Clinical Phenotyping of Polymyxin-Associated Nephrotoxicity and Phenotype-Specific Prognostic Utility of Neutrophil-to-Platelet Ratio in Carbapenem-Resistant Acinetobacter baumannii Infections

Clinical Phenotyping of Polymyxin-Associated Nephrotoxicity and Phenotype-Specific Prognostic Utility of Neutrophil-to-Platelet Ratio in Carbapenem-Resistant Acinetobacter baumannii Infections | 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 Research Article Clinical Phenotyping of Polymyxin-Associated Nephrotoxicity and Phenotype-Specific Prognostic Utility of Neutrophil-to-Platelet Ratio in Carbapenem-Resistant Acinetobacter baumannii Infections Haixing Zhu, Dake Shi, Guangwei Li, Lijuan Wu, Xiaoqian Ma, Jiebai Zhou, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8146364/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in BMC Infectious Diseases → Version 1 posted 12 You are reading this latest preprint version Abstract Background Carbapenem-resistant Acinetobacter baumannii (CRAB) infections remain therapeutically challenging, often necessitating last-line polymyxin therapy despite substantial nephrotoxicity. Conventional risk assessments for polymyxin-associated acute kidney injury (AKI) frequently overlook patient heterogeneity. We sought to delineate clinical phenotypes of polymyxin-associated AKI and determine whether the prognostic value of the neutrophil-to-platelet ratio (NPR) for mortality varies by phenotype. Methods We conducted a retrospective cohort study (2020–2025) of 547 patients with CRAB infections treated with polymyxins. Latent class analysis (LCA) identified clinical phenotypes using baseline risk factors and renal outcomes. Multivariable logistic regression incorporating restricted cubic splines (RCS), followed by piecewise regression, evaluated non-linear associations between NPR and 28-day mortality and tested effect modification by phenotype. Results CA revealed three phenotypes: Severe Renal Failure (Class 1, 15.2%), Low-risk/Stable (Class 2, 62.0%), and High-risk/Non-dialysis-dependent Injury (Class 3, 22.8%). Baseline characteristics and 28-day mortality differed across phenotypes (13.3% vs 5.6% vs 10.4%; p = 0.033). The prognostic value of NPR was confined to Class 2: within this subgroup (n = 339), NPR showed a significant association with mortality (p < 0.05). Piecewise regression identified a breakpoint at NPR = 0.0167 (likelihood-ratio test p = 0.00061); NPR ≤ 0.0167 was associated with higher 28-day mortality (p = 0.00276), with no association above this threshold. Conclusion Patients with CRAB infections receiving polymyxins exhibit three distinct nephrotoxicity phenotypes, and the prognostic utility of NPR is phenotype-specific, with a pronounced non-linear threshold confined to the Low-risk/Stable subgroup; phenotype-based stratification is therefore essential for valid interpretation of commonly used prognostic biomarkers. Carbapenem-Resistant Acinetobacter baumannii Polymyxin Nephrotoxicity Acute Kidney Injury Clinical Phenotype Neutrophil-to-Platelet Ratio Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Infections caused by carbapenem-resistant Acinetobacter baumannii (CRAB) constitute a pressing global public health threat, particularly in intensive care units (ICUs) [ 1 ] . Polymyxins, including polymyxin B and polymyxin E (colistin), have been reinstated as last-line agents for these life-threatening infections [ 2 ] . Nevertheless, their clinical use remains challenging. Clinicians must choose between agents with distinct pharmacokinetic and toxicity profiles [ 3 ] while contending with the high incidence of polymyxin-associated acute kidney injury (AKI) [ 4 ] . This dilemma is most acute when treating complex patients—such as older adults or those with pre-existing renal impairment—leading to uncertainty and therapeutic hesitation in practice. Although numerous meta-analyses have compared the efficacy and overall toxicity of polymyxin agents [ 5,6 ] , they often treat the critically ill population as a homogeneous whole. Such aggregation obscures substantial heterogeneity in patient susceptibility and fails to provide the precision models required for individualized risk assessment. Meanwhile, despite ongoing efforts to discover novel AKI biomarkers [ 7,8 ] , single markers frequently underperform in capturing the complex, multidimensional nature of vulnerability. To address these gaps, we hypothesized that patients exhibit distinct response patterns to polymyxin exposure. Accordingly, we employed latent class analysis (LCA) to derive clinical phenotypes from baseline risks and renal outcomes, a strategy that has successfully revealed hidden heterogeneity in other critical illnesses [ 9–11 ] . Using this approach, we identified three distinct clinical phenotypes of polymyxin-associated nephrotoxicity. We then examined whether these phenotypes refine prognostication by assessing the predictive value of the neutrophil-to-platelet ratio (NPR) [ 12–14 ] . Our overarching goal is to move beyond generalized risk scores and to establish a stratified, phenotype-guided framework to improve prognostication in this critically ill population. Methods Study Design and Population Our analysis proceeded in three stages. First, we used latent class analysis (LCA) to identify clinical phenotypes of polymyxin-associated nephrotoxicity. The LCA model was based on seven dichotomous indicator variables: age ≥ 65 years, immunosuppressed state, baseline CKD, baseline eGFR < 60 mL/min/1.73m², ≥ 2 comorbidities, severe AKI, and RRT requirement. The optimal number of classes was selected based on information criteria (AIC, BIC, aBIC), the Lo-Mendell-Rubin Likelihood Ratio Test (LMR-LRT), and clinical interpretability.After assigning each patient to their most likely phenotype, we compared baseline characteristics and clinical outcomes across the identified classes using Chi-square or Fisher’s exact tests for categorical variables and the Kruskal-Wallis test for continuous variables. we also performed a phenotype-stratified restricted cubic spline (RCS) analysis to explore whether the relationships between continuous predictors (age and NPR) and 28-day mortality were moderated by the identified phenotypes. We used multivariable logistic regression models with 4-knot RCS terms and tested for a statistical interaction between the predictor and the phenotype. Models were adjusted for polymyxin type, gender, immunosuppression, and number of comorbidities. All analyses were performed using R software (Version 4.2.0). A two-sided p-value < 0.05 was considered statistically significant. Data Collection and Definitions We conducted a retrospective cohort study at Ruijin Hospital, a tertiary academic center affiliated with Shanghai Jiao Tong University School of Medicine. We identified all adults (≥ 18 years) with confirmed carbapenem-resistant Acinetobacter baumannii (CRAB) infection who received any polymyxin between January 1, 2020 and December 31, 2025. Inclusion criteria were age ≥ 18 years, isolation of CRAB from a clinical culture, and receipt of polymyxin B, colistin sulfate, or colistimethate sodium for ≥ 72 hours. Exclusion criteria were renal replacement therapy (RRT) at polymyxin initiation and missing baseline serum creatinine. The Institutional Review Board of Ruijin Hospital approved the study and waived informed consent owing to its retrospective design. We extracted data from the hospital electronic medical record, including demographics; comorbidities (immunosuppression and chronic kidney disease [CKD]); baseline laboratory values (serum creatinine and neutrophil and platelet counts for calculating the neutrophil-to-platelet ratio [NPR]); and polymyxin regimen details. The primary outcome was 28-day all-cause mortality. Secondary outcomes were 14-day mortality, ICU length of stay, and severe acute kidney injury (AKI; KDIGO stage 2–3). Statistical Analysis The analysis proceeded in three stages. First, we applied latent class analysis (LCA) to identify clinical phenotypes of polymyxin-associated nephrotoxicity. The LCA model included seven dichotomous indicators: age ≥ 65 years, immunosuppression, baseline CKD, baseline eGFR < 60 mL/min/1.73 m², ≥ 2 comorbidities, severe AKI, and RRT requirement. The optimal number of classes was determined using information criteria (AIC, BIC, aBIC), the Lo–Mendell–Rubin likelihood ratio test (LMR-LRT), and clinical interpretability. After assigning each patient to the most likely phenotype, we compared baseline characteristics and outcomes across classes using χ² or Fisher’s exact tests for categorical variables and the Kruskal–Wallis test for continuous variables. We additionally performed phenotype-stratified restricted cubic spline (RCS) analyses to examine whether the relationships between continuous predictors (age and NPR) and 28-day mortality differed by phenotype. We fit multivariable logistic regression models with four-knot RCS terms and tested interactions between each predictor and phenotype. Models adjusted for polymyxin type, gender, immunosuppression, and comorbidity count. All analyses were conducted in R (version 4.2.0). Two-sided p values < 0.05 were considered statistically significant. Results Baseline Characteristics of the Study Cohort A total of 792 patients treated with polymyxins for carbapenem-resistant Acinetobacter baumannii (CRAB) infections were initially assessed for eligibility. After applying the exclusion criteria, 245 patients were excluded, resulting in a final cohort of 547 patients for the analysis (Fig. 1 ). The baseline demographic and clinical characteristics of the study population are detailed in Table 1 . The cohort had a median age of 69.0 years (Interquartile Range [IQR], 58.0–77.0), and a majority of patients were male (392, 71.7%). The burden of comorbidities was high; 163 patients (29.8%) were in an immunosuppressed state, and 87 (15.9%) had pre-existing chronic kidney disease (CKD). Table 1 Baseline demographic and clinical characteristics of the study cohort (N = 547). C Colistin Sulfate ​Polymyxin E (Neb) Polymyxin B Polymyxin E (IV) statistics p-value n 547 177 137 197 36 Age 69.00 (58.00–77.00) 71.00 (60.00–78.00) 70.00 (60.00–78.00) 69.00 (56.00–77.00) 58.50 (48.25–68.25) 19.47 < 0.001 Gender Male 392 (71.66%) 126 (71.19%) 98 (71.53%) 139 (70.56%) 29 (80.56%) 1.54 0.67 Female 155 (28.34%) 51 (28.81%) 39 (28.47%) 58 (29.44%) 7 (19.44%) Diabetes 170 (31.08%) 52 (29.38%) 51 (37.23%) 59 (29.95%) 8 (22.22%) 4.09 0.25 Cardiac Disease 265 (48.45%) 84 (47.46%) 67 (48.91%) 95 (48.22%) 19 (52.78%) 0.36 0.95 ​Cerebrovascular Disease 179 (32.72%) 59 (33.33%) 49 (35.77%) 63 (31.98%) 8 (22.22%) 2.46 0.48 ​Malignancy 89 (16.27%) 28 (15.82%) 17 (12.41%) 36 (18.27%) 8 (22.22%) 3.04 0.39 Chronic Kidney Disease 87 (15.90%) 22 (12.43%) 26 (18.98%) 30 (15.23%) 9 (25.00%) 4.86 0.18 COPD 42 (7.68%) 14 (7.91%) 16 (11.68%) 11 (5.58%) 1 (2.78%) 5.55 0.14 Clinical Efficacy Failed 132 (24.13%) 38 (21.47%) 26 (18.98%) 61 (30.96%) 7 (19.44%) Improved 413 (75.50%) 139 (78.53%) 110 (80.29%) 135 (68.53%) 29 (80.56%) Microbiological outcome Eradicated 77 (14.08%) 27 (15.25%) 17 (12.41%) 27 (13.71%) 6 (16.67%) 9.79 0.63 Invalid Code 1 (0.18%) 0 (0.00%) 1 (0.73%) 0 (0.00%) 0 (0.00%) Persisted 435 (79.52%) 136 (76.84%) 108 (78.83%) 162 (82.23%) 29 (80.56%) Presumed Eradicated 24 (4.39%) 10 (5.65%) 9 (6.57%) 5 (2.54%) 0 (0.00%) Recurred 10 (1.83%) 4 (2.26%) 2 (1.46%) 3 (1.52%) 1 (2.78%) 14-Day Mortality 28 (5.12%) 6 (3.39%) 3 (2.19%) 15 (7.61%) 4 (11.11%) 8.7 0.03 28-Day Mortality 43 (7.86%) 17 (9.60%) 6 (4.38%) 17 (8.63%) 3 (8.33%) 3.21 0.36 WBC 11.27 (8.25–17.18) 11.40 (8.56–15.94) 10.34 (7.90–15.34) 11.78 (8.48–19.00) 9.86 (7.65–20.28) 5.85 0.12 Neu 88.10 (81.20–92.40) 88.80 (82.60–92.80) 87.00 (80.70–92.40) 88.60 (81.40–92.30) 86.85 (78.95–91.15) 2.33 0.51 PLT 170.00 (107.00–249.00) 171.00 (94.00–249.00) 168.00 (107.00–247.00) 169.00 (118.00–248.00) 180.00 (108.50–248.00) 0.34 0.95 CRP 88.00 (42.00–158.00) 86.00 (38.00–159.10) 76.00 (38.00–147.00) 98.00 (48.00–166.00) 76.95 (37.58–174.25) 3.31 0.35 PCT 0.80 (0.24–2.56) 0.64 (0.21–2.00) 0.66 (0.17–2.89) 1.11 (0.31–3.00) 0.86 (0.17–4.45) 6.75 0.08 DD 2.70 (1.20–5.03) 2.50 (1.22–4.29) 2.36 (1.02–5.30) 3.00 (1.30–5.14) 3.42 (1.28–5.27) 2.58 0.46 Baseline eGFR 87.80 (52.90–107.30) 91.20 (62.00–108.40) 84.90 (44.40–104.20) 87.50 (52.00–105.60) 85.45 (56.77–112.83) 2.98 0.4 Peak eGFR 63.60 (29.35–96.05) 77.60 (44.50–98.60) 71.20 (26.10–98.10) 52.30 (27.10–90.10) 51.40 (28.82–89.05) 14.98 < 0.001 Baseline Cr 71.00 (53.00–117.50) 69.00 (53.00–100.00) 71.00 (52.00–125.00) 72.00 (53.00–125.00) 77.00 (56.75–120.25) 2.05 0.56 Peak Cr 97.00 (66.50–185.50) 81.00 (64.00–136.00) 87.00 (66.00–214.00) 124.00 (69.00–192.00) 142.50 (92.25–233.75) 17.68 < 0.001 Regarding the polymyxin regimens administered, polymyxin B was the most common agent (197, 36.0%), followed by nebulized polymyxin E (137, 25.0%), colistin sulfate (177, 32.36%), and intravenous polymyxin E (colistimethate sodium) (36, 6.58%). The patient population was critically ill, with a median length of ICU stay of 29.0 days (IQR, 17.0–49.0). The overall 14-day and 28-day mortality rates for the cohort were 5.12% and 7.86%, respectively. Latent Class Analysis and Identification of Clinical Phenotypes We performed latent class analysis (LCA) using seven indicators of baseline risk and renal outcomes to identify distinct patient subgroups. Model fit supported a three-class solution as optimal (Table 2 ), with the lowest AIC (4051.276) and adjusted BIC (aBIC 4084.863). The Lo–Mendell–Rubin likelihood ratio test (LMR-LRT) favored the three-class over the two-class model (p < 0.001), whereas the four-class model did not improve fit (p = 1.000). The entropy (0.758) indicated good classification accuracy. Table 2 Model fit statistics for latent class analysis. Classes LogLik AIC BIC aBIC χ² Entropy Class_Probs p_LMRT p_BLRT 1 -2155.547 4325.093 4354.897 4332.677 690.465 2 -2029.205 4096.411 4177.306 4116.996 199.462 0.662 0.366/0.634 0.0000 0.0000 3 -1994.638 4051.276 4183.264 4084.863 122.209 0.758 0.267/0.587/0.146 0.0000 0.0000 4 -2110.688 4307.375 4490.455 4353.963 319.838 0.966 0.011/0.568/0.044/0.377 1.0000 0.0000 5 -2103.632 4317.264 4551.436 4376.853 377.132 0.980 0.053/0.052/0.201/0/0.693 0.3747 0.0000 The conditional probabilities of the indicator variables for each class are illustrated in Fig. 2 . Based on these distinct profiles, the three phenotypes were characterized and named as follows: Class 1 ( Severe Renal Failure ; n = 83, 15.2%) was characterized by very high probabilities of renal replacement therapy (RRT) and severe acute kidney injury (AKI), with a high prevalence of pre-existing CKD and low baseline eGFR. Class 2 ( Low-risk/Stable ; n = 339, 61.9%) was the largest subgroup and showed very low probabilities across adverse renal indicators, including severe AKI, RRT, baseline CKD, and low baseline eGFR. Class 3 ( High-risk /Non-dialysis-dependent Injury; n = 125, 22.9%) showed a high probability of severe AKI but a low probability of requiring RRT, with more comorbidities, older age, and lower baseline eGFR, and a lower prevalence of baseline CKD than Class 1. Comparison of Characteristics and Outcomes Among Clinical Phenotypes Across phenotypes (Table 3 ), 14-day (p = 0.039) and 28-day mortality (p = 0.033) differed significantly, with Class 2 having the lowest rates (3.2% and 5.6%, respectively) compared with Class 1 (8.4% and 13.3%) and Class 3 (8.0% and 10.4%). ICU length of stay also differed (p = 0.005), being longest in Class 3 (median 32 days). The distribution of polymyxin agents differed significantly (p < 0.001). Intravenous polymyxin E was used more often in Class 3 (16.0%) than in Class 2 (4.1%) or Class 1 (2.4%). Baseline immunosuppression and comorbidity burden were also highest in Class 3, whereas age and sex did not differ significantly across groups. Phenotype-Specific Prognostic Value of the Neutrophil-to-Platelet Ratio (NPR) Motivated by mortality differences across phenotypes, we evaluated the prognostic utility of baseline NPR. Stratified restricted cubic spline (RCS) analyses assessed non-linear associations between NPR and 28-day mortality within each phenotype, adjusted for age, sex, and ≥ 2 comorbidities (Fig. 3 ). A significant association was detected only in the Low-risk/Stable phenotype (Class 2; p for overall 0.05), suggesting attenuation by severe baseline illness. To quantify this threshold in Class 2 (n = 339), we fit a piecewise regression model adjusted for sex, age, ≥ 2 comorbidities, and immunosuppression. The optimal breakpoint for NPR was 0.0167, indicating a clear threshold: at NPR ≤ 0.0167 (n = 322), NPR was strongly associated with 28-day mortality (OR 4.07×10⁶⁰; 95% CI 1.46×10²⁸–1.13×10¹²⁷; p = 0.00276), whereas above 0.0167 (n = 17) the association was not significant (OR 0.00; p = 0.65662). A log-likelihood ratio test confirmed superior fit over a linear model (χ²=11.7488; p = 0.00061), validating a significant non-linear breakpoint at 0.0167 (Table 4 ; Fig. 5 ). These results indicate that NPR’s prognostic utility is confined to the Low-risk/Stable phenotype and is defined by an extremely low threshold. Discussion In this large retrospective cohort of patients with CRAB infections, we applied a novel framework to address heterogeneity in polymyxin-associated nephrotoxicity. We report three principal findings. First, latent class analysis (LCA) delineated three clinically meaningful phenotypes of polymyxin-associated AKI: Low-risk/Stable (Class 2), High-risk/Non-dialysis-dependent Injury (Class 3), and Severe Renal Failure (Class 1). Second, these phenotypes differed in baseline features, polymyxin use, and key outcomes, including ICU length of stay and mortality. Third—and most importantly—the prognostic value of the neutrophil-to-platelet ratio (NPR) was phenotype-specific, showing strong predictive capacity for 28-day mortality only in the Low-risk/Stable subgroup, with a distinct non-linear threshold at NPR 0.0167. Our results extend understanding of polymyxin nephrotoxicity beyond traditional AKI staging—which assigns severity after injury but overlooks patient heterogeneity—by providing a more holistic, data-driven classification. This application of LCA is consistent with emerging efforts in critical care to elucidate hidden heterogeneity within complex syndromes, as shown in heart failure [ 10 , 11 ] and sepsis-associated AKI [ 9 ] , indicating that such syndromes are not monolithic. The phenotypes have direct clinical interpretations: the Low-risk/Stable phenotype (Class 2), constituting most of the cohort (62.0%), likely reflects preserved renal reserve and physiological resilience to both CRAB infection and polymyxin exposure. By contrast, the separation of the two high-risk phenotypes reflects a common and complex ICU dilemma. The Severe Renal Failure phenotype (Class 1) denotes poor baseline renal function (34.9% with pre-existing CKD) with a severe insult, leading to rapid progression to RRT. The High-risk/Non-dialysis-dependent Injury phenotype (Class 3), despite the highest burdens of comorbidity and baseline CKD (70.4%), develops severe AKI without progressing to RRT. This distinction bears directly on the debate over polymyxin selection—particularly when choosing between polymyxin B (stable pharmacokinetics) and polymyxin E (complex pharmacokinetics requiring renal dosing) in patients with renal impairment. Polymyxin use differed significantly across phenotypes. Intravenous polymyxin E (colistimethate sodium) was used more frequently in Class 3 (16.0%) than in Class 1 (2.4%) or Class 2 (4.1%). This observation warrants caution: the retrospective design introduces substantial risk of confounding by indication. Clinicians may have preferentially selected polymyxin E for patients at very high baseline risk (as in Class 3), given its dosing adjustments tied to renal function. Ongoing debate over differential toxicity—some meta-analyses report higher AKI risk with colistin than with polymyxin B [ 5 ] —could also influence prescribing. Accordingly, we interpret this pattern as reflective of real-world decision-making rather than causal evidence. We selected the neutrophil-to-platelet ratio (NPR) as a prognostic biomarker because it is readily available from a complete blood count and reflects two key post-infection processes: systemic inflammation (neutrophil-driven) and coagulopathy or platelet consumption (platelet-reflected). Its utility in critical illness is increasingly recognized, with studies linking inflammatory ratios to infection severity and prognosis in multiple conditions [ 12 , 13 ] . Prior work shows that higher neutrophil-based ratios (e.g., N/LP) are associated with greater risk of sepsis-associated AKI, with RCS analyses confirming a dose-response relationship [ 14 ] , and other studies support inflammatory ratios for predicting complications and disease activity [ 15 , 16 ] . Accordingly, NPR was chosen as an accessible risk-stratification marker in this cohort. The key contribution of this study is that prognostic assessment should be anchored to clinical subtype. Our RCS and breakpoint analyses show that NPR’s prognostic value is not uniform but highly phenotype-dependent. In the two high-risk phenotypes (Classes 1 and 3), NPR was not predictive, likely because mortality risk is dominated by stronger drivers such as renal failure and a very high comorbidity burden.. In contrast, within the majority Low-risk/Stable subgroup (Class 2), NPR was a strong non-linear predictor. Breakpoint analysis identified a precise threshold at 0.0167: NPR ≤ 0.0167 was strongly associated with 28-day mortality, whereas no association was evident above this value. That an extremely low NPR signals high risk has practical implications. This is particularly pertinent because clinicians often hesitate to use polymyxins in patients perceived to be high risk given reported nephrotoxicity rates [ 4 , 5 ] . Our data also suggest that, in this phenotype, conventional inflammatory markers may not behave as expected—underscoring the necessity of LCA-based stratification. Without first identifying the Low-risk/Stable group, this subgroup-specific non-linear threshold would likely be obscured by noise from high-risk groups, leading to the erroneous conclusion that NPR lacks value in the overall population. These observations argue for more granular clinical stratification for CRAB patients [ 1 ] . At the initiation of polymyxin therapy, clinicians should strengthen screening for the likely renal phenotype. Baseline risk factors can guide phenotype assignment and, combined with NPR, support individualized risk assessment. Such stratification may refine treatment strategies—including polymyxin-based combinations that have shown promise [ 17 , 18 ] —by aligning therapy with phenotype-specific risks. This study has several strengths, including a large cohort, the novel use of LCA to interrogate polymyxin nephrotoxicity, and advanced RCS and breakpoint analyses to reveal complex non-linear relationships. Nonetheless, several limitations merit consideration. The retrospective design limits causal inference, particularly regarding polymyxin selection. Single-center data may constrain generalizability of the identified phenotypes [6]. In the Class 2 breakpoint analysis, few patients had NPR above 0.0167 (n = 17), rendering estimates unstable and necessitating validation of this threshold in larger samples. Unmeasured confounding may persist [ 17 , 19 ] . Finally, NPR was measured at a single time point; longitudinal dynamics may provide additional prognostic insight. In conclusion, patients with CRAB infections receiving polymyxins exhibit three distinct nephrotoxicity phenotypes with materially different mortality risks. Our core contribution is the demonstration that NPR’s prognostic value is phenotype-specific, showing a pronounced non-linear threshold only in the Low-risk/Stable subgroup. Recognizing and classifying this heterogeneity is prerequisite to moving from generalized risk scores to personalized prognostication in this critically ill population. Abbreviations AKI: Acute Kidney Injury aBIC: Adjusted Bayesian Information Criterion AIC: Akaike Information Criterion BIC: Bayesian Information Criterion CKD: Chronic Kidney Disease CRAB: Carbapenem-resistant Acinetobacter baumannii eGFR: Estimated Glomerular Filtration Rate ICU: Intensive Care Unit LCA: Latent Class Analysis LMR-LRT: Lo-Mendell-Rubin Likelihood Ratio Test NPR: Neutrophil-to-Platelet Ratio RCS: Restricted Cubic Splines RRT: Renal Replacement Therapy Declarations Competing interests The authors declare that they have no competing interests. Funding This study was supported by Grant 81600014,82170086 from the National Natural Science Foundation of China, Grant 20dz2261100 from the Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Grant shslczdzk02202 from Shanghai Municipal Key Clinical Specialty, and Grant 20dz2210500 from the Cultivation Project of Shanghai Major Infectious Disease Research Base. Author’s contributions Haixing Zhu, Dake Shi, Guangwei Li, Lijuan Wu, and Xiaoqian Ma contributed equally as first authors; they were involved in the study design, data collection, analysis, and manuscript drafting. Yumin Xu and Yun Feng are the co-corresponding authors; they conceived the study, supervised the project, and revised the manuscript for important intellectual content. All authors read and approved the final manuscript. Ethical approval and informed consent The study was approved by the Ruijin Hospital Ethics Committee, Shanghai Jiao Tong University School of Medicine. The study abides by the principles in the Declaration of Helsinki for the use of human samples. Informed consent was obtained from all patients. Clinical trial number: not applicable. Data Access Statement The de-identified dataset generated and analyzed during this study is not publicly available due to institutional patient privacy regulations. However, anonymized data may be made available to qualified researchers upon reasonable request, subject to approval by the institutional review board (IRB) of Ruijin Hospital Affiliated Shanghai Jiao Tong University School of Medicine. Requests should be directed to [email protected] and must include a detailed research proposal and compliance with data protection agreements. 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J Med Virol . 2022;94(1):211-221. doi:10.1002/jmv.27297 Xiao W, Lu Z, Liu Y, et al. Influence of the Initial Neutrophils to Lymphocytes and Platelets Ratio on the Incidence and Severity of Sepsis-Associated Acute Kidney Injury: A Double Robust Estimation Based on a Large Public Database. Front Immunol . 2022;13:925494. doi:10.3389/fimmu.2022.925494 Liang T, Chen J, Xu G, et al. Platelet-to-Lymphocyte Ratio as an Independent Factor Was Associated With the Severity of Ankylosing Spondylitis. Front Immunol . 2021;12:760214. doi:10.3389/fimmu.2021.760214 Ma M, Li G, Zhou B, et al. Comprehensive analysis of the association between inflammation indexes and complications in patients undergoing pancreaticoduodenectomy. Front Immunol . 2023;14:1303283. doi:10.3389/fimmu.2023.1303283 Chaudhary M, Kumar D, Meena DS, et al. ‘Effectiveness of various sulbactam-based combination antibiotic therapy in the management of ventilator-associated pneumonia caused by carbapenem-resistant Acinetobacter baumannii in a tertiary care Health centre.’ Indian J Med Microbiol . 2024;52:100737. doi:10.1016/j.ijmmb.2024.100737 Liu H. [analysis on the clinical pulmonary infection score on the detection of multidrug resistance organisms in lower respiratory tract in ventilated patients in intensive care unit]. Zhongguo Wei Zhong Bing Ji Jiu Yi Xue Chin Crit Care Med Zhongguo Weizhongbing Jijiuyixue . 2012;24(11):680-682. Bian X, Li M, Liu X, et al. Transcriptomic investigations of polymyxins and colistin/sulbactam combination against carbapenem-resistant Acinetobacter baumannii. Comput Struct Biotechnol J . 2024;23:2595-2605. doi:10.1016/j.csbj.2024.05.043 Table 3 and 4 Table 3 and 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementFigure1.pdf SupplementFigure2.pdf Table34.docx Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2026 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 02 Jan, 2026 Reviews received at journal 31 Dec, 2025 Reviewers agreed at journal 29 Dec, 2025 Reviews received at journal 07 Dec, 2025 Reviews received at journal 26 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 25 Nov, 2025 Editor invited by journal 24 Nov, 2025 Editor assigned by journal 24 Nov, 2025 Submission checks completed at journal 24 Nov, 2025 First submitted to journal 18 Nov, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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5","display":"","copyAsset":false,"role":"figure","size":28121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSegmented Regression Plot\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8146364/v1/5fe65cfaaad3db836172fd4f.png"},{"id":106344419,"identity":"4ae27064-bd29-42fc-9e0a-2073d3a5caba","added_by":"auto","created_at":"2026-04-07 16:14:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2395883,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8146364/v1/ed228ce4-9e09-4d99-91d2-10085189c870.pdf"},{"id":97128244,"identity":"ec9e2947-93aa-4d22-abe3-0bbaaa174d28","added_by":"auto","created_at":"2025-12-01 08:33:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":90472,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8146364/v1/1bf9a586990a6c96f2ab6a51.pdf"},{"id":97142230,"identity":"24edc6dd-46cf-4bf2-b9d7-09210b2dae00","added_by":"auto","created_at":"2025-12-01 10:07:26","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":94019,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementFigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8146364/v1/d120333bbb2c7a2adaac9da9.pdf"},{"id":97141921,"identity":"2219aab1-66ef-41d8-832f-75caba2ef2d8","added_by":"auto","created_at":"2025-12-01 10:07:09","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19111,"visible":true,"origin":"","legend":"","description":"","filename":"Table34.docx","url":"https://assets-eu.researchsquare.com/files/rs-8146364/v1/5f65998bdaf1505b312edfeb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Phenotyping of Polymyxin-Associated Nephrotoxicity and Phenotype-Specific Prognostic Utility of Neutrophil-to-Platelet Ratio in Carbapenem-Resistant Acinetobacter baumannii Infections","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfections caused by carbapenem-resistant Acinetobacter baumannii (CRAB) constitute a pressing global public health threat, particularly in intensive care units (ICUs) \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Polymyxins, including polymyxin B and polymyxin E (colistin), have been reinstated as last-line agents for these life-threatening infections\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Nevertheless, their clinical use remains challenging. Clinicians must choose between agents with distinct pharmacokinetic and toxicity profiles \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e while contending with the high incidence of polymyxin-associated acute kidney injury (AKI)\u003csup\u003e\u0026nbsp;[\u003c/sup\u003e\u003csup\u003e4\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This dilemma is most acute when treating complex patients\u0026mdash;such as older adults or those with pre-existing renal impairment\u0026mdash;leading to uncertainty and therapeutic hesitation in practice. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough numerous meta-analyses have compared the efficacy and overall toxicity of polymyxin agents\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e5,6\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, they often treat the critically ill population as a homogeneous whole. Such aggregation obscures substantial heterogeneity in patient susceptibility and fails to provide the precision models required for individualized risk assessment. Meanwhile, despite ongoing efforts to discover novel AKI biomarkers\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e7,8\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, single markers frequently underperform in capturing the complex, multidimensional nature of vulnerability. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address these gaps, we hypothesized that patients exhibit distinct response patterns to polymyxin exposure. Accordingly, we employed latent class analysis (LCA) to derive clinical phenotypes from baseline risks and renal outcomes, a strategy that has successfully revealed hidden heterogeneity in other critical illnesses \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e9\u0026ndash;11\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Using this approach, we identified three distinct clinical phenotypes of polymyxin-associated nephrotoxicity. We then examined whether these phenotypes refine prognostication by assessing the predictive value of the neutrophil-to-platelet ratio (NPR)\u003csup\u003e\u0026nbsp;[\u003c/sup\u003e\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Our overarching goal is to move beyond generalized risk scores and to establish a stratified, phenotype-guided framework to improve prognostication in this critically ill population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Population\u003c/h2\u003e\u003cp\u003eOur analysis proceeded in three stages. First, we used latent class analysis (LCA) to identify clinical phenotypes of polymyxin-associated nephrotoxicity. The LCA model was based on seven dichotomous indicator variables: age\u0026thinsp;\u0026ge;\u0026thinsp;65 years, immunosuppressed state, baseline CKD, baseline eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73m\u0026sup2;, \u0026ge;\u0026thinsp;2 comorbidities, severe AKI, and RRT requirement. The optimal number of classes was selected based on information criteria (AIC, BIC, aBIC), the Lo-Mendell-Rubin Likelihood Ratio Test (LMR-LRT), and clinical interpretability.After assigning each patient to their most likely phenotype, we compared baseline characteristics and clinical outcomes across the identified classes using Chi-square or Fisher\u0026rsquo;s exact tests for categorical variables and the Kruskal-Wallis test for continuous variables. we also performed a phenotype-stratified restricted cubic spline (RCS) analysis to explore whether the relationships between continuous predictors (age and NPR) and 28-day mortality were moderated by the identified phenotypes. We used multivariable logistic regression models with 4-knot RCS terms and tested for a statistical interaction between the predictor and the phenotype. Models were adjusted for polymyxin type, gender, immunosuppression, and number of comorbidities. All analyses were performed using R software (Version 4.2.0). A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Collection and Definitions\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study at Ruijin Hospital, a tertiary academic center affiliated with Shanghai Jiao Tong University School of Medicine. We identified all adults (\u0026ge;\u0026thinsp;18 years) with confirmed carbapenem-resistant \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (CRAB) infection who received any polymyxin between January 1, 2020 and December 31, 2025. Inclusion criteria were age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, isolation of CRAB from a clinical culture, and receipt of polymyxin B, colistin sulfate, or colistimethate sodium for \u0026ge;\u0026thinsp;72 hours. Exclusion criteria were renal replacement therapy (RRT) at polymyxin initiation and missing baseline serum creatinine. The Institutional Review Board of Ruijin Hospital approved the study and waived informed consent owing to its retrospective design.\u003c/p\u003e\u003cp\u003eWe extracted data from the hospital electronic medical record, including demographics; comorbidities (immunosuppression and chronic kidney disease [CKD]); baseline laboratory values (serum creatinine and neutrophil and platelet counts for calculating the neutrophil-to-platelet ratio [NPR]); and polymyxin regimen details. The primary outcome was 28-day all-cause mortality. Secondary outcomes were 14-day mortality, ICU length of stay, and severe acute kidney injury (AKI; KDIGO stage 2\u0026ndash;3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe analysis proceeded in three stages. First, we applied latent class analysis (LCA) to identify clinical phenotypes of polymyxin-associated nephrotoxicity. The LCA model included seven dichotomous indicators: age\u0026thinsp;\u0026ge;\u0026thinsp;65 years, immunosuppression, baseline CKD, baseline eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;, \u0026ge;\u0026thinsp;2 comorbidities, severe AKI, and RRT requirement. The optimal number of classes was determined using information criteria (AIC, BIC, aBIC), the Lo\u0026ndash;Mendell\u0026ndash;Rubin likelihood ratio test (LMR-LRT), and clinical interpretability. After assigning each patient to the most likely phenotype, we compared baseline characteristics and outcomes across classes using χ\u0026sup2; or Fisher\u0026rsquo;s exact tests for categorical variables and the Kruskal\u0026ndash;Wallis test for continuous variables. We additionally performed phenotype-stratified restricted cubic spline (RCS) analyses to examine whether the relationships between continuous predictors (age and NPR) and 28-day mortality differed by phenotype. We fit multivariable logistic regression models with four-knot RCS terms and tested interactions between each predictor and phenotype. Models adjusted for polymyxin type, gender, immunosuppression, and comorbidity count. All analyses were conducted in R (version 4.2.0). Two-sided p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics of the Study Cohort\u003c/h2\u003e\u003cp\u003eA total of 792 patients treated with polymyxins for carbapenem-resistant \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (CRAB) infections were initially assessed for eligibility. After applying the exclusion criteria, 245 patients were excluded, resulting in a final cohort of 547 patients for the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe baseline demographic and clinical characteristics of the study population are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The cohort had a median age of 69.0 years (Interquartile Range [IQR], 58.0\u0026ndash;77.0), and a majority of patients were male (392, 71.7%). The burden of comorbidities was high; 163 patients (29.8%) were in an immunosuppressed state, and 87 (15.9%) had pre-existing chronic kidney disease (CKD).\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\u003eBaseline demographic and clinical characteristics of the study cohort (N\u0026thinsp;=\u0026thinsp;547).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC Colistin Sulfate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e​Polymyxin E (Neb)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePolymyxin B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePolymyxin E (IV)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003estatistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\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\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.00 (58.00\u0026ndash;77.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.00 (60.00\u0026ndash;78.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.00 (60.00\u0026ndash;78.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.00 (56.00\u0026ndash;77.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58.50 (48.25\u0026ndash;68.