The APACHE III Prognostic System

Clinical Prediction Rules: Applications and Methodological Standards

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Medical Futility: Its Meaning and Ethical Implications

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Ventilation with Lower Tidal Volumes as Compared with Traditional Tidal Volumes for Acute Lung Injury and the Acute Respiratory Distress Syndrome

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Higher versus Lower Positive End-Expiratory Pressures in Patients with the Acute Respiratory Distress Syndrome

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Inadequate Antimicrobial Treatment of Infections

Kollef, M. H., Sherman, G., Ward, S., & Fraser, V. J. (1999) · Chest · doi:10.1378/chest.115.2.462 · Cited by 1,805

The influence of inadequate antimicrobial treatment of bloodstream infections on patient outcomes in the ICU setting

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Current Concepts in Validity and Reliability for Psychometric Instruments: Theory and Application

Cook, D. A., & Beckman, T. J. (2006) · The American Journal of Medicine · doi:10.1016/j.amjmed.2005.10.036 · Cited by 1,745

Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients*

Zimmerman, J. E., Kramer, A. A., McNair, D. S., & Malila, F. M. (2006) · Critical Care Medicine · doi:10.1097/01.ccm.0000215112.84523.f0 · Cited by 1,764

Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis

Seymour, C. W., Kennedy, J. N., Wang, S., Chang, C.-C. H., Elliott, C. F., Xu, Z., Berry, S., Clermont, G., Cooper, G., Gomez, H., et al. (2019) · JAMA · doi:10.1001/jama.2019.5791 · Cited by 1,567

Early Neuromuscular Blockade in the Acute Respiratory Distress Syndrome

The National Heart, L. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1901686 · Cited by 886

Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited*

Kramer, A. A., & Zimmerman, J. E. (2007) · Critical Care Medicine · doi:10.1097/01.ccm.0000275267.64078.b0 · Cited by 886

Measurements of Serum Free Cortisol in Critically Ill Patients

Hamrahian, A. H., Oseni, T. S., & Arafah, B. M. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa020266 · Cited by 726

Comprehensive mapping of immune perturbations associated with severe COVID-19

Kuri-Cervantes, L., Pampena, M. B., Meng, W., Rosenfeld, A. M., Ittner, C. A. G., Weisman, A. R., Agyekum, R. S., Mathew, D., Baxter, A. E., Vella, L. A., et al. (2020) · Science Immunology · doi:10.1126/sciimmunol.abd7114 · Cited by 723

Quick sepsis-related organ failure assessment, systemic inflammatory response syndrome, and early warning scores for detecting clinical deterioration in infected patients outside the intensive care unit

Churpek, M. M., Snyder, A., Han, X., Sokol, S., Pettit, N., Howell, M. D., & Edelson, D. P. (2017) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201604-0854oc · Cited by 689

Severe hypoglycemia in critically ill patients: risk factors and outcomes

Krinsley, J. S., & Grover, A. (2007) · Critical Care Medicine · doi:10.1097/01.ccm.0000282073.98414.4b · Cited by 671

A bedside scoring system (“Candida score”) for early antifungal treatment in nonneutropenic critically ill patients with Candida colonization*

León, C., Ruiz-Santana, S., Saavedra, P., Almirante, B., Nolla-Salas, J., Álvarez-Lerma, F., Garnacho-Montero, J., & León, M. Á. (2006) · Critical Care Medicine · doi:10.1097/01.ccm.0000202208.37364.7d · Cited by 659

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards

Churpek, M. M., Yuen, T. C., Winslow, C., Meltzer, D. O., Kattan, M. W., & Edelson, D. P. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001571 · Cited by 638

Prospective, randomized, controlled clinical trial comparing traditional versus reduced tidal volume ventilation in acute respiratory distress syndrome patients

Brower, R. G., Shanholtz, C. B., Fessler, H. E., Shade, D. M., White, P., Wiener, C. M., Teeter, J. G., Dodd-o, J. M., Almog, Y., & Piantadosi, S. (1999) · Critical Care Medicine · doi:10.1097/00003246-199908000-00015 · Cited by 575

Hyperglycemia-related mortality in critically ill patients varies with admission diagnosis

Falciglia, M., Freyberg, R. W., Almenoff, P. L., D’Alessio, D. A., & Render, M. L. (2009) · Critical Care Medicine · doi:10.1097/ccm.0b013e3181b083f7 · Cited by 576

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

Recovery after Acute Kidney Injury

Kellum, J. A., Sileanu, F. E., Bihorac, A., Hoste, E. A. J., & Chawla, L. S. (2017) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201604-0799oc · Cited by 520

Tidal Volume Reduction in Patients with Acute Lung Injury When Plateau Pressures Are Not High

