2,230 research outputs found

    How real-world data can facilitate the development of precision medicine treatment in psychiatry

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    Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification, and holds great potential in mental disorders. However, several important factors are needed to transform current practice into a “precision psychiatry” framework. Most important are (1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, (2) the development and validation of advanced analytical tools for stratification and prediction, and (3) the development of clinically useful management platforms for patient monitoring that can be integrated into healthcare systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements – well-powered samples from large biobanks, integrated with electronic health records and health registry data using novel artificial intelligence algorithms – to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We include use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders

    Enhancing healthcare services through cloud service: a systematic review

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    Although cloud-based healthcare services are booming, in-depth research has not yet been conducted in this field. This study aims to address the shortcomings of previous research by analyzing all journal articles from the last five years using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) systematic literature review methodology. The findings of this study highlight the benefits of cloud-based healthcare services for healthcare providers and patients, including enhanced healthcare services, data security, privacy issues, and innovative information technology (IT) service delivery models. However, this study also identifies challenges associated with using cloud services in healthcare, such as security and privacy concerns, and proposes solutions to address these issues. This study concludes by discussing future research directions and the need for a complete solution that addresses the conflicting requirements of the security, privacy, efficiency, and scalability of cloud technologies in healthcare

    Leveraging real-world data to assess treatment sequences in health economic evaluations: a study protocol for emulating target trials using the English Cancer Registry and US Electronic Health Records-Derived Database

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    Background Considering the sequence of treatments is vital for optimising healthcare resource allocation, especially in cancer care, where sequence changes can affect patients’ overall survival and associated costs. A key challenge in evaluating treatment sequences in health technology assessments (HTA) is the scarce evidence on effectiveness, leading to uncertainties in decision making. While randomised controlled trials (RCTs) and meta-analyses are viewed as the gold standards for evidence, applying them to determine the effectiveness of treatment sequences in economic models often necessitates making arbitrary assumptions due to insufficient information on patients' treatment histories and subsequent therapies. In contrast, real-world data (RWD) presents a promising alternative source of evidence, often encompassing details across treatment lines. However, due to its non-randomised nature, estimates of the treatment effectiveness based on RWD analyses can be susceptible to biases if not properly adjusted for confounding factors. To date, several international initiatives have been investigating methods to derive reliable treatment effects from RWD — by emulating Target Trials that replicate existing RCTs (i.e. benchmarks) and comparing the emulated results against the benchmarks. These studies primarily seek to determine the viability of obtaining trial-equivalent results through deploying specific analytical methodologies and study designs within the Target Trial emulation framework, using a given database. Adopting the Target Trial emulation framework facilitates the analyses to be operated under causal inference principles. Upon validation in a particular database, these techniques can be applied to address similar questions (e.g., same disease area, same outcome type), but in populations lacking clinical trial evidence, leveraging the same RWD source. Studies to date, however, have predominantly focused on the comparison of individual treatments rather than treatment sequences. Moreover, the majority of these investigations have been undertaken in non-English contexts. Consequently, the use of RWD in evaluating treatment sequences for HTA, especially in an English setting, remains largely unexplored. Objectives The goal of this project is to investigate the feasibility of leveraging RWD to produce reliable, trial-like effectiveness estimates for treatment sequences. We aim to assess the capability of two oncology databases: the US-based Flatiron electronic health record and the National Cancer Registration and Analysis Service (NCRAS) database of England. To achieve this, we plan to harness the Target Trial Emulation (TTE) framework for replicating two existing oncology RCTs that compared treatment sequences, with the intent of benchmarking our results against the original studies. Further, we aim to detail the practicalities involved with implementing TTE in diverse databases and outline the challenges encountered. Methods 1. We aim to emulate existing RCTs that compare the effect of different treatment sequences by constructing the study design and analysis plan following the TTE framework. Specifically, the following case studies are planned: (1) Prostate cancer case study 1 (PC1) - US direct proof-of-concept study (method direct validation): replicating the GUTG-001 trial using Flatiron data (2) Prostate cancer case study 2 (PC2) - US-England bridging study (method extension): emulating Target Trials that compare treatment sequences that have been common in England using Flatiron data (3) Prostate cancer case study 3 (PC3) - English indirect proof-of-concept study (method indirect validation): emulating the same Target Trial in PC2 using English NCRAS data (4) Renal cell carcinoma case study (RCC) - method direct validation in a single-arm setting: emulating the sunitinib followed by everolimus arm in the RECORD-3 trial using English NCRAS data 2. We will compare results of the emulated Target Trials with those from the benchmark trials. 3. We plan to compare different advanced causal inference methods (e.g. marginal structural models using IPW and other g-methods) in estimating the effect of treatment sequences in RWD. Expected results This study will provide evidence on whether it is feasible to obtain reliable estimates of the (comparative) effectiveness of treatment sequences using Flatiron data and English NCRAS data. If applicable, we intend to develop a framework that provides a systematic way of obtaining the (comparative) effectiveness of treatment sequences using RWD. It is possible that the data quality is insufficient to emulate the planned Target Trials. In this case, we will report reasons for the implausibility of data analysis. If applicable, we will make suggestions to whether the national health data collection may be enhanced to make the analyses possible. The results of this study will be submitted to peer-reviewed journals and international conferences

