Predicting 30-day mortality using point-of-care testing; an external validation and derivation study

Abstract

Background Early risk stratification for guiding treatment priority in the emergency department (ED) is becoming increasingly important. Existing prediction models typically use demographics, vital signs and laboratory parameters. Laboratory-based models require blood testing, which may cause substantial delay. However, these delays can be prevented by the use of point-of-care testing (POCT), where results are readily available. We aimed to externally validate a laboratory-based model for mortality and subsequently assessed whether a POCT model yields comparable performance. Methods All adult patients visiting the ED of a university hospital between January 1 st , 2012 and December 31 st , 2016 were retrospectively reviewed for inclusion. Primary outcome was defined as 30-day mortality after ED presentation. We externally validated one existing prediction model including age, glucose, urea, sodium, haemoglobin, platelet count and white blood cell count. We assessed the predictive performance by discrimination, expressed as Area under the Curve (AUC). We compared the existing model to an equivalent model using predictors that are available with POCT (i.e. glucose, urea, sodium and haemoglobin). Additionally, we internally validated these models with bootstrapping. Results We included 34,437 patients of whom 1,942 (5.6%) died within 30 days. The AUC of the laboratory-based model was 0.794. We refitted this model to our ED population and found an AUC of 0.812, which decreased only slightly to 0.790 with only POCT parameters. Conclusions Our POCT-model performs similar to existing laboratory-based models in identifying patients at high risk for mortality, with results available within minutes. Although the model

Parameters of the complete blood count predict in hospital mortality

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Mortality rates are used to evaluate the quality of hospital care after adjusting for disease severity, age, comorbidity, and laboratory data that include complete blood counts (CBC). Previous studies have focused on selected CBC parameters only and have variously found that white blood counts (WBC), [1][2][3][4][5] proportion of neutrophils, 4 red cell counts (RBC), 3,4 red cell distribution widths (RDW), [6][7][8][9][10][11][12][13] mean corpuscular hemoglobin concentrations (MCHC), 1 platelet counts, 1,2,14 mean platelet volumes (MPV), 1,5,6 and neutrophil-lymphocyte ratios (NLR) 3,5,8,15,16 predict short-term mortality. We know of no attempts to identify the predictive value of all CBC parameters, with and without those provided by some modern analyzers, such as the proportion of large unstained cells (LUC), hypochromic cells, and macrocytes.

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