Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset.
Journal
Anesthesia and analgesia
ISSN: 1526-7598
Titre abrégé: Anesth Analg
Pays: United States
ID NLM: 1310650
Informations de publication
Date de publication:
01 2021
01 2021
Historique:
pubmed:
4
7
2020
medline:
20
1
2021
entrez:
4
7
2020
Statut:
ppublish
Résumé
Craniosynostosis is the premature fusion of ≥1 cranial sutures and often requires surgical intervention. Surgery may involve extensive osteotomies, which can lead to substantial blood loss. Currently, there are no consensus recommendations for guiding blood conservation or transfusion in this patient population. The aim of this study is to develop a machine-learning model to predict blood product transfusion requirements for individual pediatric patients undergoing craniofacial surgery. Using data from 2143 patients in the Pediatric Craniofacial Surgery Perioperative Registry, we assessed 6 machine-learning classification and regression models based on random forest, adaptive boosting (AdaBoost), neural network, gradient boosting machine (GBM), support vector machine, and elastic net methods with inputs from 22 demographic and preoperative features. We developed classification models to predict an individual's overall need for transfusion and regression models to predict the number of blood product units to be ordered preoperatively. The study is reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist for prediction model development. The GBM performed best in both domains, with an area under receiver operating characteristic curve of 0.87 ± 0.03 (95% confidence interval) and F-score of 0.91 ± 0.04 for classification, and a mean squared error of 1.15 ± 0.12, R-squared (R) of 0.73 ± 0.02, and root mean squared error of 1.05 ± 0.06 for regression. GBM feature ranking determined that the following variables held the most information for prediction: platelet count, weight, preoperative hematocrit, surgical volume per institution, age, and preoperative hemoglobin. We then produced a calculator to show the number of units of blood that should be ordered preoperatively for an individual patient. Anesthesiologists and surgeons can use this continually evolving predictive model to improve clinical care of patients presenting for craniosynostosis surgery.
Sections du résumé
BACKGROUND
Craniosynostosis is the premature fusion of ≥1 cranial sutures and often requires surgical intervention. Surgery may involve extensive osteotomies, which can lead to substantial blood loss. Currently, there are no consensus recommendations for guiding blood conservation or transfusion in this patient population. The aim of this study is to develop a machine-learning model to predict blood product transfusion requirements for individual pediatric patients undergoing craniofacial surgery.
METHODS
Using data from 2143 patients in the Pediatric Craniofacial Surgery Perioperative Registry, we assessed 6 machine-learning classification and regression models based on random forest, adaptive boosting (AdaBoost), neural network, gradient boosting machine (GBM), support vector machine, and elastic net methods with inputs from 22 demographic and preoperative features. We developed classification models to predict an individual's overall need for transfusion and regression models to predict the number of blood product units to be ordered preoperatively. The study is reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist for prediction model development.
RESULTS
The GBM performed best in both domains, with an area under receiver operating characteristic curve of 0.87 ± 0.03 (95% confidence interval) and F-score of 0.91 ± 0.04 for classification, and a mean squared error of 1.15 ± 0.12, R-squared (R) of 0.73 ± 0.02, and root mean squared error of 1.05 ± 0.06 for regression. GBM feature ranking determined that the following variables held the most information for prediction: platelet count, weight, preoperative hematocrit, surgical volume per institution, age, and preoperative hemoglobin. We then produced a calculator to show the number of units of blood that should be ordered preoperatively for an individual patient.
CONCLUSIONS
Anesthesiologists and surgeons can use this continually evolving predictive model to improve clinical care of patients presenting for craniosynostosis surgery.
Identifiants
pubmed: 32618624
doi: 10.1213/ANE.0000000000004988
pii: 00000539-202101000-00024
doi:
Types de publication
Journal Article
Observational Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
160-171Investigateurs
Christopher Abruzzese
(C)
Jesus Apuya
(J)
Angelina Bhandari
(A)
Amy Beethe
(A)
Hubert Benzon
(H)
Wendy Binstock
(W)
Victoria Bradford
(V)
Alyssa Brzenski
(A)
Stefan Budac
(S)
Veronica Busso
(V)
Surendrasingh Chhabada
(S)
Franklin Chiao
(F)
Franklyn Cladis
(F)
Danielle Claypool
(D)
Michael Collins
(M)
Lynnie Correll
(L)
Andrew Costandi
(A)
Rachel Dabek
(R)
Nicholas Dalesio
(N)
Piedad Echeverry
(P)
Ricardo Falcon
(R)
Patrick Fernandez
(P)
John Fiadjoe
(J)
Meera Gangadharan
(M)
Katherine Gentry
(K)
Chris Glover
(C)
Susan M Goobie
(SM)
Amanda Gosman
(A)
Anastasia Grivoyannis
(A)
Shannon Grap
(S)
Heike Gries
(H)
Allison Griffin
(A)
John Hajduk
(J)
Thorsten Haas
(T)
Rebecca Hall
(R)
Jennifer Hansen
(J)
Mali Hetmaniuk
(M)
H Mayumi Homi
(HM)
Vincent Hsieh
(V)
Henry Huang
(H)
Pablo Ingelmo
(P)
Iskra Ivanova
(I)
Ranu Jain
(R)
Siri Kanmanthreddy
(S)
Michelle Kars
(M)
Mike King
(M)
John Koller
(J)
Courtney Kowalczyk-Derderian
(C)
Jane Kugler
(J)
Kristen Labovsky
(K)
Indrani Lakheeram
(I)
Alina Lazar
(A)
Andrew Lee
(A)
Jennifer Lee
(J)
Jose Luis Martinez
(J)
Brian Masel
(B)
Aaron Mason
(A)
Eduardo Medellin
(E)
Vivek Mehta
(V)
Petra Meier
(P)
Heather Mitzel Levy
(H)
Wallis T Muhly
(WT)
Bridget Muldowney
(B)
Jonathon Nelson
(J)
Julie Nicholson
(J)
Kim-Phuong Nguyen
(KP)
Thanh Nguyen
(T)
Margaret Owens-Stubblefield
(M)
Matt Pankratz
(M)
Uma Ramesh Parekh
(U)
Jasmine Patel
(J)
Roshan Patel
(R)
Carolina Perez-Pradilla
(C)
Timothy Petersen
(T)
Julian Post
(J)
Kim Poteet-Schwartz
(K)
Pavithra Ranganathan
(P)
Srijaya Reddy
(S)
Russell Reid
(R)
Karene Ricketts
(K)
Megan Rodgers McCormick
(M)
Laura Ryan
(L)
Kaitlyn Sbrollini
(K)
Peggy Seidman
(P)
Davinder Singh
(D)
Neil R Singhal
(NR)
Rochelle Skitt
(R)
Codruta Soneru
(C)
Emad Sorial
(E)
Rachel Spitznagel
(R)
Bobbie Stubbeman
(B)
Rani Sunder
(R)
Wai Sung
(W)
Tariq Syed
(T)
Peter Szmuk
(P)
Brad M Taicher
(BM)
Jenna Taylor
(J)
Douglas Thompson
(D)
Lisa Tretault
(L)
Galit Ungar-Kastner
(G)
John Wieser
(J)
Karen Wong
(K)
Hannah Yates
(H)
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