Development and Validation of a Clinical Prediction Model for Venous Thromboembolism Following Neurosurgery: A 6-Year, Multicenter, Retrospective and Prospective Diagnostic Cohort Study
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SUBMITTER: Liu D
PROVIDER: S-EPMC10670076 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
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Project description:BackgroundColorectal cancer (CRC) is a heterogeneous group of malignancies distinguished by distinct clinical features. The association of these features with venous thromboembolism (VTE) is yet to be clarified. Machine learning (ML) models are well suited to improve VTE prediction in CRC due to their ability to receive the characteristics of a large number of features and understand the dataset to obtain implicit correlations.MethodsData were extracted from 4,914 patients with colorectal cancer between August 2019 and August 2022, and 1,191 patients who underwent surgery on the primary tumor site with curative intent were included. The variables analyzed included patient-level factors, cancer-level factors, and laboratory test results. Model training was conducted on 30% of the dataset using a ten-fold cross-validation method and model validation was performed using the total dataset. The primary outcome was VTE occurrence in postoperative 30 days. Six ML algorithms, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), weighted support vector machine (SVM), a multilayer perception (MLP) network, and a long short-term memory (LSTM) network, were applied for model fitting. The model evaluation was based on six indicators, including receiver operating characteristic curve-area under the curve (ROC-AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and Brier score. Two previous VTE models (Caprini and Khorana) were used as the benchmarks.ResultsThe incidence of postoperative VTE was 10.8%. The top ten significant predictors included lymph node metastasis, C-reactive protein, tumor grade, anemia, primary tumor location, sex, age, D-dimer level, thrombin time, and tumor stage. In our results, the XGBoost model showed the best performance, with a ROC-AUC of 0.990, a SEN of 96.9%, a SPE of 96.1% in training dataset and a ROC-AUC of 0.908, a SEN of 77.5%, a SPE of 93.7% in validation dataset. All ML models outperformed the previously developed models (Caprini and Khorana).ConclusionsThis study developed postoperative VTE predictive models using six ML algorithms. The XGBoost VTE model might supply a complementary tool for clinical VTE prophylaxis decision-making and the proposed risk factors could shed some light on VTE risk stratification in CRC patients.
| S-EPMC10007945 | biostudies-literature
Project description:BackgroundEarly treatment and prevention are the keys to reducing the mortality of VTE in patients with thoracic trauma. This study aimed to develop and validate an automatic prediction model based on machine learning for VTE risk screening in patients with thoracic trauma.MethodsIn this national multicenter retrospective study, the clinical data of chest trauma patients hospitalized in 33 hospitals in China from October 2020 to September 2021 were collected for model training and testing. The data of patients with thoracic trauma at Shanghai Sixth People's Hospital from October 2021 to September 2022 were included for further verification. The performance of the model was measured mainly by the area under the receiver operating characteristic curve (AUROC) and the mean accuracy (mAP), and the sensitivity, specificity, positive predictive value, and negative predictive value were also measured.ResultsA total of 3116 patients were included in the training and validation of the model. External validation was performed in 408 patients. The random forest (RF) model was selected as the final model, with an AUROC of 0·879 (95% CI 0·856-0·902) in the test dataset. In the external validation, the AUROC was 0.83 (95% CI 0.794-0.866), the specificity was 0.756 (95% CI 0.713-0.799), the sensitivity was 0.821 (95% CI 0.692-0.923), the negative predictive value was 0.976 (95% CI 0.958-0.993), and the positive likelihood ratio was 3.364.ConclusionsThis model can be used to quickly screen for the risk of VTE in patients with thoracic trauma. More than 90% of unnecessary VTE tests can be avoided, which can help clinicians target interventions to high-risk groups and ensure resource optimization. Although further validation and improvement are needed, this study has considerable clinical value.