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e19.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e392 (71.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126 (71.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98 (71.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e139 (70.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29 (80.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.67\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\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155 (28.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51 (28.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39 (28.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58 (29.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7 (19.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170 (31.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (29.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51 (37.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59 (29.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8 (22.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiac Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e265 (48.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (47.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67 (48.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95 (48.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19 (52.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e​Cerebrovascular Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e179 (32.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (33.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49 (35.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e63 (31.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8 (22.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e​Malignancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (16.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (15.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (12.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36 (18.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8 (22.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic Kidney Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87 (15.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (12.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (18.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30 (15.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (7.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (7.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (11.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11 (5.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (2.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFailed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132 (24.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (21.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (18.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61 (30.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7 (19.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003eImproved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e413 (75.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139 (78.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e110 (80.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e135 (68.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29 (80.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicrobiological outcome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEradicated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (14.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (15.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (12.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27 (13.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6 (16.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.63\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\u003eInvalid Code\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (0.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003ePersisted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e435 (79.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (76.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e108 (78.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e162 (82.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29 (80.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003ePresumed Eradicated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (4.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (5.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (6.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (2.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003eRecurred\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (1.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (2.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (1.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (1.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (2.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14-Day Mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (5.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (3.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (2.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (7.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4 (11.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28-Day Mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (7.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (9.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (4.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (8.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (8.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.27 (8.25\u0026ndash;17.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.40 (8.56\u0026ndash;15.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.34 (7.90\u0026ndash;15.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.78 (8.48\u0026ndash;19.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.86 (7.65\u0026ndash;20.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.10 (81.20\u0026ndash;92.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.80 (82.60\u0026ndash;92.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87.00 (80.70\u0026ndash;92.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.60 (81.40\u0026ndash;92.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e86.85 (78.95\u0026ndash;91.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170.00 (107.00\u0026ndash;249.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e171.00 (94.00\u0026ndash;249.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e168.00 (107.00\u0026ndash;247.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e169.00 (118.00\u0026ndash;248.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e180.00 (108.50\u0026ndash;248.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.00 (42.00\u0026ndash;158.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.00 (38.00\u0026ndash;159.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.00 (38.00\u0026ndash;147.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.00 (48.00\u0026ndash;166.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76.95 (37.58\u0026ndash;174.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.80 (0.24\u0026ndash;2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.64 (0.21\u0026ndash;2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66 (0.17\u0026ndash;2.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.11 (0.31\u0026ndash;3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.86 (0.17\u0026ndash;4.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.70 (1.20\u0026ndash;5.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.50 (1.22\u0026ndash;4.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.36 (1.02\u0026ndash;5.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.00 (1.30\u0026ndash;5.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.42 (1.28\u0026ndash;5.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline eGFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.80 (52.90\u0026ndash;107.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.20 (62.00\u0026ndash;108.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.90 (44.40\u0026ndash;104.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87.50 (52.00\u0026ndash;105.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e85.45 (56.77\u0026ndash;112.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeak eGFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.60 (29.35\u0026ndash;96.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.60 (44.50\u0026ndash;98.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.20 (26.10\u0026ndash;98.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.30 (27.10\u0026ndash;90.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51.40 (28.82\u0026ndash;89.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e14.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\u003eBaseline Cr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.00 (53.00\u0026ndash;117.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.00 (53.00\u0026ndash;100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.00 (52.00\u0026ndash;125.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72.00 (53.00\u0026ndash;125.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77.00 (56.75\u0026ndash;120.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeak Cr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.00 (66.50\u0026ndash;185.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.00 (64.00\u0026ndash;136.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87.00 (66.00\u0026ndash;214.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e124.00 (69.00\u0026ndash;192.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e142.50 (92.25\u0026ndash;233.