https://orcid.org/0000-0002-5108-0579
Abstract
To summarize the totality of evidence validating the Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Antiplatelet Therapy (PRECISE-DAPT) score, ascertaining its aggregate discrimination and validation power in multiple population subsets.
We searched electronic databases from 2017 (PRECISE-DAPT proposal) up to March 2023 for studies that reported the occurrence of out-of-hospital bleedings according to the PRECISE-DAPT score in patients receiving DAPT following percutaneous coronary intervention (PCI). Pooled odds ratios (OR) with 95% confidence interval (CI) were used as summary statistics and were calculated using a random-effects model. Primary and secondary endpoints were the occurrence of any and major bleeding, respectively. A total of 21 studies and 67 283 patients were included; 24.7% of patients (N = 16 603) were at high bleeding risk (PRECISE-DAPT score ≥25), and when compared to those at low bleeding risk, they experienced a significantly higher rate of any out-of-hospital bleeding (OR: 2.71; 95% CI: 2.24–3.29; P-value <0.001) and major bleedings (OR: 3.51; 95% CI: 2.71–4.55; P-value <0.001). Pooling data on c-stat whenever available, the PRECISE-DAPT score showed a moderate discriminative power in predicting major bleeding events at 1 year (pooled c-stat: 0.71; 95% CI: 0.64–0.77).
This systematic review and meta-analysis confirms the external validity of the PRECISE-DAPT score in predicting out-of-hospital bleeding outcomes in patients on DAPT following PCI. The moderate discriminative ability highlights the need for future improved risk prediction tools in the field.
Ability of the PRECISE-DAPT score in predicting bleeding events.
Introduction
In patients treated with percutaneous coronary intervention (PCI) or suffering from acute coronary syndrome (ACS), dual antiplatelet therapy (DAPT) with aspirin and a P2Y12 inhibitor represents the standard of care to prevent both coronary and systemic ischaemic events.1–3 The ischaemic benefits of DAPT come at the expense of an increased risk of bleeding.4 Multiple studies have demonstrated the negative prognostic impact of bleeding in this clinical scenario, which is of similar or even worse magnitude than a recurrent myocardial infarction.4–7 Accordingly, promptly identifying patients at high bleeding risk (HBR) so that antiplatelet regimens with lower bleeding potential can be implemented has represented a topic of numerous investigations.3,8
Current international guidelines recommend the use of dedicated risk prediction tools, such as the PRECISE-DAPT (Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Antiplatelet Therapy) score, to guide and inform decision-making regarding DAPT duration.2 The PRECISE-DAPT is a five-item risk score that was introduced to predict the risk of out-of-hospital bleeding at 1 year, and was validated in a large population of patients randomized to multiple DAPT duration strategies. According to the results of the derivation study, patients deemed at HBR, defined as PRECISE-DAPT score ≥25, do not benefit from longer courses of DAPT and should instead be considered for shorter treatment duration.9 Multiple studies have externally validated the score in clinical registries and randomized trials.10–12 The aim of this systematic review and meta-analysis was to provide a summary of the totality of evidence validating the PRECISE-DAPT score to ascertain its aggregate discrimination and validation power in multiple population subsets.
Methods
Literature search and data extraction
All observational studies and post hoc analyses of randomized controlled trials from 2017 (the year of publication of the PRECISE-DAPT score) to March 2023 validating the PRECISE-DAPT score for bleeding event prediction were evaluated for inclusion. Medline, Embase, Google Scholar, and the Cochrane Central Register of Controlled Trials were carefully screened; additionally, backward snowballing (i.e. review of references from identified articles) was performed. Results were reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Figure 1). The full search protocol is available in Supplementary material online, Method 1 and was registered on PROSPERO (CRD42022359560).
PRISMA flowchart.
Two investigators (M.F. and L.P.) independently determined study eligibility after reviewing titles, abstracts, and full texts of studies identified by the literature search. Disagreements, if any, were resolved by consensus and by the mediation of a third author (A.R.M.). Two investigators (M.F. and L.P.) extracted all data from the included studies and collected them into a dedicated electlronic spreadsheet. Accuracy of the extracted data was verified by a third author (C.M.).
The present work was conducted in accordance with the PRISMA and Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines.13,14
Selection criteria and quality assessment
Studies were considered eligible if (i) they included patients with acute or chronic coronary syndromes on DAPT following PCI, (ii) the risk of out-of-hospital bleeding was estimated using the five-item PRECISE-DAPT score, and (iii) outcomes were compared between patients considered at HBR (i.e. PRECISE-DAPT score ≥25) and non-HBR (i.e. PRECISE-DAPT score <25). Studies including exclusively patients with an indication to be on oral anticoagulants (OACs), not reporting bleeding events at follow-up, or estimating the risk of bleeding using the four-item PRECISE-DAPT score were excluded.
Two authors (M.F. and L.P.) independently assessed the quality of studies and the risk of bias according to the PROBAST (Prediction Model Study Risk of Bias Assessment Tool).15
Outcomes
The primary endpoint of the present meta-analysis was out-of-hospital major and minor bleeding events at the longest available follow-up. The secondary endpoint was out-of-hospital major bleeding events at longest available follow-up. For the secondary outcome, occurrence of events was also evaluated at 1 year, if available. Definitions of major and minor bleeding events used in each included study are provided in (Table 1).