| S-EPMC11823207 | biostudies-literature
Project description:BackgroundPatients with COVID-19 infection are commonly reported to have an increased risk of venous thrombosis. The choice of anti-thrombotic agents and doses are currently being studied in randomized controlled trials and retrospective studies. There exists a need for individualized risk stratification of venous thromboembolism (VTE) to assist clinicians in decision-making on anticoagulation. We sought to identify the risk factors of VTE in COVID-19 patients, which could help physicians in the prevention, early identification, and management of VTE in hospitalized COVID-19 patients and improve clinical outcomes in these patients.MethodThis is a multicenter, retrospective database of four main health systems in Southeast Michigan, United States. We compiled comprehensive data for adult COVID-19 patients who were admitted between 1st March 2020 and 31st December 2020. Four models, including the random forest, multiple logistic regression, multilinear regression, and decision trees, were built on the primary outcome of in-hospital acute deep vein thrombosis (DVT) and pulmonary embolism (PE) and tested for performance. The study also reported hospital length of stay (LOS) and intensive care unit (ICU) LOS in the VTE and the non-VTE patients. Four models were assessed using the area under the receiver operating characteristic curve and confusion matrix.ResultsThe cohort included 3531 admissions, 3526 had discharge diagnoses, and 6.68% of patients developed acute VTE (N = 236). VTE group had a longer hospital and ICU LOS than the non-VTE group (hospital LOS 12.2 days vs. 8.8 days, p < 0.001; ICU LOS 3.8 days vs. 1.9 days, p < 0.001). 9.8% of patients in the VTE group required more advanced oxygen support, compared to 2.7% of patients in the non-VTE group (p < 0.001). Among all four models, the random forest model had the best performance. The model suggested that blood pressure, electrolytes, renal function, hepatic enzymes, and inflammatory markers were predictors for in-hospital VTE in COVID-19 patients.ConclusionsPatients with COVID-19 have a high risk for VTE, and patients who developed VTE had a prolonged hospital and ICU stay. This random forest prediction model for VTE in COVID-19 patients identifies predictors which could aid physicians in making a clinical judgment on empirical dosages of anticoagulation.
| S-EPMC9100286 | biostudies-literature
Project description:Venous thromboembolism (VTE), including pulmonary embolism (PE) and deep vein thrombosis (DVT) occurs more frequently in cancer patients than in the general population. A retrospective cross-sectional study was carried out in patients with solid tumor complicated with VTE admitted to the Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology between January 1st, 2008 and December 31th, 2017. The incidence of VTE in hospitalized cancer patients was 1.8%, twice the incidence of VTE in hospitalized non-cancer patients. The annual incidence of cancer-associated VTE in our center varied between 1.6% in 2015 and 0.4% in 2009 with an overall average incidence of 1.3% over the research decade. BMI values of 549(67.7%) cancer patients were within the normal range, but none of patients had BMI greater than 35 kg/m2. 747(92.1%) cancer patients had ECOG PS score ≤ 2 and 481(59.3%) had distant metastasis. Patients with pancreatic, bladder, ovarian and endometrial cancer had the highest incidence of VTE. Upper extremity DVT (47.2%) was more common in cancer patients and might be closely associated with CVC (74.9%), while lower extremities DVT (36.1%) intended to PE development (15.0%). The annual incidence rates showed a fluctuating and upward trend over the research decade. VTE occurrence was closely related to tumor stage, tumor site, catheterization and anti-neoplasm therapy in cancer patients.
| S-EPMC7894580 | biostudies-literature
Project description:Impact of pandemic on the incidence of venous thromboembolism (VTE) in non-COVID-19 patients is undetermined. Thus, a nationwide multicenter retrospective survey was conducted to evaluate the disease burden in non-COVID-19 population. This multi-center survey involved 94 hospitals from 24 provinces in the mainland of China, and collected data on non-COVID-19 patients admitted to the radiology departments due to VTE between January 24 and April 16, 2020. Baseline characteristics, VTE risk factors, clinical manifestations and the treatments were compared with those in the same period of 2019. 3,358 patients with VTE from 74 hospitals were included in this study (1,458 in 2020, 1,900 in 2019). Most aged ≥ 50 years (80.6% in the pandemic, 81.2% in 2019). The number of patients aged 30-39 years increased from 3.9% in 2019 period to 5.8% in the pandemic (p = 0.009). Among the VTE risk factors, the rate of decreased activity increased significantly in the pandemic, and was much higher than that in 2019 (30.7% vs 22.6%, p < 0.0001). Under the risk of decreased activity, patients with comorbidities chronic diseases, especially diabetes, showed significantly a higher incidence of VTE (30.4% vs 22.0%, p < 0.0001). In the pandemic period, fewer patients were treated with anticoagulation alone (33.5% vs 36.7%, p = 0.05), and more underwent inferior vena cava filter (IVCF) implantation, compared with those in 2019 (66.5% vs 63.2%, p = 0.046). The pandemic increased the VTE risk of decreased activity among the non-COVID-19 population. Patients with comorbidities, especially diabetes, have a significant higher risk of VTE during the pandemic.
