APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life Sustaining Therapies Prediction Model Article Swipe
Parisa Rashidi
,
Miguel Á. Contreras
,
Brandon Silva
,
Benjamin Shickel
,
Andréa Davidson
,
Tezcan Ozrazgat‐Baslanti
,
Yuanfang Ren
,
Ziyuan Guan
,
Jeremy A. Balch
,
Jiaqing Zhang
,
Sabyasachi Bandyopadhyay
,
Tyler J. Loftus
,
Kia Khezeli
,
Subhash Nerella
,
Azra Bihorac
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4790824/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4790824/v1
On average, more than 5 million patients are admitted to intensive care units (ICUs) in the US, with mortality rates ranging from 10 to 29%. The acuity state of patients in the ICU can quickly change from stable to unstable, sometimes leading to life-threatening conditions. Early detection of deteriorating conditions can assist in more timely interventions and improved survival rates. While Artificial Intelligence (AI)-based models show potential for assessing acuity in a more granular and automated manner, they typically use mortality as a proxy of acuity in the ICU. Furthermore, these methods do not determine the acuity state of a patient (i.e., stable or unstable), the transition between acuity states, or the need for life-sustaining therapies. In this study, we propose APRICOT-M (Acuity Prediction in Intensive Care Unit-Mamba), a 1M-parameter state space-based neural network to predict acuity state, transitions, and the need for life-sustaining therapies in real-time among ICU patients. The model integrates ICU data in the preceding four hours (including vital signs, laboratory results, assessment scores, and medications) and patient characteristics (age, sex, race, and comorbidities) to predict the acuity outcomes in the next four hours. Our state space-based model can process sparse and irregularly sampled data without manual imputation, thus reducing the noise in input data and increasing inference speed. The model was trained on data from 107,473 patients (142,062 ICU admissions) from 55 hospitals between 2014-2017 and validated externally on data from 74,901 patients (101,356 ICU admissions) from 143 hospitals. Additionally, it was validated temporally on data from 12,927 patients (15,940 ICU admissions) from one hospital in 2018-2019 and prospectively on data from 215 patients (369 ICU admissions) from one hospital in 2021-2023. Three datasets were used for training and evaluation: the University of Florida Health (UFH) dataset, the electronic ICU Collaborative Research Database (eICU), and the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. APRICOT-M significantly outperforms the baseline acuity assessment, Sequential Organ Failure Assessment (SOFA), for mortality prediction in both external (AUROC 0.95 CI: 0.94-0.95 compared to 0.78 CI: 0.78-0.79) and prospective (AUROC 0.99 CI: 0.97-1.00 compared to 0.80 CI: 0.65-0.92) cohorts, as well as for instability prediction (external AUROC 0.75 CI: 0.74-0.75 compared to 0.51 CI: 0.51-0.51, and prospective AUROC 0.69 CI: 0.64-0.74 compared to 0.53 CI: 0.50-0.57). This tool has the potential to help clinicians make timely interventions by predicting the transition between acuity states and decision-making on life-sustaining within the next four hours in the ICU.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-4790824/v1
- https://www.researchsquare.com/article/rs-4790824/latest.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401378355
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401378355Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-4790824/v1Digital Object Identifier
- Title
-
APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life Sustaining Therapies Prediction ModelWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-06Full publication date if available
- Authors
-
Parisa Rashidi, Miguel Á. Contreras, Brandon Silva, Benjamin Shickel, Andréa Davidson, Tezcan Ozrazgat‐Baslanti, Yuanfang Ren, Ziyuan Guan, Jeremy A. Balch, Jiaqing Zhang, Sabyasachi Bandyopadhyay, Tyler J. Loftus, Kia Khezeli, Subhash Nerella, Azra BihoracList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-4790824/v1Publisher landing page
- PDF URL
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https://www.researchsquare.com/article/rs-4790824/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-4790824/latest.pdfDirect OA link when available
- Concepts
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Intensive care unit, Proxy (statistics), Intensive care, Medicine, Psychological intervention, Intensive care medicine, Emergency medicine, Medical emergency, Computer science, Machine learning, NursingTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.without | 199 |
| abstract_inverted_index.(101,356 | 238 |
| abstract_inverted_index.(142,062 | 222 |
| abstract_inverted_index.Database | 297 |
| abstract_inverted_index.Research | 296 |
