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Zoli M, Staartjes VE, Guaraldi F, Friso F, Rustici A, Asioli S, Sollini G, Pasquini E, Regli L, Serra C, Mazzatenta D. Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming? Neurosurg Focus 2021; 48:E5. [PMID: 32480364 DOI: 10.3171/2020.3.focus2060] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 03/04/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD). METHODS All consecutive patients in our center who underwent surgery for CD through the endoscopic endonasal approach were retrospectively reviewed. Study endpoints were gross-tumor removal (GTR), postsurgical remission, and long-term control of disease. Several demographic, radiological, and histological factors were assessed as potential predictors. For ML-based modeling, data were randomly divided into 2 sets with an 80% to 20% ratio for bootstrapped training and testing, respectively. Several algorithms were tested and tuned for the area under the curve (AUC). RESULTS The study included 151 patients. GTR was achieved in 137 patients (91%), and postsurgical hypersecretion remission was achieved in 133 patients (88%). At last follow-up, 116 patients (77%) were still in remission after surgery and in 21 patients (14%), CD was controlled with complementary treatment (overall, of 131 cases, 87% were under control at follow-up). At internal validation, the endpoints were predicted with AUCs of 0.81-1.00, accuracy of 81%-100%, and Brier scores of 0.035-0.151. Tumor size and invasiveness and histological confirmation of adrenocorticotropic hormone (ACTH)-secreting cells were the main predictors for the 3 endpoints of interest. CONCLUSIONS ML algorithms were used to train and internally validate robust models for all the endpoints, giving accurate outcome predictions in CD cases. This analytical method seems promising for potentially improving future patient care and counseling; however, careful clinical interpretation of the results remains necessary before any clinical adoption of ML. Moreover, further studies and increased sample sizes are definitely required before the widespread adoption of ML to the study of CD.
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Affiliation(s)
- Matteo Zoli
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy
| | - Victor E Staartjes
- 3Department of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, Switzerland.,4Neurosurgery, Amsterdam Movement Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, The Netherlands
| | - Federica Guaraldi
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy
| | - Filippo Friso
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna
| | - Arianna Rustici
- 5Department of Neuroradiology, IRCCS Istitute of Neurological Sciences of Bologna.,6Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna
| | - Sofia Asioli
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy.,7Section of Anatomic Pathology 'M. Malpighi' at Bellaria Hospital, Bologna; and
| | - Giacomo Sollini
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,8ENT Department, Bellaria Hospital, Bologna, Italy
| | - Ernesto Pasquini
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,8ENT Department, Bellaria Hospital, Bologna, Italy
| | - Luca Regli
- 3Department of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, Switzerland
| | - Carlo Serra
- 3Department of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, Switzerland
| | - Diego Mazzatenta
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy
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Kalagara S, Eltorai AEM, Durand WM, DePasse JM, Daniels AH. Machine learning modeling for predicting hospital readmission following lumbar laminectomy. J Neurosurg Spine 2019; 30:344-352. [PMID: 30544346 DOI: 10.3171/2018.8.spine1869] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 08/15/2018] [Indexed: 01/01/2023]
Abstract
In BriefAuthors of this study analyzed hospital readmissions following laminectomy and developed predictive models to identify readmitted patients with an accuracy >95% when using all variables and >79% when using only predischarge variables. A model capable of predicting 40% of readmitted patients was created using only the variables known predischarge. This investigation is important in its provision of data that will assist the development of predictive models for readmission as well as interventions to prevent readmission in high-risk patients.
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Affiliation(s)
| | | | | | | | - Alan H Daniels
- 1Division of Spine Surgery and.,2Department of Orthopaedic Surgery, Warren Alpert Medical School of Brown University, Providence, Rhode Island
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