Literature DB >> 25137401

ICU severity of illness scores: APACHE, SAPS and MPM.

Jorge I F Salluh1, Márcio Soares.   

Abstract

PURPOSE OF REVIEW: This review aims to evaluate the latest versions of the Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Mortality Probability Model scores, make comparisons and describe their strengths and limitations. Additionally, we provide critical analysis and recommendations for the use of these scoring systems in different scenarios. RECENT
FINDINGS: The last generation of ICU scoring systems (Acute Physiology and Chronic Health Evaluation IV, Mortality Probability Model 0-III (MPM0-III) and Simplified Acute Physiology Score 3) was widely validated in different regions of the world and in distinct settings comprising general ICU patients as well as specific subgroups such as critically ill cancer patients, cardiovascular, surgical, acute kidney injury requiring renal replacement therapy and those in need of extra-corporeal membrane oxygen. Conflicting results are reported, and in general the scores presented a good discrimination despite a worse calibration as compared with the ones described in the original studies that generated them. Nonetheless, such calibration is often improved when customizations are performed both at ICU and region or country level.
SUMMARY: ICU scoring systems provide a valuable framework to characterize patients' severity of illness for the evaluation of ICU performance, for quality improvement initiatives and for benchmarking purposes. However, to ensure the best accuracy, constant updates as well as regional customizations are required.

Entities:  

Mesh:

Year:  2014        PMID: 25137401     DOI: 10.1097/MCC.0000000000000135

Source DB:  PubMed          Journal:  Curr Opin Crit Care        ISSN: 1070-5295            Impact factor:   3.687


  61 in total

1.  Plasma cell-free DNA predicts pediatric cerebral malaria severity.

Authors:  Iset Medina Vera; Anne Kessler; Li-Min Ting; Visopo Harawa; Thomas Keller; Dylan Allen; Madi Njie; McKenze Moss; Monica Soko; Ajisa Ahmadu; Innocent Kadwala; Stephen Ray; Tonney S Nyirenda; Wilson L Mandala; Terrie E Taylor; Stephen J Rogerson; Karl B Seydel; Kami Kim
Journal:  JCI Insight       Date:  2020-06-18

2.  EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings.

Authors:  Nils Rethmeier; Necip Oguz Serbetci; Sebastian Möller; Roland Roller
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

3.  Dissociation of ACTH and cortisol in septic and non-septic ICU patients.

Authors:  Hershel Raff; Nebiyu Biru; Neil Reisinger; David J Kramer
Journal:  Endocrine       Date:  2016-07-18       Impact factor: 3.633

4.  The effects of performance status one week before hospital admission on the outcomes of critically ill patients.

Authors:  Fernando G Zampieri; Fernando A Bozza; Giulliana M Moralez; Débora D S Mazza; Alexandre V Scotti; Marcelo S Santino; Rubens A B Ribeiro; Edison M Rodrigues Filho; Maurício M Cabral; Marcelo O Maia; Patrícia S D'Alessandro; Sandro V Oliveira; Márcia A M Menezes; Eliana B Caser; Roberto S Lannes; Meton S Alencar Neto; Maristela M Machado; Marcelo F Sousa; Jorge I F Salluh; Marcio Soares
Journal:  Intensive Care Med       Date:  2016-09-29       Impact factor: 17.440

5.  Hemophagocytic lymphohistiocytosis as a harbinger of aggressive lymphoma: a case series.

Authors:  Oren Pasvolsky; Adi Zoref-Lorenz; Uri Abadi; Karyn Revital Geiger; Lucille Hayman; Iuliana Vaxman; Pia Raanani; Avi Leader
Journal:  Int J Hematol       Date:  2019-03-08       Impact factor: 2.490

6.  Hypoglycemia but Not Hyperglycemia Is Associated with Mortality in Critically Ill Patients with Diabetes.

Authors:  Bernhard Wernly; Peter Jirak; Michael Lichtenauer; Marcus Franz; Bjoern Kabisch; Paul C Schulze; Kristina Braun; Johanna Muessig; Maryna Masyuk; Bernhard Paulweber; Alexander Lauten; Uta C Hoppe; Malte Kelm; Christian Jung
Journal:  Med Princ Pract       Date:  2018-12-13       Impact factor: 1.927

7.  A Novel Composite Indicator of Predicting Mortality Risk for Heart Failure Patients With Diabetes Admitted to Intensive Care Unit Based on Machine Learning.

Authors:  Boshen Yang; Yuankang Zhu; Xia Lu; Chengxing Shen
Journal:  Front Endocrinol (Lausanne)       Date:  2022-06-29       Impact factor: 6.055

Review 8.  Does Calculated Prognostic Estimation Lead to Different Outcomes Compared With Experience-Based Prognostication in the ICU? A Systematic Review.

Authors:  Melissa Basile; Anne Press; Alexander C Adia; Jason J Wang; Saori Wendy Herman; Janice Lester; Nisha Parikh; Negin Hajizadeh
Journal:  Crit Care Explor       Date:  2019-02-01

9.  E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database.

Authors:  Nima Safaei; Babak Safaei; Seyedhouman Seyedekrami; Mojtaba Talafidaryani; Arezoo Masoud; Shaodong Wang; Qing Li; Mahdi Moqri
Journal:  PLoS One       Date:  2022-05-05       Impact factor: 3.752

10.  Outcomes of ICU Admission of Patients With Progressive Metastatic Gastrointestinal Cancer.

Authors:  Andrew S Epstein; Andrew Yang; Lauren E Colbert; Louis P Voigt; Jason Meadows; Jessica I Goldberg; Leonard B Saltz
Journal:  J Intensive Care Med       Date:  2017-12-20       Impact factor: 3.510

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