SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification.

Developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of thousands of biological samples. The exploitation of this rich source of information requires a detailed quantification of spectral features. However, the development of a consistent and automatic workflow has been challenging because of extensive signal overlap. To address this challenge, we introduce the software Spectral Automated NMR Decomposition (SAND). SAND follows on from the previous success of time-domain modeling and automatically quantifies entire spectra without manual interaction. The SAND approach uses hybrid optimization with Markov chain Monte Carlo methods, employing subsampling in both time and frequency domains. In particular, SAND randomly divides the time-domain data into training and validation sets to help avoid overfitting. We demonstrate the accuracy of SAND, which provides a correlation of ∼0.9 with ground truth on cases including highly overlapped simulated data sets, a two-compound mixture, and a urine sample spiked with different amounts of a four-compound mixture. We further demonstrate an automated annotation using correlation networks derived from SAND decomposed peaks, and on average, 74% of peaks for each compound can be recovered in single clusters. SAND is available in NMRbox, the cloud computing environment for NMR software hosted by the Network for Advanced NMR (NAN). Since the SAND method uses time-domain subsampling (i.e., random subset of time-domain points), it has the potential to be extended to a higher dimensionality and nonuniformly sampled data.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Implementation of an audit and feedback module targeting low-value clinical practices in a provincial trauma quality assurance program: a cost-effectiveness study.

BACKGROUND: Audit and Feedback (A&F) interventions based on quality indicators have been shown to lead to significant improvements in compliance with evidence-based care including de-adoption of low-value practices (LVPs). Our primary aim was to evaluate the cost-effectiveness of adding a hypothetical A&F module targeting LVPs for trauma admissions to an existing quality assurance intervention targeting high-value care and risk-adjusted outcomes. A secondary aim was to assess how certain A&F characteristics might influence its cost-effectiveness. METHODS: We conducted a cost-effectiveness analysis using a probabilistic static decision analytic model in the Québec trauma care continuum. We considered the Québec Ministry of Health perspective. Our economic evaluation compared a hypothetical scenario in which the A&F module targeting LVPs is implemented in a Canadian provincial trauma quality assurance program to a status quo scenario in which the A&F module is not implemented. In scenarios analyses we assessed the impact of A&F characteristics on its cost-effectiveness. Results are presented in terms of incremental costs per LVP avoided. RESULTS: Results suggest that the implementation of A&F module (Cost = $1,480,850; Number of LVPs = 6,005) is associated with higher costs and higher effectiveness compared to status quo (Cost = $1,124,661; Number of LVPs = 8,228). The A&F module would cost $160 per LVP avoided compared to status quo. The A&F module becomes more cost-effective with the addition of facilitation visits; more frequent evaluation; and when only high-volume trauma centers are considered. CONCLUSION: A&F module targeting LVPs is associated with higher costs and higher effectiveness than status quo and has the potential to be cost-effective if the decision-makers' willingness-to-pay is at least $160 per LVP avoided. This likely represents an underestimate of true ICER due to underestimated costs or missed opportunity costs. Results suggest that virtual facilitation visits, frequent evaluation, and implementing the module in high-volume centers can improve cost-effectiveness.

A Tutorial on Net Benefit Regression for Real-World Cost-Effectiveness Analysis Using Censored Data from Randomized or Observational Studies.

HIGHLIGHTS: We illustrate the steps involved in carrying out cost-effectiveness analysis using net benefit regressions with possibly censored demo data by providing step-by-step guidance and code applied to a data set.We demonstrate the importance of these new methods by illustrating how naïve methods for handling censoring can lead to biased cost-effectiveness results.

What is a star worth to Medicare beneficiaries? A discrete choice experiment of hospital quality ratings.

Hospital quality ratings are widely available to help Medicare beneficiaries make an informed choice about where to receive care. However, how beneficiaries' trade-off between different quality domains (clinical outcomes, patient experience, safety, efficiency) and other considerations (out-of-pocket cost, travel distance) is not well understood. We sought to study how beneficiaries make trade-offs when choosing a hypothetical hospital. We administered an online survey that included a discrete choice experiment to a nationally representative sample of 1025 Medicare beneficiaries. On average, beneficiaries were willing to pay $1698 more for a hospital with a 1-star higher rating on clinical outcomes. This was over twice the value of the patient experience ($691) and safety ($615) domains and nearly 8 times the value of the efficiency domain ($218). We also found that the value of a 1-star improvement depends not only on the quality domain but also the baseline level of performance of the hospital. Generally, it is more valuable for low-performing hospitals to achieve average performance than for average hospitals to achieve excellence.

Barriers and Facilitators of Using R for Decision Analytic Modeling in Health Technology Assessment: Focus Group Results.

BACKGROUND AND OBJECTIVE: Decision models for health technology assessment (HTA) are largely submitted to HTA agencies using commercial software, which has known limitations. The use of the open-source programming language R has been suggested because of its efficiency, transparency, reproducibility, and ability to consider complex analyses. However, its use in HTA remains limited. This qualitative study aimed to explore the main reasons for this slow uptake of R in HTA and identify tangible facilitators. METHODS: We undertook two semi-structured focus group discussions with 24 key stakeholders from government agencies, consultancy, pharmaceutical companies, and academia. Two 1.5-hour discussions reflected on barriers identified in a previous study and highlighted additional barriers. Discussions were recorded and semi-transcribed, and data were organized and summarized into key themes. RESULTS: Human resources constraints were identified as a key barrier, including a lack of training, prioritization and collaboration, and resistance to change. Another key barrier was the lack of acceptance, or clear guidance, around submissions in R by HTA agencies. Participants also highlighted a lack of communication around accepted packages and decision model structures, and between HTA agencies on standard decision modeling structures. CONCLUSIONS: There is a need for standardization, which can facilitate decision model sharing, coding homogeneity, and improved country adaptations. The creation of training materials and tailored workshops was identified as a key short-term facilitator. Increased communication and engagement of stakeholders could also facilitate the use of R by identifying needs and opportunities, encouraging HTA agencies to address structural barriers, and increasing incentives to use R.

Cancer incidence among Armenians in California.

INTRODUCTION: California is home to the largest population of Armenians in the United States. The historical categorization of Armenians as 'White' or 'Some Other Race' in population databases has likely masked cancer incidence patterns in this population. This is the first study considering cancer incidence among Armenians in California. METHODS: We used the Armenian Surname List and birthplace information in the California Cancer Registry to identify Armenians with cancer diagnosed during 1988-2019. We calculated proportional incidence ratios (PIR) among Armenians compared with non-Hispanic Whites (NHWs). As an exploratory analysis, we calculated incidence rate ratios (IRR) during 2006-2015 using Armenian population denominators from the American Community Survey (ACS). We selected PIR as our primary method given uncertainty regarding the use of ACS population estimates for rate calculations. RESULTS: There were 27,212 cancer diagnoses among Armenians in California, 13,754 among males and 13,458 among females. Armenian males had notably higher proportions of stomach (PIR = 2.39), thyroid (PIR = 1.45), and tobacco-related cancers including bladder (PIR = 1.53), colorectal (PIR = 1.29), and lung (PIR = 1.16) cancers. Higher proportional incidence of cancers including stomach (PIR = 3.24), thyroid (PIR = 1.47), and colorectal (PIR = 1.29) were observed among Armenian females. Exploratory IRR analyses showed higher stomach (IRR = 1.78), bladder (IRR = 1.13), and colorectal (IRR = 1.12) cancers among Armenian males and higher stomach (IRR = 2.54) cancer among Armenian females. CONCLUSION: We observed higher stomach, colorectal and thyroid cancer incidence among males and females, and tobacco-related cancers among males. Further research is needed to refine Armenian population estimates and understand and address risk factors associated with specific cancers among Armenians in California.

Cost-Effectiveness Analysis of a Community-Based Telewellness Weight Loss Program.

