Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Source and Study Population
2.2. Data Collection
2.3. Cluster Analysis
2.4. Outcomes
2.5. Statistical Analysis
3. Results
3.1. Clinical Characteristics Based on Clusters of Kidney Transplant Recipients from High KDPI Deceased Donors
3.2. Posttransplant Outcomes of Kidney Transplant Recipients from High KDPI Deceased Donors
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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All (n = 8935) | Cluster 1 (n = 1984) | Cluster 2 (n = 2135) | Cluster 3 (n = 357) | Cluster 4 (n = 335) | Cluster 5 (n = 1069) | Cluster 6 (n = 3055) | p-Value | |
---|---|---|---|---|---|---|---|---|
Recipient age (year) | 62.4 ± 9.5 | 57.2 ± 9.9 | 68.0 ± 6.9 | 57.3 ± 11.0 | 51.7 ± 13.7 | 63.3 ± 8.7 | 63.3 ± 7.2 | <0.001 |
Recipient male sex | 5699 (63.8) | 1213 (61.1) | 1371 (64.2) | 223 (62.5) | 170 (50.8) | 598 (55.9) | 2124 (69.5) | <0.001 |
Recipient race
| 3341 (37.4) 3080 (34.5) 1549 (17.3) 965 (10.8) | 204 (10.3) 1298 (65.4) 308 (15.5) 174 (8.8) | 1554 (72.7) 200 (9.4) 185 (8.7) 196 (9.2) | 163 (45.6) 130 (36.4) 32 (9.0) 32 (9.0) | 85 (25.3) 82 (24.5) 71 (21.2) 97 (28.9) | 510 (47.7) 260 (24.3) 213 (19.9) 86 (8.1) | 825 (27.0) 1110 (36.3) 740 (24.2) 380 (12.5) | <0.001 |
ABO blood group
| 2846 (31.9) 1374 (15.4) 340 (3.8) 4375 (48.9) | 377 (19.0) 392 (19.8) 61 (3.1) 1154 (58.1) | 903 (42.3) 222 (10.4) 79 (3.7) 931 (43.6) | 112 (31.4) 63 (17.7) 18 (5.0) 164 (45.9) | 87 (26.0) 69 (20.6) 15 (4.5) 164 (48.9) | 381 (35.6) 145 (13.6) 40 (3.7) 503 (47.1) | 986 (32.3) 483 (15.8) 127 (4.2) 1459 (47.7) | <0.001 |
Body mass index (kg/m2) | 28.5 ± 5.0 | 28.6 ± 5.3 | 27.3 ± 4.7 | 27.4 ± 5.4 | 24.8 ± 4.1 | 28.7 ± 5.0 | 29.6 ± 4.8 | <0.001 |
Kidney retransplant | 364 (4.1) | 0 (0) | 0 (0) | 357 (100) | 7 (2.1) | 0 (0) | 0 (0) | <0.001 |
Dialysis duration
| 803 (9.0) 2234 (25.0) 775 (8.7) 5123 (57.3) | 99 (5.0) 315 (15.9) 107 (5.4) 1463 (73.7) | 359 (16.8) 661 (31.0) 253 (11.9) 862 (40.4) | 35 (9.8) 88 (24.7) 36 (10.1) 198 (55.5) | 35 (10.5) 112 (33.4) 39 (11.6) 149 (44.5) | 110 (10.3) 306 (28.6) 111 (10.4) 542 (50.7) | 165 (5.4) 752 (24.6) 229 (7.5) 1909 (62.5) | <0.001 |
Cause of end-stage kidney disease
| 3623 (40.5) 2525 (28.3) 1104 (12.4) 602 (6.7) 1081 (12.1) | 18 (0.9) 1318 (66.4) 328 (16.5) 164 (8.3) 156 (7.9) | 61 (2.9) 784 (36.7) 489 (22.9) 292 (13.7) 509 (23.8) | 32 (9.0) 61 (17.1) 48 (13.5) 14 (3.9) 202 (56.5) | 80 (23.9) 89 (26.5) 76 (22.7) 30 (9.0) 60 (17.9) | 460 (43.0) 253 (23.7) 139 (13.0) 84 (7.9) 133 (12.4) | 2972 (97.2) 20 (0.7) 24 (0.8) 18 (0.6) 21 (0.7) | <0.001 |
