Research methods: Clinical studies based on routine laboratory tests
Clinical research using routine laboratory tests can provide important opportunities to investigators, especially those with limited resources, and can improve patient care, especially if the result improves clinical decision making without the use of more sophisticated or expensive tests. Laboratory analysis of biological parameters can be used for screening, diagnostic testing, predicting prognosis, and measuring treatment responses. Often the same parameter can be used for several purposes, depending on the clinical scenario and the patient population. For example, several studies have suggested the mean platelet volume (MPV) is different in patients with acute coronary syndrome compared to patients with coronary disease but no acute syndrome. Given this information it might seem relatively easy to start studies using this laboratory test. However, multiple questions need to be considered before starting any research using MPV measurements. We will discuss some of these considerations in this review article. This approach applies to most research projects based on laboratory tests.
Vizioli L, Muscari S, Muscari A. The relationship of mean
platelet volume with the risk and prognosis of cardiovascular
diseases. Int J Clin Pract 2009;63:1509–15.
Cakici M, Cetin M, Balli M, et al. Predictors of thrombus
burden and no-reflow of infarct-related artery in patients
with ST-segment elevation myocardial infarction: importance
of platelet indices. Blood Coagul Fibrinolysis 2014;
Sansanayudh N, Numthavaj P, Muntham D, et al. Prognostic
effect of mean platelet volume in patients with coronary
artery disease. A systematic review and meta-analysis.
Thromb Haemost 2015;114:1299–309.
Li N, Yu Z, Zhang X, et al. Elevated mean platelet volume
predicts poor prognosis in colorectal cancer. Sci Rep 2017;
Yang S, Berdine G. The receiver operating characteristic
(ROC) curve. Southwest Respiratory and Critical Care
Yang S, Berdine G. Categorical data analysis—logistic
regression. Southwest Respiratory and Critical Care Chronicles
Hozo SP, Djulbegovic B, Hozo I. Estimating the mean and
variance from the median, range, and the size of a sample.
BMC Med Res Methodol 2005;5:13.
Wan X, Wang W, Liu J,et al. Estimating the sample mean and
standard deviation from the sample size, median, range and/or
interquartile range. BMC Med Res Methodol 2014; 14:135.
Vránová J, Horák J, Krátká K, et al. Receiver operating
characteristic analysis and the cost–benefit analysis in determination
of the optimal cut-off point. Cas Lek Cesk 2009;
Fox J. Applied regression analysis, linear models, and related
methods. Thousand Oaks, CA, US: Sage Publications, Inc.
Kumar TK. Multicollinearity in regression analysis. Rev
Econ Stat 1975;57(3):365–366.
Lancé MD, Sloep M, Henskens YM, et al. Mean platelet volume
as a diagnostic marker for cardiovascular disease: drawbacks
of preanalytical conditions and measuring techniques.
Clin Appl Thromb Hemost 2012;18:561–8.
Latger-Cannard V, Hoarau M, Salignac S, et al. Mean platelet
volume: comparison of three analysers towards standardization
of platelet morphological phenotype. Int J Lab Hematol