PDF herunterladen
Thromb Haemost 2022; 122(04): 570-577
DOI: 10.1055/a-1525-7220
Stroke, Systemic or Venous Thromboembolism

Machine Learning to Predict Outcomes in Patients with Acute Pulmonary Embolism Who Prematurely Discontinued Anticoagulant Therapy

Damián Mora
1   Department of Internal Medicine, Hospital Virgen de la Luz, Cuenca, Spain
,
José A. Nieto
1   Department of Internal Medicine, Hospital Virgen de la Luz, Cuenca, Spain
,
Jorge Mateo
2   Neurobiological Research Group, Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
,
Behnood Bikdeli
3   Cardiovascular Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
4   Yale/YNHH Center for Outcomes Research and Evaluation, New Haven, Connecticut, United States
5   Cardiovascular Research Foundation (CRF), New York, New York, United States
,
Stefano Barco 
6   Clinic of Angiology, University Hospital Zurich, Zurich, Switzerland
7   Center for Thrombosis and Hemostasis, University Hospital Mainz, Mainz, Germany
,
Javier Trujillo-Santos
8   Department of Internal Medicine, Hospital General Universitario Santa Lucía, Universidad Católica de Murcia, Murcia, Spain
,
Silvia Soler
9   Department of Internal Medicine, Hospital Olot i Comarcal de la Garrotxa, Gerona, Spain
,
Llorenç Font
10   Department of Haematology, Hospital de Tortosa Verge de la Cinta, Tarragona, Spain
,
Marijan Bosevski 
11   Faculty of Medicine, University Cardiology Clinic, Skopje, Republic of Macedonia
,
Manuel Monreal
12   Department of Internal Medicine, Hospital Germans Trias i Pujol, Badalona, Barcelona, Spain
13   Department of Medicine, Universidad Católica de Murcia, Murcia, Spain
,
the RIETE Investigators › Institutsangaben Funding The sponsors of RIETE had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. B.B was supported by the National Heart, Lung, and Blood Institute, National Institutes of Health (NIH), through grant number T32 HL007854. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Dr. Bikdeli reports that he has been a consulting expert (on behalf of the plaintiff) for litigation related to a specific type of IVC filters. The current study is the idea of the investigators and has not been performed at the request of a third party.
› Weitere Informationen
Auch verfügbar auf
    Lizenzen und Reprints

Abstract

Background Patients with pulmonary embolism (PE) who prematurely discontinue anticoagulant therapy (<90 days) are at an increased risk for death or recurrences.

Methods We used the data from the RIETE (Registro Informatizado de Pacientes con Enfermedad TromboEmbólica) registry to compare the prognostic ability of five machine-learning (ML) models and logistic regression to identify patients at increased risk for the composite of fatal PE or recurrent venous thromboembolism (VTE) 30 days after discontinuation. ML models included decision tree, k-nearest neighbors algorithm, support vector machine, Ensemble, and neural network [NN]. A “full” model with 70 variables and a “reduced” model with 23 were analyzed. Model performance was assessed by confusion matrix metrics on the testing data for each model and a calibration plot.

Results Among 34,447 patients with PE, 1,348 (3.9%) discontinued therapy prematurely. Fifty-one (3.8%) developed fatal PE or sudden death and 24 (1.8%) had nonfatal VTE recurrences within 30 days after discontinuation. ML-NN was the best method for identification of patients experiencing the composite endpoint, predicting the composite outcome with an area under receiver operating characteristic (ROC) curve of 0.96 (95% confidence interval [CI]: 0.95–0.98), using either 70 or 23 variables captured before discontinuation. Similar numbers were obtained for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. The discrimination of logistic regression was inferior (area under ROC curve, 0.76 [95% CI: 0.70–0.81]). Calibration plots showed similar deviations from the perfect line for ML-NN and logistic regression.

Conclusion The ML-NN method very well predicted the composite outcome after premature discontinuation of anticoagulation and outperformed traditional logistic regression.

Keywords

artificial intelligence - machine-learning - neural networks - pulmonary embolism - anticoagulation - outcomes - prediction

* A full list of the RIETE investigators is given in [ Supplementary Appendix A ] .


