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Tang M, Zhao Y, Xiao J, Jiang S, Tan J, Xu Q, Pan C, Wang J. Development and validation of a predictive model for prolonged length of stay in elderly type 2 diabetes mellitus patients combined with cerebral infarction. Front Neurol 2024; 15:1405096. [PMID: 39148703 PMCID: PMC11325865 DOI: 10.3389/fneur.2024.1405096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 07/12/2024] [Indexed: 08/17/2024] Open
Abstract
Background This study aimed to identify the predictive factors for prolonged length of stay (LOS) in elderly type 2 diabetes mellitus (T2DM) patients suffering from cerebral infarction (CI) and construct a predictive model to effectively utilize hospital resources. Methods Clinical data were retrospectively collected from T2DM patients suffering from CI aged ≥65 years who were admitted to five tertiary hospitals in Southwest China. The least absolute shrinkage and selection operator (LASSO) regression model and multivariable logistic regression analysis were conducted to identify the independent predictors of prolonged LOS. A nomogram was constructed to visualize the model. The discrimination, calibration, and clinical practicality of the model were evaluated according to the area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results A total of 13,361 patients were included, comprising 6,023, 2,582, and 4,756 patients in the training, internal validation, and external validation sets, respectively. The results revealed that the ACCI score, OP, PI, analgesics use, antibiotics use, psychotropic drug use, insurance type, and ALB were independent predictors for prolonged LOS. The eight-predictor LASSO logistic regression displayed high prediction ability, with an AUROC of 0.725 (95% confidence interval [CI]: 0.710-0.739), a sensitivity of 0.662 (95% CI: 0.639-0.686), and a specificity of 0.675 (95% CI: 0.661-0.689). The calibration curve (bootstraps = 1,000) showed good calibration. In addition, the DCA and CIC also indicated good clinical practicality. An operation interface on a web page (https://xxmyyz.shinyapps.io/prolonged_los1/) was also established to facilitate clinical use. Conclusion The developed model can predict the risk of prolonged LOS in elderly T2DM patients diagnosed with CI, enabling clinicians to optimize bed management.
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Affiliation(s)
- Mingshan Tang
- Department of Neurology, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Yan Zhao
- Department of Neurology, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Xiao
- Department of Neurology, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Side Jiang
- Department of Neurology, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Juntao Tan
- Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Qian Xu
- Library, Chongqing Medical University, Chongqing, China
| | - Chengde Pan
- Department of Neurology, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Wang
- Department of Neurology, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China
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Wen P, Zhao Y, Yang M, Yang P, Nan K, Liu L, Xu P. Identification of necroptosis-related genes in ankylosing spondylitis by bioinformatics and experimental validation. J Cell Mol Med 2024; 28:e18557. [PMID: 39031474 PMCID: PMC11258886 DOI: 10.1111/jcmm.18557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/15/2024] [Accepted: 07/08/2024] [Indexed: 07/22/2024] Open
Abstract
The pathogenesis of ankylosing spondylitis (AS) remains unclear, and while recent studies have implicated necroptosis in various autoimmune diseases, an investigation of its relationship with AS has not been reported. In this study, we utilized the Gene Expression Omnibus database to compare gene expressions between AS patients and healthy controls, identifying 18 differentially expressed necroptosis-related genes (DENRGs), with 8 upregulated and 10 downregulated. Through the application of three machine learning algorithms-least absolute shrinkage and selection operation, support vector machine-recursive feature elimination and random forest-two hub genes, FASLG and TARDBP, were pinpointed. These genes demonstrated high specificity and sensitivity for AS diagnosis, as evidenced by receiver operating characteristic curve analysis. These findings were further supported by external datasets and cellular experiments, which confirmed the downregulation of FASLG and upregulation of TARDBP in AS patients. Immune cell infiltration analysis suggested that CD4+ T cells, CD8+ T cells, NK cells and neutrophils may be associated with the development of AS. Notably, in the group with high FASLG expression, there was a significant infiltration of CD8+ T cells, memory-activated CD4+ T cells and resting NK cells, with relatively less infiltration of memory-resting CD4+ T cells and neutrophils. Conversely, in the group with high TARDBP expression, there was enhanced infiltration of naïve CD4+ T cells and M0 macrophages, with a reduced presence of memory-resting CD4+ T cells. In summary, FASLG and TARDBP may contribute to AS pathogenesis by regulating the immune microenvironment and immune-related signalling pathways. These findings offer new insights into the molecular mechanisms of AS and suggest potential new targets for therapeutic strategies.
