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Table 1 Relevant studies about mortality prediction for sepsis patients

From: Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

Authors Title Dataset Methodology Predictors Outcome Sepsis definition
Masson, S. et al. [19] Presepsin (soluble CD14 subtype) and procalcitonin levels for mortality prediction in sepsis: data from the Albumin Italian Outcome Sepsis trial A multicentre, randomised Albumin Italian Outcome Sepsis trial, 100 patients Cox regression model Presepsin level, procalcitonin level and some covariates 28-day/ICU/90-day mortality Sepsis-2
Adrie C. et al. [20] Model for predicting short-term mortality of severe sepsis A multicentre database including data from 12 ICUs, 2268 patients Generalised linear model SAPS II and LOD scores at ICU admission, septic shock, multiple organ failure, comorbidities, procedures, agents, bacteraemia and sources of infection 14-day mortality within ICU stay Sepsis-2
Ripoll, V.J.R. et al. [21] Sepsis mortality prediction with the Quotient Basis Kernel MIMIC II Support vector machines (SVMs), LR, SAPS SOFA and SAPS scores at ICU admission ICU mortality Sepsis-2
Fang W-F et al. [22] Development and validation of immune dysfunction score to predict 28-day mortality of sepsis patients Sepsis patients admitted to ICU at a hospital in Taiwan, 151 patients LR Monocyte HLA-DR* expression, plasma G-CSF* level, plasma IL*-10 level, and serum SeMo* ratio 28-day mortality Sepsis-3
Xie, Y. et al. [23] Using clinical features and biomarkers to predict 60-day mortality of sepsis patients Protocol-based care in early septic shock trial, around 530 patients LR Clinical features and biomarkers obtained during the first 24 h of hospital admission 60-day mortality Not mentioned
Poucke, S.V. et al. [31] Scalable predictive analysis in critically ill patients using a visual open data analysis platform MIMIC II Naïve Bayes, LR, RF, AdaBoost, Bagging, Stacking, SVM Demographics, comorbidities, types of care unit, platelet count ICU mortality NA
Zhang, Z.
& Hong, Y [32].
Development of a novel score for the prediction of hospital mortality in patients with severe sepsis: the use of electronic healthcare records with LASSO regression MIMIC III LASSO, LR Demographics, clinical and laboratory variables recorded during the first 24 h in ICU Hospital mortality Sepsis-2
Taylor, R.A. et al. [34] Prediction of in-hospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach Adult ED* visits over 12 months, 4676 patients RF, CART, LR Demographics, previous health status, ED health status, ED services rendered and operational details Hospital mortality Sepsis-2
Pregernig, A. et al. [35] Prediction of mortality in adult patients with sepsis using six biomarkers: a systematic review and meta-analysis 44 articles in English Qualitative analysis, meta-analysis Angiopoietin 1 and 2, HMGB1*, sRAGE*, sTREM*-1, suPAR* 28-day/30-day/ICU/hospital/90-day mortality Sepsis-1/Sepsis-2/Sepsis-3
  1. *Abbreviations: HLA-DR Human leukocyte antigen D-related, *G-CSF Granulocyte-colony stimulating factor, IL Interleukin, SeMo Segmented neutrophil-to-monocyte, ED Emergency department, HMGB1 High mobility group box 1 protein, sRAGE soluble receptor for advanced glycation endproducts, sTREM soluble triggering receptor expressed on myeloid cells 1, suPAR soluble urokinase-type plasminogen activator receptor