ICU Classification · Mbalizi Hospital

Level of Care Framework · Nates et al. (2016) Table 3 & Table 4

Guide to Resource Allocation of Intensive Monitoring and Care

Nates et al. (2016) Table 3 · Crit Care Med 44:1553–1602

Level	Type of Patient	Nurse : Patient	Key Interventions
ICU / Level 3	Critically ill, hourly or invasive monitoring, continuous arterial BP via cannula	1:1 – 1:2	Invasive mechanical ventilation, vasopressors, ECMO, IABP, LVAD, CRRT, CSF drainage, extracorporeal support
IMU / Level 2	Unstable, needs nursing interventions and monitoring every 2–4 hours	≤ 1:3	Non-invasive ventilation, IV infusions, titration of vasodilators or antiarrhythmics
Telemetry / Level 1	Stable, needs close ECG monitoring for non-malignant arrhythmias or labs every 2–4 hours	≤ 1:4	IV infusions, titration of vasodilators or antiarrhythmics
Ward / Level 0	Stable, needs testing and monitoring no more frequently than every 4 hours	≤ 1:5	IV antibiotics, IV chemotherapy, laboratory and radiographic work

ICU Admission Prioritization Framework

Nates et al. (2016) Table 4 · Updated from Egol (1999)

The Nates et al. (2016) ADT Task Force updated the original Egol (1999) Priority 1–4 model by adding Priority 5 (Palliative Care) and splitting Priority 3 to also cover the Intermediate Medical Unit (IMU), providing clearer differentiation between ICU-level and step-down care. Scoring systems alone should not determine level of care (Grade 2C recommendation).

Priority 1 — ICU · Critical ICU

Critically ill, requires life support for organ failure, intensive monitoring, and ICU-only therapies. Includes invasive ventilation, CRRT, hemodynamic monitoring, ECMO, IABP, severe hypoxemia, or shock. No therapeutic limits.

Priority 2 — ICU · Guarded ICU

As Priority 1, but with significantly lower probability of recovery. Would accept intensive care therapies but not CPR. Example: metastatic cancer with respiratory failure or septic shock requiring vasopressors.

Priority 3 — IMU · Monitoring IMU/HDU

Organ dysfunction requiring intensive monitoring and/or NIV, or postoperative patients needing close monitoring. May need ICU if early management fails. Examples: intermittent NIV, postoperative high-risk monitoring.

Priority 4 — IMU · Lower Acuity IMU/Ward

As Priority 3, but with lower probability of recovery/survival. Patients with underlying metastatic disease who do not want intubation or resuscitation. May access ICU in special circumstances if no IMU available.

Priority 5 — Palliative Care NOT ICU (New — Nates et al. 2016)

Terminal or moribund patients with no possibility of recovery. Generally not appropriate for ICU admission unless potential organ donors. When individuals have unequivocally declined intensive care or have irreversible processes (metastatic cancer, no further treatment options), palliative care should be initially offered. This priority level was added in the 2016 update to the original 1999 Egol guidelines.

⚕ Key 2016 SCCM Evidence-Based Recommendations (Nates et al.)

Overtriage preferred over undertriage — Some overtriage is more acceptable and preferable to undertriage (Grade 2D). An ideal triage model identifies all patients needing ICU care with an acceptable overtriage level.

ED-to-ICU transfer time — Minimize transfer time from the emergency department to ICU (<6 hours in non-trauma patients). A delay of ≥6 hours was associated with significantly higher ICU and hospital mortality in cross-sectional studies (Grade 2D).

Elderly patients (>80 years) — Base ICU admission on comorbidities, severity of illness, prehospital functional status, and patient preferences — not on chronological age alone (Grade 2C, 100% Eldicus consensus).

Mechanically ventilated / septic patients — Should be treated in an ICU. Patients should not be weaned from mechanical ventilation on the general ward unless it is a high-dependency/intermediate unit (Grade 2C).

Scoring systems alone — Do not use scoring systems alone to determine level of care or removal from higher levels; these are not accurate in predicting individual mortality (Grade 2C).

Triage bias — Ethnic origin, race, sex, social status, sexual preference, or financial status should never be considered in triage decisions (Ungraded — both Egol 1999 and Nates 2016 are in full agreement).

GRADE Level	Certainty of Evidence	Strength	Meaning
1A	High	Strong	Benefits definitively outweigh costs; most patients should receive the intervention
1B	Moderate	Strong	Benefits worth costs; most patients should receive the intervention
2C	Low	Weak	Uncertain balance; help patients make informed decisions
2D	Very Low	Weak	Costs and burdens might outweigh benefits; debatable value
Ungraded	—	Best practice	No evidence available; alternative statement does not make sense

Clinical Evidence Base & References

Reference 1 · Updated SCCM ADT Guidelines (2016)

ICU Admission, Discharge, and Triage Guidelines: A Framework to Enhance Clinical Operations, Development of Institutional Policies, and Further Research

Nates JL, Nunnally M, Kleinpell R, Blosser S, Goldner J, Birriel B, Fowler CS, Byrum D, Miles WS, Bailey H, Sprung CL.
Critical Care Medicine. 2016;44(8):1553–1602. doi:10.1097/CCM.0000000000001856

The ACCM ADT Task Force updated the original 1999 Egol guidelines using the GRADE evidence-based methodology, reviewing 2,404 articles published 1998–2013. Key updates include an expanded Priority 1–5 model (adding Priority 5 for Palliative Care), a Level 0–3 care framework with nursing ratios (Table 3), GRADE-rated recommendations, and specific guidance on overtriage, elderly patients, and ED transfer times. The guidelines explicitly state that "ethnic origin, race, sex, social status, sexual preference, or financial status should never be considered in triage decisions." The best predictors from multivariate analysis for ICU admission and adverse outcome were: heart rate >111 bpm, SpO₂ <89%, GCS <8. Severe metabolic acidemia (pH <7.20) was associated with >57% mortality.

Select Evidence-Based Recommendations with GRADE Ratings

High-intensity ICU staffing model (intensivist as primary physician): Grade 1B
Do not recommend 24-hr/7-day intensivist model when high-intensity coverage already in place: Grade 1A
Mechanically ventilated / sepsis → ICU (not general ward): Grade 2C
Expeditious transfer of critically ill from ED or ward → ICU: Grade 2D
Overtriage preferred over undertriage: Grade 2D
Minimize ED→ICU transfer time (<6 hr non-trauma): Grade 2D
Elderly (>80 yr): base on severity + functional status, not age alone: Grade 2C
Do not use scoring systems alone for level-of-care decisions: Grade 2C
Avoid after-hours (>7 PM) ICU discharge: Grade 2C
Discharge high-risk patients to step-down unit, not general ward: Grade 2C

Reference 2 · Original ACCM/SCCM Objective Parameter Model (1999)

Guidelines for Intensive Care Unit Admission, Discharge, and Triage

Egol A, Fromm R, Guntupalli KK, Fitzpatrick M, Kaufman D, Nasraway S, Ryon D, Zimmerman J. Task Force of the ACCM/SCCM.
Critical Care Medicine. 1999;27(3):633–638.

Foundational ACCM/SCCM guidelines establishing the Prioritization Model (Priority 1–4), Diagnosis Model, and Objective Parameter Model. The objective thresholds below are directly incorporated into this tool's clinical flagging system.

Pulse

<40 or >150 bpm

Systolic BP

<80 mmHg

MAP

<60 mmHg

Diastolic BP

>120 mmHg

Resp Rate

>35 /min

Serum Sodium

<110 or >170 mEq/L

Serum Potassium

<2.0 or >7.0 mEq/L

PaO₂

<50 torr

Arterial pH

<7.1 or >7.7

Serum Glucose

>800 mg/dL

Serum Calcium

>15 mg/dL

GCS / Coma

Coma = ICU indicator

Reference 3 · MDMT Clinical Profile Scoring & Social Bias Evidence

The Role of Clinical and Social Criteria in ICU Admission Decisions: Evidence from a Medical Decision-Making Tool