Studies characteristics
| Author(s) | Journal (year) | Study type (enrollment period) | N | PRECISE-DAPT score ≥25 (%) | Minor bleeding definition | Major bleeding definition | Follow-up, days |
|---|---|---|---|---|---|---|---|
| Choi et al. | Circ Cardiovasc Interv (2020) | RCT (2012–15) | 2712 | 745 (27.5) | BARC 2 | BARC 3–5 | 540 |
| Okabe et al. | Circ J (2022) | Single-centre registry (2010–13) | 3410 | 1504 (44.1) | NA | BARC 3–5 | 2555 |
| Lee et al. | Korean Circ J (2022) | RCT (2015–18) | 2980 | 504 (17) | NA | Intracranial bleeding, bleeding with a ≥5 g/dL decrease in Hb, or fatal bleeding | 360 |
| Montalto et al. | Int J Cardiol (2020) | 2 RCT and 1 registry (2012–17) | 1883 | 961 (51) | BARC 2 | BARC 3–5 | 365 |
| Gragnano et al. | Eur Heart J—Cardiovasc Pharmacother (2022) | RCT (2013–15) | 7134 | 1180 (16.5) | NA | BARC 3–5 | 730 |
| Dannenberg et al. | Int J Cardiol Heart Vasc (2021) | Single-centre registry (2015–16) | 994 | 470 (47.3) | TIMI minor | TIMI Major | 365 |
| Fujii et al. | Circ J (2021) | Single-centre registry (2006–19) | 939 | 439 (46.8) | NA | BARC 3–5 | 365 |
| Lyu et al. | Platelets (2021) | Single-centre registry (2019–20) | 1911 | 185 (9.7) | BARC 2 | BARC 3–5 | 365 |
| Bianco et al. | Int J Cardiol (2019) | Multicentre registry (2012–16) | 4424 | 903 (20.4) | NA | BARC 3–5 | 540 |
| Morici et al. | Angiology (2019) | Single-centre registry (2014–17) | 995 | 359 (36) | BARC 2 | BARC 3–5 | 496 |
| Abu-Assi et al. | EuroIntervention (2018) | Single-centre registry (2012–15) | 1926 | 759 (39.4) | BARC 2 | BARC 3–5 | 365 |
| Guerrero et al. | J Geriatr Cardiol (2018) | Multicentre registry (NA) | 208 | 193 (92.8) | NA | Bleeding leading to hospitalization, transfusion, intervention, stop of antithrombotic drugs or death | 861 |
| Young Choi et al. | Circ Cardiovasc Interv (2018) | Single-centre registry (2008–15) | 904 | 335 (37) | NA | BARC 3–5 | 365 |
| Muñoz et al. | Curr Probl Cardiol (2022) | Multicentre registry (2017–18) | 862 | 210 (24.4) | BARC 2 | BARC 3–5 | 450 |
| Boudreau et al. | Am J Cardiol (2021) | Multicentre registry (2017–18) | 451 | 94 (20.8) | NA | BARC 3–5 | 365 |
| Zhao et al. | Eur Heart J Qual Care Clin Outcomes (2021) | Single-centre registry (2013) | 10 109 | 445 (4.4) | BARC 2 | BARC 3–5 | 1825 |
| Jang et al. | Cardiovasc Drugs Ther (2021) | Pooled analysis of 4 RCTs (2008–14) | 5131 | 663 (12.9) | TIMI minor | TIMI Major | 365 |
| Rozemeijer et al. | Neth Heart J (2021) | RCTs (2014–17) | 1492 | 190 (12.7) | BARC 2 | BARC 3–5 | 365 |
| Asif et al. | Catheter Cardiovasc Interv (2021) | Multicentre registry (2010–19) | 16 905 | 5762 (34.1) | BARC 2 | NA | 1095 |
| Kubota et al. | Circ Rep (2022) | Single-centre registry (2016–20) | 842 | 431 (51.2) | NA | BARC 3–5 | 365 |
| Celik et al. | Angiology (2022) | Single-centre registry (2008–15) | 1071 | 271 (25.3) | BARC 1–2 | BARC 3–5 | 2665 |
| Author(s) | Journal (year) | Study type (enrollment period) | N | PRECISE-DAPT score ≥25 (%) | Minor bleeding definition | Major bleeding definition | Follow-up, days |
|---|---|---|---|---|---|---|---|
| Choi et al. | Circ Cardiovasc Interv (2020) | RCT (2012–15) | 2712 | 745 (27.5) | BARC 2 | BARC 3–5 | 540 |
| Okabe et al. | Circ J (2022) | Single-centre registry (2010–13) | 3410 | 1504 (44.1) | NA | BARC 3–5 | 2555 |
| Lee et al. | Korean Circ J (2022) | RCT (2015–18) | 2980 | 504 (17) | NA | Intracranial bleeding, bleeding with a ≥5 g/dL decrease in Hb, or fatal bleeding | 360 |
| Montalto et al. | Int J Cardiol (2020) | 2 RCT and 1 registry (2012–17) | 1883 | 961 (51) | BARC 2 | BARC 3–5 | 365 |
| Gragnano et al. | Eur Heart J—Cardiovasc Pharmacother (2022) | RCT (2013–15) | 7134 | 1180 (16.5) | NA | BARC 3–5 | 730 |
| Dannenberg et al. | Int J Cardiol Heart Vasc (2021) | Single-centre registry (2015–16) | 994 | 470 (47.3) | TIMI minor | TIMI Major | 365 |
| Fujii et al. | Circ J (2021) | Single-centre registry (2006–19) | 939 | 439 (46.8) | NA | BARC 3–5 | 365 |
| Lyu et al. | Platelets (2021) | Single-centre registry (2019–20) | 1911 | 185 (9.7) | BARC 2 | BARC 3–5 | 365 |
| Bianco et al. | Int J Cardiol (2019) | Multicentre registry (2012–16) | 4424 | 903 (20.4) | NA | BARC 3–5 | 540 |
| Morici et al. | Angiology (2019) | Single-centre registry (2014–17) | 995 | 359 (36) | BARC 2 | BARC 3–5 | 496 |
| Abu-Assi et al. | EuroIntervention (2018) | Single-centre registry (2012–15) | 1926 | 759 (39.4) | BARC 2 | BARC 3–5 | 365 |
| Guerrero et al. | J Geriatr Cardiol (2018) | Multicentre registry (NA) | 208 | 193 (92.8) | NA | Bleeding leading to hospitalization, transfusion, intervention, stop of antithrombotic drugs or death | 861 |
| Young Choi et al. | Circ Cardiovasc Interv (2018) | Single-centre registry (2008–15) | 904 | 335 (37) | NA | BARC 3–5 | 365 |
| Muñoz et al. | Curr Probl Cardiol (2022) | Multicentre registry (2017–18) | 862 | 210 (24.4) | BARC 2 | BARC 3–5 | 450 |
| Boudreau et al. | Am J Cardiol (2021) | Multicentre registry (2017–18) | 451 | 94 (20.8) | NA | BARC 3–5 | 365 |
| Zhao et al. | Eur Heart J Qual Care Clin Outcomes (2021) | Single-centre registry (2013) | 10 109 | 445 (4.4) | BARC 2 | BARC 3–5 | 1825 |
| Jang et al. | Cardiovasc Drugs Ther (2021) | Pooled analysis of 4 RCTs (2008–14) | 5131 | 663 (12.9) | TIMI minor | TIMI Major | 365 |
| Rozemeijer et al. | Neth Heart J (2021) | RCTs (2014–17) | 1492 | 190 (12.7) | BARC 2 | BARC 3–5 | 365 |
| Asif et al. | Catheter Cardiovasc Interv (2021) | Multicentre registry (2010–19) | 16 905 | 5762 (34.1) | BARC 2 | NA | 1095 |
| Kubota et al. | Circ Rep (2022) | Single-centre registry (2016–20) | 842 | 431 (51.2) | NA | BARC 3–5 | 365 |
| Celik et al. | Angiology (2022) | Single-centre registry (2008–15) | 1071 | 271 (25.3) | BARC 1–2 | BARC 3–5 | 2665 |
BARC = Bleeding Academic Research Consortium; NA = not available; RCT = randomized controlled trial; TIMI = thrombolysis in myocardial infarction.
