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 How Much? How Frequent? How Long? A Clinical Guide to New Therapies in Myelodysplastic Syndromes

Hematology ◽  
2010 ◽  
Vol 2010 (1) ◽  
pp. 314-321 ◽  
Author(s):  
William Blum
Keyword(s):  
Quality Of Life ◽  
Myelodysplastic Syndromes ◽  
Clinical Decision Making ◽  
Treatment Options ◽  
Clinical Decision ◽  
Clinical Results ◽  
Improve Quality ◽  
Substantial Impact ◽  
History Of

AbstractAdvances in the treatment of myelodysplastic syndromes (MDSs) over the last decade have given patients and their hematologists a multitude of treatment options. Therapeutic options now exist that reduce disease-related symptoms, improve quality of life, and alter the natural history of the disease. Three drugs are now specifically Food and Drug Administration-approved for treatment of MDS: (1) azacitidine, (2) decitabine, and (3) lenalidomide. Clinical results with each of these agents, plus results with immunosuppressive therapy, are reviewed to guide clinical decision making. Although each therapy has made a substantial impact in improving the care of patients with MDS, unfortunately MDS treatment in 2010 ultimately fails in most patients, but these therapies provide a foundation on which we can build to further improve outcomes.

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.

Management of thoracic aortic lesions - the future is endovascular

VASA ◽  
2012 ◽  
Vol 41 (3) ◽  
pp. 163-176 ◽  
Author(s):  
Weidenhagen ◽  
Bombien ◽  
Meimarakis ◽  
Geisler ◽  
A. Koeppel
Keyword(s):  
Thoracic Aorta ◽  
Clinical Decision Making ◽  
Treatment Options ◽  
Clinical Decision ◽  
Clinical Results ◽  
Treatment Modalities ◽  
Traumatic Rupture ◽  
Descending Thoracic Aorta ◽  
New Treatment ◽  
Aneurysm Dissection

Open surgical repair of lesions of the descending thoracic aorta, such as aneurysm, dissection and traumatic rupture, has been the “state-of-the-art” treatment for many decades. However, in specialized cardiovascular centers, thoracic endovascular aortic repair and hybrid aortic procedures have been implemented as novel treatment options. The current clinical results show that these procedures can be performed with low morbidity and mortality rates. However, due to a lack of randomized trials, the level of reliability of these new treatment modalities remains a matter of discussion. Clinical decision-making is generally based on the experience of the vascular center as well as on individual factors, such as life expectancy, comorbidity, aneurysm aetiology, aortic diameter and morphology. This article will review and discuss recent publications of open surgical, hybrid thoracic aortic (in case of aortic arch involvement) and endovascular repair in complex pathologies of the descending thoracic aorta.

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 Warning systems in a computerized nursing process for Intensive Care Units

2014 ◽  
Vol 48 (1) ◽  
pp. 125-132 ◽  
Author(s):  
Daniela Couto Carvalho Barra ◽  
Grace Teresinha Marcon Dal Sasso ◽  
Camila Rosália Antunes Baccin
Keyword(s):  
Patient Safety ◽  
Intensive Care ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Healthcare Research ◽  
Pelvic Surgery ◽  
Warning Systems ◽  
Nursing Process ◽  
Promote Patient Safety

A hybrid study combining technological production and methodological research aiming to establish associations between the data and information that are part of a Computerized Nursing Process according to the ICNP® Version 1.0, indicators of patient safety and quality of care. Based on the guidelines of the Agency for Healthcare Research and Quality and the American Association of Critical Care Nurses for the expansion of warning systems, five warning systems were developed: potential for iatrogenic pneumothorax, potential for care-related infections, potential for suture dehiscence in patients after abdominal or pelvic surgery, potential for loss of vascular access, and potential for endotracheal extubation. The warning systems are a continuous computerized resource of essential situations that promote patient safety and enable the construction of a way to stimulate clinical reasoning and support clinical decision making of nurses in intensive care.

