病原宏基因组二代测序在肺部感染所致脓毒症患者中的应用

doi:10.3969/j.issn.1674-8115.2025.02.005

上海交通大学学报（医学版） ›› 2025, Vol. 45 ›› Issue (2): 169-178.doi: 10.3969/j.issn.1674-8115.2025.02.005

• 论著 · 临床研究 • 上一篇

病原宏基因组二代测序在肺部感染所致脓毒症患者中的应用

徐斐翔¹(), 俞凤², 王瑞兰³, 宋振举⁴, 童朝阳⁴, 朱长清¹()

^1.上海交通大学医学院附属仁济医院急诊科，上海 200127
^2.安徽医科大学第一附属医院急诊科，合肥 230032
^3.上海交通大学医学院附属第一人民医院急诊与重症医学科，上海 200080
^4.复旦大学附属中山医院急诊科，上海 200032

收稿日期:2024-09-27 接受日期:2024-11-22 出版日期:2025-02-28 发布日期:2025-02-28
通讯作者: 朱长清 E-mail:1303917815@qq.com;zhuzhangqing@renji.com
作者简介:徐斐翔（1991—），男，主治医师，硕士生；电子信箱：1303917815@qq.com。
基金资助:
上海市急危重症临床研究中心项目(21MC1930400)

Application of metagenomics next-generation sequencing of pathogen in patients with pneumonia-induced sepsis

XU Feixiang¹(), YU Feng², WANG Ruilan³, SONG Zhenju⁴, TONG Chaoyang⁴, ZHU Changqing¹()

^1.Department of Emergency, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
^2.Department of Emergency, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
^3.Department of Emergency and Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
^4.Department of Emergency, Zhongshan Hospital, Fudan University, Shanghai 200032, China

Received:2024-09-27 Accepted:2024-11-22 Online:2025-02-28 Published:2025-02-28
Contact: ZHU Changqing E-mail:1303917815@qq.com;zhuzhangqing@renji.com
Supported by:
Shanghai Committee of Science and Technology(21MC1930400)

摘要/Abstract

摘要：

目的·探讨病原宏基因组二代测序（metagenomics next-generation sequencing，mNGS）在肺部感染所致脓毒症患者中协助病原体早期诊断、指导临床抗感染方案调整、改善患者近期预后的价值。方法·该研究由一项多中心、前瞻性、非随机对照试验及一项诊断试验构成。纳入2020年3月—2021年10月在4家医院住院治疗的肺部感染所致脓毒症患者，所有患者均符合美国重症医学会与欧洲重症医学会发布的脓毒症3.0标准和肺部感染的临床诊断标准。入组患者根据自身意愿选择感染部位标本的病原学检测方法，包括仅送检传统病原学检测（传统病原学检测组），或在送检传统病原学检测的基础上同步送检mNGS（联合mNGS检测组）。患者预后的主要评价指标为7 d全因死亡率，次要评价指标包括7 d序贯器官衰竭评分（Sequential Organ Failure Assessment，SOFA）变化、7 d急性生理与慢性健康状况评分Ⅱ（Acute Physiology and Chronic Health Evaluation Ⅱ，APACHEⅡ）变化、28 d全因死亡率、28 d机械通气或死亡的复合终点发生率、28 d无呼吸机天数、28 d非住院天数和住院期间日均花费。采用倾向性评分匹配均衡2组的协变量分布，采用Kaplan-Meier生存曲线和Cox比例风险模型比较2组死亡率。使用联合mNGS检测组患者感染部位标本的病原学检测结果进行诊断试验。以临床综合判定的责任病原体为参考标准，分别将传统病原学检测和mNGS检测结果与参考标准相比较，计算2种方法和参考标准之间的阳性百分比一致性、阴性百分比一致性、阳性预测值和阴性预测值，并采用McNemar配对χ²检验比较2种检测方法的责任病原体检出能力。结果·共入组患者533例，其中311例选择接受额外的mNGS检测，222例仅接受传统病原学检测。非随机对照试验中，经倾向性评分匹配平衡协变量后，联合mNGS检测组的7 d全因死亡率低于传统病原学检测组［4.8% vs 8.6%，HR 0.37（95%CI 0.15~0.91），P=0.031］，联合mNGS检测组的28 d无呼吸机天数多于传统病原学检测组（19.9 d vs 18.4 d，P=0.041）。2组在28 d全因死亡率和日均住院花费上差异无统计学意义。诊断试验提示，mNGS检测结果与临床综合判定的责任病原体的阳性百分比一致性高于传统病原学检测［91.9%（95%CI 87.7%~95.0%） vs 56.1%（95%CI 49.7%~62.4%），P<0.001］，阴性百分比一致性低于传统病原学检测［29.2%（95%CI 18.6%~41.8%） vs 69.2%（95%CI 56.6%~80.1%），P<0.001］，阴性预测值高于传统病原学检测［48.7%（95%CI 32.4%~65.2%）vs 29.4%（95%CI 22.3%~37.3%），P=0.001］。结论·与传统病原学检测相比，肺部感染所致脓毒症患者感染部位标本行mNGS可提高责任病原体的检出率。相比于仅行传统病原学检测，联合mNGS检测的患者7 d全因死亡率更低，提示mNGS在肺部感染所致脓毒症患者中具有临床价值及应用前景。

