Prevalence of spinal meningeal diverticula in autosomal dominant polycystic kidney disease.

BACKGROUND AND PURPOSE: Autosomal dominant polycystic kidney disease (ADPKD) patients develop cysts in the kidneys, liver, spleen, pancreas, prostate and arachnoid spaces. In addition, spinal meningeal diverticula have been reported. To determine whether spinal meningeal diverticula are associated with ADPKD, we compare their prevalence in ADPKD subjects to a control cohort without ADPKD. MATERIALS AND METHODS: ADPKD subjects and age-and gender-matched controls without ADPKD undergoing abdominal MRI from mid-thorax to the pelvis from 2003 to 2023 were retrospectively evaluated for spinal meningeal diverticula by 4 blinded observers. Prevalence of spinal meningeal diverticula in ADPKD was compared to control subjects, using t-test and correlated with clinical and laboratory data, and magnetic resonance imaging (MRI) features, including cyst volumes and cyst counts. RESULTS: Identification of spinal meningeal diverticula in ADPKD (n=285, median age, 47 [37,56]; 54% female) and control (n=285, median age, 47 [37,57]; 54% female) subjects had high inter-observer agreement (Pairwise Cohen kappa=0.74). Spinal meningeal diverticula were observed in 145 of 285 (51%) ADPKD subjects compared with 66 of 285 (23%) control subjects without ADPKD (p<0.001). Spinal meningeal diverticula in ADPKD were more prevalent in women (98 of 153 [64%]) than men (47 of 132 [36%], p<0.001). The mean number of spinal meningeal diverticula per affected ADPKD subject was 3.6 + 2.9 compared to 2.4 + 1.9 in controls with cysts (p<0.001). The median volume/interquartile range (IQR, 25%/75%) of spinal meningeal diverticula was 400 mm3 (210, 740) in ADPKD compared to 250 mm3 (180, 440) in controls (p<0.001). Mean/SD spinal meningeal diverticulum diameter was greater in the sacrum (7.3 + 4.1 mm) compared to thoracic (5.4 + 1.8 mm) and lumbar spine (5.8 + 2.0 mm), p<0.001, suggesting that that hydrostatic pressure contributed to enlargement. CONCLUSIONS: ADPKD has a high prevalence of spinal meningeal diverticula, particularly in women. ABBREVIATIONS: ADPKD = Autosomal dominant polycystic kidney disease.

Automatically Detecting Pancreatic Cysts in Autosomal Dominant Polycystic Kidney Disease on MRI Using Deep Learning.

BACKGROUND: Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with PKD2 mutations, which have a different phenotype than PKD1 mutations. However, pancreatic cysts are commonly overlooked by radiologists. Here, we automate the detection of pancreatic cysts on abdominal MRI in ADPKD. METHODS: Eight nnU-Net-based segmentation models with 2D or 3D configuration and various loss functions were trained on positive-only or positive-and-negative datasets, comprising axial and coronal T2-weighted MR images from 254 scans on 146 ADPKD patients with pancreatic cysts labeled independently by two radiologists. Model performance was evaluated on test subjects unseen in training, comprising 40 internal, 40 external, and 23 test-retest reproducibility ADPKD patients. RESULTS: Two radiologists agreed on 52% of cysts labeled on training data, and 33%/25% on internal/external test datasets. The 2D model with a loss of combined dice similarity coefficient and cross-entropy trained with the dataset with both positive and negative cases produced an optimal dice score of 0.7 ± 0.5/0.8 ± 0.4 at the voxel level on internal/external validation and was thus used as the best-performing model. In the test-retest, the optimal model showed superior reproducibility (83% agreement between scan A and B) in segmenting pancreatic cysts compared to six expert observers (77% agreement). In the internal/external validation, the optimal model showed high specificity of 94%/100% but limited sensitivity of 20%/24%. CONCLUSIONS: Labeling pancreatic cysts on T2 images of the abdomen in patients with ADPKD is challenging, deep learning can help the automated detection of pancreatic cysts, and further image quality improvement is warranted.

Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting.

Mayo Imaging Classification (MIC) for predicting future kidney growth in autosomal dominant polycystic kidney disease (ADPKD) patients is calculated from a single MRI/CT scan assuming exponential kidney volume growth and height-adjusted total kidney volume at birth to be 150 mL/m. However, when multiple scans are available, how this information should be combined to improve prediction accuracy is unclear. Herein, we studied ADPKD subjects ( n = 36 ) with 8+ years imaging follow-up (mean = 11 years) to establish ground truth kidney growth trajectory. MIC annual kidney growth rate predictions were compared to ground truth as well as 1- and 2-parameter least squares fitting. The annualized mean absolute error in MIC for predicting total kidney volume growth rate was 2.1 % ± 2 % compared to 1.1 % ± 1 % ( p = 0.002 ) for a 2-parameter fit to the same exponential growth curve used for MIC when 4 measurements were available or 1.4 % ± 1 % ( p = 0.01 ) with 3 measurements averaging together with MIC. On univariate analysis, male sex ( p = 0.05 ) and PKD2 mutation ( p = 0.04 ) were associated with poorer MIC performance. In ADPKD patients with 3 or more CT/MRI scans, 2-parameter least squares fitting predicted kidney volume growth rate better than MIC, especially in males and with PKD2 mutations where MIC was less accurate.

Barriers and enablers to improving integrated primary healthcare for non-communicable diseases and mental health conditions in Ethiopia: a mixed methods study.

BACKGROUND: The Ethiopian Primary Healthcare Clinical Guidelines (EPHCG) seek to improve quality of primary health care, while also expanding access to care for people with Non-Communicable Diseases and Mental Health Conditions (NCDs/MHCs). The aim of this study was to identify barriers and enablers to implementation of the EPHCG with a particular focus on NCDs/MHCs. METHODS: A mixed-methods convergent-parallel design was employed after EPHCG implementation in 18 health facilities in southern Ethiopia. Semi-structured interviews were conducted with 10 primary healthcare clinicians and one healthcare administrator. Organisational Readiness for Implementing Change (ORIC) questionnaire was self-completed by 124 health workers and analysed using Kruskal Wallis ranked test to investigate median score differences. Qualitative data were mapped to the Consolidated Framework for Implementation Science (CFIR) and the Theoretical Domains Framework (TDF). Expert Recommendations for Implementing Change (ERIC) were employed to select implementation strategies to address barriers. RESULTS: Four domains were identified: EPHCG training and implementation, awareness and meeting patient needs (demand side), resource constraints/barriers (supply side) and care pathway bottlenecks. The innovative facility-based training to implement EPHCG had a mixed response, especially in busy facilities where teams reported struggling to find protected time to meet. Key barriers to implementation of EPHCG were non-availability of resources (CFIR inner setting), such as laboratory reagents and medications that undermined efforts to follow guideline-based care, the way care was structured and lack of familiarity with providing care for people with NCDs-MHCs. Substantial barriers arose because of socio-economic problems that were interlinked with health but not addressable within the health system (CFIR outer setting). Other factors influencing effective implementation of EPHCG (TDF) included low population awareness about NCDs/MHCs and unaffordable diagnostic and treatment services (TDF). Implementation strategies were identified. ORIC findings indicated high scores of organisational readiness to implement the desired change with likely social desirability bias. CONCLUSION: Although perceived as necessary, practical implementation of EPHCG was constrained by challenges across domains of internal/external determinants. This was especially marked in relation to expansion of care responsibilities to include NCDs/MHCs. Attention to social determinants of health outcomes, community engagement and awareness-raising are needed to maximize population impact.

A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression.

