Predicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regression
Hospital readmission is considered a key research area for improving care coordination and achieving potential savings. This is important because hospital readmissions can have negative consequences in terms of good health and recovery for patients. It is thus important to significantly reduce such...
- Autores:
-
Ortiz-Barrios, Miguel
Altamar-Maldonado, Zenaida
Martínez-Solano, Cielo
Petrillo, Antonella
De Felice, Fabio
Jiménez-Delgado, Genett
García-Cuan, Aracely
Medina-Buelvas, Ana M.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8513
- Acceso en línea:
- https://hdl.handle.net/11323/8513
http://dx.doi.org/10.4067/S0718-33052021000200378
https://repositorio.cuc.edu.co/
- Palabra clave:
- Hospital readmission
logistic regression
quality of care
health policy
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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| dc.title.spa.fl_str_mv |
Predicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regression |
| dc.title.translated.spa.fl_str_mv |
Prediciendo reingresos hospitalarios no planificados antes de 15 días: una aplicación de la regresión logística |
| title |
Predicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regression |
| spellingShingle |
Predicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regression Hospital readmission logistic regression quality of care health policy |
| title_short |
Predicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regression |
| title_full |
Predicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regression |
| title_fullStr |
Predicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regression |
| title_full_unstemmed |
Predicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regression |
| title_sort |
Predicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regression |
| dc.creator.fl_str_mv |
Ortiz-Barrios, Miguel Altamar-Maldonado, Zenaida Martínez-Solano, Cielo Petrillo, Antonella De Felice, Fabio Jiménez-Delgado, Genett García-Cuan, Aracely Medina-Buelvas, Ana M. |
| dc.contributor.author.spa.fl_str_mv |
Ortiz-Barrios, Miguel Altamar-Maldonado, Zenaida Martínez-Solano, Cielo Petrillo, Antonella De Felice, Fabio Jiménez-Delgado, Genett García-Cuan, Aracely Medina-Buelvas, Ana M. |
| dc.subject.spa.fl_str_mv |
Hospital readmission logistic regression quality of care health policy |
| topic |
Hospital readmission logistic regression quality of care health policy |
| description |
Hospital readmission is considered a key research area for improving care coordination and achieving potential savings. This is important because hospital readmissions can have negative consequences in terms of good health and recovery for patients. It is thus important to significantly reduce such readmissions. Unfortunately, there isn't a one-size-fits-all solution to preventing hospital readmissions. There are many variables outside of hospitals' direct control, such as social determinants and patient lifestyle factors, impacting readmissions. Although several studies have been undertaken to investigate 30-day readmissions, predicting revisits in shorter intervals (e.g., within 15 days after discharge) is highly needed to capture hospital-attributable returns better and develop more effective improvement plans. Hence, the aim of this paper is three-fold: i) to develop a comprehensive experimental study for identifying factors affecting 15-day readmission risk, ii) to classify patients according to the risk of 15-day readmission using logistic regression, and iii) provide general recommendations to reduce the 15-day readmission risk considering different predictors. To this end, the patients' characteristics were first described. Then, the significance of potential predictors, their interactions, and their effects were assessed. After this, a logistic regression model was derived to predict the likelihood of 15-day readmission in each patient. Finally, general recommendations were provided to reduce 15-day revisits. A real case study in Colombia was considered to validate the proposed methodology. |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-08-11T15:59:09Z |
| dc.date.available.none.fl_str_mv |
2021-08-11T15:59:09Z |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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acceptedVersion |
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https://hdl.handle.net/11323/8513 |
| dc.identifier.doi.spa.fl_str_mv |
http://dx.doi.org/10.4067/S0718-33052021000200378 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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https://hdl.handle.net/11323/8513 http://dx.doi.org/10.4067/S0718-33052021000200378 https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
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Jr. Fargason, and M. Hall. "How well can hospital readmission be predicted in a cohort of hospitalized children? A retrospective multi-center study". Pediatrics. Vol. 123 N° 1, pp. 286-293. 2009. ISSN: 0031-4005. DOI: 10.1542/peds.2007-3395. O. Hasan, D.O. Meltzer, S.A. Shaykevich, C.M. Bell, P.J. Kaboli, A.D. Auerbach, T.D. Wetterneck, V.M. Arora, J. Zhang, and J.L. Schnipper. "Hospital readmission in general medicine patients: A prediction model". Journal of General Internal Medicine. Vol. 25, N° 3, pp. 211-219. 2010. ISSN: 1525-1497 DOI: 10.1007/s11606-009-1196-1. M.D. Silverstein, H. Qin, S.Q. Mercer, J. Fong, and Z. Haydar. "Risk factors for 30-day hospital readmission in patients ≥65 years of age". Baylor University Medical Center Proceedings. Vol. 21, N° 4, pp. 363-372.2008. PubMed PMID: 18982076. PubMed Central PMCID: PMC2566906. D.L. Chin, H. Bang, R.N. Manickam, and P.S. Romano. "Rethinking thirty-day hospital readmissions: shorter intervals might be better indicators of quality of care". Health Affairs. Vol. 35 N° 10, pp. 1867-1875. 2016. ISSN: 0278-2715. DOI: 10.1377/hlthaff.2016.0205. P.N. Pham, H. Xiao, A. Sarayani, M. Chen, and J.D. Brown. "Risk factors associated with 7-versus 30-day readmission among patients with heart failure using the Nationwide Readmission Database". Medical care. Vol. 57 N° 1, pp. 1-7. 2019. ISSN: 1537-1948. DOI: 10.1097/MLR.0000000000001006. A.L. Leppin, M.R. Gionfriddo, M. Kessler, J.P. Brito, F.S. Mair, K. Gallacher, and H.H. Ting. "Preventing 30-day hospital readmissions: a systematic review and meta-analysis of randomized trials". JAMA internal medicine. Vol. 174 N° 7, pp. 1095-1107. 2014. ISSN: 2168-6114. DOI: 10.1001/jamainternmed.2014.1608. M.O. Barrios, and H.F. Jiménez. "Reduction of average lead time in outpatient service of obstetrics through six sigma methodology". In Ambient Intelligence for Health. Springer Cham. 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International journal of cardiology. Vol. 230, pp. 14-20. 2017. ISSN: 0167-5273. DOI: 10.1016/j.ijcard.2016.12.121. A.A. Elsamadicy, O. Adogwa, V.D. Vuong, A.I. Mehta, R.A. Vasquez, J. Cheng, and C.A. Bagley. "Patient body mass index is an independent predictor of 30-day hospital readmission after elective spine surgery". World neurosurgery. Vol. 96, pp. 148-151. 2016. ISSN: 1878-8750. DOI: 10.1016/j.wneu.2016.08.097. M.L. Metersky, M.J. Fine, and E.M. Mortensen. "The effect of marital status on the presentation and outcomes of elderly male veterans hospitalized for pneumonia". Chest, Vol. 142, N° 4, pp. 982-987. 2012. ISSN: 0012-3692. DOI: 10.1378/chest.11-3183. C.S. Kwok, J. Potts, M. Gulati, M. Alasnag, M. Rashid, A. Shoaib, and M.A. Mamas. "Effect of gender on unplanned readmissions after percutaneous coronary intervention (from the Nationwide Readmissions Database)". The American journal of cardiology. Vol. 121 N° 7, pp. 810-817. 2018. ISSN: 0002-9149. DOI: https://doi.org/10.1016/j.amjcard.2017.12.032. A.N. Mahmoud, and I.Y. Elgendy. "Gender impact on 30-day readmissions after hospitalization with acute myocardial infarction complicated by cardiogenic shock (from the 2013 to 2014 National Readmissions Database)". The American journal of cardiology. Vol. 121 N° 5, pp. 523-528. 2018. ISSN: 0002-9149. DOI: https://doi.org/10.1016/j.amjcard.2017.11.023. Ministerio de Salud y Protección Social. "Evaluación de la frecuencia de eventos adversos y monitoreo de aspectos claves relacionados con la seguridad del paciente". 2018. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/DE/CA/Evaluar-frecuencia-eventos-adversos.pdf J.J. Brennan, T.C. Chan, J.P. Killeen, and E.M. Castillo. "Inpatient readmissions and emergency department visits within 30 days of a hospital admission". Western Journal of Emergency Medicine. Vol. 16 N° 7, pp. 1025-1029. 2015. ISSN: 1936-900X. DOI: 10.5811/westjem.2015.8.26157. E.Y. Boateng, and D.A. Abaye. "A Review of the Logistic Regression Model with Emphasis on Medical Research". Journal of Data Analysis and Information Processing. Vol. 7 N° 4, pp. 190-207. 2019. ISSN: 2327-7203. DOI: 10.4236/jdaip.2019.74012. |
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Ortiz-Barrios, MiguelAltamar-Maldonado, ZenaidaMartínez-Solano, CieloPetrillo, AntonellaDe Felice, FabioJiménez-Delgado, GenettGarcía-Cuan, AracelyMedina-Buelvas, Ana M.2021-08-11T15:59:09Z2021-08-11T15:59:09Z2021https://hdl.handle.net/11323/8513http://dx.doi.org/10.4067/S0718-33052021000200378Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Hospital readmission is considered a key research area for improving care coordination and achieving potential savings. This is important because hospital readmissions can have negative consequences in terms of good health and recovery for patients. It is thus important to significantly reduce such readmissions. Unfortunately, there isn't a one-size-fits-all solution to preventing hospital readmissions. There are many variables outside of hospitals' direct control, such as social determinants and patient lifestyle factors, impacting readmissions. Although several studies have been undertaken to investigate 30-day readmissions, predicting revisits in shorter intervals (e.g., within 15 days after discharge) is highly needed to capture hospital-attributable returns better and develop more effective improvement plans. Hence, the aim of this paper is three-fold: i) to develop a comprehensive experimental study for identifying factors affecting 15-day readmission risk, ii) to classify patients according to the risk of 15-day readmission using logistic regression, and iii) provide general recommendations to reduce the 15-day readmission risk considering different predictors. To this end, the patients' characteristics were first described. Then, the significance of potential predictors, their interactions, and their effects were assessed. After this, a logistic regression model was derived to predict the likelihood of 15-day readmission in each patient. Finally, general recommendations were provided to reduce 15-day revisits. A real case study in Colombia was considered to validate the proposed methodology.Ortiz-Barrios, MiguelAltamar-Maldonado, ZenaidaMartínez-Solano, CieloPetrillo, AntonellaDe Felice, FabioJiménez-Delgado, GenettGarcía-Cuan, AracelyMedina-Buelvas, Ana M.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ingeniarehttps://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052021000200378&lng=en&nrm=iso&tlng=enHospital readmissionlogistic regressionquality of carehealth policyPredicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regressionPrediciendo reingresos hospitalarios no planificados antes de 15 días: una aplicación de la regresión logísticaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionC. 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