{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T19:14:40Z","timestamp":1788808480319,"version":"build-2803163510"},"reference-count":32,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T00:00:00Z","timestamp":1636588800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T00:00:00Z","timestamp":1636588800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T00:00:00Z","timestamp":1636588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knee surg. sports traumatol. arthrosc."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to develop and validate machine\u2010learning models for the prediction of recurrent infection in patients following revision total knee arthroplasty for periprosthetic joint infection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 618 consecutive patients underwent revision total knee arthroplasty for periprosthetic joint infection. The patient cohort included 165 patients with confirmed recurrent periprosthetic joint infection (PJI). Potential risk factors including patient demographics and surgical characteristics served as input to three machine\u2010learning models which were developed to predict recurrent periprosthetic joint. The machine\u2010learning models were assessed by discrimination, calibration and decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The factors most significantly associated with recurrent PJI in patients following revision total knee arthroplasty for PJI included irrigation and debridement with\/without modular component exchange (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001),\u2009&gt;\u20094 prior open surgeries (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), metastatic disease (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), drug abuse (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), HIV\/AIDS (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01), presence of Enterococcus species (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01) and obesity (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01). The machine\u2010learning models all achieved excellent performance across discrimination (AUC range 0.81\u20130.84).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study developed three machine\u2010learning models for the prediction of recurrent infections in patients following revision total knee arthroplasty for periprosthetic joint infection. The strongest predictors were previous irrigation and debridement with or without modular component exchange and prior open surgeries. The study findings show excellent model performance, highlighting the potential of these computational tools in quantifying increased risks of recurrent PJI to optimize patient outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of evidence<\/jats:title>\n                    <jats:p>IV.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00167-021-06794-3","type":"journal-article","created":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T23:37:36Z","timestamp":1636587456000},"page":"2582-2590","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection"],"prefix":"10.1002","volume":"30","author":[{"given":"Christian","family":"Klemt","sequence":"first","affiliation":[{"name":"<!--<label>1<\/label>--> Bioengineering Laboratory, Department of Orthopaedic Surgery Massachusetts General Hospital, Harvard Medical School 55 Fruit St 02114 Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Laurencin","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Bioengineering Laboratory, Department of Orthopaedic Surgery Massachusetts General Hospital, Harvard Medical School 55 Fruit St 02114 Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akachimere Cosmas","family":"Uzosike","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Bioengineering Laboratory, Department of Orthopaedic Surgery Massachusetts General Hospital, Harvard Medical School 55 Fruit St 02114 Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jillian C.","family":"Burns","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Bioengineering Laboratory, Department of Orthopaedic Surgery Massachusetts General Hospital, Harvard Medical School 55 Fruit St 02114 Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy G.","family":"Costales","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Bioengineering Laboratory, Department of Orthopaedic Surgery Massachusetts General Hospital, Harvard Medical School 55 Fruit St 02114 Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ingwon","family":"Yeo","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Bioengineering Laboratory, Department of Orthopaedic Surgery Massachusetts General Hospital, Harvard Medical School 55 Fruit St 02114 Boston MA 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