Prediction of Length of Stay After Colorectal Surgery Using Intraoperative Risk Factors
Objective:. The primary objective of this study was to develop a length of stay (LOS) prediction model. Background:. Predicting the LOS is crucial for patient care, planning, managing expectations, and optimizing hospital resources. Prolonged LOS after colorectal surgery is largely influenced by com...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Health
2024-09-01
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| Series: | Annals of Surgery Open |
| Online Access: | http://journals.lww.com/10.1097/AS9.0000000000000478 |
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| author | Daitlin Esmee Huisman, MD Erik Wouter Ingwersen, MD Joanna Luttikhold, MD, PhD Gerrit Dirk Slooter, MD, PhD Geert Kazemier, MD, PhD Freek Daams, MD, PhD LekCheck Study Group Audrey Jongen Carlo V. Feo Simone Targa Hidde M. Kroon Emmanuel A. G. L. Lagae Aalbert K. Talsma Johannes A. Wegdam Bob van Wely Dirk J. A. Sonneveld Sanne C. Veltkamp Emiel G. G. Verdaasdonk Rudi M. H. Roumen Freek Daams |
| author_facet | Daitlin Esmee Huisman, MD Erik Wouter Ingwersen, MD Joanna Luttikhold, MD, PhD Gerrit Dirk Slooter, MD, PhD Geert Kazemier, MD, PhD Freek Daams, MD, PhD LekCheck Study Group Audrey Jongen Carlo V. Feo Simone Targa Hidde M. Kroon Emmanuel A. G. L. Lagae Aalbert K. Talsma Johannes A. Wegdam Bob van Wely Dirk J. A. Sonneveld Sanne C. Veltkamp Emiel G. G. Verdaasdonk Rudi M. H. Roumen Freek Daams |
| author_sort | Daitlin Esmee Huisman, MD |
| collection | DOAJ |
| description | Objective:. The primary objective of this study was to develop a length of stay (LOS) prediction model.
Background:. Predicting the LOS is crucial for patient care, planning, managing expectations, and optimizing hospital resources. Prolonged LOS after colorectal surgery is largely influenced by complications, and an accurate prediction model could significantly benefit patient outcomes and healthcare efficiency.
Methods:. This study included patients who underwent colorectal surgery in 14 different hospitals between January 2016 and December 2020. Two distinct random forest models were developed: one solely based on preoperative variables (preoperative prediction model [PP model]) and the other incorporating both preoperative and intraoperative variables (intraoperative prediction model [IP model]). Both models underwent validation using 10-fold cross-validation. The discriminative power of the model was assessed using the area under the curve (AUC), and calibration was evaluated using a calibration curve. The 2 developed models were compared using DeLong test.
Results:. A total of 2140 patients were included in the analysis. After internal validation, the PP model achieved an AUC of 0.75 (95% confidence interval [CI]: 0.73–0.77), and the IP model achieved an AUC of 0.84 (95% CI: 0.82–0.85). The difference in discrimination between the 2 models was statistically significant (DeLong test, P < 0.001). Both models exhibited good calibration.
Conclusions:. Incorporating intraoperative parameters enhances the accuracy of the predictive model for LOS after colorectal surgery. Improving LOS prediction can assist in managing the increasing number of patients and optimizing the allocation of healthcare resources. |
| format | Article |
| id | doaj-art-7fe992fd2bed40bf9e1a7ebff1af2f15 |
| institution | Kabale University |
| issn | 2691-3593 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wolters Kluwer Health |
| record_format | Article |
| series | Annals of Surgery Open |
| spelling | doaj-art-7fe992fd2bed40bf9e1a7ebff1af2f152025-01-24T09:18:49ZengWolters Kluwer HealthAnnals of Surgery Open2691-35932024-09-0153e47810.1097/AS9.0000000000000478202409000-00028Prediction of Length of Stay After Colorectal Surgery Using Intraoperative Risk FactorsDaitlin Esmee Huisman, MD0Erik Wouter Ingwersen, MD1Joanna Luttikhold, MD, PhD2Gerrit Dirk Slooter, MD, PhD3Geert Kazemier, MD, PhD4Freek Daams, MD, PhD5LekCheck Study GroupAudrey JongenCarlo V. FeoSimone TargaHidde M. KroonEmmanuel A. G. L. LagaeAalbert K. TalsmaJohannes A. WegdamBob van WelyDirk J. A. SonneveldSanne C. VeltkampEmiel G. G. VerdaasdonkRudi M. H. RoumenFreek Daams* From the Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands* From the Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands‡ Department of Surgery, Amstelland Hospital, Amstelveen, Netherlands§ Department of Surgery, Maxima Medical Center Veldhoven, Eindhoven, Netherlands.* From the Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands* From the Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, NetherlandsObjective:. The primary objective of this study was to develop a length of stay (LOS) prediction model. Background:. Predicting the LOS is crucial for patient care, planning, managing expectations, and optimizing hospital resources. Prolonged LOS after colorectal surgery is largely influenced by complications, and an accurate prediction model could significantly benefit patient outcomes and healthcare efficiency. Methods:. This study included patients who underwent colorectal surgery in 14 different hospitals between January 2016 and December 2020. Two distinct random forest models were developed: one solely based on preoperative variables (preoperative prediction model [PP model]) and the other incorporating both preoperative and intraoperative variables (intraoperative prediction model [IP model]). Both models underwent validation using 10-fold cross-validation. The discriminative power of the model was assessed using the area under the curve (AUC), and calibration was evaluated using a calibration curve. The 2 developed models were compared using DeLong test. Results:. A total of 2140 patients were included in the analysis. After internal validation, the PP model achieved an AUC of 0.75 (95% confidence interval [CI]: 0.73–0.77), and the IP model achieved an AUC of 0.84 (95% CI: 0.82–0.85). The difference in discrimination between the 2 models was statistically significant (DeLong test, P < 0.001). Both models exhibited good calibration. Conclusions:. Incorporating intraoperative parameters enhances the accuracy of the predictive model for LOS after colorectal surgery. Improving LOS prediction can assist in managing the increasing number of patients and optimizing the allocation of healthcare resources.http://journals.lww.com/10.1097/AS9.0000000000000478 |
| spellingShingle | Daitlin Esmee Huisman, MD Erik Wouter Ingwersen, MD Joanna Luttikhold, MD, PhD Gerrit Dirk Slooter, MD, PhD Geert Kazemier, MD, PhD Freek Daams, MD, PhD LekCheck Study Group Audrey Jongen Carlo V. Feo Simone Targa Hidde M. Kroon Emmanuel A. G. L. Lagae Aalbert K. Talsma Johannes A. Wegdam Bob van Wely Dirk J. A. Sonneveld Sanne C. Veltkamp Emiel G. G. Verdaasdonk Rudi M. H. Roumen Freek Daams Prediction of Length of Stay After Colorectal Surgery Using Intraoperative Risk Factors Annals of Surgery Open |
| title | Prediction of Length of Stay After Colorectal Surgery Using Intraoperative Risk Factors |
| title_full | Prediction of Length of Stay After Colorectal Surgery Using Intraoperative Risk Factors |
| title_fullStr | Prediction of Length of Stay After Colorectal Surgery Using Intraoperative Risk Factors |
| title_full_unstemmed | Prediction of Length of Stay After Colorectal Surgery Using Intraoperative Risk Factors |
| title_short | Prediction of Length of Stay After Colorectal Surgery Using Intraoperative Risk Factors |
| title_sort | prediction of length of stay after colorectal surgery using intraoperative risk factors |
| url | http://journals.lww.com/10.1097/AS9.0000000000000478 |
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