Madeira F, Ferreira JM, Correia D, Gaibino N, Reis R, Pereira CR.
Journal of Clinical and Translational Research. 2025;11(6):39–49. doi:10.36922/JCTR025280040

MDMT scoring (6 attributes, scale 6–12). Profile acceptance rates — Score 7: 13.5%; Score 8: 37.5%; Score 9: 52.5%; Score 10: 67.4%. Gender bias (b=−0.51, p<0.001) and racial overcorrection (b=−0.52, p<0.001) identified. Clinical criteria (age, comorbidities, RR, O₂ sat, organ failure, prognosis) were primary drivers.

⚠ Social Bias Findings — Clinician Awareness

Male candidates admitted significantly more often than female (57% vs. 32%; b=−0.51, p<0.001). Black candidates admitted more than White (51% vs. 32%; b=−0.52, p<0.001), possibly reflecting compensatory overcorrection. Both Egol (1999) and Nates (2016) explicitly state race, sex, social status must never be considered in triage decisions. This tool excludes social characteristics from its scoring model.

Score 713.5%

Score 837.5%

Score 952.5%

Score 1067.4%

Reference 4 · Scoping Review of ICU Admission Criteria

Intensive Care Unit Admission Criteria: A Scoping Review

Soares J, Leung C, Campbell V, Van Der Vegt A, Malycha J, Andersen C.
Journal of the Intensive Care Society. 2024;25(3):296–307. doi:10.1177/17511437241246901

Reviewed 68 publications across 5 domains. The six most consistently proposed objective major admission criteria: GCS, hypertension, hypotension, arterial O₂ saturation, PaO₂:FiO₂, and tachypnoea — all captured in this tool. Variability in thresholds suggests heterogeneity in critical deterioration; no single consensus-driven set.

Full Reference List — APA 7th Edition

Patient Classification Tool · Mbalizi Hospital · Tanzania · Tool Version 3.0 · 49 References · March 2026

Primary Evidence Base — Directly Incorporated into the Classification Tool

Egol, A., Fromm, R., Guntupalli, K. K., Fitzpatrick, M., Kaufman, D., Nasraway, S., Ryon, D., & Zimmerman, J. (1999). Guidelines for intensive care unit admission, discharge, and triage. Task Force of the American College of Critical Care Medicine, Society of Critical Care Medicine. Critical Care Medicine, 27(3), 633–638. https://doi.org/10.1097/00003246-199903000-00048

Madeira, F., Ferreira, J. M., Correia, D., Gaibino, N., Reis, R., & Pereira, C. R. (2025). The role of clinical and social criteria in intensive care unit admission decisions: Evidence from a medical decision-making tool. Journal of Clinical and Translational Research, 11(6), 39–49. https://doi.org/10.36922/JCTR025280040

Nates, J. L., Nunnally, M., Kleinpell, R., Blosser, S., Goldner, J., Birriel, B., Fowler, C. S., Byrum, D., Miles, W. S., Bailey, H., & Sprung, C. L. (2016). ICU admission, discharge, and triage guidelines: A framework to enhance clinical operations, development of institutional policies, and further research. Critical Care Medicine, 44(8), 1553–1602. https://doi.org/10.1097/CCM.0000000000001856

Soares, J., Leung, C., Campbell, V., Van Der Vegt, A., Malycha, J., & Andersen, C. (2024). Intensive care unit admission criteria: A scoping review. Journal of the Intensive Care Society, 25(3), 296–307. https://doi.org/10.1177/17511437241246901

SCCM/ACCM Guidelines & Ethics Committee Statements

American Thoracic Society. (1997). Fair allocation of intensive care unit resources. American Journal of Respiratory and Critical Care Medicine, 156(4), 1282–1301. https://doi.org/10.1164/ajrccm.156.4.ats7-97

Council on Ethical and Judicial Affairs, American Medical Association. (1995). Ethical considerations in the allocation of organs and other scarce medical resources among patients. Archives of Internal Medicine, 155(1), 29–40. https://doi.org/10.1001/archinte.1995.00430010033003