关键词: 宏基因组二代测序, 脓毒症, 肺部感染, 全因死亡

Abstract:

Objective ·To explore the diagnostic, therapeutic, and prognostic value of metagenomics next-generation sequencing (mNGS) in patients with pneumonia-induced sepsis. Methods ·This study consisted of a multicenter, prospective, non-randomized controlled trial and a diagnostic test. Patients with pneumonia-induced sepsis who were hospitalized in four hospitals across China were enrolled between March 2020 and October 2021. All patients met the Sepsis-3 criteria issued by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine, as well as the clinical diagnostic standard of pneumonia. Enrolled patients were assigned based on their preference to either the conventional test-only group [receiving only conventional test (CMT)] or the combined mNGS test group (receiving CMT and mNGS concurrently). The primary outcome was the 7-day all-cause mortality rate, and secondary outcomes included the changes in SOFA and APACHE Ⅱ scores from baseline to day 7, 28-day all-cause mortality rate, the composite endpoint of mechanical ventilation or death within 28 d, 28 d ventilation-free days, 28 d hospital-free days, and the average daily hospitalization cost. Propensity score matching was used to balance covariates between the two groups. Kaplan-Meier curves were plotted and Cox proportional hazards models were built to compare the risk of death between the two groups. Pathogen detection results from infection site samples in the combined mNGS test group were used for the diagnostic test. The clinically-adjudicated causative pathogens was used as the reference standard. The results of traditional pathogen detection and mNGS detection were compared respectively with the reference standard. The positive percent agreement, negative percent agreement, positive predictive value, and negative predictive value between the two methods and the reference standard were calculated. McNemar's χ²test was used to evaluate the causative pathogen detection capabilities of the two methods. Results ·A total of 533 patients were enrolled, of whom 311 opted for additional mNGS testing, while 222 received only conventional pathogenetic testing. In the non-randomized controlled trial, after propensity score matching to balance covariates, the 7-day all-cause mortality was lower in the combined mNGS test group compared to the conventional test-only group [4.8% vs 8.6%, HR 0.37 (95%CI 0.15‒0.91), P=0.031]. Additionally, the 28-day ventilation-free days were increased in the combined mNGS test group (19.9 d vs 18.4 d, P=0.041). No significant difference was observed between the two groups in terms of 28-day all-cause mortality or the average daily hospitalization costs. In the diagnostic test, compared to the reference standard, the positive percent agreement of mNGS with the clinical composite judgment for causative pathogens was higher than that of CMT [91.9% (95%CI 87.7%‒95.0%) vs 56.1% (95%CI 49.7%‒62.4%), P<0.001]. Conversely, the negative percent agreement of mNGS was lower than that of CMT [29.2% (95%CI 18.6%‒41.8%) vs 69.2% 95%CI 56.6%‒80.1%), P<0.001]. The negative predictive value of nNGS was higher than that of CMT [48.7%(95%CI 32.4%‒65.2%) vs 29.4% (95%CI 22.3%‒37.3%), P=0.001]. Conclusion ·In patients with pneumonia-induced sepsis, mNGS of infection site samples demonstrated a higher detection rate of causative pathogen compared to CMT. Furthermore, the combination of mNGS with CMT may help reduce the 7-day all-cause mortality, suggesting that mNGS has clinical value and potential for application in the management of sepsis caused by pulmonary infections.

