morbidity and mortality, but the benefit of continuous positive airway pressure (CPAP) is uncertain. However, most randomized controlled trials have focused on the role of CPAP in secondary prevention, although there is growing evidence of a potential benefit on early CV disease. Weight loss in combination with CPAP may be superior but is difficult to achieve and maintain with conventional measures alone. Objectives: The aim of this study was to gain insights into the effect of CPAP on early atherosclerotic processes and to compare it with a glucagon-like peptide (GLP)-1-mediated weight loss regimen in patients with OSA. Methods: We performed a randomized proof-of-concept study comparing CPAP, a GLP1-mediated weight-loss regimen (liraglutide [Lir]), and both in combination for 24 weeks in 30 consecutive patients with OSA (apnea-hypopnea index >15 events/h; body mass index 30-40 kg/m2; and no history of diabetes, heart failure, or unstable CV disease). In addition to extensive evaluation for CV risk factors and endothelial function at baseline and end of study, subjects underwent 18F-fluoro-2-deoxy-D-glucose positron emission tomography-computed tomography (18F-FDG PET-CT) for the measurement of aortic wall inflammation (target-to-background ratio) and coronary computed tomography angiography for semiautomated coronary plaque analysis. Results: Baseline characteristics were similar between groups. CPAP alone and in combination resulted in greater reduction in apnea-hypopnea index than Lir alone (mean difference, -45 and -43 events/h, respectively, vs. -12 events/h; P < 0.05). Both Lir and combination treatment led to significant weight loss, but only CPAP alone resulted in significant decrease in vascular inflammation (aortic wall target-to-background ratio from 2.03 ± 0.34 to 1.84 ± 0.43; P = 0.010), associated with an improvement in endothelial function and a decrease in C-reactive protein. Low-attenuation coronary artery plaque volume as a marker of unstable plaque also decreased with CPAP (from 571 ± 490 to 334 ± 185 mm3) and with combination therapy (from 401 ± 145 to 278 ± 126 mm3) but not with Lir. Conclusions: These data suggest that CPAP therapy, but not GLP1-mediated weight loss, improves vascular inflammation and reduces unstable plaque volume in patients with OSA. Further large randomized controlled studies are warranted to assess the benefit of CPAP therapy in modifying early CV disease. Clinical trial registered with www.clinicaltrials.gov (NCT04186494).

A Combined Assessment Tool of Teamwork, Communication, and Workload in Hospital Procedural Units.

Teamwork, communication, and workload issues continue to contribute to patient safety events. The authors developed a diagnostic mixed methods toolkit combining a behavior observation tool, semistructured interview guide, and surveys to proactively identify relevant gaps. Applied across 14 units at three hospitals, this toolkit yielded 344 findings with 156 associated recommendations and took, on average, four days of observation. On a scale from 1 (not at all helpful) to 6 (substantially helpful), leaders indicated that the assessment and its recommendations were very helpful (median 5, interquartile range 5-6, 34 survey respondents, 47.9% individual-level response rate, 85.7% unit-level response rate). Integrating this tool into a broader safety strategy can help inform organizational improvement efforts.

Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.

Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors. This was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 h between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload (a positive fluid balance ≥ 10% of the admission body weight) in the 48-72 h after ICU admission were created. Potential patient and medication fluid overload predictor variables (n = 28) were collected at either baseline or 24 h after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models. A total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance (AUROC 0.78, PPV 0.27, NPV 0.94) for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model (AUROC 0.70; PPV 0.20, NPV 0.94). Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload. In the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload.

Analysis of tissue lipidomics and computed tomography pulmonary fat attenuation volume (CTPFAV ) in idiopathic pulmonary fibrosis.