Society of Critical Care Medicine Ethics Committee. (1994). Consensus statement on the triage of critically ill patients. JAMA, 271(15), 1200–1203. https://doi.org/10.1001/jama.1994.03510390076038

Society of Critical Care Medicine Ethics Committee. (1997). Consensus statement of the Society of Critical Care Medicine's Ethics Committee regarding futile and other possibly inadvisable treatments. Critical Care Medicine, 25(5), 887–891. https://doi.org/10.1097/00003246-199705000-00028

Sprung, C. L., Danis, M., Iapichino, G., Artigas, A., Kesecioglu, J., Moreno, R., Lippert, A., Hargreaves, C., Pezzi, A., Pirracchio, R., Edbrooke, D., Cohen, S., Damas, P., Gjedsted, J., Ranieri, M., Rogante, S., Baras, M., & Bjorn Weiss, Y. (2013). Triage of intensive care patients: Identifying agreement and controversy. Intensive Care Medicine, 39(11), 1916–1924. https://doi.org/10.1007/s00134-013-3033-6

Social Bias in Clinical Decision-Making

Burgess, D. J., van Ryn, M., Dovidio, J., & Saha, S. (2007). Reducing racial bias among health care providers: Lessons from social-cognitive psychology. Journal of General Internal Medicine, 22(6), 882–887. https://doi.org/10.1007/s11606-007-0160-1

Chapman, E. N., Kaatz, A., & Carnes, M. (2013). Physicians and implicit bias: How doctors may unwittingly perpetuate health care disparities. Journal of General Internal Medicine, 28(11), 1504–1510. https://doi.org/10.1007/s11606-013-2441-1

Dovidio, J. F., & Gaertner, S. L. (2004). Aversive racism. Advances in Experimental Social Psychology, 36, 1–52. https://doi.org/10.1016/S0065-2601(04)36001-6

Green, A. R., Carney, D. R., Pallin, D. J., Ngo, L. H., Raymond, K. L., Iezzoni, L. I., & Banaji, M. R. (2007). Implicit bias among physicians and its prediction of thrombolysis decisions for Black and White patients. Journal of General Internal Medicine, 22(9), 1231–1238. https://doi.org/10.1007/s11606-007-0258-5

Krosch, A. R., Tyler, T. R., & Amodio, D. M. (2017). Race and recession: Effects of economic scarcity on racial discrimination. Journal of Personality and Social Psychology, 113(6), 892–909. https://doi.org/10.1037/pspi0000103

Madeira, F., Do Bu, E. A., Freitas, G., & Pereira, C. R. (2023). Distributive justice criteria and social categorization processes predict healthcare allocation bias. British Journal of Health Psychology, 28(2), 552–566. https://doi.org/10.1111/bjhp.12640

Mohammed, S., Matos, J., Doutreligne, M., Celi, L. A., & Struja, T. (2023). Racial disparities in invasive ICU treatments among septic patients: High-resolution electronic health records analysis from MIMIC-IV. Yale Journal of Biology and Medicine, 96(3), 293–312. https://doi.org/10.59249/WDJI8829

Schulman, K. A., Berlin, J. A., Harless, W., Kerner, J. F., Sistrunk, S., Gersh, B. J., Dube, R., Taleghani, C. K., Burke, J. E., Williams, S., Eisenberg, J. M., & Escarce, J. J. (1999). The effect of race and sex on physicians' recommendations for cardiac catheterization. New England Journal of Medicine, 340(8), 618–626. https://doi.org/10.1056/NEJM199902253400806

ICU Triage, Outcomes, and Resource Allocation

Azoulay, E., Pochard, F., Chevret, S., Vinsonneau, C., Garrouste-Orgeas, M., Cohen, Y., Thuong, M., Paugam, C., Dominguez-Berjon, F., & Schlemmer, B. (2001). Compliance with triage to intensive care recommendations. Critical Care Medicine, 29(11), 2132–2136. https://doi.org/10.1097/00003246-200111000-00011

Chalfin, D. B., Trzeciak, S., Likourezos, A., Baumann, B. M., & Dellinger, R. P. (2007). Impact of delayed transfer of critically ill patients from the emergency department to the intensive care unit. Critical Care Medicine, 35(6), 1477–1483. https://doi.org/10.1097/01.CCM.0000266585.74905.5A

Edbrooke, D. L., Minelli, C., Mills, G. H., Azzopardi, D., Campion, J., Cohen, S. L., Grounds, R. M., Hermon, M., Luchetti, M., Maia, P., Meredith, M. J., Olufolabi, A., Withy, G., & the Eldicus Study Group. (2011). Implications of ICU triage decisions on patient mortality: A cost-effectiveness analysis. Critical Care, 15(1), R56. https://doi.org/10.1186/cc10029

Gabler, N. B., Ratcliffe, S. J., Wagner, J., Asch, D. A., Rubenfeld, G. D., Angus, D. C., & Halpern, S. D. (2013). Mortality among patients admitted to strained intensive care units. American Journal of Respiratory and Critical Care Medicine, 188(7), 800–806. https://doi.org/10.1164/rccm.201304-0622OC

Gopalan, P. D., & Pershad, S. (2019). Decision-making in ICU: A systematic review of factors considered important by ICU clinician decision makers with regard to ICU triage decisions. Journal of Critical Care, 50, 99–110. https://doi.org/10.1016/j.jcrc.2018.11.027

Iapichino, G., Corbella, D., Minelli, C., Mills, G. H., Bellomo, R., Bertolini, G., Ferrer, M., Guerin, C., Raimondi, F., Rowan, K., & Sprung, C. L. (2010). Reasons for refusal of admission to intensive care and impact on mortality. Intensive Care Medicine, 36(10), 1772–1779. https://doi.org/10.1007/s00134-010-2020-x

Joynt, G. M., Gomersall, C. D., Tan, P., Lee, A., Cheng, C. A., & Wong, E. L. (2001). Prospective evaluation of patients refused admission to an intensive care unit: Triage, futility and outcome. Intensive Care Medicine, 27(9), 1459–1465. https://doi.org/10.1007/s001340101041

Mery, E., & Kahn, J. M. (2013). Does space make waste? The influence of ICU bed capacity on admission decisions. Critical Care, 17(5), 315. https://doi.org/10.1186/cc12714

Simchen, E., Sprung, C. L., Galai, N., Zitser-Gurevich, Y., Bar-Lavi, Y., Gurman, G., Klein, M., Lev, A., Levi, L., Zveibel, F., & Mandel, M. (2004). Survival of critically ill patients hospitalized in and out of intensive care units under paucity of intensive care unit beds. Critical Care Medicine, 32(8), 1654–1661. https://doi.org/10.1097/01.CCM.0000133021.22188.35

Sinuff, T., Kahnamoui, K., Cook, D. J., Luce, J. M., Levy, M. M., & Values Ethics and Rationing in Critical Care Task Force. (2004). Rationing critical care beds: A systematic review. Critical Care Medicine, 32(7), 1588–1597. https://doi.org/10.1097/01.CCM.0000130175.38521.9F

Sprung, C. L., Geber, D., Eidelman, L. A., Baras, M., Pizov, R., Nimrod, A., Oppenheim, A., Epstein, L., & Cotev, S. (1999). Evaluation of triage decisions for intensive care admission. Critical Care Medicine, 27(6), 1073–1079. https://doi.org/10.1097/00003246-199906000-00011

Vanhecke, T. E., Gandhi, M., McCullough, P. A., Kim, S., & Rajagopalan, S. (2008). Outcomes of patients considered for, but not admitted to, the intensive care unit. Critical Care Medicine, 36(3), 812–817. https://doi.org/10.1097/CCM.0B013E3181651FF9

