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Background <p>Frailty is a state of increased vulnerability to stressors that predisposes individuals to adverse outcomes. Hospital-related adverse events (AEs) are complications not directly caused by patients’ pre-existing conditions. While age has been widely studied, less is known about the effect of frailty and its interaction with patient demographic and clinical characteristics on the risk of having at least one adverse event (AE) among acutely admitted older adults.</p> Methods <p>In this study, AEs were operationally defined as Datix-reported patient safety incidents with the potential to cause harm. Poisson regression models were used to estimate Relative Risk (RRs) and 95% Confidence Intervals (CIs) for the association between frailty and the likelihood of experiencing at least one AE during admission. Frailty was assessed at the Emergency Department (ED) using the Clinical Frailty Scale (CFS) and modelled as the primary predictor. Individual models were then used to assess crude associations for age, gender, ethnicity, ED wait time, and In-patient (IP) Length of Stay (LOS). Interaction terms between frailty and each characteristic were tested using likelihood ratio tests. Backward stepwise elimination was used to obtain a multivariable model retaining only variables that significantly improved model fit (<i>p</i> &lt; 0.01).</p> Results <p>A total of 158,470 hospital admissions with a recorded frailty score were included. Statistically significant interactions were found between frailty and age, ED wait time, and IP LOS in relation to the risk of experiencing at least one AE (all <i>p</i> &lt; 0.01). Multivariable modelling showed that the interaction between frailty and IP LOS was the only interaction that significantly improved model fit.</p> Conclusion <p>The risk of AEs was associated with increasing frailty, and this association varied with IP LOS. Frailty assessment may help identify patients at increased risk of Datix-reported patient safety incidents, although the observed interaction with IP LOS does not imply causation.</p>

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The interaction between frailty and patient characteristics and its association with hospital-related adverse events in older adults

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月23日

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  • Faris Alotaibi,
  • Bradley Manktelow,
  • Abdullah Alshibani,
  • Adam Linton,
  • Jay Banerjee

摘要

Background

Frailty is a state of increased vulnerability to stressors that predisposes individuals to adverse outcomes. Hospital-related adverse events (AEs) are complications not directly caused by patients’ pre-existing conditions. While age has been widely studied, less is known about the effect of frailty and its interaction with patient demographic and clinical characteristics on the risk of having at least one adverse event (AE) among acutely admitted older adults.

Methods

In this study, AEs were operationally defined as Datix-reported patient safety incidents with the potential to cause harm. Poisson regression models were used to estimate Relative Risk (RRs) and 95% Confidence Intervals (CIs) for the association between frailty and the likelihood of experiencing at least one AE during admission. Frailty was assessed at the Emergency Department (ED) using the Clinical Frailty Scale (CFS) and modelled as the primary predictor. Individual models were then used to assess crude associations for age, gender, ethnicity, ED wait time, and In-patient (IP) Length of Stay (LOS). Interaction terms between frailty and each characteristic were tested using likelihood ratio tests. Backward stepwise elimination was used to obtain a multivariable model retaining only variables that significantly improved model fit (p < 0.01).

Results

A total of 158,470 hospital admissions with a recorded frailty score were included. Statistically significant interactions were found between frailty and age, ED wait time, and IP LOS in relation to the risk of experiencing at least one AE (all p < 0.01). Multivariable modelling showed that the interaction between frailty and IP LOS was the only interaction that significantly improved model fit.

Conclusion

The risk of AEs was associated with increasing frailty, and this association varied with IP LOS. Frailty assessment may help identify patients at increased risk of Datix-reported patient safety incidents, although the observed interaction with IP LOS does not imply causation.