Hager, D. N., Krishnan, J. A., Hayden, D. L., & Brower, R. G. (2005) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200501-048cp · Cited by 509

Postinjury Multiple Organ Failure: A Bimodal Phenomenon

Moore, F. A., Sauaia, A., Moore, E. E., Haenel, J. B., Burch, J. M., & Lezotte, D. C. (1996) · The Journal of Trauma: Injury, Infection, and Critical Care · doi:10.1097/00005373-199604000-00001 · Cited by 491

The MIMIC Code Repository: enabling reproducibility in critical care research

Johnson, A. E. W., Stone, D. J., Celi, L. A., & Pollard, T. J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx084 · Cited by 442

Inflammatory Markers at Hospital Discharge Predict Subsequent Mortality after Pneumonia and Sepsis

Yende, S., D'Angelo, G., Kellum, J. A., Weissfeld, L., Fine, J., Welch, R. D., Kong, L., Carter, M., & Angus, D. C. (2008) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200712-1777oc · Cited by 434

Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study.

Pirracchio, R., Petersen, M. L., Carone, M., Rigon, M. R., Chevret, S., & van der Laan, M. J. (2015) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(14)70239-5 · Cited by 374

Nurse working conditions and patient safety outcomes.

Stone, P. W., Mooney-Kane, C., Larson, E. L., Horan, T., Glance, L. G., Zwanziger, J., & Dick, A. W. (2007) · Medical Care · doi:10.1097/mlr.0b013e3180383667 · Cited by 360

Biofilm Production by Candida Species and Inadequate Antifungal Therapy as Predictors of Mortality for Patients with Candidemia

Tumbarello, M., Posteraro, B., Trecarichi, E. M., Fiori, B., Rossi, M., Porta, R., de Gaetano Donati, K., La Sorda, M., Spanu, T., Fadda, G., et al. (2007) · Journal of Clinical Microbiology · doi:10.1128/jcm.00131-07 · Cited by 343

Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records

Thorsen-Meyer, H.-C., Nielsen, A. B., Nielsen, A. P., Kaas-Hansen, B. S., Toft, P., Schierbeck, J., Strøm, T., Chmura, P. J., Heimann, M., Dybdahl, L., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30018-2 · Cited by 336

The role of infection and comorbidity: Factors that influence disparities in sepsis

Esper, A. M., Moss, M., Lewis, C. A., Nisbet, R., Mannino, D. M., & Martin, G. S. (2006) · Critical Care Medicine · doi:10.1097/01.ccm.0000239114.50519.0e · Cited by 336

Impact of Body Mass Index on Outcomes Following Critical Care

Tremblay, A., & Bandi, V. (2003) · Chest · doi:10.1378/chest.123.4.1202 · Cited by 318

Restricted versus continued standard caloric intake during the management of refeeding syndrome in critically ill adults: a randomised, parallel-group, multicentre, single-blind controlled trial

Doig, G. S., Simpson, F., Heighes, P. T., Bellomo, R., Chesher, D., Caterson, I. D., Reade, M. C., & Harrigan, P. W. J. (2015) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(15)00418-x · Cited by 308

Predictors of Adverse Outcome in Cancer Patients with Candidemia

Anaissie, E. J., Rex, J. H., Uzun, Ö., & Vartivarian, S. (1998) · The American Journal of Medicine · doi:10.1016/s0002-9343(98)00030-8 · Cited by 295

Recovery Rate and Prognosis in Older Persons Who Develop Acute Lung Injury and the Acute Respiratory Distress Syndrome

Ely, E. W., Wheeler, A. P., Thompson, B. T., Ancukiewicz, M., Steinberg, K. P., Bernard, G. R., & for the Acute Respiratory Distress Syndrome Network (2002) · Annals of Internal Medicine · doi:10.7326/0003-4819-136-1-200201010-00007 · Cited by 268

Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm.

Dybowski, R., Gant, V, Weller, P., & Chang, R. (1996) · The Lancet · doi:10.1016/s0140-6736(96)90609-1 · Cited by 240

A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome

Ambler, G., Omar, R. Z., & Royston, P. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074466 · Cited by 233

Outcomes of Percutaneous Endoscopic Gastrostomy Among Older Adults in a Community Setting

Callahan, C. M., Haag, K. M., Weinberger, M., Tierney, W. M., Buchanan, N. N., Stump, T. E., & Nisi, R. (2000) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2000.tb04779.x · Cited by 225

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

Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach

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

Decision‐Making and Outcomes of Prolonged ICU Stays in Seriously Ill Patients

Teno, J. M., Fisher, E., Hamel, M. B., Wu, A. W., Murphy, D. J., Wenger, N. S., Lynn, J., & Harrell, F. E. (2000) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2000.tb03144.x · Cited by 182

