{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:27:21Z","timestamp":1765888041587,"version":"3.40.5"},"reference-count":40,"publisher":"American Academy of Pediatrics (AAP)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,9,1]]},"abstract":"<jats:sec><jats:title>OBJECTIVES:<\/jats:title><jats:p>To develop a model to assist clinicians in reducing 30-day unplanned pediatric readmissions and to enhance understanding of risk factors leading to such readmissions.<\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS:<\/jats:title><jats:p>Data consisting of 38\u2009143 inpatient clinical encounters at a tertiary pediatric hospital were retrieved, and 50% were used for training on a multivariate logistic regression model. The pediatric Rothman Index (pRI) was 1 of the novel candidate predictors considered. Multivariate model selection was conducted by minimization of Akaike Information Criteria. The area under the receiver operator characteristic curve (AUC) and values for sensitivity, specificity, positive predictive value, relative risk, and accuracy were computed on the remaining 50% of the data.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS:<\/jats:title><jats:p>The multivariate logistic regression model of readmission consists of 7 disease diagnosis groups, 4 measures of hospital resource use, 3 measures of disease severity and\/or medical complexities, and 2 variables derived from the pRI. Four of the predictors are novel, including history of previous 30-day readmissions within last 6 months (P &amp;lt; .001), planned admissions (P &amp;lt; .001), the discharge pRI score (P &amp;lt; .001), and indicator of whether the maximum pRI occurred during the last 24 hours of hospitalization (P = .005). An AUC of 0.79 (0.77\u20130.80) was obtained on the independent test data set.<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSIONS:<\/jats:title><jats:p>Our model provides significant performance improvements in the prediction of unplanned 30-day pediatric readmissions with AUC higher than the LACE readmission model and other general unplanned 30-day pediatric readmission models. The model is expected to provide an opportunity to capture 39% of readmissions (at a selected operating point) and may therefore assist clinicians in reducing avoidable readmissions.<\/jats:p><\/jats:sec>","DOI":"10.1542\/hpeds.2017-0220","type":"journal-article","created":{"date-parts":[[2018,8,31]],"date-time":"2018-08-31T08:29:34Z","timestamp":1535704174000},"page":"578-587","source":"Crossref","is-referenced-by-count":28,"title":["A Novel Model for Enhanced Prediction and Understanding of Unplanned 30-Day Pediatric Readmission"],"prefix":"10.1542","volume":"8","author":[{"given":"Louis","family":"Ehwerhemuepha","sequence":"first","affiliation":[{"name":"Children\u2019s Hospital of Orange County, Orange, California;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stacey","family":"Finn","sequence":"additional","affiliation":[{"name":"Cedar Gate Technologies, Greenwich, Connecticut;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Rothman","sequence":"additional","affiliation":[{"name":"PeraHealth, Inc, Charlotte, North Carolina; and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cyril","family":"Rakovski","sequence":"additional","affiliation":[{"name":"School of Computational and Data Science, Chapman University, Orange, California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William","family":"Feaster","sequence":"additional","affiliation":[{"name":"Children\u2019s Hospital of Orange County, Orange, California;"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"417","published-online":{"date-parts":[[2018,9,1]]},"reference":[{"issue":"15","key":"2021120817304220800_B1","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1056\/NEJMp1201268","article-title":"Medicare\u2019s readmissions-reduction program\u2014a positive alternative","volume":"366","author":"Berenson","year":"2012","journal-title":"N Engl J Med"},{"issue":"13","key":"2021120817304220800_B2","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1056\/NEJMp1300122","article-title":"A path forward on Medicare readmissions","volume":"368","author":"Joynt","year":"2013","journal-title":"N Engl J Med"},{"issue":"3","key":"2021120817304220800_B3","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.jpeds.2014.12.020","article-title":"Preventing pediatric readmissions: which ones and how?","volume":"166","author":"Payne","year":"2015","journal-title":"J Pediatr"},{"issue":"17","key":"2021120817304220800_B4","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1001\/jama.2010.533","article-title":"Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure","volume":"303","author":"Hernandez","year":"2010","journal-title":"JAMA"},{"issue":"1","key":"2021120817304220800_B5","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.jpeds.2013.04.063","article-title":"Significant variability in 30-day unplanned readmission rates among children\u2019s hospitals","volume":"163","author":"Srivastava","year":"2013","journal-title":"J Pediatr"},{"issue":"4","key":"2021120817304220800_B6","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1001\/jama.2012.188351","article-title":"Pediatric readmission prevalence and variability across hospitals [published correction appears in JAMA. 2013;309(10):986]","volume":"309","author":"Berry","year":"2013","journal-title":"JAMA"},{"issue":"24","key":"2021120817304220800_B7","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1056\/NEJMsa1101942","article-title":"The relationship between hospital admission rates and rehospitalizations","volume":"365","author":"Epstein","year":"2011","journal-title":"N Engl J Med"},{"issue":"4","key":"2021120817304220800_B8","doi-asserted-by":"crossref","first-page":"290","DOI":"10.2174\/1574887112666170816144455","article-title":"Discharge interventions and modifiable risk factors for preventing hospital readmissions in children with medical complexity","volume":"12","author":"Stephens","year":"2017","journal-title":"Rev Recent Clin Trials"},{"issue":"2","key":"2021120817304220800_B9","doi-asserted-by":"crossref","first-page":"e20154182","DOI":"10.1542\/peds.2015-4182","article-title":"Potentially preventable 30-day hospital readmissions at a children\u2019s hospital","volume":"138","author":"Toomey","year":"2016","journal-title":"Pediatrics"},{"issue":"9","key":"2021120817304220800_B10","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1080\/02770903.2017.1281294","article-title":"Multifaceted quality improvement initiative to decrease pediatric asthma readmissions","volume":"54","author":"Krupp","year":"2017","journal-title":"J Asthma"},{"issue":"6","key":"2021120817304220800_B11","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.pedhc.2017.04.017","article-title":"The effect of a comprehensive care transition model on cost and utilization for medically complex children with cerebral palsy","volume":"31","author":"Howard","year":"2017","journal-title":"J Pediatr Health Care"},{"issue":"4","key":"2021120817304220800_B12","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1002\/jhm.2134","article-title":"Pediatric hospital discharge interventions to reduce subsequent utilization: a systematic review","volume":"9","author":"Auger","year":"2014","journal-title":"J Hosp Med"},{"issue":"3","key":"2021120817304220800_B13","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.jpeds.2014.10.052","article-title":"Rates and impact of potentially preventable readmissions at children\u2019s hospitals","volume":"166","author":"Gay","year":"2015","journal-title":"J Pediatr"},{"issue":"3","key":"2021120817304220800_B14","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11606-009-1196-1","article-title":"Hospital readmission in general medicine patients: a prediction model","volume":"25","author":"Hasan","year":"2010","journal-title":"J Gen Intern Med"},{"key":"2021120817304220800_B15","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.jpeds.2017.03.056","article-title":"Adding social determinant data changes children\u2019s hospitals\u2019 readmissions performance","volume":"186","author":"Sills","year":"2017","journal-title":"J Pediatr"},{"issue":"4","key":"2021120817304220800_B16","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.jpeds.2013.04.013","article-title":"Predictors of 30-day readmission and association with primary care follow-up plans","volume":"163","author":"Coller","year":"2013","journal-title":"J Pediatr"},{"key":"2021120817304220800_B17","first-page":"1072","article-title":"Pediatric readmission classification using stacked regularized logistic regression models","volume":"2014","author":"Stiglic","year":"2014","journal-title":"AMIA Annu Symp Proc"},{"issue":"8","key":"2021120817304220800_B18","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.jpedsurg.2016.11.043","article-title":"Rates and risk factors of unplanned 30-day readmission following general and thoracic pediatric surgical procedures","volume":"52","author":"Polites","year":"2017","journal-title":"J Pediatr Surg"},{"issue":"5","key":"2021120817304220800_B19","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/j.jbi.2013.06.011","article-title":"Development and validation of a continuous measure of patient condition using the electronic medical record","volume":"46","author":"Rothman","year":"2013","journal-title":"J Biomed Inform"},{"key":"2021120817304220800_B20","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.jbi.2016.12.013","article-title":"Development and validation of a continuously age-adjusted measure of patient condition for hospitalized children using the electronic medical record","volume":"66","author":"Rothman","year":"2017","journal-title":"J Biomed Inform"},{"issue":"4","key":"2021120817304220800_B21","doi-asserted-by":"crossref","first-page":"e000646","DOI":"10.1136\/bmjopen-2012-000849","article-title":"Clinical implications and validity of nursing assessments: a longitudinal measure of patient condition from analysis of the electronic medical record","volume":"2","author":"Rothman","year":"2012","journal-title":"BMJ Open"},{"issue":"2","key":"2021120817304220800_B22","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1002\/jhm.2132","article-title":"Measuring the modified early warning score and the Rothman index: advantages of utilizing the electronic medical record in an early warning system","volume":"9","author":"Finlay","year":"2014","journal-title":"J Hosp Med"},{"issue":"3","key":"2021120817304220800_B23","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.clnu.2011.11.001","article-title":"Malnutrition and its impact on cost of hospitalization, length of stay, readmission and 3-year mortality","volume":"31","author":"Lim","year":"2012","journal-title":"Clin Nutr"},{"issue":"4","key":"2021120817304220800_B24","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1542\/hpeds.2017-0195","article-title":"The effect of malnutrition on the risk of unplanned 7-day readmission in pediatrics","volume":"8","author":"Ehwerhemuepha","year":"2018","journal-title":"Hosp Pediatr"},{"issue":"12","key":"2021120817304220800_B25","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1016\/j.jclinepi.2004.03.012","article-title":"New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality","volume":"57","author":"Sundararajan","year":"2004","journal-title":"J Clin Epidemiol"},{"issue":"1 pt 2","key":"2021120817304220800_B26","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1542\/peds.106.S1.205","article-title":"Pediatric deaths attributable to complex chronic conditions: a population-based study of Washington State, 1980-1997","volume":"106","author":"Feudtner","year":"2000","journal-title":"Pediatrics"},{"key":"2021120817304220800_B27","volume-title":"Applied Missing Data Analysis","author":"Enders","year":"2010","edition":"1st ed"},{"issue":"5","key":"2021120817304220800_B28","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s11135-006-9018-6","article-title":"A caution regarding rules of thumb for variance inflation factors","volume":"41","author":"O\u2019brien","year":"2007","journal-title":"Qual Quant"},{"issue":"1","key":"2021120817304220800_B29","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1186\/s13054-015-0999-1","article-title":"Why the C-statistic is not informative to evaluate early warning scores and what metrics to use","volume":"19","author":"Romero-Brufau","year":"2015","journal-title":"Crit Care"},{"key":"2021120817304220800_B30","unstructured":"R Core Team. R: a language and environment for statistical computing. 2017. Available at: https:\/\/www.r-project.org\/ Accessed October 18, 2017"},{"issue":"6","key":"2021120817304220800_B31","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1503\/cmaj.091117","article-title":"Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community","volume":"182","author":"van Walraven","year":"2010","journal-title":"CMAJ"},{"issue":"3","key":"2021120817304220800_B32","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1176\/ajp.154.3.337","article-title":"Predicting readmission to the psychiatric hospital in a managed care environment: implications for quality indicators","volume":"154","author":"Lyons","year":"1997","journal-title":"Am J Psychiatry"},{"issue":"15","key":"2021120817304220800_B33","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1001\/jama.2011.1515","article-title":"Risk prediction models for hospital readmission: a systematic review","volume":"306","author":"Kansagara","year":"2011","journal-title":"JAMA"},{"article-title":"Mining a large-scale EHR with machine learning methods to predict all-cause 30-day unplanned readmissions","volume-title":"Proceedings of the 2nd ASE International Conference on Big Data Science and Computing","author":"Zhai","key":"2021120817304220800_B34"},{"issue":"3","key":"2021120817304220800_B35","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11606-009-1196-1","article-title":"Hospital readmission in general medicine patients: a prediction model","volume":"25","author":"Hasan","year":"2010","journal-title":"J Gen Intern Med"},{"issue":"11","key":"2021120817304220800_B36","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1097\/MLR.0b013e3181ef60d9","article-title":"An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data","volume":"48","author":"Amarasingham","year":"2010","journal-title":"Med Care"},{"issue":"417","key":"2021120817304220800_B37","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1080\/01621459.1992.10475190","article-title":"Generalized collinearity diagnostics","volume":"87","author":"Fox","year":"1992","journal-title":"J Am Stat Assoc"},{"issue":"4","key":"2021120817304220800_B38","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.resuscitation.2013.12.017","article-title":"Widely used track and trigger scores: are they ready for automation in practice?","volume":"85","author":"Romero-Brufau","year":"2014","journal-title":"Resuscitation"},{"issue":"1","key":"2021120817304220800_B39","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1542\/peds.2007-3395","article-title":"How well can hospital readmission be predicted in a cohort of hospitalized children? A retrospective, multicenter study","volume":"123","author":"Feudtner","year":"2009","journal-title":"Pediatrics"},{"key":"2021120817304220800_B40","doi-asserted-by":"crossref","first-page":"S39","DOI":"10.1016\/j.acap.2014.06.012","article-title":"Measuring pediatric hospital readmission rates to drive quality improvement","volume":"14","author":"Nakamura","year":"2014","journal-title":"Acad Pediatr"}],"container-title":["Hospital Pediatrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/publications.aap.org\/hospitalpediatrics\/article-pdf\/8\/9\/578\/815101\/hpeds_2017-0220.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/publications.aap.org\/hospitalpediatrics\/article-pdf\/8\/9\/578\/815101\/hpeds_2017-0220.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:42:18Z","timestamp":1661906538000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.aap.org\/hospitalpediatrics\/article\/8\/9\/578\/26635\/A-Novel-Model-for-Enhanced-Prediction-and"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,1]]},"references-count":40,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9,1]]},"published-print":{"date-parts":[[2018,9,1]]}},"URL":"https:\/\/doi.org\/10.1542\/hpeds.2017-0220","relation":{},"ISSN":["2154-1663","2154-1671"],"issn-type":[{"type":"print","value":"2154-1663"},{"type":"electronic","value":"2154-1671"}],"subject":[],"published":{"date-parts":[[2018,9,1]]}}}