scholarly journals Standardised clinical data from patients with primary ciliary dyskinesia: FOLLOW-PCD

2020 ◽  
Vol 6 (1) ◽  
pp. 00237-2019 ◽  
Author(s):  
Myrofora Goutaki ◽  
Jean-François Papon ◽  
Mieke Boon ◽  
Carmen Casaulta ◽  
Ernst Eber ◽  
...  
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Clinical Data ◽  
Primary Ciliary Dyskinesia ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Routine Care ◽  
Standards Of Care ◽  
Patient Questionnaire ◽  
Ciliary Dyskinesia

Clinical data on primary ciliary dyskinesia (PCD) are limited, heterogeneous and mostly derived from retrospective chart reviews, leading to missing data and unreliable symptoms and results of physical examinations. We need standardised prospective data collection to study phenotypes, severity and prognosis and improve standards of care.A large, international and multidisciplinary group of PCD experts developed FOLLOW-PCD, a standardised clinical PCD form and patient questionnaire. We identified existing forms for clinical data collection via the Better Experimental Approaches to Treat PCD (BEAT-PCD) COST Action network and a literature review. We selected and revised the content items with the working group and patient representatives. We then revised several drafts in an adapted Delphi process, refining the content and structure.FOLLOW-PCD has a modular structure, to allow flexible use based on local practice and research focus. It includes patient-completed versions for the modules on symptoms and lifestyle. The form allows a comprehensive standardised clinical assessment at baseline and for annual reviews and a short documentation for routine follow-up. It can either be completed using printable paper forms or using an online REDCap database.Data collected in FOLLOW-PCD version 1.0 is available in real-time for national and international monitoring and research. The form will be adapted in the future after extensive piloting in different settings and we encourage the translation of the patient questionnaires to multiple languages. FOLLOW-PCD will facilitate quality research based on prospective standardised data from routine care, which can be pooled between centres, to provide first-line and real-time evidence for clinical decision-making.

scholarly journals Collecting clinical data in primary ciliary dyskinesia- challenges and opportunities

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2031 ◽  
Author(s):  
Israel Amirav ◽  
Mary Roduta Roberts ◽  
Huda Mussaffi ◽  
Avigdor Mandelberg ◽  
Yehudah Roth ◽  
...  
Keyword(s):  
Data Collection ◽  
Clinical Research ◽  
Clinical Data ◽  
Primary Ciliary Dyskinesia ◽  
Completion Rates ◽  
End User ◽  
Revision Process ◽  
Data Collection And Analysis ◽  
Challenges And Opportunities ◽  
Ciliary Dyskinesia

Rationale: Primary ciliary dyskinesia (PCD) is under diagnosed and underestimated. Most clinical research has used some form of questionnaires to capture data but none has been critically evaluated particularly with respect to its end-user feasibility and utility. Objective: To critically appraise a clinical data collection questionnaire for PCD used in a large national PCD consortium in order to apply conclusions in future PCD research. Methods: We describe the development, validation and revision process of a clinical questionnaire for PCD and its evaluation during a national clinical PCD study with respect to data collection and analysis, initial completion rates and user feedback. Results: 14 centers participating in the consortium successfully completed the revised version of the questionnaire for 173 patients with various completion rates for various items. While content and internal consistency analysis demonstrated validity, there were methodological deficiencies impacting completion rates and end-user utility. These deficiencies were addressed resulting in a more valid questionnaire. Conclusions: Our experience may be useful for future clinical research in PCD. Based on the feedback collected on the questionnaire through analysis of completion rates, judgmental analysis of the content, and feedback from experts and end users, we suggest a practicable framework for development of similar tools for various future PCD research.

scholarly journals Collecting clinical data in primary ciliary dyskinesia- challenges and opportunities

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2031
Author(s):  
Israel Amirav ◽  
Mary Roduta Roberts ◽  
Huda Mussaffi ◽  
Avigdor Mandelberg ◽  
Yehudah Roth ◽  
...  
Keyword(s):  
Data Collection ◽  
Clinical Research ◽  
Clinical Data ◽  
Primary Ciliary Dyskinesia ◽  
Completion Rates ◽  
End User ◽  
Revision Process ◽  
Data Collection And Analysis ◽  
Challenges And Opportunities ◽  
Ciliary Dyskinesia

Rationale: Primary ciliary dyskinesia (PCD) is under diagnosed and underestimated. Most clinical research has used some form of questionnaires to capture data but none has been critically evaluated particularly with respect to its end-user feasibility and utility. Objective: To critically appraise a clinical data collection questionnaire for PCD used in a large national PCD consortium in order to apply conclusions in future PCD research. Methods: We describe the development, validation and revision process of a clinical questionnaire for PCD and its evaluation during a national clinical PCD study with respect to data collection and analysis, initial completion rates and user feedback. Results: 14 centers participating in the consortium successfully completed the revised version of the questionnaire for 173 patients with various completion rates for various items. While content and internal consistency analysis demonstrated validity, there were methodological deficiencies impacting completion rates and end-user utility. These deficiencies were addressed resulting in a more valid questionnaire. Conclusions: Our experience may be useful for future clinical research in PCD. Based on the feedback collected on the questionnaire through analysis of completion rates, judgmental analysis of the content, and feedback from experts and end users, we suggest a practicable framework for development of similar tools for various future PCD research.

scholarly journals Generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report

2021 ◽  
Vol 28 (1) ◽  
pp. e100447
Author(s):  
Davy van de Sande ◽  
Michel E. Van Genderen ◽  
Joost Huiskens ◽  
Robert E. R. Veen ◽  
Yvonne Meijerink ◽  
...  
Keyword(s):  
Real Time ◽  
Clinical Data ◽  
Clinical Decision Making ◽  
Treatment Options ◽  
Clinical Decision ◽  
Data Infrastructure ◽  
Clinical Patient ◽  
Use Of Data ◽  
Learning From Data

Introduction In the current situation, clinical patient data are often siloed in multiple hospital information systems. Especially in the intensive care unit (ICU), large volumes of clinical data are routinely collected through continuous patient monitoring. Although these data often contain useful information for clinical decision making, they are not frequently used to improve quality of care. During, but also after, pressing times, data-driven methods can be used to mine treatment patterns from clinical data to determine the best treatment options from a hospitals own clinical data.Methods In this implementer report, we describe how we implemented a data infrastructure that enabled us to learn in real time from consecutive COVID-19 ICU admissions. In addition, we explain our step-by-step multidisciplinary approach to establish such a data infrastructure.Conclusion By sharing our steps and approach, we aim to inspire others, in and outside ICU walls, to make more efficient use of data at hand, now and in the future.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

scholarly journals Single-copy detection of somatic variants from solid and liquid biopsy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ana-Luisa Silva ◽  
Paulina Klaudyna Powalowska ◽  
Magdalena Stolarek ◽  
Eleanor Ruth Gray ◽  
Rebecca Natalie Palmer ◽  
...  
Keyword(s):  
Real Time ◽  
Single Molecule ◽  
Real Time Pcr ◽  
Clinical Decision Making ◽  
High Sensitivity ◽  
Clinical Decision ◽  
Single Copy ◽  
Turnaround Time ◽  
Copy Detection ◽  
Allele Specific

AbstractAccurate detection of somatic variants, against a background of wild-type molecules, is essential for clinical decision making in oncology. Existing approaches, such as allele-specific real-time PCR, are typically limited to a single target gene and lack sensitivity. Alternatively, next-generation sequencing methods suffer from slow turnaround time, high costs, and are complex to implement, typically limiting them to single-site use. Here, we report a method, which we term Allele-Specific PYrophosphorolysis Reaction (ASPYRE), for high sensitivity detection of panels of somatic variants. ASPYRE has a simple workflow and is compatible with standard molecular biology reagents and real-time PCR instruments. We show that ASPYRE has single molecule sensitivity and is tolerant of DNA extracted from plasma and formalin fixed paraffin embedded (FFPE) samples. We also demonstrate two multiplex panels, including one for detection of 47 EGFR variants. ASPYRE presents an effective and accessible method that simplifies highly sensitive and multiplexed detection of somatic variants.

scholarly journals Accounting for healthcare-seeking behaviours and testing practices in real-time influenza forecasts