| S-EPMC8120766 | biostudies-literature
Project description:ImportanceThe assessment of the risk of venous thromboembolism (VTE) among outpatients with cancer represents an unsolved topic. Current international guidelines recommend primary prophylaxis for patients at intermediate to high risk of VTE, indicated by a Khorana score of 2 or more. A previous prospective study developed the ONKOTEV score, a 4-variable risk assessment model (RAM) consisting of a Khorana score of more than 2, metastatic disease, vascular or lymphatic compression, and previous VTE event.ObjectiveTo validate the ONKOTEV score as a novel RAM to assess the risk of VTE among outpatients with cancer.Design, setting, and participantsONKOTEV-2 is a noninterventional prognostic study conducted in 3 European centers located in Italy, Germany, and the United Kingdom among a prospective cohort of 425 ambulatory patients with a histologically confirmed diagnosis of a solid tumor who were receiving active treatments. The total study duration was 52 months, with an accrual period of 28 months (from May 1, 2015, to September 30, 2017) and an overall follow up-period of 24 months (data were censored September 30, 2019). Statistical analysis was performed in October 2019.ExposuresThe ONKOTEV score was calculated for each patient at baseline by collecting clinical, laboratory, and imaging data from tests performed for routine practice. Each patient was then observed to detect any thromboembolic event throughout the study period.Main outcomes and measuresThe primary outcome of the study was the incidence of VTE, including deep vein thrombosis and pulmonary embolism.ResultsA total of 425 patients (242 women [56.9%]; median age, 61 years [range, 20-92 years]) were included in the validation cohort of the study. The cumulative incidences for the risk of developing VTE at 6 months were 2.6% (95% CI, 0.7%-6.9%), 9.1% (95% CI, 5.8%-13.2%), 32.3% (95% CI, 21.0%-44.1%), and 19.3% (95% CI, 2.5%-48.0%), respectively, among 425 patients with an ONKOTEV score of 0, 1, 2, and greater than 2 (P < .001). The time-dependent area under the curve at 3, 6, and 12 months was 70.1% (95% CI, 62.1%-78.7%), 72.9% (95% CI, 65.6%-79.1%), and 72.2% (95% CI, 65.2%-77.3%), respectively.Conclusions and relevanceThis study suggests that, because the ONKOTEV score has been validated in this independent study population as a novel predictive RAM for cancer-associated thrombosis, it can be adopted into practice and into clinical interventional trials as a decision-making tool for primary prophylaxis.
| S-EPMC9936336 | biostudies-literature
Project description:BackgroundCritically ill children and young adults with diabetic ketoacidosis are thought to be in a prothrombotic state. However, the rate of hospital-acquired venous thromboembolism and associated risk factors in this population have not been identified.ObjectivesChildren hospitalized for diabetic ketoacidosis (DKA) may be at increased risk of hospital-acquired venous thromboembolism (HA-VTE). We sought to estimate the incidence of HA-VTE and identify unique prothrombotic risk factors in this population.MethodsWe performed a multicenter, retrospective cohort study using the Pediatric Health Information Systems registry including patients aged 0 to 21 years hospitalized for DKA from January 2017 to December 2023 within 48 participating centers. The primary outcome was the frequency of HA-VTE. Secondary outcomes were rates of cerebral edema, central venous catheterization (CVC), invasive mechanical ventilation (IMV), infection, and length of stay (LOS). An adjusted logistic regression was employed to identify potential HA-VTE risk factors.ResultsOf the 27,613 patients studied, 93 (0.3%) developed a HA-VTE. Compared with those without HA-VTE, those with HA-VTE had a greater median LOS (10 [IQR, 5-21] vs 2 [IQR, 2-3] days) and rates of cerebral edema (25.8% vs 6.6%), CVC (23.7% vs 1.1%), infection (72% vs 23.5%), and IMV (39.8% vs 1.4%; all P < .001). In an adjusted logistic model, factors independently associated with increased HA-VTE were CVC (adjusted odds ratio [aOR], 3.04; 95% CI, 1.49-6.19), infection (aOR, 4.61; 95% CI, 2.81-7.56), IMV (aOR, 9.24; 95% CI, 4.83-17.56), and increasing LOS (aOR, 1.05; 95% CI, 1.02-1.06; all P < .01).ConclusionThe frequency of HA-VTE among critically ill children and young adults hospitalized for DKA was 0.3%. After prospective validation, putative risk factors (ie, CVC, IMV, infection, and extended LOS) may be incorporated into the design of forthcoming pediatric thromboprophylaxis trials.
| S-EPMC11567947 | biostudies-literature
Project description:BackgroundStudies evaluating risk factors for in-hospital venous thromboembolism in children are limited by quality assurance of case definition and/or lack of controlled comparison. The objective of this study is to determine risk factors for the development of in-hospital venous thromboembolism in children.Design and methodsIn a case-control study at The Children's Hospital, Colorado, from 1(st) January 2003 to 31(st) December 2009 we employed diagnostic validation methods to determine pediatric in-hospital venous thromboembolism risk factors. Clinical data on putative risk factors were retrospectively collected from medical records of children with International Classification of Diseases, 9th edition codes of venous thromboembolism at discharge, in whom radiological reports confirmed venous thromboembolism and no signs/symptoms of venous thromboembolism were noted on admission.ResultsWe verified 78 cases of in-hospital venous thromboembolism, yielding an average incidence of 5 per 10,000 hospitalized children per year. Logistical regression analyses revealed that mechanical ventilation, systemic infection, and hospitalization duration of five days or over were statistically significant, independent risk factors for in-hospital venous thromboembolism (OR=3.29, 95%CI=1.53-7.06, P=0.002; OR=3.05, 95%CI=1.57-5.94, P=0.001; and OR=1.03, 95%CI=1.01-1.04, P=0.001, respectively). Using these factors in a risk model, post-test probability of venous thromboembolism was 3.6%.ConclusionsThese data indicate that risk of in-hospital venous thromboembolism in children with this risk factor combination may exceed that of hospitalized adults in whom prophylactic anticoagulation is indicated. Substantiation of these findings via multicenter studies could provide the basis for future risk-stratified randomized control trials of pediatric venous thromboembolism prevention.
| S-EPMC3347665 | biostudies-literature
Project description:Objective To develop and validate a risk prediction model for venous thromboembolism in the first six weeks after delivery (early postpartum).Design Cohort study.Setting Records from England based Clinical Practice Research Datalink (CPRD) linked to Hospital Episode Statistics (HES) and data from Sweden based registry.Participants All pregnant women registered with CPRD-HES linked data between 1997 and 2014 and Swedish medical birth registry between 2005 and 2011 with postpartum follow-up.Main outcome measure Multivariable logistic regression analysis was used to develop a risk prediction model for postpartum venous thromboembolism based on the English data, which was externally validated in the Swedish data.Results 433 353 deliveries were identified in the English cohort and 662 387 in the Swedish cohort. The absolute rate of venous thromboembolism was 7.2 per 10 000 deliveries in the English cohort and 7.9 per 10 000 in the Swedish cohort. Emergency caesarean delivery, stillbirth, varicose veins, pre-eclampsia/eclampsia, postpartum infection, and comorbidities were the strongest predictors of venous thromboembolism in the final multivariable model. Discrimination of the model was similar in both cohorts, with a C statistic above 0.70, with excellent calibration of observed and predicted risks. The model identified more venous thromboembolism events than the existing national English (sensitivity 68% v 63%) and Swedish guidelines (30% v 21%) at similar thresholds.Conclusion A new prediction model that quantifies absolute risk of postpartum venous thromboembolism has been developed and externally validated. It is based on clinical variables that are available in many developed countries at the point of delivery and could serve as the basis for real time decisions on obstetric thromboprophylaxis.
| S-EPMC5137302 | biostudies-literature
Project description:BackgroundHigh incidence of asymptomatic venous thromboembolism (VTE) has been observed in severe COVID-19 patients, but the characteristics of symptomatic VTE in general COVID-19 patients have not been described.ObjectivesTo comprehensively explore the prevalence and reliable risk prediction for VTE in COVID-19 patients.Methods/resultsThis retrospective study enrolled all COVID-19 patients with a subsequent VTE in 16 centers in China from January 1 to March 31, 2020. A total of 2779 patients were confirmed with COVID-19. In comparison to 23,434 non-COVID-19 medical inpatients, the odds ratios (ORs) for developing symptomatic VTE in severe and non-severe hospitalized COVID-19 patients were 5.94 (95% confidence interval [CI] 3.91-10.09) and 2.79 (95% CI 1.43-5.60), respectively. When 104 VTE cases and 208 non-VTE cases were compared, pulmonary embolism cases had a higher rate for in-hospital death (OR 6.74, 95% CI 2.18-20.81). VTE developed at a median of 21 days (interquartile range 13.25-31) since onset. Independent factors for VTE were advancing age, cancer, longer interval from symptom onset to admission, lower fibrinogen and higher D-dimer on admission, and D-dimer increment (DI) ≥1.5-fold; of these, DI ≥1.5-fold had the most significant association (OR 14.18, 95% CI 6.25-32.18, p = 2.23 × 10-10 ). A novel model consisting of three simple coagulation variables (fibrinogen and D-dimer levels on admission, and DI ≥1.5-fold) showed good prediction for symptomatic VTE (area under the curve 0.865, 95% CI 0.822-0.907, sensitivity 0.930, specificity 0.710).ConclusionsThere is an excess risk of VTE in hospitalized COVID-19 patients. This novel model can aid early identification of patients who are at high risk for VTE.
| S-EPMC8014692 | biostudies-literature