Abdominal imaging of autosomal dominant polycystic kidney disease (ADPKD) has historically focused on detecting complications such as cyst rupture, cyst infection, obstructing renal calculi, and pyelonephritis; discriminating complex cysts from renal cell carcinoma; and identifying sources of abdominal pain. Many imaging features of ADPKD are incompletely evaluated or not deemed to be clinically significant, and because of this, treatment options are limited. However, total kidney volume (TKV) measurement has become important for assessing the risk of disease progression (i.e., Mayo Imaging Classification) and predicting tolvaptan treatment's efficacy. Deep learning for segmenting the kidneys has improved these measurements' speed, accuracy, and reproducibility. Deep learning models can also segment other organs and tissues, extracting additional biomarkers to characterize the extent to which extrarenal manifestations complicate ADPKD. In this concept paper, we demonstrate how deep learning may be applied to measure the TKV and how it can be extended to measure additional features of this disease.

Quantitative susceptibility mapping for detection of kidney stones, hemorrhage differentiation, and cyst classification in ADPKD.

BACKGROUND AND PURPOSE: The objective is to demonstrate feasibility of quantitative susceptibility mapping (QSM) in autosomal dominant polycystic kidney disease (ADPKD) patients and to compare imaging findings with traditional T1/T2w magnetic resonance imaging (MRI). METHODS: Thirty-three consecutive patients (11 male, 22 female) diagnosed with ADPKD were initially selected. QSM images were reconstructed from the multiecho gradient echo data and compared to co-registered T2w, T1w, and CT images. Complex cysts were identified and classified into distinct subclasses based on their imaging features. Prevalence of each subclass was estimated. RESULTS: QSM visualized two renal calcifications measuring 9 and 10 mm and three pelvic phleboliths measuring 2 mm but missed 24 calcifications measuring 1 mm or less and 1 larger calcification at the edge of the field of view. A total of 121 complex T1 hyperintense/T2 hypointense renal cysts were detected. 52 (43%) Cysts appeared hyperintense on QSM consistent with hemorrhage; 60 (49%) cysts were isointense with respect to simple cysts and normal kidney parenchyma, while the remaining 9 (7%) were hypointense. The presentation of the latter two complex cyst subtypes is likely indicative of proteinaceous composition without hemorrhage. CONCLUSION: Our results indicate that QSM of ADPKD kidneys is possible and uniquely suited to detect large renal calculi without ionizing radiation and able to identify properties of complex cysts unattainable with traditional approaches.

Primary biliary cholangitis drug evaluation and regulatory approval: Where do we go from here?

Primary biliary cholangitis (PBC) is a chronic cholestatic liver disease. The management landscape was transformed 20 years ago with the advent of ursodeoxycholic acid. Up to 40% of patients do not, however, respond adequately to ursodeoxycholic acid and therefore still remain at risk of disease progression to cirrhosis. The introduction of obeticholic acid as a second-line therapy for patients failing ursodeoxycholic acid has improved outcomes for patients with PBC. There remains, however, a need for better treatment for patients at higher risk. The greatest threat facing our efforts to improve treatment in PBC is, paradoxically, the regulatory approval model providing conditional marketing authorization for new drugs based on biochemical markers on the condition that long-term, randomized placebo-controlled outcome trials are performed to confirm efficacy. As demonstrated by the COBALT confirmatory study with obeticholic acid, it is difficult to retain patients in the required follow-on confirmatory placebo-controlled PBC outcome trials when a licensed drug is commercially available. New PBC therapies in development, such as the peroxisome proliferator-activated receptor agonists, face even greater challenges in demonstrating outcome benefit through randomized placebo-controlled studies once following conditional marketing authorization, as there will be even more treatment options available. A recently published EMA Reflection Paper provides some guidance on the regulatory pathway to full approval but fails to recognize the importance of real-world data in providing evidence of outcome benefit in rare diseases. Here we explore the impact of the EMA reflection paper on PBC therapy and offer pragmatic solutions for generating evidence of long-term outcomes through real-world data collection.

"Problems you can live with" versus emergencies: how community members in rural Ethiopia contend with conditions requiring surgery.