2018 ◽  
Author(s):  
Robert Moss ◽  
Alexander E Zarebski ◽  
Sandra J Carlson ◽  
James M McCaw
Keyword(s):  
Real Time ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surveillance Data ◽  
Surveillance Systems ◽  
Healthcare Seeking ◽  
Public Health Decision ◽  
Predictive Skill ◽  
Health Decision ◽  
Substantial Change

AbstractFor diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., in order to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for RT-PCR influenza tests. In this study we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data.

scholarly journals Challenges in treatment of renal echinococcosis with gross hydatiduria and unsalvageable kidney: a case report

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Sameera Shuaibi ◽  
Abdelrahman AlAshqar ◽  
Munirah Alabdulhadi ◽  
Wasl Al-Adsani
Keyword(s):  
Clinical Decision Making ◽  
Scientific Evidence ◽  
Renal Involvement ◽  
Clinical Decision ◽  
Response To Treatment ◽  
Standards Of Care ◽  
Future Research ◽  
Positive Serology ◽  
Grape Skin

Abstract Introduction Renal echinococcosis is of rare occurrence, and although often asymptomatic, it can present with various mild to drastic presentations, of which hydatiduria is pathognomonic. Diagnosis can be preliminarily established by imaging, and treatment is primarily surgical. We present a patient with renal echinococcosis treated successfully with exclusive antiparasitic pharmacotherapy after refusing surgery despite extensive renal involvement. We hope through this report to help establish future solid guidelines regarding this uncommon therapeutic approach. Case presentation This is a case of a 49-year-old Syrian shepherd presenting with flank pain and passage of grape-skin-like structures in urine. A diagnosis of renal echinococcosis with hydatiduria and significant parenchymal destruction was established based on exposure history, positive serology, imaging findings, and renal scintigraphy. After proper counseling, the patient refused nephrectomy and was therefore started on dual pharmacotherapy (albendazole and praziquantel) and is having an uneventful follow-up and a satisfactory response to treatment. Conclusion This case embodies the daily challenges physicians navigate as they uphold the ethical principles of their practice and support their patients’ autonomy while delivering the best standards of care and consulting the scientific evidence. Although surgery is the cornerstone of renal echinococcosis treatment, treating physicians should be prepared to tackle situations where surgery cannot be done and offer the best next available option for patients who refuse surgery. As data on exclusive pharmacotherapy are limited, future research should thoroughly investigate the efficacy of this uncommon approach and outline reliable recommendations, facilitating future clinical decision-making in this avenue.

scholarly journals Early Identification of Patients with Acute Gastrointestinal Bleeding using Electronic Health Record Phenotyping

2020 ◽  
Author(s):  
Dennis Shung ◽  
Cynthia Tsay ◽  
Loren Laine ◽  
Prem Thomas ◽  
Caitlin Partridge ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Gastrointestinal Bleeding ◽  
Real Time ◽  
Decision Rule ◽  
Language Processing ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Internal Validation ◽  
Electronic Health

Background and AimGuidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify (“phenotype”) patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients.MethodsWe specified criteria using structured data elements to create rules for identifying patients, and also developed a natural-language-processing (NLP)-based algorithm for automated phenotyping of patients, tested them with tenfold cross-validation (n=7144) and external validation (n=2988), and compared them with the standard method for encoding patient conditions in the EHR, Systematized Nomenclature of Medicine (SNOMED). The gold standard for GIB diagnosis was independent dual manual review of medical records. The primary outcome was positive predictive value (PPV).ResultsA decision rule using GIB-specific terms from ED triage and from ED review-of-systems assessment performed better than SNOMED on internal validation (PPV=91% [90%-93%] vs. 74% [71%-76%], P<0.001) and external validation (PPV=85% [84%-87%] vs. 69% [67%-71%], P<0.001). The NLP algorithm (external validation PPV=80% [79-82%]) was not superior to the structured-datafields decision rule.ConclusionsAn automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision-making in real time for patients with acute GIB presenting to the ED.

