Short-Term Adverse Outcomes Related to Medication Use in Older Adults Visiting Emergency Department – a Retrospective Observational Study

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Drug-related emergency department (ED) visits are often encountered in the ED but remain unidentified, especially among older adults. Although medication use should be screened in the ED, little is known about their effect on short-term adverse ED outcomes. Therefore, we aimed to determine the association between polypharmacy and potentially inappropriate medication (PIM) use and short-term adverse outcomes in older ED patients. Methods. We retrospectively determined prescribed medications of 392 non-urgently transported community-dwelling patients aged ≥ 75 years. We measured polypharmacy and PIM use with dichotomous and ordinal variables. Comorbidities were assessed with Charlson Comorbidity Index (CCI). Primary outcomes were 90-day mortality, hospital admissions and 90-day ED revisits. Statistically, we used adjusted logistic regression analysis. Results. 80% of the patients had polypharmacy (≥ 5 regular medications) and 30% had excessive polypharmacy (≥ 10 regular medications). Polypharmacy did not predict higher risk of any study outcomes but was associated with a lower risk of 90-day mortality [adjusted OR 0.17 (95% CI 0.06–0.45), p < 0.001]. Excessive polypharmacy predicted a higher risk of 90-day ED revisits [adjusted OR 1.35 (95% CI 1.12–4.93), p = 0.024]. An increasing number of regular medications was associated with a higher risk of 90-day ED revisits [OR 1.09 (95% CI 1.03–1.16), p = 0.014] and a lower risk of 90-day mortality [OR 0.83 (95% CI 0.72–0.94, p = 0.005]. PIM use did not increase risks for any study outcomes. Increasing CCI predicted higher 90-day mortality rates [OR 1.70 (95% CI 1.37–2.10), p < 0.001]. Conclusions. Polypharmacy, defined as use of five or more medications is common among older ED patients but does not increase the risk of short-term adverse outcomes. Rising number of regular medications and excessive polypharmacy increases the risk for 90-day ED revisits. Instead of assessing polypharmacy with currently used numerical thresholds, EDs should screen excessive polypharmacy or use novel numerical thresholds to screen high-risk patients. Older Emergency Department Emergency Medicine Medication Polypharmacy Potentially Inappropriate Medication Drug-Related ED visit Outcome Medication review Figures Figure 1 Background Drug-related problems (DRPs), such as adverse drug events (ADEs) and drug interactions, are defined as actual or potential adverse health outcomes involving or resulting from pharmacological therapy ( 1 ). DRPs are often encountered in the emergency departments (EDs) and may even contribute to the ED visit itself: the prevalence of drug-related ED visits varies between 2.3–28.6%, and two-thirds of them have been considered as preventable ( 2 – 8 ). However, DRPs often remain unidentified in the ED, which may potentially result in worse clinical outcomes ( 3 – 5 , 9 – 11 ). Polypharmacy, often defined as use of five or more regular medications, is associated with a higher risk of drug-related ED visits, functional decline, higher use of healthcare resources, hospitalization, ED revisits and mortality ( 8 , 12 ). However, an independent association between polypharmacy and some adverse outcomes such as mortality could not have been confirmed ( 12 , 13 ). In recent years there has been an increasing interest in the use of potentially inappropriate medications (PIMs), which are defined as medications with greater potential risks than benefits in older people. Various criteria, such as the Beers criteria have been developed to detect PIM use. Regardless of polypharmacy, use of PIMs has been independently associated with higher risks of hospitalization, functional decline and ADEs ( 14 , 15 ). Despite the risks, PIM use is very common in the older population, varying between 11–57% depending on the criteria ( 16 ). Older adults are a growing group of ED patients with various risk factors for DRPs. Consequently, they are at higher risk of having drug-related ED visits ( 8 , 17 – 20 ). However, only one study has investigated the association between polypharmacy and short-term adverse outcomes following ED visits considering important confounding factors such as comorbidities( 21 ) and there are no earlier studies regarding PIM use. Hellinger et al. recently investigated pharmacist-led medication reviews in the ED and reported promising results of the intervention ‘s ability to identify drug-related ED visits in older patient population. However, the study suggested that more automatized methods should be developed to find the high-risk patients that would benefit from the intervention ( 22 ). This study’s objective was to determine if polypharmacy or the use of PIMs predict short-term adverse outcomes in the older ED patient population. We hypothesized that a higher number of regular medications or PIMs increases the risks of 90-day mortality, hospital admissions and 90-day ED revisits, and for that reason, they could be screened to identify high-risk patients eligible for acute medication reviews. Materials and methods Study design and population. This was a retrospective observational single center study conducted in Espoo, which is the second most populous city of Finland with over 300,000 inhabitants. From the original study population of an emergency medical services (EMS) screening study ( 23 ), we included community-dwelling patients aged 75 years or older who were non-urgently transported to the ED by the EMS between 10th November 2018 and 30th July 2019. Nine patients were excluded from the study population due to either double inclusion or missing health records of the index ED visit. All data was gathered retrospectively from electronic health records. Variables . For included patients, we determined currently used medications at the time of the index ED visit. We defined medications as medicinal substances used regularly to treat or prevent a disease and were administered perorally, transdermally, subcutaneously or taken by inhalation. Regularly administered supplements and vitamins of regular administration were also included. Medicines administered or prescribed during the index ED visit were excluded. Polypharmacy was defined as the use of ≥ 5 regular medications and excessive polypharmacy as the use of ≥ 10 regular medications. PIM use was measured by determining both the use of at least one PIM and the number of PIM prescriptions. PIMs were defined according to the Meds75 + database ( 16 , 24 ), the nationally used PIM criterion in Finland. To measure the burden of underlying comorbidities affecting the prognosis of included patients, we calculated age-adjusted Charlson Comorbidity Index (CCI) ranging from minimum of 3 to maximum of 37 points ( 25 ). Additionally, we determined the primary diagnoses and complaints related to the ED visits, using the 10th revision of International Statistical Classification of Diseases and Related Health Problems (ICD-10). Most recent estimated glomerular filtration rate (eGFR), based on serum creatinine and CKD-EPI equitation, was recorded from the electronic patient records. Outcomes. As primary outcomes we assessed 90-day mortality, hospital admissions and 90-day ED revisits. As a secondary outcome, we determined the number of ED revisits during the 90-day follow-up. ED revisits were defined as ED visits that took place in any of the Helsinki University Hospital (HUH) are EDs within 90 days of the index ED visit. Statistics. Categorical variables are presented as counts with percentages (%) and continuous variables as median with interquartile range (IQR). To evaluate the association between study variables and binary study outcomes, we used logistic regression analysis. The results of logistic regression analysis are presented as odds ratios (OR) with 95% confidence intervals (CI). In addition to univariate models, we calculated adjusted ORs for the study variables by including age, sex, CCI and renal function (eGFR) as confounding factors. Furthermore, Spearman rank correlation analysis was conducted to measure the relationship between the number of regular medications, number of PIMs and the number of 90-day ED revisits. P-values of < 0.05 were considered statistically significant. All statistical analyses were performed with RStudio and IBM SPSS Statistics (Versions 28 and 29). GraphPad Prism (v9.0.0.121) was used for graphical illustration. Results We included 392 older adults with a median age of 84 years (IQR 79–89). Two-thirds of the patients were female. The median CCI for the whole study population was 6 (IQR 5–7), the maximum CCI being 12. Nonspecific clinical signs or complaints (R00-R99) accounted for 36% (n = 141), and traumatic