Dataset Information

Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19.

ABSTRACT: SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

SUBMITTER: McRae MP

PROVIDER: S-EPMC7360344 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundElectronic medical records are widely used in family practices across Canada and can improve health outcomes. However, recent reports indicate that physicians using electronic medical records work longer and have less direct patient contact which may contribute to burnout. Therefore, new and innovative digital tools are essential to reduce physician workloads and improve patient-physician interaction to address physician burnout. The objective of this study was to assess the efficiency and accuracy of clinical decision-making when using a new preventive care point-of-care clinical decision support system (CDSS). An estimate of the potential annual time savings was also determined. This study also assessed physician reported perceived usefulness and ease of use of the CDSS.MethodsQuantitative and qualitative data were collected during this study. Each participant evaluated two simulated patient charts and identified which preventive care metrics were due. The participants recorded their decisions and the time required to assess each chart. Participants then completed a Technology Acceptance Model survey regarding the perceived usefulness and ease of use of the CDSS, which included qualitative feedback. The amount of time saved was determined and participants' clinical decision-making accuracy was scored against current Canadian preventive care guidelines. The number of preventive care specific visits completed per year was determined using clinic billing data.ResultsThe preventive care CDSS saved an average of 195.7 s of chart review time (249.5 s vs 445.2 s; P < 0.001). A total of 1520 preventive visits were performed at Primrose and Bruyère Family Medicine Centres. Extrapolated across the organization, implementation of the new tool could save 82.6 h per year. Decision-making accuracy was not affected by the new tool (78.4% vs 80.9%, P > 0.05). Participants rated the perceived ease of use and usefulness to be very high.ConclusionsNew digital tools may reduce providers' workload without impacting clinical decision-making accuracy. Participants indicated that the preventive care CDSS was useful and easy to use. Further software development and clinical studies are required to further improve and characterize the effect this new CDSS has when implemented in clinical practice.

Project description:BackgroundThe aim of RApid community Point-of-care Testing fOR COVID-19 (RAPTOR-C19) is to assess the diagnostic accuracy of multiple current and emerging point-of-care tests (POCTs) for active and past SARS-CoV2 infection in the community setting. RAPTOR-C19 will provide the community testbed to the COVID-19 National DiagnOstic Research and Evaluation Platform (CONDOR).MethodsRAPTOR-C19 incorporates a series of prospective observational parallel diagnostic accuracy studies of SARS-CoV2 POCTs against laboratory and composite reference standards in patients with suspected current or past SARS-CoV2 infection attending community settings. Adults and children with suspected current SARS-CoV2 infection who are having an oropharyngeal/nasopharyngeal (OP/NP) swab for laboratory SARS-CoV2 reverse transcriptase Digital/Real-Time Polymerase Chain Reaction (d/rRT-PCR) as part of clinical care or community-based testing will be invited to participate. Adults (≥ 16 years) with suspected past symptomatic infection will also be recruited. Asymptomatic individuals will not be eligible. At the baseline visit, all participants will be asked to submit samples for at least one candidate point-of-care test (POCT) being evaluated (index test/s) as well as an OP/NP swab for laboratory SARS-CoV2 RT-PCR performed by Public Health England (PHE) (reference standard for current infection). Adults will also be asked for a blood sample for laboratory SARS-CoV-2 antibody testing by PHE (reference standard for past infection), where feasible adults will be invited to attend a second visit at 28 days for repeat antibody testing. Additional study data (e.g. demographics, symptoms, observations, household contacts) will be captured electronically. Sensitivity, specificity, positive, and negative predictive values for each POCT will be calculated with exact 95% confidence intervals when compared to the reference standard. POCTs will also be compared to composite reference standards constructed using paired antibody test results, patient reported outcomes, linked electronic health records for outcomes related to COVID-19 such as hospitalisation or death, and other test results.DiscussionHigh-performing POCTs for community use could be transformational. Real-time results could lead to personal and public health impacts such as reducing onward household transmission of SARS-CoV2 infection, improving surveillance of health and social care staff, contributing to accurate prevalence estimates, and understanding of SARS-CoV2 transmission dynamics in the population. In contrast, poorly performing POCTs could have negative effects, so it is necessary to undertake community-based diagnostic accuracy evaluations before rolling these out.Trial registrationISRCTN, ISRCTN14226970.

Project description:BackgroundClinical pathways are one of the main tools to manage the health care's quality and concerned with the standardization of care processes. They have been used to help frontline healthcare workers by presenting summarized evidence and generating clinical workflows involving a series of tasks performed by various people within and between work environments to deliver care. Integrating clinical pathways into Clinical Decision Support Systems (CDSSs) is a common practice today. However, in a low-resource setting (LRS), this kind of decision support systems is often not readily accessible or even not available. To fill this gap, we developed a computer aided CDSS that swiftly identifies which cases require a referral and which ones may be managed locally. The computer aided CDSS is designed primarily for use in primary care settings for maternal and childcare services, namely for pregnant patients, antenatal and postnatal care. The purpose of this paper is to assess the user acceptance of the computer aided CDSS at the point of care in LRSs.MethodsFor evaluation, we used a total of 22 parameters structured in to six major categories, namely "ease of use, system quality, information quality, decision changes, process changes, and user acceptance." Based on these parameters, the caregivers from Jimma Health Center's Maternal and Child Health Service Unit evaluated the acceptability of a computer aided CDSS. The respondents were asked to express their level of agreement using 22 parameters in a think-aloud approach. The evaluation was conducted in the caregiver's spare-time after the clinical decision. It was based on eighteen cases over the course of two days. The respondents were then asked to score their level of agreement with some statements on a five-point scale: strongly disagree, disagree, neutral, agree, and strongly agree.ResultsThe CDSS received a favorable agreement score in all six categories by obtaining primarily strongly agree and agree responses. In contrast, a follow-up interview revealed a variety of reasons for disagreement based on the neutral, disagree, and strongly disagree responses.ConclusionsThough the study had a positive outcome, it was limited to the Jimma Health Center Maternal and Childcare Unit, and hence a wider scale evaluation and longitudinal measurements, including computer aided CDSS usage frequency, speed of operation and impact on intervention time are needed.

Project description:Point-of-care (POC) technologies-including HIV viral load (VL) monitoring-are expanding globally, including in resource-limited settings. Modelling could allow decision-makers to consider the optimal strategy(ies) to maximize coverage and access, minimize turnaround time (TAT) and minimize cost with limited machines. Informed by formative qualitative focus group discussions with stakeholders focused on model inputs, outputs and format, we created an optimization model incorporating queueing theory and solved it using integer programming methods to reflect HIV VL monitoring in Kisumu County, Kenya. We modelled three scenarios for sample processing: (1) centralized laboratories only, (2) centralized labs with 7 existing POC 'hub' facilities and (3) centralized labs with 7 existing and 1-7 new 'hub' facilities. We calculated total TAT using the existing referral network for scenario 1 and solved for the optimal referral network by minimizing TAT for scenarios 2 and 3. We conducted one-way sensitivity analyses, including distributional fairness in each sub-county. Through two focus groups, stakeholders endorsed the provisionally selected model inputs, outputs and format with modifications incorporated during model-building. In all three scenarios, the largest component of TAT was time spent at a facility awaiting sample batching and transport (scenarios 1-3: 78.7%, 89.9%, 91.8%) and waiting time at the testing site (18.7%, 8.7%, 7.5%); transportation time contributed minimally to overall time (2.6%, 1.3%, 0.7%). In scenario 1, the average TAT was 39.8 h (SD: 2.9), with 1077 h that samples spent cumulatively in the VL processing system. In scenario 2, the average TAT decreased to 33.8 h (SD: 4.8), totalling 430 h. In scenario 3, the average TAT decreased nearly monotonically with each new machine to 31.1 h (SD: 8.4) and 346 total hours. Frequency of sample batching and processing rate most impacted TAT, and inclusion of distributional fairness minimally impacted TAT. In conclusion, a stakeholder-informed resource allocation model identified optimal POC VL hub allocations and referral networks. Using existing-and adding new-POC machines could markedly decrease TAT, as could operational changes.

Project description:BackgroundWith the consequences of inadequate dosing ranging from increased bleeding risk to excessive drug costs and undesirable administration regimens, the antihemophilic factors are uniquely suited to dose individualization. However, existing options for individualization are limited and exist outside the flow of care. We developed clinical decision support (CDS) software that is integrated with our electronic health record (EHR) and designed to streamline the process for our hematology providers.ObjectivesThe aim of this study is to develop and examine the usability of a CDS tool for antihemophilic factor dose individualization.MethodsOur development strategy was based on the features associated with successful CDS tools and driven by a formal requirements analysis. The back-end code was based on algorithms developed for manual individualization and unit tested with 23,000 simulated patient profiles created from the range of patient-derived pharmacokinetic parameter estimates defined in children and adults. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users were recruited to participate in traditional usability testing under an institutional review board approved protocol.ResultsCDS software was developed to systematically walk the point-of-care clinician through dose individualization after seamlessly importing the requisite patient data from the EHR. Classical and population pharmacokinetic approaches were incorporated with clearly displayed estimates of reliability and uncertainty. Users can perform simulations for prophylaxis and acute bleeds by providing two of four therapeutic targets. Testers were highly satisfied with our CDS and quickly became proficient with the tool.ConclusionWith early and broad stakeholder engagement, we developed a CDS tool for hematology provider that affords seamless transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent dose selection.

Dataset Information

Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets