Oral–Gut Microbiome Coalescence and Ecosystem Fragility Drive Carbapenem-Resistant Organism Colonization and Infection in Relapsed/Refractory Acute Leukemia

Oral–Gut Microbiome Coalescence and Ecosystem Fragility Drive Carbapenem-Resistant Organism Colonization and Infection in Relapsed/Refractory Acute Leukemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Oral–Gut Microbiome Coalescence and Ecosystem Fragility Drive Carbapenem-Resistant Organism Colonization and Infection in Relapsed/Refractory Acute Leukemia xiaomeng feng, yuqing cui, Qingsong Lin, kanchao chen, ling pan, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8434940/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The microbiome actively determines infection outcomes in immunocompromised hosts. Here we reveal that oral–gut microbiome coalescence marks a fragile microbial ecosystem that predisposes relapsed/refractory acute leukemia (R/R AL) patients to carbapenem-resistant organism (CRO) colonization and infection. A longitudinal cohort (n = 18, 144 samples) and a cross-sectional validation cohort (n = 23, 47 samples) demonstrated microbial instability and increased oral–gut convergence dominated by Enterococcus and Klebsiella in R/R AL patients. In vitro assays confirmed a fourfold reduction in colonization resistance in R/R-derived microbiota (44.0 vs 11.2 CFU, p = 0.023). A clinical cohort (n = 1,821) validated R/R status as an independent risk factor for CRO infection (OR = 1.57, p = 0.038) and 30-day mortality (OR = 3.35, p < 0.001), partially mediated by colonization (22.8%). Our study integrates microbial ecology, functional validation, and clinical causality, defining oral–gut community coalescence as both a biomarker and a mechanism of infection vulnerability in R/R AL patients. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The microbiome is essential for maintaining colonization resistance and protecting against opportunistic infections, particularly in immunocompromised individuals. In patients with acute leukemia (AL), intensive cytotoxic therapy and allogeneic hematopoietic stem cell transplantation (allo-HSCT) profoundly disrupt the composition and ecological stability of microbial communities. This dysbiosis contributes to an increased risk of bloodstream infections (BSI) and elevated treatment-related mortality. Notably, multidrug-resistant gram-negative bacteria (GNB) represent a major threat in this vulnerable population 1–5 .. Emerging evidence suggests that chemotherapy can induce oral–gut microbial coalescence —a process in which the normally segregated oral and gut microbial communities merge, with oral taxa translocating to and colonizing the intestinal tract, and gut microbes potentially influencing the oral ecosystem. This bidirectional disruption, mediated by the oral-gut axis, destabilizes gut ecology and can severely impair colonization resistance. While such coalescence has been observed in AL patients undergoing initial therapy 6–9 , its extent, persistence, and clinical implications in relapsed or refractory (R/R) AL remain unknown. Ecological theory frames these events as community coalescence , wherein distinct microbial ecosystems merge, leading to altered stability and function. Whether this process further compromises colonization resistance in R/R AL—a state of heightened immunological and microbial fragility—has not been established 10–12 . We hypothesized that R/R AL patients harbor a profoundly disrupted microbial ecosystem characterized by persistent oral–gut coalescence and loss of colonization resistance, which in turn facilitates the expansion of drug-resistant pathogens and predisposes to severe infections. To test this hypothesis, we integrated longitudinal microbiome profiling (Fig. 1 ), in vitro functional assays, and large-scale clinical validation. Using a retrospective cohort of 1,821 AL patients with GNB BSI, we performed logistic regression and causal mediation analyses to identify risk factors for mortality and resistant infections. Furthermore, through the National Longitudinal Cohort of Hematological Diseases (NICHE), we collected serial oral and rectal swabs from AL patients from diagnosis through pre-HSCT, applying 16S rRNA sequencing and bioinformatics to delineate dynamic microbial shifts and identify signature taxa associated with clinical outcomes. Together, this study aims to elucidate how microbial coalescence and ecological instability underlie infection susceptibility in R/R AL, providing a rationale for microbiome-targeted monitoring and intervention strategies in high-risk patients. Methods Study design and participants This study integrated a retrospective cohort analysis with prospective longitudinal sample collection. The retrospective cohort included 1,821 acute leukemia (AL) patients with Gram-negative bloodstream infection (GNB BSI) at the Institute of Hematology, Chinese Academy of Medical Sciences (January 2017–December 2022). Only the first infection episode per hospitalization was analyzed. Prospectively, serial oral and rectal swabs were collected from a separate AL cohort (NICHE: NCT04645199), including 23 relapsed/refractory (R/R) patients, at key timepoints: diagnosis, pre-chemotherapy for each cycle, and pre-hematopoietic stem cell transplantation (HSCT). The study was approved by the Institutional Review Board, and informed consent was obtained. Definitions and outcomes The primary outcomes were 30-day all-cause mortality and multidrug-resistant (MDR) infection. Carbapenem-resistant organism (CRO) was defined as resistance to meropenem or imipenem; colonization was defined as detection without infection signs. MDR was defined as non-susceptibility to ≥ 3 antimicrobial classes. Definitions for severe neutropenia, septic shock, and prior antibiotic use followed established criteria [13]. All patients underwent screening for CRO colonization on admission and weekly thereafter. Microbiome profiling and analysis DNA was extracted from swabs, and the V4 region of the 16S rRNA gene was amplified and sequenced on an Illumina NovaSeq platform. Sequences were processed using QIIME 2. Microbial α-diversity was assessed using the Shannon index, and β-diversity was calculated based on Bray-Curtis distances. Linear discriminant analysis Effect Size (LEfSe) was employed to identify differentially abundant taxa, and Dynamic Time Warping (DTW) was used to analyze temporal community patterns. Statistical analysis Categorical and continuous variables are presented as frequencies (percentages) and medians (interquartile ranges), compared using the χ²/Fisher’s exact or Mann-Whitney U tests, as appropriate. Multivariate logistic regression identified risk factors for 30-day mortality and MDR infection. A causal mediation analysis was performed within an ‘exposure–mediator–outcome’ framework to assess pathways between R/R status and CRO infection. Analyses were conducted using SPSS (v22.0), Python (v3.8), and R (v4.2.0). A two-sided P < 0.05 was considered statistically significant. Results 1. Longitudinal Dynamics Reveal Oral–Gut Coalescence as an Ecological Signature of Relapse To dynamically characterize the microbiome shifts associated with disease status, we conducted a prospective longitudinal study in 20 acute leukemia patients from diagnosis through pre-hematopoietic stem cell transplantation. Patients achieving complete remission (CR, n = 14, 108 samples) maintained stable microbial communities over time, dominated by commensals like Streptococcus and Prevotella. In stark contrast, patients with relapsed/refractory disease (R/R, n = 4, 36 samples) exhibited marked temporal instability, with dynamic time warping clustering revealing higher fluctuations and a significantly increased species turnover rate (Fig. 2 , Figure S3 ). This instability was characterized by the progressive expansion and fluctuation