Predicting Hospital Related Adverse Events: Interactions Between Frailty and Patient Characteristics in Acutely Admitted Older Adults

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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 patients’ characteristics on the risk of having at least one adverse event among acutely admitted older adults. Methods 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 (p < 0.01). Frailty was first modelled as the primary predictor, followed by individual models assessing crude associations for age, gender, ethnicity, Emergency Department (ED) wait time, and In-patient (IP) Length of Stay (LOS). Interaction terms between frailty and each characteristic were tested using Likelihood Ratio Test. Backward stepwise elimination was used to obtain a multivariable model retaining only variables that significantly improved model fit. 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 (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 increased with frailty, and this effect was most strongly influenced by IP LOS. Early frailty identification and targeted interventions for frail patients, especially those with extended admissions, may help reduce the risk of AE and harm. Clinical trial number: Not applicable. Frailty Adverse events Patient safety Clinical frailty score Figures Figure 1 Figure 2 Figure 3 Key Points • Frailty was a strong independent predictor of adverse events during acute hospital admission. • The effect of frailty on AE risk differed by age, ED wait time, and inpatient length of stay. • A significant frailty–length of stay interaction showed AE risk rose steeply with longer admissions in frail patients. • Interactions between frailty and gender or ethnicity were not statistically significant. • Length of stay was the most influential modifier of frailty-related AE risk. Background The number of people aged 65 and older is growing in the UK. In 2020, 18.6% of the UK population was aged 65 or over. [ 1 ] This proportion is projected to reach 24% in 2038, which is associated with increased health and social care needs. [ 2 ][ 3 ] As people age, the prevalence of age-related syndromes, e.g. frailty, increases. Approximately one in ten individuals aged 65 and older live with frailty, with a higher proportion of females affected compared to males. [ 4 ] Frailty is an age-related syndrome that affects older people and is characterised by a decline in the physiologic function of many body systems. [ 5 ] It is described as a state of increased vulnerability and inability to maintain homeostasis after a stressor, which may cause adverse outcomes, like falls, delirium, disability and abrupt changes in health status. [ 5 ] [ 6 ] [ 7 ] [ 8 ] Various methods are used to assess frailty, including Fried’s phenotype model, [ 5 ] the Frailty Index based on cumulative deficits, [ 9 ] and the Clinical Frailty Scale (CFS). [ 10 ] CFS is a widely used clinician-based tool that captures functional and cognitive status. [ 10 ] As frailty is associated with the risk of adverse outcomes, the risk of hospital related adverse events (AEs) also increase for this population when receiving care. An adverse event (AE) is any injury occurred during a healthcare episode that is not primarily related to the patient illness, comorbidities or past medical history. [ 11 ] Globally, studies estimate that approximately 1 in 20 patients may experience an AE during a hospital admission. [ 12 ] and 50% of AEs are possibly preventable. [ 13 ]. AEs could lead to a prolonged hospital stay, disability, or death. [ 14 ]. There are many methods to report and identify AEs in hospitalised patients, including voluntary incident reporting, chart review, or patient interviews. [ 15 ] There is a lack of studies that address frailty’s impact and its interaction with patient demographics (age, ethnicity and gender) and clinical characteristics (LOS and wait times) on the risk of AEs in acutely admitted older patients undergoing unplanned hospitalisation for a serious condition requiring immediate assessment and treatment. This study aims to investigate the impact of frailty measured by the CFS at the Emergency Department (ED) and its interaction with both patient’s demographics and clinical characteristics on the risk of having at least one AE during a hospital admission for an older acutely admitted patients. Methods Setting This study was undertaken at a single-centre university teaching hospital in the East Midlands of the United Kingdom. It provides emergency care to 240,000 attendees annually, including 50,000 older people aged ≥ 65 at its ED. The hospital serves a local population of 1.1 million residents. [ 16 ] Routine frailty assessment using the CFS was introduced into the electronic health record (EHR) in October 2017 for all patients aged 65 years and older presenting to the ED. Study Type and Sample We performed a retrospective observational study of patients aged ≥ 65 who presented acutely to the ED or were admitted from the ED to in-patient (IP) care. Frailty, screened in the ED using the CFS, was grouped as non-frail (CFS 1–3) and further stratified into mild (CFS 4–5), moderate (CFS 6), and severe (CFS 7–9) frailty. Only admissions with a documented frailty score were eligible for inclusion. AEs of these admissions were obtained from Datix, a national NHS web-based incident reporting system used to record and investigate patient safety incidents. [ 17 ] AEs were defined as the occurrence of any incident within the following higher-level AE categories during hospital admission: pressure ulcers, moisture-associated skin damage, falls, hospital injury or trauma, infections, medication errors, adverse drug reactions, self-harm, patient abuse, and incidents related to patient discharge or transfer. To support analysis, individual Datix-reported incidents were grouped into these broader AE categories. The specific Datix terms included within each category are detailed in Supplementary Table 1. Statistical Analysis Baseline characteristics were summarised across the four predefined frailty categories; the number of admissions and percentages was relative to the total included sample. Patient’s demographics variables (age, gender, ethnicity), ED measures (wait time in minutes, ED admission indicator), IP measures (LOS in nights, discharge destination), and in-hospital mortality was summarised across the four frailty categories. Continuous data were summarised as mean and standard deviation (SD) or median and interquartile range (IQR) as appropriate based on the normality of data distribution. Categorical data were presented as counts and percentages of the total admissions withing each frailty category. A series of Poisson regression models was applied to estimate the relative risk (RR), and 95% confidence intervals (CIs) of experiencing at least one AE during an acute admission with a significance level of p < 0.01 chosen to minimise the likelihood of false-positive findings due to multiple testing and to ensure that identified associations are not only statistically significant but also clinically meaningful, given the relatively large sample size. These models examined the association of frailty and key patient characteristics, age, gender, ethnicity, ED wait time, and IP LOS, with the risk of experiencing at least one AE. The significance of each interaction of patient’s characteristics with frailty was tested using likelihood ratio test comparing the interaction model with the main-effects-only model. Where the interaction was not statistically significant, the main effects of that characteristic adjusted for frailty were reported. To enhance the model and identify the most meaningful interactions, a backwards stepwise elimination approach was used to select statistically significant variables and interactions for a multivariable model. At each step, the reduced model was compared to the previous using the Likelihood Ratio Test. The least statistically significant term in the model was removed, while keeping in the model the main effect of any variable included in any remaining interaction. This process was repeated until only the terms