Song JY, Park DW, Moon SW, et al. Diagnostic and prognostic value of interleukin-6, pentraxin 3, and procalcitonin levels among sepsis and septic shock patients: a prospective controlled study according to the sepsis-3 definitions. BMC Infect Dis. 2019a;19(1):968.
Article
PubMed
PubMed Central
CAS
Google Scholar
Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016;315(8):801–10.
Article
CAS
PubMed
PubMed Central
Google Scholar
Liu V, Escobar GJ, Greene JD, et al. Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA. 2014;312(1):90–2.
Article
CAS
PubMed
Google Scholar
Hong TH, Chang CH, Ko WJ, et al. Biomarkers of early sepsis may be correlated with outcome. J Transl Med. 2014;12:146.
Article
PubMed
PubMed Central
CAS
Google Scholar
Song J, Moon SW, Park DW, et al. Biomarker combination and SOFA score for the prediction of mortality in sepsis and septic shock. Medicine. 2020;99(22):e20495.
Article
PubMed
Google Scholar
Majdan M, Brazinova A, Rusnak M, et al. Outcome prediction after traumatic brain injury: comparison of the performance of routinely used severity scores and multivariable prognostic models. J Neurosci Rural Pract. 2017;8(1):20.
Article
PubMed
PubMed Central
Google Scholar
Song JY, Park DW, Moon SW, et al. Validation of APACHE II and SAPS II scales at the intensive care unit along with assessment of SOFA scale at the admission as an isolated risk of death predictor. Anaesthesiol Intensive Ther. 2019b;51(2):107–11.
Article
Google Scholar
Godinjak A, Iglica A, Rama A, et al. Predictive value of SAPS II and APACHE II scoring systems for patient outcome in a medical intensive care unit. Acta Med Acad. 2016;45(2):97–103.
Article
PubMed
Google Scholar
Yuan KC, Tsai LW, Lee KH, et al. The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. Int J Med Inform. 2020;141:104176.
Article
PubMed
Google Scholar
Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining-KDD 2016, San Francisco, CA, USA; 2016. p. 785–94.
Johnson AEW, Pollard TJ, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:160035.
Article
CAS
PubMed
PubMed Central
Google Scholar
Oweira H, Schmidt J, Mehrabi A, et al. Comparison of three prognostic models for predicting cancer-specific survival among patients with gastrointestinal stromal tumors. Future Oncol. 2018;14(4):379–89.
Article
CAS
PubMed
Google Scholar
Goldberger A, Amaral L, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):e215–20.
Article
CAS
PubMed
Google Scholar
Templ M, Alfons A, Filzmoser P. Exploring incomplete data using visualization techniques. Adv Data Anal Classif. 2012;6:29–47.
Article
Google Scholar
Van Buuren S, Groothuis-Oudshoorn CG. mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45:67.
Article
Google Scholar
Gall JRL, Lemeshow S, Saulnier F, et al. A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA. 1993;270:2957–63.
Article
PubMed
Google Scholar
Zhang Z, Ho KM, Hong Y. Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care. Crit Care. 2019;23(1):112.
Article
PubMed
PubMed Central
Google Scholar
Livne M, Boldsen JK, Mikkelsen IK, et al. Boosted tree model reforms multimodal magnetic resonance imaging infarct prediction in acute stroke. Stroke. 2018;49:912–8.
Article
PubMed
Google Scholar
Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD). Circulation. 2015;131(2):211–9.
Article
PubMed
PubMed Central
Google Scholar
Fleischmann-Struzek C, Mellhammar L, Reinhart K, et al. Incidence and mortality of hospital and ICU-treated sepsis: results from an updated and expanded systematic review and meta-analysis. Intensive Care Med. 2020;46(8):1552–62.
Article
CAS
PubMed
PubMed Central
Google Scholar
Torio CM, Moore BJ. National inpatient hospital costs: the most expensive conditions by payer, 2013: statistical brief #204. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs, Rockville (MD); 2006.
Martin GS, Mannino DM, Eaton S, et al. The epidemiology of sepsis in the united states from 1979 through 2000. N Engl J Med. 2003;348:1546–54.
Article
PubMed
Google Scholar
World Health Organization. World Health Assembly 70, resolution 70.7: improving the prevention, diagnosis and clinical management of sepsis. 2017. http://apps.who.int/gb/ebwha/pdf_fles/WHA70/A70_R7-en.pdf.
Jouffroy R, Saade A, Vivien B, et al. Pre-hospital mechanical ventilation in septic shock patients. Am J Emerg Med. 2019;37(10):1860–3.
Article
PubMed
Google Scholar
Wu L, Feng Q, Ai ML, et al. The dynamic change of serum S100B levels from day 1 to day 3 is more associated with sepsis associated encephalopathy. Sci Rep. 2020;10(1):7718.
Article
CAS
PubMed
PubMed Central
Google Scholar
Oud L. Epidemiology and outcomes of sepsis among hospitalizations with systemic lupus erythematosus admitted to the ICU: a population-based cohort study. J Intensive Care. 2020;8:3.