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 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.

scholarly journals Opioid Analgesia: Perspectives on Right Use and Utility

2007 ◽  
Vol 3;10 (5;3) ◽  
pp. 479-491 ◽  
Author(s):  
Jane C. Ballantyne
Keyword(s):  
Quality Of Life ◽  
Chronic Pain ◽  
Side Effects ◽  
Clinical Decision Making ◽  
Analgesic Efficacy ◽  
Clinical Decision ◽  
Opioid Analgesia ◽  
Opioid Treatment

The ability of opioids to effectively and safely control acute and cancer pain has been one of several arguments used to support extending opioid treatment to patients with chronic pain, against a backdrop of considerable caution that has been based upon fears of addiction. Of course, opioids may cause addiction, but the “principle of balance” may justify that “…efforts to address abuse should not interfere with legitimate medical practice and patient care.” Yet, situations are increasingly encountered in which opioid-maintained patients are refractory to analgesia during periods of pain, or even during the course of chronic treatment. The real question is whether analgesic efficacy of opioids can be maintained over time. Overall, the evidence supporting long-term analgesic efficacy is weak. The putative mechanisms for failed opioid analgesia may be related to tolerance or opioid-induced hyperalgesia. Advances in basic sciences may help in understanding these phenomena, but the question of whether long-term opioid treatment can improve patients’ function or quality of life remains a broader issue. Opioid side effects are well known, but with chronic use, most (except constipation) subside. Still, side effects can negatively affect the outcomes and continuity of therapy. This paper addresses 1) what evidence supports the long-term utility of opioids for chronic pain; 2) how side effects may alter quality of life; 3) the nature of addiction and why it is different in pain patients, and 4) on what grounds could pain medication be denied? These questions are discussed in light of patients’ rights, and warrant balancing particular responsibilities with risks. These are framed within the Hippocratic tradition of “producing good for the patient and protecting from harm,” so as to enable 1) more informed clinical decision making, and 2) progress towards right use and utility of opioid treatment for chronic pain. Key Words: Opioids, chronic pain, addiction, side effects, utility, ethics

Beyond the Development of Health-Related Quality-of-Life (HRQOL) Measures: A Checklist for Evaluating HRQOL Outcomes in Cancer Clinical Trials—Does HRQOL Evaluation in Prostate Cancer Research Inform Clinical Decision Making?

2003 ◽  
Vol 21 (18) ◽  
pp. 3502-3511 ◽  
Author(s):  
Fabio Efficace ◽  
Andrew Bottomley ◽  
David Osoba ◽  
Carolyn Gotay ◽  
Henning Flechtner ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Clinical Trials ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Cancer Clinical Trials ◽  
Health Related Quality ◽  
Related Quality ◽  
Health Related

Purpose: The aim of this study was to evaluate whether the inclusion of health-related quality of life (HRQOL), as a part of the trial design in a randomized controlled trial (RCT) setting, has supported clinical decision making for the planning of future medical treatments in prostate cancer. Materials and Methods: A minimum standard checklist for evaluating HRQOL outcomes in cancer clinical trials was devised to assess the quality of the HRQOL reporting and to classify the studies on the grounds of their robustness. It comprises 11 key HRQOL issues grouped into four broader sections: conceptual, measurement, methodology, and interpretation. Relevant studies were identified in a number of databases, including MEDLINE and the Cochrane Controlled Trials Register. Both their HRQOL and traditional clinical reported outcomes were systematically analyzed to evaluate their consistency and their relevance for supporting clinical decision making. Results: Although 54% of the identified studies did not show any differences in traditional clinical end points between treatment arms and 17% showed a difference in overall survival, 74% of the studies showed some difference in terms of HRQOL outcomes. One third of the RCTs provided a comprehensive picture of the whole treatment including HRQOL outcomes to support their conclusions. Conclusion: A minimum set of criteria for assessing the reported outcomes in cancer clinical trials is necessary to make informed decisions in clinical practice. Using a checklist developed for this study, it was found that HRQOL is a valuable source of information in RCTs of treatment in metastatic prostate cancer.

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.

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