External validation of a novel electronic risk score for cancer-associated thrombosis in a comprehensive cancer center.

Venous thromboembolism (VTE) is a significant complication for cancer patients undergoing systemic therapy. We performed an independent external validation for a recently derived and validated a novel electronic health record (EHR) VTE risk score in a comprehensive cancer center. Adult patients with incident cancer diagnoses were identified from MD Anderson Cancer Center Tumor Registry 1/2017-1/2021. Baseline covariates extracted at the time of first-line systemic therapy included demographics, cancer site/histology, stage, treatment, complete blood count, body mass index, recent prolonged hospitalization, and history of VTE or paralysis. VTE was ascertained using an institution-specific natural language processing radiology algorithm (positive predictive value of 94.8%). The median follow-up for 21 142 cancer patients was 8.1 months. There were 1067 (5.7%) VTE within 6 months after systemic therapy. The distribution of the novel score for 0-, 1, 2, 3, 4, 5+ was 5661, 3558, 3462, 3489, 2918, and 2054; while the corresponding 6-month VTE incidence was 1.3%, 3.1%, 5.4%, 7.3%, 9.3%, and 13.8%, respectively (c statistic 0.71 [95% CI 0.69-0.72] with excellent calibration). In comparison, the Khorana score had a c statistic of 0.64 [95% CI 0.62-0.65]. The two risk scores had 80% concordance; the novel score reclassified 20% of Khorana score (3530 low-to-high with 9.0% VTE; 734 high-to-low with 3.4% VTE) and led to a 25% increment in VTEs captured in the high-risk group. In conclusion, the novel score demonstrated consistent discrimination and calibration across cohorts with heterogenous demographics. It could become a new standard to select high-risk populations for clinical trials and VTE monitoring.

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.

Structure and morphology of charged graphene platelets in solution by small-angle neutron scattering.

Solutions of negatively charged graphene (graphenide) platelets were produced by intercalation of nanographite with liquid potassium-ammonia followed by dissolution in tetrahydrofuran. The structure and morphology of these solutions were then investigated by small-angle neutron scattering. We found that >95 vol % of the solute is present as single-layer graphene sheets. These charged sheets are flat over a length scale of >150 Å in solution and are strongly solvated by a shell of solvent molecules. Atomic force microscopy on drop-coated thin films corroborated the presence of monolayer graphene sheets. Our dissolution method thus offers a significant increase in the monodispersity achievable in graphene solutions.