Persistent Mortality Risk Following Initial Supratherapeutic Digoxin Exposure in Critically Ill Children: A Legacy Effect

Persistent Mortality Risk Following Initial Supratherapeutic Digoxin Exposure in Critically Ill Children: A Legacy Effect | 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 Persistent Mortality Risk Following Initial Supratherapeutic Digoxin Exposure in Critically Ill Children: A Legacy Effect Jingchuan Lu, Xuemei Hu, Shiying Zhao, Shuangyan Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9183203/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Purpose Digoxin is a narrow therapeutic index drug requiring therapeutic drug monitoring (TDM) in pediatric intensive care units (PICUs). Whether correcting initially elevated concentrations eliminates the associated mortality risk remains unknown. Methods This retrospective cohort study analyzed data from 317 PICU patients in the Pediatric Intensive Care database (2010–2018), stratified by initial serum digoxin concentrations (SDC): ≤1.0 ng/mL (n = 111), > 1.0 - ≤2.0 ng/mL ( n = 134), and > 2.0 ng/mL ( n = 72). A subcohort of 180 patients with serial measurements compared mortality outcomes between those with initially supratherapeutic concentrations subsequently corrected to therapeutic range ("High-Decrease" pattern) versus those maintaining consistently therapeutic levels ("Normal-Increase" pattern). Multivariable logistic regression assessed associations with hospital mortality. Results Overall hospital mortality was 12.3% (39/317). Initial SDC > 2.0 ng/mL was independently associated with increased hospital mortality compared with ≤ 1.0 ng/mL (adjusted OR 5.15, 95% CI 1.73–15.34, P = 0.003). In the longitudinal subcohort, patients in the "High-Decrease" pattern maintained a nearly 4-fold elevated mortality risk (adjusted OR 3.65, 95% CI 1.03–12.88, P = 0.045) compared with the "Normal-Increase" pattern, despite achieving comparable final concentrations. Conclusion Initial digoxin exposure exceeding 2.0 ng/mL demonstrates a persistent "legacy effect" on hospital mortality that is not eliminated by subsequent concentration correction. These findings suggest that proactive prevention of initial supratherapeutic exposure may be more effective than reactive TDM-guided dose adjustment. digoxin therapeutic drug monitoring pediatric intensive care hospital mortality legacy effect Figures Figure 1 Figure 2 Key points Initial supratherapeutic digoxin exposure (> 2.0 ng/mL) in critically ill children is associated with a 5-fold increased risk of hospital mortality. This elevated mortality risk persists even after subsequent therapeutic drug monitoring (TDM) successfully corrects the concentration to the normal range. Clinical practice should prioritize proactive prevention of initial overexposure rather than relying solely on reactive TDM-guided correction. Introduction Digoxin remains a cornerstone of therapy for pediatric heart failure and supraventricular arrhythmias[1], yet its narrow therapeutic index poses substantial safety challenges in critically ill children[2]. Current therapeutic drug monitoring (TDM) protocols operate under the assumption that prompt correction of supratherapeutic concentrations mitigates risks associated with overexposure—a paradigm that has not been rigorously tested in pediatric populations[3, 4].Evidence from adult cardiovascular medicine suggests that transient drug exposures may exert lasting biological effects[5]. However, whether initial supratherapeutic digoxin exposure produces a "legacy effect"—persistent adverse outcomes despite subsequent concentration normalization[6]—has never been investigated in pediatric intensive care settings, where patients exhibit unique pharmacokinetic vulnerabilities including immature renal function and altered volume of distribution. We hypothesized that initial supratherapeutic digoxin exposure (>2.0 ng/mL) would be associated with increased hospital mortality independent of subsequent concentration correction. Using longitudinal data from the Pediatric Intensive Care database, we compared mortality outcomes between patients whose initially supratherapeutic concentrations were corrected versus those maintaining consistently therapeutic levels throughout their PICU stay. Materials and methods Study Design and Setting This retrospective cohort study utilized the Pediatric Intensive Care (PIC) database, a comprehensive, publicly available clinical dataset derived from the Children’s Hospital affiliated with the Zhejiang University School of Medicine[7, 8]. The database captures detailed clinical information—including physiological parameters, laboratory results, and survival outcomes—for pediatric patients admitted to the intensive care unit. The study protocol was approved by the Institutional Review Board (IRB) of the Children's Hospital, Zhejiang University School of Medicine. The requirement for individual informed consent was waived due to the retrospective design and the use of de-identified data. Access to the database was conducted in strict accordance with the data use agreement of PhysioNet[9]. Participants We identified patients admitted to the PICU between 2010 and 2018. Inclusion criteria were: (1) age < 18 years; (2) administration of digoxin; and (3) availability of at least one serum digoxin concentration (SDC) measurement during the PICU stay. Patients were excluded if they had missing essential clinical data precluding multivariable analysis or if SDC measurements were identified as biologically implausible outliers/data entry errors.Two cohorts were constructed for analysis: Full Sample Cohort: Included all eligible patients with ≥ 1 SDC measurement to assess the baseline risk of supratherapeutic exposure. Longitudinal Trajectory Cohort: A subset of patients with ≥ 2 sequential SDC measurements to evaluate dynamic concentration patterns. Variables and Data Collection Baseline demographic and clinical data were extracted, including age, sex, and laboratory indicators (serum creatinine, cystatin C, total bilirubin, lactate, albumin, hemoglobin, white blood cell count, potassium, ALT, AST, BNP, arterial pH, platelet count, and INR)[10]. Baseline values were defined as the initial measurement recorded upon hospital admission, prioritized within the first 24 hours of PICU admission. PICU length of stay (LOS) was calculated as the cumulative sum of days for all PICU admissions within the index hospitalization. Covariate Selection Potential confounders were identified based on clinical plausibility and statistical screening. Variables with a P-value < 0.05 in the univariate analysis were considered for inclusion in the multivariable models. Additionally, variables showing significant baseline imbalances between digoxin groups (Table 1) were also adjusted for. Digoxin Exposure Stratification The initial SDC (T1) was defined as the first recorded level during the PICU stay. Patients were stratified into three groups based on T1: Group 1 (≤ 1.0 ng/mL), Group 2 (> 1.0 - 2.0 ng/mL), and Group 3 (> 2.0 ng/mL, defined as supratherapeutic)[11]. In the longitudinal cohort, dynamic patterns from the initial (T1) to the subsequent (T2) measurement were categorized as: Normal-Increased: T1 ≤ 2.0 ng/mL and T2 > T1. High-Decreased: T1 > 2.0 ng/mL and T2 ≤ T1 (indicating correction of supratherapeutic levels). Normal-Decreased/Stable: T1 ≤ 2.0 ng/mL and T2 ≤ T1. Outcomes ： The primary outcome was in-hospital mortality, defined as death from any cause occurring during the same hospital admission. Statistical Analysis Patient characteristics were described using medians (IQR) for continuous variables and counts (percentages) for categorical variables. Differences between groups were assessed using the Kruskal-Wallis test for continuous data and the Chi-square test or Fisher's exact test for categorical data, as appropriate. Multivariable logistic regression models were used to quantify the association between initial digoxin groups, dynamic patterns, and hospital mortality. Covariates entered the models were selected based on clinical plausibility and univariate significance. We performed restricted cubic spline (RCS) analysis to visualize the non-linear dose-response relationship between continuous initial SDC and the risk of mortality.Missing data were handled using complete-case analysis. Analyses were performed using EmpowerStats software (version 5.2.0, X&Y Solutions, Inc., Boston, MA). A two-sided P < 0.05 was considered statistically significant. Results Study Population and Baseline Characteristics During the study period, a total of 317 pediatric patients were included in the final analysis. The detailed selection process is illustrated in Figure 1. Patients were stratified into three groups based on their initial serum digoxin concentration (SDC): Group 1 (≤1.0 ng/mL, n =111), Group 2 (>1.0 and ≤2.0 ng/mL, n =134), and Group 3 (>2.0 ng/mL, n =72). Table 1 outlines the baseline characteristics stratified by initial serum digoxin concentration (SDC). Patients in the highest SDC group (>2.0 ng/mL) were significantly younger than those in the lowest group (median 0.14 vs. 0.53 years, P <0.001), while sex distribution was comparable ( P =0.791). Elevated SDC levels were associated with a progressive burden of multi-organ dysfunction. Specifically, markers of renal and hepatic stress worsened across increasing SDC groups, evidenced by a stepwise rise in Cystatin C (median 1.12 to 1.39 mg/L, P <0.001) and total bilirubin (9.7 to 32.6 µmol/L, P <0.001), concurrent with a significant decline in albumin (41.3 to 38.1 g/L, P <0.001). Electrolyte disturbances, particularly hyperkalemia ( P =0.003) and hypocalcemia ( P =0.006), were also more pronounced in patients with higher SDC.Regarding outcomes, although the PICU length of stay showed no statistical difference ( P =0.115), hospital mortality exhibited a significant upward trend, escalating from 6.3% in Group 1 to 22.2% in Group 3 ( P = 0.009) The restricted cubic spline analysis (Figure 2) visually demonstrated a continuous positive dose-response relationship between the initial serum digoxin concentration and the predicted probability of hospital mortality. As the initial digoxin concentration increased, the predicted probability of hospital mortality showed a gradual upward trend. Although the curve did not exhibit a sharply steep increase, its upward trajectory intuitively suggests that higher initial digoxin concentrations are associated with a greater risk of mortality, particularly above the supratherapeutic threshold of 2.0 ng/mL Association between Initial Serum Digoxin Concentration and Hospital Mortality Multivariable logistic regression identified initial serum digoxin