that significantly improved the model fit remained (p < 0.01). Results 158,470 acute admissions with a frailty score recorded in the ED were analysed, drawn from 369,798 admissions between October 2017 and October 2023 among patients aged ≥ 65 years (Table 1 ). Median age associated with frailty, increasing from 74 years (IQR 69–80) in non-frail patients to 84 years (IQR 77–90) in those with severe frailty, and the proportion of female patients rose from 48.9% in those with no frailty to 58.4% in severe frailty. Across frailty groups, most patients were of White ethnicity peaking at 85% in moderate frailty category, followed by Asian ethnicity peaking at 13% in mild frailty, with other ethnic groups each accounting for < 2% across all frailty categories. Median ED waiting time increased from 334 minutes in non-frail patients to 516 minutes in those with moderate frailty, before decreasing slightly in the severely frail group (460 minutes). Admission from the ED to IP care increased from 54.3% in non-frail patients to 79.5% in moderate frailty, with a modest decline in severe frailty. Inpatient length of stay rose with increasing frailty, from a median of 3 nights in the non-frail to 7 nights in both moderate and severely frail. In-hospital mortality increased from 1.5% in non-frail admissions to 11.8% in severe frailty, while discharge to non-home destinations rose from 5.9% in non-frail to 35.8% severely frail. Admissions with at least one AE also became more frequent, increasing from 3.0% in non-frail patients to 11.4% in those with moderate frailty, before falling slightly to 10.6% in the severely frail group. Table 1 Patients characteristics per frailty level. Patient characteristics Non-Frail CFS 1–3 Mild Frailty CFS 4–5 Moderate Frailty CFS 6 Severe Frailty CFS 7–9 Total Admission. N (%) 35,691 (22.5%) 62,581 (39.5%) 37,068 (23.4%) 23,130 (14.6%) Average Age in Years (IQR) 74 (69–80) 80 (74–86) 84 (78–89) 84 (77–90) Female. % (N) 48.9% (n = 17,455) 53.2% (n = 33,333) 58.2% (n = 21,599) 58.4% (n = 13,519) White Ethnicity. % (N) 82.7% (n = 29,536) 83.3% (n = 52,177) 85.1% (n = 31,551) 83.8% (n = 19,382) Median ED Wait Time in Minutes (IQR) 334 (202–556) 475 (293–771) 516 (326–815) 460 (292–730) Median IP LOS in Nights (IQR) 3 (1–7) 5 (2–11) 7 (3–14) 7 (3–14) Mortality during admission. % (N)** 1.5% (n = 553) 4.3% (n = 2,745) 7.6% (n = 2,827) 11.8% (n = 2,750) Admitted from ED to IP. % (N) 54.3% (n = 19,387) 74.1% (n = 46,377) 79.5% (n = 29,478) 76.7% (n = 17,758) Discharge to a destination other than Home. % (N) 5.9% (n = 1,063) 15.4% (n = 6,443) 29.7% (n = 7,589) 35.8% (n = 5,132) Admissions with at least one AEs % (N) 3.0% (n = 1,072) 7.7% (n = 4,870) 11.4% (n = 4,257) 10.6% (n = 2,466) Frailty as a Predictor of risk When frailty was modelled as a predictor of AE risk, risk increased across the degrees of frailty. Compared to the non-frail patients, those with mild frailty had 2.6 times the risk (RR = 2.59; 95% CI: 2.43–2.76), rising to 3.82 (95% CI: 3.58–4.08) in moderate frailty and 3.55 (95% CI: 3.31–3.80) in severe frailty all p < 0.001. Interactions Between Frailty and Patient Characteristics Age and Frailty Interaction. The interaction between age and frailty was statistically significant in predicting the risk of at least one AE (χ² = 71.11, df = 18, p < 0.001), indicating that frailty's effect varied across age groups. In patients with no frailty and mild frailty AE risk rose with increasing age; however, this pattern was not observed in those with moderate and severe frailty, where risk potentially declined with age. See Fig. 1 . Gender and Frailty Interaction The interaction between gender and frailty was not statistically significant (χ² = 0.62, df = 3, p = 0.891), indicating similar frailty effects in males and females. Given this, we examined gender's independent effect after adjusting for frailty, males had a 26% higher risk of AE than females (RR = 1.26; 95% CI: 1.22–1.30; p < 0.001). Ethnicity and Frailty Interaction. The interaction between ethnicity and frailty also did not reach statistical significance (χ² = 11.64, df = 15, p = 0.706). Accordingly, we examined the main effect of ethnicity after adjusting for frailty, compared with White patients, Asian patients (RR 0.75, 95% CI 0.70–0.79; p < 0.001) and Black patients (RR 0.79, 95% CI 0.66–0.95; p = 0.01) had lower adjusted risks of adverse events. No significant differences were observed for patients of Mixed ethnicity (RR 1.00, 95% CI 0.67–1.49; p = 0.98), other ethnicities (RR 0.85, 95% CI 0.70–1.03; p = 0.10), or unknown ethnicities (RR 0.92, 95% CI 0.81–1.05; p = 0.22). ED Wait Time and Frailty Interaction. The ED wait time and frailty interaction was statistically significant (χ² = 67.25, df = 21, p < 0.001). AE risk increased with longer waits across the degree of frailty, peaking at 1–2 nights in ED; beyond this, risk declined for all degrees of frailty, except in the severely frail, whose risk continue to rise. See Fig. 2 . IP LOS and Frailty Interaction. The interaction between IP LOS and frailty was statistically significant (χ² = 150.53, df = 21, p < 0.001), indicating that the impact of prolonged hospitalisation on AE risk differed by frailty level. The risk rose across all degrees of frailty. Moderate and severe frailty had a higher risk at shorter stays compared to non-frail and mildly frail, but this difference was less visible in longer IP stays. See Fig. 3 . Multivariable Interaction Model. Starting with a model including all main effects and all interactions, stepwise elimination showed that dropping interactions with age, gender, ethnicity, and ED wait time together with the main effect of age did not statistically significantly affect the model fit. Only the IP LOS and frailty interaction was indispensable: removing it substantially impacted the model fit (χ² = 151.32, df = 21, p < 0.001). RRs from the multivariable model, with the interaction terms of IP LOS and frailty model are presented in Supplementary Table 2. Discussion To our knowledge, this is the first study to examine how multiple patient characteristics interact with frailty on predicting the risk of having at least one hospital-related AE in older acutely admitted patients. Frailty emerged as a strong and independent predictor of AEs in older adults acutely admitted through ED. Its effect on the risk of AE was significantly modified by patient age, ED wait time, and IP LOS. When all patient characteristics were included in a multivariable model with frailty, removing the frailty-IP LOS interaction only, resulted in a substantial decline in model fit, underscoring its importance in explaining variation in AE risk, indicating that it is the most influential factor. The age and frailty significant interaction suggest that the association between frailty and AE risk significantly varies by age. At younger ages, absolute risk increased with age for the non-frail and mildly frail. In contrast, although moderately and severely frail patients had the highest risk at younger ages, their risk decreased with advancing age. Although it may sound strange, the risk declined among those aged ≥ 95 years across all degrees of frailty. Fewer frailty studies have stratified results by age but, this reduction of the frailty’s association with AEs risk at extreme older age, could be explained by the reduction of predictive validity of CFS at extreme old age where most individuals already have significant deficits, so the incremental risk added by frailty is less discernible. [ 18 ] Additionally people at extreme age may receive more individualized palliative and restricted care due to their known advanced age which could modify the relationship between frailty and the risk of AE. [ 18 ]. This pattern may also reflect survivor bias and competing risks, whereby admitted patients with advanced age represent a highly selected group, resulting in lower observed in-hospital mortality rates compared with younger older adults. [ 19 ] The significant interaction between ED wait time and frailty indicates that the effect of prolonged ED stays on adverse events differs across frailty strata. Absolute AE risk increased with longer