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Affiliation(s)
- Pengfei Wen
- Department of Joint Surgery, Honghui HospitalXi'an Jiaotong UniversityShaanxiChina
| | - Yan Zhao
- Department of Laboratory, Honghui HospitalXi'an Jiaotong UniversityShaanxiChina
| | - Mingyi Yang
- Department of Joint Surgery, Honghui HospitalXi'an Jiaotong UniversityShaanxiChina
| | - Peng Yang
- Department of Joint Surgery, Honghui HospitalXi'an Jiaotong UniversityShaanxiChina
| | - Kai Nan
- Department of Joint Surgery, Honghui HospitalXi'an Jiaotong UniversityShaanxiChina
| | - Lin Liu
- Department of Joint Surgery, Honghui HospitalXi'an Jiaotong UniversityShaanxiChina
| | - Peng Xu
- Department of Joint Surgery, Honghui HospitalXi'an Jiaotong UniversityShaanxiChina
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Früh A, Frey D, Hilbert A, Jelgersma C, Uhl C, Nissimov N, Truckenmüller P, Wasilewski D, Rallios D, Hoppe M, Bayerl S, Hecht N, Vajkoczy P, Wessels L. Preoperatively-determined Red Distribution Width (RDW) predicts prolonged length of stay after single-level spinal fusion in elderly patients. BRAIN & SPINE 2024; 4:102827. [PMID: 38784126 PMCID: PMC11112267 DOI: 10.1016/j.bas.2024.102827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 04/06/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024]
Abstract
Introduction Elderly patients receiving lumbar fusion surgeries present with a higher risk profile, which necessitates a robust predictor of postoperative outcomes. The Red Distribution Width (RDW) is a preoperative routinely determined parameter that reflects the degree of heterogeneity of red blood cells. Thereby, RDW is associated with frailty in hospital-admitted patients. Research question This study aims to elucidate the potential of RDW as a frailty biomarker predictive of prolonged hospital stays following elective mono-segmental fusion surgery in elderly patients. Material and methods In this retrospective study, we included all patients with age over 75 years that were treated via lumbar single-level spinal fusion from 2015 to 2022 at our tertiary medical center. Prolonged length of stay (pLOS) was defined as a length ≥ the 3rd quartile of LOS of all included patients. Classical correlation analysis, Receiver-operating characteristic (ROC) and new machine learning algorithms) were used. Results A total of 208 patients were included in the present study. The median age was 77 (IQR 75-80) years. The median LOS of the patients was 6 (IQR 5-8) days. The data shows a significant positive correlation between RDW and LOS. RDW is significantly enhanced in the pLOS group. New machine learning approaches with the imputation of multiple variables can enhance the performance to an AUC of 71%. Discussion and conclusion RDW may serve as a predictor for a pLOS in elderly. These results are compelling because the determination of this frailty biomarker is routinely performed at hospital admission. An improved prognostication of LOS could enable healthcare systems to distribute constrained hospital resources efficiently, fostering evidence-based decision-making processes.
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Affiliation(s)
- Anton Früh
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- BIH Biomedical Innovation Academy, BIH Charité Junior Digital Clinician Scientist Program, Charitéplatz 1, 10117, Berlin, Germany
| | - Dietmar Frey
- CLAIM – Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Adam Hilbert
- CLAIM – Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Claudius Jelgersma
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Christian Uhl
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Nitzan Nissimov
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Peter Truckenmüller
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - David Wasilewski
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Dimitrios Rallios
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Matthias Hoppe
- Medical Faculty Leipzig, Leipzig University, Leipzig, Germany
| | - Simon Bayerl
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Nils Hecht
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Lars Wessels
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
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Arjmandnia F, Alimohammadi E. The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review. Patient Saf Surg 2024; 18:11. [PMID: 38528562 DOI: 10.1186/s13037-024-00393-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 03/15/2024] [Indexed: 03/27/2024] Open
Abstract
Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.
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Affiliation(s)
- Fatemeh Arjmandnia
- Department of Aneasthesiology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ehsan Alimohammadi
- Department of Neurosurgery, Kermanshah University of Medical Sciences, Imam Reza Hospital, Kermanshah, Iran.
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Kshirsagar RS, Eide JG, Qatanani A, Harris J, Abello EH, Roman KM, Vasudev M, Jackson C, Lee JYK, Kuan EC, Palmer JN, Adappa ND. Impact of Frailty on Postoperative Outcomes in Extended Endonasal Skull Base Surgery for Suprasellar Pathologies. Otolaryngol Head Neck Surg 2024; 170:568-576. [PMID: 37746938 DOI: 10.1002/ohn.537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 08/29/2023] [Accepted: 09/05/2023] [Indexed: 09/26/2023]
Abstract
OBJECTIVE Frailty metrics estimate a patient's ability to tolerate physiologic stress and there are limited frailty data in patients undergoing expanded endonasal approaches (EEA) for suprasellar pathologies. Elevated frailty metrics have been associated with increased perioperative complications in patients undergoing craniotomies. We sought to examine this potential relationship in EEA. STUDY DESIGN Retrospective cohort study. SETTING Two tertiary academic skull base centers. METHODS Cases of patients undergoing EEA for suprasellar pathologies were reviewed. Demographic, treatment, survival, and postoperative outcomes data were recorded. Frailty was calculated using validated indexes, including the American Society of Anesthesiologists (ASA) classification, the modified 5-item frailty index (mFI-5), and the Charlson comorbidity index (CCI). Primary outcomes included 30-day medical and surgical complications. RESULTS A total of 88 patients were included, with 59 (67%) female patients and a mean age of 54 ± 15 years. The most common pathologies included 53 meningiomas (60.2%) and 21 craniopharyngiomas (23.9%). Most patients were ASA class 3 (54.5%) with mean mFI-5 0.82 ± 1.01 and CCI 4.18 ± 2.42. There was no association between increased frailty and 30-day medical or surgical outcomes (including postoperative cerebrospinal fluid leak), prolonged length of hospital stay, or mortality (all P > .05). Higher mFI-5 was associated with an increased risk for 30-day readmission (odds ratio: 2.35, 95% confidence Interval: 1.10-5.64, P = .04). CONCLUSION Despite the patient population being notably frail, we only identified an increased risk for 30-day readmission and observed no links with deteriorating surgical, medical, or mortality outcomes. This implies that conventional frailty metrics may not effectively align with EEA outcomes.
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Affiliation(s)
- Rijul S Kshirsagar
- Department of Otorhinolaryngology-Head and Neck Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jacob G Eide
- Department of Otorhinolaryngology-Head and Neck Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anas Qatanani
- Department of Otorhinolaryngology-Head and Neck Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jacob Harris
- Department of Otorhinolaryngology-Head and Neck Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Eric H Abello
- Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, Orange, California, USA
| | - Kelsey M Roman
- Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, Orange, California, USA
| | - Milind Vasudev
- Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, Orange, California, USA
| | - Christina Jackson
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John Y K Lee
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Edward C Kuan
- Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, Orange, California, USA
| | - James N Palmer
- Department of Otorhinolaryngology-Head and Neck Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nithin D Adappa
- Department of Otorhinolaryngology-Head and Neck Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Saravi B, Zink A, Ülkümen S, Couillard-Despres S, Lang G, Hassel F. Artificial intelligence-based analysis of associations between learning curve and clinical outcomes in endoscopic and microsurgical lumbar decompression surgery. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023:10.1007/s00586-023-08084-7. [PMID: 38156994 DOI: 10.1007/s00586-023-08084-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 11/22/2023] [Accepted: 12/03/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE A common spine surgery procedure involves decompression of the lumbar spine. The impact of the surgeon's learning curve on relevant clinical outcomes is currently not well examined in the literature. A variety of machine learning algorithms have been investigated in this study to determine how a surgeon's learning curve and other clinical parameters will influence prolonged lengths of stay (LOS), extended operating times (OT), and complications, as well as whether these clinical parameters can be reliably predicted. METHODS A retrospective monocentric cohort study of patients with lumbar spinal stenosis treated with microsurgical (MSD) and full-endoscopic (FED) decompression was conducted. The study included 206 patients with lumbar spinal stenosis who underwent FED (63; 30.6%) and MSD (118; 57.3%). Prolonged LOS and OT were defined as those exceeding the 75th percentile of the cohort. Furthermore, complications were assessed as a dependent variable. Using unsupervised learning, clusters were identified in the data, which helped distinguish between the early learning curve (ELC) and the late learning curve (LLC). From 15 algorithms, the top five algorithms that best fit the data were selected for each prediction task. We calculated the accuracy of prediction (Acc) and the area under the curve (AUC). The most significant predictors were determined using a feature importance analysis. RESULTS For the FED group, the median number of surgeries with case surgery type at the time of surgery was 72 in the ELC group and 274 in the LLC group. FED patients did not significantly differ in outcome variables (LOS, OT, complication rate) between the ELC and LLC group. The random forest model demonstrated the highest mean accuracy and AUC across all folds for each classification task. For OT, it achieved an accuracy of 76.08% and an AUC of 0.89. For LOS, the model reached an accuracy of 83.83% and an AUC of 0.91. Lastly, in predicting complications, the random forest model attained the highest accuracy of 89.90% and an AUC of 0.94. Feature importance analysis indicated that LOS, OT, and complications were more significantly affected by patient characteristics than the surgical technique (FED versus MSD) or the surgeon's learning curve. CONCLUSIONS A median of 72 cases of FED surgeries led to comparable clinical outcomes in the early learning curve phase compared to experienced surgeons. These outcomes seem to be more significantly affected by patient characteristics than the learning curve or the surgical technique. Several study variables, including the learning curve, can be used to predict whether lumbar decompression surgery will result in an increased LOS, OT, or complications. To introduce the provided prediction tools into clinics, the algorithms need to be implemented into open-source software and externally validated through large-scale randomized controlled trials.
