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Platelet count and mortality of H7N9 infected patients in Guangdong, China

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journal contribution
posted on 2019-09-11, 12:44 authored by Ying Chen, Yang Yang, Jian Cheng, Jiahai Lu, Wenbiao Hu

Avian influenza A (H7N9) is a serve zoonosis with a high mortality rate. Timely and effective diagnosis and early warning is crucial for the clinical treatment of H7N9 patients. The previous studies indicated that thrombocytopenia was associated with the prognosis of influenza cases, but the related evidence of platelet change within the course of the disease remains largely insufficient. A total of 130 laboratory-confirmed H7N9 cases and their corresponding medical records from August 2013 to March 2015 were collected from 23 hospitals of 13 cities in Guangdong, China. The results indicated that there was a significant difference between the outcome of H7N9 cases and their average platelet count (PC) including maximum, minimum, range, admission and discharge/death of the PC value. Furthermore, we built a classification and regression tree (CART) model to predict the fatality rate which varied with average PC. There was a 7% chance for a mortality from H7N9 if PC was over 207.0 × 10^9/L, while there was a 46.3% chance of a mortality from H7N9 when PC was between 123.9 × 10^9/L and 207.0 × 10^9/L, and 81.3% chance of a mortality from H7N9 when PC was less than 123.9 × 10^9/L. This study demonstrates that using platelet count to predict the fatality of H7N9 is significant, and lower platelet counts of H7N9 patients were associated with higher risk of mortality of H7N9 patients, which may need to be taken into consideration when planning clinical treatment.

Funding

This work was supported by the Guangdong Provincial Science and Technology Project [2018B020207013]; National Key Research and Development Program of China [2018YFD0500500]; Science and Technology Program of Guangdong [812088869118]; Science and Technology Program of Guangzhou [201704020056]; International Program for Ph.D. Candidates, Sun Yat-sen University, China [02300–18814201]; National Natural Science Fund of China [31872499]; National Science and Technology Major Project [2018ZX10101002].

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