Supplementary Material



Publikationsverlauf

Eingereicht: 03. November 2020

Angenommen: 08. Juni 2021

Accepted Manuscript online:
09. Juni 2021

Artikel online veröffentlicht:
13. Juli 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Barritt DW, Jordan SC. Anticoagulant drugs in the treatment of pulmonary embolism. A controlled trial. Lancet 1960; 1 (7138): 1309-1312
    CrossrefPubMedGoogle Scholar
  • 2 Kearon C, Akl EA, Ornelas J. et al. Antithrombotic therapy for VTE disease: CHEST Guideline and Expert Panel report. Chest 2016; 149 (02) 315-352
    CrossrefPubMedGoogle Scholar
  • 3 Nieto JA, Vicente JA, Prieto LM. et al; RIETE Investigators. Thirty-day outcomes in patients with acute pulmonary embolism who discontinued anticoagulant therapy before 90 days. Am Heart J 2018; 206: 1-10
    CrossrefPubMedGoogle Scholar
  • 4 Goldhaber SZ, Visani L, De Rosa M. Acute pulmonary embolism: clinical outcomes in the International Cooperative Pulmonary Embolism Registry (ICOPER). Lancet 1999; 353 (9162): 1386-1389
    CrossrefPubMedGoogle Scholar
  • 5 Laporte S, Mismetti P, Décousus H. et al; RIETE Investigators. Clinical predictors for fatal pulmonary embolism in 15,520 patients with venous thromboembolism: findings from the Registro Informatizado de la Enfermedad TromboEmbolica venosa (RIETE) Registry. Circulation 2008; 117 (13) 1711-1716
    CrossrefPubMedGoogle Scholar
  • 6 Jiménez D, Aujesky D, Moores L. et al; RIETE Investigators. Simplification of the pulmonary embolism severity index for prognostication in patients with acute symptomatic pulmonary embolism. Arch Intern Med 2010; 170 (15) 1383-1389
    CrossrefPubMedGoogle Scholar
  • 7 Jiménez D, Aujesky D, Díaz G. et al; RIETE Investigators. Prognostic significance of deep vein thrombosis in patients presenting with acute symptomatic pulmonary embolism. Am J Respir Crit Care Med 2010; 181 (09) 983-991
    CrossrefPubMedGoogle Scholar
  • 8 Jiménez D, Kopecna D, Tapson V. et al; On Behalf Of The Protect Investigators. Derivation and validation of multimarker prognostication for normotensive patients with acute symptomatic pulmonary embolism. Am J Respir Crit Care Med 2014; 189 (06) 718-726
    CrossrefPubMedGoogle Scholar
  • 9 Gulshan V, Peng L, Coram M. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316 (22) 2402-2410
    CrossrefPubMedGoogle Scholar
  • 10 Golden JA. Deep learning algoritms for detection of lymph node metastases from breast cancer: helping artificial intelligence be seen. JAMA 2017; 318 (22) 2184-2186
    CrossrefPubMedGoogle Scholar
  • 11 Pirracchio R, Petersen ML, Carone M, Rigon MR, Chevret S, van der Laan MJ. Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study. Lancet Respir Med 2015; 3 (01) 42-52
    CrossrefPubMedGoogle Scholar
  • 12 Mortazavi BJ, Bucholz EM, Desai NR. et al. Comparison of machine learning methods with national cardiovascular data registry models for prediction of risk of bleeding after percutaneous coronary intervention. JAMA Netw Open 2019; 2 (07) e196835
    CrossrefPubMedGoogle Scholar
  • 13 Kawaler E, Cobian A, Peissig P, Cross D, Yale S, Craven M. Learning to predict post-hospitalization VTE risk from EHR data. AMIA Annu Symp Proc 2012; 2012: 436-445
    PubMedGoogle Scholar
  • 14 Willan J, Katz H, Keeling D. The use of artificial neural network analysis can improve the risk-stratification of patients presenting with suspected deep vein thrombosis. Br J Haematol 2019; 185 (02) 289-296
    CrossrefPubMedGoogle Scholar
  • 15 Wang X, Yang YQ, Liu SH, Hong XY, Sun XF, Shi JH. Comparing different venous thromboembolism risk assessment machine learning models in Chinese patients. J Eval Clin Pract 2020; 26 (01) 26-34
    CrossrefPubMedGoogle Scholar
  • 16 Rucco M, Sousa-Rodrigues D, Merelli E. et al. Neural hypernetwork approach for pulmonary embolism diagnosis. BMC Res Notes 2015; 8: 617