Studies characteristics
| Author(s) | Journal (year) | Study type (enrollment period) | N | PRECISE-DAPT score ≥25 (%) | Minor bleeding definition | Major bleeding definition | Follow-up, days |
|---|---|---|---|---|---|---|---|
| Choi et al. | Circ Cardiovasc Interv (2020) | RCT (2012–15) | 2712 | 745 (27.5) | BARC 2 | BARC 3–5 | 540 |
| Okabe et al. | Circ J (2022) | Single-centre registry (2010–13) | 3410 | 1504 (44.1) | NA | BARC 3–5 | 2555 |
| Lee et al. | Korean Circ J (2022) | RCT (2015–18) | 2980 | 504 (17) | NA | Intracranial bleeding, bleeding with a ≥5 g/dL decrease in Hb, or fatal bleeding | 360 |
| Montalto et al. | Int J Cardiol (2020) | 2 RCT and 1 registry (2012–17) | 1883 | 961 (51) | BARC 2 | BARC 3–5 | 365 |
| Gragnano et al. | Eur Heart J—Cardiovasc Pharmacother (2022) | RCT (2013–15) | 7134 | 1180 (16.5) | NA | BARC 3–5 | 730 |
| Dannenberg et al. | Int J Cardiol Heart Vasc (2021) | Single-centre registry (2015–16) | 994 | 470 (47.3) | TIMI minor | TIMI Major | 365 |
| Fujii et al. | Circ J (2021) | Single-centre registry (2006–19) | 939 | 439 (46.8) | NA | BARC 3–5 | 365 |
| Lyu et al. | Platelets (2021) | Single-centre registry (2019–20) | 1911 | 185 (9.7) | BARC 2 | BARC 3–5 | 365 |
| Bianco et al. | Int J Cardiol (2019) | Multicentre registry (2012–16) | 4424 | 903 (20.4) | NA | BARC 3–5 | 540 |
| Morici et al. | Angiology (2019) | Single-centre registry (2014–17) | 995 | 359 (36) | BARC 2 | BARC 3–5 | 496 |
| Abu-Assi et al. | EuroIntervention (2018) | Single-centre registry (2012–15) | 1926 | 759 (39.4) | BARC 2 | BARC 3–5 | 365 |
| Guerrero et al. | J Geriatr Cardiol (2018) | Multicentre registry (NA) | 208 | 193 (92.8) | NA | Bleeding leading to hospitalization, transfusion, intervention, stop of antithrombotic drugs or death | 861 |
| Young Choi et al. | Circ Cardiovasc Interv (2018) | Single-centre registry (2008–15) | 904 | 335 (37) | NA | BARC 3–5 | 365 |
| Muñoz et al. | Curr Probl Cardiol (2022) | Multicentre registry (2017–18) | 862 | 210 (24.4) | BARC 2 | BARC 3–5 | 450 |
| Boudreau et al. | Am J Cardiol (2021) | Multicentre registry (2017–18) | 451 | 94 (20.8) | NA | BARC 3–5 | 365 |
| Zhao et al. | Eur Heart J Qual Care Clin Outcomes (2021) | Single-centre registry (2013) | 10 109 | 445 (4.4) | BARC 2 | BARC 3–5 | 1825 |
| Jang et al. | Cardiovasc Drugs Ther (2021) | Pooled analysis of 4 RCTs (2008–14) | 5131 | 663 (12.9) | TIMI minor | TIMI Major | 365 |
| Rozemeijer et al. | Neth Heart J (2021) | RCTs (2014–17) | 1492 | 190 (12.7) | BARC 2 | BARC 3–5 | 365 |
| Asif et al. | Catheter Cardiovasc Interv (2021) | Multicentre registry (2010–19) | 16 905 | 5762 (34.1) | BARC 2 | NA | 1095 |
| Kubota et al. | Circ Rep (2022) | Single-centre registry (2016–20) | 842 | 431 (51.2) | NA | BARC 3–5 | 365 |
| Celik et al. | Angiology (2022) | Single-centre registry (2008–15) | 1071 | 271 (25.3) | BARC 1–2 | BARC 3–5 | 2665 |
| Author(s) | Journal (year) | Study type (enrollment period) | N | PRECISE-DAPT score ≥25 (%) | Minor bleeding definition | Major bleeding definition | Follow-up, days |
|---|---|---|---|---|---|---|---|
| Choi et al. | Circ Cardiovasc Interv (2020) | RCT (2012–15) | 2712 | 745 (27.5) | BARC 2 | BARC 3–5 | 540 |
| Okabe et al. | Circ J (2022) | Single-centre registry (2010–13) | 3410 | 1504 (44.1) | NA | BARC 3–5 | 2555 |
| Lee et al. | Korean Circ J (2022) | RCT (2015–18) | 2980 | 504 (17) | NA | Intracranial bleeding, bleeding with a ≥5 g/dL decrease in Hb, or fatal bleeding | 360 |
| Montalto et al. | Int J Cardiol (2020) | 2 RCT and 1 registry (2012–17) | 1883 | 961 (51) | BARC 2 | BARC 3–5 | 365 |
| Gragnano et al. | Eur Heart J—Cardiovasc Pharmacother (2022) | RCT (2013–15) | 7134 | 1180 (16.5) | NA | BARC 3–5 | 730 |
| Dannenberg et al. | Int J Cardiol Heart Vasc (2021) | Single-centre registry (2015–16) | 994 | 470 (47.3) | TIMI minor | TIMI Major | 365 |
| Fujii et al. | Circ J (2021) | Single-centre registry (2006–19) | 939 | 439 (46.8) | NA | BARC 3–5 | 365 |
| Lyu et al. | Platelets (2021) | Single-centre registry (2019–20) | 1911 | 185 (9.7) | BARC 2 | BARC 3–5 | 365 |
| Bianco et al. | Int J Cardiol (2019) | Multicentre registry (2012–16) | 4424 | 903 (20.4) | NA | BARC 3–5 | 540 |
| Morici et al. | Angiology (2019) | Single-centre registry (2014–17) | 995 | 359 (36) | BARC 2 | BARC 3–5 | 496 |
| Abu-Assi et al. | EuroIntervention (2018) | Single-centre registry (2012–15) | 1926 | 759 (39.4) | BARC 2 | BARC 3–5 | 365 |
| Guerrero et al. | J Geriatr Cardiol (2018) | Multicentre registry (NA) | 208 | 193 (92.8) | NA | Bleeding leading to hospitalization, transfusion, intervention, stop of antithrombotic drugs or death | 861 |
| Young Choi et al. | Circ Cardiovasc Interv (2018) | Single-centre registry (2008–15) | 904 | 335 (37) | NA | BARC 3–5 | 365 |
| Muñoz et al. | Curr Probl Cardiol (2022) | Multicentre registry (2017–18) | 862 | 210 (24.4) | BARC 2 | BARC 3–5 | 450 |
| Boudreau et al. | Am J Cardiol (2021) | Multicentre registry (2017–18) | 451 | 94 (20.8) | NA | BARC 3–5 | 365 |
| Zhao et al. | Eur Heart J Qual Care Clin Outcomes (2021) | Single-centre registry (2013) | 10 109 | 445 (4.4) | BARC 2 | BARC 3–5 | 1825 |
| Jang et al. | Cardiovasc Drugs Ther (2021) | Pooled analysis of 4 RCTs (2008–14) | 5131 | 663 (12.9) | TIMI minor | TIMI Major | 365 |
| Rozemeijer et al. | Neth Heart J (2021) | RCTs (2014–17) | 1492 | 190 (12.7) | BARC 2 | BARC 3–5 | 365 |
| Asif et al. | Catheter Cardiovasc Interv (2021) | Multicentre registry (2010–19) | 16 905 | 5762 (34.1) | BARC 2 | NA | 1095 |
| Kubota et al. | Circ Rep (2022) | Single-centre registry (2016–20) | 842 | 431 (51.2) | NA | BARC 3–5 | 365 |
| Celik et al. | Angiology (2022) | Single-centre registry (2008–15) | 1071 | 271 (25.3) | BARC 1–2 | BARC 3–5 | 2665 |
BARC = Bleeding Academic Research Consortium; NA = not available; RCT = randomized controlled trial; TIMI = thrombolysis in myocardial infarction.
Discrimination and calibration powers of the PRECISE-DAPT score in predicting out-of-hospital major bleeding at 1 year were assessed by pooling concordance statistics (c-stat) and observed:expected (O:E) ratios from individual studies. For patients with a PRECISE-DAPT score ≥25, we considered an expected 1-year rate of major bleedings of 1.8%, as previously reported in the PRECISE-DAPT derivation study,9 and of 4%, according to the Academic Research Consortium for HBR definition.16
Statistical analysis
Pooled odds ratios (OR) with 95% confidence interval (CI) were used as summary statistics for outcomes of interest and were calculated using a random-effects model according to DerSimonian and Laird.17 Statistical heterogeneity was tested using the Cochran Q statistic and I² values. Heterogeneity was considered to be low if I2 < 25%, low to medium if 25 < I2 < 50%, medium to high if 50 < I2 < 75%, and high if I2 > 75%. Because the I2 was medium to high in our main analyses, the random effect was considered the primary model. For studies that reported major bleeding (secondary endpoint) at longer than 1 year follow-up, the number of events at 1 year were obtained extracting time-to-event data from Kaplan–Meier curves.19 Residual heterogeneity of the primary endpoint was further assessed by meta-regression of several baseline characteristics. Finally, a sensitivity analysis considering separately post-hoc analysis of randomized controlled trials and observational studies was performed.
Statistical significance was set at P-value < 0.05 (two-sided). Publication bias was assessed by visual inspection of funnel plots and by Egger's and Begg's tests. Data analysis was performed in the R environment (packages meta and metafor) (R Foundation for Statistical Computing, Vienna, Austria) and with the use of Stata version 14 (StataCorp LLC, College Station, TX, USA).
Results
Literature research and systematic review
Our literature research identified a total of 781 citations; after removing duplicates and non-relevant studies, 30 articles underwent full-text screening (Figure 1). Twenty-one studies were finally included in the systematic review, with a total of 67 283 patients receiving DAPT after PCI: 16 603 (24.7%) with a PRECISE-DAPT score ≥ 25 and 50 680 (75.3%) with a PRECISE-DAPT score < 25.10–12,20–37 Included works were retrospective observational studies or post-hoc analyses of randomized controlled trials, which clearly reported outcomes separately according to patient's bleeding risk (Table 1). Patient characteristics of each included study are shown in (Table 2). For one study that included patients with and without atrial fibrillation, baseline characteristics and outcome data were selectively extracted for the latter population only.35
Patients characteristics
| Study | Age, mean ± SD or median (IQR) | Male gender, % | ACS presentation, % | Potent p2y12 inhibitors, % | Oral anticoagulation, % | Femoral access, % | Bleeding rate, % |
|---|---|---|---|---|---|---|---|
| Choi et al. | N-HBR: 57.6 ± 9.8 | N-HBR: 83% | N-HBR: 100% | N-HBR: 20.9% | N-HBR: 0% | N-HBR: 51.2% | N-HBR: 2.2% |
| HBR: 73.7 ± 7.7 | HBR: 55.3% | HBR: 100% | HBR: 14.8% | HBR: 0% | HBR: 58% | HBR: 4.9% | |
| Okabe et al. | Overall: 70 ± 11 | Overall: 74.9% | Overall: 38.5% | Overall: NA | Overall: 12% | Overall: 58.2% | N-HBR: 5.3% |
| HBR: 10.2% | |||||||
| Lee et al. | N-HBR: 58.8 ± 10.1 | N-HBR: 83.2% | N-HBR: 100% | N-HBR: 100% | N-HBR: 0% | N-HBR: 42.6% | N-HBR: 0.5% |
| HBR: 70.8 ± 8 | HBR: 63.7% | HBR: 100% | BR: 100%H | HBR: 0% | HBR: 51.6% | HBR: 2.7% | |
| Montalto et al. | NA | Overall: 61% | Overall: 100% | Overall: 36% | Overall: 1.9% | Overall: 23% | N-HBR: 1% |
| HBR: 4.5% | |||||||
| Gragnano et al. | N-HBR: 62.8 ± 9.5 | N-HBR: 79.7% | N-HBR: 50.7% | NA | NA | N-HBR: 25.8% | N-HBR: 2% |
| HBR: 75.7 ± 7.7 | HBR: 59.2% | HBR: 55% | HBR: 30.1% | HBR: 5.2% | |||
| Dannenberg et al. | N-HBR: 63.7 ± 10.3 | N-HBR: 77.9% | Overall: 64.2% | N-HBR: 45.2% | N-HBR: 9.9% | N-HBR: 59.4% | N-HBR: 1.5% |
| HBR: 76.5 ± 9.5 | HBR: 60.2% | HBR: 27.2% | HBR: 15.1% | HBR: 74% | HBR: 4.8% | ||
| Fujii et al. | Overall: 66.3 ± 12.4 | Overall: 79.2% | Overall: 100% | Overall: 33% | Overall: 10.5% | NA | N-HBR: 4% |
| HBR: 16.1% | |||||||
| Lyu et al. | Overall: 59 (52–66) | Overall: 78.1% | Overall: 76.5% | Overall: 38% | Overall: 0.3% | NA | N-HBR: 4.4% |
| HBR: 7% | |||||||
| Bianco et al. | Overall: 60.9 ± 11.5 | Overall: 79.2% | Overall: 100% | Overall: 100% | NA | NA | N-HBR: 1.4% |
| HBR: 3.4% | |||||||
| Morici et al. | Overall: 67 (58–77) | Overall: 76.6% | Overall: 100% | NA | Overall: 6.3% | NA | N-HBR: 3% |
| HBR:4.7% | |||||||
| Abu-Assi et al. | Overall: 65.1 ± 13 | Overall: 76.8% | Overall: 100% | Overall: 30.1% | Overall: 8.2% | NA | N-HBR: 5.5% |
| HBR: 8.5% | |||||||
| Guerrero et al. | Overall: 81.9 ± 5 | Overall: 55.3% | Overall: 100% | Overall: 8.2% | Overall: 2.9% | NA | N-HBR: 13.3% |
| HBR: 11.9% | |||||||
| Young Choi et al. | Overall: 65.5 ± 10.5 | Overall: 70% | Overall: 100% | Overall: 5.2% | NA | NA | N-HBR: 7% |
| HBR: 34% | |||||||
| Muñoz et al. | Overall: 66 (58–74) | Overall: 81.2% | Overall: 100% | Overall: 26.5% | Overall: 9% | Overall: 28.4% | N-HBR: 2.9% |
| HBR: 8.1% | |||||||
| Boudreau et al. | Overall: 63.5 ± 12.1 | Overall: 79.2% | Overall: 84.5% | Overall: 52.4% | NA | NA | N-HBR: 2.8% |
| HBR: 17% | |||||||
| Zhao et al. | N-HBR: 57.7 ± 9.9 | N-HBR: 78.1% | N-HBR: 67.5% | N-HBR: 0% | NA | N-HBR: 7.1% | N-HBR: 4% |
| HBR: 72.7 ± 8.3 | HBR: 52.8% | HBR: 79.6% | HBR: 0% | HBR: 13.7% | HBR: 6.5% | ||
| Jang et al. | Overall: 63 ± 10 | Overall: 66.5% | Overall: 77% | Overall: 0% | NA | NA | N-HBR: 0.4% |
| HBR: 1.9% | |||||||
| Rozemeijer et al. | Overall: 64.9 ± 11.0 | Overall: 76.6% | Overall: 57.8% | Overall: 57.8% | Overall: 8% | Overall: 7.8% | N-HBR: 2% |
| HBR: 4.2% | |||||||
