基于SII构建及评价预测ARDS住院死亡率的在线临床风险模型

doi:10.6040/j.issn.1671-7554.0.2024.0004

摘要/Abstract

摘要： 目的基于全身免疫炎症指数(systemic immune inflammation index, SII)探讨影响急性呼吸窘迫综合征(acute respiratory distress syndrome, ARDS)患者住院死亡率的危险因素,并建立预后预测模型。方法选取山东大学齐鲁医院2022年12月至2023年9月符合ARDS诊断标准的219例患者的资料,按3∶1的比例随机分为训练组(165例)和验证组(54例)。采用受试者工作特征(receiver operating characteristic, ROC)曲线探究SII对ARDS患者住院死亡率的预测价值,利用多因素Logistic回归分析得出的独立危险因素绘制预测ARDS患者住院死亡率的列线图,通过ROC曲线下面积(area under curve, AUC)、校准曲线、决策曲线分析(decision curve analysis, DCA)评估列线图的预测效能。结果相对于血小板与淋巴细胞的比值(platelet-to-lymphocyte ratio, PLR)、单核细胞与淋巴细胞的比值(monocyte-to-lymphocyte ratio, MLR)、C反应蛋白与白蛋白的比值(C-reactive protein-to-albumin ratio, CAR)、乳酸脱氢酶与白蛋白的比值(lactate dehydrogenase-to-albumin ratio, LAR)等其他新型炎症指标,SII曲线下面积最大(AUC=0.79),最佳截断值为3 096.60×10⁹/L,其灵敏度和特异度分别为73.70%和76.40%;多因素Logistic回归分析发现,SII、年龄、C反应蛋白(C-reactive protein, CRP)、慢性肝脏疾病和慢性肾脏疾病是影响ARDS患者住院死亡率的独立危险因素(P均<0.05)。列线图模型在训练组和验证组的AUC分别为0.876、0.848,校准曲线、DCA证实,该模型临床预测效果良好。结论入院时高SII水平与ARDS患者住院死亡风险增加相关,基于SII构建在线列线图可早期预测ARDS患者的住院死亡率,具有较高的区分度、准确性及临床实用性。

关键词: 急性呼吸窘迫综合征, 全身免疫炎症指数, 列线图, 住院死亡率, 预测模型

Abstract: Objective To explore the risk factors of in-hospital mortality in patients with acute respiratory distress syndrome(ARDS)based on systemic immune inflammation index(SII), and to develop a prognostic prediction model. Methods The data of 219 patients who met the diagnostic criteria of ARDS in Qilu Hospital of Shandong University from December 2022 to September 2023 were collected and randomly divided into a training group(n=165)and a verification group(n=54)in a ratio of 3∶1. The receiver operating characteristic(ROC)curve was used to explore the predictive value of SII for in-hospital mortality in patients with ARDS. A nomogram model predicting the risk of in-hospital death in patients with ARDS using independent risk factors derived from multifactorial Logistic regression analysis was constructed. The prediction efficiency of the nomogram was evaluated by ROC area under the curve(AUC), calibration curve and decision curve analysis(DCA). Results Compared with novel inflammatory indexes such as platelet-to-lymphocyte ratio(PLR), monocyte-to-lymphocyte ratio(MLR), C-reactive protein-to-albumin ratio(CAR)and lactate dehydrogenase-to-albumin ratio(LAR), the AUC of SII was the most prominent(AUC=0.79). When the optimal cutoff value was 3 096.60×10⁹/L, the sensitivity and specificity of SII in predicting in-hospital mortality in patients with ARDS were 73.70% and 76.40%, respectively. Multivariate Logistic regression analysis showed that SII, age, CRP, chronic liver disease and chronic kidney disease were independent risk factors for in-hospital mortality in patients with ARDS(all P<0.05). The AUC of the nomogram model in the training group and the verification group were 0.876 and 0.848, respectively. The calibration curve and DCA confirmed that the model exhibited a satisfactory degree of clinical predictive efficacy. Conclusion The high levels of SII on admission are associated with increased risk of in-hospital death in patients with ARDS. The online nomogram constructed based on SII can early predict the in-hospital mortality of patients with ARDS, with high differentiation, accuracy and clinical practicability.

Key words: Acute respiratory distress syndrome, Systemic immune inflammation index, Nomogram, In-hospital mortality, Prediction model

中图分类号:

R563.9

孙丽娜,白红艳,牛宗格,张福帅,曲仪庆. 基于SII构建及评价预测ARDS住院死亡率的在线临床风险模型[J]. 山东大学学报 (医学版), 2024, 62(7): 10-20.