| abstract_inverted_index.admitted | 9 |
| abstract_inverted_index.average, | 2 |
| abstract_inverted_index.baseline | 313 |
| abstract_inverted_index.cohorts, | 347 |
| abstract_inverted_index.compared | 331, 342, 359, 370 |
| abstract_inverted_index.dataset, | 291 |
| abstract_inverted_index.dataset. | 308 |
| abstract_inverted_index.datasets | 278 |
| abstract_inverted_index.external | 326 |
| abstract_inverted_index.granular | 74 |
| abstract_inverted_index.hospital | 259, 274 |
| abstract_inverted_index.improved | 58 |
| abstract_inverted_index.outcomes | 182 |
| abstract_inverted_index.patients | 7, 30, 221, 237, 253, 268 |
| abstract_inverted_index.reducing | 203 |
| abstract_inverted_index.results, | 165 |
| abstract_inverted_index.survival | 59 |
| abstract_inverted_index.training | 282 |
| abstract_inverted_index.(external | 354 |
| abstract_inverted_index.0.64-0.74 | 369 |
| abstract_inverted_index.0.74-0.75 | 358 |
| abstract_inverted_index.0.94-0.95 | 330 |
| abstract_inverted_index.0.97-1.00 | 341 |
| abstract_inverted_index.2014-2017 | 229 |
| abstract_inverted_index.2018-2019 | 261 |
| abstract_inverted_index.APRICOT-M | 122, 309 |
| abstract_inverted_index.Intensive | 126, 305 |
| abstract_inverted_index.assessing | 69 |
| abstract_inverted_index.automated | 76 |
| abstract_inverted_index.detection | 47 |
| abstract_inverted_index.determine | 95 |
| abstract_inverted_index.hospitals | 227 |
| abstract_inverted_index.inference | 211 |
| abstract_inverted_index.intensive | 11 |
| abstract_inverted_index.mortality | 19, 81, 322 |
| abstract_inverted_index.patients. | 150 |
| abstract_inverted_index.potential | 67, 379 |
| abstract_inverted_index.preceding | 158 |
| abstract_inverted_index.real-time | 147 |
| abstract_inverted_index.sometimes | 41 |
| abstract_inverted_index.therapies | 145 |
| abstract_inverted_index.typically | 79 |
| abstract_inverted_index.unstable, | 40 |
| abstract_inverted_index.validated | 231, 247 |
| abstract_inverted_index.(AI)-based | 64 |
| abstract_inverted_index.(MIMIC)-IV | 307 |
| abstract_inverted_index.(including | 161 |
| abstract_inverted_index.0.51-0.51, | 363 |
| abstract_inverted_index.0.65-0.92) | 346 |
| abstract_inverted_index.0.78-0.79) | 335 |
| abstract_inverted_index.2021-2023. | 276 |
| abstract_inverted_index.Artificial | 62 |
| abstract_inverted_index.Assessment | 319 |
| abstract_inverted_index.Prediction | 124 |
| abstract_inverted_index.Sequential | 316 |
| abstract_inverted_index.University | 286 |
| abstract_inverted_index.assessment | 166 |
| abstract_inverted_index.clinicians | 382 |
| abstract_inverted_index.conditions | 50 |
| abstract_inverted_index.electronic | 293 |
| abstract_inverted_index.externally | 232 |
| abstract_inverted_index.hospitals. | 243 |
| abstract_inverted_index.increasing | 210 |
| abstract_inverted_index.integrates | 153 |
| abstract_inverted_index.laboratory | 164 |
| abstract_inverted_index.predicting | 387 |
| abstract_inverted_index.prediction | 323, 353 |
| abstract_inverted_index.temporally | 248 |
| abstract_inverted_index.therapies. | 116 |
| abstract_inverted_index.transition | 107, 389 |
| abstract_inverted_index.unstable), | 105 |
| abstract_inverted_index.0.50-0.57). | 374 |
| abstract_inverted_index.Information | 302 |
| abstract_inverted_index.admissions) | 224, 240, 256, 271 |
| abstract_inverted_index.assessment, | 315 |
| abstract_inverted_index.conditions. | 45 |
| abstract_inverted_index.evaluation: | 284 |
| abstract_inverted_index.imputation, | 201 |
| abstract_inverted_index.instability | 352 |
| abstract_inverted_index.irregularly | 196 |
| abstract_inverted_index.outperforms | 311 |
| abstract_inverted_index.prospective | 337, 365 |
| abstract_inverted_index.space-based | 132, 190 |
| abstract_inverted_index.1M-parameter | 130 |
| abstract_inverted_index.Furthermore, | 90 |
| abstract_inverted_index.Intelligence | 63 |
| abstract_inverted_index.Unit-Mamba), | 128 |
| abstract_inverted_index.medications) | 169 |
| abstract_inverted_index.transitions, | 139 |
| abstract_inverted_index.Additionally, | 244 |
| abstract_inverted_index.Collaborative | 295 |
| abstract_inverted_index.deteriorating | 49 |
| abstract_inverted_index.interventions | 56, 385 |
| abstract_inverted_index.prospectively | 263 |
| abstract_inverted_index.significantly | 310 |
| abstract_inverted_index.comorbidities) | 177 |
| abstract_inverted_index.characteristics | 172 |
| abstract_inverted_index.decision-making | 394 |
| abstract_inverted_index.life-sustaining | 115, 144, 396 |
| abstract_inverted_index.life-threatening | 44 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5007040136 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 15 |
| corresponding_institution_ids | https://openalex.org/I33213144 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.79004672 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |