Healthcare risk stratification model for emergency departments based on drugs, income and comorbidities: the DICER-score.
Elderly
Emergency care
Polypharmacy
Journal
BMC emergency medicine
ISSN: 1471-227X
Titre abrégé: BMC Emerg Med
Pays: England
ID NLM: 100968543
Informations de publication
Date de publication:
14 Feb 2024
14 Feb 2024
Historique:
received:
20
07
2023
accepted:
05
02
2024
medline:
15
2
2024
pubmed:
15
2
2024
entrez:
14
2
2024
Statut:
epublish
Résumé
During the last decade, the progressive increase in age and associated chronic comorbidities and polypharmacy. However, assessments of the risk of emergency department (ED) revisiting published to date often neglect patients' pharmacotherapy plans, thus overseeing the Drug-related problems (DRP) risks associated with the therapy burden. The aim of this study is to develop a predictive model for ED revisit, hospital admission, and mortality based on patient's characteristics and pharmacotherapy. Retrospective cohort study including adult patients visited in the ED (triage 1, 2, or 3) of multiple hospitals in Catalonia (Spain) during 2019. The primary endpoint was a composite of ED visits, hospital admission, or mortality 30 days after ED discharge. The study population was randomly split into a model development (60%) and validation (40%) datasets. The model included age, sex, income level, comorbidity burden, measured with the Adjusted Morbidity Groups (GMA), and number of medications. Forty-four medication groups, associated with medication-related health problems, were assessed using ATC codes. To assess the performance of the different variables, logistic regression was used to build multivariate models for ED revisits. The models were created using a "stepwise-forward" approach based on the Bayesian Information Criterion (BIC). Area under the curve of the receiving operating characteristics (AUCROC) curve for the primary endpoint was calculated. 851.649 patients were included; 134.560 (15.8%) revisited the ED within 30 days from discharge, 15.2% were hospitalized and 9.1% died within 30 days from discharge. Four factors (sex, age, GMA, and income level) and 30 ATC groups were identified as risk factors and combined into a final score. The model showed an AUCROC values of 0.720 (95%CI:0.718-0.721) in the development cohort and 0.719 (95%CI.0.717-0.721) in the validation cohort. Three risk categories were generated, with the following scores and estimated risks: low risk: 18.3%; intermediate risk: 40.0%; and high risk: 62.6%. The DICER score allows identifying patients at high risk for ED revisit within 30 days based on sociodemographic, clinical, and pharmacotherapeutic characteristics, being a valuable tool to prioritize interventions on discharge.
Sections du résumé
BACKGROUND
BACKGROUND
During the last decade, the progressive increase in age and associated chronic comorbidities and polypharmacy. However, assessments of the risk of emergency department (ED) revisiting published to date often neglect patients' pharmacotherapy plans, thus overseeing the Drug-related problems (DRP) risks associated with the therapy burden. The aim of this study is to develop a predictive model for ED revisit, hospital admission, and mortality based on patient's characteristics and pharmacotherapy.
METHODS
METHODS
Retrospective cohort study including adult patients visited in the ED (triage 1, 2, or 3) of multiple hospitals in Catalonia (Spain) during 2019. The primary endpoint was a composite of ED visits, hospital admission, or mortality 30 days after ED discharge. The study population was randomly split into a model development (60%) and validation (40%) datasets. The model included age, sex, income level, comorbidity burden, measured with the Adjusted Morbidity Groups (GMA), and number of medications. Forty-four medication groups, associated with medication-related health problems, were assessed using ATC codes. To assess the performance of the different variables, logistic regression was used to build multivariate models for ED revisits. The models were created using a "stepwise-forward" approach based on the Bayesian Information Criterion (BIC). Area under the curve of the receiving operating characteristics (AUCROC) curve for the primary endpoint was calculated.
RESULTS
RESULTS
851.649 patients were included; 134.560 (15.8%) revisited the ED within 30 days from discharge, 15.2% were hospitalized and 9.1% died within 30 days from discharge. Four factors (sex, age, GMA, and income level) and 30 ATC groups were identified as risk factors and combined into a final score. The model showed an AUCROC values of 0.720 (95%CI:0.718-0.721) in the development cohort and 0.719 (95%CI.0.717-0.721) in the validation cohort. Three risk categories were generated, with the following scores and estimated risks: low risk: 18.3%; intermediate risk: 40.0%; and high risk: 62.6%.
CONCLUSION
CONCLUSIONS
The DICER score allows identifying patients at high risk for ED revisit within 30 days based on sociodemographic, clinical, and pharmacotherapeutic characteristics, being a valuable tool to prioritize interventions on discharge.
Identifiants
pubmed: 38355411
doi: 10.1186/s12873-024-00946-7
pii: 10.1186/s12873-024-00946-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
23Informations de copyright
© 2024. The Author(s).
Références
Laureau M, Vuillot O, Gourhant V, Perier D, Pinzani V, Lohan L, et al. Adverse drug events detected by clinical pharmacists in an Emergency Department: a prospective Monocentric Observational Study. J Patient Saf. 2021;17(8):e1040–9.
doi: 10.1097/PTS.0000000000000679
pubmed: 32175969
Bos JM, Kalkman GA, Groenewoud H, van den Bemt PMLA, De Smet PAGM, Nagtegaal JE, et al. Prediction of clinically relevant adverse drug events in surgical patients. PLoS ONE. 2018;13(8):e0201645.
doi: 10.1371/journal.pone.0201645
pubmed: 30138343
pmcid: 6107128
Patel P, Zed PJ. Drug-related visits to the emergency department: how big is the problem? Pharmacotherapy. julio de. 2002;22(7):915–23.
Ukkonen M, Jämsen E, Zeitlin R, Pauniaho SL. Emergency department visits in older patients: a population-based survey. BMC Emerg Med. 2019;19(1):20.
doi: 10.1186/s12873-019-0236-3
pubmed: 30813898
pmcid: 6391758
Aminzadeh F, Dalziel WB. Older adults in the emergency department: a systematic review of patterns of use, adverse outcomes, and effectiveness of interventions. Ann Emerg Med. 2002;39(3):238–47.
doi: 10.1067/mem.2002.121523
pubmed: 11867975
Albert M, McCaig LF, Ashman JJ. Emergency department visits by persons aged 65 and over: United States, 2009–2010. NCHS data brief, no 130. Hyattsville, MD: National Center for Health Statistics; 2013.
Ravn-Nielsen LV, Duckert ML, Lund ML, Henriksen JP, Nielsen ML, Eriksen CS, et al. Effect of an In-Hospital multifaceted clinical pharmacist intervention on the risk of Readmission. JAMA Intern Med. 2018;178(3):375–82.
doi: 10.1001/jamainternmed.2017.8274
pubmed: 29379953
pmcid: 5885912
Hao S, Jin B, Shin AY, Zhao Y, Zhu C, Li Z, et al. Risk prediction of Emergency Department Revisit 30 days Post Discharge: a prospective study. PLoS ONE [Internet]; 2014.
Vela E, Carot-Sans G, Clèries M, Monterde D, Acebes X, Comella A, et al. Development and validation of a population-based risk stratification model for severe COVID-19 in the general population. Sci Rep. 2022;12(1):3277. https://doi.org/10.1038/s41598-022-07138-y .
doi: 10.1038/s41598-022-07138-y
pubmed: 35228558
pmcid: 8885698
Farré N, Vela E, Clèries M, Bustins M, Cainzos-Achirica M, Enjuanes C, et al. Real world heart failure epidemiology and outcome: a population-based analysis of 88,195 patients. PLoS ONE. 2017;12(2):e0172745. https://doi.org/10.1371/journal.pone.0172745 .
doi: 10.1371/journal.pone.0172745
pubmed: 28235067
pmcid: 5325273
Epidemiology of major. osteoporotic fractures: a population-based analysis in Catalonia, Spain - PubMed [Internet]. [cited 18 april 2022]. Available at: https://pubmed.ncbi.nlm.nih.gov/35267128/ .
Agency for Healthcare Research and Quality. Clinical Classifications Software (CCS) for ICD-9-CM [Internet]. [cited 2020 Oct 29]. Available from: https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp .
European Parliament and Council Regulation (EU). 2016/679 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General data Protection Regulation) [2016] OJ L119/1.
Cainzos-Achirica M, Capdevila C, Vela E, Cleries M, Bilal U, Garcia-Altes A, et al. Individual income, mortality and healthcare resource use in patients with chronic heart failure living in a universal healthcare system: a population-based study in Catalonia, Spain. Int J Cardiol. 2019;277:250–7.
doi: 10.1016/j.ijcard.2018.10.099
pubmed: 30413306
Bilal U, Cainzos-Achirica M, Cleries M, Santaeugènia S, Corbella X, Comin-Colet J, et al. Socioeconomic status, life expectancy and mortality in a universal healthcare setting: an individual-level analysis of > 6 million Catalan residents. Prev Med. 2019;123:91–4.
doi: 10.1016/j.ypmed.2019.03.005
pubmed: 30853378
Monterde D, Vela E, Clèries M. Los grupos de morbilidad ajustados: nuevo agrupador de morbilidad poblacional de utilidad en El ámbito de la atención primaria. Aten Primaria. 2016;48(10):674–82.
doi: 10.1016/j.aprim.2016.06.003
pubmed: 27495004
pmcid: 6879171
Monterde D, Vela E, Clèries M, Garcia-Eroles L, Roca J, Pérez-Sust P. Multimorbidity as a predictor of health service utilization in primary care: a registry-based study of the Catalan population. BMC Fam Pract. 2020;21(1):39.
doi: 10.1186/s12875-020-01104-1
pubmed: 32066377
pmcid: 7026948
Vela E, Clèries M, Monterde D, Carot-Sans G, Coca M, Valero-Bover D, et al. Performance of quantitative measures of multimorbidity: a population-based retrospective analysis. BMC Public Health. 2021;21(1):1881.
doi: 10.1186/s12889-021-11922-2
pubmed: 34663289
pmcid: 8524794
High-risk drugs in chronic patients. ISMP-Spain. [accessed 20 Mar 2022]. Available in: http://www.ismp-espana.org/ficheros/Relaci%C3%B3n%20medicamentos%20alto%20riesgo%20en%20cronicos.pdf .
Chowdhury MZI, Turin TC. Variable selection strategies and its importance in clinical prediction modelling. Fam Med Community Health. 2020;8(1):e000262.
doi: 10.1136/fmch-2019-000262
pubmed: 32148735
pmcid: 7032893
Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE. 2015;10(3):e0118432.
doi: 10.1371/journal.pone.0118432
pubmed: 25738806
pmcid: 4349800
R Core Team. R: A language and environment for statistical com-putting [Internet]. R Foundation for Statistical Computing, Vienna, Austria. 2017 [cited 2022 March 25]. Available from: https://www.r-project.org .
Biancari F, Asim Mahar MA, Kangasniemi OP. CHADS2 and CHA2DS2-VASc scores for prediction of immediate and late stroke after coronary artery bypass graft surgery. J Stroke Cerebrovasc Dis off J Natl Stroke Assoc. 2013;22(8):1304–11.
doi: 10.1016/j.jstrokecerebrovasdis.2012.11.004
Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818–29.
doi: 10.1097/00003246-198510000-00009
pubmed: 3928249
Falconer N, Barras M, Cottrell N. Systematic review of predictive risk models for adverse drug events in hospitalized patients. Br J Clin Pharmacol. 2018;84(5):846–64.
