Dataset Information

Stepwise stroke recognition through clinical information, vital signs, and initial labs (CIVIL): Electronic health record-based observational cohort study.

ABSTRACT: Background: Stroke recognition systems have been developed to reduce time delays, however, a comprehensive triaging score identifying stroke subtypes is needed to guide appropriate management. We aimed to develop a prehospital scoring system for rapid stroke recognition and identify stroke subtype simultaneously.

Methods and findings: In prospective database of regional emergency and stroke center, Clinical Information, Vital signs, and Initial Labs (CIVIL) of 1,599 patients suspected of acute stroke was analyzed from an automatically-stored electronic health record. Final confirmation was performed with neuroimaging. Using multiple regression analyses, we determined independent predictors of tier 1 (true-stroke or not), tier 2 (hemorrhagic stroke or not), and tier 3 (emergent large vessel occlusion [ELVO] or not). The diagnostic performance of the stepwise CIVIL scoring system was investigated using internal validation. A new scoring system characterized by a stepwise clinical assessment has been developed in three tiers. Tier 1: Seven CIVIL-AS3A2P items (total score from -7 to +6) were deduced for true stroke as Age (? 60 years); Stroke risks without Seizure or psychiatric disease, extreme Sugar; "any Asymmetry", "not Ambulating"; abnormal blood Pressure at a cut-off point ? 1 with diagnostic sensitivity of 82.1%, specificity of 56.4%. Tier 2: Four items for hemorrhagic stroke were identified as the CIVIL-MAPS indicating Mental change, Age below 60 years, high blood Pressure, no Stroke risks with cut-point ? 2 (sensitivity 47.5%, specificity 85.4%). Tier 3: For ELVO diagnosis: we applied with CIVIL-GFAST items (Gaze, Face, Arm, Speech) with cut-point ? 3 (sensitivity 66.5%, specificity 79.8%). The main limitation of this study is its retrospective nature and require a prospective validation of the CIVIL scoring system.

Conclusions: The CIVIL score is a comprehensive and versatile system that recognizes strokes and identifies the stroke subtype simultaneously.

SUBMITTER: Lee SE

PROVIDER: S-EPMC7159200 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Stepwise stroke recognition through clinical information, vital signs, and initial labs (CIVIL): Electronic health record-based observational cohort study.

Lee Sung Eun SE Choi Mun Hee MH Kang Hyo Jung HJ Lee Seong-Joon SJ Lee Jin Soo JS Lee Yunhwan Y Hong Ji Man JM

PloS one 20200415 4

<h4>Background</h4>Stroke recognition systems have been developed to reduce time delays, however, a comprehensive triaging score identifying stroke subtypes is needed to guide appropriate management. We aimed to develop a prehospital scoring system for rapid stroke recognition and identify stroke subtype simultaneously.<h4>Methods and findings</h4>In prospective database of regional emergency and stroke center, Clinical Information, Vital signs, and Initial Labs (CIVIL) of 1,599 patients suspect ...[more]

PMID: 32294085

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Project description:Clinical assessment of brain function relies heavily on indirect behavior-based tests. Unfortunately, behavior-based assessments are subjective and therefore susceptible to several confounding factors. Event-related brain potentials (ERPs), derived from electroencephalography (EEG), are often used to provide objective, physiological measures of brain function. Historically, ERPs have been characterized extensively within research settings, with limited but growing clinical applications. Over the past 20 years, we have developed clinical ERP applications for the evaluation of functional status following serious injury and/or disease. This work has identified an important gap: the need for a clinically accessible framework to evaluate ERP measures. Crucially, this enables baseline measures before brain dysfunction occurs, and might enable the routine collection of brain function metrics in the future much like blood pressure measures today. Here, we propose such a framework for extracting specific ERPs as potential "brain vital signs." This framework enabled the translation/transformation of complex ERP data into accessible metrics of brain function for wider clinical utilization. To formalize the framework, three essential ERPs were selected as initial indicators: (1) the auditory N100 (Auditory sensation); (2) the auditory oddball P300 (Basic attention); and (3) the auditory speech processing N400 (Cognitive processing). First step validation was conducted on healthy younger and older adults (age range: 22-82 years). Results confirmed specific ERPs at the individual level (86.81-98.96%), verified predictable age-related differences (P300 latency delays in older adults, p < 0.05), and demonstrated successful linear transformation into the proposed brain vital sign (BVS) framework (basic attention latency sub-component of BVS framework reflects delays in older adults, p < 0.05). The findings represent an initial critical step in developing, extracting, and characterizing ERPs as vital signs, critical for subsequent evaluation of dysfunction in conditions like concussion and/or dementia.

Project description:IntroductionMonitoring vital signs in hospital is an important part of safe patient care. However, there are no robust estimates of the workload it generates for nursing staff. This makes it difficult to plan adequate staffing to ensure current monitoring protocols can be delivered.ObjectiveTo estimate the time taken to measure and record one set of patient's vital signs; and to identify factors associated with the time required to measure and record one set of patient's vital signs.MethodsWe undertook a time-and-motion study of 16 acute medical or surgical wards across four hospitals in England. Two trained observers followed a standard operating procedure to record the time taken to measure and record vital signs. We used mixed-effects models to estimate the mean time using whole vital signs rounds, which included equipment preparation, time spent taking vital signs at the bedside, vital signs documentation, and equipment storing. We tested whether our estimates were influenced by nurse, ward and hospital factors.ResultsAfter excluding non-vital signs related interruptions, dividing the length of a vital signs round by the number of vital signs assessments in that round yielded an estimated time per vital signs set of 5 min and 1 second (95% Confidence Interval (CI) = 4:39-5:24). If interruptions within the round were included, the estimated time was 6:26 (95% CI = 6:01-6:50). If only time taking each patient's vital signs at the bedside was considered, after excluding non-vital signs related interruptions, the estimated time was 3:45 (95% CI = 3:32-3:58). We found no substantial differences by hospital, ward or nurse characteristics, despite different systems for recording vital signs being used across the hospitals.DiscussionThe time taken to observe and record a patient's vital signs is considerable, so changes to recommended assessment frequency could have major workload implications. Variation in estimates derived from previous studies may, in part, arise from a lack of clarity about what was included in the reported times. We found no evidence that nurses save time when using electronic vital signs recording, or that the grade of staff measuring the vital signs influenced the time taken.ConclusionsMeasuring and recording vital signs is time consuming and the impact of interruptions and preparation away from the bedside is considerable. When considering the nursing workload around vital signs assessment, no assumption of relative efficiency should be made if different technologies or staff groups are deployed.

Dataset Information

Stepwise stroke recognition through clinical information, vital signs, and initial labs (CIVIL): Electronic health record-based observational cohort study.

Publications

Stepwise stroke recognition through clinical information, vital signs, and initial labs (CIVIL): Electronic health record-based observational cohort study.

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