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Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors
OBJECTIVE: Intradural spinal tumors are uncommon and while associations between clinical characteristics and surgical outcomes have been explored, there remains a paucity of literature unifying diverse predictors into an integrated risk model. To predict postresection outcomes for patients with spin...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Spinal Neurosurgery Society
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8987552/ https://www.ncbi.nlm.nih.gov/pubmed/35378587 http://dx.doi.org/10.14245/ns.2143244.622 |
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author | Jin, Michael C. Ho, Allen L. Feng, Austin Y. Medress, Zachary A. Pendharkar, Arjun V. Rezaii, Paymon Ratliff, John K. Desai, Atman M. |
author_facet | Jin, Michael C. Ho, Allen L. Feng, Austin Y. Medress, Zachary A. Pendharkar, Arjun V. Rezaii, Paymon Ratliff, John K. Desai, Atman M. |
author_sort | Jin, Michael C. |
collection | PubMed |
description | OBJECTIVE: Intradural spinal tumors are uncommon and while associations between clinical characteristics and surgical outcomes have been explored, there remains a paucity of literature unifying diverse predictors into an integrated risk model. To predict postresection outcomes for patients with spinal tumors. METHODS: IBM MarketScan Claims Database was queried for adult patients receiving surgery for intradural tumors between 2007 and 2016. Primary outcomes-of-interest were nonhome discharge and 90-day postdischarge readmissions. Secondary outcomes included hospitalization duration and postoperative complications. Risk modeling was developed using a regularized logistic regression framework (LASSO, least absolute shrinkage and selection operator) and validated in a withheld subset. RESULTS: A total of 5,060 adult patients were included. Most surgeries utilized a posterior approach (n=5,023, 99.3%) and tumors were most commonly found in the thoracic region (n=1,941, 38.4%), followed by the lumbar (n=1,781, 35.2%) and cervical (n=1,294, 25.6%) regions. Compared to models using only tumor-specific or patient-specific features, our integrated models demonstrated better discrimination (area under the curve [AUC] [nonhome discharge] = 0.786; AUC [90-day readmissions] = 0.693) and accuracy (Brier score [nonhome discharge] = 0.155; Brier score [90-day readmissions] = 0.093). Compared to those predicted to be lowest risk, patients predicted to be highest-risk for nonhome discharge required continued care 16.3 times more frequently (64.5% vs. 3.9%). Similarly, patients predicted to be at highest risk for postdischarge readmissions were readmitted 7.3 times as often as those predicted to be at lowest risk (32.6% vs. 4.4%). CONCLUSION: Using a diverse set of clinical characteristics spanning tumor-, patient-, and hospitalization-derived data, we developed and validated risk models integrating diverse clinical data for predicting nonhome discharge and postdischarge readmissions. |
format | Online Article Text |
id | pubmed-8987552 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Korean Spinal Neurosurgery Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-89875522022-04-13 Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors Jin, Michael C. Ho, Allen L. Feng, Austin Y. Medress, Zachary A. Pendharkar, Arjun V. Rezaii, Paymon Ratliff, John K. Desai, Atman M. Neurospine Original Article OBJECTIVE: Intradural spinal tumors are uncommon and while associations between clinical characteristics and surgical outcomes have been explored, there remains a paucity of literature unifying diverse predictors into an integrated risk model. To predict postresection outcomes for patients with spinal tumors. METHODS: IBM MarketScan Claims Database was queried for adult patients receiving surgery for intradural tumors between 2007 and 2016. Primary outcomes-of-interest were nonhome discharge and 90-day postdischarge readmissions. Secondary outcomes included hospitalization duration and postoperative complications. Risk modeling was developed using a regularized logistic regression framework (LASSO, least absolute shrinkage and selection operator) and validated in a withheld subset. RESULTS: A total of 5,060 adult patients were included. Most surgeries utilized a posterior approach (n=5,023, 99.3%) and tumors were most commonly found in the thoracic region (n=1,941, 38.4%), followed by the lumbar (n=1,781, 35.2%) and cervical (n=1,294, 25.6%) regions. Compared to models using only tumor-specific or patient-specific features, our integrated models demonstrated better discrimination (area under the curve [AUC] [nonhome discharge] = 0.786; AUC [90-day readmissions] = 0.693) and accuracy (Brier score [nonhome discharge] = 0.155; Brier score [90-day readmissions] = 0.093). Compared to those predicted to be lowest risk, patients predicted to be highest-risk for nonhome discharge required continued care 16.3 times more frequently (64.5% vs. 3.9%). Similarly, patients predicted to be at highest risk for postdischarge readmissions were readmitted 7.3 times as often as those predicted to be at lowest risk (32.6% vs. 4.4%). CONCLUSION: Using a diverse set of clinical characteristics spanning tumor-, patient-, and hospitalization-derived data, we developed and validated risk models integrating diverse clinical data for predicting nonhome discharge and postdischarge readmissions. Korean Spinal Neurosurgery Society 2022-03 2022-03-31 /pmc/articles/PMC8987552/ /pubmed/35378587 http://dx.doi.org/10.14245/ns.2143244.622 Text en Copyright © 2022 by the Korean Spinal Neurosurgery Society https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Jin, Michael C. Ho, Allen L. Feng, Austin Y. Medress, Zachary A. Pendharkar, Arjun V. Rezaii, Paymon Ratliff, John K. Desai, Atman M. Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors |
title | Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors |
title_full | Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors |
title_fullStr | Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors |
title_full_unstemmed | Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors |
title_short | Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors |
title_sort | prediction of discharge status and readmissions after resection of intradural spinal tumors |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8987552/ https://www.ncbi.nlm.nih.gov/pubmed/35378587 http://dx.doi.org/10.14245/ns.2143244.622 |
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