Evaluation of the OPTN Six-Status Heart Allocation System and a Combined Prognostic Model for Waitlist Risk Evaluation Article Swipe
John Malamon
,
Elizabeth Bashain
,
Michael T. Cain
,
Bryon Bhagwandin
,
Bruce Kaplan
,
Jordan Hoffman
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.09.22.25336410
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.09.22.25336410
KEY POINTS Question Does the current national six-status heart allocation system rank waitlisted transplant patients based on medical urgency? Can this heart allocation system’s prognostic performance be improved? Findings The six-status system is poorly calibrated, lacks sufficient statistical discrimination, and underestimates risk in the highest-risk patients, or those with an observed six-month mortality probability greater than 2%. By combining the current status system with six additional patient characteristics (previous transplant, ventilation, mean pulmonary capillary wedge pressure, willingness to accept a donor after cardiac death, diabetes status, and most recent creatinine), we correctly predicted greater than 80% of six-month waitlist mortalities in 7,706 study participants. Meaning This study challenges the safety and efficacy of the current national heart allocation system. Importance In December 2016, the Organ Procurement and Transplantation Network (OPTN) approved a bylaw that restructured the national heart allocation policy from a three-status to a six-status system. This new allocation system, which aimed to assign the highest priority to the patients with the highest mortality risk, went into effect on October 18, 2018. Since then, studies have identified limitations with the current system. However, no changes have been made to improve the national heart allocation system. Objective Given the clear importance and impact of ranking patients based on medical urgency, we carefully evaluated the six-status heart allocation system to determine its correlation with observed mortality, or calibration, and its ability to predict six-month patient mortality risk and waitlist survival. We identified six additional patient characteristics associated with waitlist mortality and combined them with the six-status score to significantly improve the current allocation system’s ability to predict six-month waitlist mortality. Design A retrospective, secondary analysis of the Scientific Registry of Transplant Recipients (SRTR) database of heart transplant candidates and recipients waitlisted from October 18, 2018, to December 31, 2024. Setting The United States Participants Single-organ heart transplant candidates, 18 years of age and older who were placed on the waitlist (N = 19,275). Patients listed multi-organ transplantation were excluded. Exposures All-cause waitlist mortality Main Outcomes and Measures The primary outcome of this study was the validation of the calibration and prognostic performance of the current heart allocation system. The secondary outcome is a simple model that greatly improves upon the current system’s ability to accurately (>80%) predict waitlisted patient mortality. Results With a mean calibration slope of 0.94 (0.66, 1.21) and an area under the receiver operating curve of 0.71 (0.47, 0.87), the current allocation system is poorly calibrated, has only moderate statistical discrimination, and underestimates patient risk in the most critically ill patients. Hazard and time-series regression analysis confirmed that the six-status system does not adequately rank patients based on medical urgency. Our combined model demonstrates that the national allocation system can be improved. Conclusions and Relevance While the current heart distribution system accounts for some patient risk factors, a more objective and accurate model is needed to achieve the OPTN’s strategic objective to more reliably model and predict patient risk and survival likelihood. Our model more accurately predicts patient waitlist mortality and will better inform waitlist management and improve waitlist survival by prioritizing medical urgency.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.09.22.25336410
- https://www.medrxiv.org/content/medrxiv/early/2025/09/24/2025.09.22.25336410.full.pdf
- OA Status
- green
- References
- 24
- OpenAlex ID
- https://openalex.org/W4414458267
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4414458267Canonical identifier for this work in OpenAlex
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https://doi.org/10.1101/2025.09.22.25336410Digital Object Identifier
- Title