Key words: metagenomics next-generation sequencing (mNGS), sepsis, pneumonia, all-cause mortality

中图分类号:

R563.1

徐斐翔, 俞凤, 王瑞兰, 宋振举, 童朝阳, 朱长清. 病原宏基因组二代测序在肺部感染所致脓毒症患者中的应用[J]. 上海交通大学学报（医学版）, 2025, 45(2): 169-178.

XU Feixiang, YU Feng, WANG Ruilan, SONG Zhenju, TONG Chaoyang, ZHU Changqing. Application of metagenomics next-generation sequencing of pathogen in patients with pneumonia-induced sepsis[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025, 45(2): 169-178.

图/表 5

参考文献 23

1	SINGER M, DEUTSCHMAN C S, SEYMOUR C W, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3)[J]. JAMA, 2016, 315(8): 801-810.
2	RUDD K E, JOHNSON S C, AGESA K M, et al. Global, regional, and national sepsis incidence and mortality, 1990‒2017: analysis for the Global Burden of Disease Study[J]. Lancet, 2020, 395(10219): 200-211.
3	WENG L, XU Y, YIN P, et al. National incidence and mortality of hospitalized sepsis in China[J]. Crit Care, 2023, 27(1): 84.
4	MESSACAR K, PARKER S K, TODD J K, et al. Implementation of rapid molecular infectious disease diagnostics: the role of diagnostic and antimicrobial stewardship[J]. J Clin Microbiol, 2017, 55(3): 715-723.
5	DIAO Z L, HAN D S, ZHANG R, et al. Metagenomics next-generation sequencing tests take the stage in the diagnosis of lower respiratory tract infections[J]. J Adv Res, 2021, 38: 201-212.
6	BLAUWKAMP T A, THAIR S, ROSEN M J, et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease[J]. Nat Microbiol, 2019, 4(4): 663-674.
7	QU J M, ZHANG J, CHEN Y, et al. Aetiology of severe community acquired pneumonia in adults identified by combined detection methods: a multi-centre prospective study in China[J]. Emerg Microbes Infect, 2022, 11(1): 556-566.
8	HOGAN C A, YANG S X, GARNER O B, et al. Clinical impact of metagenomic next-generation sequencing of plasma cell-free DNA for the diagnosis of infectious diseases: a multicenter retrospective cohort study[J]. Clin Infect Dis, 2021, 72(2): 239-245.
9	GU W, DENG X D, LEE M, et al. Rapid pathogen detection by metagenomic next-generation sequencing of infected body fluids[J]. Nat Med, 2021, 27(1): 115-124.
10	UNAL I. Defining an optimal cut-point value in ROC analysis: an alternative approach[J]. Comput Math Methods Med, 2017, 2017: 3762651.
11	JING C D, CHEN H B, LIANG Y, et al. Clinical evaluation of an improved metagenomic next-generation sequencing test for the diagnosis of bloodstream infections[J]. Clin Chem, 2021, 67(8): 1133-1143.
12	ZHU Y Q, GAN M Y, GE M M, et al. Diagnostic performance and clinical impact of metagenomic next-generation sequencing for pediatric infectious diseases[J]. J Clin Microbiol, 2023, 61(6): e0011523.
13	CHEN H B, YIN Y Y, GAO H, et al. Clinical utility of in-house metagenomic next-generation sequencing for the diagnosis of lower respiratory tract infections and analysis of the host immune response[J]. Clin Infect Dis, 2020, 71(Suppl 4): S416-S426.
14	MIAO Q, MA Y Y, WANG Q Q, et al. Microbiological diagnostic performance of metagenomic next-generation sequencing when applied to clinical practice[J]. Clin Infect Dis, 2018, 67(suppl_2): S231-S240.
15	QIAN Y Y, WANG H Y, ZHOU Y, et al. Improving pulmonary infection diagnosis with metagenomic next generation sequencing[J]. Front Cell Infect Microbiol, 2020, 10: 567615.
16	ZUO Y H, WU Y X, HU W P, et al. The clinical impact of metagenomic next-generation sequencing (mNGS) test in hospitalized patients with suspected sepsis: a multicenter prospective study[J]. Diagnostics (Basel), 2023, 13(2): 323.
17	WILSON M R, SAMPLE H A, ZORN K C, et al. Clinical metagenomic sequencing for diagnosis of meningitis and encephalitis[J]. N Engl J Med, 2019, 380(24): 2327-2340.
18	ROSSOFF J, CHAUDHURY S, SONEJI M, et al. Noninvasive diagnosis of infection using plasma next-generation sequencing: a single-center experience[J]. Open Forum Infect Dis, 2019, 6(8): ofz327.
19	LIN P C, CHEN Y, SU S S, et al. Diagnostic value of metagenomic next-generation sequencing of bronchoalveolar lavage fluid for the diagnosis of suspected pneumonia in immunocompromised patients[J]. BMC Infect Dis, 2022, 22(1): 416.
20	SEYMOUR C W, GESTEN F, PRESCOTT H C, et al. Time to treatment and mortality during mandated emergency care for sepsis[J]. N Engl J Med, 2017, 376(23): 2235-2244.
21	BAGHDADI J D, BROOK R H, USLAN D Z, et al. Association of a care bundle for early sepsis management with mortality among patients with hospital-onset or community-onset sepsis[J]. JAMA Intern Med, 2020, 180(5): 707-716.
22	RHEE C, JONES T M, HAMAD Y, et al. Prevalence, underlying causes, and preventability of sepsis-associated mortality in US acute care hospitals[J]. JAMA Netw Open, 2019, 2(2): e187571.
23	STRATTON C W, SCHUTZBANK T E, TANG Y W. Use of metagenomic next-generation sequencing in the clinical microbiology laboratory: a step forward, but not an end-all[J]. J Mol Diagn, 2021, 23(11): 1415-1421.

Characteristic	Before PSM				After PSM
Characteristic	Conventional test-only group (n=222)	Combined mNGS test group (n=311)	P value	SMD	Conventional test-only group (n=187)	Combined mNGS test group (n=187)	P value	SMD
Male/n(%)	164 (73.9)	224 (72.0)	0.708	0.042	136 (72.7)	138 (73.8)	0.907	0.024
Age/year	67.0 (53.2, 75.0)	68.0 (56.0, 78.0)	0.079	0.151	67.0 (58.0, 75.0)	68.0 (54.5, 77.0)	0.996	0.006
BMI/(kg·m^-2)	22.7 (20.8, 24.6)	22.9 (20.8, 25.1)	0.285	0.040	22.6 (20.7, 24.5)	22.6 (20.4, 24.9)	0.823	0.003
ICH/n(%)	34 (15.3)	55 (17.7)	0.545	0.064	29 (15.5)	30 (16.0)	1.000	0.015
CCI	3.0 (2.0, 5.0)	4.0 (2.0, 5.0)	0.295	0.107	3.0 (2.0, 6.0)	3.0 (2.0, 5.0)	0.524	0.062
Shock/n(%)	57 (25.7)	108 (34.7)	0.033	0.198	50 (26.7)	55 (29.4)	0.645	0.060
MODS/n(%)	53 (23.9)	125 (40.2)	<0.001	0.355	52 (27.8)	47 (25.1)	0.639	0.061
HR/(beat per minute)	98.0 (84.2, 114.0)	101.0 (85.0, 117.0)	0.341	0.119	98.0 (84.0, 115.0)	100.0 (84.0, 116.0)	0.526	0.101
RR/(breath per minute)	20.0 (18.0, 24.0)	22.0 (19.0, 26.0)	0.037	0.183	20.0 (18.0, 24.0)	22.0 (19.5, 26.5)	0.009	0.277
SBP/mmHg	124.0 (109.0, 137.8)	120.0 (105.0, 139.5)	0.479	0.076	123.0 (109.0, 139.5)	120.0 (104.0, 136.5)	0.268	0.147
DBP/mmHg	71.0 (63.0, 79.0)	70.0 (60.0, 79.0)	0.162	0.122	70.0 (62.0, 78.0)	70.0 (60.0, 79.0)	0.427	0.105
Baseline WBC/(×10⁹·L^-1)	10.1 (6.8, 16.0)	10.5 (6.9, 15.6)	0.698	0.029	10.2 (6.8, 16.1)	10.3 (7.1, 14.7)	0.914	0.038
Baseline PCT/(ng·mL^-1)	0.8 (0.3, 7.5)	1.4 (0.3, 9.5)	0.318	0.091	0.8 (0.3, 7.0)	0.8 (0.2, 7.3)	0.685	0.008
Total bilirubin/(μmol·L^-1)	13.8 (9.7, 22.7)	14.1 (9.1, 22.0)	0.765	0.099	13.7 (9.7, 22.2)	13.6 (8.3, 20.8)	0.239	0.007
Serum creatinine/(μmol·L^-1)	83.4 (60.0, 130.8)	78.0 (56.8, 137.5)	0.682	0.015	80.3 (58.1, 119.4)	76.0 (57.5, 115.0)	0.507	0.016
PaO₂/FiO₂	212.0 (152.0, 287.8)	200.0 (127.5, 275.0)	0.100	0.105	214.0 (148.5, 286.0)	202.0 (140.5, 275.5)	0.482	0.034
Invasive mechanical ventilation/n(%)	67 (30.2)	167 (53.7)	<0.001	0.491	64 (34.2)	68 (36.4)	0.745	0.045
Baseline SOFA/score	5.0 (3.0, 8.0)	6.0 (4.0, 10.5)	0.009	0.228	5.0 (3.0, 9.0)	5.0 (3.0, 10.0)	0.645	0.082
Baseline APACHE Ⅱ/score	15.0 (10.2, 22.8)	18.0 (11.5, 24.0)	0.021	0.164	15.0 (10.5, 21.5)	15.0 (10.0, 23.0)	0.786	0.016
Number of antibiotics	2.0 (1.0, 2.0)	2.0 (1.5, 3.0)	0.063	0.185	2.0 (1.0, 2.0)	2.0 (1.0, 2.0)	0.874	0.041
Glucocorticoids/n(%)	58 (26.1)	87 (28.0)	0.708	0.042	50 (26.7)	55 (29.4)	0.645	0.060
Anticoagulation/n(%)	46 (20.7)	77 (24.8)	0.324	0.096	41 (21.9)	46 (24.6)	0.624	0.063
CRRT/n(%)	17 (7.7)	39 (12.5)	0.095	0.163	16 (8.6)	17 (9.1)	1.000	0.019

