| var | total |
|---|---|
| n | 794’110 |
| Age | 66.1 (13.5) |
| Haemoglobin | 12.6 (2.4) |
| WBC | 9.2 (7.8) |
| Creatinine_Clearance | 62.5 (32.5) |
| Prior_bleeding (= 1) | 83’105 (10.5%) |
| Score_new | 28.2 (16.6) |
| alternative_score | 28.6 (19.4) |
| hbr (= 1) | 437’396 (55.1%) |
| hbr_alternative (= 1) | 423’131 (53.3%) |
| new_cluster_risk | |
| High | 437’396 (55.1%) |
| Low | 118’909 (15.0%) |
| Moderate | 129’798 (16.3%) |
| Very Low | 108’007 (13.6%) |
PD Tables
PRECISE-DAPT Tables
Table 1 General features
Table 2 PRECISE-DAPT and alternative PRECISE-DAPT quantiles
| quantile_score | alternative_score | |
|---|---|---|
| 0% | 0 | 0 |
| 25% | 16 | 14 |
| 50% | 26 | 26 |
| 75% | 38 | 40 |
| 100% | 100 | 100 |
Table 3 Frequency of High Bleeding Risk (HBR) patients identified by PRECISE-DAPT and alternative PRECISE-DAPT scores
| var | total |
|---|---|
| n | 794’110 |
| No HBR (= 1) | 337’386 (42.5%) |
| HBR only alternative score (= 2) | 19’328 (2.4%) |
| HBR only conventional score (= 3) | 33’593 (4.2%) |
| HBR (= 4) | 403’803 (50.8%) |
Supplementary Table 1: Results based on computation characteristics
| var | total | BOT | CHEATER | GOOD | REPEATER | |
|---|---|---|---|---|---|---|
| n | 794’110 | 19’417 (2.4%) | 264’727 (33.3%) | 455’524 (57.4%) | 54’442 (6.9%) | |
| Age | 66.1 (13.5) | 65.9 (13.7) | 64.9 (14.6) | 67.1 (12.7) | 63.3 (13.1) | *** ’ |
| Haemoglobin | 12.6 (2.4) | 12.8 (2.4) | 12.4 (2.4) | 12.6 (2.4) | 13.3 (2.1) | *** ’ |
| WBC | 9.2 (7.8) | 9.3 (7.9) | 9.3 (9.2) | 9.2 (7.1) | 9.0 (6.6) | *** ’ |
| Creatinine_Clearance | 62.5 (32.5) | 60.2 (33.9) | 58.1 (34.7) | 63.9 (30.5) | 73.5 (33.2) | *** ’ |
| Prior_bleeding (= 1) | 83’105 (10.5%) | 2’283 (11.8%) | 42’166 (15.9%) | 36’863 (8.1%) | 1’793 (3.3%) | *** “” |
| Score_new | 28.2 (16.6) | 29.0 (17.3) | 30.4 (17.6) | 27.8 (15.9) | 21.1 (14.4) | *** ’ |
| alternative_score | 28.6 (19.4) | 29.6 (20.3) | 31.6 (20.5) | 27.9 (18.7) | 20.2 (16.7) | *** ’ |
| hbr (= 1) | 437’396 (55.1%) | 10’882 (56.0%) | 160’445 (60.6%) | 242’460 (53.2%) | 23’609 (43.4%) | *** “” |
| hbr_alternative (= 1) | 423’131 (53.3%) | 10’640 (54.8%) | 159’702 (60.3%) | 234’664 (51.5%) | 18’125 (33.3%) | *** “” |
| new_cluster_risk | *** “” | |||||
| High | 437’396 (55.1%) | 10’882 (56.0%) | 160’445 (60.6%) | 242’460 (53.2%) | 23’609 (43.4%) | |
| Low | 118’909 (15.0%) | 2’845 (14.7%) | 31’902 (12.1%) | 74’952 (16.5%) | 9’210 (16.9%) | |
| Moderate | 129’798 (16.3%) | 2’963 (15.3%) | 40’378 (15.3%) | 80’008 (17.6%) | 6’449 (11.8%) | |
| Very Low | 108’007 (13.6%) | 2’727 (14.0%) | 32’002 (12.1%) | 58’104 (12.8%) | 15’174 (27.9%) | |
| Category | *** “” | |||||
| BOT | 19’417 (2.4%) | 19’417 (100.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | |
| CHEATER | 264’727 (33.3%) | 0 (0.0%) | 264’727 (100.0%) | 0 (0.0%) | 0 (0.0%) | |
| GOOD | 455’524 (57.4%) | 0 (0.0%) | 0 (0.0%) | 455’524 (100.0%) | 0 (0.0%) | |
| REPEATER | 54’442 (6.9%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 54’442 (100.0%) |
Figures Years and Quarters
Countries
Joyplots