Young, M. P., Gooder, V. J., McBride, K., James, B., & Fisher, E. S. (2003). Inpatient transfers to the intensive care unit: Delays are associated with increased mortality and morbidity. Journal of General Internal Medicine, 18(2), 77–83. https://doi.org/10.1046/j.1525-1497.2003.20441.x

Severity Scoring Systems

Jones, A. E., Trzeciak, S., & Kline, J. A. (2009). The Sequential Organ Failure Assessment score for predicting outcome in patients with severe sepsis and evidence of hypoperfusion at the time of emergency department presentation. Critical Care Medicine, 37(5), 1649–1654. https://doi.org/10.1097/CCM.0b013e31819def97

Jung, B., Rimmele, T., Le Goff, C., Chanques, G., Corne, P., Jonquet, O., Muller, L., Lefrant, J. Y., Guervilly, C., Papazian, L., Allaouchiche, B., Jaber, S., & AzuRea Group. (2011). Severe metabolic or mixed acidemia on intensive care unit admission: Incidence, prognosis and administration of buffer therapy. A prospective, multiple-center study. Critical Care, 15(5), R238. https://doi.org/10.1186/cc10487

Sinuff, T., Adhikari, N. K., Cook, D. J., Schunemann, H. J., Griffith, L. E., Rocker, G., & Walter, S. D. (2006). Mortality predictions in the intensive care unit: Comparing physicians with scoring systems. Critical Care Medicine, 34(3), 878–885. https://doi.org/10.1097/01.CCM.0000201036.14738.A5

Sprung, C. L., Baras, M., Iapichino, G., Abizanda, R., Lev, A., Lippert, A., Reiter, A., Petrini, F., Danis, M., Kesecioglu, J., Pezzi, A., Moreno, R., Edbrooke, D., Cohen, S. L., & Sjokvist, P. (2012). The Eldicus prospective, observational study of triage decision making in European intensive care units: Part I — European Intensive Care Admission Triage Scores. Critical Care Medicine, 40(1), 125–131. https://doi.org/10.1097/CCM.0b013e3182269797

Talmor, D., Jones, A. E., Rubinson, L., Howell, M. D., & Shapiro, N. I. (2007). Simple triage scoring system predicting death and the need for critical care resources for use during epidemics. Critical Care Medicine, 35(5), 1251–1256. https://doi.org/10.1097/01.CCM.0000262385.95721.CC

Staffing, Outreach, and Discharge

Checkley, W., Martin, G. S., Brown, S. M., Chang, S. Y., Dabbagh, O., Fremont, R. D., Girard, T. D., Rice, T. W., Howell, M. D., Johnson, A. P., Kutcher, M. E., Levy, M. M., Lodato, R. F., Matthay, M. A., Peterson, M. W., Steingrub, J. S., Woodruff, P. G., & Rubenfeld, G. D. (2014). Structure, process, and annual ICU mortality across 69 centers: United States Critical Illness and Injury Trials Group Critical Illness Outcomes Study. Critical Care Medicine, 42(2), 344–356. https://doi.org/10.1097/CCM.0000000000000008

Kerlin, M. P., Small, D. S., Cooney, E., Fuchs, B. D., Bellini, L. M., Mikkelsen, M. E., Harhay, M. O., Hansen-Flaschen, J., Asch, D. A., Halpern, S. D., & Lanken, P. N. (2013). A randomized trial of nighttime physician staffing in an intensive care unit. New England Journal of Medicine, 368(23), 2201–2209. https://doi.org/10.1056/NEJMoa1302854

Niven, D. J., Bastos, J. F., & Stelfox, H. T. (2014). Critical care transition programs and the risk of readmission or death after discharge from an ICU: A systematic review and meta-analysis. Critical Care Medicine, 42(1), 179–187. https://doi.org/10.1097/CCM.0b013e3182a272c0

Wilcox, M. E., Chong, C. A., Niven, D. J., Rubenfeld, G. D., Rowan, K. M., Wunsch, H., & Fan, E. (2013). Do intensivist staffing patterns influence hospital mortality following ICU admission? A systematic review and meta-analyses. Critical Care Medicine, 41(10), 2253–2274. https://doi.org/10.1097/CCM.0b013e318292313f