concentration as an independent predictor of hospital mortality (Table 2). In the fully adjusted model (Model II), each 0.1 ng/mL increase in digoxin concentration was associated with a 5% increase in the odds of mortality (adjusted OR 1.05; 95% CI, 1.01–1.10; P = 0.015). Categorical analysis revealed that patients with concentrations > 2.0 ng/mL had a significantly elevated risk compared to the reference group (< 1.0 ng/mL) (adjusted OR 5.15; 95% CI, 1.73–15.34; P = 0.003). Patients with intermediate levels (1.0–2.0 ng/mL) showed a trend toward increased mortality (adjusted OR 2.05; 95% CI, 0.72–5.83), though this did not reach statistical significance ( P =0.180). This positive dose-response relationship was visually confirmed by restricted cubic spline analysis (Figure 2), which depicted a progressive increase in predicted mortality probability as serum digoxin concentration rose. Baseline Characteristics and Outcomes by Dynamic Concentration Patterns A sub-analysis of 180 patients with serial measurements was performed to evaluate the impact of dynamic concentration changes (Table 3). Patients were stratified into three groups: Normal-Increase ( n = 103), High-Decrease ( n = 24), and Normal-Decrease ( n = 53). The High-Decrease group was significantly younger (median age 0.11 years, P < 0.001) and exhibited more severe organ dysfunction compared to the other groups, indicated by higher baseline serum cystatin C ( P = 0.003) and total bilirubin ( P = 0.013) levels. Notably, although the High-Decrease group successfully reduced their serum digoxin concentration to a therapeutic level at follow-up (median T2: 1.29 ng/mL), which was comparable to that of the Normal-Increase group (median T2: 1.32 ng/mL), their clinical outcomes remained poor. The High-Decrease group had the highest hospital mortality rate (25.0%), compared to 15.1% in the Normal-Decrease group and 9.7% in the Normal-Increase group, although this unadjusted difference across three groups did not reach statistical significance ( P = 0.133). Association Between Dynamic Patterns and Mortality Multivariable logistic regression was conducted to determine whether the High-Decrease pattern was independently associated with hospital mortality (Table 4). In the crude analysis, the High-Decrease pattern was associated with a 3.1-fold increase in mortality risk compared to the reference Normal-Increase group (OR 3.10; 95% CI, 1.00–9.61; P = 0.050). After adjusting for confounders, this association became stronger. In the fully adjusted model (Model II), which controlled for age, gender, renal function (creatinine, cystatin C), liver function, and electrolytes, the High-Decrease pattern remained a significant and independent predictor of hospital mortality (adjusted OR 3.65; 95% CI, 1.03–12.88; P = 0.045). In contrast, the Normal-Decrease pattern showed no significant association with mortality in any model ( P > 0.05). Discussion Principal Findings This retrospective analysis of 317 critically ill children receiving digoxin reveals a clinically significant "legacy effect" of initial supratherapeutic exposure. Using longitudinal concentration trajectories from the PIC database, we demonstrate that patients with initial serum digoxin concentrations exceeding 2.0 ng/mL had five-fold increased odds of hospital mortality compared with those maintaining concentrations ≤1.0 ng/mL (adjusted OR 5.15; 95% CI, 1.73–15.34). More importantly, in patients with serial measurements, those whose initially supratherapeutic concentrations were successfully corrected to the therapeutic range retained a nearly four-fold elevated mortality risk (adjusted OR 3.65; 95% CI, 1.03–12.88) compared with patients maintaining consistently therapeutic levels. These findings challenge the prevailing assumption that reactive therapeutic drug monitoring (TDM) fully mitigates the risks of digoxin toxicity. Potential Mechanisms The observed legacy effect likely reflects a combination of direct cellular injury and confounding by disease severity. From a mechanistic perspective, supratherapeutic digoxin concentrations cause rapid intracellular sodium accumulation through Na⁺/K⁺-ATPase inhibition. Recent evidence suggests that this sodium overload triggers allosteric inactivation of the Na⁺-Ca²⁺ exchanger (NCX1), effectively impairing calcium efflux. Notably, this inactivation exhibits slow, time-dependent recovery, creating a temporal dissociation whereby toxic calcium overload persists after serum digoxin levels have normalized[12]. Sustained intracellular calcium excess may trigger mitochondrial dysfunction and apoptotic cascades that proceed to completion even after drug clearance[13], while concurrently establishing a persistent arrhythmogenic substrate[14]. Alternatively, or additionally, the "High-Decrease" pattern may represent a marker of underlying disease severity. Clinicians may intuitively administer higher digoxin doses to patients with more profound circulatory failure[15, 16]. Thus, our findings may reflect a dual phenomenon: the "High-Decrease" pattern both identifies patients with the most severe baseline illness and contributes directly to a toxic cellular cascade from which recovery is incomplete. Comparison with Existing Literature Previous pediatric digoxin research has predominantly focused on population pharmacokinetic modeling and therapeutic range attainment[17]. A central assumption in this literature is that risks associated with overexposure resolve once serum concentrations are restored to the target range. For instance, a recent physiologically based pharmacokinetic study evaluated FDA-recommended dosing strategies but focused exclusively on achieving target steady-state concentrations, without examining consequences following supratherapeutic exposure[18]. Similarly, longitudinal TDM data analyses have described concentration trends and guideline alignment but offered no evaluation of subsequent prognosis in patients experiencing supratherapeutic levels[19]. Our findings extend this literature by demonstrating that mortality risk is established at the time of initial supratherapeutic exposure and persists despite pharmacokinetic correction. While the ARISTOTLE trial analysis found that serum digoxin concentrations ≥ 1.2 ng/mL were associated with 56% increased mortality hazard in adults with atrial fibrillation[20], such population-level findings do not address real-time risk evolution in critically ill pediatric populations. Current guidelines, including ESC recommendations[3], endorse TDM-guided dose adjustment but do not account for the inherent latency of this reactive approach in vulnerable PICU populations[21]. Our data suggest that once concentrations exceed 2.0 ng/mL, mortality risk is already substantially elevated and may persist despite subsequent intervention. Clinical Implications These findings support two fundamental shifts in pediatric digoxin management. First, clinical practice should prioritize proactive prevention over reactive correction. A conservative "start low, go slow" dosing strategy is essential, particularly in high-risk populations including neonates and patients with renal impairment. The relative safety of upward titration from subtherapeutic levels outweighs the potentially irreversible consequences of initial overexposure. Second, patients experiencing initial supratherapeutic exposure should not be considered clinically resolved upon concentration normalization. Our data indicate that these patients retain significantly elevated mortality risk, warranting intensified surveillance including prolonged electrocardiographic monitoring and strict electrolyte management to mitigate their persistent pro-arrhythmic vulnerability. Strengths and Limitations This study has several strengths. First, the granular data from the PIC database enabled reconstruction of longitudinal concentration trajectories rather than reliance on static measurements. Second, adjustment for serum cystatin C—a superior biomarker compared with creatinine in critically ill children with low muscle mass—strengthens our conclusion that the observed mortality risk is independent of renal dysfunction. This approach is supported by recent evidence demonstrating the predictive value of cystatin C for supratherapeutic digoxin levels in high-risk populations[22, 23]. Several limitations warrant consideration. First, the retrospective design precludes definitive causal inference despite rigorous statistical adjustment. Second, the "High-Decrease" cohort (n=24), while sufficient for the primary analysis, precluded granular subgroup stratifications by age or comorbidity. Third, absence of adjudicated cause-of-death data and continuous electrocardiographic recordings prevented definitive attribution of excess mortality to specific mechanisms such as lethal arrhythmias. Fourth, the single-center design limits generalizability, and external validation in other PICU populations is warranted. Conclusion This study identifies a significant legacy effect in pediatric digoxin therapy whereby mortality risk from initial supratherapeutic exposure persists despite subsequent concentration normalization. These findings indicate that reactive TDM strategies alone are insufficient and support a paradigm shift toward proactive prevention of initial overexposure combined with intensified surveillance of patients with prior supratherapeutic exposure. Abbreviations ALT Alanine aminotransferase AST Aspartate aminotransferase CI Confidence interval INR International normalized ratio IQR Interquartile range OR Odds ratio PICU Pediatric intensive care unit SDC Serum digoxin concentration TDM Therapeutic drug monitoring Abbreviations ALT: Alanine aminotransferase AST: Aspartate aminotransferase CI: Confidence interval INR: International normalized ratio IQR: Interquartile range OR: Odds ratio PICU: Pediatric intensive care unit SDC: Serum digoxin concentration TDM: Therapeutic drug monitoring Declarations Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Competing interests The authors declare that they have no competing interests. Authors’ contributions Jingchuan Lu conceived and designed the study, performed the statistical analysis, and drafted the manuscript. Xuemei Hu contributed to data interpretation and manuscript revision. Shiying Zhao and Shuangyan Zhu contributed to data extraction and critical revision of the manuscript. All authors read and approved the final manuscript. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. The Pediatric Intensive Care (PIC) database protocol was approved by the Institutional Review Board of the Children’s Hospital, Zhejiang University School of Medicine (Approval number: 2019_IRB_052). The use of this publicly available, de-identified dataset for secondary analysis qualified for exemption from further ethical approval. Consent to participate The requirement for individual informed consent was waived by the Institutional Review Board (IRB) of the Children's Hospital, Zhejiang University School of Medicine due to the retrospective design and the use of de-identified data. Consent to publish Not applicable. Availability of data and materials The data analyzed in this study are available in the Pediatric Intensive Care (PIC) database and can be accessed via PhysioNet (https://physionet.org/content/picdb/). Access was granted to the first author (Jingchuan Lu) following the completion of the required Collaborative Institutional Training Initiative (CITI Program) coursework "Data or Specimens Only Research" (Record ID: 63513763) and signing of the data use agreement. Code availability Not applicable. Acknowledgments We extend our sincere gratitude to the clinical and research staff at the Children's Hospital affiliated with the Zhejiang University School of Medicine for their exceptional efforts in establishing and maintaining the Pediatric Intensive Care (PIC) database. We also express our appreciation to the MIT Laboratory for Computational Physiology and the PhysioNet platform for hosting and sharing these invaluable clinical datasets, which made this research possible. References Amdani S, Conway J, George K, et al (2024) Evaluation and Management of Chronic Heart Failure in Children and Adolescents with Congenital Heart Disease: A Scientific Statement from the American Heart Association. Circulation 150: e33–e50. https://doi.org/10.1161/CIR.0000000000001245 Khandelwal R, Vagha JD, Meshram RJ, Patel A (2024) A Comprehensive Review on Unveiling the Journey of Digoxin: Past, Present, and Future Perspectives. 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Baseline Characteristics of Pediatric Patients Stratified by Initial Serum Digoxin Concentration Characteristic Group 1 (≤1.0 ng/mL) (n=111) Group 2 (1.0–2.0 ng/mL) (n=134) Group 3 (>2.0 ng/mL) (n=72) P -value Demographics Age, years 0.53 (0.29-1.05) 0.28 (0.10-0.73) 0.14 (0.07-0.42) <0.001 Male sex, No. (%) 67 (60.4) 75 (56.0) 42 (58.3) 0.791 Digoxin Metrics Initial concentration, ng/mL 0.57 (0.36-0.78) 1.18 (0.99-1.38) 2.08 (1.49-2.55) <0.001 Patients with at least 2 measurements, No. (%) 36 (32.4) 87 (64.9) 58 (80.6) <0.001 Renal Function Serum creatinine, μmol/L 43.0 (38.0-49.0) 48.0 (41.3-68.8) 54.0 (44.8-68.0) <0.001 Cystatin C, mg/L 1.12 (0.93-1.39) 1.25 (1.04-1.65) 1.39 (1.15-1.58) <0.001 Blood urea nitrogen, mmol/L 3.30 (2.55-4.74) 3.55 (2.28-5.23) 3.64 (2.72-5.02) 0.476 Electrolytes & Blood Gas Potassium, mmol/L 3.80 (3.50-4.20) 4.10 (3.70-4.50) 4.00 (3.65-4.35) 0.003 Magnesium, mmol/L 0.88 (0.81-0.93) 0.90 (0.85-0.98) 0.90 (0.83-0.97) 0.181 Calcium, mmol/L 2.42 (2.29-2.51) 2.33 (2.20-2.49) 2.30 (2.19-2.46) 0.006 Sodium, mmol/L 136 (134-139) 135 (133-138) 136 (133-138) 0.432 Lactate, mmol/L 1.30 (0.90-2.70) 2.00 (1.00-3.48) 2.20 (1.40-3.50) <0.001 Arterial pH 7.38 (7.33-7.43) 7.36 (7.32-7.42) 7.38 (7.33-7.42) 0.342 Liver Function ALT, U/L 23.0 (16.0-37.5) 19.0 (12.3-36.0) 19.5 (12.0-31.0) 0.028 AST, U/L 45.0 (36.0-63.5) 51.5 (34.3-90.0) 45.0 (33.0-68.0) 0.330 Total bilirubin, μmol/L 9.7 (6.0-18.3) 17.7 (8.3-51.9) 32.6 (10.7-98.9) <0.001 Albumin, g/L 41.3 (38.4-44.0) 39.4 (34.4-43.1) 38.1 (34.1-41.6) <0.001 INR 1.04 (0.98-1.16) 1.09 (0.99-1.31) 1.12 (1.04-1.26) 0.030 Hematology Hemoglobin, g/L 115 (105-134) 124 (108-147) 123 (105-142) 0.118 White blood cell count, ×10⁹/L 9.29 (7.80-11.70) 9.63 (7.71-13.01) 10.89 (8.49-14.14) 0.050 Platelet count, ×10⁹/L 322 (215-404) 293 (198-384) 311 (262-392) 0.168 Outcomes PICU length of stay, days 6.9 (4.5-11.3) 9.9 (6.8-21.1) 15.8 (8.8-28.1) 0.115 Hospital Mortality, No. (%) 7 (6.3) 16 (11.9) 16 (22.2) 0.009 Note : Data are presented as median (IQR) for continuous variables and No. (%) for categorical variables. Abbreviations : ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; INR, international normalized ratio; IQR, interquartile range; PICU, pediatric intensive care unit. Statistical Analysis : P-values were calculated using the Kruskal-Wallis rank sum test for continuous variables and Fisher's exact test for categorical variables with expected counts < 10. Table 2. Multivariable Logistic Regression Analysis of the Association Between Initial Serum Digoxin Concentration and Hospital Mortality Exposure Crude Model Model I Model II OR (95% CI) P OR (95% CI) P OR (95% CI) P Exposure as Continuous Per 0.1 ng/mL increase 1.06 (1.02, 1.10) 0.0016 1.06 (1.02, 1.10) 0.0013 1.05 (1.01, 1.10) 0.0152 Exposure as Categorical Group 1 (≤ 1.0 ng/mL) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Group 2 (1.0–2.0 ng/mL) 2.01 (0.80, 5.09) 0.138 2.09 (0.82, 5.33) 0.121 2.05 (0.72, 5.83) 0.180 Group 3 (>2.0 ng/mL) 4.24 (1.65, 10.93) 0.003 4.58 (1.73, 12.14) 0.002 5.15 (1.73, 15.34) 0.003 Sample Size (n) 317 317 314 Abbreviations: OR, odds ratio; CI, confidence interval. Outcome Variable: Hospital Mortality Crude Model: Crude analysis without adjustment. Model I: Adjusted for age and gender. Model II: Adjusted for age, gender, baseline serum creatinine, Calcium, Cystatin C, potassium, ALT, total bilirubin, albumin, lactate, and WBC count Table 3. Baseline Characteristics and Clinical Outcomes Stratified by Dynamic Changes in Serum Digoxin Concentration. Variable Normal-Increase Pattern (n = 103) High-Decrease Pattern (n = 24) Normal-Decrease Pattern (n = 53) P-value Demographics Age, years 0.36 (0.21-0.87) 0.11 (0.06-0.32) 0.26 (0.10-0.52) <0.001 Male, No. (%) 66 (64.08%) 13 (54.17%) 28 (52.83%) 0.353 Digoxin Concentrations Initial (T1), ng/mL 0.80 (0.42-1.12) 2.55 (2.18-3.18) 1.31 (1.00-1.52) <0.001 Follow-up (T2), ng/mL 1.32 (0.94-1.79) 1.29 (0.95-1.68) 0.77 (0.48-1.01) <0.001 Renal Function Serum Creatinine, μmol/L 47.0 (39.5-55.5) 56.0 (44.0-77.3) 52.0 (41.0-69.0) 0.042 Serum Cystatin C, mg/L 1.16 (0.98-1.50) 1.50 (1.26-1.76) 1.37 (1.09-1.72) 0.003 Blood Urea Nitrogen, mmol/L 3.44 (2.64-4.88) 3.64 (2.57-4.56) 3.65 (2.81-5.11) 0.776 Electrolytes & Blood Gas Serum Potassium, mmol/L 3.90 (3.55-4.20) 4.20 (3.80-4.70) 4.00 (3.70-4.50) 0.068 Serum Magnesium, mmol/L 0.89 (0.82-0.98) 0.91 (0.83-0.97) 0.88 (0.85-0.99) 0.975 Serum Calcium, mmol/L 2.35 (2.25-2.48) 2.38 (2.19-2.50) 2.29 (2.12-2.47) 0.476 Serum Sodium, mmol/L 136 (134-138) 135 (133-138) 136 (133-139) 0.526 Arterial pH 7.37 (7.33-7.41) 7.38 (7.34-7.42) 7.36 (7.29-7.43) 0.584 Serum Lactate, mmol/L 1.70 (1.00-3.15) 2.40 (1.10-4.75) 2.20 (1.20-4.00) 0.161 Liver Function Alanine Aminotransferase (ALT), U/L 21.0 (14.5-36.5) 14.5 (11.8-23.8) 21.0 (13.0-35.0) 0.121 Aspartate Aminotransferase (AST), U/L 50.0 (33.5-81.0) 41.5 (30.8-74.0) 49.0 (35.0-76.0) 0.787 Total Bilirubin, μmol/L 14.0 (6.8-33.1) 66.2 (13.7-105.3) 19.6 (6.4-64.8) 0.013 Serum Albumin, g/L 39.9 (35.7-43.0) 36.5 (33.5-40.1) 39.9 (34.1-43.9) 0.187 Hematology Hemoglobin, g/L 123 (105-146) 131 (119-143) 122 (103-147) 0.646 White Blood Cell Count, ×10⁹/L 9.71 (7.72-11.55) 11.39 (9.40-15.16) 9.99 (8.13-14.13) 0.081 Platelet Count, ×10⁹/L 304 (229-400) 289 (268-354) 291 (191-379) 0.369 Outcomes PICU Length of Stay, days 12.5 (7.0-23.3) 10.8 (6.8-23.3) 14.8 (7.8-29.9) 0.745 Hospital Mortality, No. (%) 10 (9.71%) 6 (25.00%) 8 (15.09%) 0.133 Note: Data are presented as median (IQR) or n (%). P-values were calculated using the Kruskal-Wallis test or Fisher's exact test, as appropriate Table 4. Multivariable Logistic Regression Analysis of the Association Between Dynamic Digoxin Patterns and Hospital Mortality Exposure Non-adjusted(N=180) Adjust I(N=180) Adjust II(N=178) Dynamic Digoxin Pattern OR (95%CI) P OR (95%CI) P OR (95%CI) P Normal_Increased (Therapeutic) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) High_Decreased (Correction) 3.10 (1.00, 9.61) 0.0499 3.47 (1.08, 11.15) 0.0368 3.65 (1.03, 12.88) 0.0445 Normal_Decreased 1.65 (0.61, 4.47) 0.3222 1.82 (0.66, 5.06) 0.2502 1.69 (0.57, 5.02) 0.3451 Abbreviations: OR, odds ratio; CI, confidence interval. Outcome Variable: Hospital Mortality Unadjusted Model: Crude analysis without adjustment. Model I: Adjusted for age and gender. Model II: Adjusted for age, gender, serum creatinine, Cystatin C, total bilirubin, serum potassium, and WBC count. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9183203","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628539091,"identity":"c2f323fd-860f-43eb-9896-588285d7e81a","order_by":0,"name":"Jingchuan Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYBACPoYDIMqGh5+/gUgtbBAtaTKSMw4QrQUMDtsYNCQQq4Xx8OPPhW3neQwYDjB++JhDlC3HzKRntt3mMWduYJacuY0oLWfYmHmBWiwbDgAZRGph/szbdo7H4EAC8VoYpHnbDpCkBeiXGeeSeSRnHGwmzi/8EsAQKyizs+fnbz744SMxWhgkDjAwQ1iMDcSoB1nTANMyCkbBKBgFowAHAAD/IzInu7D7tAAAAABJRU5ErkJggg==","orcid":"","institution":"People's Hospital of Leshan","correspondingAuthor":true,"prefix":"","firstName":"Jingchuan","middleName":"","lastName":"Lu","suffix":""},{"id":628539092,"identity":"c3fa57d3-d7ac-4667-a0f0-ef9978603d87","order_by":1,"name":"Xuemei Hu","email":"","orcid":"","institution":"People's Hospital of Leshan","correspondingAuthor":false,"prefix":"","firstName":"Xuemei","middleName":"","lastName":"Hu","suffix":""},{"id":628539093,"identity":"51128d63-f5c8-424d-970a-dfe812532be4","order_by":2,"name":"Shiying Zhao","email":"","orcid":"","institution":"People's Hospital of Leshan","correspondingAuthor":false,"prefix":"","firstName":"Shiying","middleName":"","lastName":"Zhao","suffix":""},{"id":628539094,"identity":"0218062b-0600-43b4-9da3-9c765be0ff7d","order_by":3,"name":"Shuangyan Zhu","email":"","orcid":"","institution":"People's Hospital of Leshan","correspondingAuthor":false,"prefix":"","firstName":"Shuangyan","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2026-03-21 05:08:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9183203/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9183203/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107832647,"identity":"f79faf17-4cc5-4a90-a03a-f94e7d2e1e90","added_by":"auto","created_at":"2026-04-26 15:35:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110515,"visible":true,"origin":"","legend":"\u003cp\u003ePatient Flow and Data Processing for a Retrospective Study on Initial Digoxin Toxicity in PICU\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9183203/v1/3bf83deb75f5189e25eea176.png"},{"id":107832649,"identity":"2929a39b-c8e4-4cdb-a888-d0c785c8bb33","added_by":"auto","created_at":"2026-04-26 15:35:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85314,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable-adjusted predicted probability of hospital mortality according to initial serum digoxin concentration.