diagnoses (S00-T98) for 27% (n = 106) of all ED diagnoses. One-fifth (n = 77) of the study ED visits involved a fall. The most common ED diagnosis was Malaise and fatigue (R53) with a prevalence of 13% (Table 1 ). Of all patients, 80% had polypharmacy and 30% for excessive polypharmacy. The median number of prescribed regular medications was 7 ( 5 – 10 ), ranging from 0 to 20. In turn, 307 (78%) patients used at least one PIM with a median of 2 ( 1 – 3 ) and a maximum of 7 PIMs. Regarding study outcomes, 32 (8.2%) patients died within 90 days of the index visit, 269 (69%) patients were admitted to hospital from the ED and 165 (42%) patients had a 90-day ED revisit. Of the patients with revisits, 43% (n = 71) had at least two ED revisits. Table 1 Characteristics of the study data (n = 392). Variable N (%) / median (IQR) Age, years, median (IQR) 84 (79–89) Female, N (%) 263 (67) CCI, points, median (IQR) 6 (5–7) eGFR, ml/min, median (IQR) 61 (43–79) Polypharmacy, N (%) 315 (80) Excessive polypharmacy, N (%) 117 (30) Number of regular medications, median (IQR) 7 (5–10) Use of at least one PIM, N (%) 307 (78) Number of PIMs, median (IQR) 2 (1–3) Characteristics of the ED visits Most common ED diagnoses (ICD-10) • R53 Malaise and fatigue 50 (12.8) • S01 Open wound of head 22 (5.6) • I48 Atrial fibrillation and flutter 18 (4.6) • R42 Dizziness and giddiness 18 (4.6) • J18 Pneumonia 16 (4.1) ED visit involving a fall, N (%) 77 (20) Study outcomes N (%) 90-day mortality 32 (8.2) Hospital admission 269 (69) 90-day ED revisit 165 (42) Number of 90-day ED revisits of readmitted patients, median (IQR) 0 (0–1) IQR = interquartile range, CCI = Charlson Comorbidity Index, eGFR = estimated glomerular filtration rate, PIM = potentially inappropriate medication, ED = emergency department, ICD-10 = International Classification of Diseases, Tenth Revision Logistic regression. When investigated as ordinal variables, an increase in the number of regular medications had an unadjusted OR of 0.96 (95% CI 0.87–1.06, p = 0.419) and an adjusted OR of 0.83 (95% CI 0.72–0.94, p = 0.005) for 90-day mortality. For hospital admission, the unadjusted OR was 1.05 (95% CI 0.99–1.12, p = 0.105) and the adjusted OR was 1.04 (95% CI 0.97–1.11, p = 0.263). The unadjusted OR for 90-day ED revisit was 1.09 (95% CI 1.03–1.16, p = 0.002) and the result remained statistically significant in the adjusted model [OR 1.09 (95% CI 1.03–1.16), p = 0.014]. The increasing number of PIMs had an unadjusted OR of 0.88 (95% CI 0.54–1.43, p = 0.615) for 90-day mortality. In the adjusted model, the OR was 0.74 (95% CI 0.41–1.33, p = 0.312). For hospital admission, an increase of one in the number of PIMs had an unadjusted OR of 0.92 (95% CI 0.71–1.20, p = 0.546) and an OR of 0.89 (95% CI 0.67–1.16, p = 0.379) in the adjusted model. Regarding 90-day ED revisits, the unadjusted OR was 1.08 (95% CI 0.84–1.38, p = 0.558) and the adjusted OR was 1.08 (0.84–1.40, p = 0.548) for an increase in the number of PIMs. The logistic regression results of ordinal medication-related variables and CCI on study outcomes are illustrated in a forest plot in Fig. 1 . For 90-day mortality, an increase in CCI had an unadjusted OR of 1.60 (95% CI 1.33–1.91, p < 0.001) and an adjusted OR of 1.70 (95% CI 1.37–2.10, p < 0.001). For hospital admission, the unadjusted OR for an increase of one in CCI was 1.15 (95% CI 1.01–1.30, p = 0.035) and the adjusted OR was 1.11 (95% CI 0.95–1.30, p = 0.189). The unadjusted OR for 90-day ED revisits was 1.15 (95% CI 1.03–1.29, p = 0.016), whereas the adjusted OR was 1.03 (95% CI 0.90–1.18, p = 0.636). Table 2 illustrates the associations between dichotomous study variables, confounding factors and study outcomes. Recurrent ED visits – Spearman rank correlative analysis. Among the patients who revisited the ED within 90 days (n = 165), there was a very weak positive correlation between the number of regular medications and the number of 90-day ED revisits (ρ = 0.16, n = 165, p = 0.042). Additionally, a weak positive correlation was observed between the number of PIMs and the number of 90-day ED revisits (ρ = 0.28, n = 165, p < 0.001). Repeating the correlative analysis in the whole patient population showed similar results regarding the number of regular medications (ρ = 0.19, n = 391, p < 0.001) and the number of PIMs (ρ = 0.10, n = 386, p = 0.043) on the number of 90-day ED revisits. Table 2 Unadjusted and adjusted odds ratios of dichotomous medication-related variables and confounding factors for predicting study outcomes. Outcome / variable N (%) / median (IQR) OR 95% CI p-value 90-day mortality (N = 32) Polypharmacy* 22 (69) 0.50 0.23–1.11 0.090 Adjusted 0.17 0.06–0.45 < 0.001 Excessive polypharmacy* 11 (34) 0.70 0.28–1.73 0.433 Adjusted 0.21 0.06–0.70 0.011 Use of at least one PIM 13 (41) 1.11 0.53–2.34 0.785 Adjusted 0.88 0.38–2.04 0.768 CCI Adjusted 7 (5–10) 1.60 1.70 1.33–1.91 1.37–2.10 < 0.001 < 0.001 Female sex 18 (56) 81 (76–89) 48 (30–78) 0.62 0.30–1.29 0.199 Age 0.96 0.91–1.01 0.146 eGFR 0.98 0.96-1.00 0.046 Hospital admission (N = 269) Polypharmacy* Adjusted 222 (83) 1.52 1.18 0.91–2.56 0.66–2.13 0.111 0.573 Excessive polypharmacy* Adjusted 89 (33) 2.03 1.30 1.09–3.79 0.58–2.88 0.026 0.526 Use of at least one PIM Adjusted 101 (38) 0.80 0.72 0.52–1.24 0.45–1.14 0.319 0.158 CCI Adjusted 6 (5–7) 1.15 1.11 1.01–1.30 0.95–1.30 0.035 0.189 Female sex Age eGFR 94 (35) 83 (78–89) 61 (42–79) 0.80 0.98 1.00 0.51–1.27 0.95–1.01 0.99–1.01 0.344 0.265 0.991 90-day ED revisit (N = 165) Polypharmacy* 141 (86) 1.79 1.05–3.04 0.032 Adjusted 1.58 0.88–2.84 0.124 Excessive polypharmacy* 58 (35) 2.17 1.19–3.97 0.012 Adjusted 2.35 1.12–4.93 0.024 Use of at least one PIM 70 (42) 1.27 0.84–1.92 0.256 Adjusted 1.36 0.88–2.10 0.165 CCI Adjusted 6 (5–7) 1.15 1.03 1.03–1.29 0.90–1.18 0.016 0.636 Female sex Age eGFR 62 (38) 85 (79–90) 58 (41–78) 0.73 1.03 0.99 0.48–1.11 1.00-1.06 0.99-1.00 0.137 0.079 0.240 *Patients with < 5 regular medications as a reference group IQR = interquartile range, OR = odds ratio, CI = confidence interval, PIM = potentially inappropriate medication, eGFR = estimated glomerular filtration rate, ED = emergency department Discussion To our knowledge, this was the first study to investigate the effect of both polypharmacy and use of PIMs on short-term adverse outcomes in older ED patients. We measured medication use by using both dichotomous and ordinal variables, simultaneously aiming to limit the impact of confounding factors, such as underlying comorbidities. This study confirmed that polypharmacy and PIM use is very common among older ED patients: 80% of the study population used five or more regular medications and 30% ten or more regular medications. In comparison with the previously reported results in ED setting, we reported a clearly higher prevalence of polypharmacy and excessive polypharmacy ( 21 , 26 ). The differences in polypharmacy prevalence may be a result of the large variance among the definitions of medications and polypharmacy as well as the rapidly increasing trend in polypharmacy prevalence ( 12 , 27 ). On the other hand, the prevalence of PIM use was in line with both global and Finnish reports of community-dwelling older adults ( 16 , 28 ). Additionally, our results suggest that polypharmacy does not independently predict a higher risk of any study outcomes - polypharmacy or excessive polypharmacy were even associated with a lower risk of 90-day mortality, and the result was confirmed in two separate regression models using both dichotomous and ordinal variables. Most likely, the results reflect the effect of appropriate polypharmacy. This result is novel, as the association between appropriate polypharmacy and clinical outcomes has remained unclear in previous literature ( 29 ). We found that excessive polypharmacy and the increasing number of regular medications independently predicted 90-day ED revisits. This indicates that excessive medication use may increase the risk of ED revisits, but the current thresholds for the definition of polypharmacy may not identify the high-risk patients as the use of more than five regular medications is very common, and as mentioned earlier, continuously increasing ( 12 ). For that reason, a conclusion can be made that the existing numerical threshold for defining polypharmacy is outdated, at least among older ED patients. The current thresholds may even weaken the risk-predictive value of various acute geriatric screening tools detecting polypharmacy, such as the Identification of Seniors at Risk (ISAR) tool that uses the threshold of three regular medications ( 30 ). Therefore, we suggest that polypharmacy could screened in the ED by detecting excessive polypharmacy or by using novel numerical thresholds. Thereafter, lining the reports of previous literature suggesting that future medication reviews should focus on medication appropriateness ( 12 , 27 , 29 , 31 ), a targeted and more comprehensive clinical pharmacist-led medication review could be performed, to assess the medication appropriateness of these high-risk patients and support the identification of drug-related ED visits. Our results on PIM use and short-term adverse outcomes are in line with previous study results on long-term outcomes, which have found no association between mortality or ED visits ( 14 ). The effect of PIM detection in ED setting is scarcely investigated, but one intervention reducing PIM use in acute setting did not report improvement in clinical outcomes ( 32 ). However, regardless of the limited scientific evidence, PIM detection in the ED using STOPP/START criteria is recommended by geriEM project, a collaboration between European Society of Emergency Medicine (EUSEM) and European Geriatric Medicine Society (EuGMS) ( 33 ). Considering that the benefits of PIM detection in ED setting using the existing criteria remain unclear, we thus suggest that more prospective studies should be conducted to investigate the effects of PIM use and detection in ED setting. Polypharmacy and PIM use correlated with recurrent ED visits according to correlative analysis. However, worth mentioning is that the statistical power of the analysis was low, and these results need to be confirmed in a larger sample size. As expected, the study results confirm that underlying comorbidities play a significant role in predicting short-term mortality. Additionally, the effect of polypharmacy and PIM use on study outcomes weakened after adjusting for CCI. Several studies have reported similar results of the prognostic value of CCI in older ED patients ( 21 , 34 ). Although there exists complex interplay between comorbidities and medications, the identification of polypharmacy, with novel numerical thresholds and approaches, could be beneficial in the screening process of older adults in a higher risk of adverse outcomes. More prospective studies are, however, required to define the optimal thresholds for polypharmacy and confirm the benefits of polypharmacy screening and acute medication reviews on clinical outcomes of older ED patients. Limitations Although we adjusted for multiple patient-specific confounding factors in the analyses, the effects of underlying comorbidities could not be entirely removed. The analyses lacked frailty measurement, which may be additionally considered as a limitation. Furthermore, we did not measure medication appropriateness and thus could not recognize DRPs and ADEs. Due to the retrospective design of this study, medication timeliness and patient adherence to drug use could not be investigated. Therefore, pro re nata (PRN) medications were not included in the data analysis. Third, the study population did not include older adults living in nursing homes or long-term care facilities – thus, the study results should be considered in only the context of community-dwelling older population. Conclusions Use of more than five medications is common among the older ED population but does not increase the risk for short-term mortality, hospital admissions or ED revisits. Excessive polypharmacy predicts a higher rate of 90-day ED revisits. PIM use does not affect short-term adverse outcomes. Rather than screening polypharmacy, the screening of excessive polypharmacy might be beneficial in identifying patients eligible for acute pharmacist-led medication reviews. Prospective studies are needed to measure the accuracy of this assessment and the efficacy of medication reviews on short-term adverse outcomes in the older ED population. Abbreviations ADE = adverse drug event CI = confidence interval CCI = Charlson Comorbidity Index DRP = drug-related problem ED= emergency department eGFR = estimated glomerular filtration rate EMS = emergency medical services EuGMS = European Geriatric Medicine Society EUSEM = European Society of Emergency Medicine HUH = Helsinki University Hospital ICD-10 = International Statistical Classification of Diseases and Related Health Problems, 10 th version IQR = interquartile range ISAR = Identification of Seniors At Risk OR = odds ratio PIM = potentially inappropriate medication Declarations Ethics approval and consent to participate . The study design was approved by the Helsinki University Hospital (HUH) District (Ref. no. §85 HUS/223/2023). Due to the retrospective and observational nature of the study, patient consent or approval from the HUH Regional Committee on Medical Research Ethics was not required. Consent for publication. Not applicable. Availability of data and materials. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests. The authors declare that they have no competing interests. Funding. This study was partially funded from the government research fund of Helsinki University Hospital Emergency Medicine and Services. Authors’ contributions. RH gathered the medication lists of included patients and combined it with the previously collected clinical data, took part in the conceptualization, performed the statistical analysis, created the figures and tables and prepared the initial and final version of the manuscript. MH took part in planning and conceptualization of the study and assisted with statistical analysis and demonstration of the results. ES gathered the initial clinical data of included patients and assisted in preparing the manuscript. EJ, MC and JK took part in planning, conceptualization and project coordination and reviewing the manuscript. 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Frenkel WJ, Jongerius EJ, Mandjes-Van Uitert MJ, Van Munster BC, De Rooij SE. Validation of the Charlson Comorbidity Index in acutely hospitalized elderly adults: A prospective cohort study. J Am Geriatr Soc. 2014 Feb;62(2):342–6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4930828","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346968501,"identity":"3fb425ff-42e9-4702-bdd3-ebe7c8d66e0b","order_by":0,"name":"Ria M Holstein","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie2PsUrEQBBAZ1kwzUTbPRIuvzCwhQqn+ysbDuwOrkwZOJg0J7b6GXaWkS2uyUecHFhrpyDoJgdWbtJa7GOXmV3mMTMAkch/RA7nwmeihvcacv+UYGGOE4oC9Ip4qH08KjqswK/ik7Q+Rn91sPy8kXu5rpQxyYYPV08LLJrU0R4oDym5OyF536lyi8+NXnU3SO50aS1QcDAlgWTKyqIqOVuxQ5KoWwtfI0ry1isGixfOLvkbi82gjHXBoYvYKsGZ4BbBoZ4YDNcO+126kme3vPS7oCZLI8rZ7vGA1cIkze5VffL1vLjr9OyjIhNSeto//mhMiEQikcgUP46hQaVTq97bAAAAAElFTkSuQmCC","orcid":"","institution":"Faculty of Medicine, University of Helsinki","correspondingAuthor":true,"prefix":"","firstName":"Ria","middleName":"M","lastName":"Holstein","suffix":""},{"id":346968502,"identity":"f7cd2353-4d5c-41fb-98c7-c16f2a54a21e","order_by":1,"name":"Mari P Hongisto","email":"","orcid":"","institution":"Emergency Medicine and Services, Helsinki University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mari","middleName":"P","lastName":"Hongisto","suffix":""},{"id":346968503,"identity":"df9dd852-aea0-432d-8df1-a6c9ac8b1dde","order_by":2,"name":"Esa Jämsen","email":"","orcid":"","institution":"Department of Geriatrics, Helsinki University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Esa","middleName":"","lastName":"Jämsen","suffix":""},{"id":346968504,"identity":"e2e490a5-6e18-460a-a655-5c00274b3ea0","order_by":3,"name":"Eeva Saario","email":"","orcid":"","institution":"Faculty of Medicine, University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Eeva","middleName":"","lastName":"Saario","suffix":""},{"id":346968505,"identity":"9fb2c458-c737-4f50-b711-2ac711e4015d","order_by":4,"name":"Kirsi Kvarnström","email":"","orcid":"","institution":"Department of Pharmacy, Helsinki University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kirsi","middleName":"","lastName":"Kvarnström","suffix":""},{"id":346968506,"identity":"2d5bbe2f-1d89-4eb7-a09c-1c8523d9e955","order_by":5,"name":"Maaret K Castrén","email":"","orcid":"","institution":"Emergency Medicine and Services, Helsinki University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Maaret","middleName":"K","lastName":"Castrén","suffix":""},{"id":346968507,"identity":"d66c346e-ec80-4076-82d8-58f2d03ebfe6","order_by":6,"name":"Johanna M Kaartinen","email":"","orcid":"","institution":"Emergency Medicine and Services, Helsinki University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Johanna","middleName":"M","lastName":"Kaartinen","suffix":""}],"badges":[],"createdAt":"2024-08-17 17:41:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4930828/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4930828/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66563976,"identity":"90f603f2-91c8-4a34-b245-a189ab136be5","added_by":"auto","created_at":"2024-10-14 10:30:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2746579,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of univariate and adjusted associations between ordinal medication-related variables, CCI and study outcomes. \u0026nbsp;Number of regular medications and number of PIMs adjusted by age, sex, Charlson Comorbidity Index and estimated glomerular filtration rates, CCI adjusted by age, sex and estimated glomerular filtration rates.\u003c/p\u003e","description":"","filename":"SJTREMfigure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4930828/v1/902cbcb2e85ea1155acd3c06.jpg"},{"id":81060964,"identity":"83b5611b-2fc3-4628-aae5-1ed8132e3947","added_by":"auto","created_at":"2025-04-21 19:01:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3530675,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4930828/v1/1747fdcd-d2f0-4438-94c2-f6eea19d8a3f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short-Term Adverse Outcomes Related to Medication Use in Older Adults Visiting Emergency Department – a Retrospective Observational Study","fulltext":[{"header":"Background","content":"\u003cp\u003eDrug-related problems (DRPs), such as adverse drug events (ADEs) and drug interactions, are defined as actual or potential adverse health outcomes involving or resulting from pharmacological therapy (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). DRPs are often encountered in the emergency departments (EDs) and may even contribute to the ED visit itself: the prevalence of drug-related ED visits varies between 2.3\u0026ndash;28.6%, and two-thirds of them have been considered as preventable (\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, DRPs often remain unidentified in the ED, which may potentially result in worse clinical outcomes (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePolypharmacy, often defined as use of five or more regular medications, is associated with a higher risk of drug-related ED visits, functional decline, higher use of healthcare resources, hospitalization, ED revisits and mortality (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, an independent association between polypharmacy and some adverse outcomes such as mortality could not have been confirmed (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years there has been an increasing interest in the use of potentially inappropriate medications (PIMs), which are defined as medications with greater potential risks than benefits in older people. Various criteria, such as the Beers criteria have been developed to detect PIM use. Regardless of polypharmacy, use of PIMs has been independently associated with higher risks of hospitalization, functional decline and ADEs (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Despite the risks, PIM use is very common in the older population, varying between 11\u0026ndash;57% depending on the criteria (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOlder adults are a growing group of ED patients with various risk factors for DRPs. Consequently, they are at higher risk of having drug-related ED visits (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, only one study has investigated the association between polypharmacy and short-term adverse outcomes following ED visits considering important confounding factors such as comorbidities(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and there are no earlier studies regarding PIM use. Hellinger et al. recently investigated pharmacist-led medication reviews in the ED and reported promising results of the intervention \u0026lsquo;s ability to identify drug-related ED visits in older patient population. However, the study suggested that more automatized methods should be developed to find the high-risk patients that would benefit from the intervention (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study\u0026rsquo;s objective was to determine if polypharmacy or the use of PIMs predict short-term adverse outcomes in the older ED patient population. We hypothesized that a higher number of regular medications or PIMs increases the risks of 90-day mortality, hospital admissions and 90-day ED revisits, and for that reason, they could be screened to identify high-risk patients eligible for acute medication reviews.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003eStudy design and population.\u003c/b\u003e This was a retrospective observational single center study conducted in Espoo, which is the second most populous city of Finland with over 300,000 inhabitants. From the original study population of an emergency medical services (EMS) screening study (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), we included community-dwelling patients aged 75 years or older who were non-urgently transported to the ED by the EMS between 10th November 2018 and 30th July 2019. Nine patients were excluded from the study population due to either double inclusion or missing health records of the index ED visit. All data was gathered retrospectively from electronic health records.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVariables\u003c/b\u003e. For included patients, we determined currently used medications at the time of the index ED visit. We defined medications as medicinal substances used regularly to treat or prevent a disease and were administered perorally, transdermally, subcutaneously or taken by inhalation. Regularly administered supplements and vitamins of regular administration were also included. Medicines administered or prescribed during the index ED visit were excluded.\u003c/p\u003e \u003cp\u003ePolypharmacy was defined as the use of \u0026ge;\u0026thinsp;5 regular medications and excessive polypharmacy as the use of \u0026ge;\u0026thinsp;10 regular medications. PIM use was measured by determining both the use of at least one PIM and the number of PIM prescriptions. PIMs were defined according to the Meds75\u0026thinsp;+\u0026thinsp;database (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), the nationally used PIM criterion in Finland.\u003c/p\u003e \u003cp\u003eTo measure the burden of underlying comorbidities affecting the prognosis of included patients, we calculated age-adjusted Charlson Comorbidity Index (CCI) ranging from minimum of 3 to maximum of 37 points (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Additionally, we determined the primary diagnoses and complaints related to the ED visits, using the 10th revision of International Statistical Classification of Diseases and Related Health Problems (ICD-10). Most recent estimated glomerular filtration rate (eGFR), based on serum creatinine and CKD-EPI equitation, was recorded from the electronic patient records.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOutcomes.\u003c/b\u003e As primary outcomes we assessed 90-day mortality, hospital admissions and 90-day ED revisits. As a secondary outcome, we determined the number of ED revisits during the 90-day follow-up. ED revisits were defined as ED visits that took place in any of the Helsinki University Hospital (HUH) are EDs within 90 days of the index ED visit.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistics.\u003c/b\u003e Categorical variables are presented as counts with percentages (%) and continuous variables as median with interquartile range (IQR). To evaluate the association between study variables and binary study outcomes, we used logistic regression analysis. The results of logistic regression analysis are presented as odds ratios (OR) with 95% confidence intervals (CI). In addition to univariate models, we calculated adjusted ORs for the study variables by including age, sex, CCI and renal function (eGFR) as confounding factors. Furthermore, Spearman rank correlation analysis was conducted to measure the relationship between the number of regular medications, number of PIMs and the number of 90-day ED revisits. P-values of \u0026lt;\u0026thinsp;0.05 were considered statistically significant. All statistical analyses were performed with RStudio and IBM SPSS Statistics (Versions 28 and 29). GraphPad Prism (v9.0.0.121) was used for graphical illustration.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe included 392 older adults with a median age of 84 years (IQR 79\u0026ndash;89). Two-thirds of the patients were female. The median CCI for the whole study population was 6 (IQR 5\u0026ndash;7), the maximum CCI being 12.\u003c/p\u003e \u003cp\u003eNonspecific clinical signs or complaints (R00-R99) accounted for 36% (n\u0026thinsp;=\u0026thinsp;141), and traumatic diagnoses (S00-T98) for 27% (n\u0026thinsp;=\u0026thinsp;106) of all ED diagnoses. One-fifth (n\u0026thinsp;=\u0026thinsp;77) of the study ED visits involved a fall. The most common ED diagnosis was Malaise and fatigue (R53) with a prevalence of 13% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOf all patients, 80% had polypharmacy and 30% for excessive polypharmacy. The median number of prescribed regular medications was 7 (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), ranging from 0 to 20. In turn, 307 (78%) patients used at least one PIM with a median of 2 (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and a maximum of 7 PIMs.\u003c/p\u003e \u003cp\u003eRegarding study outcomes, 32 (8.2%) patients died within 90 days of the index visit, 269 (69%) patients were admitted to hospital from the ED and 165 (42%) patients had a 90-day ED revisit. Of the patients with revisits, 43% (n\u0026thinsp;=\u0026thinsp;71) had at least two ED revisits.\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\u003eCharacteristics of the study data (n\u0026thinsp;=\u0026thinsp;392).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%) / median (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (79\u0026ndash;89)\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\u003e263 (67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI, points, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (5\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (43\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolypharmacy, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e315 (80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive polypharmacy, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117 (30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of regular medications, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (5\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of at least one PIM, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307 (78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of PIMs, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of the ED visits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost common ED diagnoses (ICD-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; R53 Malaise and fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (12.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; S01 Open wound of head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; I48 Atrial fibrillation and flutter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; R42 Dizziness and giddiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; J18 Pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED visit involving a fall, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStudy outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (8.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269 (69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90-day ED revisit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165 (42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of 90-day ED revisits of readmitted patients, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eIQR\u0026thinsp;=\u0026thinsp;interquartile range, CCI\u0026thinsp;=\u0026thinsp;Charlson Comorbidity Index, eGFR\u0026thinsp;=\u0026thinsp;estimated glomerular filtration rate, PIM\u0026thinsp;=\u0026thinsp;potentially inappropriate medication, ED\u0026thinsp;=\u0026thinsp;emergency department, ICD-10\u0026thinsp;=\u0026thinsp;International Classification of Diseases, Tenth Revision\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLogistic regression.\u003c/b\u003e When investigated as ordinal variables, an increase in the number of regular medications had an unadjusted OR of 0.96 (95% CI 0.87\u0026ndash;1.06, p\u0026thinsp;=\u0026thinsp;0.419) and an adjusted OR of 0.83 (95% CI 0.72\u0026ndash;0.94, p\u0026thinsp;=\u0026thinsp;0.005) for 90-day mortality. For hospital admission, the unadjusted OR was 1.05 (95% CI 0.99\u0026ndash;1.12, p\u0026thinsp;=\u0026thinsp;0.105) and the adjusted OR was 1.04 (95% CI 0.97\u0026ndash;1.11, p\u0026thinsp;=\u0026thinsp;0.263). The unadjusted OR for 90-day ED revisit was 1.09 (95% CI 1.03\u0026ndash;1.16, p\u0026thinsp;=\u0026thinsp;0.002) and the result remained statistically significant in the adjusted model [OR 1.09 (95% CI 1.03\u0026ndash;1.16), p\u0026thinsp;=\u0026thinsp;0.014].\u003c/p\u003e \u003cp\u003eThe increasing number of PIMs had an unadjusted OR of 0.88 (95% CI 0.54\u0026ndash;1.43, p\u0026thinsp;=\u0026thinsp;0.615) for 90-day mortality. In the adjusted model, the OR was 0.74 (95% CI 0.41\u0026ndash;1.33, p\u0026thinsp;=\u0026thinsp;0.312). For hospital admission, an increase of one in the number of PIMs had an unadjusted OR of 0.92 (95% CI 0.71\u0026ndash;1.20, p\u0026thinsp;=\u0026thinsp;0.546) and an OR of 0.89 (95% CI 0.67\u0026ndash;1.16, p\u0026thinsp;=\u0026thinsp;0.379) in the adjusted model. Regarding 90-day ED revisits, the unadjusted OR was 1.08 (95% CI 0.84\u0026ndash;1.38, p\u0026thinsp;=\u0026thinsp;0.558) and the adjusted OR was 1.08 (0.84\u0026ndash;1.40, p\u0026thinsp;=\u0026thinsp;0.548) for an increase in the number of PIMs. The logistic regression results of ordinal medication-related variables and CCI on study outcomes are illustrated in a forest plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor 90-day mortality, an increase in CCI had an unadjusted OR of 1.60 (95% CI 1.33\u0026ndash;1.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and an adjusted OR of 1.70 (95% CI 1.37\u0026ndash;2.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For hospital admission, the unadjusted OR for an increase of one in CCI was 1.15 (95% CI 1.01\u0026ndash;1.30, p\u0026thinsp;=\u0026thinsp;0.035) and the adjusted OR was 1.11 (95% CI 0.95\u0026ndash;1.30, p\u0026thinsp;=\u0026thinsp;0.189). The unadjusted OR for 90-day ED revisits was 1.15 (95% CI 1.03\u0026ndash;1.29, p\u0026thinsp;=\u0026thinsp;0.016), whereas the adjusted OR was 1.03 (95% CI 0.90\u0026ndash;1.18, p\u0026thinsp;=\u0026thinsp;0.636).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the associations between dichotomous study variables, confounding factors and study outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRecurrent ED visits \u0026ndash; Spearman rank correlative analysis.\u003c/b\u003e Among the patients who revisited the ED within 90 days (n\u0026thinsp;=\u0026thinsp;165), there was a very weak positive correlation between the number of regular medications and the number of 90-day ED revisits (ρ\u0026thinsp;=\u0026thinsp;0.16, n\u0026thinsp;=\u0026thinsp;165, p\u0026thinsp;=\u0026thinsp;0.042). Additionally, a weak positive correlation was observed between the number of PIMs and the number of 90-day ED revisits (ρ\u0026thinsp;=\u0026thinsp;0.28, n\u0026thinsp;=\u0026thinsp;165, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Repeating the correlative analysis in the whole patient population showed similar results regarding the number of regular medications (ρ\u0026thinsp;=\u0026thinsp;0.19, n\u0026thinsp;=\u0026thinsp;391, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the number of PIMs (ρ\u0026thinsp;=\u0026thinsp;0.10, n\u0026thinsp;=\u0026thinsp;386, p\u0026thinsp;=\u0026thinsp;0.043) on the number of 90-day ED revisits.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnadjusted and adjusted odds ratios of dichotomous medication-related variables and confounding factors for predicting study outcomes.\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\u003eOutcome / variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%) / median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e90-day mortality (N\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolypharmacy*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e22 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.17\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.06\u0026ndash;0.45\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive polypharmacy*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u0026ndash;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.21\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.06\u0026ndash;0.70\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of at least one PIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e13 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u0026ndash;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.88\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.38\u0026ndash;2.04\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.768\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (5\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003cp\u003e\u003cem\u003e1.70\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33\u0026ndash;1.91\u003c/p\u003e \u003cp\u003e\u003cem\u003e1.37\u0026ndash;2.10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e18 (56)\u003c/p\u003e \u003cp\u003e81 (76\u0026ndash;89)\u003c/p\u003e \u003cp\u003e48 (30\u0026ndash;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u0026ndash;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital admission (N\u0026thinsp;=\u0026thinsp;269)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolypharmacy*\u003c/p\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222 (83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003cp\u003e\u003cem\u003e1.18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u0026ndash;2.56\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.66\u0026ndash;2.13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.573\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive polypharmacy*\u003c/p\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003cp\u003e\u003cem\u003e1.30\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09\u0026ndash;3.79\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.58\u0026ndash;2.88\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.526\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of at least one PIM\u003c/p\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.72\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u0026ndash;1.24\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.45\u0026ndash;1.14\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.158\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (5\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003cp\u003e\u003cem\u003e1.11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u0026ndash;1.30\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.95\u0026ndash;1.30\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.189\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (35)\u003c/p\u003e \u003cp\u003e83 (78\u0026ndash;89)\u003c/p\u003e \u003cp\u003e61 (42\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u0026ndash;1.27\u003c/p\u003e \u003cp\u003e0.95\u0026ndash;1.01\u003c/p\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003cp\u003e0.265\u003c/p\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e90-day ED revisit (N\u0026thinsp;=\u0026thinsp;165)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolypharmacy*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u0026ndash;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.58\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.88\u0026ndash;2.84\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.124\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive polypharmacy*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u0026ndash;3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2.35\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.12\u0026ndash;4.93\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of at least one PIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u0026ndash;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.36\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.88\u0026ndash;2.10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.165\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003cp\u003e\u003cem\u003eAdjusted\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (5\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003cp\u003e\u003cem\u003e1.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u0026ndash;1.29\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.90\u0026ndash;1.18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.636\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (38)\u003c/p\u003e \u003cp\u003e85 (79\u0026ndash;90)\u003c/p\u003e \u003cp\u003e58 (41\u0026ndash;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u0026ndash;1.11\u003c/p\u003e \u003cp\u003e1.00-1.06\u003c/p\u003e \u003cp\u003e0.99-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003cp\u003e0.079\u003c/p\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Patients with \u0026lt;\u0026thinsp;5 regular medications as a reference group\u003c/p\u003e \u003cp\u003eIQR\u0026thinsp;=\u0026thinsp;interquartile range, OR\u0026thinsp;=\u0026thinsp;odds ratio, CI\u0026thinsp;=\u0026thinsp;confidence interval, PIM\u0026thinsp;=\u0026thinsp;potentially inappropriate medication, eGFR\u0026thinsp;=\u0026thinsp;estimated glomerular filtration rate, ED\u0026thinsp;=\u0026thinsp;emergency department\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this was the first study to investigate the effect of both polypharmacy and use of PIMs on short-term adverse outcomes in older ED patients. We measured medication use by using both dichotomous and ordinal variables, simultaneously aiming to limit the impact of confounding factors, such as underlying comorbidities.\u003c/p\u003e \u003cp\u003eThis study confirmed that polypharmacy and PIM use is very common among older ED patients: 80% of the study population used five or more regular medications and 30% ten or more regular medications. In comparison with the previously reported results in ED setting, we reported a clearly higher prevalence of polypharmacy and excessive polypharmacy (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The differences in polypharmacy prevalence may be a result of the large variance among the definitions of medications and polypharmacy as well as the rapidly increasing trend in polypharmacy prevalence (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). On the other hand, the prevalence of PIM use was in line with both global and Finnish reports of community-dwelling older adults (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, our results suggest that polypharmacy does not independently predict a higher risk of any study outcomes - polypharmacy or excessive polypharmacy were even associated with a lower risk of 90-day mortality, and the result was confirmed in two separate regression models using both dichotomous and ordinal variables. Most likely, the results reflect the effect of appropriate polypharmacy. This result is novel, as the association between appropriate polypharmacy and clinical outcomes has remained unclear in previous literature (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that excessive polypharmacy and the increasing number of regular medications independently predicted 90-day ED revisits. This indicates that excessive medication use may increase the risk of ED revisits, but the current thresholds for the definition of polypharmacy may not identify the high-risk patients as the use of more than five regular medications is very common, and as mentioned earlier, continuously increasing (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). For that reason, a conclusion can be made that the existing numerical threshold for defining polypharmacy is outdated, at least among older ED patients. The current thresholds may even weaken the risk-predictive value of various acute geriatric screening tools detecting polypharmacy, such as the Identification of Seniors at Risk (ISAR) tool that uses the threshold of three regular medications (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Therefore, we suggest that polypharmacy could screened in the ED by detecting excessive polypharmacy or by using novel numerical thresholds. Thereafter, lining the reports of previous literature suggesting that future medication reviews should focus on medication appropriateness (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), a targeted and more comprehensive clinical pharmacist-led medication review could be performed, to assess the medication appropriateness of these high-risk patients and support the identification of drug-related ED visits.\u003c/p\u003e \u003cp\u003eOur results on PIM use and short-term adverse outcomes are in line with previous study results on long-term outcomes, which have found no association between mortality or ED visits (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The effect of PIM detection in ED setting is scarcely investigated, but one intervention reducing PIM use in acute setting did not report improvement in clinical outcomes (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). However, regardless of the limited scientific evidence, PIM detection in the ED using STOPP/START criteria is recommended by geriEM project, a collaboration between European Society of Emergency Medicine (EUSEM) and European Geriatric Medicine Society (EuGMS) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Considering that the benefits of PIM detection in ED setting using the existing criteria remain unclear, we thus suggest that more prospective studies should be conducted to investigate the effects of PIM use and detection in ED setting.\u003c/p\u003e \u003cp\u003ePolypharmacy and PIM use correlated with recurrent ED visits according to correlative analysis. However, worth mentioning is that the statistical power of the analysis was low, and these results need to be confirmed in a larger sample size.