BACKGROUND AND OBJECTIVE: There is increasing interest in the role of lipids in processes that modulate lung fibrosis with evidence of lipid deposition in idiopathic pulmonary fibrosis (IPF) histological specimens. The aim of this study was to identify measurable markers of pulmonary lipid that may have utility as IPF biomarkers. STUDY DESIGN AND METHODS: IPF and control lung biopsy specimens were analysed using a unbiased lipidomic approach. Pulmonary fat attenuation volume (PFAV) was assessed on chest CT images (CTPFAV ) with 3D semi-automated lung density software. Aerated lung was semi-automatically segmented and CTPFAV calculated using a Hounsfield-unit (-40 to -200HU) threshold range expressed as a percentage of total lung volume. CTPFAV was compared to pulmonary function, serum lipids and qualitative CT fibrosis scores. RESULTS: There was a significant increase in total lipid content on histological analysis of IPF lung tissue (23.16 nmol/mg) compared to controls (18.66 mol/mg, p = 0.0317). The median CTPFAV in IPF was higher than controls (1.34% vs. 0.72%, p < 0.001) and CTPFAV correlated significantly with DLCO% predicted (R2 = 0.356, p < 0.0001) and FVC% predicted (R2 = 0.407, p < 0.0001) in patients with IPF. CTPFAV correlated with CT features of fibrosis; higher CTPFAV was associated with >10% reticulation (1.6% vs. 0.94%, p = 0.0017) and >10% honeycombing (1.87% vs. 1.12%, p = 0.0003). CTPFAV showed no correlation with serum lipids. CONCLUSION: CTPFAV is an easily quantifiable non-invasive measure of pulmonary lipids. In this pilot study, CTPFAV correlates with pulmonary function and radiological features of IPF and could function as a potential biomarker for IPF disease severity assessment.

Evaluation of medication regimen complexity as a predictor for mortality.

While medication regimen complexity, as measured by a novel medication regimen complexity-intensive care unit (MRC-ICU) score, correlates with baseline severity of illness and mortality, whether the MRC-ICU improves hospital mortality prediction is not known. After characterizing the association between MRC-ICU, severity of illness and hospital mortality we sought to evaluate the incremental benefit of adding MRC-ICU to illness severity-based hospital mortality prediction models. This was a single-center, observational cohort study of adult intensive care units (ICUs). A random sample of 991 adults admitted ≥ 24 h to the ICU from 10/2015 to 10/2020 were included. The logistic regression models for the primary outcome of mortality were assessed via area under the receiver operating characteristic (AUROC). Medication regimen complexity was evaluated daily using the MRC-ICU. This previously validated index is a weighted summation of medications prescribed in the first 24 h of ICU stay [e.g., a patient prescribed insulin (1 point) and vancomycin (3 points) has a MRC-ICU = 4 points]. Baseline demographic features (e.g., age, sex, ICU type) were collected and severity of illness (based on worst values within the first 24 h of ICU admission) was characterized using both the Acute Physiology and Chronic Health Evaluation (APACHE II) and the Sequential Organ Failure Assessment (SOFA) score. Univariate analysis of 991 patients revealed every one-point increase in the average 24-h MRC-ICU score was associated with a 5% increase in hospital mortality [Odds Ratio (OR) 1.05, 95% confidence interval 1.02-1.08, p = 0.002]. The model including MRC-ICU, APACHE II and SOFA had a AUROC for mortality of 0.81 whereas the model including only APACHE-II and SOFA had a AUROC for mortality of 0.76. Medication regimen complexity is associated with increased hospital mortality. A prediction model including medication regimen complexity only modestly improves hospital mortality prediction.

Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.

BACKGROUND: Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may provide important infrastructure to clinicians and researchers to support artificial intelligence analysis of medication-related outcomes and healthcare costs. Using an unsupervised cluster analysis approach in combination with this common data model, the objective of this evaluation was to identify novel patterns of medication clusters (termed 'pharmacophenotypes') correlated with ICU adverse events (e.g., fluid overload) and patient-centered outcomes (e.g., mortality). METHODS: This was a retrospective, observational cohort study of 991 critically ill adults. To identify pharmacophenotypes, unsupervised machine learning analysis with automated feature learning using restricted Boltzmann machine and hierarchical clustering was performed on the medication administration records of each patient during the first 24 h of their ICU stay. Hierarchical agglomerative clustering was applied to identify unique patient clusters. Distributions of medications across pharmacophenotypes were described, and differences among patient clusters were compared using signed rank tests and Fisher's exact tests, as appropriate. RESULTS: A total of 30,550 medication orders for the 991 patients were analyzed; five unique patient clusters and six unique pharmacophenotypes were identified. For patient outcomes, compared to patients in Clusters 1 and 3, patients in Cluster 5 had a significantly shorter duration of mechanical ventilation and ICU length of stay (p < 0.05); for medications, Cluster 5 had a higher distribution of Pharmacophenotype 1 and a smaller distribution of Pharmacophenotype 2, compared to Clusters 1 and 3. For outcomes, patients in Cluster 2, despite having the highest severity of illness and greatest medication regimen complexity, had the lowest overall mortality; for medications, Cluster 2 also had a comparably higher distribution of Pharmacophenotype 6. CONCLUSION: The results of this evaluation suggest that patterns among patient clusters and medication regimens may be observed using empiric methods of unsupervised machine learning in combination with a common data model. These results have potential because while phenotyping approaches have been used to classify heterogenous syndromes in critical illness to better define treatment response, the entire medication administration record has not been incorporated in those analyses. Applying knowledge of these patterns at the bedside requires further algorithm development and clinical application but may have the future potential to be leveraged in guiding medication-related decision making to improve treatment outcomes.

