Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations.

Predictive tools for major bleeding (MB) using machine learning (ML) might be advantageous over traditional methods. We used data from the Registro Informatizado de Enfermedad TromboEmbólica (RIETE) to develop ML algorithms to identify patients with venous thromboembolism (VTE) at increased risk of MB during the first 3 months of anticoagulation. A total of 55 baseline variables were used as predictors. New data prospectively collected from the RIETE were used for further validation. The RIETE and VTE-BLEED scores were used for comparisons. External validation was performed with the COMMAND-VTE database. Learning was carried out with data from 49 587 patients, of whom 873 (1.8%) had MB. The best performing ML method was XGBoost. In the prospective validation cohort the sensitivity, specificity, positive predictive value and F1 score were: 33.2%, 93%, 10%, and 15.4% respectively. F1 value for the RIETE and VTE-BLEED scores were 8.6% and 6.4% respectively. In the external validation cohort the metrics were 10.3%, 87.6%, 3.5% and 5.2% respectively. In that cohort, the F1 value for the RIETE score was 17.3% and for the VTE-BLEED score 9.75%. The performance of the XGBoost algorithm was better than that from the RIETE and VTE-BLEED scores only in the prospective validation cohort, but not in the external validation cohort.

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

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.

"The bent nuns convent". Antipsycotic-induced Pisa Syndrome and Camptocormia. / «El convento de las monjas torcidas¼. Síndrome de Pisa y camptocormia inducido por antipsicóticos.

Camptocormia is an involuntary flexion between 15-45° of the thoracolumbar spine when standing, walking or sitting, which disappears completely in the supine position. Is associated to Parkinson's disease and other neurodegenerative conditions, being its physiopatology unknown. We report three cases of atypical anti-psychotic induced camptocormia.

Thirty-day outcomes in patients with proximal deep vein thrombosis who discontinued anticoagulant therapy prematurely.

INTRODUCTION: In patients receiving anticoagulation for deep vein thrombosis (DVT), a variety of reasons (including active bleeding or high-risk for bleeding) may lead into premature discontinuation of therapy (prior to completing 90 days). The relative frequency and clinical consequences of premature discontinuation in contemporary patients remain unknown. METHODS: We used the data from RIETE, an international registry of patients with venous thromboembolism (VTE), to identify patients with proximal (above knee) lower limb DVT who prematurely discontinued anticoagulation. We assessed the incidence of the composite outcome: pulmonary embolism (PE)-related death, sudden death, or recurrent VTE within the subsequent 30 days after discontinuation and compared the risk of these events vs. the risk in patients without premature discontinuation, once adjusted for demographics and clinical factors. RESULTS: Of 26,335 patients with proximal DVT recruited from 2001 to 2018, 1322 (5.02%) prematurely discontinued anticoagulation. Thirty days after discontinuation, 12 (0.91%) patients suffered fatal PE (n = 8) or sudden death (n = 4) and 33 (2.50%) had non-fatal recurrent VTE (PE = 15; recurrent DVT = 18). In patients with premature discontinuation, the 30-day incidence of the composite outcome was 1.62 per 1000 patient-days (95%CI: 0.00-3.80). During the first week after discontinuation, the incidence rate was 4.09 per 1000 patient-days (95%CI: 0.65-7.52). The adjusted odds of the composite outcome was 7.88 times (95%CI: 6.39-9.72) higher in patients who discontinued prematurely than in those without premature discontinuation. CONCLUSION: Premature discontinuation of anticoagulation occurred in 5% of patients with proximal DVT, and was associated an 8-fold increased odds for the composite outcome.