\u003c/p\u003e \u003cp\u003eAs expected, the study results confirm that underlying comorbidities play a significant role in predicting short-term mortality. Additionally, the effect of polypharmacy and PIM use on study outcomes weakened after adjusting for CCI. Several studies have reported similar results of the prognostic value of CCI in older ED patients (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough there exists complex interplay between comorbidities and medications, the identification of polypharmacy, with novel numerical thresholds and approaches, could be beneficial in the screening process of older adults in a higher risk of adverse outcomes. More prospective studies are, however, required to define the optimal thresholds for polypharmacy and confirm the benefits of polypharmacy screening and acute medication reviews on clinical outcomes of older ED patients.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eAlthough we adjusted for multiple patient-specific confounding factors in the analyses, the effects of underlying comorbidities could not be entirely removed. The analyses lacked frailty measurement, which may be additionally considered as a limitation. Furthermore, we did not measure medication appropriateness and thus could not recognize DRPs and ADEs. Due to the retrospective design of this study, medication timeliness and patient adherence to drug use could not be investigated. Therefore, pro re nata (PRN) medications were not included in the data analysis. Third, the study population did not include older adults living in nursing homes or long-term care facilities \u0026ndash; thus, the study results should be considered in only the context of community-dwelling older population.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eUse of more than five medications is common among the older ED population but does not increase the risk for short-term mortality, hospital admissions or ED revisits. Excessive polypharmacy predicts a higher rate of 90-day ED revisits. PIM use does not affect short-term adverse outcomes. Rather than screening polypharmacy, the screening of excessive polypharmacy might be beneficial in identifying patients eligible for acute pharmacist-led medication reviews. Prospective studies are needed to measure the accuracy of this assessment and the efficacy of medication reviews on short-term adverse outcomes in the older ED population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADE = adverse drug event\u003c/p\u003e\n\u003cp\u003eCI = confidence interval\u003c/p\u003e\n\u003cp\u003eCCI = Charlson Comorbidity Index\u003c/p\u003e\n\u003cp\u003eDRP = drug-related problem\u003c/p\u003e\n\u003cp\u003eED= emergency department\u003c/p\u003e\n\u003cp\u003eeGFR = estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eEMS = emergency medical services\u003c/p\u003e\n\u003cp\u003eEuGMS = European Geriatric Medicine Society\u003c/p\u003e\n\u003cp\u003eEUSEM = European Society of Emergency Medicine\u003c/p\u003e\n\u003cp\u003eHUH = Helsinki University Hospital\u003c/p\u003e\n\u003cp\u003eICD-10 = International Statistical Classification of Diseases and Related Health Problems, 10\u003csup\u003eth\u003c/sup\u003e version\u003c/p\u003e\n\u003cp\u003eIQR = interquartile range\u003c/p\u003e\n\u003cp\u003eISAR = Identification of Seniors At Risk\u003c/p\u003e\n\u003cp\u003eOR = odds ratio\u003c/p\u003e\n\u003cp\u003ePIM = potentially inappropriate medication\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e. The study design was approved by the Helsinki University Hospital (HUH) District (Ref. no.\u0026nbsp;\u0026sect;85 HUS/223/2023).\u0026nbsp;Due to the retrospective and observational nature of the study, patient consent or approval from the HUH Regional Committee on Medical Research Ethics was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication.\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials.\u003c/strong\u003e The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests.\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u003c/strong\u003e This study was partially funded from the government research fund of Helsinki University Hospital Emergency Medicine and Services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions.\u003c/strong\u003e RH gathered the medication lists of included patients and combined it with the previously collected clinical data, took part in the conceptualization, performed the statistical analysis, created the figures and tables and prepared the initial and final version of the manuscript. MH took part in planning and conceptualization of the study and assisted with statistical analysis and demonstration of the results. ES gathered the initial clinical data of included patients and assisted in preparing the manuscript. EJ, MC and JK took part in planning, conceptualization and project coordination and reviewing the manuscript. KK took part in planning the project and consulted the pharmacological aspects of the study. All authors have reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u003c/strong\u003e We acknowledge Helsinki University Hospital Emergency Medicine and Services, Faculty of Medicine in University of Helsinki and Emergency Medical Services (EMS) of Helsinki University Hospital area for the effortless collaboration regarding this research project.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eThe Pharmaceutical Care Network Europe (PCNE) Classification V9.1. Classification for Drug-Related Problems. 2003.\u003c/li\u003e\n \u003cli\u003eNymoen LD, Bj\u0026ouml;rk M, Flateb\u0026oslash; TE, Nilsen M, God\u0026oslash; A, \u0026Oslash;ie E, et al. Drug-related emergency department visits: prevalence and risk factors. Intern Emerg Med. 2022 Aug 1;17(5):1453\u0026ndash;62.\u003c/li\u003e\n \u003cli\u003eRoulet L, Ballereau F, Hardouin JB, Chiffoleau A, Moret L, Potel G, et al. Assessment of adverse drug event recognition by emergency physicians in a French teaching hospital. Emergency Medicine Journal. 2013 Jan;30(1):63\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eHohl CM, Zed PJ, Brubacher JR, Abu-Laban RB, Loewen PS, Purssell RA. Do Emergency Physicians Attribute Drug-Related Emergency Department Visits to Medication-Related Problems? Ann Emerg Med. 2010;55(6).\u003c/li\u003e\n \u003cli\u003eZed PJ, Abu-Laban RB, Balen RM, Loewen PS, Hohl CM, Brubacher JR, et al. Incidence, severity and preventability of medication-related visits to the emergency department: A prospective study. CMAJ Canadian Medical Association Journal. 2008 Jun 3;178(12):1563\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003ede Almeida SM, Romualdo A, de Abreu Ferraresi A, Zelezoglo GR, Marra AR, Edmond MB. Use of a trigger tool to detect adverse drug reactions in an emergency department. BMC Pharmacol Toxicol. 2017 Nov 15;18(71).\u003c/li\u003e\n \u003cli\u003eLo Giudice I, Mocciaro E, Giardina C, Barbieri MA, Cicala G, Gioffr\u0026egrave;-Florio M, et al. Characterization and preventability of adverse drug events as cause of emergency department visits: A prospective 1-year observational study. BMC Pharmacol Toxicol. 2019 Apr 27;20(1).\u003c/li\u003e\n \u003cli\u003eNymoen LD, Bj\u0026ouml;rk M, Flateb\u0026oslash; TE, Nilsen M, God\u0026oslash; A, \u0026Oslash;ie E, et al. Drug-related emergency department visits: prevalence and risk factors. Intern Emerg Med. 2022 Aug 1;17(5):1453\u0026ndash;62.\u003c/li\u003e\n \u003cli\u003eDalleur O, Beeler PE, Schnipper JL, Donz\u0026eacute; J. 30-Day Potentially Avoidable Readmissions Due to Adverse Drug Events. J Patient Saf [Internet]. 2017 Aug;17(5):379\u0026ndash;86. Available from: www.journalpatientsafety.com\u003c/li\u003e\n \u003cli\u003eRoulet L, Ballereau F, Hardouin JB, Chiffoleau A, Potel G, Asseray N. Adverse drug event nonrecognition in emergency departments: An exploratory study on factors related to patients and drugs. Journal of Emergency Medicine. 2014;46(6):857\u0026ndash;64.\u003c/li\u003e\n \u003cli\u003eNickel CH, Ruedinger JM, Messmer AS, Maile S, Peng A, Bodmer M, et al. Drug - related emergency department visits by elderly patients presenting with non-specific complaints. Scand J Trauma Resusc Emerg Med. 2013 Mar 5;21(1).\u003c/li\u003e\n \u003cli\u003eKhezrian M, McNeil CJ, Murray AD, Myint PK. An overview of prevalence, determinants and health outcomes of polypharmacy. Ther Adv Drug Saf. 2020 Jun;11:1\u0026ndash;10.\u003c/li\u003e\n \u003cli\u003eSch\u0026ouml;ttker B, Saum KU, Muhlack DC, Hoppe LK, Holleczek B, Brenner H. Polypharmacy and mortality: new insights from a large cohort of older adults by detection of effect modification by multi-morbidity and comprehensive correction of confounding by indication. Eur J Clin Pharmacol. 2017 Aug 1;73(8):1041\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eMekonnen AB, Redley B, de Courten B, Manias E. Potentially inappropriate prescribing and its associations with health-related and system-related outcomes in hospitalised older adults: A systematic review and meta-analysis. Br J Clin Pharmacol. 