BACKGROUND: 98% of people with surgical conditions living in low- and middle-income countries (LMICs) do not receive safe, timely and affordable surgical and anesthesia care. Research exploring barriers to receiving care has tended to be narrow in focus, often facility-based and ignoring the community beliefs, experiences and behaviours that will be an essential component of closing the gap in surgical care. Using qualitative methods, we captured diverse community perspectives in rural Ethiopia: exploring beliefs, perceptions, knowledge and experiences related to surgical conditions, with the overall aim of (re)constructing explanatory models. METHODS: Our study was nested within a community-based survey of surgical conditions conducted in the Butajira Health and Demographic Surveillance Site, southern Ethiopia, and a follow-up study of people accessing surgical care in two local hospitals. We carried out 24 semi-structured interviews. Participants were community members who needed but did/did not access surgical care, community-based healthcare workers and traditional bone-setters. Interviews were conducted in Amharic, audio-recorded, transcribed, and translated into English. We initially carried out thematic analysis and we recognized that emerging themes were aligned with Kleinman's explanatory models framework and decided to use this to guide the final stages of analysis. RESULTS: We found that community members primarily understood surgical conditions according to severity. We identified two categories: conditions you could live with and those which required urgent care, with the latter indicating a clear and direct path to surgical care whilst the former was associated with a longer, more complex and experimental pattern of help-seeking. Fear of surgery and poverty disrupted help-seeking, whilst community narratives based on individual experiences fed into the body of knowledge people used to inform decisions about care. CONCLUSIONS: We found explanatory models to be flexible, responsive to new evidence about what might work best in the context of limited community resources. Our findings have important implications for future research and policy, suggesting that community-level barriers have the potential to be responsive to carefully designed interventions which take account of local knowledge and beliefs.

Peripheral Blood Mononuclear Cell Gene Expression Associated with Pulmonary Microvascular Perfusion: The Multi-Ethnic Study of Atherosclerosis Chronic Obstructive Pulmonary Disease.

Rationale: Chronic obstructive pulmonary disease (COPD) and emphysema are associated with endothelial damage and altered pulmonary microvascular perfusion. The molecular mechanisms underlying these changes are poorly understood in patients, in part because of the inaccessibility of the pulmonary vasculature. Peripheral blood mononuclear cells (PBMCs) interact with the pulmonary endothelium. Objectives: To test the association between gene expression in PBMCs and pulmonary microvascular perfusion in COPD. Methods: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study recruited two independent samples of COPD cases and controls with ⩾10 pack-years of smoking history. In both samples, pulmonary microvascular blood flow, pulmonary microvascular blood volume, and mean transit time were assessed on contrast-enhanced magnetic resonance imaging, and PBMC gene expression was assessed by microarray. Additional replication was performed in a third sample with pulmonary microvascular blood volume measures on contrast-enhanced dual-energy computed tomography. Differential expression analyses were adjusted for age, gender, race/ethnicity, educational attainment, height, weight, smoking status, and pack-years of smoking. Results: The 79 participants in the discovery sample had a mean age of 69 ± 6 years, 44% were female, 25% were non-White, 34% were current smokers, and 66% had COPD. There were large PBMC gene expression signatures associated with pulmonary microvascular perfusion traits, with several replicated in the replication sets with magnetic resonance imaging (n = 47) or dual-energy contrast-enhanced computed tomography (n = 157) measures. Many of the identified genes are involved in inflammatory processes, including nuclear factor-κB and chemokine signaling pathways. Conclusions: PBMC gene expression in nuclear factor-κB, inflammatory, and chemokine signaling pathways was associated with pulmonary microvascular perfusion in COPD, potentially offering new targetable candidates for novel therapies.

Developing interventions to improve detection of depression in primary healthcare settings in rural Ethiopia.