Imaging Follow-Up of Nonsurgical Therapies for Lung Cancer: AJR Expert Panel Narrative Review.

Lung cancer continues to be the most common cause of cancer-related death worldwide. In the past decade, with the implementation of lung cancer screening programs and advances in surgical and nonsurgical therapies, the survival of patients with lung cancer has increased, as has the number of imaging studies that these patients undergo. However, most patients with lung cancer do not undergo surgical re-section, because they have comorbid disease or lung cancer in an advanced stage at diagnosis. Nonsurgical therapies have continued to evolve with a growing range of systemic and targeted therapies, and there has been an associated evolution in the imaging findings encountered at follow-up examinations after such therapies (e.g., with respect to posttreatment changes, treatment complications, and recurrent tumor). This AJR Expert Panel Narrative Review describes the current status of nonsurgical therapies for lung cancer and their expected and unexpected imaging manifestations. The goal is to provide guidance to radiologists regarding imaging assessment after such therapies, focusing mainly on non-small cell lung cancer. Covered therapies include systemic therapy (conventional chemotherapy, targeted therapy, and immunotherapy), radiotherapy, and thermal ablation.

Effect of Sacubitril/Valsartan vs Valsartan on Left Atrial Volume in Patients With Pre-Heart Failure With Preserved Ejection Fraction: The PARABLE Randomized Clinical Trial.

Importance: Pre-heart failure with preserved ejection fraction (pre-HFpEF) is common and has no specific therapy aside from cardiovascular risk factor management. Objective: To investigate the hypothesis that sacubitril/valsartan vs valsartan would reduce left atrial volume index using volumetric cardiac magnetic resonance imaging in patients with pre-HFpEF. Design, Setting, and Participants: The Personalized Prospective Comparison of ARNI [angiotensin receptor/neprilysin inhibitor] With ARB [angiotensin-receptor blocker] in Patients With Natriuretic Peptide Elevation (PARABLE) trial was a prospective, double-blind, double-dummy, randomized clinical trial carried out over 18 months between April 2015 and June 2021. The study was conducted at a single outpatient cardiology center in Dublin, Ireland. Of 1460 patients in the STOP-HF program or outpatient cardiology clinics, 461 met initial criteria and were approached for inclusion. Of these, 323 were screened and 250 asymptomatic patients 40 years and older with hypertension or diabetes, elevated B-type natriuretic peptide (BNP) greater than 20 pg/mL or N-terminal pro-b-type natriuretic peptide greater than 100 pg/mL, left atrial volume index greater than 28 mL/m2, and preserved ejection fraction greater than 50% were included. Interventions: Patients were randomized to angiotensin receptor neprilysin inhibitor sacubitril/valsartan titrated to 200 mg twice daily or matching angiotensin receptor blocker valsartan titrated to 160 mg twice daily. Main Outcomes and Measures: Maximal left atrial volume index and left ventricular end diastolic volume index, ambulatory pulse pressure, N-terminal pro-BNP, and adverse cardiovascular events. Results: Among the 250 participants in this study, the median (IQR) age was 72.0 (68.0-77.0) years; 154 participants (61.6%) were men and 96 (38.4%) were women. Most (n = 245 [98.0%]) had hypertension and 60 (24.0%) had type 2 diabetes. Maximal left atrial volume index was increased in patients assigned to receive sacubitril/valsartan (6.9 mL/m2; 95% CI, 0.0 to 13.7) vs valsartan (0.7 mL/m2; 95% CI, -6.3 to 7.7; P < .001) despite reduced markers of filling pressure in both groups. Changes in pulse pressure and N-terminal pro-BNP were lower in the sacubitril/valsartan group (-4.2 mm Hg; 95% CI, -7.2 to -1.21 and -17.7%; 95% CI, -36.9 to 7.4, respectively; P < .001) than the valsartan group (-1.2 mm Hg; 95% CI, -4.1 to 1.7 and 9.4%; 95% CI, -15.6 to 4.9, respectively; P < .001). Major adverse cardiovascular events occurred in 6 patients (4.9%) assigned to sacubitril/valsartan and 17 (13.3%) assigned to receive valsartan (adjusted hazard ratio, 0.38; 95% CI, 0.17 to 0.89; adjusted P = .04). Conclusions and Relevance: In this trial of patients with pre-HFpEF, sacubitril/valsartan treatment was associated with a greater increase in left atrial volume index and improved markers of cardiovascular risk compared to valsartan. More work is needed to understand the observed increased cardiac volumes and long-term effects of sacubitril/valsartan in patients with pre-HFpEF. Trial Registration: ClinicalTrials.gov Identifier: NCT04687111.