Article
PubMed
PubMed Central
Google Scholar
Xiao J, Ding RF, Xu XL, et al. Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. J Transl Med. 2019;17(1):119.
Article
PubMed
PubMed Central
Google Scholar
Li YM, Li ZL, Chen F, et al. A LASSO-derived risk model for long-term mortality in Chinese patients with acute coronary syndrome. J Transl Med. 2020;18(1):157.
Article
PubMed
PubMed Central
Google Scholar
Nemati S, Holder A, Razmi F, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med. 2018;46(4):547–53.
Article
PubMed
PubMed Central
Google Scholar
Seymour CW, Kennedy JN, Wang S, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA. 2019;321(20):2003–17.
Article
CAS
PubMed
PubMed Central
Google Scholar
Dhungana P, Serafim LP, Ruiz AL, et al. Machine learning in data abstraction: a computable phenotype for sepsis and septic shock diagnosis in the intensive care unit. World J Crit Care Med. 2019;8(7):120–6.
Article
PubMed
PubMed Central
Google Scholar
Liu R, Greenstein JL, Granite SJ, et al. Data-driven discovery of a novel sepsis pre-shock state predicts impending septic shock in the ICU. Sci Rep. 2019;9(1):6145.
Article
PubMed
PubMed Central
CAS
Google Scholar
LANL Earthquake Prediction. 2019. https://www.kaggle.com/c/LANL-EarthquakePrediction. Accessed 15 Mar 2020.
Franzosi OS, Nunes DSL, Klanovicz TM, et al. Hemodynamic and skin perfusion is associated with successful enteral nutrition therapy in septic shock patients. Clin Nutr. 2020;S0261–5614(20):30151–5.
Google Scholar
Pinheiro KHE, Azêdo FA, Areco KCN, et al. Risk factors and mortality in patients with sepsis, septic and non septic acute kidney injury in ICU. J Bras Nefrol. 2019;41(4):462–71.
Article
PubMed
PubMed Central
Google Scholar
Lin PC, Huang HC, Komorowski M, et al. A machine learning approach for predicting urine output after fluid administration. Comput Methods Programs Biomed. 2019;177:155–9.
Article
PubMed
Google Scholar
Teixeira C, Garzotto F, Piccinni P, et al. Fluid balance and urine volume are independent predictors of mortality in acute kidney injury. Crit Care. 2013;17(1):R14. https://doi.org/10.1186/cc12484.
Article
PubMed
PubMed Central
Google Scholar
Shirazy M, Omar I, Abduljabbar D, et al. Prevalence and prognostic impact of hypernatremia in sepsis and septic shock patients in the intensive care unit: a single centre experience. J Crit Care Med. 2020;6(1):52–8.
Article
Google Scholar
Zhang K, Lv D, Deng Y, et al. STAPLAg: a convenient early warning score for use in infected patients in the intensive care unit. Medicine. 2020;99(22):e20274.
Article
PubMed
Google Scholar
Ding XF, Yang ZY, Xu ZT, et al. Early goal-directed and lactate-guided therapy in adult patients with severe sepsis and septic shock: a meta-analysis of randomized controlled trials. J Transl Med. 2018;16(1):331.
Article
CAS
PubMed
PubMed Central
Google Scholar
Liu YL, Zheng J, Zhang DS, et al. Neutrophil-lymphocyte ratio and plasma lactate predict 28-day mortality in patients with sepsis. J Clin Lab Anal. 2019;33(7):e22942.
CAS
PubMed
PubMed Central
Google Scholar
Velissaris D, Karamouzos V, Pantzaris ND, et al. Relation between central venous, peripheral venous and arterial lactate levels in patients with sepsis in the emergency department. J Clin Med Res. 2019;11(9):629–34.
Article
CAS
PubMed
PubMed Central
Google Scholar
Lyons PG, Micek ST, Hampton N, et al. Sepsis-associated coagulopathy severity predicts hospital mortality. Crit Care Med. 2018;46(5):736–42.
Article
PubMed
Google Scholar
Casado-Méndez M, Fernandez-Pacheco J, Arellano-Orden V, et al. Relationship of thromboelastography and conventional clotting test values with severe bleeding in critically ill patients with coagulopathy: a prospective study. Int J Lab Hematol. 2019;41(5):671–8.
Article
PubMed
Google Scholar
Lemiale V, Pons S, Mirouse A, et al. Sepsis and septic shock in patients with malignancies: a Groupe de Recherche Respiratoire en Réanimation Onco-Hématologique study. Crit Care Med. 2020;48(6):822–9.
Article
CAS
PubMed
Google Scholar
Seok H, Jeon JH, Park DW. Antimicrobial therapy and antimicrobial stewardship in sepsis. Infect Chemother. 2020;52(1):19–30.
Article
PubMed
PubMed Central
Google Scholar