Introduction: The purpose of this study was to perform a cost-effectiveness analysis of the Koa Family Program, a community-based telewellness weight reduction intervention for overweight and obese women aged 21-45 years with low income. The Koa Family Program resulted in an approximately 8-pound weight loss as demonstrated in an RCT published previously. Methods: Estimates for the cost-effectiveness were derived from the prospective 25-week RCT including 70 women (25 kg/m2≤BMI<40 kg/m2). The analysis was from a program-funder perspective. Base case costs, as well as low and high scenario costs, were estimated from the services provided to intervention participants. The incremental costs were compared with the incremental effectiveness, with weight loss being the outcome of interest. Costs were in 2021 U.S. dollars. Cost-effectiveness was assessed using the incremental cost-effectiveness ratio and the incremental net benefit. The statistical uncertainty was characterized using an incremental net benefit by willingness-to-pay plot and a cost-effectiveness acceptability curve. Results: The base case average cost per participant was $564.39. The low and high scenario average costs per participant were $407.34 and $726.22, respectively. Over the 25-week study timeframe, participants lost an average 7.7 pounds, yielding a base case incremental cost-effectiveness ratio of approximately $73 per extra pound lost. The probability that the Koa Family Program is cost-effective is 90%, assuming a willingness-to-pay of $115 for a 1-pound reduction, and is 95%, assuming a willingness-to-pay of $140. Conclusions: The Koa Family Program provides good value with cost-effectiveness in line with other weight-loss interventions. This is a striking finding given that the Koa Family Program serves a more vulnerable population than is typically engaged in weight loss research studies.

PDB NextGen Archive: centralizing access to integrated annotations and enriched structural information by the Worldwide Protein Data Bank.

The Protein Data Bank (PDB) is the global repository for public-domain experimentally determined 3D biomolecular structural information. The archival nature of the PDB presents certain challenges pertaining to updating or adding associated annotations from trusted external biodata resources. While each Worldwide PDB (wwPDB) partner has made best efforts to provide up-to-date external annotations, accessing and integrating information from disparate wwPDB data centers can be an involved process. To address this issue, the wwPDB has established the PDB Next Generation (or NextGen) Archive, developed to centralize and streamline access to enriched structural annotations from wwPDB partners and trusted external sources. At present, the NextGen Archive provides mappings between experimentally determined 3D structures of proteins and UniProt amino acid sequences, domain annotations from Pfam, SCOP2 and CATH databases and intra-molecular connectivity information. Since launch, the PDB NextGen Archive has seen substantial user engagement with over 3.5 million data file downloads, ensuring researchers have access to accurate, up-to-date and easily accessible structural annotations. Database URL: http://www.wwpdb.org/ftp/pdb-nextgen-archive-site.

Restraint Validation of Biomolecular Structures Determined by NMR in the Protein Data Bank.

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NMR exchange (NEF) and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB Restraint Violation Report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints.

Restraint validation of biomolecular structures determined by NMR in the Protein Data Bank.

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NEF and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB restraint violation report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints.

IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods.

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.

Community recommendations on cryoEM data archiving and validation.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

Impact of COVID-19, cancer survivorship and patient-provider communication on mental health in the US Difference-In-Difference.

Poor mental health has been found to be more prevalent among those with cancer and is considered a public health crisis since COVID-19. This study assessed the impact of COVID-19 and cancer survivorship on mental health and investigated factors, including online patient-provider communications (OPPC; email/internet/tablet/smartphone), associated with poor mental health prior to and during the early COVID-19. Nationally representative Health Information National Trends Survey data during 2017-2020 (n = 15,871) was used. While the prevalence of poor mental health was high (40-42%), Difference-In-Difference analyses revealed that cancer survivorship and COVID-19 were not associated with poor mental health. However, individuals that used OPPC had 40% higher odds of poor mental health. Low socioeconomic status (low education/income), younger age (18-64 years), and female birth gender were also associated with poor mental health. Findings highlight the persistence of long-standing mental health inequities and identify that OPPC users might be those who need mental health support.