How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.

BACKGROUND: Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. METHODS: In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. RESULTS: We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. DISCUSSION: This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. CONCLUSION: The customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems.

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which standardizes appearances to enable reliable AI analysis of multi-source medical imaging. METHODS: A literature search using PRISMA guidelines was conducted to identify relevant papers published between 2013 and 2023 analyzing multi-centric and multi-device medical imaging studies that utilized image harmonization approaches. RESULTS: Common image harmonization techniques included grayscale normalization (improving classification accuracy by up to 24.42 %), resampling (increasing the percentage of robust radiomics features from 59.5 % to 89.25 %), and color normalization (enhancing AUC by up to 0.25 in external test sets). Initially, mathematical and statistical methods dominated, but machine and deep learning adoption has risen recently. Color imaging modalities like digital pathology and dermatology have remained prominent application areas, though harmonization efforts have expanded to diverse fields including radiology, nuclear medicine, and ultrasound imaging. In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores. CONCLUSIONS: Continued progress in image harmonization represents a promising strategy for advancing healthcare by enabling large-scale, reliable analysis of integrated multi-source datasets using AI. Standardizing imaging data across clinical settings can help realize personalized, evidence-based care supported by data-driven technologies while mitigating biases associated with specific populations or acquisition protocols.

Characteristics of dentists in the National Dental Practice-Based Research Network.

OBJECTIVES: Our aims are to describe the characteristics of dentists, members of the US National Dental practice-based research network (PBRN) in the United States, and determine how often these dentists provide specific dental procedures. METHODS: Dentists completed a questionnaire when they enrolled in the Network about their demographic and training characteristics and characteristics of their practices and patients. Dentists also reported the frequency of providing specific dental procedures. Data were analyzed using descriptive statistics. RESULTS: Of 4,483 dentists in active clinical practice, 34% identified as females, 70% as white, and 73% as general dentists. Most dentists practiced in large metropolitan areas (87%) and in solo or small practices (72%). On average, they reported about one-half of their patients were children or older adults, a third were from historically underrepresented racial and ethnic groups, and one-quarter were covered by public insurance. Most dentists routinely performed restorations and fixed prosthetics (78%), extractions (59%), removable (44%) and implant (40%) prosthetics, and endodontics on incisor and premolar teeth (44%). CONCLUSIONS: Dentists participating in the National Dental PBRN have much in common with dentists at large. The network has a broad representation of dentists, practice types, patient populations, and treatments offered, including diversity regarding race/ethnicity, gender, insurance, and geography of its practitioners and patients. CLINICAL SIGNIFICANCE: Characteristics of National Dental PBRN dentists suggest that a broad range of dentists is interested in participating in national-level research studies, thereby enabling an array of clinical study settings and topics that can optimize the generalizability of study findings.

Head-to-Head Comparison of Consensus-Recommended Platelet Function Tests to Assess P2Y12 Inhibition-Insights for Multi-Center Trials.

The vasodilator-associated stimulated phosphoprotein (VASP) phosphorylation level is a highly specific method to assess P2Y12 receptor inhibition. Traditionally, VASP phosphorylation is analyzed by flow cytometry, which is laborious and restricted to specialized laboratories. Recently, a simple ELISA kit has been commercialized. The primary objective of this study was to compare the performance of VASP assessment by ELISA and flow cytometry in relation to functional platelet aggregation testing by Multiplate® whole-blood aggregometry. Blood from 24 healthy volunteers was incubated with increasing concentration of a P2Y12 receptor inhibitor (AR-C 66096). Platelet function testing was carried out simultaneously by Multiplate® aggregometry and by VASP assessment through ELISA and flow cytometry. As expected, increasing concentrations of the P2Y12 receptor inhibitor induced a proportional inhibition of platelet aggregation and P2Y12 receptor activation across the modalities. Platelet reactivity index values of both ELISA- and flow cytometry-based VASP assessment methods correlated strongly (r = 0.87, p < 0.0001) and showed minimal bias (1.05%). Correlation with Multiplate® was slightly higher for the flow cytometry-based VASP assay (r = 0.79, p < 0.0001) than for the ELISA-based assay (r = 0.69, p < 0.0001). Intraclass correlation (ICC) was moderate for all the assays tested (ICC between 0.62 and 0.84). However, categorization into low, optimal, or high platelet reactivity based on these assays was strongly concordant (κ between 0.86 and 0.92). In conclusion, the consensus-recommended assays with their standardized cut-offs should not be used interchangeably in multi-center clinical studies but, rather, they should be standardized throughout sites.

