Considering Spine Surgery

Moulton, H.; Tosteson, Tor D.; Zhao, Wenyan; Pearson, Loretta H.; Mycek, Kristina; Scherer, Emily A.; Weinstein, James Neil; Pearson, Adam M.; Abdu, William A.; Schwarz, Susan; Kelly, Michael P.; McGuire, Kevin J.; Milam, Alden; Lurie, Jonathan D.

doi:10.1097/brs.0000000000002723

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…Subsequently, a medical human-computer interface (web calculator) was designed to efficiently and accurately evaluate the risk for various E.S. patients via Internet [ 33 ]. This model could provide clinicians with quantitative information to make fast and scientific decisions and better clinical planning, for delivering precision healthcare to patients.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Novel Clinical Prediction Model to Predict the Risk of Lung Metastasis from Ewing Sarcoma for Medical Human-Computer Interface

Hong

et al. 2022

Computational Intelligence and Neuroscience

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Background. This study aimed at establishing and validating a quantitative and visual prognosis model of Ewing Sarcoma (E.S.) via a nomogram. This model was developed to predict the risk of lung metastasis (L.M.) in patients with E.S. to provide a practical tool and help in clinical diagnosis and treatment. Methods. Data of all patients diagnosed with Ewing sarcoma between 2010 and 2016 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. A training dataset from the enrolled cohorts was built (n = 929). Predictive factors for L.M. were identified based on the results of multivariable logistic regression analyses. A nomogram model and a web calculator were constructed based on those key predictors. A multicenter dataset from four medical institutions was established for model validation (n = 51). The predictive ability of the nomogram model was evaluated by the receiver operating characteristic (ROC) curve and calibration plot. Decision curve analysis (DCA) was applied to explain the accuracy of the nomogram model in clinical practice. Results. Five independent factors, including survival time, surgery, tumor (T) stage, node (N) stage, and bone metastasis, were identified to develop a nomogram model. Internal and external validation indicated significant predictive discrimination: the area under the ROC curve (AUC) value was 0.769 (95% CI: 0.740 to 0.795) in the training cohort and 0.841 (95% CI: 0.712 to 0.929) in the validation cohort, respectively. Calibration plots and DCA presented excellent performance of the nomogram model with great clinical utility. Conclusions. In this study, a nomogram model was constructed and validated to predict L.M. in patients with E.S. for medical human-computer interface—a web calculator (https://drliwenle.shinyapps.io/LMESapp/). This practical tool could help clinicians make better decisions to provide precision prognosis and treatment for patients with E.S.

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Section: Introductionmentioning

confidence: 99%

Development and Validation of a Novel Clinical Prediction Model to Predict the Risk of Lung Metastasis from Ewing Sarcoma for Medical Human-Computer Interface

Hong

et al. 2022

Computational Intelligence and Neuroscience

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“…1 No entanto, alguns grupos de pacientes não percebem tais benefícios, exigindo, portanto, intervenção cirúrgica. 2…”

Section: Introductionunclassified

“…1 However, some groups of patients do not perceive such benefits from nonsurgical treatments, therefore requiring surgical intervention. 2…”

Section: Introductionmentioning

confidence: 99%

Impacto dos parâmetros espinopélvicos pré-operatórios na correção de lordose segmentar após fusão intersomática lombar por via lateral de um nível

Arnoni

Amaral

Pokorny

et al. 2022

Rev Bras Ortop (Sao Paulo)