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Evaluation of the OPTN Six-Status Heart Allocation System and a Combined Prognostic Model for Waitlist Risk EvaluationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
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2025-09-24Full publication date if available
- Authors
-
John Malamon, Elizabeth Bashain, Michael T. Cain, Bryon Bhagwandin, Bruce Kaplan, Jordan HoffmanList of authors in order
- Landing page
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https://doi.org/10.1101/2025.09.22.25336410Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2025/09/24/2025.09.22.25336410.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.medrxiv.org/content/medrxiv/early/2025/09/24/2025.09.22.25336410.full.pdfDirect OA link when available
- Cited by
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0Total citation count in OpenAlex
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24Number of works referenced by this work
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| abstract_inverted_index.(SRTR) | 282 |
| abstract_inverted_index.0.87), | 401 |
| abstract_inverted_index.Design | 270 |
| abstract_inverted_index.Hazard | 424 |
| abstract_inverted_index.POINTS | 2 |
| abstract_inverted_index.States | 302 |
| abstract_inverted_index.United | 301 |
| abstract_inverted_index.accept | 79 |
| abstract_inverted_index.assign | 155 |
| abstract_inverted_index.better | 505 |
| abstract_inverted_index.death, | 84 |
| abstract_inverted_index.effect | 169 |
| abstract_inverted_index.impact | 203 |
| abstract_inverted_index.inform | 506 |
| abstract_inverted_index.listed | 324 |
| abstract_inverted_index.needed | 477 |
| abstract_inverted_index.placed | 316 |
| abstract_inverted_index.policy | 140 |
| abstract_inverted_index.poorly | 34, 407 |
| abstract_inverted_index.recent | 89 |
| abstract_inverted_index.safety | 110 |
| abstract_inverted_index.simple | 363 |
| abstract_inverted_index.status | 62 |
| abstract_inverted_index.system | 11, 32, 63, 218, 405, 433, 451, 463 |
| abstract_inverted_index.Meaning | 105 |
| abstract_inverted_index.Network | 129 |
| abstract_inverted_index.October | 171, 292 |
| abstract_inverted_index.Results | 380 |
| abstract_inverted_index.Setting | 299 |
| abstract_inverted_index.ability | 230, 264, 372 |
| abstract_inverted_index.achieve | 479 |
| abstract_inverted_index.cardiac | 83 |
| abstract_inverted_index.changes | 186 |
| abstract_inverted_index.current | 6, 61, 115, 182, 261, 354, 370, 403, 460 |
| abstract_inverted_index.greater | 55, 94 |
| abstract_inverted_index.greatly | 366 |
| abstract_inverted_index.highest | 157, 164 |
| abstract_inverted_index.improve | 191, 259, 510 |
| abstract_inverted_index.medical | 18, 209, 441, 515 |
| abstract_inverted_index.outcome | 339, 360 |
| abstract_inverted_index.patient | 67, 234, 244, 378, 416, 467, 490, 500 |
| abstract_inverted_index.predict | 232, 266, 376, 489 |
| abstract_inverted_index.primary | 338 |
| abstract_inverted_index.ranking | 205 |
| abstract_inverted_index.status, | 86 |
| abstract_inverted_index.studies | 176 |
| abstract_inverted_index.system, | 151 |
| abstract_inverted_index.system. | 119, 147, 183, 196, 357 |
| abstract_inverted_index.19,275). | 322 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.December | 122, 296 |
| abstract_inverted_index.Findings | 29 |
| abstract_inverted_index.However, | 184 |
| abstract_inverted_index.Measures | 336 |
| abstract_inverted_index.OPTN’s | 481 |
| abstract_inverted_index.Outcomes | 334 |
| abstract_inverted_index.Patients | 323 |
| abstract_inverted_index.Question | 3 |
| abstract_inverted_index.Registry | 278 |
| abstract_inverted_index.accounts | 464 |
| abstract_inverted_index.accurate | 474 |
| abstract_inverted_index.analysis | 274, 428 |
| abstract_inverted_index.approved | 131 |
| abstract_inverted_index.combined | 251, 444 |
| abstract_inverted_index.database | 283 |
| abstract_inverted_index.diabetes | 85 |
| abstract_inverted_index.efficacy | 112 |
| abstract_inverted_index.factors, | 469 |
| abstract_inverted_index.improves | 367 |
| abstract_inverted_index.moderate | 411 |
| abstract_inverted_index.national | 7, 116, 137, 193, 449 |
| abstract_inverted_index.observed | 51, 224 |
| abstract_inverted_index.patients | 15, 161, 206, 438 |
| abstract_inverted_index.predicts | 499 |
| abstract_inverted_index.priority | 158 |
| abstract_inverted_index.receiver | 395 |
| abstract_inverted_index.reliably | 486 |
| abstract_inverted_index.survival | 493, 512 |
| abstract_inverted_index.urgency, | 210 |
| abstract_inverted_index.urgency. | 442, 516 |
