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Predictors of adverse outcomes and factors associated with a risk-stratification based community virtual ward model for older persons with complex health and social care needs

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posted on 13.11.2020, 12:33 by Clare Lewis

Introduction

Population ageing is occurring rapidly worldwide, particularly in more developed

countries. As a result, a greater proportion of frail older adults are expected to be living

in the community but at increased risk of emergency department (ED) presentation and

hospital admission. While risk-stratification is useful in allocating limited resources, few

instruments or models are available to support healthcare professionals to manage the

complex health and social care needs of these patients. The recent introduction of a

Community Virtual Ward (CVW) for older persons with complex healthcare needs,

reduced ED presentations and unplanned hospital admissions. The research presented

in this thesis expands on previous studies examining predictors of adverse healthcare

outcomes in community-dwellers. It also investigates the impact of health trajectories

(transient health states defined as ‘unstable’, ‘deteriorated’ and ‘stable’) and factors

associated with the risk-stratification model including individual screening and

assessment scale scores on the risk of hospitalisation, institutionalisation and death

among patients admitted to a CVW.


Methods

This study is a longitudinal non-experimental descriptive and correlational design

investigating patients selected for admission to a CVW in a single centre in Ireland. The

theoretical principles of a Markov model informed the study design. Correlations and

associations were examined using Chi-square, and Spearman’s rho tests. To examine

relationships between risk scores, health states and adverse outcomes a univariate,

multinomial and multivariable regression analysis was performed.


Results

In total, 88 community-dwellers, with a mean age (+/- standard deviation) of 82.8 +/-6.4

years were included. Most were severely frail on the Rockwood Clinical Frailty Scale

(mean 6.8/9 +/-1.33) and highly dependent on the Barthel Index (2.7/20 +/-0.8). Health

states were predictive of adverse outcomes 60 observed up to 90 days. Reaching a

level of stability (‘stable’ state) within 60 days of admission to the CVW was a predictor

for stability at 90 days and remaining at home. Stability was also associated with less

episodes of care (<2), lower risk of experiencing delirium, and fewer numbers of health

care professional direct involvement in care (<7). In contrast an ‘unstable’ health state

observed at 60 days was predictive of institutionalisation or death with an increase in

the number (>2) of episodes of care, and higher numbers of healthcare professionals

(>7) direct involvement in care. Several screening and assessment instruments scores

covering pressure ulcer risk, functional and frailty status, mobility, nutrition and cognition

were predictive of reaching stability and risk of institutionalisation.


Conclusion

The CVW provides a framework for case management as a conceptual model of risk to

support older people at home or identify those at risk of institutional care. The use of

defined health states assisted in stratifying those at lower or higher risk in a high-risk

frail population. The selection of brief, targeted screening and assessment instruments

used to monitor risk in this CVW model were also helpful in stratifying participants.

Further research is needed to confirm these findings and refine this model.


History

First Supervisor

Dr. Linda Nugent

Second Supervisor

Dr. Declan Patton

Comments

A thesis submitted for the degree of Doctor of Philosophy from the Royal College of Surgeons in Ireland in 2019.

Published Citation

Lewis C. Predictors of adverse outcomes and factors associated with a risk-stratification based community virtual ward model for older persons with complex health and social care needs [PhD Thesis]. Dublin: Royal College of Surgeons in Ireland; 2019.

Degree Name

Doctor of Philosophy (PhD)

Date of award

30/06/2019

Programme

  • Doctor of Philosophy (PhD)

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