{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T03:47:18Z","timestamp":1778125638792,"version":"3.51.4"},"reference-count":37,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T00:00:00Z","timestamp":1644883200000},"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":["Health Planning &amp; Management"],"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Readmission followed by surgery to treat spinal fractures has a substantial impact on patient care costs and reflects a hospital's quality standards. This article analyzes the factors associated with hospital readmission followed by surgery to treat spinal fractures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This was a cross\u2010sectional study with time\u2010series analysis. For prediction analysis, we used Cox proportional hazards and machine\u2010learning models, using data from the Healthcare Cost and Utilization Project, Inpatient Database from Florida (USA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The sample comprised 215,999 patients, 8.8% of whom were readmitted within 30\u00a0days. The factors associated with a risk of readmission were male sex (1.1 [95% confidence interval 1.06\u20131.13]) and &gt;60\u00a0years of age (1.74 [95% CI: 1.69\u20131.8]). Surgeons with a higher annual patient volume presented a lower risk of readmission (0.61 [95% CI: 0.59\u20130.63]) and hospitals with an annual volume &gt;393 presented a lower risk (0.92 [95% CI: 0.89\u20130.95]).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Surgical procedures and other selected predictors and machine\u2010learning models can be used to reduce 30\u2010day readmissions after spinal surgery. Identification of patients at higher risk for readmission and complications is the first step to reducing unplanned readmissions.<\/jats:p><\/jats:sec>","DOI":"10.1002\/hpm.3437","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T04:04:25Z","timestamp":1644984265000},"page":"1708-1721","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predictors of hospital readmission within 30\u00a0days after surgery for thoracolumbar fractures: A mixed approach"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9934-920X","authenticated-orcid":false,"given":"Altac\u00edlio Aparecido","family":"Nunes","sequence":"first","affiliation":[{"name":"Department of Social Medicine Ribeir\u00e3o Preto Medical School, University of S\u00e3o Paulo  Ribeir\u00e3o Preto 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