Integration of patient and clinician narratives into systematic reviews: An applied proof of concept.
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ABSTRACT:
SUBMITTER: Christensen V
PROVIDER: S-EPMC8111606 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Reviews have traditionally been based on extensive searches of the available bibliography on the topic of interest. However, this approach is frequently influenced by the authors' background, leading to possible selection bias. Artificial intelligence applied to natural language processing (NLP) is a powerful tool that can be used for systematic reviews by speeding up the process and providing more objective results, but its use in scientific literature reviews is still scarce. This manuscript addresses this challenge by developing a reproducible tool that can be used to develop objective reviews on almost every topic. This tool has been used to review the antibacterial activity of Cistus genus plant extracts as proof of concept, providing a comprehensive and objective state of the art on this topic based on the analysis of 1601 research manuscripts and 136 patents. Data were processed using a publicly available Jupyter Notebook in Google Collaboratory here. NLP, when applied to the study of antibacterial activity of Cistus plants, is able to recover the main scientific manuscripts and patents related to the topic, avoiding any biases. The NLP-assisted literature review reveals that C. creticus and C. monspeliensis are the first and second most studied Cistus species respectively. Leaves and fruits are the most commonly used plant parts and methanol, followed by butanol and water, the most widely used solvents to prepare plant extracts. Furthermore, Staphylococcus. aureus followed by Bacillus. cereus are the most studied bacterial species, which are also the most susceptible bacteria in all studied assays. This new tool aims to change the actual paradigm of the review of scientific literature to make the process more efficient, reliable, and reproducible, according to Open Science standards.
| S-EPMC9952093 | biostudies-literature
Project description:BackgroundChildren with cancer are in urgent need of new therapies, as approximately 25% of patients experience a relapse and 20% succumb to their disease. Moreover, the majority of survivors suffer from clinically relevant health problems. Repurposing of targeted agents developed for adult indications could provide novel therapeutic options for paediatric cancer patients. To prioritise targeted drugs for paediatric clinical development, we applied a systematic review methodology to develop a Target Actionability Review (TAR) strategy. These TARs assess the strength and completeness of published preclinical proof-of-concept (PoC) data by structured critical appraisal of and summarising the available scientific literature for a specific target (pathway) and the associated drugs in paediatric tumours.MethodsA sensitive literature search in PubMed was performed and relevant papers were identified. For each paper, the individual experimental findings were extracted, marked for paediatric tumour type and categorised into nine separate PoC data modules. Each experimental finding was scored for experimental outcome and quality independently by two reviewers; discrepancies were assessed by a third reviewer and resolved by adjudication. Scores corresponding to one PoC module were merged for each tumour type and visualised in a heat map matrix in the publicly available R2 data portal [r2.amc.nl].Results and conclusionsTo test our TAR methodology, we conducted a pilot study on MDM2 and TP53. The heat map generated from analysis of 161 publications provides a rationale to support drug development in specific paediatric solid and brain tumour types. Furthermore, our review highlights tumour types where preclinical data are incomplete or lacking and for which additional preclinical testing is advisable.
| S-EPMC7203547 | biostudies-literature
Project description:IntroductionAdverse drug reactions (ADRs) are associated with significant health-related and financial burden, and multiple sources are currently utilized to actively discover them. Social media has been proposed as a potential resource for monitoring ADRs, but drug-specific analytical studies comparing social media with other sources are scarce.ObjectivesOur objective was to develop methods to compare ADRs mentioned in social media with those in traditional sources: the US FDA Adverse Event Reporting System (FAERS), drug information databases (DIDs), and systematic reviews.MethodsA total of 10,188 tweets mentioning adalimumab collected between June 2014 and August 2016 were included. ADRs in the corpus were extracted semi-automatically and manually mapped to standardized concepts in the Unified Medical Language System. ADRs were grouped into 16 biologic categories for comparisons. Frequencies, relative frequencies, disproportionality analyses, and rank ordering were used as metrics.ResultsThere was moderate agreement between ADRs in social media and traditional sources. "Local and injection site reactions" was the top ADR in Twitter, DIDs, and systematic reviews by frequency, ranked frequency, and index ranking. The next highest ADR in Twitter-fatigue-ranked fifth and seventh in FAERS and DIDs.ConclusionSocial media posts often express mild and symptomatic ADRs, but rates are measured differently in scientific sources. ADRs in FAERS are reported as absolute numbers, in DIDs as percentages, and in systematic reviews as percentages, risk ratios, or other metrics, which makes comparisons challenging; however, overlap is substantial. Social media analysis facilitates open-ended investigation of patient perspectives and may reveal concepts (e.g. anxiety) not available in traditional sources.