Privacy-protecting estimation of adjusted risk ratios using modified Poisson regression in multi-center studies.

BACKGROUND: Multi-center studies can generate robust and generalizable evidence, but privacy considerations and legal restrictions often make it challenging or impossible to pool individual-level data across data-contributing sites. With binary outcomes, privacy-protecting distributed algorithms to conduct logistic regression analyses have been developed. However, the risk ratio often provides a more transparent interpretation of the exposure-outcome association than the odds ratio. Modified Poisson regression has been proposed to directly estimate adjusted risk ratios and produce confidence intervals with the correct nominal coverage when individual-level data are available. There are currently no distributed regression algorithms to estimate adjusted risk ratios while avoiding pooling of individual-level data in multi-center studies. METHODS: By leveraging the Newton-Raphson procedure, we adapted the modified Poisson regression method to estimate multivariable-adjusted risk ratios using only summary-level information in multi-center studies. We developed and tested the proposed method using both simulated and real-world data examples. We compared its results with the results from the corresponding pooled individual-level data analysis. RESULTS: Our proposed method produced the same adjusted risk ratio estimates and standard errors as the corresponding pooled individual-level data analysis without pooling individual-level data across data-contributing sites. CONCLUSIONS: We developed and validated a distributed modified Poisson regression algorithm for valid and privacy-protecting estimation of adjusted risk ratios and confidence intervals in multi-center studies. This method allows computation of a more interpretable measure of association for binary outcomes, along with valid construction of confidence intervals, without sharing of individual-level data.

The acute health effects of ozone and PM2.5 on daily cardiovascular disease mortality: A multi-center time series study in China.

To explore the acute health effects of ozone(O3) and PM2.5 on daily cardiovascular disease mortality in all the cities of Jiangsu province, China. Daily average concentrations of ozone (maximum 8-h average), fine particle matter (PM2.5), meteorological data (including temperature and relative humidity) and daily cardiovascular diseases mortality data were collected from January 1, 2015, to December 31, 2017, in all the 13 cities of Jiangsu Province in China. The time-series model linked with Poisson distribution was used to estimate the correlation between O3 and daily cardiovascular diseases mortality, after adjusting for PM2.5 and other pollutants. Firstly, we evaluated the acute effects of O3 on cardiovascular mortality in each city in Jiangsu province, and then we combined the effects of O3 on cardiovascular disease mortality by meta-analysis. The single-pollutant model indicated that the acute health effects of O3 in different cities were quite different. After combined the effects of 13 cities, the mortality of cardiovascular diseases increased by 0.983%（95%CI：0.588-1.377%）for every 10 µg/m3 increases of O3. O3 showed the strongest effect on 3-day moving average concentration (Lag 0-3). After adjusting PM2.5，NO2, SO2, and CO, the results were still statistically significant at the multi-city level. The study found that O3 had a greater effect on women; the elderly population was susceptible and high-education groups were more vulnerable to O3, although the differences between different subgroups were insignificant. The dose-response curve showed that the mortality of cardiovascular disease increased with the increase of O3. The increase in O3 concentration of atmospheric can increase the daily mortality of cardiovascular diseases in Jiangsu, China.

Reproducibility of multi-shell diffusion tractography on traveling subjects: A multicenter study prospective.