Comorbidity
| 4484 (50.2) 975 (10.9) 987 (11.1) | 331 (16.7) 123 (6.2) 107 (5.4) | 314 (14.7) 419 (19.6) 148 (6.9) | 116 (32.5) 49 (13.7) 30 (8.4) | 101 (30.2) 32 (9.6) 18 (5.4) | 567 (53.0) 129 (12.1) 119 (11.1) | 3055 (100.0) 223 (7.3) 565 (18.5) | <0.001 <0.001 <0.001 |
PRA (%), median (IQR) | 0 (0–1) | 0 (0–4) | 0 (0–0) | 48 (0–95) | 0 (0–0) | 0 (0–17) | 0 (0–0) | <0.001 |
Positive HCV serostatus | 455 (5.1) | 145 (7.3) | 70 (3.3) | 34 (9.5) | 9 (2.7) | 47 (4.4) | 150 (4.9) | <0.001 |
Positive HBs antigen | 181 (2.0) | 43 (2.2) | 30 (1.4) | 14 (3.9) | 16 (4.8) | 24 (2.3) | 54 (1.8) | <0.001 |
Positive HIV serostatus | 63 (0.7) | 31 (1.6) | 9 (0.4) | 0 (0.0) | 4 (1.2) | 5 (0.5) | 14 (0.5) | <0.001 |
Functional status
| 26 (0.3) 3723 (41.7) 5186 (58.0) | 4 (0.2) 859 (43.3) 1121 (56.5) | 6 (0.3) 709 (33.2) 1420 (66.5) | 0 (0.0) 143 (40.1) 214 (59.9) | 2 (0.6) 101 (30.2) 232 (69.2) | 3 (0.3) 429 (40.1) 637 (59.6) | 11 (0.4) 1482 (48.5) 1562 (51.1) | <0.001 |
Working income | 1593 (17.8) | 415 (20.9) | 427 (20.0) | 71 (19.9) | 100 (29.8) | 192 (18.0) | 388 (12.7) | <0.001 |
Public insurance | 7320 (81.9) | 1676 (84.5) | 1672 (78.3) | 297 (83.2) | 232 (69.3) | 865 (80.9) | 2578 (84.4) | <0.001 |
US resident | 8872 (99.3) | 1966 (99.1) | 2127 (99.6) | 356 (99.7) | 323 (96.4) | 1060 (99.2) | 3040 (99.5) | <0.001 |
Undergraduate education or above | 4400 (49.2) | 863 (43.5) | 1274 (59.7) | 188 (52.7) | 188 (56.1) | 530 (49.6) | 1357 (44.4) | <0.001 |
Serum albumin (g/dL) | 3.9 ± 0.5 | 4.0 ± 0.5 | 4.0 ± 0.5 | 3.8 ± 0.6 | 4.0 ± 0.5 | 3.9 ± 0.5 | 3.9 ± 0.6 | <0.001 |
Kidney donor status
| 1673 (18.7) 7262 (81.3) | 484 (24.4) 1500 (75.6) | 184 (8.6) 1951 (91.4) | 80 (22.4) 277 (77.6) | 335 (100) 0 (0) | 155 (14.5) 914 (85.5) | 435 (14.2) 2620 (85.8) | <0.001 |
Donor age (year) | 58.3 ± 13.2 | 58.1 ± 6.6 | 62.6 ± 6.3 | 58.4 ± 8.4 | 0.7 ± 3.1 | 61.1 ± 6.9 | 60.7 ± 6.7 | <0.001 |
Donor male sex | 4124 (46.2) | 850 (42.8) | 944 (44.2) | 165 (46.2) | 189 (56.4) | 515 (48.2) | 1461 (47.8) | <0.001 |
Donor race
| 4532 (50.7) 2928 (32.7) 987 (11.1) 488 (5.5) | 605 (30.5) 1042 (52.5) 208 (10.5) 129 (6.5) | 1472 (68.9) 367 (17.2) 198 (9.3) 98 (4.6) | 158 (44.3) 139 (38.9) 35 (9.8) 25 (7.0) | 104 (31.0) 191 (57.0) 32 (9.6) 8 (2.4) | 677 (63.3) 206 (19.3) 138 (12.9) 48 (4.5) | 1516 (49.6) 983 (32.2) 376 (12.3) 180 (5.9) | <0.001 |
