Validating the DIVERT Scales, CARS, and EARLI for Predicting Emergency Department Visits in Home Health Care in Japan: a retrospective cohort study

Validating the DIVERT Scales, CARS, and EARLI for Predicting Emergency Department Visits in Home Health Care in Japan: a retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validating the DIVERT Scales, CARS, and EARLI for Predicting Emergency Department Visits in Home Health Care in Japan: a retrospective cohort study Takao Ono, Hiroko Watase, Takuma Ishihara, Taketo Watase, Kiho Kang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4206648/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The Detection of Indicators and Vulnerabilities for Emergency Room Trips (DIVERT） scale, the Community Assessment Risk Screen (CARS), and the Emergency Admission Risk Likelihood Index (EARLI) are scales that assess the risk of emergency department (ED) visits among home health care patients. This study validated these scales and explored factors that could improve their predictive accuracy among Japanese home health care patients. Methods This was a single-center retrospective cohort study. The primary outcome of unplanned ED visits was used to assess the validity of the DIVERT scale, CARS, and EARLI. Additionally, we examined whether the addition of patient age and receipt of advanced care planning as variables on these assessments could enhance their precision. Results Of the 224 eligible patients, 40 (17.8%) had at least one ED visit during the 6-month study period. In these patients, the DIVERT scale was superior compared with CARS and EARLI (both p < 0.05). The area under the curve (AUC) of the DIVERT scale, CARS, and EARLI were 0.62, 0.59, and 0.60, respectively. Adding patient age and receipt of advance care planning improved the AUC in all three scales. Conclusions Our findings suggest that these assessment scales could be applicable to home health care patients in Japan. Furthermore, adding age and receipt of advanced care planning as variables was found to enhance the predictive accuracy of the scales. CARS DIVERT scale EARLI emergency department visits home health care Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Japan has an aging population,( 1 ) and between 14.8–47.4% of the population wish to spend the last phase of their life at home.( 2 ) With the rising proportion of older people, the cost of health care has also increased, creating substantial economic burdens on the health care system. Home health care has been shown to be less expensive than hospital admission,( 3 ) leading to an increase in both the number of patients who receive home health care and the number of home health care physicians. These figures are expected to continue rising until the late 2030s.( 4 ) Most home health care patients are older adults who can require emergency transport to emergency departments (ED).( 5 ) Emergency ED visits and unexpected hospital admissions often disrupt their ability to live comfortably at home.( 6 ) Therefore, it is critical to ensure cooperation from the local community, including home health care services and the ED, to ensure their medical needs are met.( 7 ) To predict ED visits by home health care patients, different risk assessment scales have been developed: the Detection of Indicators and Vulnerabilities for Emergency Room Trips (DIVERT) scale, the Community Assessment Risk Screen (CARS), and the Emergency Admission Risk Likelihood Index (EARLI).( 8 – 10 ) The DIVERT scale was derived from the population-level Resident Assessment Instrument Home Care (RAI-HC) records,( 11 ) whereas CARS and EARLI were derived from patient questionnaires,( 9 , 10 ) Their validity has been evaluated in six European countries( 12 ) but not in Japan. To address this gap in the literature, we assessed the validity and predictive accuracy of the DIVERT scale, the CARS, and the EARLI among Japanese patients. We also investigated the extent to which the addition of age( 13 ) and receipt of advanced care planning (ACP) to the scales could improve their predictive accuracy. Both of these factors have previously been found to be associated with ED visits.( 13 – 15 ) We evaluated the impact of these factors on the area under the curve (AUC) for the development of a Japan-specific risk scale for ED visits. METHODS Study design and settings This single-center retrospective cohort study was approved by the Institutional Review Board of Fujita Health University Hospital and Midori Homon Clinic (approval numbers, HM22-368). To evaluate the risk of ED visits in home health care patients, including emergency transport and walk-in visits, we selected a home health care clinic in Nagoya City in the Aichi prefecture (the fourth biggest prefecture in Japan, with a population of 7.553 million) that receives an average of 250 patients annually. The study period was from January 1 to June 30, 2022. All study participants provided written informed consent and the study was conducted in accordance with the tenets of the 2013 revision of the Declaration of Helsinki. Participants All patients who lived in their own homes and received home health care from this clinic for over 2 months during the study period were included. Those who were hospitalized or lived in nursing homes on January 1, 2022, were excluded from the study. Data collection The codes used in insurance claim processing were used to identify age, duration since the first home health care visit, and significant diseases and comorbidities.( 16 ) Factors that we believed clinically important, including age, socioeconomic status, total number of medications, duration since the first home health care visit, presence of a caregiver, and receipt of ACP were confirmed with patients and their caregivers. Detailed information on variables and data sources is provided in Supplemental Table 1. All information was recorded using REDCap electronic data capture tools at Fujita Health University and anonymized to protect patient privacy.