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e17.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eRegarding the polymyxin regimens administered, polymyxin B was the most common agent (197, 36.0%), followed by nebulized polymyxin E (137, 25.0%), colistin sulfate (177, 32.36%), and intravenous polymyxin E (colistimethate sodium) (36, 6.58%). The patient population was critically ill, with a median length of ICU stay of 29.0 days (IQR, 17.0\u0026ndash;49.0). The overall 14-day and 28-day mortality rates for the cohort were 5.12% and 7.86%, respectively.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLatent Class Analysis and Identification of Clinical Phenotypes\u003c/h3\u003e\n\u003cp\u003eWe performed latent class analysis (LCA) using seven indicators of baseline risk and renal outcomes to identify distinct patient subgroups. Model fit supported a three-class solution as optimal (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with the lowest AIC (4051.276) and adjusted BIC (aBIC 4084.863). The Lo\u0026ndash;Mendell\u0026ndash;Rubin likelihood ratio test (LMR-LRT) favored the three-class over the two-class model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the four-class model did not improve fit (p\u0026thinsp;=\u0026thinsp;1.000). The entropy (0.758) indicated good classification accuracy.\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\u003eModel fit statistics for latent class analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClasses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLogLik\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eaBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEntropy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eClass_Probs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep_LMRT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ep_BLRT\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2155.547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4325.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4354.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4332.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e690.465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2029.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4096.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4177.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4116.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e199.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.366/0.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1994.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4051.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4183.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4084.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e122.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.267/0.587/0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2110.688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4307.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4490.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4353.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e319.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.011/0.568/0.044/0.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2103.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4317.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4551.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4376.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e377.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.053/0.052/0.201/0/0.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.3747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0000\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\u003eThe conditional probabilities of the indicator variables for each class are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Based on these distinct profiles, the three phenotypes were characterized and named as follows:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eClass 1 (\u003cb\u003eSevere Renal Failure\u003c/b\u003e; n\u0026thinsp;=\u0026thinsp;83, 15.2%) was characterized by very high probabilities of renal replacement therapy (RRT) and severe acute kidney injury (AKI), with a high prevalence of pre-existing CKD and low baseline eGFR.\u003c/p\u003e\u003cp\u003eClass 2 (\u003cb\u003eLow-risk/Stable\u003c/b\u003e; n\u0026thinsp;=\u0026thinsp;339, 61.9%) was the largest subgroup and showed very low probabilities across adverse renal indicators, including severe AKI, RRT, baseline CKD, and low baseline eGFR.\u003c/p\u003e\u003cp\u003eClass 3 (\u003cb\u003eHigh-risk\u003c/b\u003e/Non-dialysis-dependent Injury; n\u0026thinsp;=\u0026thinsp;125, 22.9%) showed a high probability of severe AKI but a low probability of requiring RRT, with more comorbidities, older age, and lower baseline eGFR, and a lower prevalence of baseline CKD than Class 1.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eComparison of Characteristics and Outcomes Among Clinical Phenotypes\u003c/h2\u003e\n \u003cp\u003eAcross phenotypes (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), 14-day (p\u0026thinsp;=\u0026thinsp;0.039) and 28-day mortality (p\u0026thinsp;=\u0026thinsp;0.033) differed significantly, with Class 2 having the lowest rates (3.2% and 5.6%, respectively) compared with Class 1 (8.4% and 13.3%) and Class 3 (8.0% and 10.4%). ICU length of stay also differed (p\u0026thinsp;=\u0026thinsp;0.005), being longest in Class 3 (median 32 days). The distribution of polymyxin agents differed significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Intravenous polymyxin E was used more often in Class 3 (16.0%) than in Class 2 (4.1%) or Class 1 (2.4%). Baseline immunosuppression and comorbidity burden were also highest in Class 3, whereas age and sex did not differ significantly across groups.\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePhenotype-Specific Prognostic Value of the Neutrophil-to-Platelet Ratio (NPR)\u003c/h3\u003e\n\u003cp\u003eMotivated by mortality differences across phenotypes, we evaluated the prognostic utility of baseline NPR. Stratified restricted cubic spline (RCS) analyses assessed non-linear associations between NPR and 28-day mortality within each phenotype, adjusted for age, sex, and \u0026ge;\u0026thinsp;2 comorbidities (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). A significant association was detected only in the Low-risk/Stable phenotype (Class 2; p for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e): risk rose steeply at very low NPR values and then plateaued. In contrast, no linear or non-linear association was observed in Classes 1 or 3 (Supplementary Figs. 1\u0026ndash;2; all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting attenuation by severe baseline illness.\u003c/p\u003e\n\u003cp\u003eTo quantify this threshold in Class 2 (n\u0026thinsp;=\u0026thinsp;339), we fit a piecewise regression model adjusted for sex, age, \u0026ge;\u0026thinsp;2 comorbidities, and immunosuppression. The optimal breakpoint for NPR was 0.0167, indicating a clear threshold: at NPR\u0026thinsp;\u0026le;\u0026thinsp;0.0167 (n\u0026thinsp;=\u0026thinsp;322), NPR was strongly associated with 28-day mortality (OR 4.07\u0026times;10⁶⁰; 95% CI 1.46\u0026times;10\u0026sup2;⁸\u0026ndash;1.13\u0026times;10\u0026sup1;\u0026sup2;⁷; p\u0026thinsp;=\u0026thinsp;0.00276), whereas above 0.0167 (n\u0026thinsp;=\u0026thinsp;17) the association was not significant (OR 0.00; p\u0026thinsp;=\u0026thinsp;0.65662). A log-likelihood ratio test confirmed superior fit over a linear model (\u0026chi;\u0026sup2;=11.7488; p\u0026thinsp;=\u0026thinsp;0.00061), validating a significant non-linear breakpoint at 0.0167 (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). These results indicate that NPR\u0026rsquo;s prognostic utility is confined to the Low-risk/Stable phenotype and is defined by an extremely low threshold.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large retrospective cohort of patients with CRAB infections, we applied a novel framework to address heterogeneity in polymyxin-associated nephrotoxicity. We report three principal findings. First, latent class analysis (LCA) delineated three clinically meaningful phenotypes of polymyxin-associated AKI: Low-risk/Stable (Class 2), High-risk/Non-dialysis-dependent Injury (Class 3), and Severe Renal Failure (Class 1). Second, these phenotypes differed in baseline features, polymyxin use, and key outcomes, including ICU length of stay and mortality. Third\u0026mdash;and most importantly\u0026mdash;the prognostic value of the neutrophil-to-platelet ratio (NPR) was phenotype-specific, showing strong predictive capacity for 28-day mortality only in the Low-risk/Stable subgroup, with a distinct non-linear threshold at NPR 0.0167.\u003c/p\u003e\u003cp\u003eOur results extend understanding of polymyxin nephrotoxicity beyond traditional AKI staging\u0026mdash;which assigns severity after injury but overlooks patient heterogeneity\u0026mdash;by providing a more holistic, data-driven classification. This application of LCA is consistent with emerging efforts in critical care to elucidate hidden heterogeneity within complex syndromes, as shown in heart failure\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e and sepsis-associated AKI \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, indicating that such syndromes are not monolithic. The phenotypes have direct clinical interpretations: the Low-risk/Stable phenotype (Class 2), constituting most of the cohort (62.0%), likely reflects preserved renal reserve and physiological resilience to both CRAB infection and polymyxin exposure. By contrast, the separation of the two high-risk phenotypes reflects a common and complex ICU dilemma. The Severe Renal Failure phenotype (Class 1) denotes poor baseline renal function (34.9% with pre-existing CKD) with a severe insult, leading to rapid progression to RRT. The High-risk/Non-dialysis-dependent Injury phenotype (Class 3), despite the highest burdens of comorbidity and baseline CKD (70.4%), develops severe AKI without progressing to RRT. This distinction bears directly on the debate over polymyxin selection\u0026mdash;particularly when choosing between polymyxin B (stable pharmacokinetics) and polymyxin E (complex pharmacokinetics requiring renal dosing) in patients with renal impairment.\u003c/p\u003e\u003cp\u003ePolymyxin use differed significantly across phenotypes. Intravenous polymyxin E (colistimethate sodium) was used more frequently in Class 3 (16.0%) than in Class 1 (2.4%) or Class 2 (4.1%). This observation warrants caution: the retrospective design introduces substantial risk of confounding by indication. Clinicians may have preferentially selected polymyxin E for patients at very high baseline risk (as in Class 3), given its dosing adjustments tied to renal function. Ongoing debate over differential toxicity\u0026mdash;some meta-analyses report higher AKI risk with colistin than with polymyxin B\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;could also influence prescribing. Accordingly, we interpret this pattern as reflective of real-world decision-making rather than causal evidence.\u003c/p\u003e\u003cp\u003eWe selected the neutrophil-to-platelet ratio (NPR) as a prognostic biomarker because it is readily available from a complete blood count and reflects two key post-infection processes: systemic inflammation (neutrophil-driven) and coagulopathy or platelet consumption (platelet-reflected). Its utility in critical illness is increasingly recognized, with studies linking inflammatory ratios to infection severity and prognosis in multiple conditions \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Prior work shows that higher neutrophil-based ratios (e.g., N/LP) are associated with greater risk of sepsis-associated AKI, with RCS analyses confirming a dose-response relationship\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, and other studies support inflammatory ratios for predicting complications and disease activity\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Accordingly, NPR was chosen as an accessible risk-stratification marker in this cohort.\u003c/p\u003e\u003cp\u003eThe key contribution of this study is that prognostic assessment should be anchored to clinical subtype. Our RCS and breakpoint analyses show that NPR\u0026rsquo;s prognostic value is not uniform but highly phenotype-dependent. In the two high-risk phenotypes (Classes 1 and 3), NPR was not predictive, likely because mortality risk is dominated by stronger drivers such as renal failure and a very high comorbidity burden..\u003c/p\u003e\u003cp\u003eIn contrast, within the majority Low-risk/Stable subgroup (Class 2), NPR was a strong non-linear predictor. Breakpoint analysis identified a precise threshold at 0.0167: NPR\u0026thinsp;\u0026le;\u0026thinsp;0.0167 was strongly associated with 28-day mortality, whereas no association was evident above this value. That an extremely low NPR signals high risk has practical implications. This is particularly pertinent because clinicians often hesitate to use polymyxins in patients perceived to be high risk given reported nephrotoxicity rates\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Our data also suggest that, in this phenotype, conventional inflammatory markers may not behave as expected\u0026mdash;underscoring the necessity of LCA-based stratification. Without first identifying the Low-risk/Stable group, this subgroup-specific non-linear threshold would likely be obscured by noise from high-risk groups, leading to the erroneous conclusion that NPR lacks value in the overall population.\u003c/p\u003e\u003cp\u003eThese observations argue for more granular clinical stratification for CRAB patients\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. At the initiation of polymyxin therapy, clinicians should strengthen screening for the likely renal phenotype. Baseline risk factors can guide phenotype assignment and, combined with NPR, support individualized risk assessment. Such stratification may refine treatment strategies\u0026mdash;including polymyxin-based combinations that have shown promise\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;by aligning therapy with phenotype-specific risks.\u003c/p\u003e\u003cp\u003eThis study has several strengths, including a large cohort, the novel use of LCA to interrogate polymyxin nephrotoxicity, and advanced RCS and breakpoint analyses to reveal complex non-linear relationships. Nonetheless, several limitations merit consideration. The retrospective design limits causal inference, particularly regarding polymyxin selection. Single-center data may constrain generalizability of the identified phenotypes [6]. In the Class 2 breakpoint analysis, few patients had NPR above 0.0167 (n\u0026thinsp;=\u0026thinsp;17), rendering estimates unstable and necessitating validation of this threshold in larger samples. Unmeasured confounding may persist\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Finally, NPR was measured at a single time point; longitudinal dynamics may provide additional prognostic insight.\u003c/p\u003e\u003cp\u003eIn conclusion, patients with CRAB infections receiving polymyxins exhibit three distinct nephrotoxicity phenotypes with materially different mortality risks. Our core contribution is the demonstration that NPR\u0026rsquo;s prognostic value is phenotype-specific, showing a pronounced non-linear threshold only in the Low-risk/Stable subgroup. Recognizing and classifying this heterogeneity is prerequisite to moving from generalized risk scores to personalized prognostication in this critically ill population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAKI:\u003c/strong\u003e Acute Kidney Injury\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eaBIC:\u003c/strong\u003e Adjusted Bayesian Information Criterion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAIC:\u003c/strong\u003e Akaike Information Criterion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBIC:\u003c/strong\u003e Bayesian Information Criterion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCKD:\u003c/strong\u003e Chronic Kidney Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRAB:\u003c/strong\u003e Carbapenem-resistant Acinetobacter baumannii\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eeGFR:\u003c/strong\u003e Estimated Glomerular Filtration Rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICU:\u003c/strong\u003e Intensive Care Unit\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLCA:\u003c/strong\u003e Latent Class Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLMR-LRT:\u003c/strong\u003e Lo-Mendell-Rubin Likelihood Ratio Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNPR:\u003c/strong\u003e Neutrophil-to-Platelet Ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRCS:\u003c/strong\u003e Restricted Cubic Splines\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRRT:\u003c/strong\u003e Renal Replacement Therapy\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Grant 81600014,82170086 from the National Natural Science Foundation of China, Grant 20dz2261100 from the Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Grant shslczdzk02202 from Shanghai Municipal Key Clinical Specialty, and Grant 20dz2210500 from the Cultivation Project of Shanghai Major Infectious Disease Research Base.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaixing Zhu, Dake Shi, Guangwei Li, Lijuan Wu, and Xiaoqian Ma contributed equally as first authors; they were involved in the study design, data collection, analysis, and manuscript drafting. Yumin Xu and Yun Feng are the co-corresponding authors; they conceived the study, supervised the project, and revised the manuscript for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthical approval and informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ruijin Hospital Ethics Committee, Shanghai Jiao Tong University School of Medicine. The study abides by the principles in the Declaration of Helsinki for the use of human samples. Informed consent was obtained from all patients.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Access Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe de-identified dataset generated and analyzed during this study is not publicly available due to institutional patient privacy regulations. However, anonymized data may be made available to qualified researchers upon reasonable request, subject to approval by the institutional review board (IRB) of Ruijin Hospital Affiliated Shanghai Jiao Tong University School of Medicine. Requests should be directed to [email protected] and must include a detailed research proposal and compliance with data protection agreements.