SwePub
Sök i LIBRIS databas

  Utökad sökning

WFRF:(Sinnott R)
 

Sökning: WFRF:(Sinnott R) > Forewarning of hypo...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00005349naa a2200493 4500
001oai:DiVA.org:uu-374422
003SwePub
008190129s2019 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3744222 URI
0247 a https://doi.org/10.1007/s10877-018-0139-y2 DOI
040 a (SwePub)uu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Donald, Robu Stats Res Ltd, Dingwall, Scotland4 aut
2451 0a Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care
264 c 2018-05-24
264 1b Springer Science and Business Media LLC,c 2019
338 a print2 rdacarrier
520 a Traumatically brain injured (TBI) patients are at risk from secondary insults. Arterial hypotension, critically low blood pressure, is one of the most dangerous secondary insults and is related to poor outcome in patients. The overall aim of this study was to get proof of the concept that advanced statistical techniques (machine learning) are methods that are able to provide early warning of impending hypotensive events before they occur during neuro-critical care. A Bayesian artificial neural network (BANN) model predicting episodes of hypotension was developed using data from 104 patients selected from the BrainIT multi-center database. Arterial hypotension events were recorded and defined using the Edinburgh University Secondary Insult Grades (EUSIG) physiological adverse event scoring system. The BANN was trained on a random selection of 50% of the available patients (n = 52) and validated on the remaining cohort. A multi-center prospective pilot study (Phase 1, n = 30) was then conducted with the system running live in the clinical environment, followed by a second validation pilot study (Phase 2, n = 49). From these prospectively collected data, a final evaluation study was done on 69 of these patients with 10 patients excluded from the Phase 2 study because of insufficient or invalid data. Each data collection phase was a prospective non-interventional observational study conducted in a live clinical setting to test the data collection systems and the model performance. No prediction information was available to the clinical teams during a patient's stay in the ICU. The final cohort (n = 69), using a decision threshold of 0.4, and including false positive checks, gave a sensitivity of 39.3% (95% CI 32.9-46.1) and a specificity of 91.5% (95% CI 89.0-93.7). Using a decision threshold of 0.3, and false positive correction, gave a sensitivity of 46.6% (95% CI 40.1-53.2) and specificity of 85.6% (95% CI 82.3-88.8). With a decision threshold of 0.3, > 15min warning of patient instability can be achieved. We have shown, using advanced machine learning techniques running in a live neuro-critical care environment, that it would be possible to give neurointensive teams early warning of potential hypotensive events before they emerge, allowing closer monitoring and earlier clinical assessment in an attempt to prevent the onset of hypotension. The multi-centre clinical infrastructure developed to support the clinical studies provides a solid base for further collaborative research on data quality, false positive correction and the display of early warning data in a clinical setting.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Anestesi och intensivvård0 (SwePub)302012 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Anesthesiology and Intensive Care0 (SwePub)302012 hsv//eng
653 a Traumatic brain injury
653 a Neuro-intensive care
653 a Bayesian prediction
653 a Clinical study results
7001 a Howells, Timu Uppsala universitet,Neurokirurgi4 aut0 (Swepub:uu)timho266
7001 a Piper, Ianu Queen Elizabeth Univ Hosp, Inst Neurol Sci, Clin Phys, Glasgow, Lanark, Scotland4 aut
7001 a Enblad, Peru Uppsala universitet,Neurokirurgi4 aut0 (Swepub:uu)perenbla
7001 a Nilsson, Pelleu Uppsala universitet,Neurokirurgi4 aut0 (Swepub:uu)pellnils
7001 a Chambers, I.u James Cook Univ Hosp, Dept Med Phys, Middlesbrough, Cleveland, England4 aut
7001 a Gregson, B.u Newcastle Univ, Neurosurg Trials Grp, Newcastle Upon Tyne, Tyne & Wear, England4 aut
7001 a Citerio, G.u Hosp San Gerardo, Neurorianimaz, Monza, Italy4 aut
7001 a Kiening, K.u Ruprecht Karls Univ Hosp, Dept Neurosurg, Heidelberg, Germany4 aut
7001 a Neumann, J.u Ruprecht Karls Univ Hosp, Dept Neurosurg, Heidelberg, Germany4 aut
7001 a Ragauskas, A.u Kaunas Univ Technol, Kaunas, Lithuania4 aut
7001 a Sahuquillo, J.u Vall dHebron Univ Hosp, Dept Neurosurg, Barcelona, Spain4 aut
7001 a Sinnott, R.u Univ Melbourne, Dept Informat Syst, Parkville, Vic, Australia4 aut
7001 a Stell, A.u Univ Glasgow, Dept Clin Phys, Glasgow, Lanark, Scotland4 aut
7102 a Stats Res Ltd, Dingwall, Scotlandb Neurokirurgi4 org
7730 t Journal of clinical monitoring and computingd : Springer Science and Business Media LLCg 33:1, s. 39-51q 33:1<39-51x 1387-1307x 1573-2614
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-374422
8564 8u https://doi.org/10.1007/s10877-018-0139-y

Hitta via bibliotek

Till lärosätets databas

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy