Predictability of persistent frequent attendance in primary care: a temporal and geographical validation study.

<h4>Background</h4>Frequent attenders are patients who visit their general practitioner exceptionally frequently. Frequent attendance is usually transitory, but some frequent attenders become persistent. Clinically, prediction of persistent frequent attendance is useful to target treatme...

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Autores principales:	Frans T Smits, Henk J Brouwer, Aeilko H Zwinderman, Marjan van den Akker, Ben van Steenkiste, Jacob Mohrs, Aart H Schene, Henk C van Weert, Gerben Ter Riet
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ae2e15fccc624ebcb6d3b2575adf874f
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spelling	oai:doaj.org-article:ae2e15fccc624ebcb6d3b2575adf874f2021-11-18T08:56:44ZPredictability of persistent frequent attendance in primary care: a temporal and geographical validation study.1932-620310.1371/journal.pone.0073125https://doaj.org/article/ae2e15fccc624ebcb6d3b2575adf874f2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24039870/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Frequent attenders are patients who visit their general practitioner exceptionally frequently. Frequent attendance is usually transitory, but some frequent attenders become persistent. Clinically, prediction of persistent frequent attendance is useful to target treatment at underlying diseases or problems. Scientifically it is useful for the selection of high-risk populations for trials. We previously developed a model to predict which frequent attenders become persistent.<h4>Aim</h4>To validate an existing prediction model for persistent frequent attendance that uses information solely from General Practitioners' electronic medical records.<h4>Methods</h4>We applied the existing model (N = 3,045, 2003-2005) to a later time frame (2009-2011) in the original derivation network (N = 4,032, temporal validation) and to patients of another network (SMILE; 2007-2009, N = 5,462, temporal and geographical validation). Model improvement was studied by adding three new predictors (presence of medically unexplained problems, prescriptions of psychoactive drugs and antibiotics). Finally, we derived a model on the three data sets combined (N = 12,539). We expressed discrimination using histograms of the predicted values and the concordance-statistic (c-statistic) and calibration using the calibration slope (1 = ideal) and Hosmer-Lemeshow tests.<h4>Results</h4>The existing model (c-statistic 0.67) discriminated moderately with predicted values between 7.5 and 50 percent and c-statistics of 0.62 and 0.63, for validation in the original network and SMILE network, respectively. Calibration (0.99 originally) was better in SMILE than in the original network (slopes 0.84 and 0.65, respectively). Adding information on the three new predictors did not importantly improve the model (c-statistics 0.64 and 0.63, respectively). Performance of the model based on the combined data was similar (c-statistic 0.65).<h4>Conclusion</h4>This external validation study showed that persistent frequent attenders can be prospectively identified moderately well using data solely from patients' electronic medical records.Frans T SmitsHenk J BrouwerAeilko H ZwindermanMarjan van den AkkerBen van SteenkisteJacob MohrsAart H ScheneHenk C van WeertGerben Ter RietPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e73125 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Frans T Smits Henk J Brouwer Aeilko H Zwinderman Marjan van den Akker Ben van Steenkiste Jacob Mohrs Aart H Schene Henk C van Weert Gerben Ter Riet Predictability of persistent frequent attendance in primary care: a temporal and geographical validation study.
description	<h4>Background</h4>Frequent attenders are patients who visit their general practitioner exceptionally frequently. Frequent attendance is usually transitory, but some frequent attenders become persistent. Clinically, prediction of persistent frequent attendance is useful to target treatment at underlying diseases or problems. Scientifically it is useful for the selection of high-risk populations for trials. We previously developed a model to predict which frequent attenders become persistent.<h4>Aim</h4>To validate an existing prediction model for persistent frequent attendance that uses information solely from General Practitioners' electronic medical records.<h4>Methods</h4>We applied the existing model (N = 3,045, 2003-2005) to a later time frame (2009-2011) in the original derivation network (N = 4,032, temporal validation) and to patients of another network (SMILE; 2007-2009, N = 5,462, temporal and geographical validation). Model improvement was studied by adding three new predictors (presence of medically unexplained problems, prescriptions of psychoactive drugs and antibiotics). Finally, we derived a model on the three data sets combined (N = 12,539). We expressed discrimination using histograms of the predicted values and the concordance-statistic (c-statistic) and calibration using the calibration slope (1 = ideal) and Hosmer-Lemeshow tests.<h4>Results</h4>The existing model (c-statistic 0.67) discriminated moderately with predicted values between 7.5 and 50 percent and c-statistics of 0.62 and 0.63, for validation in the original network and SMILE network, respectively. Calibration (0.99 originally) was better in SMILE than in the original network (slopes 0.84 and 0.65, respectively). Adding information on the three new predictors did not importantly improve the model (c-statistics 0.64 and 0.63, respectively). Performance of the model based on the combined data was similar (c-statistic 0.65).<h4>Conclusion</h4>This external validation study showed that persistent frequent attenders can be prospectively identified moderately well using data solely from patients' electronic medical records.
format	article
author	Frans T Smits Henk J Brouwer Aeilko H Zwinderman Marjan van den Akker Ben van Steenkiste Jacob Mohrs Aart H Schene Henk C van Weert Gerben Ter Riet
author_facet	Frans T Smits Henk J Brouwer Aeilko H Zwinderman Marjan van den Akker Ben van Steenkiste Jacob Mohrs Aart H Schene Henk C van Weert Gerben Ter Riet
author_sort	Frans T Smits
title	Predictability of persistent frequent attendance in primary care: a temporal and geographical validation study.
title_short	Predictability of persistent frequent attendance in primary care: a temporal and geographical validation study.
title_full	Predictability of persistent frequent attendance in primary care: a temporal and geographical validation study.
title_fullStr	Predictability of persistent frequent attendance in primary care: a temporal and geographical validation study.
title_full_unstemmed	Predictability of persistent frequent attendance in primary care: a temporal and geographical validation study.
title_sort	predictability of persistent frequent attendance in primary care: a temporal and geographical validation study.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/ae2e15fccc624ebcb6d3b2575adf874f
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Predictability of persistent frequent attendance in primary care: a temporal and geographical validation study.

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