BACKGROUND: The poor detection of depression in primary healthcare (PHC) in low- and middle-income countries continues to threaten the plan to scale up mental healthcare coverage. AIMS: To describe the process followed to develop an intervention package to improve detection of depression in PHC settings in rural Ethiopia. METHOD: The study was conducted in Sodo, a rural district in south Ethiopia. The Medical Research Council's framework for the development of complex interventions was followed. Qualitative interviews, observations of provider-patient communication, intervention development workshops and pre-testing of the screening component of the intervention were conducted to develop the intervention. RESULTS: A multicomponent intervention package was developed, which included (a) manual-based training of PHC workers for 10 days, adapted from the World Health Organization's Mental Health Gap Action Programme Intervention Guide, with emphasis on depression, locally identified depressive symptoms, communication skills, training by people with lived experience and active learning methods; (b) screening for culturally salient manifestations of depression, using a four-item tool; (c) raising awareness among people attending out-patient clinics about depression, using information leaflets and health education; and (d) system-level interventions, such as supportive supervision, use of posters at health facilities and a decision support mobile app. CONCLUSIONS: This contextualised, multicomponent intervention package may lead to meaningful impact on the detection of depression in PHC in rural Ethiopia and similar settings. The intervention will be pilot tested for feasibility, acceptability and effectiveness before its wider implementation.

Gd-EOB-DTPA enhanced MRI based radiomics combined with clinical variables in stratifying hepatic functional reserve in HBV infected patients.

PURPOSES: To evaluate radiomics from Gd-EOB-DTPA enhanced MR combined with clinical variables for stratifying hepatic functional reserve in hepatitis B virus (HBV) patients. METHODS: Our study included 279 chronic HBV patients divided 8:2 for training and test cohorts. Radiomics features were extracted from the hepatobiliary phase (HBP) MR images. Radiomics features were selected to construct a Rad-score which was combined with clinical parameters in two models differentiating hepatitis vs. Child-Pugh A and Child-Pugh A vs. B/C. Performances of these stratifying models were compared using area under curve (AUC). RESULTS: Rad-score alone discriminated hepatitis vs. Child-Pugh A with AUC = 0.890, 0.914 and Child-Pugh A vs. B/C with AUC = 0.862, 0.865 for the training and test cohorts, respectively. Model 1 [Rad-score + clinical parameters for hepatitis vs. Child-Pugh A] showed AUC = 0.978 for the test cohort, which was higher than ALBI [albumin-bilirubin] and MELD [model for end-stage liver disease], with AUCs of 0.716, 0.799, respectively (p < 0.001, < 0.001). Model 2 [Rad-score + clinical parameters for Child-Pugh A vs. B/C] showed AUC of 0.890 in the test cohort, which was similar to ALBI (AUC = 0.908, p = 0.760), and higher than MELD (AUC = 0.709, p = 0.018). CONCLUSION: Rad-score combined with clinical variables stratifies hepatic functional reserve in HBV patients.

Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning.

RATIONALE AND OBJECTIVES: Following autosomal dominant polycystic kidney disease (ADPKD) progression by measuring organ volumes requires low measurement variability. The objective of this study is to reduce organ volume measurement variability on MRI of ADPKD patients by utilizing all pulse sequences to obtain multiple measurements which allows outlier analysis to find errors and averaging to reduce variability. MATERIALS AND METHODS: In order to make measurements on multiple pulse sequences practical, a 3D multi-modality multi-class segmentation model based on nnU-net was trained/validated using T1, T2, SSFP, DWI and CT from 413 subjects. Reproducibility was assessed with test-re-test methodology on ADPKD subjects (n = 19) scanned twice within a 3-week interval correcting outliers and averaging the measurements across all sequences. Absolute percent differences in organ volumes were compared to paired students t-test. RESULTS: Dice similarlity coefficient > 97%, Jaccard Index > 0.94, mean surface distance < 1 mm and mean Hausdorff Distance < 2 cm for all three organs and all five sequences were found on internal (n = 25), external (n = 37) and test-re-test reproducibility assessment (38 scans in 19 subjects). When averaging volumes measured from five MRI sequences, the model automatically segmented kidneys with test-re-test reproducibility (percent absolute difference between exam 1 and exam 2) of 1.3% which was better than all five expert observers. It reliably stratified ADPKD into Mayo Imaging Classification (area under the curve=100%) compared to radiologist. CONCLUSION: 3D deep learning measures organ volumes on five MRI sequences leveraging the power of outlier analysis and averaging to achieve 1.3% total kidney test-re-test reproducibility.

