Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients*
Abstract
Objective To improve the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) method for predicting hospital mortality among critically ill adults and to evaluate changes in the accuracy of earlier APACHE models. Design : Observational cohort study. Setting A total of 104 intensive care units (ICUs) in 45 U.S. hospitals. Patients A total of 131,618 consecutive ICU admissions during 2002 and 2003, of which 110,558 met inclusion criteria and had complete data. Interventions None. Measurements and main results We developed APACHE IV using ICU day 1 information and a multivariate logistic regression procedure to estimate the probability of hospital death for randomly selected patients who comprised 60% of the database. Predictor variables were similar to those in APACHE III, but new variables were added and different statistical modeling used. We assessed the accuracy of APACHE IV predictions by comparing observed and predicted hospital mortality for the excluded patients (validation set). We tested discrimination and used multiple tests of calibration in aggregate and for patient subgroups. APACHE IV had good discrimination (area under the receiver operating characteristic curve = 0.88) and calibration (Hosmer-Lemeshow C statistic = 16.9, p = .08). For 90% of 116 ICU admission diagnoses, the ratio of observed to predicted mortality was not significantly different from 1.0. We also used the validation data set to compare the accuracy of APACHE IV predictions to…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Traditional severity scoring systems typically focus on mortality prediction rather than dynamic changes in patient recovery trajectories.
“Furthermore, these scoring systems typically focus on mortality prediction rather than the dynamic changes in patient recovery trajectories7,8.”
From Machine learning predicts sepsis deterioration trajectories. (2026) · cites this paper as [8]
Cites
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Pollard, T. J., Johnson, A. E. W., Raffa, J. D., Celi, L. A., Mark, R. G., & Badawi, O. (2018) · Scientific Data · doi:10.1038/sdata.2018.178 · Cited by 1,993 · see citing works
Hydrocortisone, Vitamin C, and Thiamine for the Treatment of Severe Sepsis and Septic Shock
Marik, P. E., Khangoora, V., Rivera, R., Hooper, M. H., & Catravas, J. (2017) · Chest · doi:10.1016/j.chest.2016.11.036 · Cited by 887 · see citing works
Changes in hospital mortality for United States intensive care unit admissions from 1988 to 2012
Zimmerman, J. E., Kramer, A. A., & Knaus, W. A. (2013) · Critical Care · doi:10.1186/cc12695 · Cited by 533 · see citing works
Self-citationGaies, M. G., Jeffries, H. E., Niebler, R. A., Pasquali, S. K., Donohue, J. E., Yu, S., Gall, C., Rice, T. B., & Thiagarajan, R. R. (2014) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0000000000000153 · Cited by 427 · see citing works
Combining structured and unstructured data for predictive models: a deep learning approach
Zhang, D., Yin, C., Zeng, J., Yuan, X., & Zhang, P. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01297-6 · Cited by 269 · see citing works
Methods of Blood Pressure Measurement in the ICU*
Lehman, L.-W. H., Saeed, M., Talmor, D., Mark, R., & Malhotra, A. (2013) · Critical Care Medicine · doi:10.1097/ccm.0b013e318265ea46 · Cited by 224 · see citing works
Awad, A., Bader-El-Den, M., McNicholas, J., & Briggs, J. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.10.002 · Cited by 208 · see citing works
Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU
Kong, G., Lin, K., & Hu, Y. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01271-2 · Cited by 180 · see citing works
Krinsley, J. S., & Preiser, J.-C. (2015) · Critical Care · doi:10.1186/s13054-015-0908-7 · Cited by 174 · see citing works
Shickel, B., Loftus, T. J., Adhikari, L., Ozrazgat-Baslanti, T., Bihorac, A., & Rashidi, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-38491-0 · Cited by 165 · see citing works
Churpek, M. M., Wendlandt, B., Zadravecz, F. J., Adhikari, R., Winslow, C., & Edelson, D. P. (2016) · Journal of Hospital Medicine · doi:10.1002/jhm.2630 · Cited by 143 · see citing works