Donor weight (kg) | 79 ± 26 | 82 ± 23 | 82 ± 22 | 81 ± 23 | 9 ± 9 | 81 ± 23 | 83 ± 22 | <0.001 |
Donor Height (cm) | 163 ± 22 | 167 ± 10 | 167 ± 10 | 167 ± 13 | 67 ± 17 | 167 ± 11 | 168 ± 10 | <0.001 |
Donor hypertension | 6833 (76.5) | 1633 (82.3) | 1648 (77.2) | 287 (80.4) | 6 (1.8) | 837 (78.3) | 2422 (79.3) | <0.001 |
Donor diabetes | 2416 (27) | 571 (29) | 588 (28) | 93 (26) | 3 (1) | 306 (29) | 855 (28) | <0.001 |
Donor positive HCV serostatus | 387 (4) | 110 (6) | 71 (3) | 28 (8) | 2 (1) | 45 (4) | 131 (4) | <0.001 |
Donor cerebrovascular death | 6142 (69) | 1469 (74) | 1489 (70) | 257 (72) | 14 (4) | 744 (70) | 2169 (71) | <0.001 |
Donor creatinine (mg/dL) | 1.3 ± 1.0 | 1.3 ± 0.0.7 | 1.3 ± 1.1 | 1.3 ± 0.6 | 1.0 ± 1.8 | 1.3 ± 1.1 | 1.3 ± 0.8 | <0.001 |
KDPI (%) | 91 ± 4 | 91 ± 4 | 91 ± 4 | 90 ± 4 | 89 ± 4 | 91 ± 4 | 91 ± 4 | <0.001 |
Dual kidney transplant | 840 (9.4) | 94 (4.7) | 189 (8.9) | 10 (2.8) | 280 (83.6) | 77 (7.2) | 190 (6.2) | <0.001 |
Total HLA mismatch, median (IQR) | 5 (4-5) | 5 (4-6) | 5 (4-5) | 4 (3-5) | 5 (4-6) | 3 (2-3) | 5 (4-6) | <0.001 |
Cold ischemia time (hours) | 20.2 ± 9.0 | 19.6 ± 9.0 | 20.5 ± 9.1 | 20.4 ± 9.1 | 21.7 ± 8.7 | 19.7 ± 8.4 | 20.4 ± 9.0 | <0.001 |
Kidney on pump | 5428 (60.8) | 1171 (59.0) | 1400 (65.6) | 206 (57.7) | 143 (42.7) | 623 (58.3) | 1885 (61.7) | <0.001 |
Allocation type
| 5248 (58.7) 2264 (25.3) 1423 (16.0) | 1298 (65.4) 452 (22.8) 234 (11.8) | 1269 (59.4) 531 (24.9) 335 (15.7) | 169 (47.3) 86 (24.1) 102 (28.6) | 86 (25.7) 101 (30.1) 148 (44.2) | 608 (56.9) 261 (24.4) 200 (18.7) | 1818 (59.5) 833 (27.3) 404 (13.2) | <0.001 |
EBV status
| 23 (0.3) 8197 (91.7) 715 (8.0) | 2 (0.1) 1847 (93.1) 135 (6.8) | 8 (0.4) 1916 (89.7) 211 (9.9) | 0 (0) 327 (91.6) 30 (8.4) | 9 (2.7) 316 (94.3) 10 (3.0) | 1 (0.1) 979 (91.6) 89 (8.3) | 3 (0.1) 2812 (92.0) 240 (7.9) | <0.001 |
CMV status
| 722 (8.1) 1711 (19.1) 4777 (53.5) 1725 (19.3) | 68 (3.4) 254 (12.8) 1369 (69.0) 293 (14.8) | 270 (12.7) 468 (21.9) 796 (37.3) 601 (28.1) | 28 (7.8) 70 (19.6) 196 (54.9) 63 (17.7) | 25 (7.5) 151 (45.1) 116 (34.6) 43 (12.8) | 109 (10.2) 196 (18.3) 529 (49.5) 235 (22.0) | 222 (7.3) 572 (18.7) 1771 (58.0) 490 (16.0) | <0.001 |
Induction immunosuppression
| 5021 (56.2) 1382 (15.5) 2080 (23.3) 256 (2.9) 687 (7.7) | 1130 (57.0) 342 (17.2) 386 (19.5) 82 (4.1) 170 (8.6) | 1079 (50.5) 342 (16.0) 622 (29.1) 53 (2.5) 161 (7.5) | 211 (59.1) 57 (16.0) 51 (14.3) 16 (4.5) 34 (9.5) | 268 (80.0) 22 (6.6) 33 (9.8) 10 (3.0) 13 (3.9) | 577 (54.0) 176 (16.5) 258 (24.1) 29 (2.7) 82 (7.7) | 1756 (57.5) 443 (14.5) 730 (23.9) 66 (2.2) 227 (7.4) | <0.001 <0.001 <0.001 <0.001 0.050 |