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

Should Vascular Catheters Be Removed from All Patients with Candidemia? An Evidence-Based Review

Nucci, M., & Anaissie, E. (2002) · Clinical Infectious Diseases · doi:10.1086/338714 · Cited by 175

Untitled work

(2015) · International Conference on Artificial Intelligence and Statistics · Cited by 162

Anxiety and agitation in mechanically ventilated patients

Tate, J. A., Devito Dabbs, A., Hoffman, L. A., Milbrandt, E., & Happ, M. B. (2012) · Qualitative Health Research · doi:10.1177/1049732311421616 · Cited by 136

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

Dhiman, P., Ma, J., Andaur Navarro, C. L., Speich, B., Bullock, G., Damen, J. A. A., Hooft, L., Kirtley, S., Riley, R. D., Van Calster, B., et al. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01577-x · Cited by 140

Risk Factors and Predictors of Outcome in Patients with Cancer and Breakthrough Candidemia

Uzun, O., Ascioglu, S., Anaissie, E. J., & Rex, J. H. (2001) · Clinical Infectious Diseases · doi:10.1086/320757 · Cited by 138

A comparison of intensive care unit mortality prediction models through the use of data mining techniques.

Kim, S., Kim, W., & Park, R. W. (2011) · Healthcare Informatics Research · doi:10.4258/hir.2011.17.4.232 · Cited by 132

Disparities in Hypoxemia Detection by Pulse Oximetry Across Self-Identified Racial Groups and Associations with Clinical Outcomes

Henry, N. R., Hanson, A. C., Schulte, P. J., Warner, N. S., Manento, M. N., Weister, T. J., & Warner, M. A. (2022) · Critical Care Medicine · doi:10.1097/ccm.0000000000005394 · Cited by 133

Patient length of stay and mortality prediction: A survey

Awad, A., Bader–El–Den, M., & McNicholas, J. (2017) · Health Services Management Research · doi:10.1177/0951484817696212 · Cited by 128

Effect of antifungal therapy timing on mortality in cancer patients with candidemia

Taur, Y., Cohen, N., Dubnow, S., Paskovaty, A., & Seo, S. K. (2010) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00945-09 · Cited by 127

Hepatic metabolic response to injury and sepsis

Dahn, M. S., Mitchell, R. A., Lange, M. P., Smith, S., & Jacobs, L. A. (1995) · Surgery · doi:10.1016/s0039-6060(05)80251-x · Cited by 119

Ventilator-derived carbon dioxide production to assess energy expenditure in critically ill patients: proof of concept

Stapel, S. N., de Grooth, H.-J. S., Alimohamad, H., Elbers, P. W. G., Girbes, A. R. J., Weijs, P. J. M., & Oudemans-van Straaten, H. M. (2015) · Critical Care · doi:10.1186/s13054-015-1087-2 · Cited by 118

Bias arising from missing data in predictive models.

Gorelick, M. H. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.11.029 · Cited by 107

Rapid identification of slow healing wounds

Jung, K., Covington, S., Sen, C. K., Januszyk, M., Kirsner, R. S., Gurtner, G. C., & Shah, N. H. (2016) · Wound Repair and Regeneration · doi:10.1111/wrr.12384 · Cited by 100

Benchmarking machine learning models on multi-centre eICU critical care dataset

Sheikhalishahi, S., Balaraman, V., & Osmani, V. (2020) · PLOS ONE · doi:10.1371/journal.pone.0235424 · Cited by 89

Length of Stay Predictions: Improvements Through the Use of Automated Laboratory and Comorbidity Variables

Liu, V., Kipnis, P., Gould, M. K., & Escobar, G. J. (2010) · Medical Care · doi:10.1097/mlr.0b013e3181e359f3 · Cited by 87

Chronically critically ill patients: Health-related quality of life and resource use after a disease management intervention.

Douglas, S. L., Daly, B. J., Kelley, C. G., O’Toole, E., & Montenegro, H. (2007) · American Journal of Critical Care · doi:10.4037/ajcc2007.16.5.447 · Cited by 82

Development and Validation of a Mortality Prediction Model for Patients Receiving 14 Days of Mechanical Ventilation

Hough, C. L., Caldwell, E. S., Cox, C. E., Douglas, I. S., Kahn, J. M., White, D. B., Seeley, E. J., Bangdiwala, S. I., Rubenfeld, G. D., Angus, D. C., et al. (2015) · Critical Care Medicine · doi:10.1097/ccm.0000000000001205 · Cited by 79

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology

Dhiman, P., Ma, J., Andaur Navarro, C. L., Speich, B., Bullock, G., Damen, J. A. A., Hooft, L., Kirtley, S., Riley, R. D., Van Calster, B., et al. (2022) · Diagnostic and Prognostic Research · doi:10.1186/s41512-022-00126-w · Cited by 79