Reproducibility of multicenter diffusion magnetic resonance imaging has drawn more attention recently due to rapidly increasing need for large-size brain imaging studies. Advanced multi-shell diffusion models are recommended for their potentials to provide variety of physio-pathological information. While previous studies have investigated the consistency of single-shell diffusion acquisition from various hardware and protocols, a well-controlled study with multi-shell acquisition would be necessary to understand the inherent factors of reproducibility from new complexity of such acquisition protocol. In this study, three traveling subjects were scanned at eight imaging centers equipped with the same type of scanners using the same multi-shell diffusion imaging protocol. Track density imaging and structure connectomes were investigated in local-scale distribution and in distal-scale connectivity, respectively. With evaluations of the coefficient of variation and the intra-class correlation coefficient, our results indicated: 1) similar to single-shell schemes, the intra-center reproducibility of multi-shell is higher than inter-center; 2) multi-shell schemes produce higher reproducibility and precision among centers compared to the single-shell schemes; and 3) in addition to the diffusion schemes, image quality and the presence of complex fiber structure could also associated with multicenter reproducibility.

Variability in quantitative diffusion-weighted MR imaging (DWI) across different scanners and imaging sites: is there a potential consensus that can help reducing the limits of expected bias?

KEY POINTS: • Variability of ADC measurements may be substantial across different MRI scanners and imaging sites. • DWI protocol standardization and increased awareness of frequent sources of error can help reducing the limits of expected bias. • Focusing on ADC change and normalized ADC values rather than on absolute measurements can facilitate consistent use of ADC in multi-center studies.

Bivariate Mixed Effects Analysis of Clustered Data with Large Cluster Sizes.

Linear mixed effects models are widely used to analyze a clustered response variable. Motivated by a recent study to examine and compare the hospital length of stay (LOS) between patients undertaking percutaneous coronary intervention (PCI) and coronary artery bypass graft (CABG) from several international clinical trials, we proposed a bivariate linear mixed effects model for the joint modeling of clustered PCI and CABG LOS's where each clinical trial is considered a cluster. Due to the large number of patients in some trials, commonly used commercial statistical software for fitting (bivariate) linear mixed models failed to run since it could not allocate enough memory to invert large dimensional matrices during the optimization process. We consider ways to circumvent the computational problem in the maximum likelihood (ML) inference and restricted maximum likelihood (REML) inference. Particularly, we developed an expected and maximization (EM) algorithm for the REML inference and presented an ML implementation using existing software. The new REML EM algorithm is easy to implement and computationally stable and efficient. With this REML EM algorithm, we could analyze the LOS data and obtained meaningful results.

Variability in baseline laboratory measurements of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

Multi-center epidemiological studies must ascertain that their measurements are accurate and reliable. For laboratory measurements, reliability can be assessed through investigation of reproducibility of measurements in the same individual. In this paper, we present results from the quality control analysis of the baseline laboratory measurements from the ELSA-Brasil study. The study enrolled 15,105 civil servants at 6 research centers in 3 regions of Brazil between 2008–2010, with multiple biochemical analytes being measured at a central laboratory. Quality control was ascertained through standard laboratory evaluation of intra- and inter-assay variability and test-retest analysis in a subset of randomly chosen participants. An additional sample of urine or blood was collected from these participants, and these samples were handled in the same manner as the original ones, locally and at the central laboratory. Reliability was assessed with the intraclass correlation coefficient (ICC), estimated through a random effects model. Coefficients of variation (CV) and Bland-Altman plots were additionally used to assess measurement variability. Laboratory intra and inter-assay CVs varied from 0.86% to 7.77%. From test-retest analyses, the ICCs were high for the majority of the analytes. Notably lower ICCs were observed for serum sodium (ICC=0.50; 95%CI=0.31–0.65) and serum potassium (ICC=0.73; 95%CI=0.60–0.83), due to the small biological range of these analytes. The CVs ranged from 1 to 14%. The Bland-Altman plots confirmed these results. The quality control analyses showed that the collection, processing and measurement protocols utilized in the ELSA-Brasil produced reliable biochemical measurements.

Precentral gyrus functional connectivity signatures of autism.

