Journal of Critical Care 29 (2014) 865

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Confounding factors in observational study: The Achilles heel☆

Many thanks for the thoughtful insights by Dr Varol. I would like to respond to the comments. I believe that to provide more information and commentary will further improve the quality of our work [1]. The first question goes to technical details of the laboratory measurement of platelet indices. As a matter of fact, this was a retrospective review of electronic medical records, and the details of measurements were not prespecified in study protocol. Data were entered into electronic system as part of our routine clinical practice. As a clinician, I am less concerned with how the blood sample was measured, but to respond to the concerns raised by Dr Varol, I have consulted laboratory workers for more details. All blood specimens were collected in dipotassium EDTA vacutainers. The measurement was performed within 1 hour because each sample will be sent to the laboratory immediately after it is taken. The measurement was performed by using automatic analyzer (SYSMEX CORPORATION, Hyogo, Japan), in which impedance method was used [2]. Another concern raised by the reader is about confounding factor adjustment in observational study. This is a universal problem in conducting observational studies. Theoretically, incorporating more confounding factors into analysis will increase the accuracy and reliability of the result. However, to accurately extract data from electronic database imposes great challenge to the process of data mining. As mentioned by Dr Varol, there are numerous factors that can cause increases in mean platelet volume, including but not limited to smoking, obesity, hypertension, diabetes mellitus, prediabetes, and hyperlipidemia. However, we encountered difficulties in extracting these data because they are stored as string variable. In this situation, some expressions are not standardized in our institution. For instance, the text referring to pneumonia may have 3 to 4 expressions in Chinese characters. In contrast, the laboratory measurements were

☆ Conflict of interests: The author declares that he has no competing interests. http://dx.doi.org/10.1016/j.jcrc.2014.06.015 0883-9441/© 2014 Elsevier Inc. All rights reserved.

stored as numeric variable, which is more amiable to data mining. Furthermore, we could not make sure that all information recorded in medical records or progress notes were correct. For instance, the body weight and height are usually estimated for critically ill patients because they cannot stand up to complete the measurement. In aggregate, I acknowledge that our study is preliminary that have failed to include many potential confounding factors. However, this is the Achilles heel of observational study. No one can include all known and unknown confounding factors exhaustively. The only methodology that can obliterate confounding effect is the randomization with infinite sample size. Again, I am grateful to Dr Varol for pointing out shortcomings in the current study.

Zhongheng Zhang, MM Department of Critical Care Medicine, Jinhua Municipal Central Hospital Jinhua Hospital of Zhejiang University, Zhejiang, PR China No. 351, Mingyue Road, Jinhua Zhejiang Province, China, 321000 Tel.: + 86 579 82552667 Email address: [email protected]

References [1] Zhang Z, Xu X, Ni H, Deng H. Platelet indices are novel predictors of hospital mortality in intensive care unit patients. J Crit Care 2014;29:885.e1–6. http://dx. doi.org/10.1016/j.jcrc.2014.04.020. [2] Kang SH, Kim HK, Ham CK, Lee DS, Cho HI. Comparison of four hematology analyzers, CELL-DYN Sapphire, ADVIA 120, Coulter LH 750, and Sysmex XE-2100, in terms of clinical usefulness. Int J Lab Hematol 2008;30(6):480–6.

Confounding factors in observational study: the Achilles heel.

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