Combined autologous blood patch-immediate patient rollover does not reduce the pneumothorax or chest drain rate following CT-guided lung biopsy compared to immediate patient rollover alone.

PUPROSE: The purpose of this study was to evaluate a combined autologous blood-patch (ABP)-immediate patient rollover (IPR) technique compared with the IPR technique alone on the incidence of pneumothorax and chest drainage following CT-guided lung biopsy. METHODS: In this interventional cohort study of both prospectively and retrospectively acquired data, 652 patients underwent CT-guided lung biopsy. Patient demographics, lesion characteristics and technical biopsy variables including the combined ABP-IPR versus IPR alone were evaluated as predictors of pneumothorax and chest drain rates using regression analysis. RESULTS: The combined ABP-IPR technique was performed in 259 (39.7 %) patients whilst 393 (60.3 %) underwent IPR alone. There was no significant difference in pneumothorax rate or chest drains required between the combined ABP-IPR vs IPR groups (p =.08, p =.60 respectively). Predictors of pneumothorax adjusted for the combined ABP-IPR and IPR alone groups included age (p =.02), lesion size (p =.01), location (p =.005), patient position (p =.008), emphysema along the needle track (p =.005) and lesion distance from the pleura (p =.02). Adjusted predictors of chest drain insertion included lesion location (p =.09), patient position (p =.002), bullae crossed (p =.02) and lesion distance from the pleura (p =.02). CONCLUSION: The combined ABP-IPR technique does not reduce the pneumothorax or chest drain rate compared to the IPR technique alone. Utilising IPR without an ABP following CT-guided lung biopsy results in similar pneumothorax and chest drain rates while minimising the potential risk of systemic air embolism.

Quantitative Airway Assessment of Diffuse Idiopathic Pulmonary Neuroendocrine Cell Hyperplasia (DIPNECH) on CT as a Novel Biomarker.

Objectives: Diffuse idiopathic pulmonary neuroendocrine cell hyperplasia (DIPNECH) occurs due to abnormal proliferation of pulmonary neuroendocrine cells. We hypothesized that performing a quantitative analysis of airway features on chest CT may reveal differences to matched controls, which could ultimately help provide an imaging biomarker. Methods: A retrospective quantitative analysis of chest CTs in patients with DIPNECH and age matched controls was carried out using semi-automated post-processing software. Paired segmental airway and artery diameters were measured for each bronchopulmonary segment, and the airway:artery (AA) ratio, airway wall thickness:artery ratio (AWTA ratio) and wall area percentage (WAP) calculated. Nodule number, size, shape and location was recorded. Correlation between CT measurements and pulmonary function testing was performed. Results: 16 DIPNECH and 16 control subjects were analysed (all female, mean age 61.7 +/− 11.8 years), a combined total of 425 bronchopulmonary segments. The mean AwtA ratio, AA ratio and WAP for the DIPNECH group was 0.57, 1.18 and 68.8%, respectively, compared with 0.38, 1.03 and 58.3% in controls (p < 0.001, <0.001, 0.03, respectively). DIPNECH patients had more nodules than controls (22.4 +/− 32.6 vs. 3.6 +/− 3.6, p = 0.03). AA ratio correlated with FVC (R2 = 0.47, p = 0.02). A multivariable model incorporating nodule number, AA ratio and AWTA-ratio demonstrated good performance for discriminating DIPNECH and controls (AUC 0.971; 95% CI: 0.925−1.0). Conclusions: Quantitative CT airway analysis in patients with DIPNECH demonstrates increased airway wall thickness and airway:artery ratio compared to controls. Advances in knowledge: Quantitative CT measurement of airway wall thickening offers a potential imaging biomarker for treatment response.