Maintenance Immunosuppression
| 7983 (89.3) 115 (1.3) 8185 (91.6) 21 (0.2) 94 (1.1) 5910 (66.1) | 1766 (89.0) 23 (1.2) 1821 (91.8) 1 (0.1) 20 (1.0) 1330 (67.0) | 1896 (88.8) 31 (1.4) 1952 (91.4) 9 (0.4) 31 (1.5) 1341 (62.8) | 312 (87.4) 6 (1.7) 323 (90.5) 2 (0.6) 4 (1.1) 258 (72.3) | 298 (89.0) 4 (1.2) 307 (91.6) 1 (0.3) 1 (0.3) 160 (47.8) | 954 (89.2) 15 (1.4) 962 (90.0) 2 (0.2) 11 (1.0) 736 (68.9) | 2757 (90.2) 36 (1.2) 2820 (92.3) 6 (0.2) 27 (0.9) 2085 (68.3) | 0.419 0.905 0.274 0.155 0.307 <0.001 |
Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 | Cluster 5 | Cluster 6 | |
---|---|---|---|---|---|---|
Primary non-function | 36 (1.8%) | 26 (1.2%) | 5 (1.4%) | 4 (1.2%) | 7 (0.7%) | 31 (1.0%) |
Delayed graft function | 759 (38.3%) | 535 (25.1%) | 149 (41.7%) | 101 (30.2%) | 340 (31.8%) | 1247 (40.8%) |
1-year survival | 95.3% | 93.9% | 91.0% | 98.1% | 92.2% | 92.5% |
5-year survival | 79.2% | 68.6% | 62.1% | 90.5% | 68.9% | 67.0% |
1-year death-censored graft survival | 91.2% | 94.3% | 87.8% | 85.6% | 93.4% | 92.3% |
5-year death-censored graft survival | 73.1% | 84.3% | 70.1% | 81.8% | 80.5% | 76.2% |
1-year graft survival | 88.1% | 90.2% | 81.8% | 84.8% | 88.6% | 87.3% |
5-year graft survival | 63.9% | 64.2% | 53.2% | 76.9% | 63.4% | 58.9% |
1-year acute rejection | 157 (7.9%) | 134 (6.3%) | 38 (10.6%) | 8 (2.4%) | 69 (6.5%) | 200 (6.6%) |
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Thongprayoon, C.; Radhakrishnan, Y.; Jadlowiec, C.C.; Mao, S.A.; Mao, M.A.; Vaitla, P.; Acharya, P.C.; Leeaphorn, N.; Kaewput, W.; Pattharanitima, P.; et al. Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering. J. Pers. Med. 2022, 12, 1992. https://doi.org/10.3390/jpm12121992
Thongprayoon C, Radhakrishnan Y, Jadlowiec CC, Mao SA, Mao MA, Vaitla P, Acharya PC, Leeaphorn N, Kaewput W, Pattharanitima P, et al. Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering. Journal of Personalized Medicine. 2022; 12(12):1992. https://doi.org/10.3390/jpm12121992
Chicago/Turabian StyleThongprayoon, Charat, Yeshwanter Radhakrishnan, Caroline C. Jadlowiec, Shennen A. Mao, Michael A. Mao, Pradeep Vaitla, Prakrati C. Acharya, Napat Leeaphorn, Wisit Kaewput, Pattharawin Pattharanitima, and et al. 2022. "Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering" Journal of Personalized Medicine 12, no. 12: 1992. https://doi.org/10.3390/jpm12121992