【摘要】 目的 探讨中型和重型颅脑损伤后患者血小板（platelet，Plt）参数的变化特点及临床意义。 方法 选取2009年3月-2010年3月脑外伤后24 h内入院的颅脑损伤患者75例作为观察组，于伤后1、3、7、14 d采血测定Plt数量、血小板平均体积（mean platelet volume，MPV）、血小板体积分布宽度（platelet distribution width，PDW），并同时进行格拉斯哥昏迷评分（Glasgow coma scale，GCS）。同时选取60例健康体检者，测定Plt、MPV和PDW作为对照组。 结果 观察组伤后1、3、7 d Plt计数分别为（106.21±36.31）、（102.76±35.23）、（108.37±31.32）×109/L，较对照组[（210.41±68.56）×109/L]明显降低（Plt;0.05）；观察组伤后1、3、7 d MPV分别为（12.34±1.34）、（11.21±1.52）、（10.78±1.36） fL，PDW分别为（15.78±1.26）、（17.67±1.16）、（16.72±1.21） fL，均较对照组[MPV：（8.24±1.76） fL，PDW：（12.86±1.42） fL]明显升高（Plt;0.05）；伤后14 d Plt、MPV和PDW均较对照组差异无统计学意义（Pgt;0.05）。GCS≤8分组伤后1 d Plt计数为（96.85±36.52）×109/L，明显低于GCSgt;8分组[（123.85±35.78）×109/L]，而GCS≤8分组MPV为（12.14±1.32） fL，PDW为（18.63±1.21） fL，均明显高于GCSgt;8分组[MPV：（9.78±1.34） fL，PDW：（16.72±1.34） fL]，差异均有统计学意义（Plt;0.05）。伤后第1天Plt与随访6个月GOS评分呈正相关（r=0.625，Plt;0.05）。 结论 中型和重型颅脑损伤后Plt计数明显降低，MPV和PDW值明显升高，且与伤情及预后有关。Plt及其参数的检测有助于对伤情、预后的判断。【Abstract】 Objective To investigate the platelet parameters changes and its clinical significance in medium and severe head injury patients. Methods From March 2009 to March 2010, 75 brain injury patients hospitalized within 24 h after injury were included in this study. The platelet number (Plt), mean platelet volume (MPV), platelet volume distribution width (PDW) and Glasgow coma scale were measured on the first, third, seventh and fourteenth day after injury respectively. We also measured the Plt, MPV and PDW of 60 healthy volunteers to make comparisons. Results The Plt counts were (106.21±36.31), (102.76±35.23), and (108.37±31.32)×109/L in the head injury patients on the first, third, and 7th day respectively, which were significantly lower than those in the control group [(210.41±68.56)×109/L] (Plt;0.05); the MPV and PDW values measured on the first day [MPV: (12.34±1.34) fL, PDW: (15.78±1.26) fL] and the third day [MPV: (11.21±1.52) fL, PDW: (17.67±1.16)fL] were both significantly lower than those of the control group (Plt;0.05); There was no evidence of a difference in Plt, MPV and PDW between the two groups fourteen day after injury (P＞0.05); The Plt count was (96.85±36.52)×109/L in GCS≤8 group on the first day, which was significantly lower than that of GCSgt;8 group [(123.85±35.78) fL, Plt;0.05]; However, the MPV and PDW values in GCS≤8 group [(MPV: (12.14±1.32) fL, PDW: (18.63±1.21) fL] were both significantly higher than those of GCSgt;8 group [MPV: (9.78±1.34) fL, PDW: (16.72±1.34) fL, Plt;0.05]; The Plt count was correlated with GOS score positively (r=0.625,Plt;0.05). Conclusions Medium and severe head injury patients are significantly associated with a lower Plt count and increased MPV and PDW values. The Plt parameters changes are correlated with the prognosis of patients. Therefore, the measurement of Plt parameters may contribute to the valuation of severity and prognosis, and provide new ideas for treatment of head injury patients.
ObjectiveTo observe the correlation between blood cell-related inflammatory markers and diabetic retinopathy (DR). MethodsA cross-sectional study. From June 2020 to February 2022, the phase Ⅰ data of Beichen Eye Study in Tianjin Medical University Eye Hospital were included in the study. The research contents included questionnaires, routine systemic and ocular examinations, and laboratory blood cell-related indicators including mean platelet volume (MPV), platelet distribution width (PDW), neutrophils, and lymphocytes were performed. Neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) were calculated. The diagnosis and classification of DR referred to the international clinical classification standard of DR. Monocular or binocular DR was defined as DR patients. Participants were categorized into different groups based on whether they had diabetes and whether they had DR. The groups included the no-diabetes group, the diabetes without DR group, and the DR group. The Kruskal-Wallis H test was used for the comparison of quantitative data among multiple groups. Wilcoxon test was used for comparison between the two groups. The χ2 test was used to compare the categorical variables between groups. The variables was adjusted step by step, an unadjusted univariate model was built and the different parameters of the model Ⅰ, Ⅱ, Ⅲ were adjusted. The correlation between MPV, PDW, NLR, PLR, and DR in different models was analyzed by logistic regression. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficacy of different NLR models for DR. ResultsA total of 3 328 subjects were recruited. Among them, 1 121 (33.68 %, 1 121/3 328) were males and 2 207 (66.32 %, 2 207/3 328) were females. The median age of the included participants was 61.84 (6.05) years. The no-diabetes group, the diabetes without DR group, and the DR group were 2 679, 476, and 173, respectively. There was no significant difference in MPV and PLR among the three groups (H=5.98, 1.94; P=0.051, 0.379). However, compared with no-diabetes group and the diabetes without DR group, PDW and NLR in the DR group showed an upward trend. In model Ⅲ with completely adjusted related factors, NLR was an independent risk factor for DR in no-diabetes group and DR group [odds ratio (OR)=1.440, 95% confidence interval (CI) 1.087-1.920, P=0.041], diabetes without DR group and DR group [OR=1.990, 95% CI 1.440-2.749, P＜0.001]. The results of ROC curve analysis showed that the diagnostic efficiency of NLR model Ⅲ was the highest, the area under the curve was 0.751 (95%CI 0.706-0.796, P＜0.001), the optimal cutoff value was 0.390, and the sensitivity and specificity were 74.3% and 64.8%, respectively. ConclusionsThe NLR of the DR group is significantly higher than that of the no-diabetes group and diabetes without DR group. NLR is an independent risk factor for DR.