Derivation and Validation of a Clinical Risk Assessment Model for Cancer-Associated Thrombosis in Two Unique US Health Care Systems

Abstract

PURPOSE Venous thromboembolism (VTE), especially pulmonary embolism (PE) and lower extremity deep vein thrombosis (LE-DVT), is a serious and potentially preventable complication for patients with cancer undergoing systemic therapy. METHODS Using retrospective data from patients diagnosed with incident cancer from 2011-2020, we derived a parsimonious risk assessment model (RAM) using least absolute shrinkage and selection operator regression from the Harris Health System (HHS, n = 9,769) and externally validated it using the Veterans Affairs (VA) health care system (n = 79,517). Bootstrapped c statistics and calibration curves were used to assess external model discrimination and fit. Dichotomized risk strata using integer scores were created and compared against the Khorana score (KS). RESULTS Incident VTE and PE/LE-DVT at 6 months occurred in 590 (6.2%) and 437 (4.6%) patients in HHS and 4,027 (5.1%) and 3,331 (4.2%) patients in the VA health care system. Assessed at the time of systemic therapy initiation, the new RAM included components of the KS with the modified cancer subtype, cancer staging, systemic therapy class, history of VTE, history of paralysis/immobility, recent hospitalization, and Asian/Pacific Islander race. The c statistic was 0.71 in HHS and 0.68 in the VA health care system (compared with 0.65 and 0.60, respectively, for KS). Furthermore, the new RAM appropriately reclassified 28% of patients and increased the proportion of VTEs in the high-risk group from 37% to 68% in the validation data set. CONCLUSION The novel RAM stratified patients with cancer into a high-risk group with 8%-10% cumulative incidence of VTE and 7% PE/LE-DVT at 6 months ( v 3% and 2%, respectively, in the low-risk group). The model had improved performance over the original KS and doubled the number of VTE events in the high-risk stratum. We encourage additional external validation from prospective studies.

Epidemiology of Cancer-Associated Venous Thromboembolism in Patients With Solid and Hematologic Neoplasms in the Veterans Affairs Health Care System

“…VTE events were screened using a combination of ICD-9-CM and ICD-10-CM codes and natural language processing radiology impressions from inpatient and outpatient settings, as described previously . This combined algorithm had a sensitivity of 96% and a positive predictive value of 91% when compared with manual medical record abstraction in the VA cohort as shown previously …”

“…Patients with cancer are not only at increased risk of VTE compared with the general population, but also at higher risk of VTE recurrence despite receipt of anticoagulation therapy . Our understanding of the pathophysiology of cancer-associated thrombosis (CAT) has evolved over the past few decades, as have validated tools to identify those at high risk . Despite these advances, there remains a critical need to identify the associations of race and ethnicity, cancer subtype, and systemic therapy regimens with CAT, particularly over time as the landscape of antineoplastic therapies has evolved.…”

Fine Tuning Venous Thromboembolism Risk Prediction in Patients With Cancer

“…In the articles that accompany this editorial, Li et al 10 and Muñoz et al 11 presented two new venous thromboembolism risk prediction scores to assess risk in patients with cancer. Although these scores may improve the discrimination between patients at low and high risk for venous thromboembolism and better identify those who would benefit from thromboprophylaxis, both require further validation before they can be used routinely in clinical practice.…”

“…The score incorporates oncology specific factors of stage of cancer and risks associated with cancer treatment. Known predictors of VTE in any population, including a history of VTE, history of paralysis/immobility, recent hospitalization, and Asian Pacific Islander race (conferring decreased VTE risk) were added to the model and assigned appropriate points 10 (Table 2). These factors emerged as predictive from 34 candidates in the derivation cohort and were confirmed in the validation cohort.…”

“…Prospective validation of both scores is needed to determine the utility of these models in populations that include wide representation of all the elements of the Li et al 10 model and in large numbers of patients with diverse genetic backgrounds for the Muñoz et al 11 model.…”