prikaz prve stranice dokumenta Improving risk prediction model quality in the critically ill: data linkage study
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Improving risk prediction model quality in the critically ill: data linkage study
Health and Social Care Delivery Research, 10 (2022), 39; 1-192. https://doi.org/10.3310/EQAB4594
Ferrando-Vivas, Paloma; Shankar-Hari, Manu; Thomas, Karen; Doidge, James C; Caskey, Fergus J; Forni, Lui; Harris, Steve; Ostermann, Marlies; Gornik, Ivan; Holman, Naomi; Lone, Nazir; Young, Bob; Jenkins, David; Webb, Stephen; Nolan, Jerry P; Soar, Jasmeet; Rowan, Kathryn M; Harrison, David A Više autora...

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Ferrando Vivas, P., Shankar Hari, M., Thomas, K., Doidge, J. C., Caskey, F. J., Forni, L. ... Harrison, D. A. (2022). Improving risk prediction model quality in the critically ill: data linkage study. Health and Social Care Delivery Research, 10. (39), 1-192. doi: 10.3310/EQAB4594

Ferrando Vivas, Paloma, et al. "Improving risk prediction model quality in the critically ill: data linkage study." Health and Social Care Delivery Research, vol. 10, br. 39, 2022, str. 1-192. https://doi.org/10.3310/EQAB4594

Ferrando Vivas, Paloma, Manu Shankar Hari, Karen Thomas, James C Doidge, Fergus J Caskey, Lui Forni, Steve Harris, et al. "Improving risk prediction model quality in the critically ill: data linkage study." Health and Social Care Delivery Research 10, br. 39 (2022): 1-192. https://doi.org/10.3310/EQAB4594

Ferrando Vivas, P., et al. (2022) 'Improving risk prediction model quality in the critically ill: data linkage study', Health and Social Care Delivery Research, 10(39), str. 1-192. doi: 10.3310/EQAB4594

Ferrando Vivas P, Shankar Hari M, Thomas K, Doidge JC, Caskey FJ, Forni L, i sur.. Improving risk prediction model quality in the critically ill: data linkage study. Health and Social Care Delivery Research [Internet]. 12.2022. [pristupljeno 02.05.2024.];10(39):1-192. doi: 10.3310/EQAB4594

P. Ferrando Vivas, et al., "Improving risk prediction model quality in the critically ill: data linkage study", Health and Social Care Delivery Research, vol. 10, br. 39, str. 1-192, Prosinac 2022. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:105:306197. [Citirano: 02.05.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:105:306197
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Podaci o radu
Naslov (engleski)Improving risk prediction model quality in the critically ill: data linkage study
AutorPaloma Ferrando-Vivas
AutorManu Shankar-Hari
AutorKaren Thomas
AutorJames C Doidge
AutorFergus J Caskey
AutorLui Forni
AutorSteve Harris
AutorMarlies Ostermann
AutorIvan Gornik
AutorNaomi Holman
AutorNazir Lone
AutorBob Young
AutorDavid Jenkins
AutorStephen Webb
AutorJerry P Nolan
AutorJasmeet Soar
AutorKathryn M Rowan
AutorDavid A Harrison
Autorova ustanovaSveučilište u Zagrebu
Medicinski fakultet
(KATEDRA ZA INTERNU MEDICINU)
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Kliničke medicinske znanosti
Interna medicina
Sažetak (engleski)
Large amounts of information (data) are collected about patients using NHS services, but we do not make the best possible use of these data to improve patient care. Data are held by different organisations in different databases. Joining up these databases (data linkage) can give us a more complete picture of what happened to a patient. The Intensive Care National Audit & Research Centre is an independent charity that runs national clinical audits to monitor and improve care for critically ill patients. These audits use statistical models that take information about the patient know before, or soon after, the start of their illness to make a prediction of their likely outcome. In this research study, we used data linkage to improve these models and ensure that the audits provide useful information back to hospitals to support quality improvement. However, it took over 4 years to link the databases. By linking with death certificate information, we were able to predict how many patients die by 30 days, 90 days and 1 year after their critical illness. By linking with routine hospital data, we were able to take better account of how sick patients were before they became critically ill and look at how many days they spent in hospital in the year after their critical illness and the costs of these hospital stays. By linking with two other national clinical audits, we were able to develop new models to predict important problems of kidney failure and diabetes that some patients experience after critical care. By linking with another national clinical audit, we were able to get a more complete picture of how sick patients having heart surgery were before they were admitted to an intensive care unit, helping us to improve our models to make fairer comparisons for these patients.
Jezikengleski
Vrsta publikacijeZnanstveni rad - Pregledni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaHealth and Social Care Delivery Research
Brojčani podacivol. 10, br. 39, str. 1-192
p-ISSN2755-0060
e-ISSN2755-0079
DOIhttps://doi.org/10.3310/EQAB4594
URN:NBNurn:nbn:hr:105:306197
Datum objave publikacije2022-12
URL dokumentahttps://www.journalslibrary.nihr.ac.uk/hsdr/EQAB4594
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2023-08-28 08:39:32