Size Matters: How to Characterize ADPKD Severity by Measuring Total Kidney Volume.

Following patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD) has been challenging because serum biomarkers such as creatinine often remain normal until relatively late in the disease [...].

Clinical Quality Control of MRI Total Kidney Volume Measurements in Autosomal Dominant Polycystic Kidney Disease.

Total kidney volume measured on MRI is an important biomarker for assessing the progression of autosomal dominant polycystic kidney disease and response to treatment. However, we have noticed that there can be substantial differences in the kidney volume measurements obtained from the various pulse sequences commonly included in an MRI exam. Here we examine kidney volume measurement variability among five commonly acquired MRI pulse sequences in abdominal MRI exams in 105 patients with ADPKD. Right and left kidney volumes were independently measured by three expert observers using model-assisted segmentation for axial T2, coronal T2, axial single-shot fast spin echo (SSFP), coronal SSFP, and axial 3D T1 images obtained on a single MRI from ADPKD patients. Outlier measurements were analyzed for data acquisition errors. Most of the outlier values (88%) were due to breathing during scanning causing slice misregistration with gaps or duplication of imaging slices (n = 35), slice misregistration from using multiple breath holds during acquisition (n = 25), composing of two overlapping acquisitions (n = 17), or kidneys not entirely within the field of view (n = 4). After excluding outlier measurements, the coefficient of variation among the five measurements decreased from 4.6% pre to 3.2%. Compared to the average of all sequences without errors, TKV measured on axial and coronal T2 weighted imaging were 1.2% and 1.8% greater, axial SSFP was 0.4% greater, coronal SSFP was 1.7% lower and axial T1 was 1.5% lower than the mean, indicating intrinsic measurement biases related to the different MRI contrast mechanisms. In conclusion, MRI data acquisition errors are common but can be identified using outlier analysis and excluded to improve organ volume measurement consistency. Bias toward larger volume measurements on T2 sequences and smaller volumes on axial T1 sequences can also be mitigated by averaging data from all error-free sequences acquired.

Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans.

BACKGROUND: Treatment and preventative advances for chronic obstructive pulmonary disease (COPD) have been slow due, in part, to limited subphenotypes. We tested if unsupervised machine learning on CT images would discover CT emphysema subtypes with distinct characteristics, prognoses and genetic associations. METHODS: New CT emphysema subtypes were identified by unsupervised machine learning on only the texture and location of emphysematous regions on CT scans from 2853 participants in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS), a COPD case-control study, followed by data reduction. Subtypes were compared with symptoms and physiology among 2949 participants in the population-based Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study and with prognosis among 6658 MESA participants. Associations with genome-wide single-nucleotide-polymorphisms were examined. RESULTS: The algorithm discovered six reproducible (interlearner intraclass correlation coefficient, 0.91-1.00) CT emphysema subtypes. The most common subtype in SPIROMICS, the combined bronchitis-apical subtype, was associated with chronic bronchitis, accelerated lung function decline, hospitalisations, deaths, incident airflow limitation and a gene variant near DRD1, which is implicated in mucin hypersecretion (p=1.1 ×10-8). The second, the diffuse subtype was associated with lower weight, respiratory hospitalisations and deaths, and incident airflow limitation. The third was associated with age only. The fourth and fifth visually resembled combined pulmonary fibrosis emphysema and had distinct symptoms, physiology, prognosis and genetic associations. The sixth visually resembled vanishing lung syndrome. CONCLUSION: Large-scale unsupervised machine learning on CT scans defined six reproducible, familiar CT emphysema subtypes that suggest paths to specific diagnosis and personalised therapies in COPD and pre-COPD.