2- 18 F-fluoro-2-deoxyglucose positron emission tomography in delirium

Haggstrom, L. R., Nelson, J. A., Wegner, E. A., & Caplan, G. A. (2017) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x17701764 · Cited by 78

Use of reclassification for assessment of improved prediction: an empirical evaluation

Tzoulaki, I., Liberopoulos, G., & Ioannidis, J. P. A. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr013 · Cited by 70

The association of nicotine replacement therapy with mortality in a medical intensive care unit

Lee, A. H., & Afessa, B. (2007) · Critical Care Medicine · doi:10.1097/01.ccm.0000266537.86437.38 · Cited by 64

Identifying Infected Emergency Department Patients Admitted to the Hospital Ward at Risk of Clinical Deterioration and Intensive Care Unit Transfer

Kennedy, M., Joyce, N., Howell, M. D., Lawrence Mottley, J., & Shapiro, N. I. (2010) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2010.00872.x · Cited by 63

“Reading between the lines” of flow sheet data: nurses' optional documentation associated with cardiac arrest outcomes

Collins, S. A., & Vawdrey, D. K. (2012) · Applied Nursing Research · doi:10.1016/j.apnr.2011.06.002 · Cited by 50

ICU Telemedicine Comanagement Methods and Length of Stay

Hawkins, H. A., Lilly, C. M., Kaster, D. A., Groves, R. H., & Khurana, H. (2016) · CHEST · doi:10.1016/j.chest.2016.03.030 · Cited by 49

An Elevated Low Cardiac Output Syndrome Score Is Associated With Morbidity in Infants After Congenital Heart Surgery

Ulate, K. P., Yanay, O., Jeffries, H., Baden, H., Di Gennaro, J. L., & Zimmerman, J. (2017) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0000000000000979 · Cited by 45

Predicting inpatient medication orders from electronic health record data

Rough, K., Dai, A. M., Zhang, K., Xue, Y., Vardoulakis, L. M., Cui, C., Butte, A. J., Howell, M. D., & Rajkomar, A. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1826 · Cited by 28

Supersparse Linear Integer Models for Interpretable Classification

Ustun, B., Tracà, S., & Rudin, C. (2013) · arXiv · doi:10.48550/arxiv.1306.6677 · Cited by 26

Developing an Interpretable Machine Learning Model to Predict in-Hospital Mortality in Sepsis Patients: A Retrospective Temporal Validation Study

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

Obstructive sleep apnea and adverse outcomes in surgical and nonsurgical patients on the wards

Lyons, P. G., Zadravecz, F. J., Edelson, D. P., Mokhlesi, B., & Churpek, M. M. (2015) · Journal of Hospital Medicine · doi:10.1002/jhm.2404 · Cited by 25

Predictive models for severe sepsis in adult ICU patients

Guillén, J., Liu, J., Furr, M., Wang, T., Strong, S., Moore, C. C., Flower, A., & Barnes, L. E. (2015) · 2015 Systems and Information Engineering Design Symposium · doi:10.1109/sieds.2015.7116970 · Cited by 23

Computational signatures for post-cardiac arrest trajectory prediction: Importance of early physiological time series

Kim, H. B., Nguyen, H. T., Jin, Q., Tamby, S., Gelaf Romer, T., Sung, E., Liu, R., Greenstein, J. L., Suarez, J. I., Storm, C., et al. (2022) · Anaesthesia Critical Care & Pain Medicine · doi:10.1016/j.accpm.2021.101015 · Cited by 18

Multimedia Abstract Generation of Intensive Care Data: The Automation of Clinical Processes Through AI Methodologies

Jordan, D., & Rose, S. E. (2010) · World Journal of Surgery · doi:10.1007/s00268-009-0319-5 · Cited by 12

Generalizability of predictive models for intensive care unit patients

Johnson, A. E. W., Pollard, T. J., & Naumann, T. (2018) · arXiv · doi:10.48550/arxiv.1812.02275 · Cited by 12

Development and validation of a multimodal-based prognosis and intervention prediction model for COVID-19 patients in a multicenter cohort

Lee, J. H., Ahn, J. S., Chung, M. J., Jeong, Y. J., Kim, J. H., Lim, J. K., Kim, J. Y., Kim, Y. J., Lee, J. E., & Kim, E. Y. (2022) · Sensors · doi:10.3390/s22135007 · Cited by 9

Aboriginal and Torres Strait islander patients requiring critical care: Characteristics, resource use, and outcomes

Secombe, P., Brown, A., McAnulty, G., & Pilcher, D. (2019) · Critical Care and Resuscitation · doi:10.1016/s1441-2772(23)00528-8 · Cited by 4