SUN Lina, BAI Hongyan, NIU Zongge, ZHANG Fushuai, QU Yiqing. Construction and evaluation of an online clinical risk model for predicting in-hospital mortality in patients with ARDS based on SII[J]. Journal of Shandong University (Health Sciences), 2024, 62(7): 10-20.

参考文献

[1] Fan E, Brodie D, Slutsky AS. Acute respiratory distress syndrome: advances in diagnosis and treatment[J]. JAMA, 2018, 319(7): 698-710.
[2] Bellani G, Laffey JG, Pham T, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries[J]. JAMA, 2016, 315(8): 788-800.
[3] Meyer NJ, Gattinoni L, Calfee CS. Acute respiratory distress syndrome[J]. Lancet, 2021, 398(10300): 622-637.
[4] Mokra D, Kosutova P. Biomarkers in acute lung injury[J]. Respir Physiol Neurobiol, 2015, 209: 52-58. doi:10.1016/j.resp.2014.10.006.
[5] Terpstra ML, Aman J, van Nieuw Amerongen GP, et al. Plasma biomarkers for acute respiratory distress syndrome: a systematic review and meta-analysis[J]. Crit Care Med, 2014, 42(3): 691-700.
[6] Sipahioglu H, Onuk S. Lactate dehydrogenase/albumin ratio as a prognostic factor in severe acute respiratory distress syndrome cases associated with COVID-19[J]. Medicine(Baltimore), 2022, 101(38): e30759. doi:10.1097/md.0000000000030759.
[7] Yang LJ, Gao C, Li FY, et al. Monocyte-to-lymphocyte ratio is associated with 28-day mortality in patients with acute respiratory distress syndrome: a retrospective study[J]. J Intensive Care, 2021, 9(1): 49. doi:10.1186/s40560-021-00564-6.
[8] 吴薇, 肖影, 王健, 等. CRP/Alb、NLR、PLR联合检测对重症急性胰腺炎合并ARDS的预测价值[J]. 疑难病杂志, 2023, 22(9): 951-955. WU Wei, XIAO Ying, WANG Jian, et al. The predictive value of combined detection of CRP/Alb, NLR, and PLR in severe acute pancreatitis with ARDS[J]. Chinese Journal of Difficult and Complicated Cases, 2023, 22(9): 951-955.
[9] Yu XS, Chen ZQ, Hu YF, et al. Red blood cell distribution width is associated with mortality risk in patients with acute respiratory distress syndrome based on the Berlin definition: a propensity score matched cohort study[J]. Heart Lung, 2020, 49(5): 641-645.
[10] Chen JH, Zhai ET, Yuan YJ, et al. Systemic immune-inflammation index for predicting prognosis of colorectal cancer[J]. World J Gastroenterol, 2017, 23(34): 6261-6272.
[11] Jomrich G, Paireder M, Kristo I, et al. High systemic immune-inflammation index is an adverse prognostic factor for patients with gastroesophageal adenocarcinoma[J]. Ann Surg, 2021, 273(3): 532-541.
[12] Orhan AL, ?瘙塁aylık F, Çiçek V, et al. Evaluating the systemic immune-inflammation index for in-hospital and long-term mortality in elderly non-ST-elevation myocardial infarction patients[J]. Aging Clin Exp Res, 2022, 34(7): 1687-1695.
[13] Ozer Balin S, Ozcan EC, Ugur K. A new inflammatory marker of clinical and diagnostic importance in diabetic foot infection: systemic immune-inflammation index[J]. Int J Low Extrem Wounds, 2022: 15347346221130817. doi:10.1177/15347346221130817.
[14] Zhang D, Wang T, Dong X, et al. Systemic immune-inflammation index for predicting the prognosis of critically ill patients with acute pancreatitis[J]. Int J Gen Med, 2021, 14: 4491-4498. doi:10.2147/ijgm.s314393.
[15] Matthay MA, Arabi Y, Arroliga AC, et al. A new global definition of acute respiratory distress syndrome[J]. Am J Respir Crit Care Med, 2024, 209(1): 37-47.
[16] Matthay MA, Zemans RL. The acute respiratory distress syndrome: pathogenesis and treatment[J]. Annu Rev Pathol, 2011, 6: 147-163. doi:10.1146/annurev-pathol-011110-130158.
