Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond.
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ABSTRACT:
SUBMITTER: Keim-Malpass J
PROVIDER: S-EPMC7781904 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe purpose of this article is to describe neonatal intensive care unit clinician perceptions of a continuous predictive analytics technology and how those perceptions influenced clinician adoption. Adopting and integrating new technology into care is notoriously slow and difficult; realizing expected gains remain a challenge.MethodsSemistructured interviews from a cross-section of neonatal physicians (n = 14) and nurses (n = 8) from a single U.S. medical center were collected 18 months following the conclusion of the predictive monitoring technology randomized control trial. Following qualitative descriptive analysis, innovation attributes from Diffusion of Innovation Theory-guided thematic development.ResultsResults suggest that the combination of physical location as well as lack of integration into work flow or methods of using data in care decisionmaking may have delayed clinicians from routinely paying attention to the data. Once data were routinely collected, documented, and reported during patient rounds and patient handoffs, clinicians came to view data as another vital sign. Through clinicians' observation of senior physicians and nurses, and ongoing dialogue about data trends and patient status, clinicians learned how to integrate these data in care decision making (e.g., differential diagnosis) and came to value the technology as beneficial to care delivery.DiscussionThe use of newly created predictive technologies that provide early warning of illness may require implementation strategies that acknowledge the risk-benefit of treatment clinicians must balance and take advantage of existing clinician training methods.
| S-EPMC6494616 | biostudies-literature
Project description:The lack of visualization frameworks to guide interpretation and facilitate discovery is a potential bottleneck for precision medicine, systems genetics and other studies. To address this we have developed an interactive, reproducible, web-based prioritization approach that builds on our earlier work. HitWalker2 is highly flexible and can utilize many data types and prioritization methods based upon available data and desired questions, allowing it to be utilized in a diverse range of studies such as cancer, infectious disease and psychiatric disorders.Source code is freely available at https://github.com/biodev/HitWalker2 and implemented using Python/Django, Neo4j and Javascript (D3.js and jQuery). We support major open source browsers (e.g. Firefox and Chromium/Chrome).wilmotb@ohsu.eduSupplementary data are available at Bioinformatics online. Additional information/instructions are available at https://github.com/biodev/HitWalker2/wiki.
| S-EPMC4824131 | biostudies-other
Project description:We study strategically missing data problems in predictive analytics with regression. In many real-world situations, such as financial reporting, college admission, job application, and marketing advertisement, data providers often conceal certain information on purpose in order to gain a favorable outcome. It is important for the decision-maker to have a mechanism to deal with such strategic behaviors. We propose a novel approach to handle strategically missing data in regression prediction. The proposed method derives imputation values of strategically missing data based on the Support Vector Regression models. It provides incentives for the data providers to disclose their true information. We show that with the proposed method imputation errors for the missing values are minimized under some reasonable conditions. An experimental study on real-world data demonstrates the effectiveness of the proposed approach.
| S-EPMC8459828 | biostudies-literature
Project description:Inflammatory biomarkers are useful prognostic tools in cancer patients. However, the prognostic and predictive value of inflammatory biomarkers beyond the 1st-line setting in metastatic colorectal cancer (mCRC) is unclear.In multivariate analysis 1 standard deviation increase in neutrophil-lymphocyte-ratio (NLR) was associated with an 8.5% absolute lower objective-response-rate (ORR) in 1st-line (p<0.0001), 3% lower ORR in 2nd-line (p< 0.0001), and 3% lower ORR in 3rd-line (p=0.24), respectively. Regarding progression free survival (PFS), an increase in the NLR was significantly associated with rising hazard-ratios (HR) over all treatment lines (HR=1.30, p= 0.021 1st-line); (HR=1.37, p<0.0001 2nd-line); (HR=1.44, p=0.042 3rd-line). The platelet-lymphocyte-ratio (PLR) was associated with 6-month PFS over all three treatment lines. Higher C-reactive-protein (CRP) predicted for worse PFS in the first two chemotherapy lines and in best supportive care (BSC). (HR=1.49 (p<0.0001 1st-line); HR=1.25 (p=0.007 2nd-line); HR=1.09 (95%CI 0.81-1.48, p=0.552 3rd-line and HR=1.43 (p= 0.002 in BSC)).Two-hundred-fifty-eight patients with mCRC undergoing palliative chemo(immuno-)therapy were retrospectively included. Primary endpoints were 6-month PFS and ORR during 1st-line, 2nd-line, and 3rd-line treatment, and 6-month overall survival during BSC.This study shows that inflammatory biomarkers are useful predictors of disease outcome and treatment response over several treatment lines in mCRC patients.
| S-EPMC5707080 | biostudies-other
Project description:Mechanical ventilation is a lifesaving tool and provides organ support for patients with respiratory failure. However, injurious ventilation due to inappropriate delivery of high tidal volume can initiate or potentiate lung injury. This could lead to acute respiratory distress syndrome, longer duration of mechanical ventilation, ventilator associated conditions and finally increased mortality. In this study, we explore the viability and compare machine learning methods to generate personalized predictive alerts indicating violation of the safe tidal volume per ideal body weight (IBW) threshold that is accepted as the upper limit for lung protective ventilation (LPV), prior to application to patients. We process streams of patient respiratory data recorded per minute from ventilators in an intensive care unit and apply several state-of-the-art time series prediction methods to forecast the behavior of the tidal volume metric per patient, 1 hour ahead. Our results show that boosted regression delivers better predictive accuracy than other methods that we investigated and requires relatively short execution times. Long short-term memory neural networks can deliver similar levels of accuracy but only after much longer periods of data acquisition, further extended by several hours computing time to train the algorithm. Utilizing Artificial Intelligence, we have developed a personalized clinical decision support tool that can predict tidal volume behavior within 10% accuracy and compare alerts recorded from a real world system to highlight that our models would have predicted violations 1 hour ahead and can therefore conclude that the algorithms can provide clinical decision support.
| S-EPMC7543875 | biostudies-literature
Project description:BackgroundThe assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention.Main textHerein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice.ConclusionEfforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.
| S-EPMC6912996 | biostudies-literature
Project description:Precision health refers to personalized healthcare based on a person's unique genetic, genomic, or omic composition within the context of lifestyle, social, economic, cultural and environmental influences to help individuals achieve well-being and optimal health. Precision health utilizes big data sets that combine omics (i.e. genomic sequence, protein, metabolite, and microbiome information) with clinical information and health outcomes to optimize disease diagnosis, treatment and prevention specific to each patient. Successful implementation of precision health requires interprofessional collaboration, community outreach efforts, and coordination of care, a mission that nurses are well-positioned to lead. Despite the surge of interest and attention to precision health, most nurses are not well-versed in precision health or its implications for the nursing profession. Based on a critical analysis of literature and expert opinions, this paper provides an overview of precision health and the importance of engaging the nursing profession for its implementation. Other topics reviewed in this paper include big data and omics, information science, integration of family health history in precision health, and nursing omics research in symptom science. The paper concludes with recommendations for nurse leaders in research, education, clinical practice, nursing administration and policy settings for which to develop strategic plans to implement precision health.
| S-EPMC7031154 | biostudies-literature
Project description:ObjectiveTo present clinicians at the point-of-care with real-world data on the effectiveness of various treatment options in a precision cohort of patients closely matched to the index patient.Materials and methodsWe developed disease-specific, machine-learning, patient-similarity models for hypertension (HTN), type II diabetes mellitus (T2DM), and hyperlipidemia (HL) using data on approximately 2.5 million patients in a large medical group practice. For each identified decision point, an encounter during which the patient's condition was not controlled, we compared the actual outcome of the treatment decision administered to that of the best-achieved outcome for similar patients in similar clinical situations.ResultsFor the majority of decision points (66.8%, 59.0%, and 83.5% for HTN, T2DM, and HL, respectively), there were alternative treatment options administered to patients in the precision cohort that resulted in a significantly increased proportion of patients under control than the treatment option chosen for the index patient. The expected percentage of patients whose condition would have been controlled if the best-practice treatment option had been chosen would have been better than the actual percentage by: 36% (65.1% vs 48.0%, HTN), 68% (37.7% vs 22.5%, T2DM), and 138% (75.3% vs 31.7%, HL).ConclusionClinical guidelines are primarily based on the results of randomized controlled trials, which apply to a homogeneous subject population. Providing the effectiveness of various treatment options used in a precision cohort of patients similar to the index patient can provide complementary information to tailor guideline recommendations for individual patients and potentially improve outcomes.
| S-EPMC7936526 | biostudies-literature
Project description:To support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction, (2) similarity model training, (3) precision cohort identification, and (4) treatment options analysis was developed. The similarity model is used to dynamically create a cohort of similar patients, to inform clinical decisions about an individual patient. The workflow was implemented using EHR data from a large health care provider for three different highly prevalent chronic diseases: hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia (HL). A retrospective analysis demonstrated that treatment options with better outcomes were available for a majority of cases (75%, 74%, 85% for HTN, T2DM, HL, respectively). The models for HTN and T2DM were deployed in a pilot study with primary care physicians using it during clinic visits. A novel data-analytic workflow was developed to create patient-similarity models that dynamically generate personalized treatment insights at the point-of-care. By leveraging both knowledge-driven treatment guidelines and data-driven EHR data, physicians can incorporate real-world evidence in their medical decision-making process when considering treatment options for individual patients.
| S-EPMC7806725 | biostudies-literature
Project description:IntroductionTrauma in developing countries has been on the increase, a situation perpetuated by rising road traffic collisions, terrorism and firearms proliferation. Some of the victims of trauma are left with life threatening conditions requiring urgent surgical intervention and/or intensive care. The objectives of this study were to determine the pattern of major trauma needing intensive care in the region, and to determine the outcome of major trauma admitted to intensive care unit.MethodsA six-year retrospective cohort study of trauma patients needing intensive care, set in the Intensive Care Unit of Ahmadu Bello University Teaching Hospital, Zaria, North-West Nigeria. Subjects were major trauma patients admitted into the intensive care unit of the institution, identified via an admission register kept in the unit. The main outcomes measured were length of stay and mortality.ResultsTrauma admissions represented 25.1% of the all intensive care admissions. Severe traumatic brain injury accounted for 32.1% of the trauma admissions, while burns accounted for 23.2%. Of the injuries, 15.5% were sustained in bomb blasts, and 8.3% were firearm injuries. The majority of the patients stayed for no more than seven days from admission. Burns patients had the worst outcomes, with 82.1% mortality.ConclusionMajor trauma contributes significantly to local intensive care admissions, with terrorism- related trauma now an emerging challenging cause of major trauma in our region. The observed poor outcomes in this study are a reflection of the quality of available intensive care, and lends credence to the concept of appropriately resourced, specialised intensive care units for optimisation of care.
| S-EPMC6440917 | biostudies-literature