| S-EPMC6223697 | biostudies-literature
Project description:Due to its abundant chondrogenic matrix and hypoxic tissue, chondrosarcoma is chemo- and radio-resistant. Our group has developed a proteoglycan targeting strategy by using a quaternary ammonium (QA) function as a carrier of DNA alkylating agents to chondrosarcoma environment. Here, we assessed the relevance of this strategy applied to hypoxia-activated prodrugs, by conjugating a QA to 2-nitroimidazole phosphoramidate. This derivative, named as 8-QA, was evaluated respectively to its non-QA equivalent and to a QA-conjugated but non-hypoxia activated. Firstly binding to aggrecan was confirmed from dissociation constant determined by Surface Plasmon Resonance. In vitro, in HEMC-SS chondrosarcoma cells cultured in monolayer and in spheroids, 8-QA showed higher cytotoxic activity in hypoxia versus normoxia, and led to a strong accumulation of cells in S phase and apoptosis. In vivo, a HEMC-SS xenograft model was implanted on SCID mice and characterized for hypoxia by photoacoustic imaging as well as proteoglycan content. When HEMC-SS bearing mice were given 8-QA at 47 ?mol/kg according to a q4d x 6 schedule, a significant 62.1% inhibition of tumor growth was observed, without associated hematological side effects. Mechanistic studies of treated tumors highlighted decrease in mitotic index associated to increase in both p21 and p53S15 markers. Interestingly, 8-QA treatment induced an increase of DNA damages as measured by ?H2AX predominantly found in pimonidazole-positive hypoxic areas. These preclinical results are the first to demonstrate the interest of addressing hypoxia-activated prodrugs selectively to proteoglycan of chondrogenic tumor tissue, as a promising therapeutic strategy.
| S-EPMC5707064 | biostudies-literature
Project description:In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.
| S-EPMC5813927 | biostudies-literature
Project description:BackgroundPatient narratives are illustrative, individual accounts of patients' experiences with certain health conditions. Web-based patient narratives have become widely available on the internet and in social media, as part of electronically available patient decision aids or Web-based databases. In recent years, the role of patient narratives as a source of information, insight, and support for both health care users and providers has increasingly been emphasized. Although the potential impact of patient stories has high immediate plausibility, it is of interest to know if this impact can be captured in quantitative studies.ObjectiveThis review aimed to evaluate whether research-generated Web-based patient narratives have quantifiable risks or benefits for (potential) patients, relatives, or health care professionals.MethodsWe searched the following databases from August 2017 to March 2019: Medical Literature Analysis and Retrieval System Online, PsycInfo, Sociological Abstracts, Web of Science, and EMBASE. Titles and abstracts of the retrieved studies were reviewed and assessed for the inclusion and exclusion criteria. Papers were included if they studied the following: (1) (potential) patients, relatives, or health care professionals; (2) the effects of Web-based patient narratives that were generated scientifically (eg, through qualitative research methods); and (3) were quantitative studies. Furthermore, 2 authors independently performed an assessment of the quality of the included studies using a validated checklist.ResultsOf 4226 documents, 17 studies met the inclusion criteria. The studies investigated 10 different sources of Web-based patient narratives. Sample sizes ranged from 23 to 2458. The mean score of the quality assessment was 82.6 (range 61-100). Effects regarding five different purposes were identified as follows: provide information, engage, model behavior, persuade, and comfort. We found positive effects in every category and negative effects in one category (persuade). Several of the reported effects are rather small or were identified under specific experimental conditions.ConclusionsPatient narratives seem to be a promising means to support users in improving their understanding of certain health conditions and possibly to provide emotional support and have an impact on behavioral changes. There is limited evidence for beneficial effects on some outcomes. However, narratives are characterized by considerable heterogeneity and the investigated outcomes are hardly comparable with each other, which makes the overall judgment difficult. As there are numerous possible measures and purposes of narratives, quantifying the impact of Web-based patient narratives remains a challenge. Future research is needed to define the optimal standards for quantitative approaches to narrative-based interventions.
