首页> 外文期刊>Frontiers in Medicine >Unsupervised Clustering Analysis Based on MODS Severity Identifies Four Distinct Organ Dysfunction Patterns in Severely Injured Blunt Trauma Patients
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Unsupervised Clustering Analysis Based on MODS Severity Identifies Four Distinct Organ Dysfunction Patterns in Severely Injured Blunt Trauma Patients

机译:基于MODS严重程度的无监督聚类分析识别严重受伤的钝性创伤患者的四种不同器官功能障碍模式

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Background: Multiple organ dysfunction syndrome (MODS) typically peaks within 5 days post injury and is associated with a complicated clinical course; however, the type and number of distinct organ failure patterns that occur after injury are unknown. To address this, we sought to identify a MOD score parameter that highly correlates with adverse outcomes and then use this parameter to test the hypothesis that multiple severity-based MODS clusters could be identified after blunt trauma in humans. Methods: MOD score across days (D) 2-5 was subjected to Fuzzy C-means Clustering Analysis (FCM) followed by eight Clustering Validity Indices (CVI) to derive organ dysfunction patterns among 376 trauma patients that survived to discharge. Thirty-one inflammation biomarkers were assayed (by Luminex?) in serial blood samples (3 samples within the first 24 h and then daily up to day 5 post-injury) and were analyzed using Two-Way ANOVA and Dynamic Network analysis (DyNA). Results: The FCM followed by CVI suggested four distinct clusters based on MOD score magnitude between D2-5: Cluster 1 (n=199, average MOD score=0.3); Cluster 2 (n=99, average MOD score=2); Cluster 3 (n=53, average MOD score=4); and Cluster 4 (n=25, average MOD score=7). There were statistically significant differences among the four clusters regarding to in-hospital outcomes, including intensive care unit (ICU) and total hospital stay, days on mechanical ventilation, and incidence of nosocomial infection. Of the 31 biomarkers measured, IL-6, MCP-1, IL-10, IL-8, IP-10, sST2, and MIG were elevated differentially over time across the four clusters. DyNA identified remarkable differences in inflammatory network interconnectivity over time among the four clusters. Conclusion: These results suggest the existence of four distinct organ failure patterns based on MOD score magnitude in trauma patients admitted to the ICU and survive to discharge. The organ failure patterns are preceded by distinct inflammatory responses and followed by differences in in-hospital outcomes.
机译:背景:多器官功能障碍综合征(MODS)通常在损伤后5天内峰值,与复杂的临床课程相关联;但是,损伤后发生的不同器官失效模式的类型和数量是未知的。为了解决这个问题,我们试图识别与不利结果高度相关的Mod变量参数,然后使用该参数来测试在人类中钝的创伤之后可以识别多个基于SOMS簇的假设。方法:跨越日(d)2-5的Mod得分进行模糊C-means聚类分析(FCM),然后进行八个聚类有效性指数(CVI),以衍生出376名幸存下来的患者的器官功能障碍模式。在连续血液样品中(通过Luminexα)测定三十次炎症生物标志物(在前24小时内3个样品,然后每日损伤后第5天),并使用双向ANOVA和动态网络分析(Dyna)分析。结果:FCM后跟CVI建议基于D2-5:群集1(n = 199,平均Mod得分= 0.3)基于MOD分数幅度的四个不同群集;群集2(n = 99,平均mod得分= 2);群集3(n = 53,平均mod得分= 4);和群集4(n = 25,平均mod得分= 7)。在医院内外的四个集群中存在统计学上的差异,包括重症监护单元(ICU)和总医院住宿,机械通气的日子以及医院感染的发生率。在测量的31个生物标志物中,IL-6,MCP-1,IL-10,IL-8,IP-10,SST2和MIG在四个簇上随时间差异升高。 Dyna在四个集群中确定了随着时间的推移炎症网络互连的显着差异。结论:这些结果表明基于MoM患者的MOD分数幅度存在四种不同器官失效模式的存在,进入ICU并存入放电。器官衰竭模式前面是不同的炎症反应,然后是医院内结果的差异。

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