[17] Chen W, Janz DR, Bastarache JA, et al. Prehospital aspirin use is associated with reduced risk of acute respiratory distress syndrome in critically ill patients: a propensity-adjusted analysis[J]. Crit Care Med, 2015, 43(4): 801-807.
[18] Livingstone SA, Wildi KS, Dalton HJ, et al. Coagulation dysfunction in acute respiratory distress syndrome and its potential impact in inflammatory subphenotypes[J]. Front Med(Lausanne), 2021, 8: 723217. doi:10.3389/fmed.2021.723217.
[19] 李若寒, 李佳媚, 任佳佳, 等. 急性呼吸窘迫综合征中炎症和凝血的交互作用[J]. 中国急救医学, 2023, 43(9): 752-756. LI Ruohan, LI Jiamei, REN Jiajia, et al. Cross talks between coagulation and inflammation in acute respiratory distress syndrome[J]. Chinese Journal of Critical Care Medicine, 2023, 43(9): 752-756.
[20] Venet F, Chung CS, Huang X, et al. Lymphocytes in the development of lung inflammation: a role for regulatory CD4+ T cells in indirect pulmonary lung injury[J]. J Immunol, 2009, 183(5): 3472-3480.
[21] Hu B, Yang XR, Xu Y, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma[J]. Clin Cancer Res, 2014, 20(23): 6212-6222.
[22] Tang Y, Zeng X, Feng Y, et al. Association of systemic immune-inflammation index with short-term mortality of congestive heart failure: a retrospective cohort study[J]. Front Cardiovasc Med, 2021, 8: 753133. doi:10.3389/fcvm.2021.753133.
[23] Jia L, Li C, Bi X, et al. Prognostic value of systemic immune-inflammation index among critically ill patients with acute kidney injury: a retrospective cohort study[J]. J Clin Med, 2022, 11(14): 3978. doi:10.3390/jcm11143978.
[24] Gorman EA, OKane CM, McAuley DF. Acute respiratory distress syndrome in adults: diagnosis, outcomes, long-term sequelae, and management[J]. Lancet, 2022, 400(10358): 1157-1170.
[25] Schultz MJ, Van Oosten PJ, HOL L. Mortality among elderly patients with COVID-19 ARDS-age still does matter[J]. Pulmonology, 2023, 29(5): 353-355.
[26] 徐婷, 张存泰. 免疫衰老和老年营养[J]. 中国临床保健杂志, 2023, 26(4): 446-451. XU Ting, ZHANG Cuntai. Immunosenescence and malnutrition in elderly people[J]. Chinese Journal of Clinical Healthcare, 2023, 26(4): 446-451.
[27] Shu W, Guo S, Yang F, et al. Association between ARDS etiology and risk of noninvasive ventilation failure[J]. Ann Am Thorac Soc, 2022, 19(2): 255-263.
[28] Killien EY, Milis B, Vavilala MS, et al. Association between age and acute respiratory distress syndrome development and mortality following trauma[J]. Journal of Trauma and Acute Care Surgery, 2019, 86(5): 844-852.
[29] Huang I, Pranata R, Lim MA, et al. C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: a meta-analysis[J]. Ther Adv Respir Dis, 2020, 14: 1753466620937175. doi:10.1177/1753466620937175.
[30] Iwamura APD, Tavares da Silva MR, Hümmelgen AL, et al. Immunity and inflammatory biomarkers in COVID-19: a systematic review[J]. Rev Med Virol, 2021, 31(4): e2199. doi:10.1002/rmv.2199.
[31] Xu GG, Yang YS, Du YZ, et al. Clinical pathway for early diagnosis of COVID-19: updates from experience to evidence-based practice[J]. Clin Rev Allergy Immunol, 2020, 59(1): 89-100.
[32] Saviano A, Wrensch F, Ghany MG, et al. Liver disease and coronavirus disease 2019: from pathogenesis to clinical care[J]. Hepatology, 2021, 74(2): 1088-1100.
[33] Jalan R, Gines P, Olson JC, et al. Acute-on chronic liver failure[J]. J Hepatol, 2012, 57(6): 1336-1348.
[34] Gacouin A, Locufier M, Uhel F, et al. Liver cirrhosis is independently associated with 90-day mortality in ARDS patients[J]. Shock, 2016, 45(1): 16-21.
[35] Visconti L, Santoro D, Cernaro V, et al. Kidney-lung connections in acute and chronic diseases: current perspectives[J]. J Nephrol, 2016, 29(3): 341-348.

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