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Resumo Objetivos O presente estudo teve como objetivo avaliar se os parâmetros espinopélvicos pré-operatórios podem influenciar o ganho da lordose segmental após fusão intersomática lombar por via lateral de um nível. Métodos Os seguintes parâmetros radiológicos foram medidos nos raios X: incidência pélvica, lordose lombar, versão pélvica, lordose L4S1, lordose segmental do nível operado, índice intraoperatório de lordose segmentar, mismatch pélvico (IP-LL), proporção de lordose distal, delta de lordose segmentar, PT > 20, inclinação sacral real e inclinação sacral ideal, e a correlação dessas variáveis com o ganho da lordose segmentar foi investigada. Posteriormente, foi realizada uma análise exploratória de cluster para identificar características comuns entre os pacientes e o ganho de lordose segmentar. Resultados O presente estudo contou com 144 pacientes, dos quais 76% apresentaram ganho de lordose segmentar. Os parâmetros mais correlacionados com o ganho de lordose segmentar foram lordose segmentar pré-operatória (−0,50) e delta intraoperatório de lordose (0,51). Além disso, os pacientes dos grupos de incidência pélvica (IP) alto tiveram tendência de ganho de lordose segmental maior (p < 0,05) e redução do risco de perda de lordose segmental (chances 6.08). Conclusão Pacientes com perfis de IP médios baixos apresentaram maiores chances de perda de lordose segmentar. No entanto, os parâmetros espinopélvicos pré-operatórios por si só não parecem desempenhar um papel significativo no destino do ganho da lordose segmentar.

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“…These risk assessment tools and predictive models use demographic data, the patient's medical history, diagnosis, baseline patientreported outcomes (PROs), surgical plans to predict the quality of life, return to work, and disability using regression or machine learning algorithms. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] More importantly, these risk assessment tools can be used in the clinic to help the provider discuss the risk-benefit ratio of lumbar surgery with the patient as part of shared decision-making. This is especially important as surgery for lumbar degenerative conditions is elective and impacts a patient's quality of life.…”

mentioning

confidence: 99%

Assessment of the External Validity of Dialogue Support for Predicting Lumbar Spine Surgery Outcomes in a US Cohort

Carreon

Glassman

Mummaneni

et al. 2023

Spine

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Study Design. External validation using prospectively collected data. Objectives. To determine the model performance of "Dialogue Support" (DS) in predicting outcomes after lumbar spine surgery. Summary of Background Data. To help clinicians discuss risk versus benefit with patients considering lumbar fusion surgery, DS has been made available online. As DS was created using a Swedish sample, there is a need to study how well DS performs in alternative populations. Patients and Methods. Preoperative data from patients enrolled in the Quality Outcomes Database were entered into DS. The probability for each patient to report satisfaction, achieve success (leg pain improvement ≥ 3), or have no leg pain 12 months after surgery was extracted and compared with their actual 12-month postoperative data. The ability of DS to identify patients in the Quality Outcomes Database who report satisfaction, achieve success, or have no leg pain 12 months after surgery was determined using Receiver operating characteristic curve analysis, goodness-of-fit tests, and calibration plots. Results. There was a significant improvement in all outcomes in 23,928 cases included in the analysis from baseline to 12 months postoperative. Most (84%) reported satisfaction, 67% achieved success, and 44% were pain-free 12 months postoperative. Receiver operating characteristic analysis showed that DS had a low ability to predict satisfaction [area under the curve (AUC) = 0.606], success (AUC = 0.546), and being pain-free (AUC = 0.578) at 12 months postoperative; poor fit for satisfaction (< 0.001) and being pain-free (P = 0.004), but acceptable fit for success (P = 0.052). Calibration plots showed underestimation for satisfaction and success, but acceptable estimates for being pain-free. Conclusion. DS is not directly transferable to predict satisfaction and success after lumbar surgery in a US population. This may be due to differences in patient characteristics, weights of the variables included, or the exclusion of unknown variables associated with outcomes. Future studies to better understand and improve the transferability of these models are needed.

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Considering Spine Surgery

Abstract: 2.

Cited by 12 publications

References 15 publications

Development and Validation of a Novel Clinical Prediction Model to Predict the Risk of Lung Metastasis from Ewing Sarcoma for Medical Human-Computer Interface

Development and Validation of a Novel Clinical Prediction Model to Predict the Risk of Lung Metastasis from Ewing Sarcoma for Medical Human-Computer Interface

Impacto dos parâmetros espinopélvicos pré-operatórios na correção de lordose segmentar após fusão intersomática lombar por via lateral de um nível

Assessment of the External Validity of Dialogue Support for Predicting Lumbar Spine Surgery Outcomes in a US Cohort

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