Predicting major bleeding among hospitalized patients using oral anticoagulants for atrial fibrillation after discharge

Qazi, Jakub; Schnitzer, Mireille E.; Côté, Robert; Martel, Marie-Josée; Dorais, Marc; Perreault, Sylvie

doi:10.1371/journal.pone.0246691

Cited by 10 publications

(7 citation statements)

References 68 publications

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“…Indeed, in these settings, extreme regression coefficient estimates and very wide confidence intervals can be obtained. The Firth’s model has been successfully applied in different clinical fields, for example to predict major bleeding among patients using anticoagulants, to identify rare anaesthesia-related risk factors in children and to predict rare adverse events occurrence after vaccination 39 – 41 . At the best of our knowledge there are no applications of this model to predict CTCAE in GEPTNET patients.…”

Section: Discussionmentioning

confidence: 99%

Application of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT

Scalorbi

Argiroffi

Baccini

et al. 2021

Sci Rep

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To develop predictive models of side effect occurrence in GEPNET treated with PRRT. Metastatic GEPNETs patients treated in our centre with PRRT (177Lu-Oxodotreotide) from 2019 to 2020 were considered. Haematological, liver and renal toxicities were collected and graded according to CTCAE v5. Patients were grouped according with ECOG-PS, number of metastatic sites, previous treatment lines and therapies received before PRRT. A FLIC model with backward selection was used to detect the most relevant predictors. A subsampling approach was implemented to assess variable selection stability and model performance. Sixty-seven patients (31 males, 36 females, mean age 63) treated with PRRT were considered and followed up for 30 weeks from the beginning of the therapy. They were treated with PRRT as third or further lines in 34.3% of cases. All the patients showed at least one G1–G2, meanwhile G3–G5 were rare events. No renal G3–G4 were reported. Line of PRRT administration, age, gender and ECOG-PS were the main predictors of haematological, liver and renal CTCAE. The model performance, expressed by AUC, was > 65% for anaemia, creatinine and eGFR. The application of FLIC model can be useful to improve GEPNET decision-making, allowing clinicians to identify the better therapeutic sequence to avoid PRRT-related adverse events, on the basis of patient characteristics and previous treatment lines.

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Section: Discussionmentioning

confidence: 99%

Application of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT

Scalorbi

Argiroffi

Baccini

et al. 2021

Sci Rep

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“…29 Other than HASBED, we assessed variables that showed to be significantly related to GI bleeding were age >85 years, history of GI disease, history of bleeding, peripheral artery disease and myocardial infarction. Others have reported that risk factors for bleeding other than those assessed by the HASBED score might also come to play, 30 but further research is needed for confirmation. The gender effect was not found to interact with bleeding outcomes when HASBLED ≥4 while age attenuated the benefit of DOACs over warfarin.…”

Section: Discussionmentioning

confidence: 99%

Gender Related Differences in Gastrointestinal Bleeding With Oral Anticoagulation in Atrial Fibrillation

Ferroni

Denas

Gennaro

et al. 2022

J Cardiovasc Pharmacol Ther

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Background: DOACs are characterized by a higher incidence of gastrointestinal bleeding and this may be different among males and females. Female patients were underrepresented in the DOAC pivotal trials. We aimed to assess real-world differences in gastrointestinal bleeding with oral anticoagulants (DOACs and VKAs) among males and females with atrial fibrillation. Methods: We performed a population-based retrospective analysis on linked administrative claims. Atrial fibrillation patients of 65 years and above were considered. Bleeding risk factors were assessed through HASBED and previous history of gastrointestinal disease. A time-to-event analysis compared gastrointestinal bleeding between males and females. Results: The overall cohort consisted of 15338 (55% female) DOAC and 44542 (50% female) VKA users. Most of the patients showed HASBED ≥2. Incidence rate of GI bleeding was higher in females as compared to males among DOAC users (0.90% vs 0.59%), and significant gender difference in GI bleeding was found, after adjustment, in the Cox regression analysis (HR 1.48, 95%CI 1.02-2.16). In the VKA group, no significant difference among genders was found in the time-to-event analysis. Conclusions: Our data suggest that female patients treated with DOACs have a higher risk of GI bleeding versus male patients; this difference is not observed in VKA patients.

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“… 2 This appears especially promising with regard to the heterogeneous group of AF patients who are generally older and present with a large case-mix variability. 41 Although there are many examples of prognostic models for stroke 27 or bleeding 44 in AF patients, it is not clear how these models can be used for (within-class) DOAC treatment decision making (let alone combined consideration of both aspects). For this, our approach provides a straightforward path using well-established variables that have previously been shown to be prognostic or determine treatment decisions 46 and are largely available in routine clinical care.…”

Section: Discussionmentioning

confidence: 99%

“…In this regard, the stroke models performed better than the bleeding models (see Figure 1A), possibly because many co-medications (including temporary or over-thecounter drugs) were not available in the present variables. 44 Nevertheless, the combined consideration of the composite endpoint (stroke and major bleeding) was impressively successful after weighting both aspects (and the sheer number of far more bleeding events than strokes may certainly have contributed to the significant result). Although our innovative approach overlaps only in small parts with previous work, consensus criteria for treatment-guiding risk modeling can be discussed.…”

Section: Discussionmentioning

confidence: 99%

Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

et al. 2021

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Background: Decision making for the “best” treatment is particularly challenging in situations in which individual patient response to drugs can largely differ from average treatment effects. By estimating individual treatment effects (ITEs), we aimed to demonstrate how strokes, major bleeding events, and a composite of both could be reduced by model-assisted recommendations for a particular direct oral anticoagulant (DOAC). Methods: In German claims data for the calendar years 2014–2018, we selected 29 901 new users of the DOACs rivaroxaban and apixaban. Random forests considered binary events within 1 y to estimate ITEs under each DOAC according to the X-learner algorithm with 29 potential effect modifiers; treatment recommendations were based on these estimated ITEs. Model performance was evaluated by the c-for-benefit statistics, absolute risk reduction (ARR), and absolute risk difference (ARD) by trial emulation. Results: A significant proportion of patients would be recommended a different treatment option than they actually received. The stroke model significantly discriminated patients for higher benefit and thus indicated improved decisions by reduced outcomes (c-for-benefit: 0.56; 95% confidence interval [0.52; 0.60]). In the group with apixaban recommendation, the model also improved the composite endpoint (ARR: 1.69 % [0.39; 2.97]). In trial emulations, model-assisted recommendations significantly reduced the composite event rate (ARD: −0.78 % [−1.40; −0.03]). Conclusions: If prescribers are undecided about the potential benefits of different treatment options, ITEs can support decision making, especially if evidence is inconclusive, risk-benefit profiles of therapeutic alternatives differ significantly, and the patients’ complexity deviates from “typical” study populations. In the exemplary case for DOACs and potentially in other situations, the significant impact could also become practically relevant if recommendations were available in an automated way as part of decision making. Highlights It was possible to calculate individual treatment effects (ITEs) from routine claims data for rivaroxaban and apixaban, and the characteristics between the groups with recommendation for one or the other option differed significantly. ITEs resulted in recommendations that were significantly superior to usual (observed) treatment allocations in terms of absolute risk reduction, both separately for stroke and in the composite endpoint of stroke and major bleeding. When similar patients from routine data were selected (precision cohorts) for patients with a strong recommendation for one option or the other, those similar patients under the respective recommendation showed a significantly better prognosis compared with the alternative option. Many steps may still be needed on the way to clinical practice, but the principle of decision support developed from routine data may point the way toward future decision-making processes.

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Predicting major bleeding among hospitalized patients using oral anticoagulants for atrial fibrillation after discharge

Cited by 10 publications

References 68 publications

Application of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT

Application of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT

Gender Related Differences in Gastrointestinal Bleeding With Oral Anticoagulation in Atrial Fibrillation

Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

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