Simplicity versus complexity: an existential dilemma as risk tools evolve
Authors: Alok A Khorana
Journal: The Lancet Haematology (2018)

The oft-stated rule of thumb that one in five patients with cancer will develop venous thromboembolism during their illness elides its converse: four in five patients with cancer never will. Indeed, there is substantial variation in the risk of venous thromboembolism and, by extension, its consequences: morbidity, worsened short-term and long-term mortality, and increased frequency and duration of hospitalisation. Improving the ability to predict risk of venous thromboembolism is of substantial importance to patients and health systems alike. In 2008, my colleagues and I published the first formal risk assessment tool to predict the risk of venous thromboembolism in patients with cancer starting a new systemic therapy regimen. This tool is now known as the Khorana score.1Khorana AA Kuderer NM Culakova E Lyman GH Francis CW Development and validation of a predictive model for chemotherapy-associated thrombosis.Blood. 2008; 111: 4902-4907Crossref PubMed Scopus (1398) Google Scholar The score has since been validated in multiple external settings in more than 15 000 patients, and is currently the only risk assessment algorithm recommended by multiple guideline panels.2Angelini D Khorana AA Risk assessment scores for cancer-associated venous thromboembolic disease.Semin Thromb Hemost. 2017; 43: 469-478Crossref PubMed Scopus (21) Google Scholar However, much has changed in cancer medicine in the past decade. Can risk prediction also be improved further? This clinical question underpins the research by Ingrid Pabinger and colleagues,3Pabinger I van Es N Heinze G et al.A clinical prediction model for cancer-associated venous thromboembolism: a development and validation study in two independent prospective cohorts.Lancet Haematol. 2018; (published online June 6.)http://dx.doi.org/10.1016/S2352-3026(18)30063-2Summary Full Text Full Text PDF PubMed Scopus (165) Google Scholar reported in The Lancet Haematology, of a new tool for prediction of venous thromboembolism in patients with cancer. The investigators used data from the single-centre, prospective Vienna Cancer and Thrombosis Study (CATS) to develop the model, which they then validated using data from the Dutch group of patients recruited to a separate study, the Multinational Cohort Study to Identify Cancer Patients at High Risk of Venous Thromboembolism (MICA). After univariable modelling and use of a prespecified selection process, two variables were included in the final model: tumour-site risk category (low or intermediate vs high vs very high) and D-dimer concentrations (as a continuous variable). Pabinger and colleagues' study is important because it attempts to simplify risk assessment for venous thromboembolism in patients with cancer. It eliminates numerous variables that were included in previous models and only includes a biomarker that can be readily tested in the clinic. The investigators should be commended for the enormous effort and collaboration needed to validate a biomarker through use of prospectively collected biospecimens. However, although apparently simpler than existing tools, Pabinger and colleagues' model is applied as a nomogram that requires clinicians to estimate the risk of venous thromboembolism on a point scale ranging from 0 to 100 (corresponding to D-dimer concentrations of 0·1–32 μg/mL fibrinogen equivalent units), with further stratification by tumour-risk site, eventually leading to a total points scale ranging from 0 to 200, with a corresponding cumulative 6-month incidence of venous thromboembolism of 2–29%. I am concerned about the number of steps that this process asks of busy clinicians. The authors state that their new tool is a considerable improvement on previous models, but the c-indices in the development (0·66) and validation (0·68) cohorts are actually lower than those observed for the cohorts in our study (0·7 for both cohorts).1Khorana AA Kuderer NM Culakova E Lyman GH Francis CW Development and validation of a predictive model for chemotherapy-associated thrombosis.Blood. 