Parameters of the complete blood count predict in hospital mortality.
In-hospital mortality
complete blood count
index
internal medicine department
prediction
Journal
International journal of laboratory hematology
ISSN: 1751-553X
Titre abrégé: Int J Lab Hematol
Pays: England
ID NLM: 101300213
Informations de publication
Date de publication:
Feb 2022
Feb 2022
Historique:
revised:
25
07
2021
received:
04
02
2021
accepted:
10
08
2021
pubmed:
1
9
2021
medline:
5
2
2022
entrez:
31
8
2021
Statut:
ppublish
Résumé
Mortality rates are used to evaluate the quality of hospital care after adjusting for disease severity and, commonly also, for age, comorbidity, and laboratory data with only few parameters of the complete blood count (CBC). To identify the parameters of the CBC that predict independently in-hospital mortality of acutely admitted patients. All patients were admitted to internal medicine, cardiology, and intensive care departments at the Laniado Hospital in Israel in 2018 and 2019. Independent variables were patients' age, sex, and parameters of the CBC. The outcome variable was in-hospital mortality. Logistic regression. In 2018, we identified the variables that were associated with in-hospital mortality and validated this association in the 2019 cohort. In the validation cohort, a model consisting of nine parameters that are commonly available in modern analyzers had a c-statistics (area under the receiver operator curve) of 0.86 and a 10%-90% risk gradient of 0%-21.4%. After including the proportions of large unstained cells, hypochromic, and macrocytic red cells, the c-statistic increased to 0.89, and the risk gradient to 0.1%-29.5%. The commonly available parameters of the CBC predict in-hospital mortality. Addition of the proportions of hypochromic red cells, macrocytic red cells, and large unstained cells may improve the predictive value of the CBC.
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
88-95Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2021 John Wiley & Sons Ltd.
Références
Horne BD, May HT, Muhlestein JB, et al. Exceptional mortality prediction by risk scores from common laboratory tests. Am J Med. 2009;122:550-558.
Brink A, Schuttevaer R, Alsma J, Zietse R, Schuit SCE, Lingsma HF. Predicting 30-day mortality using point-of-care testing; an external validation and derivation study. PLoS ONE. 2020;15:e0239318.
Fan L, Gui L, Chai EQ, Wei CJ. Routine hematological parameters are associated with short- and long-term prognosis of patients with ischemic stroke. J Clin Lab Anal. 2018;32:e22244.
Froom P, Shimoni Z. Prediction of hospital mortality rates by admission laboratory tests. Clin Chem. 2006;52:325-328.
de Moura Monteiro Júnior JG, de Oliveira Cipriano Torres D, Silva MCFC, et al. Performance of a hematological scoring system in predicting all-cause mortality in patients with acute myocardial infarction. Int J Cardiovasc Sci. 2020;33:380-388.
Brzeźniakiewicz-Janus K, Lancé MD, Tukiendorf A, et al. Selected hematological biomarkers to predict acute mortality in emergency department patients. recent polish hospital statistics. Dis Markers. 2020;2020:8874361.
Fernandez R, Cano S, Catalan I, et al. High red blood cell distribution width as a marker of hospital mortality after ICU discharge: a cohort study. J Intensive Care. 2018;6:74.
Zhao C, Wei Y, Chen D, Jin J, Chen H. Prognostic value of an inflammatory biomarker-based clinical algorithm in septic patients in the emergency department: an observational study. Int Immunopharmacol. 2020;80:106145.
Muhlestein JB, Lappe DL, Anderson JL, et al. Both initial red cell distribution width (RDW) and change in RDW during heart failure hospitalization are associated with length of hospital stay and 30-day outcomes. Int J Lab Hematol. 2016;38:328-337.
Zhang Z, Xu X, Ni H, Deng H. Red cell distribution width is associated with hospital mortality in unselected critically ill patients. J Thorac Dis. 2013;5:730-736.
Cournane S, Conway R, Byrne D, O'Riordan D, Silke B. Predicting outcomes in emergency medical admissions using a laboratory only nomogram. Comput Math Methods Med. 2017;5267864. https://doi.org/10.1155/2017/5267864
Foy BH, Carlson JCT, Reinertsen E, et al. Association of red blood cell distribution width with mortality risk in hospitalized Adults with SARS-CoV-2 infection. JAMA Netw Open. 2020;3(9):e2022058.
Topaz G, Kitay-Cohen Y, Peled L, et al. The association between red cell distribution width and poor outcomes in hospitalized patients with influenza. J Crit Care. 2017;41:166-169.
Blanco N, Leekha S, Magder L, et al. Admission laboratory values accurately predict in-hospital mortality: a retrospective cohort study. J Gen Intern Med. 2020;35:719-723.
Avci BŞ, Avci A, Dönmez Y, et al. The effectiveness of neutrophil-lymphocyte ratio in predicting in-hospital mortality in non-st-elevation myocardial infarction. Emerg Med Int. 2020;8718304. https://doi.org/10.1155/2020/8718304
de Jager CP, Wever PC, Gemen EF, et al. The neutrophil-lymphocyte count ratio in patients with community-acquired pneumonia. PLoS ONE. 2012;7:e4656.
Lavoignet CE, Le Borgne P, Chabrier S, et al. White blood cell count and eosinopenia as valuable tools for the diagnosis of bacterial infections in the ED. Eur J Clin Microbiol Infect Dis. 2019;38:1523-1532.
Mann B, Bhandohal JS, Mushiyev S. Prognostic significance of diastolic dysfunction with multiple comorbidities in heart failure patients. Cureus. 2020;12:e8297.
Arnold DM, Cuker A. Approach to the Adult with Unexplained Thrombocytopenia, UpToDate. https://www.uptodate.com/contents/approach-to-the-adult-with-unexplained-thrombocytopenia (September 2020, date last accessed)
Ermens AA, Hoffmann JJ, Krockenberger M, Van Wijk EM. New erythrocyte and reticulocyte parameters on CELL-DYN Sapphire: analytical and preanalytical aspects. Int J Lab Hematol. 2012;34:274-282.
Froom P, Shimoni Z, Benbassat J, Silke B. A simple index predicting mortality in acutely hospitalized patients. QJM. 2021;27(114):99-104.
Lam AP, Gundabolu K, Sridharan A, et al. Multiplicative interaction between mean corpuscular volume and red cell distribution width in predicting mortality of elderly patients with and without anemia. Am J Hematol. 2013;88:E245-E249.
Rahmatinejad Z, Reihani H, Tohidinezhad F, et al. Predictive performance of the SOFA and mSOFA scoring systems for predicting in-hospital mortality in the emergency department. Am J Emerg Med. 2019;37:1237-1241.
Langlais E, Nesseler N, Le Pabic E, Frasca D, Launey Y, Seguin P. Does the clinical frailty score improve the accuracy of the SOFA score in predicting hospital mortality in elderly critically ill patients? A prospective observational study. J Crit Care. 2018;46:67-72.
Faisal M, Scally AJ, Jackson N, et al. Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study. BMJ Open. 2018;8:e022939.
Schwartz N, Sakhnini A, Bisharat N. Predictive modelling of inpatient mortality in departments of internal medicine. Intern Emerg Med. 2018;13:205-211.
Conway R, Byrne D, O'Riordan D, Silke B. Comparative influence of acute illness severity and comorbidity on mortality. Eur J Intern Med. 2020;72:42-46.
Winkelmayer WC, Lorenz M, Kramar R, Hörl WH, Sunder-Plassmann G. Percentage of hypochromic red blood cells is an independent risk factor for mortality in kidney transplant recipients. Am J Transplant. 2004;4:2075-2081.
Thomas C, Thomas L. Biochemical markers and hematologic indices in the diagnosis of functional iron deficiency. Clin Chem. 2002;48:1066-1076.
Kleber M, Kozhuharov N, Sabti Z, et al. Relative hypochromia and mortality in acute heart failure. Int J Cardiol. 2019;286:104-110.
Meintker L, Ringwald J, Rauh M, Krause SW. Comparison of automated differential blood cell counts from abbott sapphire, Siemens Advia 120, Beckman Coulter DxH 800, and Sysmex XE-2100 in normal and pathologic samples. Am J Clin Pathol. 2013;139:641-650.
Rabizadeh E, Pickholtz I, Barak M, Isakov E, Zimra Y, Froom P. Acute leukemia detection rate by automated blood count parameters and peripheral smear review. Int J Lab Hematol. 2015;37:44-49.
Bastug A, Bastug A, Bodur H, et al. Clinical and laboratory features of COVID-19: Predictors of severe prognosis. Int Immunopharmacol. 2020;88:106950. https://doi.org/10.1016/j.intimp.2020.106950
Momeni-Boroujeni A, Momeni-Boroujeni A, Mendoza R, Stopard IJ, Lambert B, Zuretti A. A dynamic bayesian model for identifying high-mortality risk in hospitalized COVID-19 patients. Infect Dis Rep. 2021;13:239-250.
Rubio-Rivas M, Formiga F, Grillo S, Gili F, Cabrera C, Corbella X. Lymphopenia as prognostic factor for mortality and hospital length of stay for elderly hospitalized patients. Aging Clin Exp Res. 2016;28:721-727.
Bruegel M, Nagel D, Funk M, Fuhrmann P, Zander J, Teupser D. Comparison of five automated hematology analyzers in a university hospital setting: Abbott Cell-Dyn Sapphire, Beckman Coulter DxH 800, Siemens Advia 2120i, Sysmex XE-5000, and Sysmex XN-2000. Clin Chem Lab Med. 2015;53:1057-1071.
Beyan C, Beyan E. Were the measurements standardized sufficiently in published studies about mean platelet volume? Blood Coagul Fibrinolysis. 2017;28:234-236.