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe solid red line represents the estimated probability of hospital mortality derived from the logistic regression model (Model II), adjusted for age, gender, baseline serum creatinine, cystatin C, total bilirubin, albumin, lactate, calcium, potassium, ALT, and WBC count. The shaded area indicates the 95% confidence interval. The plot shows an increasing probability of mortality with higher initial serum digoxin concentrations.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9183203/v1/44f3171d109ad6117c07d133.png"},{"id":108490987,"identity":"7169b795-a765-413e-a740-ca5106201283","added_by":"auto","created_at":"2026-05-05 09:50:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":532160,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9183203/v1/3e953d74-6ed2-4c0e-92fd-4bcace931c1a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Persistent Mortality Risk Following Initial Supratherapeutic Digoxin Exposure in Critically Ill Children: A Legacy Effect","fulltext":[{"header":"Key points","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eInitial supratherapeutic digoxin exposure (\u0026gt;\u0026thinsp;2.0 ng/mL) in critically ill children is associated with a 5-fold increased risk of hospital mortality.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThis elevated mortality risk persists even after subsequent therapeutic drug monitoring (TDM) successfully corrects the concentration to the normal range.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eClinical practice should prioritize proactive prevention of initial overexposure rather than relying solely on reactive TDM-guided correction.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eDigoxin remains a cornerstone of therapy for pediatric heart failure and supraventricular arrhythmias[1], yet its narrow therapeutic index poses substantial safety challenges in critically ill children[2]. Current therapeutic drug monitoring (TDM) protocols operate under the assumption that prompt correction of supratherapeutic concentrations mitigates risks associated with overexposure\u0026mdash;a paradigm that has not been rigorously tested in pediatric populations[3, 4].Evidence from adult cardiovascular medicine suggests that transient drug exposures may exert lasting biological effects[5]. However, whether initial supratherapeutic digoxin exposure produces a \u0026quot;legacy effect\u0026quot;\u0026mdash;persistent adverse outcomes despite subsequent concentration normalization[6]\u0026mdash;has never been investigated in pediatric intensive care settings, where patients exhibit unique pharmacokinetic vulnerabilities including immature renal function and altered volume of distribution.\u003c/p\u003e\n\u003cp\u003eWe hypothesized that initial supratherapeutic digoxin exposure (\u0026gt;2.0 ng/mL) would be associated with increased hospital mortality independent of subsequent concentration correction. Using longitudinal data from the Pediatric Intensive Care database, we compared mortality outcomes between patients whose initially supratherapeutic concentrations were corrected versus those maintaining consistently therapeutic levels throughout their PICU stay.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study utilized the Pediatric Intensive Care (PIC) database, a comprehensive, publicly available clinical dataset derived from the Children\u0026rsquo;s Hospital affiliated with the Zhejiang University School of Medicine[7, 8]. The database captures detailed clinical information\u0026mdash;including physiological parameters, laboratory results, and survival outcomes\u0026mdash;for pediatric patients admitted to the intensive care unit. The study protocol was approved by the Institutional Review Board (IRB) of the Children\u0026apos;s Hospital, Zhejiang University School of Medicine. The requirement for individual informed consent was waived due to the retrospective design and the use of de-identified data. Access to the database was conducted in strict accordance with the data use agreement of PhysioNet[9].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified patients admitted to the PICU between 2010 and 2018. Inclusion criteria were: (1) age \u0026lt; 18 years; (2) administration of digoxin; and (3) availability of at least one serum digoxin concentration (SDC) measurement during the PICU stay. Patients were excluded if they had missing essential clinical data precluding multivariable analysis or if SDC measurements were identified as biologically implausible outliers/data entry errors.Two cohorts were constructed for analysis:\u003cbr\u003e\u0026nbsp;Full Sample Cohort: Included all eligible patients with \u0026ge; 1 SDC measurement to assess the baseline risk of supratherapeutic exposure.\u003cbr\u003e\u0026nbsp;Longitudinal Trajectory Cohort: A subset of patients with \u0026ge; 2 sequential SDC measurements to evaluate dynamic concentration patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables and Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline demographic and clinical data were extracted, including age, sex, and laboratory indicators (serum creatinine, cystatin C, total bilirubin, lactate, albumin, hemoglobin, white blood cell count, potassium, ALT, AST, BNP, arterial pH, platelet count, and INR)[10]. Baseline values were defined as the initial measurement recorded upon hospital admission, prioritized within the first 24 hours of PICU admission. PICU length of stay (LOS) was calculated as the cumulative sum of days for all PICU admissions within the index hospitalization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariate Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotential confounders were identified based on clinical plausibility and statistical screening. Variables with a \u003cem\u003eP-value\u003c/em\u003e \u0026lt; 0.05 in the univariate analysis were considered for inclusion in the multivariable models. Additionally, variables showing significant baseline imbalances between digoxin groups (Table 1) were also adjusted for.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDigoxin Exposure Stratification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial SDC (T1) was defined as the first recorded level during the PICU stay. Patients were stratified into three groups based on T1: Group 1 (\u0026le; 1.0 ng/mL), Group 2 (\u0026gt; 1.0 - 2.0 ng/mL), and Group 3 (\u0026gt; 2.0 ng/mL, defined as supratherapeutic)[11].\u003c/p\u003e\n\u003cp\u003eIn the longitudinal cohort, dynamic patterns from the initial (T1) to the subsequent (T2) measurement were categorized as:\u003c/p\u003e\n\u003cp\u003eNormal-Increased: T1 \u0026le; 2.0 ng/mL and T2 \u0026gt; T1.\u003c/p\u003e\n\u003cp\u003eHigh-Decreased: T1 \u0026gt; 2.0 ng/mL and T2 \u0026le; T1 (indicating correction of supratherapeutic levels).\u003c/p\u003e\n\u003cp\u003eNormal-Decreased/Stable: T1 \u0026le; 2.0 ng/mL and T2 \u0026le; T1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was in-hospital mortality, defined as death from any cause occurring during the same hospital admission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient characteristics were described using medians (IQR) for continuous variables and counts (percentages) for categorical variables. Differences between groups were assessed using the Kruskal-Wallis test for continuous data and the Chi-square test or Fisher\u0026apos;s exact test for categorical data, as appropriate. Multivariable logistic regression models were used to quantify the association between initial digoxin groups, dynamic patterns, and hospital mortality. Covariates entered the models were selected based on clinical plausibility and univariate significance. We performed restricted cubic spline (RCS) analysis to visualize the non-linear dose-response relationship between continuous initial SDC and the risk of mortality.Missing data were handled using complete-case analysis. Analyses were performed using EmpowerStats software (version 5.2.0, X\u0026amp;Y Solutions, Inc., Boston, MA). A two-sided \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy Population and Baseline Characteristics\u003c/strong\u003e\u003cbr\u003eDuring the study period, a total of 317 pediatric patients were included in the final analysis. The detailed selection process is illustrated in\u0026nbsp;Figure 1. Patients were stratified into three groups based\u003c/p\u003e\n\u003cp\u003eon their initial serum digoxin concentration (SDC): Group 1 (\u0026le;1.0 ng/mL, \u003cem\u003en\u003c/em\u003e=111), Group 2 (\u0026gt;1.0 and \u0026le;2.0 ng/mL, \u003cem\u003en\u003c/em\u003e=134), and Group 3 (\u0026gt;2.0 ng/mL,\u003cem\u003e\u0026nbsp;n\u003c/em\u003e=72). Table 1 outlines the baseline characteristics stratified by initial serum digoxin concentration (SDC). Patients in the highest SDC group (\u0026gt;2.0 ng/mL) were significantly younger than those in the lowest group (median 0.14 vs. 0.53 years, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001), while sex distribution was comparable (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e=0.791). Elevated SDC levels were associated with a progressive burden of multi-organ dysfunction. Specifically, markers of renal and hepatic stress worsened across increasing SDC groups, evidenced by a stepwise rise in Cystatin C (median 1.12 to 1.39 mg/L, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001) and total bilirubin (9.7 to 32.6 \u0026micro;mol/L, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001), concurrent with a significant decline in albumin (41.3 to 38.1 g/L, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001). Electrolyte disturbances, particularly hyperkalemia (\u003cem\u003eP\u003c/em\u003e =0.003) and hypocalcemia (\u003cem\u003eP\u003c/em\u003e =0.006), were also more pronounced in patients with higher SDC.Regarding outcomes, although the PICU length of stay showed no statistical difference (\u003cem\u003eP\u003c/em\u003e =0.115), hospital mortality exhibited a significant upward trend, escalating from 6.3% in Group 1 to 22.2% in Group 3 (\u003cem\u003eP\u003c/em\u003e = 0.009)\u003c/p\u003e\n\u003cp\u003eThe restricted cubic spline analysis (Figure 2) visually demonstrated a\u0026nbsp;continuous positive dose-response relationship\u0026nbsp;between the initial serum digoxin concentration and the predicted probability of hospital mortality. As the initial digoxin concentration increased, the predicted probability of hospital mortality showed a gradual upward trend. Although the curve did not exhibit a sharply steep increase, its upward trajectory intuitively suggests that higher initial digoxin concentrations are associated with a greater risk of