Fleiss, N., Coggins, S. A., Lewis, A. N., Zeigler, A., Cooksey, K. E., Walker, L. A., Husain, A. N., de Jong, B. S., Wallman-Stokes, A., Alrifai, M. W., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.36518 · Cited by 121 · see citing works
Tabak, Y. P., Sun, X., Nunez, C. M., & Johannes, R. S. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001790 · Cited by 107 · see citing works
Groenland, C. N. L., Termorshuizen, F., Rietdijk, W. J. R., van den Brule, J., Dongelmans, D. A., de Jonge, E., de Lange, D. W., de Smet, A. M. G. A., de Keizer, N. F., Weigel, J. D., et al. (2019) · Critical Care Medicine · doi:10.1097/ccm.0000000000003957 · Cited by 104 · see citing works
Mortality in Relation to Frailty in Patients Admitted to a Specialized Geriatric Intensive Care Unit
Zeng, A., Song, X., Dong, J., Mitnitski, A., Liu, J., Guo, Z., & Rockwood, K. (2015) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/glv084 · Cited by 96 · see citing works
Sarkar, R., Martin, C., Mattie, H., Gichoya, J. W., Stone, D. J., & Celi, L. A. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00022-4 · Cited by 85 · see citing works
Rapid Detection of Methicillin-Resistant Staphylococcus aureus in BAL
Paonessa, J. R., Shah, R. D., Pickens, C. I., Lizza, B. D., Donnelly, H. K., Malczynski, M., Qi, C., & Wunderink, R. G. (2019) · Chest · doi:10.1016/j.chest.2019.02.007 · Cited by 66 · see citing works
AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview
Stylianides, C., Nicolaou, A., Sulaiman, W. A., Alexandropoulou, C.-A., Panagiotopoulos, I., Karathanasopoulou, K., Dimitrakopoulos, G., Kleanthous, S., Politi, E., Ntalaperas, D., et al. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7010006 · Cited by 28 · see citing works
Li, S., Dou, R., Song, X., Lui, K., Xu, J., Guo, Z., Hu, X., Guan, X., & Cai, C. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12030915 · Cited by 25 · see citing works
Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU
Shickel, B., Davoudi, A., Ozrazgat-Baslanti, T., Ruppert, M., Bihorac, A., & Rashidi, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.640685 · Cited by 21 · see citing works
Machine learning predicts sepsis deterioration trajectories.
Zhang, R., Long, F., Zhao, Z., Wu, J., Tan, R., Xu, W., Li, L., Long, Y., & Qu, H. (2026) · NPJ Digital Medicine · doi:10.1038/s41746-026-02565-x · Cited by 6 · see citing works
Validation of a Retrospective Computing Model for Mortality Risk in the Intensive Care Unit
Tan, E. M., Kashyap, R., Olson, I. C., & O’Horo, J. C. (2020) · Mayo Clinic Proceedings: Innovations, Quality & Outcomes · doi:10.1016/j.mayocpiqo.2020.09.001 · Cited by 5 · see citing works
Bohlen, L., Rosenberger, J., Hambauer, N., Zähringer, D., Franz, V., Zschech, P., & Kraus, M. (2026) · Health Care Management Science · doi:10.1007/s10729-026-09760-y
“This consistency aligns with established mortality scoring systems used in intensive care, such as APACHE-IV and SAPS III [ 50], and suggests that heart rate contributes positively to model generalizability across hospitals.”
- Authors
- Zimmerman, J. E., Kramer, A. A., McNair, D. S., & Malila, F. M.
- Affiliations
- George Washington University (US)
- Venue
- Critical Care Medicine
- Published
- 2006-05
- Volume
- 34
- Issue
- 5
- Pages
- 1297-1310
- ISSN
- 0090-3493 (ISSN-L)
- OpenAlex
- W2000566182
- Cited by
- 1,764
- References
- 1
- Citations by year
- 2026: 101 · 2025: 75 · 2024: 104 · 2023: 114 · 2022: 150 · 2021: 147 · 2020: 147 · 2019: 114 · 2018: 98 · 2017: 76 · 2016: 94 · 2015: 88 · 2014: 83 · 2013: 66 · 2012: 60
- FWCI
- 36.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Critical Care Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
- Keywords
- APACHE IV, APACHE III, in-hospital mortality prediction, critically ill adults, intensive care unit, prognostic model validation, discrimination and calibration, model updating
- MeSH terms
- Adult, Aged, Aged, 80 and over, Calibration, Female, Humans, Intensive Care Units, Male, Middle Aged, Predictive Value of Tests, ROC Curve, United States, Reproducibility of Results, Cohort Studies, Multivariate Analysis, Logistic Models, Critical Illness, Hospital Mortality, Outcome Assessment, Health Care, APACHE
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