Nonbeneficial Treatment and Rationing

Huynh, T. N., Kleerup, E. C., Wiley, J. F., Savitsky, T. D., Guse, D., Garber, B. J., & Wenger, N. S. (2013). The frequency and cost of treatment perceived to be futile in critical care. JAMA Internal Medicine, 173(20), 1887–1894. https://doi.org/10.1001/jamainternmed.2013.10261

Schneiderman, L. J., Jecker, N. S., & Jonsen, A. R. (1990). Medical futility: Its meaning and ethical implications. Annals of Internal Medicine, 112(12), 949–954. https://doi.org/10.7326/0003-4819-112-12-949

Schneiderman, L. J., Gilmer, T., Teetzel, H. D., Dugan, D. O., Blustein, J., Cranford, R., Briggs, K. B., Komatsu, G. I., Goodman-Crews, P., Cohn, F., & Young, E. W. (2003). Effect of ethics consultations on nonbeneficial life-sustaining treatments in the intensive care setting: A randomized controlled trial. JAMA, 290(9), 1166–1172. https://doi.org/10.1001/jama.290.9.1166

Temel, J. S., Greer, J. A., Muzikansky, A., Gallagher, E. R., Admane, S., Jackson, V. A., Dahlin, C. M., Blinderman, C. D., Jacobsen, J., Pirl, W. F., Billings, J. A., & Lynch, T. J. (2010). Early palliative care for patients with metastatic non-small-cell lung cancer. New England Journal of Medicine, 363(8), 733–742. https://doi.org/10.1056/NEJMoa1000678

Truog, R. D., Brock, D. W., Cook, D. J., Danis, M., Luce, J. M., Rubenfeld, G. D., & Levy, M. M. (2006). Rationing in the intensive care unit. Critical Care Medicine, 34(4), 958–963. https://doi.org/10.1097/01.CCM.0000206116.10417.B9

Machine Learning Pipeline & Dataset

Barfod, C., Lauritzen, M. M., Danker, J. K., Soletormos, G., Forberg, J. L., Berlac, P. A., Lippert, F., Lundstrom, L. H., Antonsen, K., & Lange, K. H. (2012). Abnormal vital signs are strong predictors for intensive care unit admission and in-hospital mortality in adults triaged in the emergency department: A prospective cohort study. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 20(1), 28. https://doi.org/10.1186/1757-7241-20-28

Cohen, R. I., Eichorn, A., & Silver, A. (2012). Admission decisions to a medical intensive care unit are based on functional status rather than severity of illness: A single center experience. Minerva Anestesiologica, 78(11), 1226–1233.

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.

Schein, R. M., Hazday, N., Pena, M., Ruben, B. H., & Sprung, C. L. (1990). Clinical antecedents to in-hospital cardiopulmonary arrest. Chest, 98(6), 1388–1392. https://doi.org/10.1378/chest.98.6.1388

GRADE Evidence Methodology

Andrews, J., Guyatt, G., Oxman, A. D., Alderson, P., Dahm, P., Falck-Ytter, Y., Nasser, M., Meerpohl, J., Post, P. N., Kunz, R., Brozek, J., Vist, G., Rind, D., Akl, E. A., & Schunemann, H. J. (2013). GRADE guidelines: 15. Going from evidence to recommendations: The significance and presentation of recommendations. Journal of Clinical Epidemiology, 66(7), 726–735. https://doi.org/10.1016/j.jclinepi.2012.03.013

Guyatt, G. H., Oxman, A. D., Kunz, R., Falck-Ytter, Y., Vist, G. E., Liberati, A., Schunemann, H. J., & GRADE Working Group. (2008). Going from evidence to recommendations. BMJ, 336(7652), 1049–1051. https://doi.org/10.1136/bmj.39493.646875.AE

Triage decisions must never consider ethnic origin, race, sex, social status, sexual preference, or financial status.
— Egol et al. (1999); Nates et al. (2016); Madeira et al. (2025)