waits in all frailty groups, peaking around 1–2 days; beyond this, risk appeared to decline in non‑frail and mildly/moderately frail patients, but continued to rise in those with severe frailty. This pattern may be driven by the selection of higher‑risk patients to earlier admission, transfer or mortality, [ 20 ] limited observation at very long waits, [ 21 ] and the disproportionate vulnerability of severely frail patients to ED boarding‑related harms such as immobility, missed or delayed medications, sleep disruption and missed nursing care. [ 22 ] [ 21 ] IP LOS and frailty significant interactions indicates that the association between LOS and AE risk varies by degree of frailty. At short stays, AE risk was clearly stratified by frailty. As LOS increased, absolute risk rose steeply across all groups, and between-group differences narrowed; by ≥ 60 days, risks converged to similarly high levels across all degrees of frailty. This pattern suggests that prolonged hospitalisation itself becomes a dominant driver of AE risk. Consistent with this, LOS was the only factor whose removal substantially affected the multivariable model fit. Potential mechanisms include longer time at risk for hospital-acquired complications, cumulative exposure to procedures and invasive devices, and reverse causation, in which AEs extend LOS. [ 23 ] [ 24 ] Similar associations between extended hospitalisation and poor outcomes in older IP living with frailty have been reported in other studies aligning with our results. [ 25 ] [ 26 ] The lack of significance in frailty-ethnicity is likely driven, at least in part, by the limited representation of non-white ethnicities, as more than 80% of our cohort were of White background. A previous study conducted in the same population similarly reported higher admission rates among White patients. [ 28 ] This finding may reflect persistent barriers to hospital care among South Asian populations, or cultural preferences for family-based care of older adults, which may also help explain the admission patterns observed in our cohort. The lack of significant interaction between gender and frailty is not a new observation; a previous study by Hessey et al. reported the same, finding no significant interaction between gender and frailty in predicting in-hospital mortality and ICU organ support. [ 29 ] The increased AE risk in frail individuals may be linked to the reduced physiological resilience and complex clinical needs, which is a known exacerbation of frailty, including polypharmacy, cognitive impairment, and functional decline. [ 8 ] Recognising the compounded effect of frailty and prolonged IP LOS on AE risk underscores the importance of early frailty identification and proactive discharge planning. Further work is required to evaluate care options and weigh the consequences of hospital admissions to reduce the risk of AEs in the frail population, as the LOS is known to increase the risk of AEs significantly. Limitations This study has several limitations; some confounding factors could not be accounted for because relevant data were not available. The interaction between frailty and length of stay is complex, as frailty can lead to a longer LOS which may increase exposure to AE. Finally, this was a single-centre study, limiting the generalisability and relied on incident reports from Datix, which are known to be misused. Conclusion In conclusion, this study found that the degree of frailty is a strong and independent predictor of AEs during an acute hospital admission in older patients. The association between frailty and AE risk influenced by age, ED wait time and IP LOS. The interaction between frailty and IP LOS showed the strongest association with the risk of AE. These results underline the need to identify frailty early during hospital admission and to reduce the IP LOS and implement targeted strategies to reduce or prevent the risk of AE. Abbreviations AE Adverse event CFS Clinical Frailty Scale ED Emergency department EHR Electronic health record IP Inpatient LOS Length of stay IQR Interquartile range RR Relative risk CI Confidence interval df Degree of freedom WHO World Health Organization Declarations Ethical Approval This study holds the NHS Health and Research Authority (HRA) and Health and Care Research Wales (HCRW) approval (protocol number 0944) (IRAS number 330357) (REC reference 24/HRA/2100). Authors Contribution JB conceived the study. BM, FA and AA contributed to study design and methodological development. JB and AL extracted and anonymised the data and shared it with the research team. BM developed the statistical analysis plan. FA conducted the statistical analyses under the supervision of BM. FA drafted the manuscript. JB, BM, AA, and AL critically revised the manuscript. All authors reviewed and approved the final version of the manuscript. Consent for publication Not applicable. Availability of data and materials The datasets analysed during the current study are not publicly available due to institutional data governance restrictions. Competing interests Jay Banerjee is a Consultant in Geriatric Emergency Medicine and the director of Jay Banerjee Consultancy Ltd and CEMEaldor Ltd which provide training and consultancy services to improve the quality of care for older people across prehospital and hospital settings. All other authors declare no competing interests. Funding This study is part of Mr Alotaibi’s PhD thesis, which is funded by Imam Abdulrahman bin Faisal University through the Saudi Arabian Cultural Bureau in London. Acknowledgements The authors thank the University Hospitals of Leicester Business Intelligence team and patient safety team for supporting data extraction and linkage. References Park N. Office for National Statistics. Estimates of the population for the UK, England and Wales, Scotland and Northern Ireland. 2020. 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Hospital-associated complications of older people: a proposed multi-component outcome for acute care Running title: Hospital-associated complications of older people Alison M Mudge MBBS PhD. Alotaibi F, Alshibani A, Banerjee J, Manktelow B. The association between frailty and hospital-related adverse events in older hospitalised patients: a systematic literature review. Eur Geriatric Med 2025. Cunha AIL, Veronese N, de Melo Borges S, Ricci NA. Frailty as a predictor of adverse outcomes in hospitalized older adults: A systematic review and meta-analysis. Ageing Res Reviews. 2019;56:100960. Hubbard RE, Peel NM, Samanta M, Gray LC, Mitnitski A, Rockwood K. Frailty status at admission to hospital predicts multiple adverse outcomes. Age Ageing. 2017;46:801–6. Office for National Statistics. How life has changed in Leicester: Census 2021 2023. Bankart M, Baker R, Rashid A, Habiba M, Banerjee J, Hsu R, et al. Characteristics of general practices associated with emergency admission rates to hospital: a cross-sectional study. Emerg Med J. 2011;28:558–63. Hessey E, Montgomery C, Zuege DJ, Rolfson D, Stelfox HT, Fiest KM, et al. Sex-specific prevalence and outcomes of frailty in critically ill patients. J intensive care. 2020;8:75. Additional Declarations Competing interest reported. Competing interests Jay Banerjee is a Consultant in Geriatric Emergency Medicine and the director of Jay Banerjee Consultancy Ltd and CEMEaldor Ltd which provide training and consultancy services to improve the quality of care for older people across prehospital and hospital settings. All other authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviews received at journal 25 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 10 Mar, 2026 Editor invited by journal 24 Feb, 2026 Editor assigned by journal 23 Feb, 2026 Submission checks completed at journal 23 Feb, 2026 First submitted to journal 21 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8933473","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605163145,"identity":"dcb44c02-a810-4b8f-b98f-8868ab62abc0","order_by":0,"name":"Faris