The latest medical research on Vascular Surgery

To analyze the treatment methods and efficacy of inferior vena cava filter thrombosis (IVCFT).

In this retrospective study, the clinical data for 47 patients with IVCFT who underwent sequential treatment at the Department of Vascular Surgery, Tianjin Medical University Second Hospital, from January 2020 to January 2023 were analyzed. Patients were divided into three groups according to the treatment method: anticoagulant therapy (AC group), anticoagulation plus catheter-directed thrombolysis (CDT group), and anticoagulation plus AngioJet thrombectomy plus catheter-directed thrombolysis (PCDT group). The evaluation criteria for efficacy mainly included preoperative and postoperative clinical symptoms (Villalta score), thrombus diameter, thrombus clearance rate, filter retrieval rate, filter retention time, and urokinase dosage.

This study included 47 patients, of whom 31 were males (65.9%) and 16 females (34.1%), with a mean age of 72.05 ± 8.32 years. An Aegisy filter was used in seven patients, whereas an Illicium filter was used in forty patients. There were a total of nineteen patients in the anticoagulation-only group, with complete dissolution of the intraluminal thrombus in five patients, a residual thrombus with a maximum diameter ≤1 cm in three patients, and a residual thrombus with a maximum diameter >1 cm in eleven patients. The Villalta score was 7.16 ± 0.6 before treatment and decreased to 3.79 ± 0.59 after treatment. The thrombus diameter decreased from an average of 1.46 ± 0.2 cm before treatment to an average of 0.85 ± 0.14 cm after treatment. The retrieval rate for the filters was 42.11% (8/19), with an average dwell time of 27.4 ± 1.3 days for the filters. The CDT group consisted of 17 patients. Among whom we observed, complete dissolution of the intraluminal thrombus was observed in six patients, residual thrombus with a maximum diameter ≤1 cm in nine patients, and residual thrombus with a maximum diameter >1 cm in two patients. The Villalta score decreased from 7.53 ± 0.83 before treatment to 2.06 ± 0.39 after treatment. The thrombus diameter also decreased from 1.46 ± 0.16 cm before treatment to 0.35 ± 0.11 cm after treatment. The retrieval rate of the filters was 88.24% (15/17), and the average filter indwelling time was 19.25 ± 4.5 days. The PCDT group consisted of 11 patients. We observed complete dissolution of the intraluminal thrombus in four patients, residual thrombus with a maximum diameter ≤1 cm in six patients, and residual thrombus with a maximum diameter >1 cm in one patient. The Villalta score decreased from 7.45 ± 0.76 before treatment to 2.09 ± 0.55 after treatment. The thrombus diameter decreased from 1.50 ± 0.21 cm before treatment to 0.33 ± 0.35 cm after treatment, and the rate of filter retrieval was 90.91% (10/11).

The three treatments of anticoagulation therapy, CDT, and PCDT were meaningful for preoperative and postoperative thrombolysis and symptom improvement in patients with IVCFT. The application of CDT and PCDT was superior to anticoagulation therapy, while there was no significant difference between the CDT and PCDT group. The retrieval rate of filters in the anticoagulation therapy group was the lowest, with no significant difference between the CDT and PCDT group.

Employment and personal income loss after traumatic brain injury is a major source of postinjury stress and a barrier to societal reintegration. The magnitude of labor market ramifications following traumatic brain injury remains largely unknown.

To quantify the 3-year postinjury labor market consequences following traumatic brain injury in Canada. To also estimate the incurred national labor market cost over the study period.

This retrospective quasi-experimental, pan-Canadian observational cohort study used linked administrative health and federal taxation data obtained between 2007 and 2017. Mixed-effects difference-in-difference regressions were constructed to estimate the annualized magnitude of the personal income and employment loss during each of the 3 years following injury, respectively, relative to preinjury baseline. Participants included tax-filing adult (19 to 61 years old) traumatic brain injury survivors.

Traumatic brain injury.

Coprimary outcomes were personal income loss and the proportion of newly unemployed individuals per annum. Secondary objectives were to quantify income and employment loss within mild, moderate, and severe traumatic brain injury subgroups.

A total of 18 050 patients with traumatic brain injury between 2007 and 2017 were identified (mean age, 38.0 [SD, 12.4] years; 13 360 male [74.0%]), each of whom was followed up with for 3 consecutive fiscal years. Mean income was CAD $42 600 (US $31 083) in the fiscal year prior to injury and 82% were employed at time of injury. The adjusted mean loss of personal income was CAD $7635 (US $5650) in the first year after injury (Y+1) and CAD $5000 (US $3700) in the third year after injury (Y+3) relative to uninjured controls. In each of the 3 postinjury years, 7.8% individuals were newly unemployed compared with the preinjury baseline. The adjusted average personal income loss for mild, moderate, and severe traumatic brain injury subgroups were CAD $3354 (US $2482), CAD $6750 (US $4995), and CAD $17 375 (US $12 859), respectively, at Y+3; the proportion of unemployed individuals increased by 5.8%, 9.2%, and 20% across the same groups at Y+3 after injury relative to preinjury baseline. The estimated total reduction in personal income aggregated over the 3 postinjury years for the affected participants was CAD $588 million (US $435 million).

This work represents national cohort data quantifying the labor market implications of traumatic brain injury. These results may be used to inform economic evaluations and social service resource allocation.

Machine learning tools are increasingly deployed for risk prediction and clinical decision support in surgery. Class imbalance adversely impacts predictive performance, especially for low-incidence complications.

To evaluate risk-prediction model performance when trained on risk-specific cohorts.

This cross-sectional study performed from February 2024 to July 2024 deployed a deep learning model, which generated risk scores for common postoperative complications. A total of 109 445 inpatient operations performed at 2 University of Florida Health hospitals from June 1, 2014, to May 5, 2021 were examined.

The model was trained de novo on separate cohorts for high-risk, medium-risk, and low-risk Common Procedure Terminology codes defined empirically by incidence of 5 postoperative complications: (1) in-hospital mortality; (2) prolonged intensive care unit (ICU) stay (≥48 hours); (3) prolonged mechanical ventilation (≥48 hours); (4) sepsis; and (5) acute kidney injury (AKI). Low-risk and high-risk cutoffs for complications were defined by the lower-third and upper-third prevalence in the dataset, except for mortality, cutoffs for which were set at 1% or less and greater than 3%, respectively.

Model performance metrics were assessed for each risk-specific cohort alongside the baseline model. Metrics included area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), F1 scores, and accuracy for each model.

A total of 109 445 inpatient operations were examined among patients treated at 2 University of Florida Health hospitals in Gainesville (77 921 procedures [71.2%]) and Jacksonville (31 524 procedures [28.8%]). Median (IQR) patient age was 58 (43-68) years, and median (IQR) Charlson Comorbidity Index score was 2 (0-4). Among 109 445 operations, 55 646 patients were male (50.8%), and 66 495 patients (60.8%) underwent a nonemergent, inpatient operation. Training on the high-risk cohort had variable impact on AUROC, but significantly improved AUPRC (as assessed by nonoverlapping 95% confidence intervals) for predicting mortality (0.53; 95% CI, 0.43-0.64), AKI (0.61; 95% CI, 0.58-0.65), and prolonged ICU stay (0.91; 95% CI, 0.89-0.92). It also significantly improved F1 score for mortality (0.42; 95% CI, 0.36-0.49), prolonged mechanical ventilation (0.55; 95% CI, 0.52-0.58), sepsis (0.46; 95% CI, 0.43-0.49), and AKI (0.57; 95% CI, 0.54-0.59). After controlling for baseline model performance on high-risk cohorts, AUPRC increased significantly for in-hospital mortality only (0.53; 95% CI, 0.42-0.65 vs 0.29; 95% CI, 0.21-0.40).

In this cross-sectional study, by training separate models using a priori knowledge for procedure-specific risk classes, improved performance in standard evaluation metrics was observed, especially for low-prevalence complications like in-hospital mortality. Used cautiously, this approach may represent an optimal training strategy for surgical risk-prediction models.

We aimed to investigate the prognostic value of the combined arteritis damage score (CARDS) in Takayasu arteritis (TAK) patients to predict the need for biologic treatment at diagnosis and the possible contribution of wall thickness (WT).

Blind evaluation of MRA/CTA at the time of diagnosis was performed by a reader rheumatologist (RR) and an interventional radiologist (RIR). The CARDS damage score for 21 arterial regions was assessed as normal, mild or moderate/severe stenosis, occclusion or aneursym/dilatation. Additionally, WT was scored for all regions as present or absent. A modified CARDS (mCARDS) was calculated as the sum of CARDS and the number of WT areas.

According to follow-up treatment, 10 patients with non-biologic treatment (non-BT) (F/M:8/2, median age 37.5 years) and 15 patients with biologic treatment (BT) (F/M:13/2, median age 30 years) were included. Indian Takatasu Arteritis Score (ITAS), CRP, and ESR levels were similar in both groups. CARDS (1.4 (0-7.2) vs 4.5 (.6-19), P: .003), WT (1.5 (0-8) vs 7 (1-21), P < .001), and mCARDS (4 (0-14.2) vs 11.4 (1.6-40), P < .001) scores were significantly higher in the BT group compared to nonBT group. Cohen's kappa coefficient between RR and RIR for WT was .99 with 99.6% aggrement, and CARDS was .98 with 99.6% agreement. The AUC values for CARDS, WT, and mCARDS scores were .748 (.605-.892), .837 (.723-.950), and .847 (.735-.958), respectively, and P value was <.0001.

The prediction of prognosis and biologic treatment need at TAK diagnosis using non-invasive angiographic images can improve outcomes and prompt closer follow-up. The combination of CARDS and WT as mCARDS achieved the highest sensitivity and specificity, and all scores appear useful for predicting prognosis.