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the author(s) used DeepSeek to improve language clarity and fluency. After using this tool, the authors reviewed and edited the content as needed, and took full responsibility for the content of the publication\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArshad N, Azzam W, Zilberberg MD, Shorr AF. Acinetobacter baumannii Complex Infections: New Treatment Options in the Antibiotic Pipeline. \u003cem\u003eMicroorganisms\u003c/em\u003e. 2025;13(2):356. doi:10.3390/microorganisms13020356\u003c/li\u003e\n\u003cli\u003eSatlin MJ, Lewis JS, Weinstein MP, et al. Clinical and laboratory standards institute and european committee on antimicrobial susceptibility testing position statements on polymyxin B and colistin clinical breakpoints. \u003cem\u003eClin Infect Dis Off Publ Infect Dis Soc Am\u003c/em\u003e. 2020;71(9):e523-e529. doi:10.1093/cid/ciaa121\u003c/li\u003e\n\u003cli\u003eGuzman L, Rabanal F, Garcia J, et al. B-rich colistin and B-pure colistin as novel strategies to increase the therapeutic window of polymyxin antibiotic therapy. \u003cem\u003eBiomed Pharmacother Biomedecine Pharmacother\u003c/em\u003e. 2025;190:118366. doi:10.1016/j.biopha.2025.118366\u003c/li\u003e\n\u003cli\u003eJeon CH, Kim SH, Kim H, Park KJ, Wi YM. Ineffectiveness of colistin monotherapy in treating carbapenem-resistant Acinetobacter baumannii Pneumonia: A retrospective single-center cohort study. \u003cem\u003eJ Infect Public Health\u003c/em\u003e. 2024;17(5):774-779. doi:10.1016/j.jiph.2024.03.007\u003c/li\u003e\n\u003cli\u003eSisay M, Hagos B, Edessa D, Tadiwos Y, Mekuria AN. Polymyxin-induced nephrotoxicity and its predictors: A systematic review and meta-analysis of studies conducted using RIFLE criteria of acute kidney injury. \u003cem\u003ePharmacol Res\u003c/em\u003e. 2021;163:105328. doi:10.1016/j.phrs.2020.105328\u003c/li\u003e\n\u003cli\u003eBu W, Wang C, Wu Y, et al. Efficacy and safety of polymyxin B sulfate versus colistin sulfate in ICU patients with nosocomial pneumonia caused by carbapenem-resistant Acinetobacter baumannii: a multicenter, propensity score-matched, real-world cohort study. \u003cem\u003eBMC Infect Dis\u003c/em\u003e. 2025;25(1):390. doi:10.1186/s12879-025-10773-1\u003c/li\u003e\n\u003cli\u003eJantti T, Tarvasmaki T, Harjola VP, et al. Predictive value of plasma proenkephalin and neutrophil gelatinase-associated lipocalin in acute kidney injury and mortality in cardiogenic shock. \u003cem\u003eAnn Intensive Care\u003c/em\u003e. 2021;11(1):25. doi:10.1186/s13613-021-00814-8\u003c/li\u003e\n\u003cli\u003eZhao Y, Chang W, Li X, Su P, Zhang H, Zhang J. Early Diagnosis of Kidney Injury via Ultrasensitive Detection of Urine-Derived Exosomes Using a Dual-Signal Amplification Strategy. \u003cem\u003eACS Sens\u003c/em\u003e. Published online September 25, 2025. doi:10.1021/acssensors.5c00287\u003c/li\u003e\n\u003cli\u003eHuang F, Thokerunga E, He F, Zhu X, Wang Z, Tu J. Research progress of the application of mesenchymal stem cells in chronic inflammatory systemic diseases. \u003cem\u003eStem Cell Res Ther\u003c/em\u003e. 2022;13(1):1. doi:10.1186/s13287-021-02613-1\u003c/li\u003e\n\u003cli\u003eHafkamp F, Dekker L, Tio R, et al. Characterizing High Risk Patients in Heart Failure: A Latent Class Analysis of Rehospitalization and Mortality. \u003cem\u003eJ Cardiovasc Nurs\u003c/em\u003e. 2025;40(5):E297-E307. doi:10.1097/JCN.0000000000001208\u003c/li\u003e\n\u003cli\u003eMatsuoka Y, Sotomi Y, Nakatani D, et al. Phenotypic Trajectories From Acute to Stable Phase in Heart Failure With Preserved Ejection Fraction: Insights From the PURSUIT‐HFpEF Study. \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e. 2025;14(3):e037567. doi:10.1161/JAHA.124.037567\u003c/li\u003e\n\u003cli\u003eOkuyan O, Elgormus Y, Sayili U, Dumur S, Isik OE, Uzun H. The Effect of Virus-Specific Vaccination on Laboratory Infection Markers of Children with Acute Rotavirus-Associated Acute Gastroenteritis. \u003cem\u003eVaccines\u003c/em\u003e. 2023;11(3). doi:10.3390/vaccines11030580\u003c/li\u003e\n\u003cli\u003eSarkar S, Kannan S, Khanna P, Singh AK. Role of platelet-to-lymphocyte count ratio (PLR), as a prognostic indicator in COVID-19: A systematic review and meta-analysis. \u003cem\u003eJ Med Virol\u003c/em\u003e. 2022;94(1):211-221. doi:10.1002/jmv.27297\u003c/li\u003e\n\u003cli\u003eXiao W, Lu Z, Liu Y, et al. Influence of the Initial Neutrophils to Lymphocytes and Platelets Ratio on the Incidence and Severity of Sepsis-Associated Acute Kidney Injury: A Double Robust Estimation Based on a Large Public Database. \u003cem\u003eFront Immunol\u003c/em\u003e. 2022;13:925494. doi:10.3389/fimmu.2022.925494\u003c/li\u003e\n\u003cli\u003eLiang T, Chen J, Xu G, et al. Platelet-to-Lymphocyte Ratio as an Independent Factor Was Associated With the Severity of Ankylosing Spondylitis. \u003cem\u003eFront Immunol\u003c/em\u003e. 2021;12:760214. doi:10.3389/fimmu.2021.760214\u003c/li\u003e\n\u003cli\u003eMa M, Li G, Zhou B, et al. Comprehensive analysis of the association between inflammation indexes and complications in patients undergoing pancreaticoduodenectomy. \u003cem\u003eFront Immunol\u003c/em\u003e. 2023;14:1303283. doi:10.3389/fimmu.2023.1303283\u003c/li\u003e\n\u003cli\u003eChaudhary M, Kumar D, Meena DS, et al. \u0026lsquo;Effectiveness of various sulbactam-based combination antibiotic therapy in the management of ventilator-associated pneumonia caused by carbapenem-resistant Acinetobacter baumannii in a tertiary care Health centre.\u0026rsquo; \u003cem\u003eIndian J Med Microbiol\u003c/em\u003e. 2024;52:100737. doi:10.1016/j.ijmmb.2024.100737\u003c/li\u003e\n\u003cli\u003eLiu H. [analysis on the clinical pulmonary infection score on the detection of multidrug resistance organisms in lower respiratory tract in ventilated patients in intensive care unit]. \u003cem\u003eZhongguo Wei Zhong Bing Ji Jiu Yi Xue Chin Crit Care Med Zhongguo Weizhongbing Jijiuyixue\u003c/em\u003e. 2012;24(11):680-682.\u003c/li\u003e\n\u003cli\u003eBian X, Li M, Liu X, et al. Transcriptomic investigations of polymyxins and colistin/sulbactam combination against carbapenem-resistant Acinetobacter baumannii. \u003cem\u003eComput Struct Biotechnol J\u003c/em\u003e. 2024;23:2595-2605. doi:10.1016/j.csbj.2024.05.043\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 3 and 4","content":"\u003cp\u003eTable 3 and 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Carbapenem-Resistant Acinetobacter baumannii, Polymyxin, Nephrotoxicity, Acute Kidney Injury, Clinical Phenotype, Neutrophil-to-Platelet Ratio","lastPublishedDoi":"10.21203/rs.3.rs-8146364/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8146364/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCarbapenem-resistant Acinetobacter baumannii (CRAB) infections remain therapeutically challenging, often necessitating last-line polymyxin therapy despite substantial nephrotoxicity. Conventional risk assessments for polymyxin-associated acute kidney injury (AKI) frequently overlook patient heterogeneity. We sought to delineate clinical phenotypes of polymyxin-associated AKI and determine whether the prognostic value of the neutrophil-to-platelet ratio (NPR) for mortality varies by phenotype.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study (2020\u0026ndash;2025) of 547 patients with CRAB infections treated with polymyxins. Latent class analysis (LCA) identified clinical phenotypes using baseline risk factors and renal outcomes. Multivariable logistic regression incorporating restricted cubic splines (RCS), followed by piecewise regression, evaluated non-linear associations between NPR and 28-day mortality and tested effect modification by phenotype.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCA revealed three phenotypes: Severe Renal Failure (Class 1, 15.2%), Low-risk/Stable (Class 2, 62.0%), and High-risk/Non-dialysis-dependent Injury (Class 3, 22.8%). Baseline characteristics and 28-day mortality differed across phenotypes (13.3% vs 5.6% vs 10.4%; p\u0026thinsp;=\u0026thinsp;0.033). The prognostic value of NPR was confined to Class 2: within this subgroup (n\u0026thinsp;=\u0026thinsp;339), NPR showed a significant association with mortality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Piecewise regression identified a breakpoint at NPR\u0026thinsp;=\u0026thinsp;0.0167 (likelihood-ratio test p\u0026thinsp;=\u0026thinsp;0.00061); NPR\u0026thinsp;\u0026le;\u0026thinsp;0.0167 was associated with higher 28-day mortality (p\u0026thinsp;=\u0026thinsp;0.00276), with no association above this threshold.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePatients with CRAB infections receiving polymyxins exhibit three distinct nephrotoxicity phenotypes, and the prognostic utility of NPR is phenotype-specific, with a pronounced non-linear threshold confined to the Low-risk/Stable subgroup; phenotype-based stratification is therefore essential for valid interpretation of commonly used prognostic biomarkers.\u003c/p\u003e","manuscriptTitle":"Clinical Phenotyping of Polymyxin-Associated Nephrotoxicity and Phenotype-Specific Prognostic Utility of Neutrophil-to-Platelet Ratio in Carbapenem-Resistant Acinetobacter baumannii Infections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:33:50","doi":"10.21203/rs.3.rs-8146364/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-02T07:16:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-01T04:16:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211134620898241821780673905009078735408","date":"2025-12-30T04:28:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T03:24:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T05:26:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214296810788753780297145087184445021236","date":"2025-11-26T04:36:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266992405927031849769941901281396439127","date":"2025-11-25T10:16:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T09:44:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-24T11:13:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T07:11:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-24T07:10:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-11-18T13:53:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"842589de-61a5-44d2-a9df-76ef42010e7e","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:10:06+00:00","versionOfRecord":{"articleIdentity":"rs-8146364","link":"https://doi.org/10.1186/s12879-026-12964-w","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2026-04-02 15:59:30","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2025-12-01 08:33:50","video":"","vorDoi":"10.1186/s12879-026-12964-w","vorDoiUrl":"https://doi.org/10.1186/s12879-026-12964-w","workflowStages":[]},"version":"v1","identity":"rs-8146364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8146364","identity":"rs-8146364","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}