| S-EPMC7146251 | biostudies-literature
Project description:MAIN OUTCOME:Thirty patients (60%) found it satisfying or very satisfying to communicate their pain with the app. Pain experienced after surgery was scored by patients as 'no': 3 (6%), 'little': 5 (10%), 'bearable': 25 (50%), 'considerable': 13 (26%) and 'severe': 1 (2%). Forty-five patients (90%) were positive about the ease of recording. Forty-five patients (90%) could correctly record their pain with the app. Thirty-eight patients (76%) agreed that in-app notifications to record pain were useful. Two patients (4%) were too ill to use the application. Based on usability feedback, we will redesign the pain intensity wheel and the in-app pain chart to improve clarity for patients to understand the course of their pain. SECONDARY OUTCOMES:The median patient recorded pain app score 4.0 (range 0 to 10) and nurse recorded numerical rating scale (NRS) for pain NRS 4.0 (range 0 to 9) were not statistically different (p = 0.06). Forty-two percent from a total of 307 patient pain app scores were ? 5 (on a scale from 0 no pain at all to 10 worst imaginable pain). Of these, 83% were recorded as 'bearable' while only in 18% of the recordings patients asked for additional analgesia. The results suggest that self-recording the severity of postoperative pain by patients with a smartphone application could be useful for postoperative pain management. The application was perceived as user-friendly and had high satisfaction rates from both patients and stakeholders. Further research is needed to validate the 11-point numeric and faces pain scale with the current gold standards visual analogue scale (VAS) and NRS for pain.
| S-EPMC7209286 | biostudies-literature
Project description:Target enrichment using parallel nanoliter quantitative PCR amplification
| PRJEB4408 | ENA
Project description:BACKGROUND:Most electronic health (eHealth) interventions offered to patients serve a single purpose and lack integration with other tools or systems. This is problematic because the majority of patients experience comorbidity and chronic disease, see multiple specialists, and therefore have different needs regarding access to patient data, communication with peers or providers, and self-monitoring of vital signs. A multicomponent digital health cloud service that integrates data sharing, collection, and communication could facilitate patient-centered care in combination with a hospital patient portal and care professionals. OBJECTIVE:This study aimed to assess the feasibility and functionality of a new cloud-based and multicomponent outpatient clinic, the "Virtual Outpatient Clinic" (VOC). METHODS:The VOC consists of 6 digital tools that facilitate self-monitoring (blood pressure, weight, and pain) and communication with peers and providers (chat and videoconferencing) connected to a cloud-based platform and the hospital patient portal to facilitate access to (self-collected) medical data. In this proof-of-concept study, 10 patients from both Departments of Internal Medicine and Dermatology (N=20) used all options of the VOC for 6 weeks. An eNurse offered support to participants during the study. We assessed the feasibility, usage statistics, content, adherence, and identified technical issues. Moreover, we conducted qualitative interviews with all participants by following a standard interview guide to identify user experiences, including barriers, facilitators, and potential effects. RESULTS:Most participants successfully used all options of the VOC and were positive about different tools and apps and the integral availability of their information. The adherence was 37% (7/19) for weight scale, 58% (11/19) for blood pressure monitor, and 70% (14/20) and 85% (17/20) for pain score and daily questions, respectively. The adherence for personal health record was 65% (13/20) and 60% (12/20) for the patient portal system. Qualitative data showed that performance and effort expectancy scored high among participants, indicating that using the VOC is convenient, easy, and time-saving. CONCLUSIONS:The VOC is a promising integrated Web-based technology that combines self-management, data sharing, and communication between patients and professionals. The system can be personalized by connecting various numbers of components, which could make it a relevant tool for other patient groups. Before a system, such as the VOC, can be implemented in daily practice, prospective studies focused on evaluating outcomes, costs, and patient-centeredness are needed.
| S-EPMC6231839 | biostudies-other
Project description:BACKGROUND:Nearly one-third of heart failure (HF) patients do not respond to cardiac resynchronization therapy (CRT) despite having left bundle branch block (LBBB). The aim of the study was to investigate a novel method of quantifying left ventricular (LV) contractile asymmetry in HF. METHODS:Patients with HF and LBBB undergoing CRT (n = 89, 37.1% females, 68 ± 9 years, ischemic etiology in 61%, LV ejection fraction 27.1 ± 7.1%) were analyzed. LV longitudinal systolic strain rate values were extracted from curved anatomical M-mode plots of standard long-axis 2D-echocardiography images and cubic spline interpolation was used to generate a 3D-phantom. Index of contractile asymmetry (ICA) was calculated based on standard deviation of differences in strain rate of opposing walls. Average ICA was individually assessed pairwise in 12 opposing 30-degree LV sectors. Reduction in LV end-systolic volume (ESV) ≥15% after 6 months was considered as positive response to CRT. RESULTS:CRT response was found in 66 (74.2%) patients. Responders with both ischemic and non-ischemic cardiomyopathy had a higher and more extensive contractile asymmetry at baseline and achieved a greater ICA reduction after CRT than non-responders. Higher baseline ICA predicted higher degree and wider extent of ICA improvement. Also, both ICA at baseline and reduction of ICA correlated with the degree of ESV reduction after CRT. CONCLUSIONS:Quantification of asymmetrical LV activation in 3D by ICA provides valuable insights into LV contraction in case of LBBB and is a promising tool for improved patient selection for CRT.
| S-EPMC6788085 | biostudies-other