| Asif et al. | N-HBR: 61 ± 10.5 | N-HBR: 74% | Overall: 61.6% | NA | N-HBR: 3.3% | NA | N-HBR: 5.1% |
| HBR: 74.4 ± 10.3 | HBR: 55.3% | HBR: 5.7% | HBR: 10% | ||||
| Kubota et al. | N-HBR: 63 ± 10 | N-HBR: 83% | N-HBR: 54% | N-HBR: 28% | N-HBR: 16% | N-HBR: 73% | N-HBR: 1.5% |
| HBR: 77 ± 8 | HBR: 68% | HBR: 47% | HBR: 15% | HBR: 23% | HBR: 78% | HBR: 4.6% | |
| Celik et al. | N-HBR: 59.6 ± 10.3 | N-HBR: 80% | N-HBR: 100% | N-HBR: 3% | N-HBR: 0% | NA | N-HBR: 8.5% |
| HBR: 70.3 ± 10.7 | HBR: 52% | HBR: 100% | HBR: 4% | HBR: 0% | HBR: 18.1% |
| Study | Age, mean ± SD or median (IQR) | Male gender, % | ACS presentation, % | Potent p2y12 inhibitors, % | Oral anticoagulation, % | Femoral access, % | Bleeding rate, % |
|---|---|---|---|---|---|---|---|
| Choi et al. | N-HBR: 57.6 ± 9.8 | N-HBR: 83% | N-HBR: 100% | N-HBR: 20.9% | N-HBR: 0% | N-HBR: 51.2% | N-HBR: 2.2% |
| HBR: 73.7 ± 7.7 | HBR: 55.3% | HBR: 100% | HBR: 14.8% | HBR: 0% | HBR: 58% | HBR: 4.9% | |
| Okabe et al. | Overall: 70 ± 11 | Overall: 74.9% | Overall: 38.5% | Overall: NA | Overall: 12% | Overall: 58.2% | N-HBR: 5.3% |
| HBR: 10.2% | |||||||
| Lee et al. | N-HBR: 58.8 ± 10.1 | N-HBR: 83.2% | N-HBR: 100% | N-HBR: 100% | N-HBR: 0% | N-HBR: 42.6% | N-HBR: 0.5% |
| HBR: 70.8 ± 8 | HBR: 63.7% | HBR: 100% | BR: 100%H | HBR: 0% | HBR: 51.6% | HBR: 2.7% | |
| Montalto et al. | NA | Overall: 61% | Overall: 100% | Overall: 36% | Overall: 1.9% | Overall: 23% | N-HBR: 1% |
| HBR: 4.5% | |||||||
| Gragnano et al. | N-HBR: 62.8 ± 9.5 | N-HBR: 79.7% | N-HBR: 50.7% | NA | NA | N-HBR: 25.8% | N-HBR: 2% |
| HBR: 75.7 ± 7.7 | HBR: 59.2% | HBR: 55% | HBR: 30.1% | HBR: 5.2% | |||
| Dannenberg et al. | N-HBR: 63.7 ± 10.3 | N-HBR: 77.9% | Overall: 64.2% | N-HBR: 45.2% | N-HBR: 9.9% | N-HBR: 59.4% | N-HBR: 1.5% |
| HBR: 76.5 ± 9.5 | HBR: 60.2% | HBR: 27.2% | HBR: 15.1% | HBR: 74% | HBR: 4.8% | ||
| Fujii et al. | Overall: 66.3 ± 12.4 | Overall: 79.2% | Overall: 100% | Overall: 33% | Overall: 10.5% | NA | N-HBR: 4% |
| HBR: 16.1% | |||||||
| Lyu et al. | Overall: 59 (52–66) | Overall: 78.1% | Overall: 76.5% | Overall: 38% | Overall: 0.3% | NA | N-HBR: 4.4% |
| HBR: 7% | |||||||
| Bianco et al. | Overall: 60.9 ± 11.5 | Overall: 79.2% | Overall: 100% | Overall: 100% | NA | NA | N-HBR: 1.4% |
| HBR: 3.4% | |||||||
| Morici et al. | Overall: 67 (58–77) | Overall: 76.6% | Overall: 100% | NA | Overall: 6.3% | NA | N-HBR: 3% |
| HBR:4.7% | |||||||
| Abu-Assi et al. | Overall: 65.1 ± 13 | Overall: 76.8% | Overall: 100% | Overall: 30.1% | Overall: 8.2% | NA | N-HBR: 5.5% |
| HBR: 8.5% | |||||||
| Guerrero et al. | Overall: 81.9 ± 5 | Overall: 55.3% | Overall: 100% | Overall: 8.2% | Overall: 2.9% | NA | N-HBR: 13.3% |
| HBR: 11.9% | |||||||
| Young Choi et al. | Overall: 65.5 ± 10.5 | Overall: 70% | Overall: 100% | Overall: 5.2% | NA | NA | N-HBR: 7% |
| HBR: 34% | |||||||
| Muñoz et al. | Overall: 66 (58–74) | Overall: 81.2% | Overall: 100% | Overall: 26.5% | Overall: 9% | Overall: 28.4% | N-HBR: 2.9% |
| HBR: 8.1% | |||||||
| Boudreau et al. | Overall: 63.5 ± 12.1 | Overall: 79.2% | Overall: 84.5% | Overall: 52.4% | NA | NA | N-HBR: 2.8% |
| HBR: 17% | |||||||
| Zhao et al. | N-HBR: 57.7 ± 9.9 | N-HBR: 78.1% | N-HBR: 67.5% | N-HBR: 0% | NA | N-HBR: 7.1% | N-HBR: 4% |
| HBR: 72.7 ± 8.3 | HBR: 52.8% | HBR: 79.6% | HBR: 0% | HBR: 13.7% | HBR: 6.5% | ||
| Jang et al. | Overall: 63 ± 10 | Overall: 66.5% | Overall: 77% | Overall: 0% | NA | NA | N-HBR: 0.4% |
| HBR: 1.9% | |||||||
| Rozemeijer et al. | Overall: 64.9 ± 11.0 | Overall: 76.6% | Overall: 57.8% | Overall: 57.8% | Overall: 8% | Overall: 7.8% | N-HBR: 2% |
| HBR: 4.2% | |||||||
| Asif et al. | N-HBR: 61 ± 10.5 | N-HBR: 74% | Overall: 61.6% | NA | N-HBR: 3.3% | NA | N-HBR: 5.1% |
| HBR: 74.4 ± 10.3 | HBR: 55.3% | HBR: 5.7% | HBR: 10% | ||||
| Kubota et al. | N-HBR: 63 ± 10 | N-HBR: 83% | N-HBR: 54% | N-HBR: 28% | N-HBR: 16% | N-HBR: 73% | N-HBR: 1.5% |
| HBR: 77 ± 8 | HBR: 68% | HBR: 47% | HBR: 15% | HBR: 23% | HBR: 78% | HBR: 4.6% | |
| Celik et al. | N-HBR: 59.6 ± 10.3 | N-HBR: 80% | N-HBR: 100% | N-HBR: 3% | N-HBR: 0% | NA | N-HBR: 8.5% |
| HBR: 70.3 ± 10.7 | HBR: 52% | HBR: 100% | HBR: 4% | HBR: 0% | HBR: 18.1% |
For each study, data are reported separately for patients at high bleeding risk (HBR) and non-high bleeding risk (NHBR), whenever available.
ACS = acute coronary syndrome; IQR = interquartile range; NA = not available; SD = standard deviation.
Patients characteristics
| Study | Age, mean ± SD or median (IQR) | Male gender, % | ACS presentation, % | Potent p2y12 inhibitors, % | Oral anticoagulation, % | Femoral access, % | Bleeding rate, % |
|---|---|---|---|---|---|---|---|
| Choi et al. | N-HBR: 57.6 ± 9.8 | N-HBR: 83% | N-HBR: 100% | N-HBR: 20.9% | N-HBR: 0% | N-HBR: 51.2% | N-HBR: 2.2% |
| HBR: 73.7 ± 7.7 | HBR: 55.3% | HBR: 100% | HBR: 14.8% | HBR: 0% | HBR: 58% | HBR: 4.9% | |
| Okabe et al. | Overall: 70 ± 11 | Overall: 74.9% | Overall: 38.5% | Overall: NA | Overall: 12% | Overall: 58.2% | N-HBR: 5.3% |
| HBR: 10.2% | |||||||
| Lee et al. | N-HBR: 58.8 ± 10.1 | N-HBR: 83.2% | N-HBR: 100% | N-HBR: 100% | N-HBR: 0% | N-HBR: 42.6% | N-HBR: 0.5% |
| HBR: 70.8 ± 8 | HBR: 63.7% | HBR: 100% | BR: 100%H | HBR: 0% | HBR: 51.6% | HBR: 2.7% | |
| Montalto et al. | NA | Overall: 61% | Overall: 100% | Overall: 36% | Overall: 1.9% | Overall: 23% | N-HBR: 1% |
| HBR: 4.5% | |||||||
| Gragnano et al. | N-HBR: 62.8 ± 9.5 | N-HBR: 79.7% | N-HBR: 50.7% | NA | NA | N-HBR: 25.8% | N-HBR: 2% |
| HBR: 75.7 ± 7.7 | HBR: 59.2% | HBR: 55% | HBR: 30.1% | HBR: 5.2% | |||
| Dannenberg et al. | N-HBR: 63.7 ± 10.3 | N-HBR: 77.9% | Overall: 64.2% | N-HBR: 45.2% | N-HBR: 9.9% | N-HBR: 59.4% | N-HBR: 1.5% |
| HBR: 76.5 ± 9.5 | HBR: 60.2% | HBR: 27.2% | HBR: 15.1% | HBR: 74% | HBR: 4.8% | ||
| Fujii et al. | Overall: 66.3 ± 12.4 | Overall: 79.2% | Overall: 100% | Overall: 33% | Overall: 10.5% | NA | N-HBR: 4% |
| HBR: 16.1% | |||||||
| Lyu et al. | Overall: 59 (52–66) | Overall: 78.1% | Overall: 76.5% | Overall: 38% | Overall: 0.3% | NA | N-HBR: 4.4% |
| HBR: 7% | |||||||
| Bianco et al. | Overall: 60.9 ± 11.5 | Overall: 79.2% | Overall: 100% | Overall: 100% | NA | NA | N-HBR: 1.4% |
| HBR: 3.4% | |||||||
| Morici et al. | Overall: 67 (58–77) | Overall: 76.6% | Overall: 100% | NA | Overall: 6.3% | NA | N-HBR: 3% |
| HBR:4.7% | |||||||
| Abu-Assi et al. | Overall: 65.1 ± 13 | Overall: 76.8% | Overall: 100% | Overall: 30.1% | Overall: 8.2% | NA | N-HBR: 5.5% |
| HBR: 8.5% | |||||||
| Guerrero et al. | Overall: 81.9 ± 5 | Overall: 55.3% | Overall: 100% | Overall: 8.2% | Overall: 2.9% | NA | N-HBR: 13.3% |
| HBR: 11.9% | |||||||
| Young Choi et al. | Overall: 65.5 ± 10.5 | Overall: 70% | Overall: 100% | Overall: 5.2% | NA | NA | N-HBR: 7% |
| HBR: 34% | |||||||
| Muñoz et al. | Overall: 66 (58–74) | Overall: 81.2% | Overall: 100% | Overall: 26.5% | Overall: 9% | Overall: 28.4% | N-HBR: 2.9% |
| HBR: 8.1% | |||||||
| Boudreau et al. | Overall: 63.5 ± 12.1 | Overall: 79.2% | Overall: 84.5% | Overall: 52.4% | NA | NA | N-HBR: 2.8% |
| HBR: 17% | |||||||
| Zhao et al. | N-HBR: 57.7 ± 9.9 | N-HBR: 78.1% | N-HBR: 67.5% | N-HBR: 0% | NA | N-HBR: 7.1% | N-HBR: 4% |
| HBR: 72.7 ± 8.3 | HBR: 52.8% | HBR: 79.6% | HBR: 0% | HBR: 13.7% | HBR: 6.5% | ||
| Jang et al. | Overall: 63 ± 10 | Overall: 66.5% | Overall: 77% | Overall: 0% | NA | NA | N-HBR: 0.4% |
| HBR: 1.9% | |||||||
| Rozemeijer et al. | Overall: 64.9 ± 11.0 | Overall: 76.6% | Overall: 57.8% | Overall: 57.8% | Overall: 8% | Overall: 7.8% | N-HBR: 2% |
| HBR: 4.2% | |||||||
| Asif et al. | N-HBR: 61 ± 10.5 | N-HBR: 74% | Overall: 61.6% | NA | N-HBR: 3.3% | NA | N-HBR: 5.1% |
| HBR: 74.4 ± 10.3 | HBR: 55.3% | HBR: 5.7% | HBR: 10% | ||||
| Kubota et al. | N-HBR: 63 ± 10 | N-HBR: 83% | N-HBR: 54% | N-HBR: 28% | N-HBR: 16% | N-HBR: 73% | N-HBR: 1.5% |
| HBR: 77 ± 8 | HBR: 68% | HBR: 47% | HBR: 15% | HBR: 23% | HBR: 78% | HBR: 4.6% | |
| Celik et al. | N-HBR: 59.6 ± 10.3 | N-HBR: 80% | N-HBR: 100% | N-HBR: 3% | N-HBR: 0% | NA | N-HBR: 8.5% |
| HBR: 70.3 ± 10.7 | HBR: 52% | HBR: 100% | HBR: 4% | HBR: 0% | HBR: 18.1% |
| Study | Age, mean ± SD or median (IQR) | Male gender, % | ACS presentation, % | Potent p2y12 inhibitors, % | Oral anticoagulation, % | Femoral access, % | Bleeding rate, % |
|---|---|---|---|---|---|---|---|
| Choi et al. | N-HBR: 57.6 ± 9.8 | N-HBR: 83% | N-HBR: 100% | N-HBR: 20.9% | N-HBR: 0% | N-HBR: 51.2% | N-HBR: 2.2% |
| HBR: 73.7 ± 7.7 | HBR: 55.3% | HBR: 100% | HBR: 14.8% | HBR: 0% | HBR: 58% | HBR: 4.9% | |
| Okabe et al. | Overall: 70 ± 11 | Overall: 74.9% | Overall: 38.5% | Overall: NA | Overall: 12% | Overall: 58.2% | N-HBR: 5.3% |
| HBR: 10.2% | |||||||
| Lee et al. | N-HBR: 58.8 ± 10.1 | N-HBR: 83.2% | N-HBR: 100% | N-HBR: 100% | N-HBR: 0% | N-HBR: 42.6% | N-HBR: 0.5% |
| HBR: 70.8 ± 8 | HBR: 63.7% | HBR: 100% | BR: 100%H | HBR: 0% | HBR: 51.6% | HBR: 2.7% | |
| Montalto et al. | NA | Overall: 61% | Overall: 100% | Overall: 36% | Overall: 1.9% | Overall: 23% | N-HBR: 1% |
| HBR: 4.5% | |||||||
| Gragnano et al. | N-HBR: 62.8 ± 9.5 | N-HBR: 79.7% | N-HBR: 50.7% | NA | NA | N-HBR: 25.8% | N-HBR: 2% |
| HBR: 75.7 ± 7.7 | HBR: 59.2% | HBR: 55% | HBR: 30.1% | HBR: 5.2% | |||
| Dannenberg et al. | N-HBR: 63.7 ± 10.3 | N-HBR: 77.9% | Overall: 64.2% | N-HBR: 45.2% | N-HBR: 9.9% | N-HBR: 59.4% | N-HBR: 1.5% |
| HBR: 76.5 ± 9.5 | HBR: 60.2% | HBR: 27.2% | HBR: 15.1% | HBR: 74% | HBR: 4.8% | ||
| Fujii et al. | Overall: 66.3 ± 12.4 | Overall: 79.2% | Overall: 100% | Overall: 33% | Overall: 10.5% | NA | N-HBR: 4% |
| HBR: 16.1% | |||||||
| Lyu et al. | Overall: 59 (52–66) | Overall: 78.1% | Overall: 76.5% | Overall: 38% | Overall: 0.3% | NA | N-HBR: 4.4% |
| HBR: 7% | |||||||
| Bianco et al. | Overall: 60.9 ± 11.5 | Overall: 79.2% | Overall: 100% | Overall: 100% | NA | NA | N-HBR: 1.4% |
| HBR: 3.4% | |||||||
| Morici et al. | Overall: 67 (58–77) | Overall: 76.6% | Overall: 100% | NA | Overall: 6.3% | NA | N-HBR: 3% |
