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A clinical prediction model for cancer-associated venous thromboembolism: a development and validation study in two independent prospective cohorts.

ABSTRACT: BACKGROUND:Venous thromboembolism is a common complication of cancer, but the risk of developing venous thromboembolism varies greatly among individuals and depends on numerous factors, including type of cancer. We aimed to develop and externally validate a clinical prediction model for cancer-associated venous thromboembolism. METHODS:We used data from the prospective Vienna Cancer and Thrombosis Study (CATS) cohort (n=1423) to select prognostic variables for inclusion in the model. We then validated the model in the prospective Multinational Cohort Study to Identify Cancer Patients at High Risk of Venous Thromboembolism (MICA) cohort (n=832). We calculated c-indices to show how the predicted incidence of objectively confirmed venous thromboembolism at 6 months compared with the cumulative 6-month incidences observed in both cohorts. FINDINGS:Two variables were selected for inclusion in the final clinical prediction model: tumour-site risk category (low or intermediate vs high vs very high) and continuous D-dimer concentrations. The multivariable subdistribution hazard ratios were 1·96 (95% CI 1·41-2·72; p=0·0001) for high or very high versus low or intermediate and 1·32 (95% CI 1·12-1·56; p=0·001) per doubling of D-dimer concentration. The cross-validated c-indices of the final model were 0·66 (95% CI 0·63-0·67) in CATS and 0·68 (0·62-0·74) in MICA. The clinical prediction model was adequately calibrated in both cohorts. INTERPRETATION:An externally validated clinical prediction model incorporating only one clinical factor (tumour-site category) and one biomarker (D-dimer) predicted the risk of venous thromboembolism in ambulatory patients with solid cancers. This simple model is a considerable improvement on previous models for predicting cancer-associated venous thromboembolism, and could aid physicians in selection of patients who will likely benefit from thromboprophylaxis. FUNDING:Austrian Science Fund, Austrian National Bank Memorial Fund, and participating hospitals.

SUBMITTER: Pabinger I

PROVIDER: S-EPMC7338218 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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A clinical prediction model for cancer-associated venous thromboembolism: a development and validation study in two independent prospective cohorts.

Pabinger Ingrid I van Es Nick N Heinze Georg G Posch Florian F Riedl Julia J Reitter Eva-Maria EM Di Nisio Marcello M Cesarman-Maus Gabriela G Kraaijpoel Noémie N Zielinski Christoph Carl CC Büller Harry Roger HR Ay Cihan C

The Lancet. Haematology 20180607 7

<h4>Background</h4>Venous thromboembolism is a common complication of cancer, but the risk of developing venous thromboembolism varies greatly among individuals and depends on numerous factors, including type of cancer. We aimed to develop and externally validate a clinical prediction model for cancer-associated venous thromboembolism.<h4>Methods</h4>We used data from the prospective Vienna Cancer and Thrombosis Study (CATS) cohort (n=1423) to select prognostic variables for inclusion in the mod ...[more]

PMID: 29885940

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Project description:PurposeVenous thromboembolism (VTE) is a leading cause of death among patients with cancer. The Khorana score was developed for assessing the risk of VTE in outpatients with cancer receiving chemotherapy, but its accuracy in identifying patients at high risk has been questioned. The aim of this study was to develop and validate a clinical-genetic score that improves the assessment of VTE risk in oncology outpatients within 6 months of diagnosis.MethodsThe new score was developed using the data of 364 outpatients belonging to the Spanish ONCOTHROMB 12-01 population. In this cohort, clinical data associated with the risk of VTE were collected at the time of diagnosis, including the Khorana score. These patients were also genotyped for the 51 genetic variants known to be associated with VTE. Multivariate logistic regression was performed to determine the weight of each genetic and clinical variable in relation to VTE risk, allowing a clinical-genetic risk score (the ONCOTHROMB score) to be developed. The Khorana and the ONCOTHROMB scores were then compared via the area under the receiver operating characteristic curve (AUC), calibration, and the number of patients needed to treat. The new score was then validated in a study of 263 patients in the Vienna Cancer and Thrombosis Study population.ResultsNine genetic variants, tumor site, TNM stage, and a body mass index of > 25 kg/m2 were found to be associated with VTE and were used to build the ONCOTHROMB score, which better predicted the overall risk of VTE than did the Khorana score (AUC, 0.781 v 0.580; P < .001). Similar AUC results were recorded in the validation study the Vienna Cancer and Thrombosis Study cohort involving patients with the same type of tumor (AUC for the ONCOTHROMB score v the Khorana score: 0.686 v 0.577; P < .001) and with all type of tumors (AUC for the ONCOTHROMB score v the Khorana score: 0.720 v 0.561; P < .0001).ConclusionThe ONCOTHROMB score for VTE risk in outpatients with cancer, which takes into account both clinical and genetic variables, better identifies patients who might benefit from primary thromboprophylaxis than does the Khorana score.