    Development and Validation of the Phoenix Criteria for Pediatric Sepsis and Septic Shock

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    ImportanceThe Society of Critical Care Medicine Pediatric Sepsis Definition Task Force sought to develop and validate new clinical criteria for pediatric sepsis and septic shock using measures of organ dysfunction through a data-driven approach.ObjectiveTo derive and validate novel criteria for pediatric sepsis and septic shock across differently resourced settings.Design, Setting, and ParticipantsMulticenter, international, retrospective cohort study in 10 health systems in the US, Colombia, Bangladesh, China, and Kenya, 3 of which were used as external validation sites. Data were collected from emergency and inpatient encounters for children (aged &amp;amp;lt;18 years) from 2010 to 2019: 3 049 699 in the development (including derivation and internal validation) set and 581 317 in the external validation set.ExposureStacked regression models to predict mortality in children with suspected infection were derived and validated using the best-performing organ dysfunction subscores from 8 existing scores. The final model was then translated into an integer-based score used to establish binary criteria for sepsis and septic shock.Main Outcomes and MeasuresThe primary outcome for all analyses was in-hospital mortality. Model- and integer-based score performance measures included the area under the precision recall curve (AUPRC; primary) and area under the receiver operating characteristic curve (AUROC; secondary). For binary criteria, primary performance measures were positive predictive value and sensitivity.ResultsAmong the 172 984 children with suspected infection in the first 24 hours (development set; 1.2% mortality), a 4-organ-system model performed best. The integer version of that model, the Phoenix Sepsis Score, had AUPRCs of 0.23 to 0.38 (95% CI range, 0.20-0.39) and AUROCs of 0.71 to 0.92 (95% CI range, 0.70-0.92) to predict mortality in the validation sets. Using a Phoenix Sepsis Score of 2 points or higher in children with suspected infection as criteria for sepsis and sepsis plus 1 or more cardiovascular point as criteria for septic shock resulted in a higher positive predictive value and higher or similar sensitivity compared with the 2005 International Pediatric Sepsis Consensus Conference (IPSCC) criteria across differently resourced settings.Conclusions and RelevanceThe novel Phoenix sepsis criteria, which were derived and validated using data from higher- and lower-resource settings, had improved performance for the diagnosis of pediatric sepsis and septic shock compared with the existing IPSCC criteria.</jats:sec

    Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study

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    Introduction Heart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However, many cases of HF remain undetected until presentation with more advanced symptoms, often requiring hospitalisation. Predicting incident HF is challenging and statistical models are limited by performance and scalability in routine clinical practice. An HF prediction model implementable in nationwide electronic health records (EHRs) could enable targeted diagnostics to enable earlier identification of HF. Methods and analysis We will investigate a range of development techniques (including logistic regression and supervised machine learning methods) on routinely collected primary care EHRs to predict risk of new-onset HF over 1, 5 and 10 years prediction horizons. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation (training and testing) and the CPRD-AURUM dataset for external validation. Both comprise large cohorts of patients, representative of the population of England in terms of age, sex and ethnicity. Primary care records are linked at patient level to secondary care and mortality data. The performance of the prediction model will be assessed by discrimination, calibration and clinical utility. We will only use variables routinely accessible in primary care. Ethics and dissemination Permissions for CPRD-GOLD and CPRD-AURUM datasets were obtained from CPRD (ref no: 21_000324). The CPRD ethical approval committee approved the study. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences. Trial registration details The study was registered on Clinical Trials.gov (NCT 05756127). A systematic review for the project was registered on PROSPERO (registration number: CRD42022380892)
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