Combined data mining techniques based patient data outlier detection for healthcare safety

2016 ◽  
Vol 9 (1) ◽  
pp. 42-68 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He ◽  
Dawit Haile
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Patient Safety ◽  
Outlier Detection ◽  
Clinical Data ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Healthcare Services ◽  
Content Type ◽  
Outliers Detection

Purpose – Among the growing number of data mining (DM) techniques, outlier detection has gained importance in many applications and also attracted much attention in recent times. In the past, outlier detection researched papers appeared in a safety care that can view as searching for the needles in the haystack. However, outliers are not always erroneous. Therefore, the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care, in particular. Design/methodology/approach – It is a combined DM (clustering and the nearest neighbor) technique for outliers’ detection, which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare safety. The outcomes or the knowledge implicit is vitally essential to a proper clinical decision-making process. The method is important to the semantic, and the novel tactic of patients’ events and situations prove that play a significant role in the process of patient care safety and medications. Findings – The outcomes of the paper is discussing a novel and integrated methodology, which can be inferring for different biological data analysis. It is discussed as integrated DM techniques to optimize its performance in the field of health and medical science. It is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient factors. Based on these facts, outliers are detected as clusters and point events, and novel ideas proposed to empower clinical services in consideration of customers’ satisfactions. It is also essential to be a baseline for further healthcare strategic development and research works. Research limitations/implications – This paper mainly focussed on outliers detections. Outlier isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not touch. Therefore, the research can be extended more about the hierarchy of patient problems. Originality/value – DM is a dynamic and successful gateway for discovering useful knowledge for enhancing healthcare performances and patient safety. Clinical data based outlier detection is a basic task to achieve healthcare strategy. Therefore, in this paper, the authors focussed on combined DM techniques for a deep analysis of clinical data, which provide an optimal level of clinical decision-making processes. Proper clinical decisions can obtain in terms of attributes selections that important to know the influential factors or parameters of healthcare services. Therefore, using integrated clustering and nearest neighbors techniques give more acceptable searched such complex data outliers, which could be fundamental to further analysis of healthcare and patient safety situational analysis.

scholarly journals Real-time utilisation of administrative data in the ED to identify older patients at risk: development and validation of the Dynamic Silver Code

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033374 ◽  
Author(s):  
Daniela Balzi ◽  
Giulia Carreras ◽  
Francesco Tonarelli ◽  
Luca Degli Esposti ◽  
Paola Michelozzi ◽  
...  
Keyword(s):  
At Risk ◽  
Cohort Study ◽  
Real Time ◽  
Administrative Data ◽  
Older Patients ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Patients At Risk ◽  
Ed Visits

ObjectiveIdentification of older patients at risk, among those accessing the emergency department (ED), may support clinical decision-making. To this purpose, we developed and validated the Dynamic Silver Code (DSC), a score based on real-time linkage of administrative data.Design and settingThe ‘Silver Code National Project (SCNP)’, a non-concurrent cohort study, was used for retrospective development and internal validation of the DSC. External validation was obtained in the ‘Anziani in DEA (AIDEA)’ concurrent cohort study, where the DSC was generated by the software routinely used in the ED.ParticipantsThe SCNP contained 281 321 records of 180 079 residents aged 75+ years from Tuscany and Lazio, Italy, admitted via the ED to Internal Medicine or Geriatrics units. The AIDEA study enrolled 4425 subjects aged 75+ years (5217 records) accessing two EDs in the area of Florence, Italy.InterventionsNone.Outcome measuresPrimary outcome: 1-year mortality. Secondary outcomes: 7 and 30-day mortality and 1-year recurrent ED visits.ResultsAdvancing age, male gender, previous hospital admission, discharge diagnosis, time from discharge and polypharmacy predicted 1-year mortality and contributed to the DSC in the development subsample of the SCNP cohort. Based on score quartiles, participants were classified into low, medium, high and very high-risk classes. In the SCNP validation sample, mortality increased progressively from 144 to 367 per 1000 person-years, across DSC classes, with HR (95% CI) of 1.92 (1.85 to 1.99), 2.71 (2.61 to 2.81) and 5.40 (5.21 to 5.59) in class II, III and IV, respectively versus class I (p<0.001). Findings were similar in AIDEA, where the DSC predicted also recurrent ED visits in 1 year. In both databases, the DSC predicted 7 and 30-day mortality.ConclusionsThe DSC, based on administrative data available in real time, predicts prognosis of older patients and might improve their management in the ED.

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