mortality, particularly above the supratherapeutic threshold of 2.0 ng/mL\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between Initial Serum Digoxin Concentration and Hospital Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression identified initial serum digoxin concentration as an independent predictor of hospital mortality (Table 2). In the fully adjusted model (Model II), each 0.1 ng/mL increase in digoxin concentration was associated with a 5% increase in the odds of mortality (adjusted OR 1.05; 95% CI, 1.01\u0026ndash;1.10; \u003cem\u003eP\u003c/em\u003e = 0.015). Categorical analysis revealed that patients with concentrations \u0026gt; 2.0 ng/mL had a significantly elevated risk compared to the reference group (\u0026lt; 1.0 ng/mL) (adjusted OR 5.15; 95% CI, 1.73\u0026ndash;15.34; \u003cem\u003eP\u003c/em\u003e = 0.003). Patients with intermediate levels (1.0\u0026ndash;2.0 ng/mL) showed a trend toward increased mortality (adjusted OR 2.05; 95% CI, 0.72\u0026ndash;5.83), though this did not reach statistical significance (\u003cem\u003eP\u003c/em\u003e =0.180). This positive dose-response relationship was visually confirmed by restricted cubic spline analysis (Figure 2), which depicted a progressive increase in predicted mortality probability as serum digoxin concentration rose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline Characteristics and Outcomes by Dynamic Concentration Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA sub-analysis of 180 patients with serial measurements was performed to evaluate the impact of dynamic concentration changes (Table 3). Patients were stratified into three groups: Normal-Increase (\u003cem\u003en\u003c/em\u003e = 103), High-Decrease (\u003cem\u003en\u003c/em\u003e = 24), and Normal-Decrease (\u003cem\u003en\u003c/em\u003e = 53).\u003c/p\u003e\n\u003cp\u003eThe High-Decrease group was significantly younger (median age 0.11 years, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and exhibited more severe organ dysfunction compared to the other groups, indicated by higher baseline serum cystatin C (\u003cem\u003eP\u003c/em\u003e = 0.003) and total bilirubin (\u003cem\u003eP\u003c/em\u003e = 0.013) levels. Notably, although the High-Decrease group successfully reduced their serum digoxin concentration to a therapeutic level at follow-up (median T2: 1.29 ng/mL), which was comparable to that of the Normal-Increase group (median T2: 1.32 ng/mL), their clinical outcomes remained poor. The High-Decrease group had the highest hospital mortality rate (25.0%), compared to 15.1% in the Normal-Decrease group and 9.7% in the Normal-Increase group, although this unadjusted difference across three groups did not reach statistical significance (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.133).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between Dynamic Patterns and Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression was conducted to determine whether the High-Decrease pattern was independently associated with hospital mortality (Table 4). In the crude analysis, the High-Decrease pattern was associated with a 3.1-fold increase in mortality risk compared to the reference Normal-Increase group (OR 3.10; 95% CI, 1.00\u0026ndash;9.61; \u003cem\u003eP\u003c/em\u003e = 0.050).\u003c/p\u003e\n\u003cp\u003eAfter adjusting for confounders, this association became stronger. In the fully adjusted model (Model II), which controlled for age, gender, renal function (creatinine, cystatin C), liver function, and electrolytes, the High-Decrease pattern remained a significant and independent predictor of hospital mortality (adjusted OR 3.65; 95% CI, 1.03\u0026ndash;12.88; \u003cem\u003eP\u003c/em\u003e = 0.045). In contrast, the Normal-Decrease pattern showed no significant association with mortality in any model (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003ePrincipal Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective analysis of 317 critically ill children receiving digoxin reveals a clinically significant \u0026quot;legacy effect\u0026quot; of initial supratherapeutic exposure. Using longitudinal concentration trajectories from the PIC database, we demonstrate that patients with initial serum digoxin concentrations exceeding 2.0 ng/mL had five-fold increased odds of hospital mortality compared with those maintaining concentrations \u0026le;1.0 ng/mL (adjusted OR 5.15; 95% CI, 1.73\u0026ndash;15.34). More importantly, in patients with serial measurements, those whose initially supratherapeutic concentrations were successfully corrected to the therapeutic range retained a nearly four-fold elevated mortality risk (adjusted OR 3.65; 95% CI, 1.03\u0026ndash;12.88) compared with patients maintaining consistently therapeutic levels. These findings challenge the prevailing assumption that reactive therapeutic drug monitoring (TDM) fully mitigates the risks of digoxin toxicity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential Mechanisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe observed legacy effect likely reflects a combination of direct cellular injury and confounding by disease severity. From a mechanistic perspective, supratherapeutic digoxin concentrations cause rapid intracellular sodium accumulation through Na⁺/K⁺-ATPase inhibition. Recent evidence suggests that this sodium overload triggers allosteric inactivation of the Na⁺-Ca\u0026sup2;⁺ exchanger (NCX1), effectively impairing calcium efflux. Notably, this inactivation exhibits slow, time-dependent recovery, creating a temporal dissociation whereby toxic calcium overload persists after serum digoxin levels have normalized[12]. Sustained intracellular calcium excess may trigger mitochondrial dysfunction and apoptotic cascades that proceed to completion even after drug clearance[13], while concurrently establishing a persistent arrhythmogenic substrate[14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlternatively, or additionally, the \u0026quot;High-Decrease\u0026quot; pattern may represent a marker of underlying disease severity. Clinicians may intuitively administer higher digoxin doses to patients with more profound circulatory failure[15, 16]. Thus, our findings may reflect a dual phenomenon: the \u0026quot;High-Decrease\u0026quot; pattern both identifies patients with the most severe baseline illness and contributes directly to a toxic cellular cascade from which recovery is incomplete.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with Existing Literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious pediatric digoxin research has predominantly focused on population pharmacokinetic modeling and therapeutic range attainment[17]. A central assumption in this literature is that risks associated with overexposure resolve once serum concentrations are restored to the target range. For instance, a recent physiologically based pharmacokinetic study evaluated FDA-recommended dosing strategies but focused exclusively on achieving target steady-state concentrations, without examining consequences following supratherapeutic exposure[18]. Similarly, longitudinal TDM data analyses have described concentration trends and guideline alignment but offered no evaluation of subsequent prognosis in patients experiencing supratherapeutic levels[19].\u003c/p\u003e\n\u003cp\u003eOur findings extend this literature by demonstrating that mortality risk is established at the time of initial supratherapeutic exposure and persists despite pharmacokinetic correction. While the ARISTOTLE trial analysis found that serum digoxin concentrations \u0026ge; 1.2 ng/mL were associated with 56% increased mortality hazard in adults with atrial fibrillation[20], such population-level findings do not address real-time risk evolution in critically ill pediatric populations. Current guidelines, including ESC recommendations[3], endorse TDM-guided dose adjustment but do not account for the inherent latency of this reactive approach in vulnerable PICU populations[21]. Our data suggest that once concentrations exceed 2.0 ng/mL, mortality risk is already substantially elevated and may persist despite subsequent intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings support two fundamental shifts in pediatric digoxin management. First, clinical practice should prioritize proactive prevention over reactive correction. A conservative \u0026quot;start low, go slow\u0026quot; dosing strategy is essential, particularly in high-risk populations including neonates and patients with renal impairment. The relative safety of upward titration from subtherapeutic levels outweighs the potentially irreversible consequences of initial overexposure.\u003c/p\u003e\n\u003cp\u003eSecond, patients experiencing initial supratherapeutic exposure should not be considered clinically resolved upon concentration normalization. Our data indicate that these patients retain significantly elevated mortality risk, warranting intensified surveillance including prolonged electrocardiographic monitoring and strict electrolyte management to mitigate their persistent pro-arrhythmic vulnerability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several strengths. First, the granular data from the PIC database enabled reconstruction of longitudinal concentration trajectories rather than reliance on static measurements. Second, adjustment for serum cystatin C\u0026mdash;a superior biomarker compared with creatinine in critically ill children with low muscle mass\u0026mdash;strengthens our conclusion that the observed mortality risk is independent of renal dysfunction. This approach is supported by recent evidence demonstrating the predictive value of cystatin C for supratherapeutic digoxin levels in high-risk populations[22, 23].\u003c/p\u003e\n\u003cp\u003eSeveral limitations warrant consideration. First, the retrospective design precludes definitive causal inference despite rigorous statistical adjustment. Second, the \u0026quot;High-Decrease\u0026quot; cohort (n=24), while sufficient for the primary analysis, precluded granular subgroup stratifications by age or comorbidity. Third, absence of adjudicated cause-of-death data and continuous electrocardiographic recordings prevented definitive attribution of excess mortality to specific mechanisms such as lethal arrhythmias. Fourth, the single-center design limits generalizability, and external validation in other PICU populations is warranted.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies a significant legacy effect in pediatric digoxin therapy whereby mortality risk from initial supratherapeutic exposure persists despite subsequent concentration normalization. These findings indicate that reactive TDM strategies alone are insufficient and support a paradigm shift toward proactive prevention of initial overexposure combined with intensified surveillance of patients with prior supratherapeutic exposure.