Venous Thromboembolism Risk and Outcomes Following Decompressive Craniectomy in Severe Traumatic Brain Injury: An Analysis of the Nationwide Inpatient Sample Database

Ali, Ali Basil; Khawaja, Ayaz; Reilly, Aoife; Tahir, Zabreen; Rao, Singiresu S.; Bernstock, Joshua D.; Chen, Patrick; Molino, Janine; Gormley, William B.; Izzy, Saef

doi:10.1016/j.wneu.2022.02.069

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…31 Immobilization represents a significant risk for DVT among hospitalized patients who had longer hospital stays and was associated with high medical costs. 32 A lower BI has been shown to be a risk factor for DVT; BI is currently the most widely used clinical assessment method for ADL and is an important evaluation index in rehabilitation departments. The lower is the score, the less ability the patient gains and the more severe the functional impairment.…”

Section: Discussionmentioning

confidence: 99%

The Use of Machine Learning Techniques to Predict Deep Vein Thrombosis in Rehabilitation Inpatients

Hou

Wang

Song

et al. 2023

Clin Appl Thromb Hemost

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Background Rehabilitation is crucial to recovering patients’ dysfunction, improving their life quality, and promoting an early return to their family and society. In China, most patients in rehabilitation units are patients transferred from neurology, neurosurgery, and orthopedics, and most of these patients face problems such as continuously bedridden or varying degrees of limb dysfunction, all of which are risk factors for deep venous thrombosis. The formation of deep venous thrombosis can delay the recovery process and result in significant morbidity, mortality, and higher healthcare costs, so early detection and individualized treatment are needed. Machine learning algorithms can help develop more precise prognostic models, which can be of great significance in the development of rehabilitation training programs. In this study, we aimed to develop a model of deep venous thrombosis for inpatients in the Department of Rehabilitation Medicine at the Affiliated Hospital of Nantong University using machine learning methods. Methods We analyzed and compared 801 patients in the Department of Rehabilitation Medicine using machine learning. Support vector machine, logistic regression, decision tree, random forest classifier, and artificial neural network were used to build models. Results Artificial neural network was the better predictor than other traditional machine learnings. D-dimer levels, bedridden time, Barthel Index, and fibrinogen degradation products were common predictors of adverse outcomes in these models. Conclusions Risk stratification can help healthcare practitioners to achieve improvements in clinical efficiency and specify appropriate rehabilitation training programs.

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Section: Discussionmentioning

confidence: 99%

The Use of Machine Learning Techniques to Predict Deep Vein Thrombosis in Rehabilitation Inpatients

Hou

Wang

Song

et al. 2023

Clin Appl Thromb Hemost

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“…Another large retrospective database study using the National Inpatient Sample database evaluated in 2022 risk factors for VTE complications in adult patients with severe TBI [ 35 ]. This study evaluated 349,165 TBI hospitalizations.…”

Section: Discussionmentioning

confidence: 99%

Risk factors for thromboembolic complications in isolated severe head injury

Jakob

Mûller

Lewis

et al. 2023

Eur J Trauma Emerg Surg

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Purpose Patients with traumatic brain injury (TBI) are at high risk for venous thromboembolism (VTE). The aim of the present study is to identify factors independently associated with VTE events. Specifically, we hypothesized that the mechanism of penetrating head trauma might be an independent factor associated with increased VTE events when compared with blunt head trauma. Methods The ACS-TQIP database (2013–2019) was queried for all patients with isolated severe head injuries (AIS 3–5) who received VTE prophylaxis with either unfractionated heparin or low-molecular-weight heparin. Transfers, patients who died within 72 h and those with a hospital length of stay < 48 h were excluded. Multivariable analysis was used as the primary analysis to identify independent risk factors for VTE in isolated severe TBI. Results A total of 75,570 patients were included in the study, 71,593 (94.7%) with blunt and 3977 (5.3%) with penetrating isolated TBI. Penetrating trauma mechanism (OR 1.49, CI 95% 1.26–1.77), increasing age (age 16–45: reference; age > 45–65: OR 1.65, CI 95% 1.48–1.85; age > 65–75: OR 1.71, CI 95% 1.45–2.02; age > 75: OR 1.73, CI 95% 1.44–2.07), male gender (OR 1.53, CI 95% 1.36–1.72), obesity (OR 1.35, CI 95% 1.22–1.51), tachycardia (OR 1.31, CI 95% 1.13–1.51), increasing head AIS (AIS 3: reference; AIS 4: OR 1.52, CI 95% 1.35–1.72; AIS 5: OR 1.76, CI 95% 1.54–2.01), associated moderate injuries (AIS = 2) of the abdomen (OR 1.31, CI 95% 1.04–1.66), spine (OR 1.35, CI 95% 1.19–1.53), upper extremity (OR 1.16, CI 95% 1.02–1.31), lower extremity (OR 1.46, CI 95% 1.26–1.68), craniectomy/craniotomy or ICP monitoring (OR 2.96, CI 95% 2.65–3.31) and pre-existing hypertension (OR 1.18, CI 95% 1.05–1.32) were identified as independent risk factors for VTE complications in isolated severe head injury. Increasing GCS (OR 0.93, CI 95% 0.92–0.94), early VTE prophylaxis (OR 0.48, CI 95% 0.39–0.60) and LMWH compared to heparin (OR 0.74, CI 95% 0.68–0.82) were identified as protective factors for VTE complications. Conclusion The identified factors independently associated with VTE events in isolated severe TBI need to be considered in VTE prevention measures. In penetrating TBI, an even more aggressive VTE prophylaxis management may be justified as compared to that in blunt.

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Coagulopathy and Prophylaxis of Venous Thromboembolism in Traumatic Brain Injury

Lindsay,

Green,

Henry

et al. 2024

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Venous Thromboembolism Risk and Outcomes Following Decompressive Craniectomy in Severe Traumatic Brain Injury: An Analysis of the Nationwide Inpatient Sample Database

Cited by 5 publications

References 38 publications

The Use of Machine Learning Techniques to Predict Deep Vein Thrombosis in Rehabilitation Inpatients

The Use of Machine Learning Techniques to Predict Deep Vein Thrombosis in Rehabilitation Inpatients

Risk factors for thromboembolic complications in isolated severe head injury

Coagulopathy and Prophylaxis of Venous Thromboembolism in Traumatic Brain Injury

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