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Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Medical Centre - Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Hugstetterstrasse 55, 79106, Freiburg, Germany.
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany.
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020, Salzburg, Austria.
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
| | - Sara Ülkümen
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020, Salzburg, Austria
- Austrian Cluster for Tissue Regeneration, Vienna, Austria
| | - Gernot Lang
- Department of Orthopedics and Trauma Surgery, Medical Centre - Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Hugstetterstrasse 55, 79106, Freiburg, Germany
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
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Fan G, Wang D, Li Y, Xu Z, Wang H, Liu H, Liao X. Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography. Diagnostics (Basel) 2023; 14:53. [PMID: 38201362 PMCID: PMC10795799 DOI: 10.3390/diagnostics14010053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients. METHODS A total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision-recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not. RESULTS The embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis. CONCLUSIONS ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.
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Affiliation(s)
- Guoxin Fan
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Dongdong Wang
- Department of Orthopaedics, Putuo People’s Hospital, Tongji University, Shanghai 200060, China;
| | - Yufeng Li
- Department of Sports Medicine, Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China;
| | - Zhipeng Xu
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
| | - Hong Wang
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
| | - Huaqing Liu
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Guangzhou 510700, China
| | - Xiang Liao
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
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Ghanem M, Ghaith AK, El-Hajj VG, Bhandarkar A, de Giorgio A, Elmi-Terander A, Bydon M. Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review. Brain Sci 2023; 13:1723. [PMID: 38137171 PMCID: PMC10741524 DOI: 10.3390/brainsci13121723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.
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Affiliation(s)
- Marc Ghanem
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, MN 55902, USA; (M.G.); (A.K.G.); (V.G.E.-H.); (A.B.); (M.B.)
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN 55902, USA
- School of Medicine, Lebanese American University, Byblos 4504, Lebanon
| | - Abdul Karim Ghaith
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, MN 55902, USA; (M.G.); (A.K.G.); (V.G.E.-H.); (A.B.); (M.B.)
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Victor Gabriel El-Hajj
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, MN 55902, USA; (M.G.); (A.K.G.); (V.G.E.-H.); (A.B.); (M.B.)
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Archis Bhandarkar
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, MN 55902, USA; (M.G.); (A.K.G.); (V.G.E.-H.); (A.B.); (M.B.)
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Andrea de Giorgio
- Artificial Engineering, Via del Rione Sirignano, 80121 Naples, Italy;
| | - Adrian Elmi-Terander
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, 75236 Uppsala, Sweden
| | - Mohamad Bydon
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, MN 55902, USA; (M.G.); (A.K.G.); (V.G.E.-H.); (A.B.); (M.B.)
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN 55902, USA
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Vollmer A, Nagler S, Hörner M, Hartmann S, Brands RC, Breitenbücher N, Straub A, Kübler A, Vollmer M, Gubik S, Lang G, Wollborn J, Saravi B. Performance of artificial intelligence-based algorithms to predict prolonged length of stay after head and neck cancer surgery. Heliyon 2023; 9:e20752. [PMID: 37928044 PMCID: PMC10623164 DOI: 10.1016/j.heliyon.2023.e20752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Background Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort. Methods We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning algorithms for the prediction modeling of prolonged LOS. Results The algorithms reached accuracy values of 75.40 (radial basis function neural network) to 97.92 (Random Trees) for the training set and 64.90 (multilayer perceptron neural network) to 84.14 (Random Trees) for the testing set. The leading parameters predicting prolonged LOS were operation time, ischemia time, the graft used, the ASA score, the intensive care stay, and the pathological stages. The results revealed that patients who had a higher number of harvested lymph nodes (LN) had a lower probability of recurrence but also a greater LOS. However, patients with prolonged LOS were also at greater risk of recurrence, particularly when fewer (LN) were extracted. Further, LOS was more strongly correlated with the overall number of extracted lymph nodes than with the number of positive lymph nodes or the ratio of positive to overall extracted lymph nodes, indicating that particularly unnecessary lymph node extraction might be associated with prolonged LOS. Conclusions The results emphasize the need for a closer follow-up of patients who experience prolonged LOS. Prospective trials are warranted to validate the present results.
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Affiliation(s)
- Andreas Vollmer
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
| | - Simon Nagler
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
| | - Marius Hörner
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
| | - Stefan Hartmann
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
| | - Roman C. Brands
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
| | - Niko Breitenbücher
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
| | - Anton Straub
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
| | - Alexander Kübler
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
| | - Michael Vollmer
- Department of Oral and Maxillofacial Surgery, University Hospital of Tübingen, 72076, Tübingen, Germany
| | - Sebastian Gubik
- Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
| | - Gernot Lang
- Department of Orthopedics and Trauma Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jakob Wollborn
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Babak Saravi
- Department of Orthopedics and Trauma Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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10
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Saravi B, Zink A, Ülkümen S, Couillard-Despres S, Wollborn J, Lang G, Hassel F. Clinical and radiomics feature-based outcome analysis in lumbar disc herniation surgery. BMC Musculoskelet Disord 2023; 24:791. [PMID: 37803313 PMCID: PMC10557221 DOI: 10.1186/s12891-023-06911-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 09/24/2023] [Indexed: 10/08/2023] Open
Abstract
BACKGROUND Low back pain is a widely prevalent symptom and the foremost cause of disability on a global scale. Although various degenerative imaging findings observed on magnetic resonance imaging (MRI) have been linked to low back pain and disc herniation, none of them can be considered pathognomonic for this condition, given the high prevalence of abnormal findings in asymptomatic individuals. Nevertheless, there is a lack of knowledge regarding whether radiomics features in MRI images combined with clinical features can be useful for prediction modeling of treatment success. The objective of this study was to explore the potential of radiomics feature analysis combined with clinical features and artificial intelligence-based techniques (machine learning/deep learning) in identifying MRI predictors for the prediction of outcomes after lumbar disc herniation surgery. METHODS We included n = 172 patients who underwent discectomy due to disc herniation with preoperative T2-weighted MRI examinations. Extracted clinical features included sex, age, alcohol and nicotine consumption, insurance type, hospital length of stay (LOS), complications, operation time, ASA score, preoperative CRP, surgical technique (microsurgical versus full-endoscopic), and information regarding the experience of the performing surgeon (years of experience with the surgical technique and the number of surgeries performed at the time of surgery). The present study employed a semiautomatic region-growing volumetric segmentation algorithm to segment herniated discs. In addition, 3D-radiomics features, which characterize phenotypic differences based on intensity, shape, and texture, were extracted from the computed magnetic resonance imaging (MRI) images. Selected features identified by feature importance analyses were utilized for both machine learning and deep learning models (n = 17 models). RESULTS The mean accuracy over all models for training and testing in the combined feature set was 93.31 ± 4.96 and 88.17 ± 2.58. The mean accuracy for training and testing in the clinical feature set was 91.28 ± 4.56 and 87.69 ± 3.62. CONCLUSIONS Our results suggest a minimal but detectable improvement in predictive tasks when radiomics features are included. However, the extent of this advantage should be considered with caution, emphasizing the potential of exploring multimodal data inputs in future predictive modeling.
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Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany.
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany.
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, Salzburg, 5020, Austria.
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
| | - Sara Ülkümen
- Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, Salzburg, 5020, Austria
- Austrian Cluster for Tissue Regeneration, Vienna, Austria
| | - Jakob Wollborn
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Gernot Lang
- Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
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Saravi B, Zink A, Ülkümen S, Couillard-Despres S, Wollborn J, Lang G, Hassel F. Automated Detection and Measurement of Dural Sack Cross-Sectional Area in Lumbar Spine MRI Using Deep Learning. Bioengineering (Basel) 2023; 10:1072. [PMID: 37760174 PMCID: PMC10525778 DOI: 10.3390/bioengineering10091072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/21/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Lumbar spine magnetic resonance imaging (MRI) is a critical diagnostic tool for the assessment of various spinal pathologies, including degenerative disc disease, spinal stenosis, and spondylolisthesis. The accurate identification and quantification of the dural sack cross-sectional area are essential for the evaluation of these conditions. Current manual measurement methods are time-consuming and prone to inter-observer variability. Our study developed and validated deep learning models, specifically U-Net, Attention U-Net, and MultiResUNet, for the automated detection and measurement of the dural sack area in lumbar spine MRI, using a dataset of 515 patients with symptomatic back pain and externally validating the results based on 50 patient scans. The U-Net model achieved an accuracy of 0.9990 and 0.9987 on the initial and external validation datasets, respectively. The Attention U-Net model reported an accuracy of 0.9992 and 0.9989, while the MultiResUNet model displayed a remarkable accuracy of 0.9996 and 0.9995, respectively. All models showed promising precision, recall, and F1-score metrics, along with reduced mean absolute errors compared to the ground truth manual method. In conclusion, our study demonstrates the potential of these deep learning models for the automated detection and measurement of the dural sack cross-sectional area in lumbar spine MRI. The proposed models achieve high-performance metrics in both the initial and external validation datasets, indicating their potential utility as valuable clinical tools for the evaluation of lumbar spine pathologies. Future studies with larger sample sizes and multicenter data are warranted to validate the generalizability of the model further and to explore the potential integration of this approach into routine clinical practice.
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Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (S.Ü.); (G.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (A.Z.); (F.H.)
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (A.Z.); (F.H.)
| | - Sara Ülkümen
- Department of Orthopedics and Trauma Surgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (S.Ü.); (G.L.)
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Austrian Cluster for Tissue Regeneration, 1200 Vienna, Austria
| | - Jakob Wollborn
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Gernot Lang
- Department of Orthopedics and Trauma Surgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (S.Ü.); (G.L.)
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (A.Z.); (F.H.)
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Dai Y, Ouyang C, Luo G, Cao Y, Peng J, Gao A, Zhou H. Risk factors for high CAD-RADS scoring in CAD patients revealed by machine learning methods: a retrospective study. PeerJ 2023; 11:e15797. [PMID: 37551346 PMCID: PMC10404399 DOI: 10.7717/peerj.15797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/05/2023] [Indexed: 08/09/2023] Open
Abstract
OBJECTIVE This study aimed to investigate a variety of machine learning (ML) methods to predict the association between cardiovascular risk factors and coronary artery disease-reporting and data system (CAD-RADS) scores. METHODS This is a retrospective cohort study. Demographical, cardiovascular risk factors and coronary CT angiography (CCTA) characteristics of the patients were obtained. Coronary artery disease (CAD) was evaluated using CAD-RADS score. The stenosis severity component of the CAD-RADS was stratified into two groups: CAD-RADS score 0-2 group and CAD-RADS score 3-5 group. CAD-RADS scores were predicted with random forest (RF), k-nearest neighbors (KNN), support vector machines (SVM), neural network (NN), decision tree classification (DTC) and linear discriminant analysis (LDA). Prediction sensitivity, specificity, accuracy and area under the curve (AUC) were calculated. Feature importance analysis was utilized to find the most important predictors. RESULTS A total of 442 CAD patients with CCTA examinations were included in this study. 234 (52.9%) subjects were CAD-RADS score 0-2 group and 208 (47.1%) were CAD-RADS score 3-5 group. CAD-RADS score 3-5 group had a high prevalence of hypertension (66.8%), hyperlipidemia (50%) and diabetes mellitus (DM) (35.1%). Age, systolic blood pressure (SBP), mean arterial pressure, pulse pressure, pulse pressure index, plasma fibrinogen, uric acid and blood urea nitrogen were significantly higher (p < 0.001), and high-density lipoprotein (HDL-C) lower (p < 0.001) in CAD-RADS score 3-5 group compared to the CAD-RADS score 0-2 group. Nineteen features were chosen to train the models. RF (AUC = 0.832) and LDA (AUC = 0.81) outperformed SVM (AUC = 0.772), NN (AUC = 0.773), DTC (AUC = 0.682), KNN (AUC = 0.707). Feature importance analysis indicated that plasma fibrinogen, age and DM contributed most to CAD-RADS scores. CONCLUSION ML algorithms are capable of predicting the correlation between cardiovascular risk factors and CAD-RADS scores with high accuracy.
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Affiliation(s)
- Yueli Dai
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Chenyu Ouyang
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Guanghua Luo
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Yi Cao
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Jianchun Peng
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Anbo Gao
- Clinical Research Institute, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Department of Cardiovascular Medicine, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
- Key Laboratory of Heart Failure Prevention & Treatment of Hengyang, Clinical Medicine Research Center of Arteriosclerotic Disease of Hunan Province, Hengyang, Hunan, China
| | - Hong Zhou
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
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Tan J, Zhang Z, He Y, Yu Y, Zheng J, Liu Y, Gong J, Li J, Wu X, Zhang S, Lin X, Zhao Y, Wu X, Tang S, Chen J, Zhao W. A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010-2022) multicenter retrospective case-control study. J Transl Med 2023; 21:91. [PMID: 36750951 PMCID: PMC9903472 DOI: 10.1186/s12967-023-03959-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Length of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged LOS. METHODS A 13-year multicenter retrospective study was conducted on 83,776 patients with T2DM to develop and validate a clinical predictive tool for prolonged LOS. Least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis were adopted to build the risk model for prolonged LOS, and a nomogram was taken to visualize the model. Furthermore, receiver operating characteristic curves, calibration curves, and decision curve analysis and clinical impact curves were used to respectively validate the discrimination, calibration, and clinical applicability of the model. RESULTS The result showed that age, cerebral infarction, antihypertensive drug use, antiplatelet and anticoagulant use, past surgical history, past medical history, smoking, drinking, and neutrophil percentage-to-albumin ratio were closely related to the prolonged LOS. Area under the curve values of the nomogram in the training, internal validation, external validation set 1, and external validation set 2 were 0.803 (95% CI [confidence interval] 0.799-0.808), 0.794 (95% CI 0.788-0.800), 0.754 (95% CI 0.739-0.770), and 0.743 (95% CI 0.722-0.763), respectively. The calibration curves indicated that the nomogram had a strong calibration. Besides, decision curve analysis, and clinical impact curves exhibited that the nomogram had favorable clinical practical value. Besides, an online interface ( https://cytjt007.shinyapps.io/prolonged_los/ ) was developed to provide convenient access for users. CONCLUSION In sum, the proposed model could predict the possible prolonged LOS of inpatients with T2DM and help the clinicians to improve efficiency in bed management.