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational normalized ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePediatric intensive care unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSerum digoxin concentration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTherapeutic drug monitoring\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALT: Alanine aminotransferase\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;AST: Aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;CI: Confidence interval\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;INR: International normalized ratio\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;IQR: Interquartile range\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;OR: Odds ratio\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;PICU: Pediatric intensive care unit\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;SDC: Serum digoxin concentration\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;TDM: Therapeutic drug monitoring\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Jingchuan Lu conceived and designed the study, performed the statistical analysis, and drafted the manuscript. Xuemei Hu contributed to data interpretation and manuscript revision. Shiying Zhao and Shuangyan Zhu contributed to data extraction and critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study was performed in line with the principles of the Declaration of Helsinki. The Pediatric Intensive Care (PIC) database protocol was approved by the Institutional Review Board of the Children\u0026rsquo;s Hospital, Zhejiang University School of Medicine (Approval number: 2019_IRB_052). The use of this publicly available, de-identified dataset for secondary analysis qualified for exemption from further ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to\u0026nbsp;participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The requirement for individual informed consent was waived by the Institutional Review Board (IRB) of the Children\u0026apos;s Hospital, Zhejiang University School of Medicine due to the retrospective design and the use of de-identified data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The data analyzed in this study are available in the Pediatric Intensive Care (PIC) database and can be accessed via PhysioNet (https://physionet.org/content/picdb/). Access was granted to the first author (Jingchuan Lu) following the completion of the required Collaborative Institutional Training Initiative (CITI Program) coursework \u0026quot;Data or Specimens Only Research\u0026quot; (Record ID: 63513763) and signing of the data use agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003cbr\u003e\u003c/strong\u003eWe extend our sincere gratitude to the clinical and research staff at the Children\u0026apos;s Hospital affiliated with the Zhejiang University School of Medicine for their exceptional efforts in establishing and maintaining the Pediatric Intensive Care (PIC) database. We also express our appreciation to the MIT Laboratory for Computational Physiology and the PhysioNet platform for hosting and sharing these invaluable clinical datasets, which made this research possible.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmdani S, Conway J, George K, et al (2024) Evaluation and Management of Chronic Heart Failure in Children and Adolescents with Congenital Heart Disease: A Scientific Statement from the American Heart Association. Circulation 150: e33\u0026ndash;e50. https://doi.org/10.1161/CIR.0000000000001245\u003c/li\u003e\n\u003cli\u003eKhandelwal R, Vagha JD, Meshram RJ, Patel A (2024) A Comprehensive Review on Unveiling the Journey of Digoxin: Past, Present, and Future Perspectives. Cureus 16: e56755. https://doi.org/10.7759/cureus.56755\u003c/li\u003e\n\u003cli\u003eMcDonagh TA, Metra M, Adamo M, et al (2021) 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J 42:3599\u0026ndash;3726. https://doi.org/10.1093/eurheartj/ehab368\u003c/li\u003e\n\u003cli\u003eHack JB, Wingate S, Zolty R, et al (2025) Expert Consensus on the Diagnosis and Management of Digoxin Toxicity. Am J Med 138:25-33. e14. https://doi.org/10.1016/j.amjmed.2024.08.018\u003c/li\u003e\n\u003cli\u003eGraafsma J, Cimic N, Dijkman M, et al (2025) Digoxin toxicity with therapeutic serum digoxin concentrations. Toxicol Rep 15:102079. https://doi.org/10.1016/j.toxrep.2025.102079\u003c/li\u003e\n\u003cli\u003eOzrazgat-Baslanti T, Loftus TJ, Ren Y, et al (2021) Association of persistent acute kidney injury and renal recovery with mortality in hospitalised patients. BMJ Health Care Inform 28:e100458. https://doi.org/10.1136/bmjhci-2021-100458\u003c/li\u003e\n\u003cli\u003eZeng X, Yu G, Lu Y, et al (2020) PIC, a paediatric-specific intensive care database. Sci Data 7:14. https://doi.org/10.1038/s41597-020-0355-4\u003c/li\u003e\n\u003cli\u003eChang J, Liu L, Han Z (2025) Association between hypothermia and hyperthermia and 28-day mortality in pediatric intensive care unit patients: a retrospective cohort study. Sci Rep 15:9141. https://doi.org/10.1038/s41598-025-93862-0\u003c/li\u003e\n\u003cli\u003eGoldberger AL, Amaral LA, Glass L, et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101:E215-220. https://doi.org/10.1161/01.cir.101.23.e215\u003c/li\u003e\n\u003cli\u003eZhang L, Wu Y, Huang H, et al (2021) Performance of PRISM III, PELOD-2, and P-MODS Scores in Two Pediatric Intensive Care Units in China. Front Pediatr 9:626165. https://doi.org/10.3389/fped.2021.626165\u003c/li\u003e\n\u003cli\u003eAndrews P, Anseeuw K, Kotecha D, et al (2023) Diagnosis and practical management of digoxin toxicity: a narrative review and consensus. Eur J Emerg Med 30:395\u0026ndash;401. https://doi.org/10.1097/MEJ.0000000000001065\u003c/li\u003e\n\u003cli\u003eScranton K, John S, Angelini M, et al (2025) The mechanism of action of digoxin requires the sodium-dependent inactivation of the sodium-calcium exchanger. Sci Adv 11:eady9596. https://doi.org/10.1126/sciadv.ady9596\u003c/li\u003e\n\u003cli\u003eMatuz-Mares D, Gonz\u0026aacute;lez-Andrade M, Araiza-Villanueva MG, et al (2022) Mitochondrial Calcium: Effects of Its Imbalance in Disease. Antioxidants (Basel) 11:801. https://doi.org/10.3390/antiox11050801\u003c/li\u003e\n\u003cli\u003eLandstrom AP, Dobrev D, Wehrens XHT (2017) Calcium Signaling and Cardiac Arrhythmias. Circ Res 120:1969\u0026ndash;1993. https://doi.org/10.1161/CIRCRESAHA.117.310083\u003c/li\u003e\n\u003cli\u003eThakkar N, Salerno S, Hornik CP, Gonzalez D (2017) Clinical Pharmacology Studies in Critically Ill Children. Pharm Res 34:7\u0026ndash;24. https://doi.org/10.1007/s11095-016-2033-y\u003c/li\u003e\n\u003cli\u003eBouajram RH, Awdishu L (2021) A Clinician\u0026rsquo;s Guide to Dosing Analgesics, Anticonvulsants, and Psychotropic Medications in Continuous Renal Replacement Therapy. Kidney Int Rep 6:2033\u0026ndash;2048. https://doi.org/10.1016/j.ekir.2021.05.004\u003c/li\u003e\n\u003cli\u003eAguirre D\u0026aacute;vila L, Weber K, Bavendiek U, et al (2019) Digoxin-mortality: randomized vs. observational comparison in the DIG trial. Eur Heart J 40:3336\u0026ndash;3341. https://doi.org/10.1093/eurheartj/ehz395\u003c/li\u003e\n\u003cli\u003eZhang Y, Liu Y, He H, Hao K (2026) Physiologically Based Pharmacokinetic Modeling of Digoxin in Adult and Pediatric Patients with Heart Failure. Pharmaceutics 18:112. https://doi.org/10.3390/pharmaceutics18010112\u003c/li\u003e\n\u003cli\u003eLarsson A, Hamberg A-K, Cedernaes J, et al (2025) New Monitoring Recommendations for Digoxin During the Last Decade Are Associated with Decreased Serum Digoxin Concentrations in Patient Samples. Basic Clin Pharmacol Toxicol 137: e70083. https://doi.org/10.1111/bcpt.70083\u003c/li\u003e\n\u003cli\u003eLopes RD, Rordorf R, De Ferrari GM, et al (2018) Digoxin and Mortality in Patients with Atrial Fibrillation. J Am Coll Cardiol 71:1063\u0026ndash;1074. https://doi.org/10.1016/j.jacc.2017.12.060\u003c/li\u003e\n\u003cli\u003eCoggins SA, Wade KC, Downes KJ (2026) Advances in Pediatric Therapeutic Drug Monitoring. Pediatrics 157: e2025073013. https://doi.org/10.1542/peds.2025-073013\u003c/li\u003e\n\u003cli\u003eLu J-J, Liu T-T (2024) Serum Cystatin C as a Risk Factor for Supratherapeutic Digoxin Concentration in Elderly Patients with Heart Failure and Chronic Kidney Disease. Am J Cardiovasc Drugs 24:303\u0026ndash;311. https://doi.org/10.1007/s40256-024-00629-5\u003c/li\u003e\n\u003cli\u003eWang Y, Zheng X, Yang Z, et al (2026) Cystatin C for predicting acute kidney injury in critically ill children with bacterial infections: a retrospective cohort study. BMJ Paediatr Open 10: e004152. https://doi.org/10.1136/bmjpo-2025-004152\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable1. Baseline Characteristics of Pediatric Patients Stratified by Initial Serum Digoxin Concentration\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 154px;\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003cp\u003e(\u0026le;1.0 ng/mL)\u003c/p\u003e\n \u003cp\u003e(n=111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGroup 2\u003cbr\u003e\u0026nbsp;(1.0\u0026ndash;2.0 ng/mL)\u003cbr\u003e\u0026nbsp;(n=134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;Group 3\u003cbr\u003e(\u0026gt;2.0 ng/mL)\u003cbr\u003e(n=72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 613px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e0.53 (0.29-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.28 (0.10-0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e0.14 (0.07-0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMale sex, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e67 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e75 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e42 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" style=\"width: 613px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigoxin Metrics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eInitial concentration, ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e0.57 (0.36-0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.18 (0.99-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e2.08 (1.49-2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePatients with\u0026nbsp;at least 2 measurements, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 154px;\"\u003e\n \u003cp\u003e36 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e87 (64.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e58 (80.