( 17 ) Outcome measurement The outcome measure of interest was ED visits in the 6 months between January 1 and June 30, 2022. These included both ambulance and walk-in arrivals. Statistical analysis The patients’ characteristics were described using the mean ± standard deviation (SD) for continuous variables and number and percentage (%) for categorical variables. A two-sided p -value of < 0.05 was considered statistically significant. All analyses were performed using R statistical software v. 4.2.0 (R Foundation for Statistical Computing). RESULTS A total of 228 eligible patients were recruited by January 1, 2022, four of whom were excluded because they did not consent to participation. Of the remaining 224 patients, 40 (17.8%) had made at least one ED visit during the study period. The mean age of the patients with1 ED visit (n = 40) was 83.1 ± 12.3 years and that of the patients with no ED visits (n = 184) was 78.4 ± 16.6 years. There was no significant difference between the ages of the two groups ( p = 0.09). Approximately 45.0% of the patients with1 ED visit and 54.3% of the patients with no ED visits were male ( p > 0.99). The DIVERT scale scores of the patients with1 ED and no ED visits were 2.5 ± 1.4 and 1.9 ± 1.0 ( p = 0.001), respectively. The CARS scores of the two groups were 4.5 ± 2.8 and 3.6 ± 2.3, respectively ( p = 0.05); and the EARLI scores were 13.2 ± 5.2 and 11.0 ± 5.0 ( p = 0.02), respectively. The clinical characteristics of the 224 patients are shown in Table 1. Univariate analyses found that the patients with 1 ED visit were more likely to be in receipt of ACP (odds ratio [OR], 2.35; 95% confidence interval [CI], 1.17–4.70; p = 0.02), to have lost weight (OR, 3.16; 95% CI, 1.40–7.09; p = 0.005), and to use a urinary catheter (OR, 3.33; 95% CI, 1.47–7.51; p = 0.004) (Table 2). The AUC of the DIVERT scale for predicting ED visits was 0.62 (95% CI: 0.52–0.72), that of the CARS was 0.59 (95% CI: 0.48–0.69), and that of the EARLI was 0.60 (95% CI: 0.50–0.70) (Fig. 1). After the receipt of ACP and age were incorporated into each representative risk score in this study, the AUC of the DIVERT scale for predicting ED visits was 0.69 (95% CI: 0.60–0.78), and those of the CARS and the EARLI improved to 0.66 (95% CI: 0.57–0.76) and 0.68 (95% CI: 0.60–0.77), respectively (Fig. 2). We integrated ACP receipt and age into the DIVERT scale and constructed a nomogram for predicting ED visits over a 6-month period. The modified DIVERT scale assessment was used to estimate the probability of ED visits as shown in Figure 3. DISCUSSION To the best of our knowledge, this study is the first to validate the risk assessment scales for predicting ED visits among home health care patients in Japan. Among our sample of 224 patients, the AUC was 0.62, 0.59, and 0.60 for the DIVERT scale, the CARS, and the EARLI, respectively. The AUC for the DIVERT scale was superior to those of the CARS and the EARLI, and all AUCs were improved after receipt of ACP and age were integrated. While these scales have previously been validated in other countries, health care systems differ significantly between countries, and the results of these other measure validations might not be applicable in Japan. ED visits affect the quality of life of home health care patients and their families. With developments and increased use of home health care in Japan, it has become critical to predict ED visits using risk assessment measures such as the DIVERT scale, the CARS, and the EARLI. Previous studies( 8 – 10 , 12 ) that validated these scales in other countries calculated the receiver operating characteristics (ROC) curve. For the EARLI, all hospital admissions over a year were included. For the CARS, hospital admissions, or ED visits over a year were included. Based on previous research, we hypothesized that receipt of ACP ( 14 , 15 ) and age( 13 ) could improve the prediction of ED visits. Therefore, these factors were integrated into the risk scales. The majority of ED visits are made by older individuals,( 5 ) and the likelihood of ED visits increases with age( 13 ) Thus, this factor was thought likely to improve the accuracy of the scales. Receipt of ACP may avoid unnecessary ED visits.( 14 , 15 ) However, previous studies have found that the risk of emergency transport does not change with ACP receipt.( 18 , 19 ) We found higher rates of ACP receipt among patients who visited the ED than those who did not. This highlights the importance of ACP in the last stage of life.( 20 , 21 ) Home health care physicians discuss ACP more frequently with patients with more severe health issues. Thus, our finding that those in receipt of ACP were more likely to visit the ED is likely due to poorer health among these patients. This study validated the DIVERT scale, the CARS, and the EARLI in Japan. Adding age and receipt of ACP as variables on the scales improved their prediction of ED visits. This offers clinicians an improved ability to stratify the level of likelihood of ED visits for each patient and to provide preventive care for those at high risk. While a uniform preventive measure for all patients might be impossible, the findings of this study offer health policymakers increased opportunities to implement risk-based preventive measures. Lastly, it is hoped that this study will provide a springboard for further multicenter studies offering more robust scale validation. Our study had some potential limitations. First, there was the possibility of selection bias due to the focus on a single center. However, the majority of the patients were older, with a wide range of conditions. Therefore, we believe our findings to be broadly representative of Japanese home health care patients. Second, given the variability in the health care and emergency medical systems between countries, as well as differences in the practices of individual clinics, these results may not be directly comparable with those from other countries. CONCLUSIONS This study validated the risk assessment scales used to predict ED visits by home health care patients in Japan. The inclusion of receipt of ACP and age as variables improved the predictive accuracy of the scales, suggesting that modifying existing risk scales may improve the ability of home health care professionals to identify patients at higher risk of ED visits. Declarations Ethics approval and consent to participate: All study participants provided written informed consent and the study was conducted in accordance with the tenets of the 2013 revision of the Declaration of Helsinki Consent for publication: Not Applicable Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: None Funding: This study was funded by the Yuumi Memorial Foundation. Authors’ Contributions: Takao Ono: Study concept and design, analysis and interpretation of the data, and drafting of the manuscript. Hiroko Watase: Study concept and design, and critical revision of the manuscript for important intellectual content. Takuma Ishihara: Analysis and interpretation of the data, and critical revision of the manuscript for important intellectual content. Taketo Watase: Critical revision of the manuscript for important intellectual content. Kang Kiho: Acquisition of the data, and critical revision of the manuscript for important intellectual content. Mitsunaga Iwata: Critical revision of the manuscript for important intellectual content. Acknowledgements: We thank Yamasita Michinori, Miho Aiba, Tadashi Kumai and Saki Kawamura at Midori Homon Clinic for their invaluable assistance in the data collection process. References Nakatani H. Population aging in Japan: policy transformation, sustainable development goals, universal health coverage, and social determinates of health. Glob Heal Med. 2019;1(1):3–10. Japan Ministry of Health L and W, Report. Survey on attitudes toward End-of-life care. 2018;1–123. https://www.mhlw.go.jp/toukei/list/dl/saisyuiryo_a_h29.pdf . Conley J, O’Brien CW, Leff BA, Bolen S, Zulman D. Alternative strategies to inpatient hospitalization for acute medical conditions: A systematic review. JAMA Intern Med. 2016;176(11):1693–702. Iwata H, Matsushima M, Watanabe T, Sugiyama Y, Yokobayashi K, Son D, et al. The need for home care physicians in Japan – 2020 to 2060. BMC Health Serv Res. 2020;20(1):1–11. Šteinmiller J, Routasalo P, Suominen T. Older people in the emergency department: A literature review. Int J Older People Nurs. 2015;10(4):284–305. Hastings SN, Schmader KE, Sloane RJ, Weinberger M, Goldberg KC, Oddone EZ. Adverse health outcomes after discharge from the emergency department - Incidence and risk factors in a veteran population. J Gen Intern Med. 2007;22(11):1527–31. Kellermann AL, Martinez R, The ER. 50 Years On. N Engl J Med. 2011;364(24):2278–9. Costa AP, Hirdes JP, Bell CM, Bronskill SE, Heckman GA, Mitchell L, et al. Derivation and validation of the detection of indicators and vulnerabilities for emergency room trips scale for classifying the risk of emergency department use in frail community-dwelling older adults. J Am Geriatr Soc. 2015;63(4):763–9. Shelton P, Sager MA, Schraeder C. The Community Assessment Risk Screen (CARS): Identifying elderly persons at risk for hospitalization or emergency department visit. Am J Manag Care. 2000;6(8):925–33. Lyon D, Lancaster GA, Taylor S, Dowrick C, Chellaswamy H. Predicting the likelihood of emergency admission to hospital of older people: Development and validation of the Emergency Admission Risk Likelihood Index (EARLI). Fam Pract. 2007;24(2):158–67. E FL, C TGOBCAS. Minimum data set for home care: a valid instrument to assess frail older people living in the community. Med Care. 2000;38(12):1184–90. Klunder JH, Bordonis V, Heymans MW, van der Roest HG, Declercq A, Smit JH et al. Predicting unplanned hospital visits in older home care recipients: a cross-country external validation study. BMC Geriatr. 2021;21(1):1–9. https://doi.org/10.1186/s12877-021-02521-2 . Albert M, McCaig LF, Ashman JJ. Emergency department visits by persons aged 65 and over: United States, 2009–2010. NCHS Data Brief. 2013;(130):1–8. Sakamoto A, Inokuchi R, Iwagami M, Sun Y, Tamiya N. Association between advanced care planning and emergency department visits: A systematic review. Am J Emerg Med. 2023;68:84–91. https://doi.org/10.1016/j.ajem.2023.03.004 . Inoue Y, Nishi K, Mayumi T, Sasaki J. Factors in Avoidable Emergency Visits for Ambulatory Care-sensitive Conditions among Older Patients Receiving Home Care in Japan: A Retrospective Study. Intern Med. 2022;61(2):177–83. Kimura S, Sato T, Ikeda S, Noda M, Nakayama T. Development of a database of health insurance claims: Standardization of disease classifications and anonymous record linkage. J Epidemiol. 2010;20(5):413–9. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42(2):377–81. Sinclair C, Auret KA, Evans SF, Jane F, Dormer S, Wilkinson A et al. Impact of a Nurse-Led Advance Care Planning Intervention on Satisfaction, Health-Related Quality of Life, and Health Care Utilization Among Patients With Severe Respiratory Disease: A Randomized Patient-Preference Trial. J Pain Symptom Manage. 2020;59(4):848–55. https://doi.org/10.1016/j.jpainsymman.2019.11.018 . Stephens CE, Newcomer R, Blegen M, Miller B, Harrington C. Emergency Department use by nursing home residents: Effect of severity of cognitive impairment. Gerontologist. 2012;52(3):383–93. Houben CHM, Spruit MA, Groenen MTJ, Wouters EFM, Janssen DJA. Efficacy of advance care planning: A systematic review and meta-analysis. J Am Med Dir Assoc. 2014;15(7):477–89. http://dx.doi.org/10.1016/j.jamda.2014.01.008 . Brinkman-Stoppelenburg A, Rietjens JAC, Van Der Heide A. The effects of advance care planning on end-of-life care: A systematic review. Palliat Med. 2014;28(8):1000–25. Tables Table 1. Patient Characteristics in Home Care Patients With and Without Recent ED Visits Mean ± SD or n (%) All (N = 224) ≧1 ED visits (n = 40) No ED visits (n = 184) Age, years 79.2 ± 16.0 83.1 ± 12.3 78.3 ± 16.6 Sex, male 122 (54.5) 18 (45.0） 84 (45.7） DIVERT scale 2.0 ± 1.1 2.5 ± 1.4 1.9 ± 1.0 CARS 3.8 ± 2.4 4.5 ± 2.8 3.6 ± 2.3 EARLI 11.4 ± 5.1 13.2 ± 5.2 11.0 ± 5.0 Duration since the first visit, months 30.0 ± 26.8 25.9 ± 25.6 31.0 ± 27.1 Number of medications 7.1 ± 4.2 6.9 ± 3.9 7.1 ± 4.2 Prior ED visits within 6 months 22 (9.8) 9 (22.5) 13 (7.0) In receipt of ACP 85 (37.9) 22 (55.0） 63 (34.2） Presence of the caregiver 190 (84.8) 36 (90.0） 154 (83.7） Weight loss 34 (15.2) 12 (30.0) 22 (12.0) Use of a urinary catheter 33 (14.7) 12 (30.0) 21 (11.4) Medical history Urinary tract infection 11 (4.9) 4 (10.0） 7 (3.8） Heart failure 119 (53.1) 26 (65.0） 93 (50.5） COPD 12 (5.4) 2 (5.0） 10 (5.4） Renal failure 18 (8.0) 4 (10.0） 14 (7.6） Pneumonia 9 (4.0) 2 (5.0） 7 (3.8） Stroke 70 (31.2) 9 (22.5） 61 (33.2） Diabetes 70 (31.2) 12 (30.0) 58 (31.5) Coronary artery disease 9 (4.0) 3 (7.5) 6 (3.3) Abbreviations: ACP, advanced care planning; COPD, chronic obstructive pulmonary disease; ED, emergency department; SD, standard deviation; Table 2. Relationships between Patient Variables and ED Visits in Home Care Patients Variables ≧1 (vs no) ED visits Odds Ratio (95% CI) p -value Age, years 1.02 (1.00, 1.05) 0.09 Sex, male 1.03 (0.52, 2.04) 0.94 DIVERT scale 1.57 (1.16, 2.11) 0.003 CARS score 1.16 (1.00, 1.34) 0.047 EARLI score 1.09 (1.02, 1.17) 0.02 Duration since the first visit, months 1.00 (1.00, 1.00) 0.28 Number of medications 0.99 (0.91, 1.07) 0.73 Prior ED visits within 6 months 3.14 (1.44, 6.84) 0.004 Receipt of ACP 2.35 (1.17, 4.70) 0.02 Presence of the caregiver 1.75 (0.58, 5.29) 0.32 Weight loss 3.16 (1.40, 7.09) 0.005 Use of a urinary catheter 3.33 (1.47, 7.51) 0.004 Medical history Urinary tract infection 2.81 (0.78, 10.1) 0.11 Heart failure 1.82 (0.89, 3.70) 0.10 COPD 0.92 (0.19, 4.35) 0.91 Renal failure 1.35 (0.42, 4.34) 0.62 Pneumonia 1.33 (0.27, 6.66) 0.73 Stroke 0.59 (0.26, 1.31) 0.19 Diabetes 0.93 (0.44, 1.96) 0.85 Coronary artery disease 2.41 (0.58, 10.06) 0.23 Abbreviations: ACP, advanced care planning; CI, confidential interval; COPD, chronic obstructive pulmonary disease; ED, emergency department; Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4206648","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288314473,"identity":"5a0b66b0-f12a-4b18-b2d9-6c74c99586da","order_by":0,"name":"Takao Ono","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYFACNjCZwMDAfIAhgQ3E52FgeIBHAw9CC1sCQksCWIqgFh4DqJUEtNizH0v88IHBJs+cvefrhgdlNvl8DLwHPyQw3JGzx2ULT9phyRkMacWWPWe33Ug4l2bZxsCXLJHA8MwYt8PSG6R5GA4nbriRu+1GYtthAzb5N2ZAhx1O7MGlhf9582+IlpxnEC0MPGAt9Ti1SKQdg9qSw4aiJQGnw248S7OcYZCWuOHMMTOQX0BajCUSDA4b9hzAroW9P834xocKm8QNx5uf3fxRZmMg38Bj+OFDxWF59gYc1oCBAREio2AUjIJRMApIAAA1PVYZ7sUWBgAAAABJRU5ErkJggg==","orcid":"","institution":"Fujita Health University","correspondingAuthor":true,"prefix":"","firstName":"Takao","middleName":"","lastName":"Ono","suffix":""},{"id":288314474,"identity":"a446891f-9135-4994-a53a-f5da362ae44d","order_by":1,"name":"Hiroko Watase","email":"","orcid":"","institution":"Fujita Health University","correspondingAuthor":false,"prefix":"","firstName":"Hiroko","middleName":"","lastName":"Watase","suffix":""},{"id":288314475,"identity":"c62b4fa7-7e7c-49ba-aee6-fb259414b523","order_by":2,"name":"Takuma Ishihara","email":"","orcid":"","institution":"Gifu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Takuma","middleName":"","lastName":"Ishihara","suffix":""},{"id":288314476,"identity":"c0dfe9c0-f01e-40a0-80fe-4ca715d16007","order_by":3,"name":"Taketo Watase","email":"","orcid":"","institution":"Fujita Health University","correspondingAuthor":false,"prefix":"","firstName":"Taketo","middleName":"","lastName":"Watase","suffix":""},{"id":288314477,"identity":"6ea1dede-d041-4e1e-88d3-6a63e244da6b","order_by":4,"name":"Kiho Kang","email":"","orcid":"","institution":"Midori Homon Clinic","correspondingAuthor":false,"prefix":"","firstName":"Kiho","middleName":"","lastName":"Kang","suffix":""},{"id":288314478,"identity":"726088e3-e430-44e5-8d12-bcbee7074615","order_by":5,"name":"Mitsunaga Iwata","email":"","orcid":"","institution":"Fujita Health University","correspondingAuthor":false,"prefix":"","firstName":"Mitsunaga","middleName":"","lastName":"Iwata","suffix":""}],"badges":[],"createdAt":"2024-04-02 12:21:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4206648/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4206648/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54371116,"identity":"30bd2561-a457-4d6f-af24-a8005d85bd39","added_by":"auto","created_at":"2024-04-09 13:13:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33010,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve analyses for each assessment scale: (a) the DIVERT scale, (b) the CARS, and (c) the EARLI.\u003c/p\u003e\n\u003cp\u003eThese ROC curves assessed the predictive performance of each assessment scale.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4206648/v1/13e9ccce1ed08a84e635aef6.png"},{"id":54371118,"identity":"3112586d-26f7-44b4-8e39-0198bf642d71","added_by":"auto","created_at":"2024-04-09 13:13:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29676,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve analyses for each modified assessment scale: (a) the DIVERT scale, (b) the CARS, and (c) the EARLI.\u003c/p\u003e\n\u003cp\u003eThese ROC curves assessed the predictive performance of each modified assessment scale.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4206648/v1/27afb159ca9592c932cf9117.png"},{"id":54371117,"identity":"a1707142-102d-4386-a187-6f05799ebbe3","added_by":"auto","created_at":"2024-04-09 13:13:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5548,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for the modified DIVERT assessment scale for the prediction of emergency department visits by home health care patients in Japan\u003c/p\u003e\n\u003cp\u003ePoints were assigned for the DIVERT scale, receipt of ACP, and age by drawing a line upward from each variable to the point. The sum of these three points became the total points. From that point, a vertical line was drawn downward, from which the linear predictor or predicted value was calculated.\u003c/p\u003e\n\u003cp\u003eACP, advanced care planning\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4206648/v1/9572642af89f80719433c533.png"},{"id":54435225,"identity":"91fe3b03-b7ab-4568-9f07-0c79d8ce726b","added_by":"auto","created_at":"2024-04-10 12:58:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":352690,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4206648/v1/6fa06087-c2fc-4eda-bb89-ead119df3714.pdf"},{"id":54371115,"identity":"447fba06-d069-42b3-9fd7-5da6955aa74e","added_by":"auto","created_at":"2024-04-09 13:13:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15549,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4206648/v1/4593f9dcb055f6db1313cf42.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validating the DIVERT Scales, CARS, and EARLI for Predicting Emergency Department Visits in Home Health Care in Japan: a retrospective cohort study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eJapan has an aging population,(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and between 14.8\u0026ndash;47.4% of the population wish to spend the last phase of their life at home.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) With the rising proportion of older people, the cost of health care has also increased, creating substantial economic burdens on the health care system. Home health care has been shown to be less expensive than hospital admission,(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) leading to an increase in both the number of patients who receive home health care and the number of home health care physicians. These figures are expected to continue rising until the late 2030s.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eMost home health care patients are older adults who can require emergency transport to emergency departments (ED).(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Emergency ED visits and unexpected hospital admissions often disrupt their ability to live comfortably at home.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Therefore, it is critical to ensure cooperation from the local community, including home health care services and the ED, to ensure their medical needs are met.(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eTo predict ED visits by home health care patients, different risk assessment scales have been developed: the Detection of Indicators and Vulnerabilities for Emergency Room Trips (DIVERT) scale, the Community Assessment Risk Screen (CARS), and the Emergency Admission Risk Likelihood Index (EARLI).(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) The DIVERT scale was derived from the population-level Resident Assessment Instrument Home Care (RAI-HC) records,(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) whereas CARS and EARLI were derived from patient questionnaires,(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Their validity has been evaluated in six European countries(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) but not in Japan.\u003c/p\u003e \u003cp\u003eTo address this gap in the literature, we assessed the validity and predictive accuracy of the DIVERT scale, the CARS, and the EARLI among Japanese patients. We also investigated the extent to which the addition of age(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and receipt of advanced care planning (ACP) to the scales could improve their predictive accuracy. Both of these factors have previously been found to be associated with ED visits.(\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) We evaluated the impact of these factors on the area under the curve (AUC) for the development of a Japan-specific risk scale for ED visits.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and settings\u003c/h2\u003e \u003cp\u003e This single-center retrospective cohort study was approved by the Institutional Review Board of Fujita Health University Hospital and Midori Homon Clinic (approval numbers, HM22-368). To evaluate the risk of ED visits in home health care patients, including emergency transport and walk-in visits, we selected a home health care clinic in Nagoya City in the Aichi prefecture (the fourth biggest prefecture in Japan, with a population of 7.553\u0026nbsp;million) that receives an average of 250 patients annually. The study period was from January 1 to June 30, 2022. All study participants provided written informed consent and the study was conducted in accordance with the tenets of the 2013 revision of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eAll patients who lived in their own homes and received home health care from this clinic for over 2 months during the study period were included. Those who were hospitalized or lived in nursing homes on January 1, 2022, were excluded from the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe codes used in insurance claim processing were used to identify age, duration since the first home health care visit, and significant diseases and comorbidities.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) Factors that we believed clinically important, including age, socioeconomic status, total number of medications, duration since the first home health care visit, presence of a caregiver, and receipt of ACP were confirmed with patients and their caregivers. Detailed information on variables and data sources is provided in Supplemental Table\u0026nbsp;1. All information was recorded using REDCap electronic data capture tools at Fujita Health University and anonymized to protect patient privacy.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome measurement\u003c/h2\u003e \u003cp\u003eThe outcome measure of interest was ED visits in the 6 months between January 1 and June 30, 2022. These included both ambulance and walk-in arrivals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe patients\u0026rsquo; characteristics were described using the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variables and number and percentage (%) for categorical variables. A two-sided \u003cem\u003ep\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were performed using R statistical software v. 4.2.0 (R Foundation for Statistical Computing).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 228 eligible patients were recruited by January 1,\u003csup\u003e\u0026nbsp;\u003c/sup\u003e2022, four of whom were excluded because they did not consent to participation. Of the remaining 224 patients, 40 (17.8%) had made at least one ED visit during the study period.\u003c/p\u003e\n\u003cp\u003eThe mean age of the patients with1 ED visit (n = 40) was 83.1 \u0026plusmn; 12.3 years and that of the patients with no ED visits (n = 184) was 78.4 \u0026plusmn; 16.6 years. There was no significant difference between the ages of the two groups (\u003cem\u003ep\u003c/em\u003e = 0.09). Approximately 45.0% of the patients with1 ED visit and 54.3% of the patients with no ED visits were male (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.99). The DIVERT scale scores of the patients with1 ED and no ED visits were 2.5 \u0026plusmn; 1.4 and 1.9 \u0026plusmn; 1.0 (\u003cem\u003ep\u003c/em\u003e = 0.001), respectively. The CARS scores of the two groups were 4.5 \u0026plusmn; 2.8 and 3.6 \u0026plusmn; 2.3, respectively (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.05); and the EARLI scores were 13.2 \u0026plusmn; 5.2 and 11.0 \u0026plusmn; 5.0 (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.02), respectively. The clinical characteristics of the 224 patients are shown in Table 1.\u003c/p\u003e\n\u003cp\u003eUnivariate analyses found that the patients with\u0026nbsp;1 ED visit were more likely to be in receipt of ACP (odds ratio [OR], 2.35; 95% confidence interval [CI], 1.17\u0026ndash;4.70;\u0026nbsp;\u003cem\u003ep\u003c/em\u003e = 0.02), to have lost weight (OR, 3.16; 95% CI, 1.40\u0026ndash;7.09;\u0026nbsp;\u003cem\u003ep\u003c/em\u003e = 0.005), and to use a urinary catheter (OR, 3.33; 95% CI, 1.47\u0026ndash;7.51;\u0026nbsp;\u003cem\u003ep\u003c/em\u003e = 0.004) (Table 2). The AUC of the DIVERT scale for predicting ED visits was 0.62 (95% CI: 0.52\u0026ndash;0.72), that of the CARS was 0.59 (95% CI: 0.48\u0026ndash;0.69), and that of the EARLI was 0.60 (95% CI: 0.50\u0026ndash;0.70) (Fig. 1).\u003c/p\u003e\n\u003cp\u003eAfter the receipt of ACP\u0026nbsp;and age were incorporated into each representative risk score in this study, the AUC of the DIVERT scale for predicting ED visits was 0.69 (95% CI: 0.60\u0026ndash;0.78), and those of the CARS and the EARLI improved to 0.66 (95% CI: 0.57\u0026ndash;0.76) and 0.68 (95% CI: 0.60\u0026ndash;0.77), respectively (Fig. 2).\u003c/p\u003e\n\u003cp\u003eWe integrated ACP receipt and age into the DIVERT scale and constructed a nomogram for predicting ED visits over a 6-month period. The modified DIVERT scale assessment was used to estimate the probability of ED visits as shown in Figure 3.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTo the best of our knowledge, this study is the first to validate the risk assessment scales for predicting ED visits among home health care patients in Japan. Among our sample of 224 patients, the AUC was 0.62, 0.59, and 0.60 for the DIVERT scale, the CARS, and the EARLI, respectively. The AUC for the DIVERT scale was superior to those of the CARS and the EARLI, and all AUCs were improved after receipt of ACP and age were integrated.\u003c/p\u003e \u003cp\u003eWhile these scales have previously been validated in other countries, health care systems differ significantly between countries, and the results of these other measure validations might not be applicable in Japan. ED visits affect the quality of life of home health care patients and their families. With developments and increased use of home health care in Japan, it has become critical to predict ED visits using risk assessment measures such as the DIVERT scale, the CARS, and the EARLI. Previous studies(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) that validated these scales in other countries calculated the receiver operating characteristics (ROC) curve. For the EARLI, all hospital admissions over a year were included. For the CARS, hospital admissions, or ED visits over a year were included.\u003c/p\u003e \u003cp\u003eBased on previous research, we hypothesized that receipt of ACP (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and age(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) could improve the prediction of ED visits. Therefore, these factors were integrated into the risk scales. The majority of ED visits are made by older individuals,(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and the likelihood of ED visits increases with age(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) Thus, this factor was thought likely to improve the accuracy of the scales. Receipt of ACP may avoid unnecessary ED visits.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) However, previous studies have found that the risk of emergency transport does not change with ACP receipt.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) We found higher rates of ACP receipt among patients who visited the ED than those who did not. This highlights the importance of ACP in the last stage of life.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) Home health care physicians discuss ACP more frequently with patients with more severe health issues. Thus, our finding that those in receipt of ACP were more likely to visit the ED is likely due to poorer health among these patients.\u003c/p\u003e \u003cp\u003eThis study validated the DIVERT scale, the CARS, and the EARLI in Japan. Adding age and receipt of ACP as variables on the scales improved their prediction of ED visits. This offers clinicians an improved ability to stratify the level of likelihood of ED visits for each patient and to provide preventive care for those at high risk. While a uniform preventive measure for all patients might be impossible, the findings of this study offer health policymakers increased opportunities to implement risk-based preventive measures. Lastly, it is hoped that this study will provide a springboard for further multicenter studies offering more robust scale validation.\u003c/p\u003e \u003cp\u003eOur study had some potential limitations. First, there was the possibility of selection bias due to the focus on a single center. However, the majority of the patients were older, with a wide range of conditions. Therefore, we believe our findings to be broadly representative of Japanese home health care patients. Second, given the variability in the health care and emergency medical systems between countries, as well as differences in the practices of individual clinics, these results may not be directly comparable with those from other countries.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study validated the risk assessment scales used to predict ED visits by home health care patients in Japan. The inclusion of receipt of ACP and age as variables improved the predictive accuracy of the scales, suggesting that modifying existing risk scales may improve the ability of home health care professionals to identify patients at higher risk of ED visits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll study participants provided written informed consent and the study was conducted in accordance with the tenets of the 2013 revision of the Declaration of Helsinki\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Yuumi Memorial Foundation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTakao Ono:\u0026nbsp;\u003c/strong\u003eStudy concept and design, analysis and interpretation of the data, and drafting of the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHiroko Watase:\u0026nbsp;\u003c/strong\u003eStudy concept and design, and critical revision of the manuscript for important intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTakuma Ishihara:\u003c/strong\u003e Analysis and interpretation of the data, and critical revision of the manuscript for important intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTaketo Watase:\u0026nbsp;\u003c/strong\u003eCritical revision of the manuscript for important intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKang Kiho:\u0026nbsp;\u003c/strong\u003eAcquisition of the data, and critical revision of the manuscript for important intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMitsunaga Iwata:\u0026nbsp;\u003c/strong\u003eCritical revision of the manuscript for important intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Yamasita Michinori, Miho Aiba, Tadashi Kumai and Saki Kawamura at Midori Homon Clinic for their invaluable assistance in the data collection process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNakatani H. Population aging in Japan: policy transformation, sustainable development goals, universal health coverage, and social determinates of health. Glob Heal Med. 2019;1(1):3\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJapan Ministry of Health L and W, Report. Survey on attitudes toward End-of-life care. 2018;1\u0026ndash;123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mhlw.go.jp/toukei/list/dl/saisyuiryo_a_h29.pdf\u003c/span\u003e\u003cspan address=\"https://www.mhlw.go.jp/toukei/list/dl/saisyuiryo_a_h29.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConley J, O\u0026rsquo;Brien CW, Leff BA, Bolen S, Zulman D. Alternative strategies to inpatient hospitalization for acute medical conditions: A systematic review. JAMA Intern Med. 2016;176(11):1693\u0026ndash;702.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIwata H, Matsushima M, Watanabe T, Sugiyama Y, Yokobayashi K, Son D, et al. The need for home care physicians in Japan \u0026ndash;\u0026thinsp;2020 to 2060. BMC Health Serv Res. 2020;20(1):1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠteinmiller J, Routasalo P, Suominen T. Older people in the emergency department: A literature review. Int J Older People Nurs. 2015;10(4):284\u0026ndash;305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHastings SN, Schmader KE, Sloane RJ, Weinberger M, Goldberg KC, Oddone EZ. Adverse health outcomes after discharge from the emergency department - Incidence and risk factors in a veteran population. J Gen Intern Med. 2007;22(11):1527\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKellermann AL, Martinez R, The ER. 50 Years On. N Engl J Med. 2011;364(24):2278\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta AP, Hirdes JP, Bell CM, Bronskill SE, Heckman GA, Mitchell L, et al. Derivation and validation of the detection of indicators and vulnerabilities for emergency room trips scale for classifying the risk of emergency department use in frail community-dwelling older adults. J Am Geriatr Soc. 2015;63(4):763\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShelton P, Sager MA, Schraeder C. The Community Assessment Risk Screen (CARS): Identifying elderly persons at risk for hospitalization or emergency department visit. Am J Manag Care. 2000;6(8):925\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLyon D, Lancaster GA, Taylor S, Dowrick C, Chellaswamy H. Predicting the likelihood of emergency admission to hospital of older people: Development and validation of the Emergency Admission Risk Likelihood Index (EARLI). Fam Pract. 2007;24(2):158\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE FL, C TGOBCAS. Minimum data set for home care: a valid instrument to assess frail older people living in the community. Med Care. 2000;38(12):1184\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlunder JH, Bordonis V, Heymans MW, van der Roest HG, Declercq A, Smit JH et al. Predicting unplanned hospital visits in older home care recipients: a cross-country external validation study. BMC Geriatr. 2021;21(1):1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-021-02521-2\u003c/span\u003e\u003cspan address=\"10.1186/s12877-021-02521-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbert M, McCaig LF, Ashman JJ. Emergency department visits by persons aged 65 and over: United States, 2009\u0026ndash;2010. NCHS Data Brief. 2013;(130):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakamoto A, Inokuchi R, Iwagami M, Sun Y, Tamiya N. Association between advanced care planning and emergency department visits: A systematic review. Am J Emerg Med. 2023;68:84\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajem.2023.03.004\u003c/span\u003e\u003cspan address=\"10.1016/j.ajem.2023.03.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInoue Y, Nishi K, Mayumi T, Sasaki J. Factors in Avoidable Emergency Visits for Ambulatory Care-sensitive Conditions among Older Patients Receiving Home Care in Japan: A Retrospective Study. Intern Med. 2022;61(2):177\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimura S, Sato T, Ikeda S, Noda M, Nakayama T. Development of a database of health insurance claims: Standardization of disease classifications and anonymous record linkage. J Epidemiol. 2010;20(5):413\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42(2):377\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinclair C, Auret KA, Evans SF, Jane F, Dormer S, Wilkinson A et al. Impact of a Nurse-Led Advance Care Planning Intervention on Satisfaction, Health-Related Quality of Life, and Health Care Utilization Among Patients With Severe Respiratory Disease: A Randomized Patient-Preference Trial. J Pain Symptom Manage. 2020;59(4):848\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jpainsymman.2019.11.018\u003c/span\u003e\u003cspan address=\"10.1016/j.jpainsymman.2019.11.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStephens CE, Newcomer R, Blegen M, Miller B, Harrington C. Emergency Department use by nursing home residents: Effect of severity of cognitive impairment. Gerontologist. 2012;52(3):383\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHouben CHM, Spruit MA, Groenen MTJ, Wouters EFM, Janssen DJA. Efficacy of advance care planning: A systematic review and meta-analysis. J Am Med Dir Assoc. 2014;15(7):477\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.jamda.2014.01.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jamda.2014.01.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrinkman-Stoppelenburg A, Rietjens JAC, Van Der Heide A. The effects of advance care planning on end-of-life care: A systematic review. Palliat Med. 2014;28(8):1000\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Patient Characteristics in Home Care Patients With and Without Recent ED Visits\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" \u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47.2936%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 44.0449%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean \u0026plusmn; SD or n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14.7668%;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003cp\u003e(N = 224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e≧1 ED visits\u003c/p\u003e\n \u003cp\u003e(n = 40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16.9618%;\"\u003e\n \u003cp\u003eNo ED visits\u003c/p\u003e\n \u003cp\u003e(n = 184)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e79.2 \u0026plusmn; 16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e83.1 \u0026plusmn; 12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e78.3 \u0026plusmn; 16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eSex, male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e122 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e18 (45.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e84 (45.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eDIVERT scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e2.0 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e1.9 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eCARS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e3.8 \u0026plusmn; 2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e4.5 \u0026plusmn; 2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eEARLI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e11.4 \u0026plusmn; 5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e13.2 \u0026plusmn; 5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e11.0 \u0026plusmn; 5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eDuration since the first visit, months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e30.0 \u0026plusmn; 26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e25.9 \u0026plusmn; 25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e31.0 \u0026plusmn; 27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eNumber of medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e7.1 \u0026plusmn; 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e6.9 \u0026plusmn; 3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e7.1 \u0026plusmn; 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003ePrior ED visits within 6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e22 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e9 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e13 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eIn receipt of ACP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e85 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e22 (55.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e63 (34.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003ePresence of the caregiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e190 (84.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e36 (90.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e154 (83.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eWeight loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e34 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e12 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e22 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eUse of a urinary catheter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e33 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e12 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e21 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eMedical history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eUrinary tract infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e11 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e4 (10.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e7 (3.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e119 (53.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e26 (65.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e93 (50.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e12 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e2 (5.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e10 (5.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eRenal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e18 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e4 (10.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e14 (7.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e9 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e2 (5.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e7 (3.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e70 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e9 (22.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e61 (33.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e70 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e12 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e58 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47.2936%;\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7668%;\"\u003e\n \u003cp\u003e9 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7623%;\"\u003e\n \u003cp\u003e3 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9618%;\"\u003e\n \u003cp\u003e6 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: ACP,\u0026nbsp;advanced care planning; COPD,\u0026nbsp;chronic obstructive pulmonary disease; ED, emergency department; SD, standard deviation;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Relationships between Patient Variables and ED Visits in Home Care Patients\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e≧1 (vs no) ED visits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (1.00, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex, male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03 (0.52, 2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDIVERT scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.57 (1.16, 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCARS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.16 (1.00, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEARLI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.09 (1.02, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDuration since the first visit, months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99 (0.91, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior ED visits within 6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.14 (1.44, 6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eReceipt of ACP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.35 (1.17, 4.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePresence of the caregiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.75 (0.58, 5.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWeight loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.16 (1.40, 7.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUse of a urinary catheter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.33 (1.47, 7.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedical history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrinary tract infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.81 (0.78, 10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.82 (0.89, 3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.92 (0.19, 4.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRenal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.35 (0.42, 4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.33 (0.27, 6.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.59 (0.26, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93 (0.44, 1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.41 (0.58, 10.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: ACP, advanced care planning; CI, confidential interval; COPD, chronic obstructive pulmonary disease; ED, emergency department;\u003c/p\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":"CARS, DIVERT scale, EARLI, emergency department visits, home health care","lastPublishedDoi":"10.21203/rs.3.rs-4206648/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4206648/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Detection of Indicators and Vulnerabilities for Emergency Room Trips (DIVERT） scale, the Community Assessment Risk Screen (CARS), and the Emergency Admission Risk Likelihood Index (EARLI) are scales that assess the risk of emergency department (ED) visits among home health care patients. This study validated these scales and explored factors that could improve their predictive accuracy among Japanese home health care patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a single-center retrospective cohort study. The primary outcome of unplanned ED visits was used to assess the validity of the DIVERT scale, CARS, and EARLI. Additionally, we examined whether the addition of patient age and receipt of advanced care planning as variables on these assessments could enhance their precision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 224 eligible patients, 40 (17.8%) had at least one ED visit during the 6-month study period. In these patients, the DIVERT scale was superior compared with CARS and EARLI (both \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05). The area under the curve (AUC) of the DIVERT scale, CARS, and EARLI were 0.62, 0.59, and 0.60, respectively. Adding patient age and receipt of advance care planning improved the AUC in all three scales.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings suggest that these assessment scales could be applicable to home health care patients in Japan. Furthermore, adding age and receipt of advanced care planning as variables was found to enhance the predictive accuracy of the scales.\u003c/p\u003e","manuscriptTitle":"Validating the DIVERT Scales, CARS, and EARLI for Predicting Emergency Department Visits in Home Health Care in Japan: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 13:13:36","doi":"10.21203/rs.3.rs-4206648/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":"d74bd45e-d385-461d-ac9b-10faeda3204d","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-10T12:50:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-09 13:13:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4206648","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4206648","identity":"rs-4206648","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}