Characteristic	Before PSM				After PSM
Characteristic	Conventional test-only group (n=222)	Combined mNGS test group (n=311)	P value	SMD	Conventional test-only group (n=187)	Combined mNGS test group (n=187)	P value	SMD
Male/n(%)	164 (73.9)	224 (72.0)	0.708	0.042	136 (72.7)	138 (73.8)	0.907	0.024
Age/year	67.0 (53.2, 75.0)	68.0 (56.0, 78.0)	0.079	0.151	67.0 (58.0, 75.0)	68.0 (54.5, 77.0)	0.996	0.006
BMI/(kg·m^-2)	22.7 (20.8, 24.6)	22.9 (20.8, 25.1)	0.285	0.040	22.6 (20.7, 24.5)	22.6 (20.4, 24.9)	0.823	0.003
ICH/n(%)	34 (15.3)	55 (17.7)	0.545	0.064	29 (15.5)	30 (16.0)	1.000	0.015
CCI	3.0 (2.0, 5.0)	4.0 (2.0, 5.0)	0.295	0.107	3.0 (2.0, 6.0)	3.0 (2.0, 5.0)	0.524	0.062
Shock/n(%)	57 (25.7)	108 (34.7)	0.033	0.198	50 (26.7)	55 (29.4)	0.645	0.060
MODS/n(%)	53 (23.9)	125 (40.2)	<0.001	0.355	52 (27.8)	47 (25.1)	0.639	0.061
HR/(beat per minute)	98.0 (84.2, 114.0)	101.0 (85.0, 117.0)	0.341	0.119	98.0 (84.0, 115.0)	100.0 (84.0, 116.0)	0.526	0.101
RR/(breath per minute)	20.0 (18.0, 24.0)	22.0 (19.0, 26.0)	0.037	0.183	20.0 (18.0, 24.0)	22.0 (19.5, 26.5)	0.009	0.277
SBP/mmHg	124.0 (109.0, 137.8)	120.0 (105.0, 139.5)	0.479	0.076	123.0 (109.0, 139.5)	120.0 (104.0, 136.5)	0.268	0.147
DBP/mmHg	71.0 (63.0, 79.0)	70.0 (60.0, 79.0)	0.162	0.122	70.0 (62.0, 78.0)	70.0 (60.0, 79.0)	0.427	0.105
Baseline WBC/(×10⁹·L^-1)	10.1 (6.8, 16.0)	10.5 (6.9, 15.6)	0.698	0.029	10.2 (6.8, 16.1)	10.3 (7.1, 14.7)	0.914	0.038
Baseline PCT/(ng·mL^-1)	0.8 (0.3, 7.5)	1.4 (0.3, 9.5)	0.318	0.091	0.8 (0.3, 7.0)	0.8 (0.2, 7.3)	0.685	0.008
Total bilirubin/(μmol·L^-1)	13.8 (9.7, 22.7)	14.1 (9.1, 22.0)	0.765	0.099	13.7 (9.7, 22.2)	13.6 (8.3, 20.8)	0.239	0.007
Serum creatinine/(μmol·L^-1)	83.4 (60.0, 130.8)	78.0 (56.8, 137.5)	0.682	0.015	80.3 (58.1, 119.4)	76.0 (57.5, 115.0)	0.507	0.016
PaO₂/FiO₂	212.0 (152.0, 287.8)	200.0 (127.5, 275.0)	0.100	0.105	214.0 (148.5, 286.0)	202.0 (140.5, 275.5)	0.482	0.034
Invasive mechanical ventilation/n(%)	67 (30.2)	167 (53.7)	<0.001	0.491	64 (34.2)	68 (36.4)	0.745	0.045
Baseline SOFA/score	5.0 (3.0, 8.0)	6.0 (4.0, 10.5)	0.009	0.228	5.0 (3.0, 9.0)	5.0 (3.0, 10.0)	0.645	0.082
Baseline APACHE Ⅱ/score	15.0 (10.2, 22.8)	18.0 (11.5, 24.0)	0.021	0.164	15.0 (10.5, 21.5)	15.0 (10.0, 23.0)	0.786	0.016
Number of antibiotics	2.0 (1.0, 2.0)	2.0 (1.5, 3.0)	0.063	0.185	2.0 (1.0, 2.0)	2.0 (1.0, 2.0)	0.874	0.041
Glucocorticoids/n(%)	58 (26.1)	87 (28.0)	0.708	0.042	50 (26.7)	55 (29.4)	0.645	0.060
Anticoagulation/n(%)	46 (20.7)	77 (24.8)	0.324	0.096	41 (21.9)	46 (24.6)	0.624	0.063
CRRT/n(%)	17 (7.7)	39 (12.5)	0.095	0.163	16 (8.6)	17 (9.1)	1.000	0.019