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" style=\"width: 613px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRenal Function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eSerum creatinine,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e43.0 (38.0-49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e48.0 (41.3-68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e54.0 (44.8-68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCystatin C, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e1.12 (0.93-1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.25 (1.04-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e1.39 (1.15-1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eBlood urea nitrogen, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e3.30 (2.55-4.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.55 (2.28-5.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e3.64 (2.72-5.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" style=\"width: 613px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectrolytes \u0026amp; Blood Gas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePotassium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e3.80 (3.50-4.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4.10 (3.70-4.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e4.00 (3.65-4.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMagnesium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e0.88 (0.81-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.90 (0.85-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e0.90 (0.83-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCalcium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e2.42 (2.29-2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.33 (2.20-2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e2.30 (2.19-2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eSodium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e136 (134-139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e135 (133-138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e136 (133-138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eLactate, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e1.30 (0.90-2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.00 (1.00-3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e2.20 (1.40-3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eArterial pH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e7.38 (7.33-7.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e7.36 (7.32-7.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e7.38 (7.33-7.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 613px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver Function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e23.0 (16.0-37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e19.0 (12.3-36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e19.5 (12.0-31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eAST, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e45.0 (36.0-63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e51.5 (34.3-90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e45.0 (33.0-68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eTotal bilirubin,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e9.7 (6.0-18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e17.7 (8.3-51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e32.6 (10.7-98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAlbumin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e41.3 (38.4-44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e39.4 (34.4-43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e38.1 (34.1-41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e1.04 (0.98-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.09 (0.99-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e1.12 (1.04-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" style=\"width: 613px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHematology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eHemoglobin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e115 (105-134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e124 (108-147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e123 (105-142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eWhite blood cell count,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e9.29 (7.80-11.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e9.63 (7.71-13.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e10.89 (8.49-14.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePlatelet count, \u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e322 (215-404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e293 (198-384)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e311 (262-392)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" style=\"width: 613px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePICU length of stay, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e6.9 (4.5-11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e9.9 (6.8-21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e15.8 (8.8-28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp;Hospital Mortality, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e7 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e16 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e16 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" valign=\"top\" style=\"width: 613px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Data are presented as median (IQR) for continuous variables and No. (%) for categorical variables.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; INR, international normalized ratio; IQR, interquartile range; PICU, pediatric intensive care unit.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e: P-values were calculated using the Kruskal-Wallis rank sum test for continuous variables and Fisher\u0026apos;s exact test for categorical variables with expected counts \u0026lt; 10.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Multivariable Logistic Regression Analysis of the Association Between Initial Serum Digoxin Concentration and Hospital Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCrude Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003eModel I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 23px;\"\u003e\n \u003cp\u003eModel II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eOR (95% CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eOR (95% CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eOR (95% CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"32\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eExposure as Continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"32\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003ePer 0.1 ng/mL increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.06 (1.02, 1.10) 0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1.06 (1.02, 1.10) 0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.05 (1.01, 1.10) 0.0152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"32\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eExposure as Categorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"30\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eGroup 1 (\u0026le; 1.0 ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"30\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eGroup 2 (1.0\u0026ndash;2.0 ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e2.01 (0.80, 5.09) 0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e2.09 (0.82, 5.33) 0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e2.05 (0.72, 5.83) 0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"30\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eGroup 3 (\u0026gt;2.0 ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e4.24 (1.65, 10.93) 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e4.58 (1.73, 12.14) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e5.15 (1.73, 15.34) 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"32\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eSample Size (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"32\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: OR, odds ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003eOutcome Variable: Hospital Mortality\u003c/p\u003e\n\u003cp\u003eCrude Model: Crude analysis without adjustment.\u003c/p\u003e\n\u003cp\u003eModel I: Adjusted for age and gender.\u003c/p\u003e\n\u003cp\u003eModel II: Adjusted for age, gender, baseline serum creatinine, Calcium, Cystatin C, potassium, ALT, total bilirubin, albumin, lactate, and WBC count\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Baseline Characteristics and Clinical Outcomes Stratified by Dynamic Changes in Serum Digoxin Concentration.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eNormal-Increase Pattern (n = 103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eHigh-Decrease Pattern (n = 24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eNormal-Decrease Pattern\u0026nbsp;(n = 53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.36 (0.21-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.11 (0.06-0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.26 (0.10-0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eMale, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e66 (64.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e13 (54.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e28 (52.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigoxin Concentrations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eInitial (T1), ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.80 (0.42-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.55 (2.18-3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.31 (1.00-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eFollow-up (T2), ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.32 (0.94-1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.29 (0.95-1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.77 (0.48-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRenal Function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSerum Creatinine, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e47.0 (39.5-55.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e56.0 (44.0-77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e52.0 (41.0-69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSerum Cystatin C, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.16 (0.98-1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.50 (1.26-1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.37 (1.09-1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eBlood Urea Nitrogen, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e3.44 (2.64-4.