of pro-inflammatory, antibiotic-resistant genera such as Enterococcus (Fig. 2 , Figure S1 b). Relapse patients exhibited a distinct microbial ecological profile characterized by an enhanced oral–gut axis. At the patient level, oral–gut fusion metrics, including shared genus ratio and Bray–Curtis similarity, were consistently higher in the relapse group, indicating increased cross-site overlap and structural resemblance between oral and gut microbial communities. Fusion contribution analysis further revealed that this enhanced fusion was primarily driven by several genera classically associated with the oral microbiota, such as Haemophilus , Streptococcus , Actinomyces , Capnocytophaga , and Eikenella , which displayed higher simultaneous abundance in both the oral cavity and the gut in relapse patients (Fig. 3 ). Notably, integration with canonical correspondence analysis (CCA) demonstrated a strong association between relapse status, CRO infection, and sequences annotated as unidentified_Chloroplast (Figure S2 ). Together, these findings suggest that relapse is accompanied by a coordinated ecological shift marked by increased cross-site microbial co-occurrence of oral-associated taxa and broader community restructuring linked to infection-related host perturbations. Importantly, these results describe an association-level enhancement of oral–gut microbial fusion rather than direct evidence of microbial translocation or stable colonization. Expanding this analysis to a larger cross-sectional cohort (23 R/R vs. matched CR patients), we confirmed that this instability culminates in a profound structural breakdown: a pronounced oral–gut microbiome coalescence. R/R patients exhibited significantly lower alpha diversity and distinct beta diversity (p < 0.001, Fig. 4 a–b). The merged ecosystem was dominated by facultative anaerobes such as Enterococcus, Klebsiella, and Stenotrophomonas—taxa known to thrive under antibiotic pressure and mucosal injury (Fig. 4 c). In microbial co-occurrence networks, Lactobacillaceae was negatively correlated with Prevotellaceae in the RR group, while showing a positive correlation in the CR group (Table S2 , Fig. 4 e), implying altered microbial interactions in disease progression. 2. Functional Consequence: Loss of Colonization Resistance and a Pro-Inflammatory Metabolite Profile We next sought to determine the functional consequence of this dysbiotic state. In vitro co-culture assays confirmed that microbiota from R/R patients were significantly less capable of inhibiting carbapenem-resistant organism (CRO) expansion (44.0 vs. 11.2 CFU, p = 0.023), demonstrating a critical breakdown of colonization resistance—a core ecological service of a healthy microbiome. This functional decline was underpinned by distinct genomic and metabolic features. LEfSe analysis confirmed the enrichment of facultative anaerobes (e.g., Lacticaseibacillus, family Lactobacillaceae) in R/R patients, while CR patients retained a higher abundance of SCFA-producing and symbiotic bacteria (e.g., Prevotellaceae, Bacteroidia) (Fig. 4 e). Consequently, functional pathway analysis showed a marked upregulation of lipopolysaccharide (LPS) biosynthesis and the TCA cycle in the R/R group (Fig. 4 d). This metabolic shift away from SCFA production toward pathways associated with immune activation and energy stress explains the loss of mucosal integrity and provides a mechanistic link between coalescence, dysbiosis, and functional impairment, as recently highlighted in reviews of the oral–gut axis. 3. Clinical Consequence: Translational Validation in a Large Retrospective Cohort The ecological and functional fragility identified above must ultimately translate to patient outcomes to be clinically meaningful. In a large retrospective cohort of 1,821 patients, R/R AL status was validated as an independent predictor of both subsequent CRO infection and all-cause mortality. Crucially, mediation analysis established that CRO colonization accounts for nearly one-quarter of the effect of relapse status on infection risk (Fig. 5 , Table 1 – 2 ). This robust epidemiologic evidence closes the loop, confirming that the ecological cascade—initiated by relapse and mediated through microbiome collapse—directly drives poor clinical outcomes. Table 1 Patients characteristics baseline in the retrospective cohort Overall (n = 1821) Complete remission (n = 1018) Relapsed/ refractory (n = 363) First-Induction (n = 440) p Age (median [IQR]) 38.00[22.00,50.00] 37.00[21.00,48.75] 42.00[26.00,53.00] 40.00[22.00,53.00] < 0.001 Hospital stay [median [IQR]) 28.00[23.00,40.00] 27.00[22.00,35.00] 34.00[25.00,52.50] 28.00[24.00,41.00] < 0.001 Gender (%) male 964(52.9) 545(53.5) 187(51.5) 232(52.7) 0.799 Diabetes mellitus (%) 153(8.9) 77(8.0) 36(10.6) 40(9.7) 0.289 Disease (%) < 0.001 ALL 646(35.5) 334(32.8) 108(29.8) 204(46.4) AML 1175(64.5) 684(67.2) 255(70.2) 236(53.6) Chemotherapy (%) 1633(89.7) 901(88.5) 309(85.1) 423(96.1) < 0.001 Allo-HSCT (%) 179(9.8) 133(13.1) 46(12.7) 0(0.0) < 0.001 Pneumonia (%) 471(25.9) 237(23.3) 130(35.8) 104(23.6) < 0.001 Perianal infection (%) 182(10.0) 109(10.7) 45(12.4) 28(6.4) 0.009 Shock (%) 94(5.2) 43(4.2) 27(7.4) 24(5.5) 0.056 IET48h 87(4.8) 31(3.0) 29(8.0) 27(6.1) < 0.001 Severe neutropenia (%) 1235(67.8) 691(67.9) 239(65.8) 305(69.3) 0.575 Duration of neutropenia [median [IQR]IQR]) 10.00[5.00,16.00] 8.00[5.00,12.00] 14.00[7.00,23.00] 12.00[7.00,19.00] < 0.001 Prior piperacillin-tazobactam use (%) 275(15.1) 169(16.6) 52(14.3) 54(12.3) 0.095 Prior carbapenems use (%) 983(54.0) 612(60.1) 193(53.2) 178(40.5) < 0.001 Prior fluoroquinolones use (%) 211(11.6) 127(12.5) 44(12.1) 40(9.1) 0.169 Hypoproteinemia, n(%) 528(29.0) 245(24.1) 129(35.5) 154(35.0) < 0.001 Primary BSI, n(%) 1004(55.1) 606(59.5) 173(47.7) 225(51.1) < 0.001 Pulmonary source, n(%) 257(14.1) 128(12.6) 63(17.4) 66(15.0) 0.066 Perianal source, n(%) 252(13.8) 121(11.9) 53(14.6) 78(17.7) 0.011 CRO Colonization (%) 106(5.8) 54(5.3) 31(8.5) 21(4.8) 0.043 TZPNS (%) 232(12.7) 91(8.9) 56(15.4) 85(19.3) < 0.001 FQNS (%) 789(43.3) 366(36.0) 165(45.5) 258(58.6) < 0.001 TZPR (%) 161(8.8) 57(5.6) 40(11.0) 64(14.5) < 0.001 CER (%) 355(19.5) 146(14.3) 78(21.5) 131(29.8) < 0.001 FQR (%) 640(35.1) 291(28.6) 138(38.0) 211(48.0) < 0.001 CRO (%) 190(10.4) 90(8.8) 56(15.4) 44(10.0) 0.002 Escherichia coli 699 (38.4) 300 (29.5) 120 (33.1) 279 (63.4) TZPR (%) 72(10.3) 20(6.7) 10(8.3) 42(15.1) 0.003 CER (%) 213(30.5) 71(23.7) 37(30.8) 105(37.6) 0.001 FQR (%) 431(61.7) 166(55.3) 78(65.0) 187(67.0) 0.011 AGR (%) 237(33.9) 96(32.0) 42(35.0) 99(35.5) 0.650 CRE (%) 36(5.2) 12(4.0) 4(3.3) 20(7.2) 0.139 ESBL (%) 381(54.5) 145(48.3) 70(58.3) 166(59.5) 0.017 Klebsiella Pneumoniae 601 (33.0) 406 (40.0) 115 (31.7) 80 (18.2) TZPR (%) 60(10.0) 24(5.9) 20(17.4) 16(20.0) < 0.001 CER (%) 96(16.0) 49(12.1) 30(26.1) 17(21.2) 0.001 FQR (%) 152(25.3) 98(24.1) 38(33.0) 16(20.0) 0.077 AGR (%) 89(14.8) 53(13.1) 24(20.9) 12(15.0) 0.114 CRE (%) 40(6.7) 17(4.2) 11(9.6) 12(15.0) 0.001 ESBL (%) 168(28.0) 101(24.9) 45(39.1) 22(27.5) 0.011 Pseudomonas aeruginosa 421 (23.1) 260 (25.6) 106 (29.2) 55 (12.5) TZPR (%) 18(4.3) 9(3.5) 7(6.6) 2(3.6) 0.115 CER (%) 27(6.4) 17(6.5) 7(6.6) 3(5.5) 0.003 FQR (%) 38(9.0) 20(7.7) 15(14.2) 3(5.5) 0.002 CRPA (%) 104(24.7) 56(21.5) 37(34.9) 11(20.0) < 0.001 MDR (%) 59 (14.0) 31(11.9) 23(21.7) 5(9.1) < 0.001 Enterobacter cloacae 100 (5.5) 52 (5.1) 22 (6.1) 26 (5.9) TZPR (%) 11(11.0) 4(7.7) 3(13.6) 4(15.4) 0.584 CER (%) 19(19.0) 9(17.3) 4(18.2) 6(23.1) 0.321 FQR (%) 19(19.0) 7(13.5) 7(31.8) 5(19.2) 0.956 CRE (%) 10(10.0) 5(9.6) 4(18.2) 1(3.8) 0.779 30d mortality (%) 97 ( 5.3) 25 ( 2.5) 40 ( 11.0) 32 ( 7.3) < 0.001 Notes , AML , Acute Myeloid Leukemia, ALL , Acute Lymphoblastic Leukemia, CRO , carbapenems-resistant organisms, piperacillin/tazobactam-resistant isolates ( TZPR ),3/4th cephalosporins-resistant isolates ( CER ),NS means non-susceptible, fluoroquinolones-resistant isolates ( FQR ), aminoglycoside-resistant isolates ( AGR ), and carbapenems-resistant isolates ( CR ), ESBL , Extended-Spectrum β-Lactamases producing isolates, Multidrug-resistant isoltes ( MDR ). Table 2 Causal mediation analysis: association of relapsed/refractory AL with the proportions of CRO BSI CRO colonization Estimate 95% CI Lower 95% CI Upper P-value Indirect effect 0.016 0.001 0.03 0.04 Direct effect 0.053 0.009 0.09 < 2e-16 *** Total effect 0.069 0.024 0.12 < 2e-16 *** Prop. Mediated 0.228 0.025 0.60 0.04 Hypoproteinemia Estimate 95% CI Lower 95% CI Upper P-value Indirect effect 0.008 0.003 0.02 < 2e-16 *** Direct effect 0.062 0.017 0.10 < 2e-16 *** Total Effect 0.070 0.025 0.11 < 2e-16 *** Prop. Mediated 0.114 0.047 0.38 < 2e-16 *** 4. Integrative Model and Future Directions Collectively, our data support a unified, mechanistic sequence: R/R AL → Barrier Breakdown & Oral–Gut Coalescence → Dysbiosis, Diversity Loss & Metabolic Shift → Functional Decline in Colonization Resistance → CRO Colonization and Infection → Increased Mortality. Table 3 Baseline for microbiome sampling cohort Characteristic Longitudinal cohort (n = 18) Relapsed cohort (n = 23) p SMD Age (median IQR) 39.00 [25.25, 53.00] 38.00 [28.00, 46.00] 0.844 0.015 Gender Female 8 (44.4) 7 (30.4) 0.55 0.293 Male 10 (55.6) 16 (69.6) Disease ALL 5 (27.8) 4 (17.4) 0.677 0.25 AML 13 (72.2) 19 (82.6) Comorbidities No 16 (88.9) 20 (87.0) 1 0.059 Yes 2 (11.1) 3 (13.0) Induction regimen DA 8 (44.4) 11 (47.8) < 0.001 2.164 DAV 4 (22.2) 0 (0.0) Lipo-MIT-AraC 1 (5.6) 0 (0.0) VA 0 (0.0) 7 (30.4) VDCLP 0 (0.0) 5 (21.7) VDCLP + VEN 5 (27.8) 0 (0.0) Induction response CR 13 (72.2) 8 (34.8) 0.039 0.810 NR 5 (27.8) 15 (65.2) Chemotherapy cycles (median IQR) 0.00 [0.00, 0.00] 5.00 [2.50, 6.00] < 0.001 2.995 Neutropenic febrile No 3 (16.7) 13 (56.5) 0.023 0.909 Yes 15 (83.3) 10 (43.5) BSI No 17 (94.4) 19 (82.6) 0.504 0.378 Yes 1 (5.6) 4 (17.4) CRO colonization No 16 (88.9) 17 (73.9) 0.422 0.392 Yes 2 (11.1) 6 (26.1) Antibiotic treatment No 5 (27.8) 11 (47.8) 0.325 0.423 Yes 13 (72.2) 12 (52.2) Time points (median IQR) 4.00 [4.00, 5.00] 1.00 [1.00, 1.00] < 0.001 3.72 Conclusions This framework integrates ecological theory with clinical infection biology. The longitudinal instability and coalescence signature we identified offer a novel paradigm for surveillance and intervention. Future trials should explore using combined oral–gut diversity and similarity indices as early, predictive biomarkers of clinical relapse and infection risk. Furthermore, this ecological understanding paves the way for testing targeted microbiome restoration strategies—such as selective probiotics, prebiotics, or fecal microbiota transplantation—designed specifically to rebuild colonization resistance and restore a stable, health-associated ecosystem in high-risk R/R AL patients. By connecting ecological coalescence, functional impairment, and clinical outcomes, this study defines oral–gut microbiome fragility as a key determinant of infection vulnerability in relapsed/refractory leukemia. These findings extend the ecological concept of community coalescence into a clinical context and underscore the potential for microbiome-based precision infection prevention. Declarations Authors’ contributions Author Xiaomeng Feng, Qingsong Lin, Yuqing Cui, Ling Pan were responsible for the data curation methodology, samples collection and writing-original draft, and they contributed equally to this article. Authors Kanchao Chen, Ruonan Shao, Jiali Sun contributed to the data collection and analysis, and figure drawing. Authors Xiaoyuan Gong, Benfa Gong, Zhiying Tian, Bingcheng Liu, Erlie Jiang, Yingchang Mi finished the formal analysis and supervision. Author Sizhou Feng, Jianxiang Wang supplied the conceptualization, funding acquisition, resources, supervision, and writing - review & editing. All authors reviewed the manuscript. Ethics approval and consent to participate This study was approved by the Institutional Review Board and Ethics Committee of the Institute of Hematology and Blood Diseases Hospital (IIT2022071-EC-1), and informed consent was obtained from all participating individuals. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available considering the privacy or ethical restrictions but are available from the corresponding author on a reasonable request. Declaration of Competing Interest The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Funding This work was supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (grant numbers 2021-I2M-1-017, 2021-I2M-1-060) and the Tianjin Municipal Science and Technology Commission Grant (grant number 21JCZDJC01170). 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DOI: 10.1016/j.chom.2023.06.009 From NLM. Additional Declarations No competing interests reported. Supplementary Files supplementtable.xls Supplementmethods.docx alldata.xlsx FigureS12.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Mar, 2026 Reviews received at journal 28 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 18 Mar, 2026 Editor assigned by journal 14 Jan, 2026 Submission checks completed at journal 13 Jan, 2026 First submitted to journal 12 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8434940","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":608418302,"identity":"2ccc7969-a8d8-49d8-a39f-36d802f24823","order_by":0,"name":"xiaomeng feng","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qingsong","middleName":"","lastName":"Lin","suffix":""},{"id":608418305,"identity":"12ce4b26-1571-4dff-a6ad-e83416ad48e4","order_by":3,"name":"kanchao chen","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"kanchao","middleName":"","lastName":"chen","suffix":""},{"id":608418306,"identity":"a8f23e5b-37eb-4f22-90ec-951e88080865","order_by":4,"name":"ling pan","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"ling","middleName":"","lastName":"pan","suffix":""},{"id":608418307,"identity":"5c9cb6dc-dec1-4103-b14c-ea916b520cb3","order_by":5,"name":"ruonan shao","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"ruonan","middleName":"","lastName":"shao","suffix":""},{"id":608418308,"identity":"3700ff46-1fd0-4004-b3ee-7543aa1e86de","order_by":6,"name":"Jiali Sun","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jiali","middleName":"","lastName":"Sun","suffix":""},{"id":608418310,"identity":"42de8a82-2692-414b-b688-d0fc5d0ec81e","order_by":7,"name":"xiaoyuan Gong","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"xiaoyuan","middleName":"","lastName":"Gong","suffix":""},{"id":608418312,"identity":"b57aad0f-bc81-4198-ad82-2b6f896b9737","order_by":8,"name":"benfa gong","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"benfa","middleName":"","lastName":"gong","suffix":""},{"id":608418315,"identity":"3a765f06-23bf-4586-9f44-dd7f6b46774a","order_by":9,"name":"zhiying tian","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"zhiying","middleName":"","lastName":"tian","suffix":""},{"id":608418317,"identity":"47e15cdb-4ff1-4f08-864f-a17e5cd71701","order_by":10,"name":"Bingcheng Liu","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bingcheng","middleName":"","lastName":"Liu","suffix":""},{"id":608418318,"identity":"9b969707-6fbe-486f-b085-a7467b242b17","order_by":11,"name":"Erlie Jiang","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Erlie","middleName":"","lastName":"Jiang","suffix":""},{"id":608418319,"identity":"b93edd92-6f44-4cff-834f-02c0e3ae95ef","order_by":12,"name":"yingchang mi","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"yingchang","middleName":"","lastName":"mi","suffix":""},{"id":608418321,"identity":"f48de982-613f-45bd-935d-6305be6ca196","order_by":13,"name":"jianxiang wang","email":"","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"jianxiang","middleName":"","lastName":"wang","suffix":""},{"id":608418323,"identity":"c55d483d-2692-4982-9b86-d9de6831c810","order_by":14,"name":"sizhou feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYFACxgdAwobHgAQtzCDFaaRrOcxAvBb5/sOMjwt+nZcxZz/8dOMPBjt5BvazB/BqMThwmNl4Zt9tHsueNLPbPAzJhg08eQn4tTD2H5Pm7bnNY3CDwew20JkJDBIE/CXfzMz+m7fnHFAL+7ebPxjqCWthOMbMxszz4wBQC4/ZDR6Gw4S1GJxhZpbmbUjmMTiTUwZ03nHDNp4cAg4Dhthnnj929gbHj2+7+aOiWp6f/QwRAc7YBreUgYGNsHoQ+EOcslEwCkbBKBihAADBcT1H/nSE1wAAAABJRU5ErkJggg==","orcid":"","institution":"National Clinical Research Center for Blood Disease, Institute of Hematology and Blood Diseases Hospital \u0026 Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"sizhou","middleName":"","lastName":"feng","suffix":""}],"badges":[],"createdAt":"2025-12-23 14:39:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8434940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8434940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105564292,"identity":"877b94a3-4098-499a-88e3-41655310ff3c","added_by":"auto","created_at":"2026-03-27 12:49:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":447816,"visible":true,"origin":"","legend":"\u003cp\u003eStudy workflow\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8434940/v1/274bed1a01e77a31592c9422.png"},{"id":105259342,"identity":"9ab76434-2f42-484f-9ec4-3369a7e86b62","added_by":"auto","created_at":"2026-03-24 05:50:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":486957,"visible":true,"origin":"","legend":"\u003cp\u003eAverage Relative Abundance of Top 5 ASVs by Time Category and Group. Bar plots display the average relative abundance of the top 5 most abundant ASVs for each group and time category. Each ASV is labeled on the y-axis, and colors distinguish between non-remission (red) and remission (blue) groups.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8434940/v1/c39b9ebc70f4ba33e57c5fa9.png"},{"id":105259348,"identity":"ed128aab-a0e9-490b-bcda-ec06d427bafe","added_by":"auto","created_at":"2026-03-24 05:50:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":551397,"visible":true,"origin":"","legend":"\u003cp\u003eGenera contributing to enhanced oral–gut fusion in relapse.\u003c/p\u003e\n\u003cp\u003eTop 20 genera ranked by the difference in fusion contribution between relapse and remission (Δ = relapse − remission). Fusion contribution was defined as the minimum relative abundance of each genus in the oral cavity and the gut. Positive values indicate stronger cross-site co-occurrence in relapse patients.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8434940/v1/2e052a991ea75a20d79676d1.png"},{"id":105259350,"identity":"64f22ad1-39d2-44b5-aa71-e397ae7d6696","added_by":"auto","created_at":"2026-03-24 05:50:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":411901,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of microbiota between RR patients (RR) and CR patients (CR). (A-B) Comparisons of alpha diversity using the Shannon index, and beta diversity using the Bray-Curtis index. (C) Comparisons of the relative abundance at the Genus level between RR patients and CR patients. (D) Functional pathway predictions between remission group (CR) and non-remission group (RR) based on PICRUSt2 analysis. (E) LEfSe analysis of taxonomic biomarkers for microbiome in relapsed/refractory AL patients (RR) and complete-remission AL patients.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8434940/v1/dae8f5eb0fda9e5d7403a48b.png"},{"id":105259347,"identity":"55bd3db1-4eb4-4507-89b4-3ecd95205b36","added_by":"auto","created_at":"2026-03-24 05:50:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":145361,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The proportion of CRO colonization and infection in relapsed/refractory and remission AL patients. (b) Directed acyclic graph for mediation relationships.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8434940/v1/dc58ec2c5365d7ccf448539b.png"},{"id":105569447,"identity":"270b51c8-2a57-477f-ba7b-26709b81f1ab","added_by":"auto","created_at":"2026-03-27 13:12:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3655312,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8434940/v1/f0a5175a-b4bf-4a3b-8b34-8f0d5e353ad7.pdf"},{"id":105564591,"identity":"eecbbcd8-28a9-4161-a6b8-daaa81e726e7","added_by":"auto","created_at":"2026-03-27 12:50:09","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":111104,"visible":true,"origin":"","legend":"","description":"","filename":"supplementtable.xls","url":"https://assets-eu.researchsquare.com/files/rs-8434940/v1/8970ea403874b914159f6fd0.xls"},{"id":105259341,"identity":"7617471e-0ec3-4a3e-9d52-c19941123d79","added_by":"auto","created_at":"2026-03-24 05:50:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19206,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementmethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-8434940/v1/4a34ae33153c88781caedd29.docx"},{"id":105564311,"identity":"b83c77eb-a942-488d-9aa5-53c2f7ec427d","added_by":"auto","created_at":"2026-03-27 12:49:17","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":238020,"visible":true,"origin":"","legend":"","description":"","filename":"alldata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8434940/v1/9a01e597df7446967b6a0bde.xlsx"},{"id":105259345,"identity":"22485fd9-31f0-4171-8d29-0c3c5d91ff67","added_by":"auto","created_at":"2026-03-24 05:50:16","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":514424,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS12.docx","url":"https://assets-eu.researchsquare.com/files/rs-8434940/v1/8668896a1221a093cc229303.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Oral–Gut Microbiome Coalescence and Ecosystem Fragility Drive Carbapenem-Resistant Organism Colonization and Infection in Relapsed/Refractory Acute Leukemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe microbiome is essential for maintaining colonization resistance and protecting against opportunistic infections, particularly in immunocompromised individuals. In patients with acute leukemia (AL), intensive cytotoxic therapy and allogeneic hematopoietic stem cell transplantation (allo-HSCT) profoundly disrupt the composition and ecological stability of microbial communities. This dysbiosis contributes to an increased risk of bloodstream infections (BSI) and elevated treatment-related mortality. Notably, multidrug-resistant gram-negative bacteria (GNB) represent a major threat in this vulnerable population\u003csup\u003e1\u0026ndash;5\u003c/sup\u003e..\u003c/p\u003e \u003cp\u003eEmerging evidence suggests that chemotherapy can induce \u003cb\u003eoral\u0026ndash;gut microbial coalescence\u003c/b\u003e\u0026mdash;a process in which the normally segregated oral and gut microbial communities merge, with oral taxa translocating to and colonizing the intestinal tract, and gut microbes potentially influencing the oral ecosystem. This bidirectional disruption, mediated by the oral-gut axis, destabilizes gut ecology and can severely impair colonization resistance. While such coalescence has been observed in AL patients undergoing initial therapy\u003csup\u003e6\u0026ndash;9\u003c/sup\u003e, its extent, persistence, and clinical implications in relapsed or refractory (R/R) AL remain unknown. Ecological theory frames these events as \u003cem\u003ecommunity coalescence\u003c/em\u003e, wherein distinct microbial ecosystems merge, leading to altered stability and function. Whether this process further compromises colonization resistance in R/R AL\u0026mdash;a state of heightened immunological and microbial fragility\u0026mdash;has not been established\u003csup\u003e10\u0026ndash;12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe hypothesized that R/R AL patients harbor a profoundly disrupted microbial ecosystem characterized by \u003cb\u003epersistent oral\u0026ndash;gut coalescence\u003c/b\u003e and loss of colonization resistance, which in turn facilitates the expansion of drug-resistant pathogens and predisposes to severe infections. To test this hypothesis, we integrated longitudinal microbiome profiling (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), in vitro functional assays, and large-scale clinical validation. Using a retrospective cohort of 1,821 AL patients with GNB BSI, we performed logistic regression and causal mediation analyses to identify risk factors for mortality and resistant infections. Furthermore, through the National Longitudinal Cohort of Hematological Diseases (NICHE), we collected serial oral and rectal swabs from AL patients from diagnosis through pre-HSCT, applying 16S rRNA sequencing and bioinformatics to delineate dynamic microbial shifts and identify signature taxa associated with clinical outcomes.