| HBR:4.7% | |||||||
| Abu-Assi et al. | Overall: 65.1 ± 13 | Overall: 76.8% | Overall: 100% | Overall: 30.1% | Overall: 8.2% | NA | N-HBR: 5.5% |
| HBR: 8.5% | |||||||
| Guerrero et al. | Overall: 81.9 ± 5 | Overall: 55.3% | Overall: 100% | Overall: 8.2% | Overall: 2.9% | NA | N-HBR: 13.3% |
| HBR: 11.9% | |||||||
| Young Choi et al. | Overall: 65.5 ± 10.5 | Overall: 70% | Overall: 100% | Overall: 5.2% | NA | NA | N-HBR: 7% |
| HBR: 34% | |||||||
| Muñoz et al. | Overall: 66 (58–74) | Overall: 81.2% | Overall: 100% | Overall: 26.5% | Overall: 9% | Overall: 28.4% | N-HBR: 2.9% |
| HBR: 8.1% | |||||||
| Boudreau et al. | Overall: 63.5 ± 12.1 | Overall: 79.2% | Overall: 84.5% | Overall: 52.4% | NA | NA | N-HBR: 2.8% |
| HBR: 17% | |||||||
| Zhao et al. | N-HBR: 57.7 ± 9.9 | N-HBR: 78.1% | N-HBR: 67.5% | N-HBR: 0% | NA | N-HBR: 7.1% | N-HBR: 4% |
| HBR: 72.7 ± 8.3 | HBR: 52.8% | HBR: 79.6% | HBR: 0% | HBR: 13.7% | HBR: 6.5% | ||
| Jang et al. | Overall: 63 ± 10 | Overall: 66.5% | Overall: 77% | Overall: 0% | NA | NA | N-HBR: 0.4% |
| HBR: 1.9% | |||||||
| Rozemeijer et al. | Overall: 64.9 ± 11.0 | Overall: 76.6% | Overall: 57.8% | Overall: 57.8% | Overall: 8% | Overall: 7.8% | N-HBR: 2% |
| HBR: 4.2% | |||||||
| Asif et al. | N-HBR: 61 ± 10.5 | N-HBR: 74% | Overall: 61.6% | NA | N-HBR: 3.3% | NA | N-HBR: 5.1% |
| HBR: 74.4 ± 10.3 | HBR: 55.3% | HBR: 5.7% | HBR: 10% | ||||
| Kubota et al. | N-HBR: 63 ± 10 | N-HBR: 83% | N-HBR: 54% | N-HBR: 28% | N-HBR: 16% | N-HBR: 73% | N-HBR: 1.5% |
| HBR: 77 ± 8 | HBR: 68% | HBR: 47% | HBR: 15% | HBR: 23% | HBR: 78% | HBR: 4.6% | |
| Celik et al. | N-HBR: 59.6 ± 10.3 | N-HBR: 80% | N-HBR: 100% | N-HBR: 3% | N-HBR: 0% | NA | N-HBR: 8.5% |
| HBR: 70.3 ± 10.7 | HBR: 52% | HBR: 100% | HBR: 4% | HBR: 0% | HBR: 18.1% |
For each study, data are reported separately for patients at high bleeding risk (HBR) and non-high bleeding risk (NHBR), whenever available.
ACS = acute coronary syndrome; IQR = interquartile range; NA = not available; SD = standard deviation.
No publication bias was identified by visually inspecting funnel plots and by mathematical testing (Supplementary material online, Figure S1). Based on the PROBAST, 13 and 5 studies were deemed to be at low and high risk of bias, respectively; for 3 studies, risk of bias was considered unclear (Supplementary material online, Methods 2).
Study outcomes, discrimination, and calibration of the PRECISE-DAPT score
Compared to those with a PRECISE-DAPT score <25, patients with a PRECISE-DAPT score ≥25 experienced a significantly higher rate of out-of-hospital major and minor bleedings (OR: 2.71; 95% CI: 2.24–3.29; P-value < 0.001) (Figure 2). Analysis of studies reporting on major bleeding outcomes showed that a PRECISE-DAPT score ≥25 was associated with a higher risk of major bleeding both at longest available follow-up (OR: 3.51; 95% CI: 2.71–4.55; P-value < 0.001) and at 1 year (OR: 4.13; 95% CI: 3.27–5.21; P-value < 0.001) (Figure 3). After pooling data on c-stat whenever available, the PRECISE-DAPT score showed a moderate discriminative power in predicting major bleeding events at 1 year (pooled c-stat: 0.71; 95% CI: 0.64–0.77) (Figure 4). Assuming the theoretical 1.8% of major bleedings at 1 year for patients at HBR, score calibration ability in the higher risk category was suboptimal, with a tendency towards under-predicting major bleeding events (pooled O:E ratio: 2.64; 95% CI: 1.52–4.55). Settling the expected 1 year rate of major bleedings at 4%, a trend towards a better calibration power was found (pooled O:E ratio: 1.19; 95% CI: 0.68–2.05) (Figure 5).
Forest plot of primary endpoint. FU = follow-up.
Forest plot of secondary endpoint. (A) Major bleeding at longest available follow-up (FU). (B) Major bleeding at 1 year.
Meta-analysis of validation index. Pooled c-statistics (c-stat).
Meta-analysis of calibration index. Pooled observed: expected(O:E) ratios.
Meta-regression and sensitivity analyses
At meta-regression analyses, no significant interactions were identified between any of the following potential effect modifiers and our primary endpoint results: proportion of patients admitted with ACS (P-value = 0.300), proportion of female patients (P-value = 0.845), proportion of patients treated with potent P2Y12 inhibitors (prasugrel or ticagrelor, P-value = 0.622), proportion of patients who were managed invasively by a femoral approach (P-value = 0.576), and proportion of patients with an indication to OAC (P-value = 0.990); (Supplementary material online, Figure S2).
No significant interaction was also identified between the above-mentioned potential effect modifiers and the discriminative power (pooled c-statistics) of the PRECISE-DAPT score (Supplementary material online, Figure S3).
The higher risk of any bleedings (primary endpoint) and of major bleedings (secondary endpoint) associated with a PRECISE-DAPT score ≥25 was also confirmed considering separately post-hoc analysis of randomized controlled trials and observational studies, (Supplementary material online, Figures 4 and 5).
Discussion
The present systematic review and meta-analysis sought to examine the external validity of the PRECISE-DAPT score in predicting bleeding outcomes: considering a large population of 67 283 subjects on DAPT after PCI, a PRECISE-DAPT score ≥25 confirmed to appropriately identify patients at an increased risk of out-of-hospital major and minor bleeding at 1 year or more, with moderate discriminative power (pooled c-stat: 0.71; 95% CI: 0.64–0.77).