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e3.64 (2.57-4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e3.65 (2.81-5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectrolytes \u0026amp; Blood Gas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSerum Potassium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e3.90 (3.55-4.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e4.20 (3.80-4.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e4.00 (3.70-4.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSerum Magnesium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.89 (0.82-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.91 (0.83-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.88 (0.85-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSerum Calcium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.35 (2.25-2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.38 (2.19-2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.29 (2.12-2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSerum Sodium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e136 (134-138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e135 (133-138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e136 (133-139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eArterial pH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e7.37 (7.33-7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e7.38 (7.34-7.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e7.36 (7.29-7.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSerum Lactate, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.70 (1.00-3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.40 (1.10-4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2.20 (1.20-4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver Function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eAlanine Aminotransferase (ALT), U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e21.0 (14.5-36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e14.5 (11.8-23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 22px;\"\u003e\n \u003cp\u003e21.0 (13.0-35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAspartate Aminotransferase (AST), U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e50.0 (33.5-81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e41.5 (30.8-74.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e49.0 (35.0-76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eTotal Bilirubin, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 22px;\"\u003e\n \u003cp\u003e14.0 (6.8-33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e66.2 (13.7-105.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e19.6 (6.4-64.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSerum Albumin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 22px;\"\u003e\n \u003cp\u003e39.9 (35.7-43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e36.5 (33.5-40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 22px;\"\u003e\n \u003cp\u003e39.9 (34.1-43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHematology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHemoglobin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e123 (105-146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e131 (119-143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e122 (103-147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eWhite Blood Cell Count, \u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e9.71 (7.72-11.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e11.39 (9.40-15.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e9.99 (8.13-14.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003ePlatelet Count, \u0026times;10⁹/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e304 (229-400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 22px;\"\u003e\n \u003cp\u003e289 (268-354)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;291 (191-379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003ePICU Length of Stay, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 22px;\"\u003e\n \u003cp\u003e12.5 (7.0-23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 22px;\"\u003e\n \u003cp\u003e10.8 (6.8-23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 22px;\"\u003e\n \u003cp\u003e14.8 (7.8-29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHospital Mortality, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e10 (9.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e6 (25.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e8 (15.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Data are presented as median (IQR) or n (%).\u003c/p\u003e\n\u003cp\u003eP-values were calculated using the Kruskal-Wallis test or Fisher\u0026apos;s exact test, as appropriate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multivariable Logistic Regression Analysis of the Association Between Dynamic Digoxin Patterns and Hospital Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eNon-adjusted(N=180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eAdjust I(N=180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAdjust II(N=178)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 24px;\"\u003e\n \u003cp\u003eDynamic Digoxin Pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eOR (95%CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eOR (95%CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eOR (95%CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eNormal_Increased (Therapeutic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eHigh_Decreased (Correction)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e3.10 (1.00, 9.61) 0.0499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e3.47 (1.08, 11.15) 0.0368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e3.65 (1.03, 12.88) 0.0445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eNormal_Decreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e1.65 (0.61, 4.47) 0.3222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1.82 (0.66, 5.06) 0.2502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.69 (0.57, 5.02) 0.3451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: OR, odds ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003eOutcome Variable: Hospital Mortality\u003c/p\u003e\n\u003cp\u003eUnadjusted Model: Crude analysis without adjustment.\u003c/p\u003e\n\u003cp\u003eModel I: Adjusted for age and gender.\u003c/p\u003e\n\u003cp\u003eModel II: Adjusted for age, gender, serum creatinine, Cystatin C, total bilirubin, serum potassium, and WBC count.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"digoxin, therapeutic drug monitoring, pediatric intensive care, hospital mortality, legacy effect","lastPublishedDoi":"10.21203/rs.3.rs-9183203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9183203/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eDigoxin is a narrow therapeutic index drug requiring therapeutic drug monitoring (TDM) in pediatric intensive care units (PICUs). Whether correcting initially elevated concentrations eliminates the associated mortality risk remains unknown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study analyzed data from 317 PICU patients in the Pediatric Intensive Care database (2010\u0026ndash;2018), stratified by initial serum digoxin concentrations (SDC): \u0026le;1.0 ng/mL (n\u0026thinsp;=\u0026thinsp;111), \u0026gt;\u0026thinsp;1.0 - \u0026le;2.0 ng/mL (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;134), and \u0026gt;\u0026thinsp;2.0 ng/mL (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;72). A subcohort of 180 patients with serial measurements compared mortality outcomes between those with initially supratherapeutic concentrations subsequently corrected to therapeutic range (\"High-Decrease\" pattern) versus those maintaining consistently therapeutic levels (\"Normal-Increase\" pattern). Multivariable logistic regression assessed associations with hospital mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall hospital mortality was 12.3% (39/317). Initial SDC\u0026thinsp;\u0026gt;\u0026thinsp;2.0 ng/mL was independently associated with increased hospital mortality compared with \u0026le;\u0026thinsp;1.0 ng/mL (adjusted OR 5.15, 95% CI 1.73\u0026ndash;15.34, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). In the longitudinal subcohort, patients in the \"High-Decrease\" pattern maintained a nearly 4-fold elevated mortality risk (adjusted OR 3.65, 95% CI 1.03\u0026ndash;12.88, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045) compared with the \"Normal-Increase\" pattern, despite achieving comparable final concentrations.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eInitial digoxin exposure exceeding 2.0 ng/mL demonstrates a persistent \"legacy effect\" on hospital mortality that is not eliminated by subsequent concentration correction. These findings suggest that proactive prevention of initial supratherapeutic exposure may be more effective than reactive TDM-guided dose adjustment.\u003c/p\u003e","manuscriptTitle":"Persistent Mortality Risk Following Initial Supratherapeutic Digoxin Exposure in Critically Ill Children: A Legacy Effect","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 15:35:09","doi":"10.21203/rs.3.rs-9183203/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-28T18:22:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T17:52:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129768335188169802869556598785122875441","date":"2026-04-23T16:57:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183705856560354874959648951134230693081","date":"2026-04-16T19:39:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333834070795708952047092672844940585425","date":"2026-04-16T10:46:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T10:43:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-24T06:17:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T05:19:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T05:18:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2026-03-21T04:51:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"387baf02-32ab-4163-9af2-304e971e7bd9","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-26T15:35:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 15:35:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9183203","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9183203","identity":"rs-9183203","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}