Dataset Information

Establishment and assessment of a nomogram for predicting blood transfusion risk in posterior lumbar spinal fusion.

ABSTRACT:

Background

The aim of this study was to determine the risk factors and develop a nomogram for blood transfusions after posterior lumbar spinal fusion (PSL).

Methods

We conducted a retrospective, single-center study based on 885 patients receiving PSL, and data was obtained from May 2015 to September 2019. Univariable and multivariable logistics regression analysis were conducted to identify risk factors for blood transfusion, and a nomogram was constructed to individually evaluate the risk of blood transfusion. Discrimination, calibration, and clinical usefulness were validated by the receiver operating characteristics (ROC), C-index, calibration plot, and decision curve analysis, respectively. Bootstrapping validation was performed to assess the performance of the model.

Results

Of 885 patients, 885 were enrolled in the final study population, and 289 received blood transfusion. Statistical analyses showed that low preoperative hemoglobin (Hb), longer time to surgery, operative time, levels of fusion > 1, longer surgery duration, and higher total intraoperative blood loss (IBL) were the risk factors for transfusion. The C-index was 0.898 (95% CI 0.847-0.949) in this dataset and 0.895 in bootstrapping validation, respectively. Calibration curve showed satisfied discrimination and calibration of the nomogram. Decision curve analysis (DCA) shown that the nomogram was clinical utility.

Conclusions

In summary, we investigated the relationship between the blood transfusion requirement and predictors: levels of fusion, operative time, time to surgery, total intraoperative EBL, and preoperative Hb level. Our nomogram with a robust performance in the assessment of risk of transfusion can contribute to clinicians in making clinical decision. However, external validation is still needed in the further.

SUBMITTER: Wang H

PROVIDER: S-EPMC7798229 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Establishment and assessment of a nomogram for predicting blood transfusion risk in posterior lumbar spinal fusion.

Wang Haosheng H Wang Kai K Lv Bin B Xu Haotian H Jiang Weibo W Zhao Jianwu J Kang Mingyang M Dong Rongpeng R Qu Yang Y

Journal of orthopaedic surgery and research 20210111 1

<h4>Background</h4>The aim of this study was to determine the risk factors and develop a nomogram for blood transfusions after posterior lumbar spinal fusion (PSL).<h4>Methods</h4>We conducted a retrospective, single-center study based on 885 patients receiving PSL, and data was obtained from May 2015 to September 2019. Univariable and multivariable logistics regression analysis were conducted to identify risk factors for blood transfusion, and a nomogram was constructed to individually evaluate ...[more]

PMID: 33430895

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Project description:Background: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct a machine learning-based perioperative transfusion risk prediction model for lumbar posterior interbody fusion in order to improve the efficacy of surgical decision-making. Methods: We retrospectively collected clinical data on 1905 patients who underwent lumbar posterior interbody fusion surgery at the Second Hospital of Shanxi Medical University between January 2021 and March 2023. All the data was randomly divided into a training set and a validation set, and the "feature_importances" method provided by eXtreme Gradient Boosting (XGBoost) algorithm was applied to select statistically significant features on the training set to establish five machine learning prediction models. The optimal model was identified by utilizing the area under the curve (AUC) and the probability calibration curve on the validation set. Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) were employed for interpretable analysis of the optimal model. Results: In the postoperative outcomes of patients, the number of hospital days in the transfusion group was longer than that in the non-transfusion group. Additionally, the transfusion group experienced higher total hospital costs, 90-day readmission rates, and complication rates within 90 days after surgery than the non-transfusion group. A total of 9 features were selected for the models. The XGBoost model performed best with an AUC value of 0.958. The SHAP values showed that intraoperative blood loss, intraoperative fluid infusion, and number of fused segments were the top 3 most important features affecting perioperative blood transfusion in lumbar posterior interbody fusion. The LIME algorithm was used to interpret the individualized prediction. Conclusion: Surgery, ASA class, levels fused, total intraoperative blood loss, operative time, and preoperative Hb are viable predictors of perioperative blood transfusion in lumbar posterior interbody fusion. The XGBoost model has demonstrated superior predictive efficacy compared to the traditional logistic regression model, making it a more effective decision-making tool for perioperative blood transfusion.

Project description:PurposePosterior spinal fusion (PSF) activates the fibrinolytic protease plasmin, which is implicated in blood loss and transfusion. While antifibrinolytic drugs have improved blood loss and reduced transfusion, variable blood loss has been observed in similar PSF procedures treated with the same dose of antifibrinolytics. However, both the cause of this and the appropriate measures to determine antifibrinolytic efficacy during high-blood-loss spine surgery are unknown, making clinical trials to optimize antifibrinolytic dosing in PSF difficult. We hypothesized that patients undergoing PSF respond differently to antifibrinolytic dosing, resulting in variable blood loss, and that specific diagnostic markers of plasmin activity will accurately measure the efficacy of antifibrinolytics in PSF.MethodsA prospective study of 17 patients undergoing elective PSF with the same dosing regimen of TXA was conducted. Surgery-induced plasmin activity was exhaustively analyzed in perioperative blood samples and correlated to measures of inflammation, bleeding, and transfusion.ResultsWhile markers of in vivo plasmin activation (PAP and D-dimer) suggested significant breakthrough plasmin activation and fibrinolysis (P < 0.01), in vitro plasmin assays, including TEG, did not detect plasmin activation. In vivo measures of breakthrough plasmin activation correlated with blood loss (R2 = 0.400, 0.264; P < 0.01), transfusions (R2 = 0.388; P < 0.01), and complement activation (R2 = 0.346, P < 0.05).ConclusionsDespite all patients receiving a high dose of TXA, its efficacy among patients was variable, indicated by notable intra-operative plasmin activity. Markers of in vivo plasmin activation best correlated with clinical outcomes. These findings suggest that the efficacy of antifibrinolytic therapy to inhibit plasmin in PSF surgery should be determined by markers of in vivo plasmin activation in future studies.Level of evidenceLevel II-diagnostic.

Dataset Information

Establishment and assessment of a nomogram for predicting blood transfusion risk in posterior lumbar spinal fusion.

Background

Methods

Results

Conclusions

Publications

Establishment and assessment of a nomogram for predicting blood transfusion risk in posterior lumbar spinal fusion.

Similar Datasets

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