2008; 111: 4902-4907Crossref PubMed Scopus (1398) Google Scholar The authors did re-evaluate the performance of the Khorana score and found lower c-indices than originally reported, but this finding might reflect a major limitation of their validation cohort: 70% of patients in MICA were enrolled a few months after starting chemotherapy, but pre-chemotherapy blood counts (which account for three of the five variables in the Khorana score) begin to decrease within a few days of starting treatment. However, the fact that this new tool works in the extended setting is a strength, and could broaden the applicability of risk assessment to patients who are already on chemotherapy. Whether or not use of this new prediction tool will improve outcomes in other applications of venous thromboembolism risk assessment is unknown. In recent years, risk assessment has been used to target education about the warning signs of venous thromboembolism to high-risk patients; for early detection of subclinical venous thromboembolism;4Khorana AA Francis CW Kuderer NM et al.Dalteparin thromboprophylaxis in cancer patients at high risk for venous thromboembolism: a randomized trial.Thromb Res. 2017; 151: 89-95Summary Full Text Full Text PDF PubMed Scopus (89) Google Scholar for prediction of venous thromboembolism in hospitalised patients;5Patell R Rybicki L McCrae KR Khorana AA Predicting risk of venous thromboembolism in hospitalized cancer patients: utility of a risk assessment tool.Am J Hematol. 2017; 92: 501-507Crossref PubMed Scopus (52) Google Scholar and, perhaps most importantly, for prediction of benefit from thromboprophylaxis.4Khorana AA Francis CW Kuderer NM et al.Dalteparin thromboprophylaxis in cancer patients at high risk for venous thromboembolism: a randomized trial.Thromb Res. 2017; 151: 89-95Summary Full Text Full Text PDF PubMed Scopus (89) Google Scholar Many other risk tools have been proposed since the Khorana score was introduced (table). In their report, Pabinger and colleagues further highlight what might be viewed as an existential issue as risk tools evolve: simplicity (favouring clinical applicability) versus complexity (favouring improved prediction). For risk tools to improve, new variables or biomarkers need to be added, or populations need to be narrowed (eg, by type of cancer). However, focusing on narrower populations could result in the proliferation of risk scores for every single type or subtype of cancer, which might overwhelm clinicians and lead to these tools never being used. Future iterations of risk tools might incorporate 'omics approaches or microRNA profiling, but how and when these would be applied clinically is uncertain.TableCharacteristics of risk scores for cancer-associated VTEInnovation or modificationValidated?Improves prediction?Predicts benefit from thrombo-prophylaxis?Identifies early VTE?Predicts VTE once new regimen already initiated?Predicts inpatient VTE?Khorana1Khorana AA Kuderer NM Culakova E Lyman GH Francis CW Development and validation of a predictive model for chemotherapy-associated thrombosis.Blood. 2008; 111: 4902-4907Crossref PubMed Scopus (1398) Google ScholarOriginalYesRefYesYesUnknownYesVienna6Ay C Dunkler D Marosi C et al.Prediction of venous thromboembolism in cancer patients.Blood. 2010; 116: 5377-5382Crossref PubMed Scopus (567) Google ScholarAdds D-dimer and soluble P-selectinNoYes (improved PPV)UnknownUnknownUnknownUnknownProtecht7Verso M Agnelli G Barni S Gasparini G LaBianca R A modified Khorana risk assessment score for venous thromboembolism in cancer patients receiving chemotherapy: the Protecht score.Intern Emerg Med. 2012; 7: 291-292Crossref PubMed Scopus (228) Google ScholarRemoves body-mass index and adds chemotherapyNoUnclearYesUnknownUnknownUnknownONKOTEV8Cella CA Di Minno G Carlomagno C et al.Preventing venous thromboembolism in ambulatory cancer patients: the ONKOTEV study.Oncologist. 2017; 22: 601-608Crossref PubMed Scopus (74) Google ScholarAdds metastatic disease, previous VTE, and compressionNoYes*Further validation ongoing. (improved AUC)UnknownUnknownUnknownUnknownCOMPASS-CAT9Gerotziafas GT Taher A Abdel-Razeq H et al.A predictive score for thrombosis associated with breast, colorectal, lung, or ovarian cancer: the prospective COMPASS-Cancer-Associated Thrombosis study.Oncologist. 