Innovations in research and clinical care using patient-generated health data.

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived in-silico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of disease-specific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy = 0.73), Meta-RNN (Accuracy = 0.72), ClinPred (Accuracy = 0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy = 0.70). While on HGMD dataset they were ClinPred (Accuracy = 0.72), MetaRNN (Accuracy = 0.71), CADD (Accuracy = 0.69), Fathmm-MKL (Accuracy = 0.68), and Fathmm-XF (Accuracy = 0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions.

Power to the people: why person-generated health data are important for pharmacoepidemiology.

Person-generated health data (PGHD) are valuable for studying outcomes relevant to everyday living, for obtaining information not otherwise available, for long-term follow-up, and in situations where decisions cannot wait for traditional clinical research to be completed. While there is no dispute that these data are subject to bias, insights gained may be better than having an information void, provided the biases are understood and addressed. People will share information known uniquely to them about exposures that may affect drug tolerance, safety, and effectiveness (eg, nonprescription and complementary medications, alcohol, tobacco, illicit drugs, exercise, etc). Patients may be the best source of safety information when long-term follow-up is needed (eg, the 5- to 15-year follow-up required for some gene therapies). Validation studies must be performed to evaluate what people can accurately report and when supplementary confirmation information is needed. However, PGHD has already proven valuable in quantifying and contrasting COVID-19 vaccine benefits and risks and for evaluating disease transmission and the accuracy of COVID-19 testing. Going forward, PGHD will be used for patient-measured and patient-relevant outcomes, including for regulatory purposes, and will be linked to broader health data networks using tokenization, becoming a mainstay for signals about risks and benefits for diverse populations. This article is part of a Special Collection on Pharmacoepidemiology.

Validation of algorithms in studies based on routinely collected health data: general principles.

Clinicians, researchers, regulators, and other decision-makers increasingly rely on evidence from real-world data (RWD), including data routinely accumulating in health and administrative databases. RWD studies often rely on algorithms to operationalize variable definitions. An algorithm is a combination of codes or concepts used to identify persons with a specific health condition or characteristic. Establishing the validity of algorithms is a prerequisite for generating valid study findings that can ultimately inform evidence-based health care. This paper aims to systematize terminology, methods, and practical considerations relevant to the conduct of validation studies of RWD-based algorithms. We discuss measures of algorithm accuracy; gold/reference standard; study size; prioritizing accuracy measures; algorithm portability; and implication for interpretation. Information bias is common in epidemiologic studies, underscoring the importance of transparency in decisions regarding choice and prioritizing measures of algorithm validity. The validity of an algorithm should be judged in the context of a data source, and one size does not fit all. Prioritizing validity measures within a given data source depends on the role of a given variable in the analysis (eligibility criterion, exposure, outcome or covariate). Validation work should be part of routine maintenance of RWD sources.

EuMAR stakeholder engagement: an analysis of medically assisted reproduction (MAR) data collection practices in EU countries†.

STUDY QUESTION: What are the current national medically assisted reproduction (MAR) data collection systems across EU Member States, and how can these countries contribute to a unique, cycle-by-cycle registry for the European Monitoring of Medically Assisted Reproduction (EuMAR) project? SUMMARY ANSWER: The study identified significant variation in MAR data collection practices across Member States, with differences in data types, collection methods, and reporting requirements; the EuMAR project emerges as an opportunity to enhance data standardization and improve MAR data collection in the EU. WHAT IS KNOWN ALREADY: There is a need for new approaches in MAR data collection that include long-term and cross border follow-up. The EuMAR project intends to establish a unified, cycle-by-cycle registry of data on MAR treatments in EU countries, from which accurate cumulative outcomes can be calculated. STUDY DESIGN, SIZE, DURATION: This cross-sectional study involved a survey and interviews with stakeholders from 26 EU Member States conducted in 2023 over a period of seven months. PARTICIPANTS/MATERIALS, SETTING, METHODS: Representatives from national competent authorities and professional associations involved in MAR data collection in EU countries were invited to complete the survey and interviewed to assess current data flows, information requirements, and their interest in the EuMAR project. MAIN RESULTS AND THE ROLE OF CHANCE: Half of the participating countries reported having a national MAR registry with cycle-by-cycle data (n = 13), while 31% reported having a national registry with aggregated data (n = 8) and 19% reported having no national registry (n = 5). Of the countries with a national cycle-by-cycle registry, eight countries collect identifiable data, five countries collect pseudonymized data, and one country collects fully anonymized data. Informed consent is required in 10 countries. The main advantages that participants expected from a European registry like EuMAR were the possibility of obtaining national statistics in the absence of a national registry and improving the calculation of cumulative outcomes. LIMITATIONS, REASONS FOR CAUTION: The results of the study are based on self-reported data, which may be subject to bias, however, the validity of the collected information was verified with different means, including follow-up calls for clarifications and sharing final transcript reports. The feasibility of the proposed data flow models will be tested in a pilot study. WIDER IMPLICATIONS OF THE FINDINGS: Despite the heterogeneity of data collection practices across EU countries, the results show that stakeholders have high expectations of the benefits that the EuMAR registry can bring, namely the improvement of data consistency, cross-border comparability, and cumulative live birth rates, leading to better information for patients, health care providers and policy makers. STUDY FUNDING/COMPETING INTEREST(S): The EuMAR project was co-founded by ESHRE and the European Commission (101079865-EuMAR-EU4H-2021-PJ2). No competing interests were declared. TRIAL REGISTRATION NUMBER: N/A.

Safety and effectiveness of mogamulizumab in relapsed or refractory CC chemokine receptor 4-positive peripheral T-cell lymphoma and relapsed or refractory cutaneous T-cell lymphoma: A post-marketing surveillance in Japan.

Mogamulizumab is a humanized antibody targeting CC chemokine receptor 4 (CCR4). This post-marketing surveillance was conducted in Japan as a regulatory requirement from 2014 to 2020 to ensure the safety and effectiveness of mogamulizumab in patients with relapsed or refractory (r/r) CCR4-positive peripheral T-cell lymphoma (PTCL) or r/r cutaneous T-cell lymphoma (CTCL). Safety and effectiveness data were collected for up to 31 weeks after treatment initiation. A total of 142 patients were registered; safety was evaluated in 136 patients. The median number of doses was 8.0 (range, 1-18). The main reasons for treatment termination were insufficient response (22.1%) and adverse events (13.2%). The frequency of any grade adverse drug reaction was 57.4%, including skin disorders (26.5%), infections and immune system disorders (16.2%), and infusion-related reactions (13.2%). Graft-versus-host disease, grade 2, developed in one of two patients who underwent allogeneic-hematopoietic stem cell transplantation after receiving mogamulizumab. Effectiveness was evaluated in 131 patients (103 with PTCL; 28 with CTCL). The best overall response rate was 45.8% (PTCL, 47.6%; CTCL, 39.3%). At week 31, the survival rate was 69.0% (95% confidence interval, 59.8%-76.5%) [PTCL, 64.4% (54.0%-73.0%); CTCL, 90.5% (67.0%-97.5%)]. Safety and effectiveness were comparable between patients <70 and ≥ 70 years old and between those with relapsed and refractory disease. The safety and effectiveness of mogamulizumab for PTCL and CTCL in the real world were comparable with the data reported in previous clinical trials. Clinical Trial Registration.

Health Data Sciences and Cardiovascular Disease in Africa: Needs and the Way Forward.

PURPOSE OF REVIEW: The rising burden of cardiovascular disease (CVD) in Africa is of great concern. Health data sciences is a rapidly developing field which has the potential to improve health outcomes, especially in low-middle income countries with burdened healthcare systems. We aim to explore the current CVD landscape in Africa, highlighting the importance of health data sciences in the region and identifying potential opportunities for application and growth by leveraging health data sciences to improve CVD outcomes. RECENT FINDINGS: While there have been a number of initiatives aimed at developing health data sciences in Africa over the recent decades, the progress and growth are still in their early stages. Its maximum potential can be leveraged through adequate funding, advanced training programs, focused resource allocation, encouraging bidirectional international partnerships, instituting best ethical practices, and prioritizing data science health research in the region. The findings of this review explore the current landscape of CVD and highlight the potential benefits and utility of health data sciences to address CVD challenges in Africa. By understanding and overcoming the barriers associated with health data sciences training, research, and application in the region, focused initiatives can be developed to promote research and development. These efforts will allow policymakers to form informed, evidence-based frameworks for the prevention and management of CVDs, and ultimately result in improved CVD outcomes in the region.

Health Data Sciences and Cardiovascular Diseases in South Asia: Innovations and Challenges in Digital Health.

PURPOSE OF REVIEW: Health data sciences can help mitigate high burden of cardiovascular disease (CVD) management in South Asia by increasing availability and affordability of healthcare services. This review explores the current landscape, challenges, and strategies for leveraging digital health technologies to improve CVD outcomes in the region. RECENT FINDINGS: Several South Asian countries are implementing national digital health strategies that aim to provide unique health account numbers for patients, creating longitudinal digital health records while others aim to digitize healthcare services and improve health outcomes. Significant challenges impede progress, including lack of interoperability, inadequate training of healthcare workers, cultural barriers, and data privacy concerns. Leveraging digital health for CVD management involves using big data for early detection, employing artificial intelligence for diagnostics, and integrating multiomics data for health insights. Addressing these challenges through policy frameworks, capacity building, and international cooperation is crucial for improving CVD outcomes in region.

Use of pragmatic randomized trials in multiple sclerosis: A systematic overview.

BACKGROUND: Pragmatic trials are increasingly recognized for providing real-world evidence on treatment choices. OBJECTIVE: The objective of this study is to investigate the use and characteristics of pragmatic trials in multiple sclerosis (MS). METHODS: Systematic literature search and analysis of pragmatic trials on any intervention published up to 2022. The assessment of pragmatism with PRECIS-2 (PRagmatic Explanatory Continuum Indicator Summary-2) is performed. RESULTS: We identified 48 pragmatic trials published 1967-2022 that included a median of 82 participants (interquartile range (IQR) = 42-160) to assess typically supportive care interventions (n = 41; 85%). Only seven trials assessed drugs (15%). Only three trials (6%) included >500 participants. Trials were mostly from the United Kingdom (n = 18; 38%), Italy (n = 6; 13%), the United States and Denmark (each n = 5; 10%). Primary outcomes were diverse, for example, quality-of-life, physical functioning, or disease activity. Only 1 trial (2%) used routinely collected data for outcome ascertainment. No trial was very pragmatic in all design aspects, but 14 trials (29%) were widely pragmatic (i.e. PRECIS-2 score ⩾ 4/5 in all domains). CONCLUSION: Only few and mostly small pragmatic trials exist in MS which rarely assess drugs. Despite the widely available routine data infrastructures, very few trials utilize them. There is an urgent need to leverage the potential of this pioneering study design to provide useful randomized real-world evidence.

Use of multiple sclerosis disease-modifying therapies during pregnancy in France: Nationwide study between 2010 and 2021.

BACKGROUND: Multiple sclerosis (MS) frequently affects women of childbearing age and pregnant women. OBJECTIVE: To assess the use of MS disease-modifying therapies (DMTs) during pregnancy in France over the last decade, marked by an increasing DMTs availability. METHODS: All pregnancies ended from April 2010 to December 2021 in women with MS were identified based on the nationwide Mother-Child Register EPI-MERES, built from the French National Health Data System (Système National des Données de Santé (SNDS)). RESULTS: Of a total of 20,567 pregnancies in women with MS, 7587 were exposed to DMT. The number of DMT-exposed pregnancies markedly increased from 1079 in 2010-2012 to 2413 in 2019-2021 (+124%), especially those exposed to glatiramer acetate, natalizumab, dimethyl fumarate, and anti-CD20. Among pregnancies of women on DMT 6 months before pregnancy, 78.0% underwent DMT discontinuation and 7.6% switched DMT, generally before (33.0% and 77.0%, respectively) or during the first trimester of pregnancy (58.3% and 17.8%, respectively). DMT discontinuation decreased from 84.0% in 2010-2012 to 72.4% in 2019-2021 and was less frequent among women aged ⩾35 years and those socioeconomically disadvantaged. CONCLUSION: Despite MS therapeutic management adaptations to pregnancy, exposure during pregnancy to treatments whose safety profile has not yet been clearly established has increased sharply over the last decade.

Ascertaining the Francophone population in Ontario: validating the language variable in health data.

