Outcome prediction for critical care patients with respiratory neoplasms using a multilayer perceptron neural network

Referencias bibliográficas

• 1 Peng X, Huang Y, Fu H, Zhang Z, He A, Luo R. Prognostic Value of blood urea nitrogen to serum albumin ratio in intensive care unit patients with lung cancer. Int J Gen Med. 2021;14:7349-59.
• 2 Sauer CM, Dong J, Celi LA, Ramazzotti D. Improved survival of cancer patients admitted to the intensive care unit between 2002 and 2011 at a U.S. Teaching Hospital. Cancer Res Treat. 2019;51(3):973-81.
• 3 Almansour IM, Aldalaykeh MK, Saleh ZT, Yousef KM, Alnaeem MM. Predictive performance of two measures of prognostic mortality of cancer patients in intensive care unit in Jordan: a comparative single-centre study. Open Nurs J. 2020;14:168-73.
• 4 Li J, Yang X, Ma J, Gong F, Chen Q. Relationship of red blood cell distribution width with cancer mortality in hospital. Biomed Res Int. 2018;2018:8914617.
• 5 Le Gall JR, Klar J, Lemeshow S, Saulnier F, Alberti C, Artigas A, et al. The Logistic organ dysfunction system. a new way to assess organ dysfunction in the intensive care unit. ICU Scoring Group. JAMA. 1996;276(10):802-10.
• 6 Johnson AE, Kramer AA, Clifford GD. A new severity of illness scale using a subset of Acute Physiology And Chronic Health Evaluation data elements shows comparable predictive accuracy. Crit Care Med. 2013;41(7):1711-8.
• 7 Le Gall JR, Loirat P, Alperovitch A, Glaser P, Granthil C, Mathieu D, et al. A simplified acute physiology score for ICU patients. Crit Care Med. 1984; 12(11):975-7.
• 8 Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA. 1993;270(24):2957-63. Erratum in: JAMA. 1994;271(17):1321.
• 9 Moreno RP, Metnitz PG, Almeida E, Jordan B, Bauer P, Campos RA, Iapichino G, Edbrooke D, Capuzzo M, Le Gall JR; SAPS 3 Investigators. SAPS 3--From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med. 2005;31(10):1345-55. Erratum in: Intensive Care Med. 2006;32(5):796.
• 10 Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22(7):707-10.
• 11 Ladha KS, Zhao K, Quraishi SA, Kurth T, Eikermann M, Kaafarani HM, et al. The Deyo-Charlson and Elixhauser-van Walraven Comorbidity Indices as predictors of mortality in critically ill patients. BMJ Open. 2015;5(9):e008990.
• 12 Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8-27.
• 13 van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. 2009;47(6):626-33.
• 14 Gao S, Wang Y, Yang L, Wang Z, Huang W. Characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases. Ann Transl Med. 2021;9(1):13.
• 15 Galdiero MR, Marone G, Mantovani A. Cancer inflammation and cytokines. Cold Spring Harb Perspect Biol. 2018;10(8):a028662. Review.
• 16 Im JH, Fu W, Wang H, Bhatia SK, Hammer DA, Kowalska MA, et al. Coagulation facilitates tumor cell spreading in the pulmonary vasculature during early metastatic colony formation. Cancer Res. 2004;64(23):8613-9.
• 17 Yang N, Han X, Yu J, Shu W, Qiu F, Han J. Hemoglobin, albumin, lymphocyte, and platelet score and neutrophil-to-lymphocyte ratio are novel significant prognostic factors for patients with small-cell lung cancer undergoing chemotherapy. J Cancer Res Ther. 2020;16(5):1134-9.
• 18 Wen Y, Yang J, Han X. Fibrinogen-to-Albumin Ratio is Associated with All-Cause Mortality in Cancer Patients. Int J Gen Med. 2021;14:4867-75.
• 19 Kos M, Hocazade C, Kos FT, Uncu D, Karakas E, Dogan M, et al. Evaluation of the effects of red blood cell distribution width on survival in lung cancer patients. Contemp Oncol (Pozn). 2016;20(2):153-7.
• 20 Warwick R, Mediratta N, Shackcloth M, Shaw M, McShane J, Poullis M. Preoperative red cell distribution width in patients undergoing pulmonary resections for non-small-cell lung cancer. Eur J Cardiothorac Surg. 2014;45(1):108-13.
• 21 Ding J, Karp JE, Emadi A. Elevated lactate dehydrogenase (LDH) can be a marker of immune suppression in cancer: Interplay between hematologic and solid neoplastic clones and their microenvironments. Cancer Biomark. 2017;19(4):353-63. Review.
• 22 Qi J, Gu C, Wang W, Xiang M, Chen X, Fu J. Elevated Lactate Dehydrogenase Levels Display a Poor Prognostic Factor for Non-Hodgkin's Lymphoma in Intensive Care Unit: An Analysis of the MIMIC-III Database Combined With External Validation. Front Oncol. 2021;11:753712.
• 23 Scott A, Salgia R. Biomarkers in lung cancer: from early detection to novel therapeutics and decision making. Biomark Med. 2008;2(6):577-86.
• 24 Santos HG, Zampieri FG, Normilio-Silva K, Silva GT, Lima AC, Cavalcanti AB, et al. Machine learning to predict 30-day quality-adjusted survival in critically ill patients with cancer. J Crit Care. 2020;55:73-8.
• 25 Johnson AE, Pollard TJ, Shen L, Lehman L, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:160035.
• 26 Nistal-Nuño B. “Replication Data for: Outcome prediction for patients with respiratory neoplasms in the Intensive Care Unit”. Harvard Dataverse, V1; 2022 [cited 2022 Sep 16]. Available from: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZCSIQY
• 27 Johnson AE, Stone DJ, Celi LA, Pollard TJ. The MIMIC Code Repository: enabling reproducibility in critical care research. J Am Med Inform Assoc. 2018;25(1):32-9.
• 28 Díaz-Díaz D, Villanova Martínez M, Palencia Herrejón E. Oncological patients admitted to an intensive care unit. Analysis of predictors of in-hospital mortality. Med Intensiva (Engl Ed). 2018;42(6):346-53.
• 29 Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29(7):1303-10.
• 30 Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, et al. KNIME: the Konstanz Information Miner. In: Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R, editors. Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Berlin: Springer; 2008. p. 319-26.