2017; 22: 1222-1231Crossref PubMed Scopus (109) Google ScholarOnly for use in breast, colorectal, lung, and ovarian cancerLung onlyYes*Further validation ongoing. (improved AUC)UnknownUnknownUnknownUnknownTic-ONCO10Munoz Martin AJ Ortega I Font C et al.Multivariable clinical-genetic risk model for predicting venous thromboembolic events in patients with cancer.Br J Cancer. 2018; 118: 1056-1061Crossref PubMed Scopus (63) Google ScholarAdds genetic risk factorsNoYes*Further validation ongoing. (improved AUC and PPV)UnknownUnknownUnknownUnknownPabinger et al3Pabinger I van Es N Heinze G et al.A clinical prediction model for cancer-associated venous thromboembolism: a development and validation study in two independent prospective cohorts.Lancet Haematol. 2018; (published online June 6.)http://dx.doi.org/10.1016/S2352-3026(18)30063-2Summary Full Text Full Text PDF PubMed Scopus (165) Google ScholarAdds D-dimer and removes all other variables except tumour siteYesYes (improved PPV)UnknownUnknownYesUnknownVTE=venous thromboembolism. PPV=positive predictive value. AUC=area under the curve.* Further validation ongoing. Open table in a new tab VTE=venous thromboembolism. PPV=positive predictive value. AUC=area under the curve. How, then, can those of us interested in reducing the burden of cancer-associated venous thromboembolism help move the ball forward? My own bias is that applicability is key and risk stratification should be as automated as possible for optimal clinical use. If risk tools must be made more complex, the implementation bar should be set at a much higher positive predictive value (minimum 70%). Perhaps we can heed the advice of the aphorism attributed to many sources, including Albert Einstein: everything should be made as simple as possible, but not any simpler. This, too, is more complicated than it seems. This online publication has been corrected. The corrected version first appeared at thelancet.com/haematology on June 15, 2018 This online publication has been corrected. The corrected version first appeared at thelancet.com/haematology on June 15, 2018 The development of the Khorana score was sponsored by a grant from the National Cancer Institute (K23 CA120587). AAK acknowledges additional research support from the Sondra and Stephen Hardis Chair in Oncology Research, the National Heart, Lung and Blood Institute (R34HL127156), and the Scott Hamilton Cancer Alliance for Research, Education and Survivorship Initiative. A clinical prediction model for cancer-associated venous thromboembolism: a development and validation study in two independent prospective cohortsAn 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. Full-Text PDF Open AccessCorrection to Lancet Haematol 2018; 5: e273–74Khorana AA. Simplicity versus complexity: an existential dilemma as risk tools evolve. Lancet Haematol 2018; published online June 6. http://dx.doi.org/10.1016/S2352-3026(18)30067-X—a copyright line has been added to this Comment to reflect that it is now Open Access. This correction has been made to the online version as of June 15, 2018. Full-Text PDF Artificial intelligence for cancer-associated thrombosis risk assessment – Author's replyI appreciate the comments from Patrizia Ferroni and colleagues regarding the use of artificial intelligence and machine-learning approaches to better predict the risk of venous thromboembolism (VTE) in patients with cancer. I agree with the authors that automated extraction of data from electronic health records in real-time is the ideal way forward to identify patients at high risk of thrombotic events—either for education, early detection or screening, or for prophylaxis—and I stressed this point in my Comment. Full-Text PDF Artificial intelligence for cancer-associated thrombosis risk assessmentWe read, with great interest, the Lancet Haematology Article by Ingrid Pabinger and colleagues1 reporting the development and validation of a nomogram (including only tumour site risk category and D-dimer) to assess the risk of venous thromboembolism (VTE) in chemotherapy-treated cancer patients. The development of risk assessment models to predict the risk of cancer-associated VTE is a compelling challenge in contemporary oncology, and the prediction tool described by Pabinger and colleagues1 has the potential for a substantial improvement over the Khorana score. Full-Text PDF