Literature DB >> 9203878

Heterogeneity in intensive care units: fact or fiction?

S Ridley1, K Burchett, K Gunning, A Burns, A Kong, M Wright, P Hunt, S Ross.   

Abstract

Reports and guidelines concerning intensive care practice have been issued recently. However, the introduction of such centrally issued recommendations may be difficult because of marked heterogeneity between intensive care units. This study examined the facilities (number of beds, consultant sessions, nursing establishment), annual workload (number and types of patients admitted) and outcome (intensive care unit mortality) in the (old) Anglia Region. There were significant differences in the distribution of patients' ages, severities of illness, diagnoses, durations of admission and outcomes. Such heterogeneity may make multicentre trials more difficult to conduct and create problems when uniform measures designed to improve intensive care services are being planned.

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Year:  1997        PMID: 9203878     DOI: 10.1111/j.1365-2222.1997.109-az0109.x

Source DB:  PubMed          Journal:  Anaesthesia        ISSN: 0003-2409            Impact factor:   6.955


  5 in total

Review 1.  The prevalence of post traumatic stress disorder in survivors of ICU treatment: a systematic review.

Authors:  John Griffiths; Gillian Fortune; Vicki Barber; J Duncan Young
Journal:  Intensive Care Med       Date:  2007-06-09       Impact factor: 17.440

Review 2.  Long-term survival from intensive care: a review.

Authors:  Teresa A Williams; Geoffrey J Dobb; Judith C Finn; Steve A R Webb
Journal:  Intensive Care Med       Date:  2005-08-24       Impact factor: 17.440

3.  Changes in health-related quality of life from 6 months to 2 years after discharge from intensive care.

Authors:  Reidar Kvale; Hans Flaatten
Journal:  Health Qual Life Outcomes       Date:  2003-03-24       Impact factor: 3.186

4.  Population pharmacokinetics and target attainment of ciprofloxacin in critically ill patients.

Authors:  Alan Abdulla; Omar Rogouti; Nicole G M Hunfeld; Henrik Endeman; Annemieke Dijkstra; Teun van Gelder; Anouk E Muller; Brenda C M de Winter; Birgit C P Koch
Journal:  Eur J Clin Pharmacol       Date:  2020-04-19       Impact factor: 2.953

5.  A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients.

Authors:  Nils Schweingruber; Marius Marc Daniel Mader; Anton Wiehe; Frank Röder; Jennifer Göttsche; Stefan Kluge; Manfred Westphal; Patrick Czorlich; Christian Gerloff
Journal:  Brain       Date:  2022-08-27       Impact factor: 15.255

  5 in total

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