{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T03:43:57Z","timestamp":1773114237721,"version":"3.50.1"},"reference-count":20,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,11,29]],"date-time":"2020-11-29T00:00:00Z","timestamp":1606608000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Eur J Clin Investigation"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Prolonged length of stay (LOS) and post\u2010acute care after percutaneous coronary intervention (PCI) is common and costly. Risk models for predicting prolonged LOS and post\u2010acute care have limited accuracy. Our goal was to develop and validate models using artificial neural networks (ANN) to predict prolonged LOS\u00a0&gt;\u00a07days and need for post\u2010acute care after PCI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We defined prolonged LOS as \u22657\u00a0days and post\u2010acute care as patients discharged to: extended care, transitional care unit, rehabilitation, other acute care hospital, nursing home or hospice care. Data from 22\u00a0675 patients who presented with ACS and underwent PCI was shuffled and split into a derivation set (75% of dataset) and a validation dataset (25% of dataset). Calibration plots were used to examine the overall predictive performance of the MLP by plotting observed and expected risk deciles and fitting a lowess smoother to the data. Classification accuracy was assessed by a receiver\u2010operating characteristic (ROC) and area under the ROC curve (AUC).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our MLP\u2010based model predicted prolonged LOS with an accuracy of 90.87% and 88.36% in training and test sets, respectively. The post\u2010acute care model had an accuracy of 90.22% and 86.31% in training and test sets, respectively. This accuracy was achieved with quick convergence. Predicted probabilities from the MLP models showed good (prolonged LOS) to excellent calibration (post\u2010acute care).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our ANN\u2010based models accurately predicted LOS and need for post\u2010acute care. Larger studies for replicability and longitudinal studies for evidence of impact are needed to establish these models in current PCI practice.<\/jats:p><\/jats:sec>","DOI":"10.1111\/eci.13406","type":"journal-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T05:18:27Z","timestamp":1602479907000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Artificial neural network\u2010based prediction of prolonged length of stay and need for post\u2010acute care in acute coronary syndrome patients undergoing percutaneous coronary intervention"],"prefix":"10.1111","volume":"51","author":[{"given":"Hemant","family":"Kulkarni","sequence":"first","affiliation":[{"name":"M &amp; H Research, LLC San Antonio TX USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4033-0904","authenticated-orcid":false,"given":"Manoj","family":"Thangam","sequence":"additional","affiliation":[{"name":"Cardiovascular Division Washington University School of Medicine St. Louis MO USA"},{"name":"Barnes\u2010Jewish Hospital St. Louis MO USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6007-0519","authenticated-orcid":false,"given":"Amit P.","family":"Amin","sequence":"additional","affiliation":[{"name":"Cardiovascular Division Washington University School of Medicine St. Louis MO USA"},{"name":"Barnes\u2010Jewish Hospital St. Louis MO USA"},{"name":"Center for Value and Innovation Washington University School of Medicine St. Louis MO USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2020,11,29]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2019.00466"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2017.05.011"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/heartjnl-2012-303379"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcha.2015.06.009"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/HPC.0b013e318292f168"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/HPC.0b013e318211bb08"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjcard.2019.10.019"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.118.004635"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2019.0054"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/ma9060483"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpclett.5b01660"},{"issue":"2","key":"e_1_2_8_13_1","first-page":"14","article-title":"Introduction to machine learning, neural networks, and deep learning","volume":"9","author":"Choi RY","year":"2020","journal-title":"Transl Vis Sci Technol"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mayocp.2020.01.038"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0042519"},{"key":"e_1_2_8_16_1","unstructured":"onehot: Fast Onehot Encoding for Data.frames. [computer program].2017."},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-3800(02)00064-9"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v046.i07"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v085.i11"},{"key":"e_1_2_8_20_1","unstructured":"EnsorJ SnellKIE MartinEC.PMCALPOT: Stata module to produce a caibration plot of predction model performance. Statistical software components.2018:S458486."},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.2307\/2531595"}],"container-title":["European Journal of Clinical Investigation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/eci.13406","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/eci.13406","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/eci.13406","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,3]],"date-time":"2023-09-03T02:24:32Z","timestamp":1693707872000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/eci.13406"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,29]]},"references-count":20,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["10.1111\/eci.13406"],"URL":"https:\/\/doi.org\/10.1111\/eci.13406","archive":["Portico"],"relation":{},"ISSN":["0014-2972","1365-2362"],"issn-type":[{"value":"0014-2972","type":"print"},{"value":"1365-2362","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,29]]},"assertion":[{"value":"2020-05-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-09-04","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-11-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e13406"}}