Alotaibi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBAC+wNA4gEQs0kwNj5IAIsl4NdiAFFjANLSbECaFgYJoC4GorSIHX/4IaHijzyfdHNbxYOawwz87DkGjD9qcGuxl84xlkg4Y2DYJnOw7UbCscMMkj1vDJh5juGxRTqHQSKxzYCxDUjeSGA7zGBwI8eAmYENn5b0xz+AWuxBWgoS/h1msL8Bctg/fFoSzEC2JIK0MCS2AW2RyDFg4G3D6zAzi4QzxslAvzRLJPal80iceVZwmLcPv8NufKiQs50/u/3hxx/frOX425M3PvzxDbcWDMADIg6QoGEUjIJRMApGARYAAIhYT2kepHm0AAAAAElFTkSuQmCC","orcid":"","institution":"University of Leicester","correspondingAuthor":true,"prefix":"","firstName":"Faris","middleName":"","lastName":"Alotaibi","suffix":""},{"id":605163146,"identity":"a2b0513e-c86a-4f73-a0ab-2a62d677df36","order_by":1,"name":"Bradley Manktelow","email":"","orcid":"","institution":"University of Leicester","correspondingAuthor":false,"prefix":"","firstName":"Bradley","middleName":"","lastName":"Manktelow","suffix":""},{"id":605163147,"identity":"5236b65a-9e95-49cb-a9d7-50a4d911af7c","order_by":2,"name":"Abdullah Alshibani","email":"","orcid":"","institution":"King Saud bin Abdulaziz University for Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"Alshibani","suffix":""},{"id":605163148,"identity":"2fa212c1-ce98-43f7-beec-2937fafbade8","order_by":3,"name":"Adam Linton","email":"","orcid":"","institution":"University Hospitals of Leicester NHS Trust","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Linton","suffix":""},{"id":605163149,"identity":"455c08db-8a4d-4245-8315-6cdb96a78f69","order_by":4,"name":"Jay Banerjee","email":"","orcid":"","institution":"University Hospitals of Leicester NHS Trust","correspondingAuthor":false,"prefix":"","firstName":"Jay","middleName":"","lastName":"Banerjee","suffix":""}],"badges":[],"createdAt":"2026-02-21 12:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8933473/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8933473/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104593861,"identity":"689c9afa-9330-46d7-afd5-7dce5124cf1e","added_by":"auto","created_at":"2026-03-13 17:46:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132833,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probability (absolute risk) of experiencing at least one AE by age group and the degrees of frailty, based on the Poisson regression model including their interaction.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8933473/v1/6d43543d712aead34463d5c6.png"},{"id":104781827,"identity":"a4744381-d157-485c-b82f-a19ef877b45c","added_by":"auto","created_at":"2026-03-17 07:56:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140113,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probability (absolute risk) of experiencing at least one AE by ED wait time group and the degrees of frailty, based on the Poisson regression model including their interaction.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8933473/v1/60fe21ccdcfcf00b3be6c7d4.png"},{"id":104593859,"identity":"0889797a-2084-407e-b147-c98cb698f8e0","added_by":"auto","created_at":"2026-03-13 17:46:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153663,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probability (absolute risk) of experiencing at least one AE by IP LOS group and the degrees of frailty, based on the Poisson regression model including their interaction.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8933473/v1/e1266e22364d4d478a4ac4df.png"},{"id":104784701,"identity":"5f2f602c-3d30-4689-9cfe-3d1f515c284a","added_by":"auto","created_at":"2026-03-17 08:08:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":858131,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8933473/v1/b52e6a5e-1af1-4e87-865c-2c1bccfc4c3a.pdf"},{"id":104593857,"identity":"68dea5a4-d399-42ed-91ad-d5c6752de003","added_by":"auto","created_at":"2026-03-13 17:46:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23097,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8933473/v1/78b81411c886becdbde998ac.docx"}],"financialInterests":"Competing interest reported. Competing interests \nJay Banerjee is a Consultant in Geriatric Emergency Medicine and the director of Jay Banerjee Consultancy Ltd and CEMEaldor Ltd which provide training and consultancy services to improve the quality of care for older people across prehospital and hospital settings. All other authors declare no competing interests.","formattedTitle":"Predicting Hospital Related Adverse Events: Interactions Between Frailty and Patient Characteristics in Acutely Admitted Older Adults","fulltext":[{"header":"Key Points","content":"\u003cp\u003e\u0026bull; Frailty was a strong independent predictor of adverse events during acute hospital admission.\u003c/p\u003e\u003cp\u003e\u0026bull; The effect of frailty on AE risk differed by age, ED wait time, and inpatient length of stay.\u003c/p\u003e\u003cp\u003e\u0026bull; A significant frailty\u0026ndash;length of stay interaction showed AE risk rose steeply with longer admissions in frail patients.\u003c/p\u003e\u003cp\u003e\u0026bull; Interactions between frailty and gender or ethnicity were not statistically significant.\u003c/p\u003e\u003cp\u003e\u0026bull; Length of stay was the most influential modifier of frailty-related AE risk.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eThe number of people aged 65 and older is growing in the UK. In 2020, 18.6% of the UK population was aged 65 or over. [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] This proportion is projected to reach 24% in 2038, which is associated with increased health and social care needs. [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e] As people age, the prevalence of age-related syndromes, e.g. frailty, increases. Approximately one in ten individuals aged 65 and older live with frailty, with a higher proportion of females affected compared to males. [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFrailty is an age-related syndrome that affects older people and is characterised by a decline in the physiologic function of many body systems. [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e] It is described as a state of increased vulnerability and inability to maintain homeostasis after a stressor, which may cause adverse outcomes, like falls, delirium, disability and abrupt changes in health status. [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] Various methods are used to assess frailty, including Fried’s phenotype model, [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e] the Frailty Index based on cumulative deficits, [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] and the Clinical Frailty Scale (CFS). [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] CFS is a widely used clinician-based tool that captures functional and cognitive status. [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] As frailty is associated with the risk of adverse outcomes, the risk of hospital related adverse events (AEs) also increase for this population when receiving care.\u003c/p\u003e \u003cp\u003eAn adverse event (AE) is any injury occurred during a healthcare episode that is not primarily related to the patient illness, comorbidities or past medical history. [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e] Globally, studies estimate that approximately 1 in 20 patients may experience an AE during a hospital admission. [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] and 50% of AEs are possibly preventable. [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. AEs could lead to a prolonged hospital stay, disability, or death. [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. There are many methods to report and identify AEs in hospitalised patients, including voluntary incident reporting, chart review, or patient interviews. [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThere is a lack of studies that address frailty’s impact and its interaction with patient demographics (age, ethnicity and gender) and clinical characteristics (LOS and wait times) on the risk of AEs in acutely admitted older patients undergoing unplanned hospitalisation for a serious condition requiring immediate assessment and treatment. This study aims to investigate the impact of frailty measured by the CFS at the Emergency Department (ED) and its interaction with both patient’s demographics and clinical characteristics on the risk of having at least one AE during a hospital admission for an older acutely admitted patients.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eSetting\u003c/p\u003e\u003cp\u003eThis study was undertaken at a single-centre university teaching hospital in the East Midlands of the United Kingdom. It provides emergency care to 240,000 attendees annually, including 50,000 older people aged ≥ 65 at its ED. The hospital serves a local population of 1.1\u0026nbsp;million residents. [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] Routine frailty assessment using the CFS was introduced into the electronic health record (EHR) in October 2017 for all patients aged 65 years and older presenting to the ED.\u003c/p\u003e\u003cp\u003eStudy Type and Sample\u003c/p\u003e\u003cp\u003eWe performed a retrospective observational study of patients aged ≥ 65 who presented acutely to the ED or were admitted from the ED to in-patient (IP) care. Frailty, screened in the ED using the CFS, was grouped as non-frail (CFS 1–3) and further stratified into mild (CFS 4–5), moderate (CFS 6), and severe (CFS 7–9) frailty. Only admissions with a documented frailty score were eligible for inclusion. AEs of these admissions were obtained from Datix, a national NHS web-based incident reporting system used to record and investigate patient safety incidents. [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] AEs were defined as the occurrence of any incident within the following higher-level AE categories during hospital admission: pressure ulcers, moisture-associated skin damage, falls, hospital injury or trauma, infections, medication errors, adverse drug reactions, self-harm, patient abuse, and incidents related to patient discharge or transfer. To support analysis, individual Datix-reported incidents were grouped into these broader AE categories. The specific Datix terms included within each category are detailed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eBaseline characteristics were summarised across the four predefined frailty categories; the number of admissions and percentages was relative to the total included sample. Patient’s demographics variables (age, gender, ethnicity), ED measures (wait time in minutes, ED admission indicator), IP measures (LOS in nights, discharge destination), and in-hospital mortality was summarised across the four frailty categories. Continuous data were summarised as mean and standard deviation (SD) or median and interquartile range (IQR) as appropriate based on the normality of data distribution. Categorical data were presented as counts and percentages of the total admissions withing each frailty category.\u003c/p\u003e\u003cp\u003eA series of Poisson regression models was applied to estimate the relative risk (RR), and 95% confidence intervals (CIs) of experiencing at least one AE during an acute admission with a significance level of \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 chosen to minimise the likelihood of false-positive findings due to multiple testing and to ensure that identified associations are not only statistically significant but also clinically meaningful, given the relatively large sample size. These models examined the association of frailty and key patient characteristics, age, gender, ethnicity, ED wait time, and IP LOS, with the risk of experiencing at least one AE.\u003c/p\u003e\u003cp\u003eThe significance of each interaction of patient’s characteristics with frailty was tested using likelihood ratio test comparing the interaction model with the main-effects-only model. Where the interaction was not statistically significant, the main effects of that characteristic adjusted for frailty were reported.\u003c/p\u003e\u003cp\u003eTo enhance the model and identify the most meaningful interactions, a backwards stepwise elimination approach was used to select statistically significant variables and interactions for a multivariable model. At each step, the reduced model was compared to the previous using the Likelihood Ratio Test. The least statistically significant term in the model was removed, while keeping in the model the main effect of any variable included in any remaining interaction. This process was repeated until only the terms that significantly improved the model fit remained (p \u0026lt; 0.01).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e158,470 acute admissions with a frailty score recorded in the ED were analysed, drawn from 369,798 admissions between October 2017 and October 2023 among patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Median age associated with frailty, increasing from 74 years (IQR 69\u0026ndash;80) in non-frail patients to 84 years (IQR 77\u0026ndash;90) in those with severe frailty, and the proportion of female patients rose from 48.9% in those with no frailty to 58.4% in severe frailty. Across frailty groups, most patients were of White ethnicity peaking at 85% in moderate frailty category, followed by Asian ethnicity peaking at 13% in mild frailty, with other ethnic groups each accounting for \u0026lt;\u0026thinsp;2% across all frailty categories.\u003c/p\u003e \u003cp\u003eMedian ED waiting time increased from 334 minutes in non-frail patients to 516 minutes in those with moderate frailty, before decreasing slightly in the severely frail group (460 minutes). Admission from the ED to IP care increased from 54.3% in non-frail patients to 79.5% in moderate frailty, with a modest decline in severe frailty. Inpatient length of stay rose with increasing frailty, from a median of 3 nights in the non-frail to 7 nights in both moderate and severely frail.\u003c/p\u003e \u003cp\u003eIn-hospital mortality increased from 1.5% in non-frail admissions to 11.8% in severe frailty, while discharge to non-home destinations rose from 5.9% in non-frail to 35.8% severely frail. Admissions with at least one AE also became more frequent, increasing from 3.0% in non-frail patients to 11.4% in those with moderate frailty, before falling slightly to 10.6% in the severely frail group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatients characteristics per frailty level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Frail\u003c/p\u003e \u003cp\u003eCFS 1\u0026ndash;3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild Frailty\u003c/p\u003e \u003cp\u003eCFS 4\u0026ndash;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate Frailty CFS 6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere Frailty\u003c/p\u003e \u003cp\u003eCFS 7\u0026ndash;9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Admission.\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35,691 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62,581 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37,068 (23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23,130 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Age in Years (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (69\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (74\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (78\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84 (77\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale. % (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.9% (n\u0026thinsp;=\u0026thinsp;17,455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.2% (n\u0026thinsp;=\u0026thinsp;33,333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.2% (n\u0026thinsp;=\u0026thinsp;21,599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.4% (n\u0026thinsp;=\u0026thinsp;13,519)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Ethnicity.\u003c/p\u003e \u003cp\u003e% (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.7% (n\u0026thinsp;=\u0026thinsp;29,536)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.3% (n\u0026thinsp;=\u0026thinsp;52,177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.1% (n\u0026thinsp;=\u0026thinsp;31,551)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.8% (n\u0026thinsp;=\u0026thinsp;19,382)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian ED Wait Time in Minutes (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334 (202\u0026ndash;556)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e475 (293\u0026ndash;771)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e516 (326\u0026ndash;815)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e460 (292\u0026ndash;730)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian IP LOS in Nights (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (3\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (3\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality during admission. % (N)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5% (n\u0026thinsp;=\u0026thinsp;553)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3% (n\u0026thinsp;=\u0026thinsp;2,745)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6% (n\u0026thinsp;=\u0026thinsp;2,827)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8% (n\u0026thinsp;=\u0026thinsp;2,750)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmitted from ED to IP. % (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.3% (n\u0026thinsp;=\u0026thinsp;19,387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.1% (n\u0026thinsp;=\u0026thinsp;46,377)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.5% (n\u0026thinsp;=\u0026thinsp;29,478)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.7% (n\u0026thinsp;=\u0026thinsp;17,758)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDischarge to a destination other than Home. % (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9% (n\u0026thinsp;=\u0026thinsp;1,063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.4% (n\u0026thinsp;=\u0026thinsp;6,443)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.7% (n\u0026thinsp;=\u0026thinsp;7,589)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.8% (n\u0026thinsp;=\u0026thinsp;5,132)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmissions with at least one AEs % (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0% (n\u0026thinsp;=\u0026thinsp;1,072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7% (n\u0026thinsp;=\u0026thinsp;4,870)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.4% (n\u0026thinsp;=\u0026thinsp;4,257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.6% (n\u0026thinsp;=\u0026thinsp;2,466)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003ch2\u003eFrailty as a Predictor of risk\u003c/h2\u003e \u003cp\u003eWhen frailty was modelled as a predictor of AE risk, risk increased across the degrees of frailty. Compared to the non-frail patients, those with mild frailty had 2.6 times the risk (RR\u0026thinsp;=\u0026thinsp;2.59; 95% CI: 2.43\u0026ndash;2.76), rising to 3.82 (95% CI: 3.58\u0026ndash;4.08) in moderate frailty and 3.55 (95% CI: 3.31\u0026ndash;3.80) in severe frailty all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eInteractions Between Frailty and Patient Characteristics\u003c/p\u003e \u003cp\u003eAge and Frailty Interaction.\u003c/p\u003e \u003cp\u003eThe interaction between age and frailty was statistically significant in predicting the risk of at least one AE (χ\u0026sup2; = 71.11, df\u0026thinsp;=\u0026thinsp;18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that frailty's effect varied across age groups. In patients with no frailty and mild frailty AE risk rose with increasing age; however, this pattern was not observed in those with moderate and severe frailty, where risk potentially declined with age. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGender and Frailty Interaction\u003c/p\u003e \u003cp\u003eThe interaction between gender and frailty was not statistically significant (χ\u0026sup2; = 0.62, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.891), indicating similar frailty effects in males and females. Given this, we examined gender's independent effect after adjusting for frailty, males had a 26% higher risk of AE than females (RR\u0026thinsp;=\u0026thinsp;1.26; 95% CI: 1.22\u0026ndash;1.30; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eEthnicity and Frailty Interaction.\u003c/p\u003e \u003cp\u003eThe interaction between ethnicity and frailty also did not reach statistical significance (χ\u0026sup2; = 11.64, df\u0026thinsp;=\u0026thinsp;15, p\u0026thinsp;=\u0026thinsp;0.706). Accordingly, we examined the main effect of ethnicity after adjusting for frailty, compared with White patients, Asian patients (RR 0.75, 95% CI 0.70\u0026ndash;0.79; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Black patients (RR 0.79, 95% CI 0.66\u0026ndash;0.95; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) had lower adjusted risks of adverse events. No significant differences were observed for patients of Mixed ethnicity (RR 1.00, 95% CI 0.67\u0026ndash;1.49; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98), other ethnicities (RR 0.85, 95% CI 0.70\u0026ndash;1.03; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10), or unknown ethnicities (RR 0.92, 95% CI 0.81\u0026ndash;1.05; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22).\u003c/p\u003e \u003cp\u003eED Wait Time and Frailty Interaction.\u003c/p\u003e \u003cp\u003eThe ED wait time and frailty interaction was statistically significant (χ\u0026sup2; = 67.25, df\u0026thinsp;=\u0026thinsp;21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). AE risk increased with longer waits across the degree of frailty, peaking at 1\u0026ndash;2 nights in ED; beyond this, risk declined for all degrees of frailty, except in the severely frail, whose risk continue to rise. See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIP LOS and Frailty Interaction.\u003c/p\u003e \u003cp\u003eThe interaction between IP LOS and frailty was statistically significant (χ\u0026sup2; = 150.53, df\u0026thinsp;=\u0026thinsp;21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the impact of prolonged hospitalisation on AE risk differed by frailty level. The risk rose across all degrees of frailty. Moderate and severe frailty had a higher risk at shorter stays compared to non-frail and mildly frail, but this difference was less visible in longer IP stays. See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMultivariable Interaction Model.\u003c/p\u003e \u003cp\u003eStarting with a model including all main effects and all interactions, stepwise elimination showed that dropping interactions with age, gender, ethnicity, and ED wait time together with the main effect of age did not statistically significantly affect the model fit. Only the IP LOS and frailty interaction was indispensable: removing it substantially impacted the model fit (χ\u0026sup2; = 151.32, df\u0026thinsp;=\u0026thinsp;21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RRs from the multivariable model, with the interaction terms of IP LOS and frailty model are presented in Supplementary Table\u0026nbsp;2.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to examine how multiple patient characteristics interact with frailty on predicting the risk of having at least one hospital-related AE in older acutely admitted patients. Frailty emerged as a strong and independent predictor of AEs in older adults acutely admitted through ED. Its effect on the risk of AE was significantly modified by patient age, ED wait time, and IP LOS. When all patient characteristics were included in a multivariable model with frailty, removing the frailty-IP LOS interaction only, resulted in a substantial decline in model fit, underscoring its importance in explaining variation in AE risk, indicating that it is the most influential factor.\u003c/p\u003e \u003cp\u003eThe age and frailty significant interaction suggest that the association between frailty and AE risk significantly varies by age. At younger ages, absolute risk increased with age for the non-frail and mildly frail. In contrast, although moderately and severely frail patients had the highest risk at younger ages, their risk decreased with advancing age. Although it may sound strange, the risk declined among those aged\u0026thinsp;\u0026ge;\u0026thinsp;95 years across all degrees of frailty. Fewer frailty studies have stratified results by age but, this reduction of the frailty\u0026rsquo;s association with AEs risk at extreme older age, could be explained by the reduction of predictive validity of CFS at extreme old age where most individuals already have significant deficits, so the incremental risk added by frailty is less discernible. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Additionally people at extreme age may receive more individualized palliative and restricted care due to their known advanced age which could modify the relationship between frailty and the risk of AE. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This pattern may also reflect survivor bias and competing risks, whereby admitted patients with advanced age represent a highly selected group, resulting in lower observed in-hospital mortality rates compared with younger older adults. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe significant interaction between ED wait time and frailty indicates that the effect of prolonged ED stays on adverse events differs across frailty strata. Absolute AE risk increased with longer waits in all frailty groups, peaking around 1\u0026ndash;2 days; beyond this, risk appeared to decline in non‑frail and mildly/moderately frail patients, but continued to rise in those with severe frailty. This pattern may be driven by the selection of higher‑risk patients to earlier admission, transfer or mortality, [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] limited observation at very long waits, [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and the disproportionate vulnerability of severely frail patients to ED boarding‑related harms such as immobility, missed or delayed medications, sleep disruption and missed nursing care. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIP LOS and frailty significant interactions indicates that the association between LOS and AE risk varies by degree of frailty. At short stays, AE risk was clearly stratified by frailty. As LOS increased, absolute risk rose steeply across all groups, and between-group differences narrowed; by \u0026ge;\u0026thinsp;60 days, risks converged to similarly high levels across all degrees of frailty. This pattern suggests that prolonged hospitalisation itself becomes a dominant driver of AE risk. Consistent with this, LOS was the only factor whose removal substantially affected the multivariable model fit. Potential mechanisms include longer time at risk for hospital-acquired complications, cumulative exposure to procedures and invasive devices, and reverse causation, in which AEs extend LOS. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Similar associations between extended hospitalisation and poor outcomes in older IP living with frailty have been reported in other studies aligning with our results. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe lack of significance in frailty-ethnicity is likely driven, at least in part, by the limited representation of non-white ethnicities, as more than 80% of our cohort were of White background. A previous study conducted in the same population similarly reported higher admission rates among White patients. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] This finding may reflect persistent barriers to hospital care among South Asian populations, or cultural preferences for family-based care of older adults, which may also help explain the admission patterns observed in our cohort.\u003c/p\u003e \u003cp\u003eThe lack of significant interaction between gender and frailty is not a new observation; a previous study by Hessey et al. reported the same, finding no significant interaction between gender and frailty in predicting in-hospital mortality and ICU organ support. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe increased AE risk in frail individuals may be linked to the reduced physiological resilience and complex clinical needs, which is a known exacerbation of frailty, including polypharmacy, cognitive impairment, and functional decline. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Recognising the compounded effect of frailty and prolonged IP LOS on AE risk underscores the importance of early frailty identification and proactive discharge planning. Further work is required to evaluate care options and weigh the consequences of hospital admissions to reduce the risk of AEs in the frail population, as the LOS is known to increase the risk of AEs significantly.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eThis study has several limitations; some confounding factors could not be accounted for because relevant data were not available. The interaction between frailty and length of stay is complex, as frailty can lead to a longer LOS which may increase exposure to AE. Finally, this was a single-centre study, limiting the generalisability and relied on incident reports from Datix, which are known to be misused.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study found that the degree of frailty is a strong and independent predictor of AEs during an acute hospital admission in older patients. The association between frailty and AE risk influenced by age, ED wait time and IP LOS. The interaction between frailty and IP LOS showed the strongest association with the risk of AE. These results underline the need to identify frailty early during hospital admission and to reduce the IP LOS and implement targeted strategies to reduce or prevent the risk of AE.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdverse event\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Frailty Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEmergency department\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic health record\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInpatient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLength of stay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003edf\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDegree of freedom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study holds the NHS Health and Research Authority (HRA) and Health and Care Research Wales (HCRW) approval (protocol number 0944) (IRAS number 330357) (REC reference 24/HRA/2100).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthors Contribution\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eJB conceived the study. BM, FA and AA contributed to study design and methodological development. JB and AL extracted and anonymised the data and shared it with the research team. BM developed the statistical analysis plan. FA conducted the statistical analyses under the supervision of BM. FA drafted the manuscript. JB, BM, AA, and AL critically revised the manuscript. All authors reviewed and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe datasets analysed during the current study are not publicly available due to institutional data governance restrictions.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eJay Banerjee is a Consultant in Geriatric Emergency Medicine and the director of Jay Banerjee Consultancy Ltd and CEMEaldor Ltd which provide training and consultancy services to improve the quality of care for older people across prehospital and hospital settings. All other authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study is part of Mr Alotaibi\u0026rsquo;s PhD thesis, which is funded by Imam Abdulrahman bin Faisal University through the Saudi Arabian Cultural Bureau in London.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors thank the University Hospitals of Leicester Business Intelligence team and patient safety team for supporting data extraction and linkage.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePark N. Office for National Statistics. Estimates of the population for the UK, England and Wales, Scotland and Northern Ireland. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinclair DR, Maharani A, Chandola T, Bower P, Hanratty B, Nazroo J, et al. Frailty among older adults and its distribution in England. 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Prevalence of Frailty in Community-Dwelling Older Persons: A Systematic Review. J Am Geriatr Soc. 2012;60:1487\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: Evidence for a phenotype. J GERONTOL A-BIOL. 2001;56:M146\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEeles EM, White SV, O'Mahony SM, Bayer AJ, Hubbard RE. The impact of frailty and delirium on mortality in older inpatients. Age Ageing. 