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Prediction model of deep vein thrombosis risk after lower extremity orthopedic surgery.
Zhang, Jiannan; Shao, Yang; Zhou, Hongmei; Li, Ronghua; Xu, Jie; Xiao, Zhongzhou; Lu, Lu; Cai, Liangyu.
Afiliación
  • Zhang J; Department of Anesthesiology, Wuxi TCM Hospital, Wuxi, 214071, PR China.
  • Shao Y; Department of Anesthesiology, Wuxi TCM Hospital, Wuxi, 214071, PR China.
  • Zhou H; Department of Anesthesiology, Wuxi TCM Hospital, Wuxi, 214071, PR China.
  • Li R; Department of Anesthesiology, Wuxi TCM Hospital, Wuxi, 214071, PR China.
  • Xu J; Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, PR China.
  • Xiao Z; Université de Montpellier, Montpellier, Languedoc-Roussillon, France.
  • Lu L; Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, PR China.
  • Cai L; Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, PR China.
Heliyon ; 10(9): e29517, 2024 May 15.
Article en En | MEDLINE | ID: mdl-38720714
ABSTRACT

Purpose:

This investigation was conceived to engineer and appraise a pioneering clinical nomogram, crafted to bridge the extant chasm in literature regarding the postoperative risk stratification for deep vein thrombosis (DVT) in the aftermath of lower extremity orthopedic procedures. This novel tool offers a sophisticated and discerning algorithm for risk prediction, heretofore unmet by existing methodologies.

Methods:

In this retrospective observational study, clinical records of hospitalized patients who underwent lower extremity orthopedic surgery were collected at the Wuxi TCM Hospital Affiliated to the Nanjing University of Chinese Medicine between Jan 2017 and Oct 2019. The univariate and multivariate analysis with the backward stepwise method was applied to select features for the predictive nomogram. The performance of the nomogram was evaluated with respect to its discriminant capability, calibration ability, and clinical utility.

Result:

A total of 5773 in-hospital patients were eligible for the study, with the incidence of deep vein thrombosis being approximately 1 % in this population. Among 31 variables included, 5 of them were identified to be the predictive features in the nomogram, including age, mean corpuscular hemoglobin concentration (MCHC), D-dimer, platelet distribution width (PDW), and thrombin time (TT). The area under the receiver operating characteristic (ROC) curve in the training and validation cohort was 85.9 % (95%CI 79.96 %-90.04 %) and 85.7 % (95%CI 78.96 %-90.69 %), respectively. Both the calibration curves and decision curve analysis demonstrated the overall satisfactory performance of the model.

Conclusion:

Our groundbreaking nomogram is distinguished by its unparalleled accuracy in discriminative and calibrating functions, complemented by its tangible clinical applicability. This innovative instrument is set to empower clinicians with a robust framework for the accurate forecasting of postoperative DVT, thus facilitating the crafting of bespoke and prompt therapeutic strategies, aligning with the rigorous standards upheld by the most esteemed biomedical journals.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido