Table2_Personalized, disease-stage specific, rapid identification of immunosuppression in sepsis.docx

IntroductionData overlapping of different biological conditions prevents personalized medical decision-making. For example, when the neutrophil percentages of surviving septic patients overlap with those of non-survivors, no individualized assessment is possible. To ameliorate this problem, an immunological method was explored in the context of sepsis.MethodsBlood leukocyte counts and relative percentages as well as the serum concentration of several proteins were investigated with 4072 longitudinal samples collected from 331 hospitalized patients classified as septic (n=286), non-septic (n=43), or not assigned (n=2). Two methodological approaches were evaluated: (i) a reductionist alternative, which analyzed variables in isolation; and (ii) a non-reductionist version, which examined interactions among six (leukocyte-, bacterial-, temporal-, personalized-, population-, and outcome-related) dimensions.ResultsThe reductionist approach did not distinguish outcomes: the leukocyte and serum protein data of survivors and non-survivors overlapped. In contrast, the non-reductionist alternative differentiated several data groups, of which at least one was only composed of survivors (a finding observable since hospitalization day 1). Hence, the non-reductionist approach promoted personalized medical practices: every patient classified within a subset associated with 100% survival subset was likely to survive. The non-reductionist method also revealed five inflammatory or disease-related stages (provisionally named ‘early inflammation, early immunocompetence, intermediary immuno-suppression, late immuno-suppression, or other’). Mortality data validated these labels: both ‘suppression’ subsets revealed 100% mortality, the ‘immunocompetence’ group exhibited 100% survival, while the remaining sets reported two-digit mortality percentages. While the ‘intermediary’ suppression expressed an impaired monocyte-related function, the ‘late’ suppression displayed renal-related dysfunctions, as indicated by high concentrations of urea and creatinine.DiscussionThe data-driven differentiation of five data groups may foster early and non-overlapping biomedical decision-making, both upon admission and throughout their hospitalization. This approach could evaluate therapies, at personalized level, earlier. To ascertain repeatability and investigate the dynamics of the ‘other’ group, additional studies are recommended.

不同生物条件下的数据重叠现象阻碍了个性化医疗决策的实施。例如，在存活患者的嗜中性粒细胞百分比与非存活患者相重叠的情况下，无法进行个体化评估。为缓解这一问题，本研究在脓毒症背景下探索了一种免疫学方法。研究方法：通过分析331名住院患者（脓毒症组n=286，非脓毒症组n=43，未分类组n=2）的4072个纵向样本中的血液白细胞计数及其相对百分比以及几种蛋白质的血清浓度，评估了两种方法学途径：（i）一种还原主义替代方法，该方法独立分析变量；（ii）一种非还原主义版本，该方法考察了六个维度（白细胞、细菌、时间、个性化、人口和结果相关）之间的相互作用。研究结果：还原主义方法无法区分结果：存活者与非存活者的白细胞和血清蛋白数据存在重叠。相比之下，非还原主义方法区分了多个数据组，其中至少有一个仅由存活者组成（这一发现自住院第一天起即可观察到）。因此，非还原主义方法促进了个性化医疗实践：在关联100%存活子集中的每位患者均有可能存活。非还原主义方法还揭示了五个炎症或疾病相关阶段（暂命名为‘早期炎症、早期免疫竞争力、中介免疫抑制、晚期免疫抑制或其他’）。死亡率数据验证了这些标签：两个‘抑制’子集均显示100%的死亡率，‘免疫竞争力’组显示出100%的存活率，而其他子集报告了两位数的死亡率百分比。其中，‘中介’抑制表现出与单核细胞相关的功能受损，而‘晚期’抑制则显示出与肾脏相关的功能障碍，如尿素和肌酐的高浓度所示。讨论：基于数据的五个数据组的区分可能促进早期且无重叠的生物医学决策，这不仅适用于入院时，也适用于整个住院期间。这种方法可以在个性化层面上更早地评估治疗方案。为确保可重复性并研究‘其他’组的动态变化，建议进行进一步研究。