2021 Nov 1;87(11):4150\u0026ndash;72.\u003c/li\u003e\n \u003cli\u003eLu WH, Wen YW, Chen LK, Hsiao FY. Effect of polypharmacy, potentially inappropriate medications and anticholinergic burden on clinical outcomes: A retrospective cohort study. CMAJ. 2015 Mar 3;187(4):E130\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003ePaulam\u0026auml;ki J, Jyrkk\u0026auml; J, Hyttinen V, J\u0026auml;msen E. Prevalence of potentially inappropriate medication use in older population: comparison of the Finnish Meds75+ database with eight published criteria. BMC Geriatr. 2023 Dec 1;23(1).\u003c/li\u003e\n \u003cli\u003eAhern F, Sahm LJ, Lynch D, McCarthy S. Determining the frequency and preventability of adverse drug reaction-related admissions to an Irish University Hospital: A cross-sectional study. Emergency Medicine Journal. 2014 Jan;31(1):24\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eGoldberg RM, Mabee, Pa-C J, Chan L, Wong S. Drug-Drug and Drug-Disease Interactions in the ED: Analysis of a High-Risk Population. Am J Emerg Med. 1996 Sep;14(5):447\u0026ndash;50.\u003c/li\u003e\n \u003cli\u003eCarpenter CR, Shelton E, Fowler S, Suffoletto B, Platts-Mills TF, Rothman RE, et al. Risk factors and screening instruments to predict adverse outcomes for undifferentiated older emergency department patients: A systematic review and meta-analysis. Academic Emergency Medicine. 2015 Jan 1;22(1):1\u0026ndash;21.\u003c/li\u003e\n \u003cli\u003eHohl CM, Dankoff J, Colacone A, Afilalo M. Polypharmacy, adverse drug-related events, and potential adverse drug interactions in elderly patients presenting to an emergency department. Ann Emerg Med. 2001;38(6):666\u0026ndash;71. \u003c/li\u003e\n \u003cli\u003evan Dam CS, Labuschagne HA, van Keulen K, Kramers C, Kleipool EE, Hoogendijk EO, et al. Polypharmacy, comorbidity and frailty: a complex interplay in older patients at the emergency department. Eur Geriatr Med. 2022 Aug 1;13(4):849\u0026ndash;57.\u003c/li\u003e\n \u003cli\u003eHellinger BJ, Gries A, Schiek S, Remane Y, Bertsche T. A prospective intervention study to identify drug-related emergency department visits comparing a standard care group and a pharmaceutical care group. European Journal of Emergency Medicine. 2024 Feb 1;31(1):9\u0026ndash;17.\u003c/li\u003e\n \u003cli\u003eSaario EL, M\u0026auml;kinen MT, J\u0026auml;msen ERK, Nikander P, Castr\u0026eacute;n MK. Screening of community-dwelling older patients by the emergency medical services: An observational retrospective registry study. Int Emerg Nurs. 2021 Nov 1;59. \u003c/li\u003e\n \u003cli\u003eJyrkk\u0026auml; J, Paulam\u0026auml;ki J, Hartikainen S, Ahonen J, Antikainen R, Jauhonen H, et al. Prescribing appropriate medicines to older adults: A Finnish experience with the web-based Meds75+ database. . Drugs Aging, in press.\u003c/li\u003e\n \u003cli\u003eCharlson ME, Pompei P, Ales KL, Mackenzie CR. A New Method of Classifying Prognostic Comorbidity in Longitudinal Studies: Development and Validation. J Chronic Dis. 1987;40(5):373\u0026ndash;83.\u003c/li\u003e\n \u003cli\u003eBanerjee A, Mbamalu D, Ebrahimi S, Khan AA, Chan TF. The prevalence of polypharmacy in elderly attenders to an emergency department - a problem with a need for an effective solution. Int J Emerg Med. 2011 Oct 9;4(22).\u003c/li\u003e\n \u003cli\u003eDelara M, Murray L, Jafari B, Bahji A, Goodarzi Z, Kirkham J, et al. Prevalence and factors associated with polypharmacy: a systematic review and Meta-analysis. BMC Geriatr. 2022 Dec 1;22(1).\u003c/li\u003e\n \u003cli\u003eTian F, Chen Z, Zeng Y, Feng Q, Chen X. Prevalence of Use of Potentially Inappropriate Medications among Older Adults Worldwide: A Systematic Review and Meta-Analysis. Vol. 6, JAMA Network Open. American Medical Association; 2023. p. E2326910.\u003c/li\u003e\n \u003cli\u003eRankin A, Cadogan CA, Patterson SM, Kerse N, Cardwell CR, Bradley MC, et al. Interventions to improve the appropriate use of polypharmacy for older people. Cochrane Database of Systematic Reviews. 2018 Sep 3;9(9).\u003c/li\u003e\n \u003cli\u003eMcCusker J, Bellavance F, Cardin S, Tr\u0026eacute;panier S, Verdon J, Ardman O. Detection of older people at increased risk of adverse health outcomes after an emergency visit: The ISAR screening tool. J Am Geriatr Soc. 1999;47(10):1229\u0026ndash;37.\u003c/li\u003e\n \u003cli\u003eKoehl JL. Adverse Drug Event Prevention and Detection in Older Emergency Department Patients. Clin Geriatr Med. 2023 Nov 1;39(4):635\u0026ndash;45.\u003c/li\u003e\n \u003cli\u003eSantolaya-Perr\u0026iacute;n R, Calder\u0026oacute;n-Hernanz B, Jim\u0026eacute;nez-D\u0026iacute;az G, Gal\u0026aacute;n-Ramos N, Moreno-Carvajal MT, Rodr\u0026iacute;guez-Camacho JM, et al. The efficacy of a medication review programme conducted in an emergency department. Int J Clin Pharm. 2019 Jun 15;41(3):757\u0026ndash;66.\u003c/li\u003e\n \u003cli\u003eEuropean Geriatric Medicine Society and European Society of Emergency Medicine. Medication reviews in the ED for older adults.\u003c/li\u003e\n \u003cli\u003eFrenkel WJ, Jongerius EJ, Mandjes-Van Uitert MJ, Van Munster BC, De Rooij SE. Validation of the Charlson Comorbidity Index in acutely hospitalized elderly adults: A prospective cohort study. J Am Geriatr Soc. 2014 Feb;62(2):342\u0026ndash;6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Older, Emergency Department, Emergency Medicine, Medication, Polypharmacy, Potentially Inappropriate Medication, Drug-Related ED visit, Outcome, Medication review","lastPublishedDoi":"10.21203/rs.3.rs-4930828/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4930828/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eDrug-related emergency department (ED) visits are often encountered in the ED but remain unidentified, especially among older adults. Although medication use should be screened in the ED, little is known about their effect on short-term adverse ED outcomes. Therefore, we aimed to determine the association between polypharmacy and potentially inappropriate medication (PIM) use and short-term adverse outcomes in older ED patients.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eWe retrospectively determined prescribed medications of 392 non-urgently transported community-dwelling patients aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years. We measured polypharmacy and PIM use with dichotomous and ordinal variables. Comorbidities were assessed with Charlson Comorbidity Index (CCI). Primary outcomes were 90-day mortality, hospital admissions and 90-day ED revisits. Statistically, we used adjusted logistic regression analysis.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003e80% of the patients had polypharmacy (\u0026ge;\u0026thinsp;5 regular medications) and 30% had excessive polypharmacy (\u0026ge;\u0026thinsp;10 regular medications). Polypharmacy did not predict higher risk of any study outcomes but was associated with a lower risk of 90-day mortality [adjusted OR 0.17 (95% CI 0.06\u0026ndash;0.45), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. Excessive polypharmacy predicted a higher risk of 90-day ED revisits [adjusted OR 1.35 (95% CI 1.12\u0026ndash;4.93), p\u0026thinsp;=\u0026thinsp;0.024]. An increasing number of regular medications was associated with a higher risk of 90-day ED revisits [OR 1.09 (95% CI 1.03\u0026ndash;1.16), p\u0026thinsp;=\u0026thinsp;0.014] and a lower risk of 90-day mortality [OR 0.83 (95% CI 0.72\u0026ndash;0.94, p\u0026thinsp;=\u0026thinsp;0.005]. PIM use did not increase risks for any study outcomes. Increasing CCI predicted higher 90-day mortality rates [OR 1.70 (95% CI 1.37\u0026ndash;2.10), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001].\u003c/p\u003e\u003ch2\u003eConclusions.\u003c/h2\u003e \u003cp\u003ePolypharmacy, defined as use of five or more medications is common among older ED patients but does not increase the risk of short-term adverse outcomes. Rising number of regular medications and excessive polypharmacy increases the risk for 90-day ED revisits. Instead of assessing polypharmacy with currently used numerical thresholds, EDs should screen excessive polypharmacy or use novel numerical thresholds to screen high-risk patients.\u003c/p\u003e","manuscriptTitle":"Short-Term Adverse Outcomes Related to Medication Use in Older Adults Visiting Emergency Department – a Retrospective Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 10:30:51","doi":"10.21203/rs.3.rs-4930828/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"63fa5424-d723-4cf4-ae45-c4077a4b28e3","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T18:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-14 10:30:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4930828","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4930828","identity":"rs-4930828","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}