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Affiliation(s)
- Juntao Tan
- Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, China
| | - Zhengyu Zhang
- Medical Records Department, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang, China
| | - Yuxin He
- Department of Medical Administration, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, China
| | - Yue Yu
- Senior Bioinformatician Department of Quantitative Health Sciences Mayo Clinic, Rochester, MN, 55905, USA
| | - Jing Zheng
- Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, China
| | - Yunyu Liu
- Medical Records Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Jun Gong
- Department of Information Center, The University Town Hospital of Chongqing Medical University, Chongqing, 401331, China
| | - Jianjun Li
- Department of Cardiothoracic Surgery, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, China
| | - Xin Wu
- Department of Gastrointestinal Surgery, The Third People's Hospital of Chongqing, Chongqing Medical University, Chongqing, 400038, China
| | - Shengying Zhang
- Department of Respiratory, Yinzhou Second Hospital, Ningbo, 315153, Zhejiang, China
| | - Xiantian Lin
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qing Chun Road, Hangzhou, 310003, Zhejiang, China
| | - Yuxi Zhao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qing Chun Road, Hangzhou, 310003, Zhejiang, China
| | - Xiaoxin Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qing Chun Road, Hangzhou, 310003, Zhejiang, China.
| | - Songjia Tang
- Plastic and Aesthetic Surgery Department, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China.
| | - Jingjing Chen
- Department of Digital Urban Governance, Zhejiang University City College, Hangzhou, 310015, Zhejiang, China.
| | - Wenlong Zhao
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China.
- Medical Data Science Academy, Chongqing Medical University, Chongqing, 400016, China.
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Lee S, Reddy Mudireddy A, Kumar Pasupula D, Adhaduk M, Barsotti EJ, Sonka M, Statz GM, Bullis T, Johnston SL, Evans AZ, Olshansky B, Gebska MA. Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department. J Pers Med 2022; 13:jpm13010007. [PMID: 36675668 PMCID: PMC9864075 DOI: 10.3390/jpm13010007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/25/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
Background: Syncope, a common problem encountered in the emergency department (ED), has a multitude of causes ranging from benign to life-threatening. Hospitalization may be required, but the management can vary substantially depending on specific clinical characteristics. Models predicting admission and hospitalization length of stay (LoS) are lacking. The purpose of this study was to design an effective, exploratory model using machine learning (ML) technology to predict LoS for patients presenting with syncope. Methods: This was a retrospective analysis using over 4 million patients from the National Emergency Department Sample (NEDS) database presenting to the ED with syncope between 2016−2019. A multilayer perceptron neural network with one hidden layer was trained and validated on this data set. Results: Receiver Operator Characteristics (ROC) were determined for each of the five ANN models with varying cutoffs for LoS. A fair area under the curve (AUC of 0.78) to good (AUC of 0.88) prediction performance was achieved based on sequential analysis at different cutoff points, starting from the same day discharge and ending at the longest analyzed cutoff LoS ≤7 days versus >7 days, accordingly. The ML algorithm showed significant sensitivity and specificity in predicting short (≤48 h) versus long (>48 h) LoS, with an AUC of 0.81. Conclusions: Using variables available to triaging ED clinicians, ML shows promise in predicting hospital LoS with fair to good performance for patients presenting with syncope.
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Affiliation(s)
- Sangil Lee
- Department of Emergency Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
| | - Avinash Reddy Mudireddy
- The Iowa Initiative of Artificial Intelligence, University of Iowa, 103 South Capitol Street, Iowa City, IA 52242, USA;
| | - Deepak Kumar Pasupula
- Division of Cardiology, Mercy One North Iowa Heart Center, 250 S Crescent Dr, Mason City, IA 50401, USA;
| | - Mehul Adhaduk
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (M.A.); (T.B.); (A.Z.E.)
| | - E. John Barsotti
- Department of Epidemiology, College of Public Health, University of Iowa, 145 N. Riverside Drive, Iowa City, IA 52242, USA;
| | - Milan Sonka
- The Iowa Initiative of Artificial Intelligence, University of Iowa, 103 South Capitol Street, Iowa City, IA 52242, USA;
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
| | - Giselle M. Statz
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
| | - Tyler Bullis
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (M.A.); (T.B.); (A.Z.E.)
| | - Samuel L. Johnston
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
| | - Aron Z. Evans
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (M.A.); (T.B.); (A.Z.E.)
