Utilisation of a Suite of Screening Tools to Determine Adverse Healthcare Outcomes in an Older Frail Population Admitted to a Community Virtual Ward

Clare Lewis, Rónán O’Caoimh, Declan Patton, Tom O’Connor, Zena Moore and Linda E. Nugent
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Clare Lewis: School of Nursing and Midwifery, Royal College of Surgeons Ireland, 123 St Stephen’s Green, Saint Peter’s, D02 YN77 Dublin, Ireland
Rónán O’Caoimh: Clinical Sciences Institute, National University of Ireland Galway, Costello Road, H91 TK33 Galway City, Ireland
Declan Patton: School of Nursing and Midwifery, Royal College of Surgeons Ireland, 123 St Stephen’s Green, Saint Peter’s, D02 YN77 Dublin, Ireland
Tom O’Connor: School of Nursing and Midwifery, Royal College of Surgeons Ireland, 123 St Stephen’s Green, Saint Peter’s, D02 YN77 Dublin, Ireland
Zena Moore: School of Nursing and Midwifery, Royal College of Surgeons Ireland, 123 St Stephen’s Green, Saint Peter’s, D02 YN77 Dublin, Ireland
Linda E. Nugent: School of Nursing and Midwifery, Royal College of Surgeons Ireland, 123 St Stephen’s Green, Saint Peter’s, D02 YN77 Dublin, Ireland

IJERPH, 2021, vol. 18, issue 11, 1-14

Abstract: Risk stratification to assess healthcare outcomes among older people is challenging due to the interplay of multiple syndromes and conditions. Different short risk-screening tools can assist but the most useful instruments to predict responses and outcomes following interventions are unknown. We examined the relationship between a suite of screening tools and risk of adverse outcomes (pre-determined clinical ‘decline’ i.e., becoming ‘unstable’ or ‘deteriorating’ at 60–90 days, and institutionalisation, hospitalisation and death at 120 days), among community dwellers (n = 88) after admission to a single-centre, Irish, Community Virtual Ward (CVW). The mean age of patients was 82.8 (±6.4) years. Most were severely frail, with mean Clinical Frailty Scale (CFS) scores of 6.8 ± 1.33. Several instruments were useful in predicting ‘decline’ and other healthcare outcomes. After adjustment for age and gender, higher frailty levels, odds ratio (OR) 3.29, ( p = 0.002), impaired cognition (Mini Mental State Examination; OR 4.23, p < 0.001), lower mobility (modified FIM) (OR 3.08, p < 0.001) and reduced functional level (Barthel Index; OR 6.39, p < 0.001) were significantly associated with clinical ‘decline’ at 90 days. Prolonged (>30 s) TUG times (OR 1.27, p = 0.023) and higher CFS scores (OR 2.29, p = 0.045) were associated with institutionalisation. Only TUG scores were associated with hospitalisation and only CFS, MMSE and Barthel scores at baseline were associated with mortality. Utilisation of a multidimensional suite of risk-screening tools across a range of domains measuring frailty, mobility and cognition can help predict clinical ‘decline’ for an already frail older population. Their association with other outcomes was less useful. A better understanding of the utility of these instruments in vulnerable populations will provide a framework to inform the impact of interventions and assist in decision-making and anticipatory care planning for older patients in CVW models.

Keywords: risk screening; clinical health states; older persons; community; virtual wards (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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