Antibiotics adjustment	Conventional test-only group (n=222)	Combined mNGS test group (n=311)	P value
Escalation/n(%)	22 (9.9)	52 (16.7)	0.034
De-escalation/n(%)	17 (7.7)	73 (23.5)	<0.001
Total adjustment/n(%)	28 (12.6)	94 (30.2)	<0.001

Antibiotics adjustment	Conventional test-only group (n=222)	Combined mNGS test group (n=311)	P value
Escalation/n(%)	22 (9.9)	52 (16.7)	0.034
De-escalation/n(%)	17 (7.7)	73 (23.5)	<0.001
Total adjustment/n(%)	28 (12.6)	94 (30.2)	<0.001

Indicator	Conventional test-only group (n=187)	Combined mNGS test group (n=187)	P value
Primary indicator
7-day all-cause mortality rate/%	8.6 (5.3‒13.5)	4.8 (2.6‒8.9)	0.031
Secondary indicator
Change of SOFA score at day 7/score	-1.2 (-1.9‒-0.6)	-1.9 (-2.5‒-1.3)	0.478
Change of APACHE Ⅱ score at day 7/score	-2.4 (-3.5‒-1.3)	-2.7 (-3.9‒-1.5)	0.843
28-day all-cause mortality rate/%	19.3 (14.2‒25.8)	16.6 (11.9‒22.6)	0.129
Mechanical ventilation or death by day 28 in patients not receiving endotracheal intubation at baseline/%	25.2 (18.4‒33.5)	16.8 (11.2‒24.5)	0.038
Ventilation-free days within 28 d/d	18.4 (16.5‒20.3)	19.9 (18.2‒21.5)	0.041
Hospital-free days within 28 d/d	6.9 (5.7‒8.1)	7.1 (6.0‒8.2)	0.760
Daily hospitalization cost/yuan	5 738 (5 049‒6 427)	5 861 (5 062‒6 661)	0.711

病原宏基因组二代测序在肺部感染所致脓毒症患者中的应用

Application of metagenomics next-generation sequencing of pathogen in patients with pneumonia-induced sepsis

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 23

相关文章 14

编辑推荐

Metrics

[1]	刘雨婷, 俞莞琦, 洪雯, 康桑, 李歆旎, 旦增曲央, 肖活源, 潘静薇. 临床衰弱指数对急性心肌梗死患者在院心脏康复后远期预后的预测价值[J]. 上海交通大学学报（医学版）, 2024, 44(5): 599-605.
[2]	宋晨璐, 向军, 杨惠忠. 血清肝素结合蛋白对重度烧伤患者预后及脓毒症发生的早期预警价值[J]. 上海交通大学学报（医学版）, 2024, 44(4): 474-481.
[3]	唐晓梦1, 2，任玉倩1，熊熙1, 2，缪惠洁1，邵卢晶1, 2，崔云1，张育才1, 2，王春霞1, 2. 成纤维细胞生长因子19预测儿童脓毒症合并胃肠功能障碍的临床价值[J]. 上海交通大学学报(医学版), 2020, 40(9): 1236-1242.
[4]	阮昕 1，张颖婷 1，韩可琪 1，林龙帅 2，陈晨 1，岳铭 1，王楚翘 1，孙英刚 3，赵庆华 2，贺明 1. SIRT7通过抑制内质网应激蛋白 GRP78减轻脂多糖或 D-氨基半乳糖 /脂多糖诱导的肝细胞凋亡[J]. 上海交通大学学报（医学版）, 2019, 39(8): 812-.
[5]	赵乙汜1，余应喜1，林时辉2，徐昉1, 2. T细胞免疫在脓毒症继发侵袭性真菌感染中作用的研究进展[J]. 上海交通大学学报（医学版）, 2019, 39(11): 1325-.
[6]	许垚，马帅，丁峰. 抗菌肽在脓毒症治疗中的应用进展[J]. 上海交通大学学报（医学版）, 2017, 37(8): 1161-.
[7]	闵丹燕，陆晓蓉，李振元，严豪，张敏芳，王琴，袁江姿，倪兆慧，方炜. 低T 3 综合征可预测腹膜透析患者的不良预后[J]. 上海交通大学学报（医学版）, 2017, 37(11): 1501-.
[8]	丁雯，沈亮，项明洁，俞淙轶，陆志俊 . 小潮气量联合肺复张策略对老年患者腹部手术后早期肺部感染的影响[J]. 上海交通大学学报（医学版）, 2016, 36(11): 1617-.
[9]	陆洋，薛飞，赵宏胜，等. 右美托咪定对脓毒症大鼠肺组织髓样细胞触发受体-1表达的影响[J]. 上海交通大学学报（医学版）, 2015, 35(9): 1274-.
[10]	王丽锋，马骏，陈怡. 脾切除术后凶险性感染致严重脓毒症1例报道[J]. 上海交通大学学报（医学版）, 2015, 35(12): 1931-.
[11]	阮莉莉，杜俊君，林艳，等. CD64指数在新生儿脓毒症早期诊断中的意义[J]. 上海交通大学学报（医学版）, 2014, 34(10): 1503-.
[12]	卢化祥，贾一韬，姚敏，等. 血栓调节蛋白对脓毒症治疗作用的研究进展[J]. 上海交通大学学报（医学版）, 2013, 33(7): 1039-.
[13]	韩亭亭, 苏布德格日乐, 胡耀敏, 等. 急性炎症状态下2型糖尿病大鼠的反应能力研究[J]. , 2012, 32(1): 9-.
[14]	沈婷, 白建文. Toll样受体4与脓毒症关系的研究进展[J]. , 2011, 31(2): 243-.