Project description:BackgroundThere is currently a lack of model for predicting the occurrence of venous thromboembolism (VTE) in patients with lung cancer. Machine learning (ML) techniques are being increasingly adapted for use in the medical field because of their capabilities of intelligent analysis and scalability. This study aimed to develop and validate ML models to predict the incidence of VTE among lung cancer patients.MethodsData of lung cancer patients from a Grade 3A cancer hospital in China with and without VTE were included. Patient characteristics and clinical predictors related to VTE were collected. The primary endpoint was the diagnosis of VTE during index hospitalization. We calculated and compared the area under the receiver operating characteristic curve (AUROC) using the selected best-performed model (Random Forest model) through multiple model comparison, as well as investigated feature contributions during the training process with both permutation importance scores and the impurity-based feature importance scores in random forest model.ResultsIn total, 3,398 patients were included in our study, 125 of whom experienced VTE during their hospital stay. The ROC curve and precision-recall curve (PRC) for Random Forest Model showed an AUROC of 0.91 (95% CI: 0.893-0.926) and an AUPRC of 0.43 (95% CI: 0.363-0.500). For the simplified model, five most relevant features were selected: Karnofsky Performance Status (KPS), a history of VTE, recombinant human endostatin, EGFR-TKI, and platelet count. We re-trained a random forest classifier with results of the AUROC of 0.87 (95% CI: 0.802-0.917) and AUPRC of 0.30 (95% CI: 0.265-0.358), respectively.ConclusionAccording to the study results, there was no conspicuous decrease in the model's performance when use fewer features to predict, we concluded that our simplified model would be more applicable in real-life clinical settings. The developed model using ML algorithms in our study has the potential to improve the early detection and prediction of the incidence of VTE in patients with lung cancer.

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.

Project description:Introduction: Effective predictive biomarkers for selection of patients benefiting from adjuvant platinum-based chemotherapy in non-small cell lung cancer (NSCLC) are needed. Based on a previously validated methodology, molecular profiles of predicted sensitivity in two patient cohorts are presented. Methods: The profiles are correlations between in vitro sensitivity to cisplatin and vinorelbine and baseline mRNA expression of the 60 cell lines in the National Cancer Institute panel. An applied clinical samples filter focused the profiles to clinically relevant genes. The profiles were tested on 1) snap-frozen tumors from 133 patients with completely resected stage 1B-2 NSCLC randomized to adjuvant cisplatin and vinorelbine (ACV, n=71) or no adjuvant treatment (OBS, n=62) [GSE14814] and 2) formalin-fixed paraffin-embedded (FFPE) pre-treatment tumors from 95 patients with completely resected stage 1A-3B NSCLC receiving adjuvant cisplatin and vinorelbine. Results: The combined cisplatin and vinorelbine profiles showed: 1) univariate Hazard Ratio (HR) for sensitive versus resistant of 0.265 (95% CI:0.079-0.889, p=0.032) in the ACV cohort and a HR of 0.28 in a multivariate model (95% CI:0.08-1.04, p=0.0573); 2) significant prediction at 3 year survival from surgery in univariate (HR=0.138 (95% CI:0.035-0.537), p=0.004) and multivariate analysis (HR=0.14 (95% CI:0.030-0.6), p=0.0081). No discrimination was found in the OBS cohort (HR=1.328, p=0.60). The cisplatin predictor alone had similar figures with 1) univariate HR of 0.37 (95% CI:0.12-1.15, p=0.09) in the ACV cohort and 2) univariate HR of 0.14 (95% CI:0.03-0.59, p=0.0076) to three years. Functional analysis on the cisplatin profile revealed a group of upregulated genes related to RNA splicing as a part of DNA damage repair and apoptosis. Conclusions: Profiles derived from snap-frozen and FFPE NSCLC tissue were prognostic and predictive in the patients that received cisplatin and vinorelbine but not in the cohort that did not receive adjuvant treatment.

Project description:BackgroundApproximately 10% of stage I colorectal cancer (CRC) patients experience unfavorable clinical outcomes after surgery. However, little is known about the subset of stage I patients who are predisposed to high risk of recurrence or death. Previous evidence was limited by small sample sizes and lack of validation.MethodsWe aimed to identify early indicators and develop a risk stratification model to inform prognosis of stage I patients by employing two large prospective cohorts. Prognostic factors for stage II tumors, including T stage, number of nodes examined, preoperative carcinoma embryonic antigen (CEA), lymphovascular invasion, perineural invasion (PNI), and tumor grade were investigated in the discovery cohort, and significant findings were further validated in the other cohort. We adopted disease-free survival (DFS) as the primary outcome for maximum statistical power and recurrence rate and overall survival (OS) as secondary outcomes. Hazard ratios (HRs) were estimated from Cox proportional hazard models, which were subsequently utilized to develop a multivariable model to predict DFS. Predictive performance was assessed in relation to discrimination, calibration and net benefit.ResultsA total of 728 and 413 patients were included for discovery and validation. Overall, 6.7% and 4.1% of the patients developed recurrences during follow-up. We identified consistent significant effects of PNI and higher preoperative CEA on inferior DFS in both the discovery (PNI: HR = 4.26, 95% CI: 1.70-10.67, p = 0.002; CEA: HR = 1.46, 95% CI: 1.13-1.87, p = 0.003) and the validation analysis (PNI: HR = 3.31, 95% CI: 1.01-10.89, p = 0.049; CEA: HR = 1.58, 95% CI: 1.10-2.28, p = 0.014). They were also significantly associated with recurrence rate. Age at diagnosis was a prominent determinant of OS. A prediction model on DFS using Age at diagnosis, CEA, PNI, and number of LYmph nodes examined (ACEPLY) showed significant discriminative performance (C-index: 0.69, 95% CI:0.60-0.77) in the external validation cohort. Decision curve analysis demonstrated added clinical benefit of applying the model for risk stratification.ConclusionsPNI and preoperative CEA are useful indicators for inferior survival outcomes of stage I CRC. Identification of stage I patients at high risk of recurrence is feasible using the ACEPLY model, although the predictive performance is yet to be improved.

Project description:AbstractMost studies on the prediction of venous thromboembolism (VTE) focused on hospitalized, surgery, and cancer patients or women receiving hormonal contraceptives or menopausal hormone therapy. No study considered diabetic and general populations to establish a VTE prediction model, especially in Asia. We developed a predictive model for VTE among type 2 diabetic patients and the general population.This study considered 2 nationwide retrospective cohort studies consisting of 52,427 diabetic participants and 508,664 participants from the general population aged 30 to 85 years during 2001 to 2004 in Taiwan. All participants were followed up until VTE event, death, or December 2011. The outcome event was VTE, including deep venous thrombosis and pulmonary embolism. Candidate predictors consisted of socio-demographic factors, diabetes-related factors and biomarkers, comorbidities, and medicine use. Our study followed the procedures proposed by the Framingham Heart Study to develop prediction models by using a Cox regression model. The predictive accuracy and performance characteristics were assessed using the area under curve of receiver operating characteristics curve and calibration of a risk score were performed by Hosmer-Lemeshow goodness-of-fit test.The common factors for persons with type 2 diabetes and general population included age, hospitalization status 1 year before the baseline, hypertension, chronic kidney disease, chronic obstructive pulmonary disease, and anti-diabetes medications; the specific factors for persons with type 2 diabetes consisted of body mass index, glycosylated hemoglobin A1C, and creatinine; and the factors for general population included gender, peripheral vascular disease, cancer, hypertension medication, cardiovascular medication, and non-steroidal anti-inflammatory drug. The area under curve of 3-, 5-, and 8-year VTE prediction models were 0.74, 0.71, and 0.69 in the diabetic population and 0.77, 0.76, and 0.75 in the general population, respectively.The new clinical prediction models can help identify a high risk of VTE and provide medical intervention in diabetic and general populations.

Dataset Information

A clinical prediction model for cancer-associated venous thromboembolism: a development and validation study in two independent prospective cohorts.

Publications

A clinical prediction model for cancer-associated venous thromboembolism: a development and validation study in two independent prospective cohorts.

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