Thrombosis risk prediction in lymphoma patients: A multi-institutional, retrospective model development and validation study.

Venous thromboembolism (VTE) poses a significant risk to cancer patients receiving systemic therapy. The generalizability of pan-cancer models to lymphomas is limited. Currently, there are no reliable risk prediction models for thrombosis in patients with lymphoma. Our objective was to create a risk assessment model (RAM) specifically for lymphomas. We performed a retrospective cohort study to develop Fine and Gray sub-distribution hazard model for VTE and pulmonary embolism (PE)/ lower extremity deep vein thrombosis (LE-DVT) respectively in adult lymphoma patients from the Veterans Affairs national healthcare system (VA). External validations were performed at the Harris Health System (HHS) and the MD Anderson Cancer Center (MDACC). Time-dependent c-statistic and calibration curves were used to assess discrimination and fit. There were 10,313 (VA), 854 (HHS), and 1858 (MDACC) patients in the derivation and validation cohorts with diverse baseline. At 6 months, the VTE incidence was 5.8% (VA), 8.2% (HHS), and 8.8% (MDACC), respectively. The corresponding estimates for PE/LE-DVT were 3.9% (VA), 4.5% (HHS), and 3.7% (MDACC), respectively. The variables in the final RAM included lymphoma histology, body mass index, therapy type, recent hospitalization, history of VTE, history of paralysis/immobilization, and time to treatment initiation. The RAM had c-statistics of 0.68 in the derivation and 0.69 and 0.72 in the two external validation cohorts. The two models achieved a clear differentiation in risk stratification in each cohort. Our findings suggest that easy-to-implement, clinical-based model could be used to predict personalized VTE risk for lymphoma patients.

Natural history of cancer-associated splanchnic vein thrombosis.

BACKGROUND: There is uncertainty in the management of cancer-associated isolated splanchnic vein thrombosis (SpVT). OBJECTIVES: To describe the natural history of SpVT by cancer type and thrombus composition and to review anticoagulation (AC) practices and associated rates of usual-site venous thromboembolism (VTE), major and clinically relevant nonmajor bleeding (MB/CRNMB), recanalization/progression, and mortality. METHODS: We performed a retrospective cohort study in patients with SpVT at 2 cancer care centers in Houston, Texas. We estimated the incidence of usual-site VTE and MB/CRNMB at 6 months using competing risk methods and examined venous patency in a subset of patients with repeat imaging. We assessed associations with mortality using Cox regression. RESULTS: Among 15 342 patients with an incident cancer diagnosis from 2011 to 2020, we identified 298 with isolated SpVT. Patients with hepatocellular carcinoma (HCC) and SpVT (n = 146) had the highest disease prevalence (20%), lowest rate of AC treatment (2%), and similar rate of usual-site VTE (4.2%) vs those without SpVT (5.2%) at 6 months, though tumor thrombus vs bland was associated with worse overall survival. In patients with non-HCC bland SpVT (n = 114), AC (n = 37) was more common in those with non-upper gastrointestinal cancers and fewer comorbidities. AC was associated with more recanalization (44% vs 15%, P = .041) but no differences in usual-site VTE, MB/CRNMB, or mortality at 6 months. CONCLUSION: Cancer-associated isolated SpVT is a common but heterogeneous thrombotic disease that is treated differently from usual-site VTE. Tumor thrombus is a negative prognostic factor. Initiation of AC in bland thrombi requires judicious consideration of thrombotic and bleeding risk.

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

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.[Media: see text].

Developing and optimizing a computable phenotype for incident venous thromboembolism in a longitudinal cohort of patients with cancer.

Background: Research on venous thromboembolism (VTE) that relies only on the International Classification of Diseases (ICD) can misclassify outcomes. Our study aims to discover and validate an improved VTE computable phenotype for people with cancer. Methods: We used a cancer registry electronic health record (EHR)-linked longitudinal database. We derived three algorithms that were ICD/medication based, natural language processing (NLP) based, or all combined. We then randomly sampled 400 patients from patients with VTE codes (n = 1111) and 400 from those without VTE codes (n = 7396). Weighted sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated on the entire sample using inverse probability weighting, followed by bootstrapped receiver operating curve analysis to calculate the concordance statistic (c statistic). Results: Among 800 patients sampled, 280 had a confirmed acute VTE during the first year after cancer diagnosis. The ICD/medication algorithm had a weighted PPV of 95% and a weighted sensitivity of 81%, with a c statistic of 0.90 (95% confidence interval [CI], 0.89-0.91). Adding Current Procedural Terminology codes for inferior vena cava filter removal or early death did not improve the performance. The NLP algorithm had a weighted PPV of 80% and a weighted sensitivity of 90%, with a c statistic of 0.93 (95% CI, 0.92-0.94). The combined algorithm had a weighted PPV of 98% at the higher cutoff and a weighted sensitivity of 96% at the lower cutoff, with a c statistic of 0.98 (95% CI, 0.97-0.98). Conclusions: Our ICD/medication-based algorithm can accurately identify VTE phenotype among patients with cancer with a high PPV of 95%. The combined algorithm should be considered in EHR databases that have access to such capabilities.

Patterns of venous thromboembolism risk, treatment, and outcomes among patients with cancer from uninsured and vulnerable populations.

The epidemiology of cancer-associated thrombosis (CAT) among uninsured and vulnerable populations in the US is not well-characterized. We performed a retrospective cohort study for patients with newly diagnosed cancer from 2011 to 2020 at Harris Health System, which cares for uninsured residents in the Houston metropolitan area. Patient demographics, NCI comorbidity index, area of deprivation index (ADI), cancer histology, staging, and systemic therapy data were extracted. CAT included overall venous thromboembolism (VTE) or pulmonary embolism +/- lower extremity deep vein thrombosis (PE/LE-DVT) within 1 year of diagnosis. We used multivariable Fine-Gray models to assess the associations with CAT accounting for death as a competing risk. Among 15 342 patients, 74% were uninsured and 84% lived in socioeconomically disadvantaged neighborhoods. There were 16% Non-Hispanic White (NHW), 28% Non-Hispanic Black (NHB), 50% Hispanic (27% Mexican), and 6% Asian/Pacific Islanders (API). The 1-year CAT incidence rate was 14.6%. Overall VTE was lower for Hispanics versus NHW (SHR 0.87 [0.76-0.99]) and API versus NHW (SHR 0.58 [0.44-0.77]). PE/LE-DVT was higher for NHB versus NHW (SHR 1.18 [1.01-1.39]). CAT was also associated with chemotherapy-based regimens (+/- immunotherapy), age, obesity, cancer type/staging, VTE history, and recent hospitalization. NCI comorbidity and ADI scores were associated with mortality but not CAT. In a large cohort of underserved patients with cancer, we identified an elevated incidence of CAT with known and novel risk predictors. Hispanics had lower adjusted rates of CAT and mortality. Our findings highlight the need to investigate and incorporate vulnerable populations in clinical trials.