BACKGROUND: Language barriers can impact health care and outcomes. Valid and reliable language data is central to studying health inequalities in linguistic minorities. In Canada, language variables are available in administrative health databases; however, the validity of these variables has not been studied. This study assessed concordance between language variables from administrative health databases and language variables from the Canadian Community Health Survey (CCHS) to identify Francophones in Ontario. METHODS: An Ontario combined sample of CCHS cycles from 2000 to 2012 (from participants who consented to link their data) was individually linked to three administrative databases (home care, long-term care [LTC], and mental health admissions). In total, 27,111 respondents had at least one encounter in one of the three databases. Language spoken at home (LOSH) and first official language spoken (FOLS) from CCHS were used as reference standards to assess their concordance with the language variables in administrative health databases, using the Cohen kappa, sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV). RESULTS: Language variables from home care and LTC databases had the highest agreement with LOSH (kappa = 0.76 [95%CI, 0.735-0.793] and 0.75 [95%CI, 0.70-0.80], respectively) and FOLS (kappa = 0.66 for both). Sensitivity was higher with LOSH as the reference standard (75.5% [95%CI, 71.6-79.0] and 74.2% [95%CI, 67.3-80.1] for home care and LTC, respectively). With FOLS as the reference standard, the language variables in both data sources had modest sensitivity (53.1% [95%CI, 49.8-56.4] and 54.1% [95%CI, 48.3-59.7] in home care and LTC, respectively) but very high specificity (99.8% [95%CI, 99.7-99.9] and 99.6% [95%CI, 99.4-99.8]) and predictive values. The language variable from mental health admissions had poor agreement with all language variables in the CCHS. CONCLUSIONS: Language variables in home care and LTC health databases were most consistent with the language often spoken at home. Studies using language variables from administrative data can use the sensitivity and specificity reported from this study to gauge the level of mis-ascertainment error and the resulting bias.

Assessing cardiovascular disease risk in women with a history of hypertensive disorders of pregnancy: A guidance paper for studies using administrative data.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a major cause of maternal morbidity and mortality, and their association with increased cardiovascular disease (CVD) risk represents a major public health concern. However, assessing CVD risk in women with a history of these conditions presents unique challenges, especially when studies are carried out using routinely collected data. OBJECTIVES: To summarise and describe key challenges related to the design and conduct of administrative studies assessing CVD risk in women with a history of HDP and provide concrete recommendations for addressing them in future research. METHODS: This is a methodological guidance paper. RESULTS: Several conceptual and methodological factors related to the data-generating mechanism and study conceptualisation, design/data management and analysis, as well as the interpretation and reporting of study findings should be considered and addressed when designing and carrying out administrative studies on this topic. Researchers should develop an a priori conceptual framework within which the research question is articulated, important study variables are identified and their interrelationships are carefully considered. CONCLUSIONS: To advance our understanding of CVD risk in women with a history of HDP, future studies should carefully consider and address the conceptual and methodological considerations outlined in this guidance paper. In highlighting these challenges, and providing specific recommendations for how to address them, our goal is to improve the quality of research carried out on this topic.

Perspectives of healthcare professionals and people living with HIV in dialogue: on information sharing to improve communication at the consultation.

We report on a qualitative Group Survey study involving four healthcare professionals (HCPs) and eight people living with HIV who were recipients of care in the United Kingdom (UK). The survey aimed to bring participants' perspectives into dialogue and establish consensus about how communication between HCPs delivering HIV care and their patients could be improved in the context of the routine care consultation. Responses from both parties were anonymously collated, thematically analysed, and shared back with participants in two subsequent survey rounds to support consensus-building on matters of concern and identify thematic insights. In this paper, we report three themes for informing future designs of tools and services to support communication between patients and HCPs: Patient-clinician relationship for trusted sharing; Self-reporting psychosocial information to support Whole-person care; and Perceived barriers for online trusted sharing with HCPs. Our findings highlight key areas of concern and further investigation is needed to understand how self-reported information may be meaningfully captured, interpreted and processed by HCPs in ways that are trusted by patients who voice privacy and security concerns.

Desiderata for discoverability and FAIR adoption of health data hubs.

BACKGROUND: The future European Health Research and Innovation Cloud (HRIC), as fundamental part of the European Health Data Space (EHDS), will promote the secondary use of data and the capabilities to push the boundaries of health research within an ethical and legally compliant framework that reinforces the trust of patients and citizens. OBJECTIVE: This study aimed to analyse health data management mechanisms in Europe to determine their alignment with FAIR principles and data discovery generating best. practices for new data hubs joining the HRIC ecosystem. In this line, the compliance of health data hubs with FAIR principles and data discovery were assessed, and a set of best practices for health data hubs was concluded. METHODS: A survey was conducted in January 2022, involving 99 representative health data hubs from multiple countries, and 42 responses were obtained in June 2022. Stratification methods were employed to cover different levels of granularity. The survey data was analysed to assess compliance with FAIR and data discovery principles. The study started with a general analysis of survey responses, followed by the creation of specific profiles based on three categories: organization type, function, and level of data aggregation. RESULTS: The study produced specific best practices for data hubs regarding the adoption of FAIR principles and data discoverability. It also provided an overview of the survey study and specific profiles derived from category analysis, considering different types of data hubs. CONCLUSIONS: The study concluded that a significant number of health data hubs in Europe did not fully comply with FAIR and data discovery principles. However, the study identified specific best practices that can guide new data hubs in adhering to these principles. The study highlighted the importance of aligning health data management mechanisms with FAIR principles to enhance interoperability and reusability in the future HRIC.

Converting OMOP CDM to phenopackets: A model alignment and patient data representation evaluation.

OBJECTIVE: This study aims to promote interoperability in precision medicine and translational research by aligning the Observational Medical Outcomes Partnership (OMOP) and Phenopackets data models. Phenopackets is an expert knowledge-driven schema designed to facilitate the storage and exchange of multimodal patient data, and support downstream analysis. The first goal of this paper is to explore model alignment by characterizing the common data models using a newly developed data transformation process and evaluation method. Second, using OMOP normalized clinical data, we evaluate the mapping of real-world patient data to Phenopackets. We evaluate the suitability of Phenopackets as a patient data representation for real-world clinical cases. METHODS: We identified mappings between OMOP and Phenopackets and applied them to a real patient dataset to assess the transformation's success. We analyzed gaps between the models and identified key considerations for transforming data between them. Further, to improve ambiguous alignment, we incorporated Unified Medical Language System (UMLS) semantic type-based filtering to direct individual concepts to their most appropriate domain and conducted a domain-expert evaluation of the mapping's clinical utility. RESULTS: The OMOP to Phenopacket transformation pipeline was executed for 1,000 Alzheimer's disease patients and successfully mapped all required entities. However, due to missing values in OMOP for required Phenopacket attributes, 10.2 % of records were lost. The use of UMLS-semantic type filtering for ambiguous alignment of individual concepts resulted in 96 % agreement with clinical thinking, increased from 68 % when mapping exclusively by domain correspondence. CONCLUSION: This study presents a pipeline to transform data from OMOP to Phenopackets. We identified considerations for the transformation to ensure data quality, handling restrictions for successful Phenopacket validation and discrepant data formats. We identified unmappable Phenopacket attributes that focus on specialty use cases, such as genomics or oncology, which OMOP does not currently support. We introduce UMLS semantic type filtering to resolve ambiguous alignment to Phenopacket entities to be most appropriate for real-world interpretation. We provide a systematic approach to align OMOP and Phenopackets schemas. Our work facilitates future use of Phenopackets in clinical applications by addressing key barriers to interoperability when deriving a Phenopacket from real-world patient data.

Toward the European Health Data Space: The IMPaCT-Data secure infrastructure for EHR-based precision medicine research.

BACKGROUND: Art. 50 of the proposal for a Regulation on the European Health Data Space (EHDS) states that "health data access bodies shall provide access to electronic health data only through a secure processing environment, with technical and organizational measures and security and interoperability requirements". OBJECTIVE: To identify specific security measures that nodes participating in health data spaces shall implement based on the results of the IMPaCT-Data project, whose goal is to facilitate the exchange of electronic health records (EHR) between public entities based in Spain and the secondary use of this information for precision medicine research in compliance with the General Data Protection Regulation (GDPR). DATA AND METHODS: This article presents an analysis of 24 out of a list of 72 security measures identified in the Spanish National Security Scheme (ENS) and adopted by members of the federated data infrastructure developed during the IMPaCT-Data project. RESULTS: The IMPaCT-Data case helps clarify roles and responsibilities of entities willing to participate in the EHDS by reconciling technical system notions with the legal terminology. Most relevant security measures for Data Space Gatekeepers, Enablers and Prosumers are identified and explained. CONCLUSION: The EHDS can only be viable as long as the fiduciary duty of care of public health authorities is preserved; this implies that the secondary use of personal data shall contribute to the public interest and/or to protect the vital interests of the data subjects. This condition can only be met if all nodes participating in a health data space adopt the appropriate organizational and technical security measures necessary to fulfill their role.

Validation of an algorithm for identifying incident cancer cases based on long-term illness and diagnosis related group program data from the French National Health Insurance Information System (SNDS).

PURPOSE: Three generic claims-based algorithms based on the Illness Classification of Diseases (10th revision- ICD-10) codes, French Long-Term Illness (LTI) data, and the Diagnosis Related Group program (DRG) were developed to identify retirees with cancer using data from the French national health insurance information system (Système national des données de santé or SNDS) which covers the entire French population. The present study aimed to calculate the algorithms' performances and to describe false positives and negatives in detail. METHODS: Between 2011 and 2016, data from 7544 participants of the French retired self-employed craftsperson cohort (ESPrI) were first matched to the SNDS data, and then toFrench population-based cancer registries data, used as the gold standard. Performance indicators, such as sensitivity and positive predictive values, were estimated for the three algorithms in a subcohort of ESPrI. RESULTS: The third algorithm, which combined the LTI and DRG program data, presented the best sensitivities (90.9%-100%) and positive predictive values (58.1%-95.2%) according to cancer sites. The majority of false positives were in fact nearby organ sites (e.g., stomach for esophagus) and carcinoma in situ. Most false negatives were probably due to under declaration of LTI. CONCLUSION: Validated algorithms using data from the SNDS can be used for passive epidemiological follow-up for some cancer sites in the ESPrI cohort.

Validation of new medication use algorithms as proxies for worsening disease activity in patients with juvenile idiopathic arthritis.

PURPOSE: To facilitate claims-based research on populations with juvenile idiopathic arthritis (JIA), we sought to validate an algorithm of new medication use as a proxy for worsening JIA disease activity. METHODS: Using electronic health record data from three pediatric centers, we defined new JIA medication use as (re)initiation of disease-modifying antirheumatic drugs or glucocorticoids (oral or intra-articular). Data were collected from 201 randomly selected subjects with (101) or without (100) new medication use. We assessed the positive predictive value (PPV) and negative predictive value (NPV) based on a reference standard of documented worsening of JIA disease activity. The algorithm was refined to optimize test characteristics. RESULTS: Overall, the medication-based algorithm had suboptimal performance in representing worsening JIA disease activity (PPV 69.3%, NPV 77.1%). However, algorithm performance improved for definitions specifying longer times after JIA diagnosis (≥1-year post-diagnosis: PPV 82.9%, NPV 80.0%) or after initiation of prior JIA treatment (≥1-year post-treatment: PPV 89.7%, NPV 80.0%). CONCLUSION: An algorithm for new JIA medication use appears to be a reasonable proxy for worsening JIA disease activity, particularly when specifying new use ≥1 year since initiating a prior JIA medication. This algorithm will be valuable for conducting research on JIA populations within administrative claims databases.

Prevalence and risks of intravenous chemotherapy-induced severe neutropenia in solid cancers: a multicenter retrospective cohort study on real-life data.

PURPOSE: We aimed at identifying prevalence, clinical outcomes and prognostic factors in cancer patients with intravenous chemotherapy-induced severe neutropenia (ICISN). METHODS: In this multicenter retrospective cohort study on the clinical data warehouse of Greater Paris University Hospitals (AP-HP), we included all adult patients with solid cancer hospitalized between 2016 and 2021 with intravenous chemotherapy within 30 days prior to severe neutropenia (D70 or D611 ICD-10 codes AND a neutrophil count < 500/mm3). The primary endpoint was referral to intensive care unit (ICU) or death within 30 days. We collected cancer, patient, and treatment characteristics. RESULTS: Among 141,586 cancer inpatients, 40,660 received chemotherapy among whom 661 (1.6%) had ICISN. Median age was 63 years (interquartile range (IQR), 54-70) and 330 patients (49%) were female. The median Charlson score was 10 (IQR, 8-11). Main primary cancers were lung (n = 204, 31%) and breast (n = 87, 13%). Advanced cancers were found in 551 patients (83%), 331 (50%) were in 1st line of chemotherapy, 284 (42%) in the 1st cycle of the current line and 149 (22%) had primary G-CSF. Documented bacterial (mostly gram-negative bacilli) and fungal infections were observed in 113 (17%) and 19 (3%) patients; 58 (9%) were transferred to ICU and 82 (12%) died within 30 days, 372 (56%) patients received subsequent chemotherapy. Independent prognostic factors were the level of monocyte, lymphocyte counts or albuminemia and a documented bacterial infection, while Charlson index and primary prophylactic G-CSF were not associated with patient clinical outcomes. CONCLUSION: Despite the use of primary G-CSF, ICISN remains a frequent event, which leads to ICU death in one on five cases Some prognostic factors of severity have been highlighted and could help clinicians to prevent severe complications.

Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project.

BACKGROUND: Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored. METHODS: PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS' practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software. DISCUSSION: Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.