DAPT-related bleeding is the most common complication after coronary stent implantation, and it is associated with an increased risk of morbidity and mortality.4–7,38 Accordingly, several bleeding-risk scores have been proposed for the prediction of bleeding events in patients on DAPT following PCI,16,39,40 denoting the mandatory need for an accurate clinical model that could help decision-making on DAPT intensity and duration.41 Nevertheless, all risk scores are intrinsically influenced by the characteristics of the patient cohorts used for their development, often resulting in limited external validity. By including the totality of available evidence, we evaluated the external validity of the PRECISE-DAPT score through discrimination, calibration, and relative risk to provide precise estimates of the score performance in a large and diverse patient population. The PRECISE-DAPT score was found to be a useful tool to identify HBR patients, targeting a population with a two- to three-fold higher risk of bleeding and a three- to four-fold higher risk of major bleeding. The modest discriminative power is in line with the original derivation and validation score, and was higher compared to multiple similar scores in prior studies.42 Rates of major bleedings in patients deemed at HBR according to the PRECISE-DAPT score ranged from 1 to 17%. Accordingly, the score calibration power resulted to be suboptimal, with a trend towards better performance when the expected event rate was settled to 4%, as recommended by the Academic Research Consortium for HBR,16 which entails a higher risk of events in unselected external cohorts. A justification for the observed suboptimal calibration of the score in the current meta-analysis may be related to the inconsistent definition of major bleeding used within the included studies. While the PRECISE-DAPT score was generated to predict TIMI (Thrombolysis in Myocardial Infarction). Major or minor bleeding, the majority of included validation studies mainly used the Bleeding Academic Research Consortium (BARC) major or minor bleeding definition, which includes a broader spectrum of events. Hence, the PRECISE-DAPT score may under-predict events, especially when more inclusive definitions are used, which suggests the need for recalibration for contemporary scales. Nevertheless, PRECISE-DAPT score discriminative power remains similar, irrespective of the bleeding definition implemented as previously observed.9
Score performance was not influenced by clinical indication for PCI (acute or chronic coronary syndromes) or the type of DAPT used. Overall, 72% of patients included in the meta-analysis underwent PCI in the context of ACS, and no interaction was identified between this proportion of subjects and results of the primary endpoint. The applicability of the PRECISE-DAPT score in patients treated with potent P2Y12 inhibitors has always been considered a matter of debate. In the derivation cohort of the score, only 11.5% of patients were treated with a potent P2Y12 inhibitor, and score discrimination power appeared slightly lower for patients treated with prasugrel.9 In the present study, the PRECISE-DAPT score ability to identify HBR patients was consistent, irrespective of treatment with potent P2Y12 inhibitors (prasugrel or ticagrelor) or with clopidogrel.
To maximize the expected net clinical benefit of DAPT, assessing the trade-off between ischaemic and bleeding risks on an individual patient basis is necessary but often challenging, in light of the frequent overlap between ischaemic and bleeding risk factors. While the PRECISE-DAPT score has been primarily generated to predict out-of-hospital bleeding events in patients undergoing PCI and assigned DAPT, previous studies also demonstrated its association with ischaemic outcomes.43 More importantly, this score appears to be a useful tool for individually customizing the duration of DAPT according to a patientʼs estimated risk of events. Indeed, in the derivation study, the PRECISE-DAPT score proved effective in balancing ischaemic and bleeding risks: patients classified as HBR did not derive any ischaemic benefit from prolonged DAPT duration, whereas those at lower bleeding risk were associated with a reduction of ischaemic events with 12–24-month DAPT courses.9 This result was consistent irrespective of the baseline ischaemic risk, which confirms that bleeding risk evaluation should be prioritized in treatment decisions.44 Furthermore, a recent meta-analysis of randomized trials, including only HBR patients according to the PRECISE-DAPT score or ARC-HBR criteria, found that very short DAPT for 1–3 months was superior in terms of bleeding and cardiovascular mortality reduction compared with standard DAPT.45 This provides further evidence that bleeding risk evaluation is key to optimize the trade-off for ischaemic and bleeding risks and treatment decisions thereof.
The robust association between the PRECISE-DAPT score and bleeding outcomes observed in our study reinforces previous findings and supports current guideline recommendations to use the PRECISE-DAPT score for guiding and informing decision-making on DAPT duration.2 Nevertheless, the moderate discriminative power of the score confirmed in our meta-analysis highlights the need for future improved risk prediction models. The recently proposed PRedicting with Artificial Intelligence riSk aftEr acute coronary syndrome (PRAISE) score showed good discrimination ability in predicting both ischaemic and bleeding outcomes in the external validation cohort,46 paving the way for machine learning-based models in the field of prediction scores. Notwithstanding, the PRAISE score was not superior to the PRECISE-DAPT score in predicting bleeding events in patients with ACS undergoing PCI.47 New evidences are thus needed to establish whether models considering more variables or based on new tools (machine learning-based approach) could be a better solution than traditional risk scores for bleeding and ischaemic risk prediction after PCI.
Limitations
The present study has several limitations that have to be mentioned. First, follow-up length and bleeding definitions were heterogeneous between studies (Table 1). Second, even though studies focused on patients with an indication to OAC were not included in the meta-analysis, 3% of the entire population developed an indication to OAC after PCI; however, at meta-regression analysis, no interaction between this proportion of patients and primary endpoint was found (Supplementary material online, Figure S2). This is also in keeping with prior reports validating PRECISE-DAPT score in a population with atrial fibrillation undergoing PCI.48 Third, validation cohorts included different types and durations of DAPT. Fourth, although primarily focusing on long-term, out-of-hospital, bleeding events and excluding from our systematic revision studies solely reporting in-hospital events, some in-hospital bleeding might have been included in our analyses if not clearly specified or separately reported in validation studies that included both in-hospital and out-of-hospital bleeding. Nonetheless, the PRECISE-DAPT score showed similar discrimination for in-hospital events in its generation cohort, limiting the impact of this element on the overall study findings. Finally, included studies were retrospective or post hoc analyses of randomized controlled trials. While a prospective validation of these tools is desirable, few studies have prospectively validated currently available risk scores to date.49
Conclusion
In conclusion, this systematic review and meta-analysis confirms the external validity of the PRECISE-DAPT score in predicting out-of-hospital bleeding outcomes in patients on DAPT after PCI. The PRECISE-DAPT score effectively identifies patients at HBR and maintains moderate discriminative power across diverse patient populations. While this reinforces current recommendations of international guidelines endorsing the use of the PRECISE-DAPT score, future research should focus on improving the performance of risk prediction tools after PCI to better identify patients at risk and inform treatment decisions.
Funding
None.
Conflict of interest: None. D.J.A. declares that he has received consulting fees or honoraria from Ab ott, Amgen, AstraZeneca, Bayer, Biosensors, Boehringer Ingelheim, Bristol Myers Squibb, Chiesi, CSL Behring, Daiichi Sankyo, Eli Lilly, Haemonetics, Janssen, Merck, Novartis, PhaseBio, PLx Pharma, Pfizer, Sanofi, and Vectura; D.J.A. also declares that his institution has received research grants from Amgen, AstraZeneca, Bayer, Biosensors, CeloNova, CSL Behring, Daiichi Sankyo, Eisai, Eli Lilly, Gilead, Idorsia, Janssen, Matsutani Chemical Industry Co., Merck, Novartis, and the Scott R. MacKenzie Foundation, outside of the current work. M.V. has received grants and personal fees from Terumo; and has received personal fees from Alvimedica/CID, Abbott Vascular, AstraZeneca, Daiichi Sankyo, Bayer, CoreFLOW, Idorsia Pharmaceuticals Ltd, Departement Klinische Forschung of Universität Basel, Bristol Myers Squibb SA, Medscape, Biotronik, and Novartis, outside the submitted work. F.C. declares that he has received consulting fees or honoraria from Chiesi Farmaceutici, AstraZeneca, and Novo Nordisk, outside of the current work. The rest of the authors have nothing to disclose.
Data availability
The data underlying this article are available in the article, in its supplementary material, and online in each study included in the meta-analysis.