Motor impairments are prevalent in children with autism spectrum disorders (ASD) and are perhaps the earliest symptoms to develop. In addition, motor skills relate to the communicative/social deficits at the core of ASD diagnosis, and these behavioral deficits may reflect abnormal connectivity within brain networks underlying motor control and learning. Despite the fact that motor abnormalities in ASD are well-characterized, there remains a fundamental disconnect between the complexity of the clinical presentation of ASD and the underlying neurobiological mechanisms. In this study, we examined connectivity within and between functional subregions of a key component of the motor control network, the precentral gyrus, using resting state functional Magnetic Resonance Imaging data collected from a large, heterogeneous sample of individuals with ASD as well as neurotypical controls. We found that the strength of connectivity within and between distinct functional subregions of the precentral gyrus was related to ASD diagnosis and to the severity of ASD traits. In particular, connectivity involving the dorsomedial (lower limb/trunk) subregion was abnormal in ASD individuals as predicted by models using a dichotomous variable coding for the presence of ASD, as well as models using symptom severity ratings. These findings provide further support for a link between motor and social/communicative abilities in ASD.

Double-blind, placebo-controlled, randomized, multicenter studies of oxiracetam in the treatment of brain injury / 中华神经医学杂志

Objective To evaluate the efficacy and safety of oxiracetam in the treatment of neurological deficits resulting from brain injury through the comparison of oxiracetam for injection and piracetam for injection in clinical trials. Methods A multiple-center, randomized, double-blind,parallel study was performed on 239 patients; these patients were divided into experimental group (oxiracetam for injection, n=120) and control group (piracetam, n=119). National institutes of health stroke scale (NIHSS), Glasgow coma scale (GCS), myodynamia grading, mini-metal state examination (MMSE) were employed to evaluate the therapeutic effects; electrocardiogram and laboratory examination were performed, and the side effects were also observed. Results The scores of NIHSS,GCS and myodynamia grading after treatment in the 2 groups were all significantly higher than those before treatment (P＜0.05); however, no significant differences on these scores were noted between the experimental group and control group (P＞0.05). No serious adverse events were noted in both groups.Conclusion Oxiracetam, the same as piracetam, is safe and effective in the treatment of neurological deficits secondary to brain injury.

Pooling FMRI data: meta-analysis, mega-analysis and multi-center studies.

The quantitative analysis of pooled data from related functional magnetic resonance imaging (fMRI) experiments has the potential to significantly accelerate progress in brain mapping. Such data-pooling can be achieved through meta-analysis (the pooled analysis of published results), mega-analysis (the pooled analysis of raw data) or multi-site studies, which can be seen as designed mega-analyses. Current limitations in function-location brain mapping and how data-pooling can be used to remediate them are reviewed, with particular attention to power aggregation and mitigation of false positive results. Some recently developed analysis tools for meta- and mega-analysis are also presented, and recommendations for the conduct of valid fMRI data pooling are formulated.

Treatment of diarrhea-predominant irritable bowel syndrome with N-acetyl-D-glucosamine: A randomized, double-blind, placebo-controlled multi-center study Coorperative group / 中华消化杂志

Objective To evaluate the efficacy and safety of N-acetyl-D-glucosamine in treatment of diarrhea-predominant irritable bowel syndrome (D-IBS). Methods A multi-center, randomized, double-blind, placebo-controlled clinical study was performed in 430 patientswith D-IBS. After 2-week baseline period, eligible subjects were randomly either received 100 mg of N-acetyl-D-glucosamine (treated group,n=323) or placebo (control group,n= 107) 3 times a day for consecutive 4 weeks, followed by 2-week withdrawal follow-up. The major parameters were assessed by visual analogue scale (VAS) score and Common Symptom Sensation score. The minor parameters included abdominal pain or discomfort, severity of diarrhea, bloating, urgency, defecation frequencies with consistency per bowel movement which was judged by bristol stool scale and utilization of Smect. The evidence of adverse events was reeoded. Results The major parameters were singnifieantly improved in the treated group with effective rate of 65.16 % at the fourth week of treatment in comparison with control group (effective rate of 34. 29% ,P<0.01). Except the utilization of Smect (P= 1.00), the other minor parameters were significantly improved in treated group compared with control group (P< 0.01) after 1 week treatment. The occurrence of adverse events was 0.96% in the treated group and 0. 95% in the control group (P = 1. 00). Conclusions The results indicate that N-acetyl-D-glucosamine is effective and safe in the treatment of D-IBS by improving ecological environment and preventing activation of mast cells.