\u003c/p\u003e \u003cp\u003eTogether, this study aims to elucidate how microbial coalescence and ecological instability underlie infection susceptibility in R/R AL, providing a rationale for microbiome-targeted monitoring and intervention strategies in high-risk patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis study integrated a retrospective cohort analysis with prospective longitudinal sample collection. The retrospective cohort included 1,821 acute leukemia (AL) patients with Gram-negative bloodstream infection (GNB BSI) at the Institute of Hematology, Chinese Academy of Medical Sciences (January 2017\u0026ndash;December 2022). Only the first infection episode per hospitalization was analyzed. Prospectively, serial oral and rectal swabs were collected from a separate AL cohort (NICHE: NCT04645199), including 23 relapsed/refractory (R/R) patients, at key timepoints: diagnosis, pre-chemotherapy for each cycle, and pre-hematopoietic stem cell transplantation (HSCT). The study was approved by the Institutional Review Board, and informed consent was obtained.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinitions and outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcomes were 30-day all-cause mortality and multidrug-resistant (MDR) infection. Carbapenem-resistant organism (CRO) was defined as resistance to meropenem or imipenem; colonization was defined as detection without infection signs. MDR was defined as non-susceptibility to \u0026ge;\u0026thinsp;3 antimicrobial classes. Definitions for severe neutropenia, septic shock, and prior antibiotic use followed established criteria [13]. All patients underwent screening for CRO colonization on admission and weekly thereafter.\u003c/p\u003e\n\u003ch3\u003eMicrobiome profiling and analysis\u003c/h3\u003e\n\u003cp\u003eDNA was extracted from swabs, and the V4 region of the 16S rRNA gene was amplified and sequenced on an Illumina NovaSeq platform. Sequences were processed using QIIME 2. Microbial α-diversity was assessed using the Shannon index, and β-diversity was calculated based on Bray-Curtis distances. Linear discriminant analysis Effect Size (LEfSe) was employed to identify differentially abundant taxa, and Dynamic Time Warping (DTW) was used to analyze temporal community patterns.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical and continuous variables are presented as frequencies (percentages) and medians (interquartile ranges), compared using the χ\u0026sup2;/Fisher\u0026rsquo;s exact or Mann-Whitney U tests, as appropriate. Multivariate logistic regression identified risk factors for 30-day mortality and MDR infection. A causal mediation analysis was performed within an \u0026lsquo;exposure\u0026ndash;mediator\u0026ndash;outcome\u0026rsquo; framework to assess pathways between R/R status and CRO infection. Analyses were conducted using SPSS (v22.0), Python (v3.8), and R (v4.2.0). A two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003e1. Longitudinal Dynamics Reveal Oral\u0026ndash;Gut Coalescence as an Ecological Signature of Relapse\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo dynamically characterize the microbiome shifts associated with disease status, we conducted a prospective longitudinal study in 20 acute leukemia patients from diagnosis through pre-hematopoietic stem cell transplantation. Patients achieving complete remission (CR, n\u0026thinsp;=\u0026thinsp;14, 108 samples) maintained stable microbial communities over time, dominated by commensals like Streptococcus and Prevotella. In stark contrast, patients with relapsed/refractory disease (R/R, n\u0026thinsp;=\u0026thinsp;4, 36 samples) exhibited marked temporal instability, with dynamic time warping clustering revealing higher fluctuations and a significantly increased species turnover rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). This instability was characterized by the progressive expansion and fluctuation of pro-inflammatory, antibiotic-resistant genera such as Enterococcus (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eRelapse patients exhibited a distinct microbial ecological profile characterized by an enhanced oral\u0026ndash;gut axis. At the patient level, oral\u0026ndash;gut fusion metrics, including shared genus ratio and Bray\u0026ndash;Curtis similarity, were consistently higher in the relapse group, indicating increased cross-site overlap and structural resemblance between oral and gut microbial communities. Fusion contribution analysis further revealed that this enhanced fusion was primarily driven by several genera classically associated with the oral microbiota, such as \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eCapnocytophaga\u003c/em\u003e, and \u003cem\u003eEikenella\u003c/em\u003e, which displayed higher simultaneous abundance in both the oral cavity and the gut in relapse patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, integration with canonical correspondence analysis (CCA) demonstrated a strong association between relapse status, CRO infection, and sequences annotated as \u003cem\u003eunidentified_Chloroplast\u003c/em\u003e (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Together, these findings suggest that relapse is accompanied by a coordinated ecological shift marked by increased cross-site microbial co-occurrence of oral-associated taxa and broader community restructuring linked to infection-related host perturbations. Importantly, these results describe an association-level enhancement of oral\u0026ndash;gut microbial fusion rather than direct evidence of microbial translocation or stable colonization.\u003c/p\u003e \u003cp\u003eExpanding this analysis to a larger cross-sectional cohort (23 R/R vs. matched CR patients), we confirmed that this instability culminates in a profound structural breakdown: a pronounced oral\u0026ndash;gut microbiome coalescence. R/R patients exhibited significantly lower alpha diversity and distinct beta diversity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;b). The merged ecosystem was dominated by facultative anaerobes such as Enterococcus, Klebsiella, and Stenotrophomonas\u0026mdash;taxa known to thrive under antibiotic pressure and mucosal injury (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). In microbial co-occurrence networks, Lactobacillaceae was negatively correlated with Prevotellaceae in the RR group, while showing a positive correlation in the CR group (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee), implying altered microbial interactions in disease progression.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Functional Consequence: Loss of Colonization Resistance and a Pro-Inflammatory Metabolite Profile\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe next sought to determine the functional consequence of this dysbiotic state. In vitro co-culture assays confirmed that microbiota from R/R patients were significantly less capable of inhibiting carbapenem-resistant organism (CRO) expansion (44.0 vs. 11.2 CFU, p\u0026thinsp;=\u0026thinsp;0.023), demonstrating a critical breakdown of colonization resistance\u0026mdash;a core ecological service of a healthy microbiome.\u003c/p\u003e \u003cp\u003eThis functional decline was underpinned by distinct genomic and metabolic features. LEfSe analysis confirmed the enrichment of facultative anaerobes (e.g., Lacticaseibacillus, family Lactobacillaceae) in R/R patients, while CR patients retained a higher abundance of SCFA-producing and symbiotic bacteria (e.g., Prevotellaceae, Bacteroidia) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Consequently, functional pathway analysis showed a marked upregulation of lipopolysaccharide (LPS) biosynthesis and the TCA cycle in the R/R group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). This metabolic shift away from SCFA production toward pathways associated with immune activation and energy stress explains the loss of mucosal integrity and provides a mechanistic link between coalescence, dysbiosis, and functional impairment, as recently highlighted in reviews of the oral\u0026ndash;gut axis.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Clinical Consequence: Translational Validation in a Large Retrospective Cohort\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe ecological and functional fragility identified above must ultimately translate to patient outcomes to be clinically meaningful. In a large retrospective cohort of 1,821 patients, R/R AL status was validated as an independent predictor of both subsequent CRO infection and all-cause mortality. Crucially, mediation analysis established that CRO colonization accounts for nearly one-quarter of the effect of relapse status on infection risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This robust epidemiologic evidence closes the loop, confirming that the ecological cascade\u0026mdash;initiated by relapse and mediated through microbiome collapse\u0026mdash;directly drives poor clinical outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatients characteristics baseline in the retrospective cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1821)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplete remission\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1018)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelapsed/\u003c/p\u003e \u003cp\u003erefractory\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;363)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFirst-Induction\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;440)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.00[22.00,50.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.00[21.00,48.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.00[26.00,53.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.00[22.00,53.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital stay [median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.00[23.00,40.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.00[22.00,35.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.00[25.00,52.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.00[24.00,41.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e964(52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e545(53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e187(51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e232(52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e646(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e334(32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e204(46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1175(64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e684(67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255(70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e236(53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1633(89.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e901(88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e309(85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e423(96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAllo-HSCT (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133(13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePneumonia (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e471(25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237(23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130(35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104(23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerianal infection (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109(10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45(12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShock (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24(5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIET48h\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSevere neutropenia (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1235(67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e691(67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e239(65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e305(69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of neutropenia [median [IQR]IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00[5.00,16.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.00[5.00,12.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.00[7.00,23.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.00[7.00,19.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrior piperacillin-tazobactam use (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275(15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169(16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrior carbapenems use (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e983(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e612(60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193(53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e178(40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrior fluoroquinolones use (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e211(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44(12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypoproteinemia, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e528(29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245(24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e154(35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary BSI, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1004(55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e606(59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173(47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e225(51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePulmonary source, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e257(14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63(17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerianal source, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252(13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53(14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78(17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRO Colonization (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTZPNS (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e232(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56(15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85(19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFQNS (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e789(43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e366(36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165(45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e258(58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTZPR (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e161(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCER (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e355(19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e131(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFQR (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e640(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e291(28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138(38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e211(48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRO (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e190(10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56(15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEscherichia coli\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e699 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e279 (63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTZPR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10(8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42(15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCER (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e213(30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71(23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37(30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e105(37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFQR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e431(61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166(55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78(65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e187(67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e237(33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96(32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42(35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESBL (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e381(54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145(48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70(58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e166(59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKlebsiella Pneumoniae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e601 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e406 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTZPR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20(17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCER (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96(16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49(12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30(26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17(21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFQR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e152(25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98(24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38(33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89(14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53(13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24(20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESBL (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168(28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101(24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45(39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22(27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePseudomonas aeruginosa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e421 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e260 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTZPR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCER (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3(5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFQR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38(9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20(7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15(14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3(5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRPA (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104(24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37(34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23(21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnterobacter cloacae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTZPR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4(7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4(15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCER (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19(19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6(23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFQR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19(19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5(19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1(3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e30d mortality (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97 ( 5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 ( 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40 ( 11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32 ( 7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNotes\u003c/b\u003e, \u003cb\u003eAML\u003c/b\u003e, Acute Myeloid Leukemia, \u003cb\u003eALL\u003c/b\u003e, Acute Lymphoblastic Leukemia, \u003cb\u003eCRO\u003c/b\u003e, carbapenems-resistant organisms, piperacillin/tazobactam-resistant isolates (\u003cb\u003eTZPR\u003c/b\u003e),3/4th cephalosporins-resistant isolates (\u003cb\u003eCER\u003c/b\u003e),NS means non-susceptible, fluoroquinolones-resistant isolates (\u003cb\u003eFQR\u003c/b\u003e), aminoglycoside-resistant isolates (\u003cb\u003eAGR\u003c/b\u003e), and carbapenems-resistant isolates (\u003cb\u003eCR\u003c/b\u003e), \u003cb\u003eESBL\u003c/b\u003e, Extended-Spectrum β-Lactamases producing isolates, Multidrug-resistant isoltes (\u003cb\u003eMDR\u003c/b\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCausal mediation analysis: association of relapsed/refractory AL with the proportions of CRO BSI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRO colonization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI Upper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProp. Mediated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypoproteinemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI Lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI Upper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProp. Mediated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e4. Integrative Model and Future Directions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCollectively, our data support a unified, mechanistic sequence:\u003c/p\u003e \u003cp\u003eR/R AL \u0026rarr; Barrier Breakdown \u0026amp; Oral\u0026ndash;Gut Coalescence \u0026rarr; Dysbiosis, Diversity Loss \u0026amp; Metabolic Shift \u0026rarr; Functional Decline in Colonization Resistance \u0026rarr; CRO Colonization and Infection \u0026rarr; Increased Mortality.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline for microbiome sampling cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitudinal cohort (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelapsed cohort (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (median IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.00 [25.25, 53.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.00 [28.00, 46.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInduction regimen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipo-MIT-AraC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVDCLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVDCLP\u0026thinsp;+\u0026thinsp;VEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInduction response\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy cycles (median IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00 [2.50, 6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutropenic febrile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRO colonization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntibiotic treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (52.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime points (median IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00 [4.00, 5.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis framework integrates ecological theory with clinical infection biology. The longitudinal instability and coalescence signature we identified offer a novel paradigm for surveillance and intervention. Future trials should explore using combined oral\u0026ndash;gut diversity and similarity indices as early, predictive biomarkers of clinical relapse and infection risk. Furthermore, this ecological understanding paves the way for testing targeted microbiome restoration strategies\u0026mdash;such as selective probiotics, prebiotics, or fecal microbiota transplantation\u0026mdash;designed specifically to rebuild colonization resistance and restore a stable, health-associated ecosystem in high-risk R/R AL patients.\u003c/p\u003e \u003cp\u003eBy connecting ecological coalescence, functional impairment, and clinical outcomes, this study defines oral\u0026ndash;gut microbiome fragility as a key determinant of infection vulnerability in relapsed/refractory leukemia. These findings extend the ecological concept of community coalescence into a clinical context and underscore the potential for microbiome-based precision infection prevention.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor Xiaomeng Feng, Qingsong Lin, Yuqing Cui, Ling Pan were responsible for the data curation methodology, samples collection and writing-original draft, and they contributed equally to this article. Authors Kanchao Chen, Ruonan Shao, Jiali Sun contributed to the data collection and analysis, and figure drawing. Authors Xiaoyuan Gong, Benfa Gong, Zhiying Tian, Bingcheng Liu, Erlie Jiang, Yingchang Mi finished the formal analysis and supervision. Author Sizhou Feng, Jianxiang Wang supplied the conceptualization, funding acquisition, resources, supervision, and writing - review \u0026amp; editing. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board and Ethics Committee of the Institute of Hematology and Blood Diseases Hospital\u0026nbsp;(IIT2022071-EC-1), and informed consent was obtained from all participating individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available considering the privacy or ethical restrictions but are available from the corresponding author on a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (grant numbers 2021-I2M-1-017, 2021-I2M-1-060) and the Tianjin Municipal Science and Technology Commission Grant (grant number 21JCZDJC01170).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the reviewers who participated in the review, as well as - for providing English editing services during the preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXin Chen, S. F. Chinese guidelines for the clinical application of antibacterial drugs for agranulocytosis with fever (2020). \u003cem\u003eChinese Journal of Hematology \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e19\u003c/em\u003e (09), 14-17. From Cnki.\u003c/li\u003e\n\u003cli\u003eGlobal mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet \u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e400\u003c/em\u003e (10369), 2221-2248. DOI: 10.1016/s0140-6736(22)02185-7 From NLM.\u003c/li\u003e\n\u003cli\u003eYuan, F.; Li, M.; Wang, X.; Fu, Y. Risk factors and mortality of carbapenem-resistant Pseudomonas aeruginosa bloodstream infection in haematology department: A 10-year retrospective study. \u003cem\u003eJ Glob Antimicrob Resist \u003c/em\u003e\u003cstrong\u003e2024\u003c/strong\u003e, \u003cem\u003e37\u003c/em\u003e, 150-156. 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DOI: 10.1016/j.chom.2023.06.009 From NLM.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbio","sideBox":"Learn more about [Microbiome](http://microbiomejournal.biomedcentral.com/)","snPcode":"40168","submissionUrl":"https://submission.nature.com/new-submission/40168/3","title":"Microbiome","twitterHandle":"@MicrobiomeJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8434940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8434940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe microbiome actively determines infection outcomes in immunocompromised hosts. Here we reveal that \u003cb\u003eoral\u0026ndash;gut microbiome coalescence\u003c/b\u003e marks a fragile microbial ecosystem that predisposes relapsed/refractory acute leukemia (R/R AL) patients to carbapenem-resistant organism (CRO) colonization and infection.\u003c/p\u003e \u003cp\u003e A longitudinal cohort (n\u0026thinsp;=\u0026thinsp;18, 144 samples) and a cross-sectional validation cohort (n\u0026thinsp;=\u0026thinsp;23, 47 samples) demonstrated microbial instability and increased oral\u0026ndash;gut convergence dominated by Enterococcus and Klebsiella in R/R AL patients. In vitro assays confirmed a fourfold reduction in colonization resistance in R/R-derived microbiota (44.0 vs 11.2 CFU, p\u0026thinsp;=\u0026thinsp;0.023). A clinical cohort (n\u0026thinsp;=\u0026thinsp;1,821) validated R/R status as an independent risk factor for CRO infection (OR\u0026thinsp;=\u0026thinsp;1.57, p\u0026thinsp;=\u0026thinsp;0.038) and 30-day mortality (OR\u0026thinsp;=\u0026thinsp;3.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), partially mediated by colonization (22.8%).\u003c/p\u003e \u003cp\u003eOur study integrates microbial ecology, functional validation, and clinical causality, defining \u003cb\u003eoral\u0026ndash;gut community coalescence\u003c/b\u003e as both a biomarker and a mechanism of infection vulnerability in R/R AL patients.\u003c/p\u003e","manuscriptTitle":"Oral–Gut Microbiome Coalescence and Ecosystem Fragility Drive Carbapenem-Resistant Organism Colonization and Infection in Relapsed/Refractory Acute Leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 05:50:06","doi":"10.21203/rs.3.rs-8434940/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-30T12:00:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T02:05:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T12:46:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109767947376704756313875404939364501669","date":"2026-03-18T17:00:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89384214089519982551764659631389955467","date":"2026-03-18T13:39:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95434748021974682886158476024382396970","date":"2026-03-18T13:19:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38091362615191508644839550837645157583","date":"2026-03-18T11:44:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T10:46:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T12:43:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-14T04:14:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbiome","date":"2026-01-12T14:24:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbio","sideBox":"Learn more about [Microbiome](http://microbiomejournal.biomedcentral.com/)","snPcode":"40168","submissionUrl":"https://submission.nature.com/new-submission/40168/3","title":"Microbiome","twitterHandle":"@MicrobiomeJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69996f35-2a17-43b0-8dad-a3b9dc2ec7b2","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T05:50:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 05:50:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8434940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8434940","identity":"rs-8434940","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}