| abstract_inverted_index.urgency? | 19 |
| abstract_inverted_index.waitlist | 99, 238, 248, 268, 319, 331, 501, 507, 511 |
| abstract_inverted_index.(>80%) | 375 |
| abstract_inverted_index.(previous | 69 |
| abstract_inverted_index.All-cause | 330 |
| abstract_inverted_index.Exposures | 329 |
| abstract_inverted_index.Objective | 197 |
| abstract_inverted_index.Relevance | 457 |
| abstract_inverted_index.capillary | 74 |
| abstract_inverted_index.carefully | 212 |
| abstract_inverted_index.combining | 59 |
| abstract_inverted_index.confirmed | 429 |
| abstract_inverted_index.correctly | 92 |
| abstract_inverted_index.determine | 220 |
| abstract_inverted_index.evaluated | 213 |
| abstract_inverted_index.excluded. | 328 |
| abstract_inverted_index.improved. | 454 |
| abstract_inverted_index.improved? | 28 |
| abstract_inverted_index.mortality | 53, 165, 235, 249, 332, 502 |
| abstract_inverted_index.objective | 472, 483 |
| abstract_inverted_index.operating | 396 |
| abstract_inverted_index.patients, | 46 |
| abstract_inverted_index.patients. | 423 |
| abstract_inverted_index.predicted | 93 |
| abstract_inverted_index.pressure, | 76 |
| abstract_inverted_index.pulmonary | 73 |
| abstract_inverted_index.secondary | 273, 359 |
| abstract_inverted_index.six-month | 52, 98, 233, 267 |
| abstract_inverted_index.strategic | 482 |
| abstract_inverted_index.survival. | 239 |
| abstract_inverted_index.Importance | 120 |
| abstract_inverted_index.Recipients | 281 |
| abstract_inverted_index.Scientific | 277 |
| abstract_inverted_index.Transplant | 280 |
| abstract_inverted_index.accurately | 374, 498 |
| abstract_inverted_index.additional | 66, 243 |
| abstract_inverted_index.adequately | 436 |
| abstract_inverted_index.allocation | 10, 23, 118, 139, 150, 195, 217, 262, 356, 404, 450 |
| abstract_inverted_index.associated | 246 |
| abstract_inverted_index.candidates | 287 |
| abstract_inverted_index.challenges | 108 |
| abstract_inverted_index.critically | 421 |
| abstract_inverted_index.identified | 178, 241 |
| abstract_inverted_index.importance | 201 |
| abstract_inverted_index.management | 508 |
| abstract_inverted_index.mortality, | 225 |
| abstract_inverted_index.mortality. | 269, 379 |
| abstract_inverted_index.prognostic | 25, 350 |
| abstract_inverted_index.recipients | 289 |
| abstract_inverted_index.regression | 427 |
| abstract_inverted_index.six-status | 8, 31, 146, 215, 255, 432 |
| abstract_inverted_index.sufficient | 37 |
| abstract_inverted_index.system’s | 24, 263, 371 |
| abstract_inverted_index.transplant | 14, 286, 306 |
| abstract_inverted_index.validation | 345 |
| abstract_inverted_index.waitlisted | 13, 290, 377 |
| abstract_inverted_index.Conclusions | 455 |
| abstract_inverted_index.Procurement | 126 |
| abstract_inverted_index.calibrated, | 35, 408 |
| abstract_inverted_index.calibration | 348, 384 |
| abstract_inverted_index.candidates, | 307 |
| abstract_inverted_index.correlation | 222 |
| abstract_inverted_index.likelihood. | 494 |
| abstract_inverted_index.limitations | 179 |
| abstract_inverted_index.mortalities | 100 |
| abstract_inverted_index.multi-organ | 325 |
| abstract_inverted_index.performance | 26, 351 |
| abstract_inverted_index.probability | 54 |
| abstract_inverted_index.statistical | 38, 412 |
| abstract_inverted_index.time-series | 426 |
| abstract_inverted_index.transplant, | 70 |
| abstract_inverted_index.willingness | 77 |
| abstract_inverted_index.Participants | 303 |
| abstract_inverted_index.Single-organ | 304 |
| abstract_inverted_index.calibration, | 227 |
| abstract_inverted_index.creatinine), | 90 |
| abstract_inverted_index.demonstrates | 446 |
| abstract_inverted_index.distribution | 462 |
| abstract_inverted_index.highest-risk | 45 |
| abstract_inverted_index.prioritizing | 514 |
| abstract_inverted_index.restructured | 135 |
| abstract_inverted_index.three-status | 143 |
| abstract_inverted_index.ventilation, | 71 |
| abstract_inverted_index.participants. | 104 |
| abstract_inverted_index.significantly | 258 |
| abstract_inverted_index.retrospective, | 272 |
| abstract_inverted_index.underestimates | 41, 415 |
| abstract_inverted_index.Transplantation | 128 |
| abstract_inverted_index.characteristics | 68, 245 |
| abstract_inverted_index.discrimination, | 39, 413 |
| abstract_inverted_index.transplantation | 326 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5049856137 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I51713134 |
| citation_normalized_percentile.value | 0.60194945 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |