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Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery
STUDY DESIGN: retrospective cohort study. OBJECTIVES: To test and compare 2 machine learning algorithms to define characteristics associated with candidates for ambulatory same day laminectomy surgery. METHODS: The American College of Surgeons National Surgical Quality Improvement Program database w...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393988/ https://www.ncbi.nlm.nih.gov/pubmed/33406909 http://dx.doi.org/10.1177/2192568220979835 |
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author | Li, Qiyi Zhong, Haoyan Girardi, Federico P. Poeran, Jashvant Wilson, Lauren A. Memtsoudis, Stavros G. Liu, Jiabin |
author_facet | Li, Qiyi Zhong, Haoyan Girardi, Federico P. Poeran, Jashvant Wilson, Lauren A. Memtsoudis, Stavros G. Liu, Jiabin |
author_sort | Li, Qiyi |
collection | PubMed |
description | STUDY DESIGN: retrospective cohort study. OBJECTIVES: To test and compare 2 machine learning algorithms to define characteristics associated with candidates for ambulatory same day laminectomy surgery. METHODS: The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent single level laminectomy in 2017 and 2018. The main outcome was ambulatory same day discharge. Study variables of interest included demographic information, comorbidities, preoperative laboratory values, and intra-operative information. Two machine learning predictive modeling algorithms, artificial neural network (ANN) and random forest, were trained to predict same day discharge. The quality of models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) measures. RESULTS: Among 35,644 patients, 13,230 (37.1%) were discharged on the day of surgery. Both ANN and RF demonstrated a satisfactory model quality in terms of AUC (0.77 and 0.77), accuracy (0.69 and 0.70), sensitivity (0.83 and 0.58), specificity (0.55 and 0.80), PPV (0.77 and 0.69), and NPV (0.64 and 0.70). Both models highlighted several important predictive variables, including age, duration of operation, body mass index and preoperative laboratory values including, hematocrit, platelets, white blood cells, and alkaline phosphatase. CONCLUSION: Machine learning approaches provide a promising tool to identify candidates for ambulatory laminectomy surgery. Both machine learning algorithms highlighted the as yet unrecognized importance of preoperative laboratory testing on patient pathway design. |
format | Online Article Text |
id | pubmed-9393988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-93939882022-08-23 Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery Li, Qiyi Zhong, Haoyan Girardi, Federico P. Poeran, Jashvant Wilson, Lauren A. Memtsoudis, Stavros G. Liu, Jiabin Global Spine J Original Articles STUDY DESIGN: retrospective cohort study. OBJECTIVES: To test and compare 2 machine learning algorithms to define characteristics associated with candidates for ambulatory same day laminectomy surgery. METHODS: The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent single level laminectomy in 2017 and 2018. The main outcome was ambulatory same day discharge. Study variables of interest included demographic information, comorbidities, preoperative laboratory values, and intra-operative information. Two machine learning predictive modeling algorithms, artificial neural network (ANN) and random forest, were trained to predict same day discharge. The quality of models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) measures. RESULTS: Among 35,644 patients, 13,230 (37.1%) were discharged on the day of surgery. Both ANN and RF demonstrated a satisfactory model quality in terms of AUC (0.77 and 0.77), accuracy (0.69 and 0.70), sensitivity (0.83 and 0.58), specificity (0.55 and 0.80), PPV (0.77 and 0.69), and NPV (0.64 and 0.70). Both models highlighted several important predictive variables, including age, duration of operation, body mass index and preoperative laboratory values including, hematocrit, platelets, white blood cells, and alkaline phosphatase. CONCLUSION: Machine learning approaches provide a promising tool to identify candidates for ambulatory laminectomy surgery. Both machine learning algorithms highlighted the as yet unrecognized importance of preoperative laboratory testing on patient pathway design. SAGE Publications 2021-01-07 2022-09 /pmc/articles/PMC9393988/ /pubmed/33406909 http://dx.doi.org/10.1177/2192568220979835 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Articles Li, Qiyi Zhong, Haoyan Girardi, Federico P. Poeran, Jashvant Wilson, Lauren A. Memtsoudis, Stavros G. Liu, Jiabin Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery |
title | Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery |
title_full | Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery |
title_fullStr | Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery |
title_full_unstemmed | Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery |
title_short | Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery |
title_sort | machine learning approaches to define candidates for ambulatory single level laminectomy surgery |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393988/ https://www.ncbi.nlm.nih.gov/pubmed/33406909 http://dx.doi.org/10.1177/2192568220979835 |
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