| | - Brian Olshansky
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
| | - Milena A. Gebska
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
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Orooji A, Shanbehzadeh M, Mirbagheri E, Kazemi-Arpanahi H. Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19. BMC Infect Dis 2022; 22:923. [PMID: 36494613 PMCID: PMC9733380 DOI: 10.1186/s12879-022-07921-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The exponential spread of coronavirus disease 2019 (COVID-19) causes unexpected economic burdens to worldwide health systems with severe shortages in hospital resources (beds, staff, equipment). Managing patients' length of stay (LOS) to optimize clinical care and utilization of hospital resources is very challenging. Projecting the future demand requires reliable prediction of patients' LOS, which can be beneficial for taking appropriate actions. Therefore, the purpose of this research is to develop and validate models using a multilayer perceptron-artificial neural network (MLP-ANN) algorithm based on the best training algorithm for predicting COVID-19 patients' hospital LOS. METHODS Using a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020 to December 20, 2020 were analyzed. In this study, first, the correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at a P-value < 0.2 were used in model construction. Then, the prediction models were developed based on 12 training algorithms according to full and selected feature datasets (90% of the training, with 10% used for model validation). Afterward, the root mean square error (RMSE) was used to assess the models' performance in order to select the best ANN training algorithm. Finally, a total of 343 patients were used for the external validation of the models. RESULTS After implementing feature selection, a total of 20 variables were determined as the contributing factors to COVID-19 patients' LOS in order to build the models. The conducted experiments indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian regularization (BR) training algorithm for whole and selected features with an RMSE of 1.6213 and 2.2332, respectively. CONCLUSIONS MLP-ANN-based models can reliably predict LOS in hospitalized patients with COVID-19 using readily available data at the time of admission. In this regard, the models developed in our study can help health systems to optimally allocate limited hospital resources and make informed evidence-based decisions.
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Affiliation(s)
- Azam Orooji
- grid.464653.60000 0004 0459 3173Department of Medical Informatics, Department of Advanced Technologies, School of Medicine, North Khorasan University of Medical Science (NKUMS), North Khorasan, Iran
| | - Mostafa Shanbehzadeh
- grid.449129.30000 0004 0611 9408Department of Health Information Management, Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Esmat Mirbagheri
- grid.411746.10000 0004 4911 7066Department of Health Information Management, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Management, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran , Department of Health Information Management, Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
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Saravi B, Ülkümen S, Couillard-Despres S, Hassel F, Lang G. Full-endoscopic versus conventional microsurgical therapy of lumbar disc herniation: a prospective, controlled, single-center, comprehensive cohort trial (FEMT-LDH trial). Trials 2022; 23:982. [PMID: 36476361 PMCID: PMC9727855 DOI: 10.1186/s13063-022-06892-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/03/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Lumbar disc herniation is one of the leading causes of chronic low back pain. Surgery remains the therapy of choice when conservative approaches fail. Full-endoscopic approaches represent a promising alternative to the well-established microsurgical technique. However, high-grade evidence comparing these techniques is still scarce. METHODS Patients presenting with lumbar disc herniation will be included. The intervention group will obtain full-endoscopic disc decompression, whereas the control group will be treated by microsurgical disc decompression. We will apply a comprehensive cohort study design involving a randomized and a prospective non-randomized study arm. Patients who do not consent to be randomized will be assigned to the non-randomized arm. The primary outcome will be the Oswestry Disability Index (ODI). Secondary outcomes involve the visual analog scale (VAS) of pain and the SF-36 health questionnaire. Furthermore, clinical characteristics including duration of hospital stay, operation time, and complications as well as laboratory markers, such as C-reactive protein, white blood cell counts, and interleukin 6 will be determined and compared. DISCUSSION This study will significantly contribute to the current evidence available in the literature by evaluating the outcome of the full-endoscopic technique against the gold standard for lumbar disc herniation in a clinically relevant study setup. Additionally, the study design allows us to include patients not willing to be randomized in a prospective parallel study arm and to evaluate the impact of randomization on outcomes and include. The results could help to improve the future therapy in patients suffering from lumbar disc herniation. TRIAL REGISTRATION This study was prospectively registered in The German Clinical Trials Register (DRKS), a German WHO primary registry, under the registration number: DRKS00025786. Registered on July 7, 2021.
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Affiliation(s)
- Babak Saravi
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany.
- Department of Orthopedics and Trauma Surgery, Medical Centre - Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Hugstetterstrasse 55, 79106, Freiburg, Germany.
| | - Sara Ülkümen
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
- Department of Orthopedics and Trauma Surgery, Medical Centre - Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Hugstetterstrasse 55, 79106, Freiburg, Germany
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020, Salzburg, Austria
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
| | - Gernot Lang
- Department of Orthopedics and Trauma Surgery, Medical Centre - Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Hugstetterstrasse 55, 79106, Freiburg, Germany
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