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. 2019 Sep 2:2019:6826127.
doi: 10.1155/2019/6826127. eCollection 2019.

Clinical Profiles of Selected Biomarkers Identifying Infection and Cancer Patients: A Gorzów Hospital Example

Katarzyna Brzeźniakiewicz-Janus  1 , Marcus Daniel Lancé  2 , Andrzej Tukiendorf  3 , Mirosław Franków  1 , Joanna Rupa-Matysek  4 , Edyta Wolny-Rokicka  5 , Lidia Gil  4
Affiliations

Clinical Profiles of Selected Biomarkers Identifying Infection and Cancer Patients: A Gorzów Hospital Example

Katarzyna Brzeźniakiewicz-Janus et al. Dis Markers. .

Abstract

Introduction: Many pathobiological processes that manifest in a patient's organs could be associated with biomarker levels that are detectable in different human systems. However, biomarkers that promote early disease diagnosis should not be tested only in personalized medicine but also in large-scale diagnostic evaluations of patients, such as for medical management.

Objective: We aimed to create an easy algorithmic risk assessment tool that is based on obtainable "everyday" biomarkers, identifying infection and cancer patients.

Patients: We obtained the study data from the electronic medical records of 517 patients (186 infection and 331 cancer episodes) hospitalized at Gorzów Hospital, Poland, over a one and a half-year period from the 1st of January 2017 to the 30th of June 2018.

Methods and results: A set of consecutive statistical methods (cluster analysis, ANOVA, and ROC analysis) was used to predict infection and cancer. For in-hospital diagnosis, our approach showed independent clusters of patients by age, sex, MPV, and disease fractions. From the set of available "everyday" biomarkers, we established the most likely bioindicators for infection and cancer together with their classification cutoffs.

Conclusions: Despite infection and cancer being very different diseases in their clinical characteristics, it seems possible to discriminate them using "everyday" biomarkers and popular statistical methods. The estimated cutoffs for the specified biomarkers can be used to allocate patients to appropriate risk groups for stratification purposes (medical management or epidemiological administration).

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Conflict of interest statement

The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Classification tree (dendrogram) of patients.
Figure 2
Figure 2
Fraction plot of clusters of patients by sex (a), age (b), MPV (c), and cancer fraction (d).
Figure 3
Figure 3
Plot of mean WBC results in clusters of patients: (a) red blood cells, (b) haematocrit, (c) haemoglobin, (d) mean corpuscular volume, (e) mean corpuscular haemoglobin, (f) mean corpuscular haemoglobin concentration, and (g) C-reactive protein.
See this image and copyright information in PMC

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