2012;41:412\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalston J, Hadley EC, Ferrucci L, Guralnik JM, Newman AB, Studenski SA et al. Research agenda for frailty in older adults: toward a better understanding of physiology and etiology: summary from the American Geriatrics Society/National Institute on Aging Research Conference on Frailty in Older Adults. \u003cem\u003eJ Am Geriatr Soc\u003c/em\u003e 2006;54:991\u0026ndash;1001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381:752\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRockwood K, Mitnitski A. Frailty in Relation to the Accumulation of Deficits. J GERONTOL A-BIOL. 2007;62:722\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. Can Med Assoc J. 2005;173:489\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas EJ, Studdert DM, Burstin HR, Orav EJ, Zeena T, Williams EJ, et al. Incidence and types of adverse events and negligent care in Utah and Colorado. Med Care. 2000;38:261\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanagioti M, Khan K, Keers RN, Abuzour A, Phipps D, Kontopantelis E et al. Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis. BMJ 2019;366.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwendimann R, Blatter C, Dhaini S, Simon M, Ausserhofer D. The occurrence, types, consequences and preventability of in-hospital adverse events\u0026ndash;a scoping review. BMC Health Serv Res. 2018;18:521.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristi A, Grimm IG. October. Adverse Events in Hospitals: A Quarter of Medicare Patients Experienced Harm in 2018 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurff HJ, Patel VL, Hripcsak G, Bates DW. Detecting adverse events for patient safety research: a review of current methodologies. J Biomed Inf. 2003;36:131\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUniversity Hospitals of Leicester NHS Trust. Who We Are (27/11 2025, date last accessed).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwusu R, Sathiavageeswaran M. Datix reporting in University Hospitals of North Midlands: what can trainees learn from this? Future Healthc J. 2023;10:118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi Y, Chung HS, Lim JY, Kim K, Choi YH, Lee DH, et al. Prognostic value of frailty across age groups in emergency department patients aged 65 and above. BMC Geriatr. 2025;25:445.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan JA, Koh JH, Merchant RA, Tan LF. Frailty as a predictor of mortality in the oldest old: a systematic review and meta-analysis. Geriatr Gerontol Int. 2025;25:102\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy TE, Gill TM, Leo-Summers LS, Gahbauer EA, Pisani MA, Ferrante LE. The competing risk of death in longitudinal geriatric outcomes. J Am Geriatr Soc. 2019;67:357\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBjurbo C, Eriksson U, Muntlin \u0026Aring;. Frail Older Patients' Experiences During Boarding in the Emergency Department: A Qualitative Study. J Adv Nurs 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIozzo P, Spina N, Cannizzaro G, Gambino V, Patinella A, Bambi S, et al. Association between Boarding of Frail Individuals in the Emergency Department and Mortality: A Systematic Review. J Clin Med. 2024;13:1269.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhty PMB, Hubbard RE, Inouye SK. Hospital-associated complications of older people: a proposed multi-component outcome for acute care Running title: Hospital-associated complications of older people Alison M Mudge MBBS PhD.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlotaibi F, Alshibani A, Banerjee J, Manktelow B. The association between frailty and hospital-related adverse events in older hospitalised patients: a systematic literature review. Eur Geriatric Med 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCunha AIL, Veronese N, de Melo Borges S, Ricci NA. Frailty as a predictor of adverse outcomes in hospitalized older adults: A systematic review and meta-analysis. Ageing Res Reviews. 2019;56:100960.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHubbard RE, Peel NM, Samanta M, Gray LC, Mitnitski A, Rockwood K. Frailty status at admission to hospital predicts multiple adverse outcomes. Age Ageing. 2017;46:801\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOffice for National Statistics. How life has changed in Leicester: Census 2021 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBankart M, Baker R, Rashid A, Habiba M, Banerjee J, Hsu R, et al. Characteristics of general practices associated with emergency admission rates to hospital: a cross-sectional study. Emerg Med J. 2011;28:558\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHessey E, Montgomery C, Zuege DJ, Rolfson D, Stelfox HT, Fiest KM, et al. Sex-specific prevalence and outcomes of frailty in critically ill patients. J intensive care. 2020;8:75.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Frailty, Adverse events, Patient safety, Clinical frailty score","lastPublishedDoi":"10.21203/rs.3.rs-8933473/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8933473/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrailty is an age-related syndrome that increases vulnerability 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 patients’ characteristics on the risk of having at least one adverse event among acutely admitted older adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePoisson 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 (p \u0026lt; 0.01). Frailty was first modelled as the primary predictor, followed by individual models assessing crude associations for age, gender, ethnicity, Emergency Department (ED) wait time, and In-patient (IP) Length of Stay (LOS). Interaction terms between frailty and each characteristic were tested using Likelihood Ratio Test. Backward stepwise elimination was used to obtain a multivariable model retaining only variables that significantly improved model fit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 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 (all p \u0026lt; 0.01). Multivariable modelling showed that the interaction between frailty and IP LOS was the only interaction that significantly improved model fit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe risk of AEs increased with frailty, and this effect was most strongly influenced by IP LOS. Early frailty identification and targeted interventions for frail patients, especially those with extended admissions, may help reduce the risk of AE and harm.\u003c/p\u003e\n\u003cp\u003eClinical trial number: Not applicable.\u003c/p\u003e","manuscriptTitle":"Predicting Hospital Related Adverse Events: Interactions Between Frailty and Patient Characteristics in Acutely Admitted Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 17:46:23","doi":"10.21203/rs.3.rs-8933473/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-12T23:13:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T06:45:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98309165746838421720308101711844249812","date":"2026-04-05T12:30:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T17:22:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154881758873306876133416349437473694730","date":"2026-03-18T16:51:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-10T13:26:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-24T06:47:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-24T03:07:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-24T03:07:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-02-21T12:22:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d2168f3-b010-44d1-8965-63c5c8821bf5","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T16:25:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 17:46:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8933473","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8933473","identity":"rs-8933473","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}