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    The PRaCTICaL study of nurse led, intensive care follow-up programmes for improving long term outcomes from critical illness : a pragmatic randomised controlled trial
    (BMJ, 2009) Cuthbertson, Brian; Rattray, J; Campbell, Marion Kay; Gager, M; Roughton, S; Smith, A; Hull, A; Breeman, S; Norrie, John David; Jenkinson, David James; Hernández, Rodolfo Andrés; Johnston, Marie; Wilson, E; Waldman, C; PRaCTICaL Study Group; University of Aberdeen, School of Medicine & Dentistry, Division of Applied Health Sciences
    Objectives To test the hypothesis that nurse led follow-up programmes are effective and cost effective in improving quality of life after discharge from intensive care. Design A pragmatic, non-blinded, multicentre, randomised controlled trial. Setting Three UK hospitals (two teaching hospitals and one district general hospital). Participants 286 patients aged ≥18 years were recruited after discharge from intensive care between September 2006 and October 2007. Intervention Nurse led intensive care follow-up programmes versus standard care. Main outcome measure(s) Health related quality of life (measured with the SF-36 questionnaire) at 12 months after randomisation. A cost effectiveness analysis was also performed. Results 286 patients were recruited and 192 completed one year follow-up. At 12 months, there was no evidence of a difference in the SF-36 physical component score (mean 42.0 (SD 10.6) v 40.8 (SD 11.9), effect size 1.1 (95% CI −1.9 to 4.2), P=0.46) or the SF-36 mental component score (effect size 0.4 (−3.0 to 3.7), P=0.83). There were no statistically significant differences in secondary outcomes or subgroup analyses. Follow-up programmes were significantly more costly than standard care and are unlikely to be considered cost effective. Conclusions A nurse led intensive care follow-up programme showed no evidence of being effective or cost effective in improving patients’ quality of life in the year after discharge from intensive care. Further work should focus on the roles of early physical rehabilitation, delirium, cognitive dysfunction, and relatives in recovery from critical illness. Intensive care units should review their follow-up programmes in light of these results.
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    Predicting death and readmission after intensive care discharge
    (Oxford Journals, 2008-06) Campbell, Alison J.; Cook, Jonathan Alistair; Adey, Gillian; Cuthbertson, Brian; University of Aberdeen, School of Medicine & Dentistry, Division of Applied Health Sciences
    Background: Despite initial recovery from critical illness, many patients deteriorate after discharge from the intensive care unit (ICU). We examined prospectively collected data in an attempt to identify patients at risk of readmission or death after intensive care discharge. Methods: This was a secondary analysis of clinical audit data from patients discharged alive from a mixed medical and surgical (non-cardiac) ICU. Results: Four hundred and seventy-five patients (11.2%) died in hospital after discharge from the ICU. Increasing age, time in hospital before intensive care admission, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and discharge Therapeutic Intervention Scoring System (TISS) score were independent risk factors for death after intensive care discharge. Three hundred and eighty-five patients (8.8%) were readmitted to intensive care during the same hospital admission. Increasing age, time in hospital before intensive care, APACHE II score, and discharge to a high dependency unit were independent risk factors for readmission. One hundred and forty-three patients (3.3%) were readmitted within 48 h of intensive care discharge. APACHE II scores and discharge to a high dependency or other ICU were independent risk factors for early readmission. The overall discriminant ability of our models was moderate with only marginal benefit over the APACHE II scores alone. Conclusions: We identified risk factors associated with death and readmission to intensive care. It was not possible to produce a definitive model based on these risk factors for predicting death or readmission in an individual patient.