    CrossrefPubMedGoogle Scholar
  • 17 Nudel J, Bishara AM, de Geus SWL. et al. Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database. Surg Endosc 2020; 35 (01) 182-191
    CrossrefPubMedGoogle Scholar
  • 18 Nafee T, Gibson CM, Travis R. et al. Machine learning to predict venous thrombosis in acutely ill medical patients. Res Pract Thromb Haemost 2020; 4 (02) 230-237
    CrossrefPubMedGoogle Scholar
  • 19 Bikdeli B, Jimenez D, Hawkins M. et al; RIETE Investigators. Rationale, Design and Methodology of the Computerized Registry of Patients with Venous Thromboembolism (RIETE). Thromb Haemost 2018; 118 (01) 214-224
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 20 Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. San Francisco, CA: Morgan Kaufmann Publishers; 2005
    Google Scholar
  • 21 Rivera-Lopez R, Canul-Reich J. Construction of near-optimal axis-parallel decision trees using a differential-evolution-based approach. IEEE Access 2018 6. 5548-5563
    PubMedGoogle Scholar
  • 22 Zhang S, Li X, Zong M, Zhu X, Wang R. Efficient kNN classification with different numbers of nearest neighbors. IEEE Trans Neural Netw Learn Syst 2018; 29 (05) 1774-1785
    CrossrefPubMedGoogle Scholar
  • 23 Xing W, Bei Y. Medical health big data classification based on KNN classification algorithm. IEEE Access 2020 8. 28808-28819
    PubMedGoogle Scholar
  • 24 Yu S, Li X, Zhang X, Wang H. The OCS-SVM: an objective-cost-sensitive SVM with sample-based misclassification cost invariance. IEEE Access 2019 7. 118931-118942
    PubMedGoogle Scholar
  • 25 Kafai M, Eshghi K. CROification: accurate kernel classification with the efficiency of sparse linear SVM. IEEE Trans Pattern Anal Mach Intell 2019; 41 (01) 34-48
    CrossrefPubMedGoogle Scholar
  • 26 Chen C, Dong D, Qi B, Petersen IR, Rabitz H. Quantum ensemble classification: a sampling-based learning control approach. IEEE Trans Neural Netw Learn Syst 2017; 28 (06) 1345-1359
    CrossrefPubMedGoogle Scholar
  • 27 Yu Z, Wang D, Zhao Z. et al. Hybrid incremental ensemble learning for noisy real-world data classification. IEEE Trans Cybern 2019; 49 (02) 403-416
    CrossrefPubMedGoogle Scholar
  • 28 Li Y, Wang XD, Luo ML, Li K, Yang XF, Guo Q. Epileptic seizure classification of EEGs using time-frequency analysis based multiscale radial basis functions. IEEE J Biomed Health Inform 2018; 22 (02) 386-397
    CrossrefPubMedGoogle Scholar
  • 29 Lam D, Wunsch D. Unsupervised feature learning classification with radial basis function extreme learning machine using graphic processors. IEEE Trans Cybern 2017; 47 (01) 224-231
    CrossrefPubMedGoogle Scholar
  • 30 Efron B, Tibshirani R. Improvements on cross-validation: the 632+ bootstrap method. J Am Stat Assoc 1977; 92: 438
    PubMedGoogle Scholar
  • 31 Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett 2006; 27: 861-874
    CrossrefPubMedGoogle Scholar
  • 32 Zhou X-H, Obuchowski NA, McClish DK. Statistical Methods in Diagnostic Medicine, 2nd ed. Hoboken, NJ: Wiley; 2011
    CrossrefGoogle Scholar
  • 33 Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J 2014; 35 (29) 1925-1931
    CrossrefPubMedGoogle Scholar
  • 34 Guo Ch, Pleiss G, Sun Y, Weinberger KQ. On calibration of modern neural networks. Paper presented at: Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 2017
    PubMedGoogle Scholar
  • 35 Snoek J, Larochelle H, Adams RP. Practical bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst 2012; 25: 2951-2959
    PubMedGoogle Scholar
  • 36 Wang HL, Hsu WY, Lee MH. et al. Automatic machine-learning-based outcome prediction in patients with primary intracerebral hemorrhage. Front Neurol 2019; 10: 910
    CrossrefPubMedGoogle Scholar
  • 37 Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019; 380 (14) 1347-1358
    CrossrefPubMedGoogle Scholar