doi: 10.1111/bcp.13514
pubmed: 29337387
pmcid: 5903258
Ruiz Ramos J, Gras-Martin L, Juanes Borrego AM, Blazquez-Andion M, Puig Campmany M, Mangues-Bafalluy MA. Development of an emergency revisit score for patients with drug-related problems. J Pharm Technol. 2021;37(4):171–7.
doi: 10.1177/87551225211011731
pubmed: 34752577
pmcid: 8255982
Lee AR, Park H, Yoo A, Kim S, Sunwoo L, Yoo S. Risk prediction of Emergency Department visits in patients with Lung Cancer using machine learning: Retrospective Observational Study. JMIR Med Inf. 2023;11:e53058.
doi: 10.2196/53058
Chae S, Davoudi A, Song J, Evans L, Hobensack M, Bowles KH, et al. Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model. J Am Med Inf Assoc. 2023;30(10):1622–33.
doi: 10.1093/jamia/ocad129
Juanes A, Garin N, Mangues MA, Herrera S, Puig M, Faus MJ, et al. Impact of a pharmaceutical care programme for patients with chronic disease initiated at the emergency department on drug-related negative outcomes: a randomised controlled trial. Eur J Hosp Pharm Sci Pract. 2018;25(5):274–80.
doi: 10.1136/ejhpharm-2016-001055
Plaza Díaz A, Ruiz Ramos J, Juanes Borrego AM, Blázquez Andión M, Puig Campmany M, Mangues Bafalluy MA. Anticholinergic burden in patients treated for constipation in an emergency department. Emerg. 2020;32(5):349–52.
Brennan JJ, Chan TC, Vilke GM, Killeen JP, Hsia RY, Castillo EM. 211 Emergency Department revisits within three days of an Emergency Department discharge for urinary tract infection among geriatric patients. Ann Emerg Med. 2017;70(4):85.
doi: 10.1016/j.annemergmed.2017.07.433
Sparks JA, Vanni KMM, Sparks MA, Xu C, Santacroce LM, Glynn RJ, Ridker PM, et al. Effect of low-dose methotrexate on eGFR and kidney adverse events: a Randomized Clinical Trial. J Am Soc Nephrol. 2021;32(12):3197–207. https://doi.org/10.1681/ASN.2021050598 .
doi: 10.1681/ASN.2021050598
pubmed: 34551998
pmcid: 8638389
Sandoval DM, Alarcón GS, Morgan SL. Adverse events in methotrexate-treated rheumatoid arthritis patients. Br J Rheumatol. 1995;34(Suppl 2):49–56.
doi: 10.1093/rheumatology/XXXIV.suppl_2.49
pubmed: 8535650
Kannan VC, Rasamimanana GN, Novack V, Hassan L, Reynolds TA. The impact of socioeconomic status on emergency department outcome in a low-income country setting: a registry-based analysis. PLoS ONE. 2019;14(10).
Guo DY, Chen KH, Chen IC, Lu KY, Lin YC, Hsiao KY. The Association between Emergency Department Revisit and Elderly patients. J Acute Med. 2020;10(1):20–6.
pubmed: 32995151
pmcid: 7517912
Rodrigues CR, Harrington AR, Murdock N, Holmes JT, Borzadek EZ, Calabro K, et al. Effect of pharmacy-supported transition-of-care interventions on 30-Day readmissions: a systematic review and Meta-analysis. Ann Pharmacother. 2017;51(10):866–89.
doi: 10.1177/1060028017712725
pubmed: 28599601
Juanes A, Ruíz J, Puig M, Blázquez M, Gilabert A, López L, Baena MI, Guiu JM, Antònia Mangues M. The Effect of the drug-related problems Prevention Bundle on early readmissions in patients from the Emergency Department: a Randomized Clinical Trial. Ann Pharmacother. 2022;10600280221143237. https://doi.org/10.1177/10600280221143237 .
de Hond AAH, Steyerberg EW, van Calster B. Interpreting area under the receiver operating characteristic curve. Lancet Digit Health. 2022;4(12):e853–5.
doi: 10.1016/S2589-7500(22)00188-1
pubmed: 36270955