Influence of the trajectory of the urine output for 24 h on the occurrence of AKI in patients with sepsis in intensive care unit.
Journal of translational medicine
BACKGROUND:Sepsis-associated acute kidney injury (S-AKI) is a common and life-threatening complication in hospitalized and critically ill patients. This condition is an independent cause of death. This study was performed to investigate the correlation between the trajectory of urine output within 24 h and S-AKI. METHODS:Patients with sepsis were studied retrospectively based on the Medical Information Mart for Intensive Care IV. Latent growth mixture modeling was used to classify the trajectory of urine output changes within 24 h of sepsis diagnosis. The outcome of this study is AKI that occurs 24 h after sepsis. Cox proportional hazard model, Fine-Gray subdistribution proportional hazard model, and doubly robust estimation method were used to explore the risk of AKI in patients with different trajectory classes. RESULTS:A total of 9869 sepsis patients were included in this study, and their 24-h urine output trajectories were divided into five classes. The Cox proportional hazard model showed that compared with class 1, the HR (95% CI) values for classes 3, 4, and 5 were 1.460 (1.137-1.875), 1.532 (1.197-1.961), and 2.232 (1.795-2.774), respectively. Competing risk model and doubly robust estimation methods reached similar results. CONCLUSIONS:The trajectory of urine output within 24 h of sepsis patients has a certain impact on the occurrence of AKI. Therefore, in the early treatment of sepsis, close attention should be paid to changes in the patient's urine output to prevent the occurrence of S-AKI.
10.1186/s12967-021-03190-w
The clinical trajectory of peripheral blood immune cell subsets, T-cell activation, and cytokines in septic patients.
Inflammation research : official journal of the European Histamine Research Society ... [et al.]
OBJECTIVE AND DESIGN:Changes in the immune status of patients with sepsis may have a major impact on their prognosis. Our research focused on changes in various immune cell subsets and T-cell activation during the progression of sepsis. METHODS AND SUBJECTS:We collected data from 188 sepsis patients at the First Affiliated Hospital of Zhejiang University School of Medicine. The main focus was on the patient's immunocyte subset typing, T-cell activation/Treg cell analysis, and cytokine assay, which can indicate the immune status of the patient. RESULTS:The study found that the number of CD4 T cells, CD8 T cells, NK cells, and B cells decreased early in the disease, and the decrease in CD4 and CD8 T cells was more pronounced in the death group. T lymphocyte activation was inhibited, and the number of Treg cells increased as the disease progressed. T lymphocyte inhibition was more significant in the death group, and the increase in IL-10 was more significant in the death group. Finally, we used patients' baseline conditions and immunological detection indicators for modeling and found that IL-10, CD4 Treg cells, CD3HLA-DR T cells, and CD3CD69 T cells could predict patients' prognosis well. CONCLUSION:Our study found that immunosuppression occurs in patients early in sepsis. Early monitoring of the patient's immune status may provide a timely warning of the disease.
10.1007/s00011-023-01825-w
Effects of growth trajectory of shock index within 24 h on the prognosis of patients with sepsis.
Frontiers in medicine
Background:Sepsis is a serious disease with high clinical morbidity and mortality. Despite the tremendous advances in medicine and nursing, treatment of sepsis remains a huge challenge. Our purpose was to explore the effects of shock index (SI) trajectory changes on the prognosis of patients within 24 h after the diagnosis of sepsis. Methods:This study was based on Medical Information Mart for Intensive Care IV (MIMIC- IV). The effects of SI on the prognosis of patients with sepsis were investigated using C-index and restricted cubic spline (RCS). The trajectory of SI in 24 h after sepsis diagnosis was classified by latent growth mixture modeling (LGMM). Cox proportional hazard model, double robust analysis, and subgroup analysis were conducted to investigate the influence of SI trajectory on in-hospital death and secondary outcomes. Results:A total of 19,869 patients were eventually enrolled in this study. C-index showed that SI had a prognostic value independent of Sequential Organ Failure Assessment for patients with sepsis. Moreover, the results of RCS showed that SI was a prognostic risk factor. LGMM divided SI trajectory into seven classes, and patients with sepsis in different classes had notable differences in prognosis. Compared with the SI continuously at a low level of 0.6, the SI continued to be at a level higher than 1.0, and the patients in the class whose initial SI was at a high level of 1.2 and then declined had a worse prognosis. Furthermore, the trajectory of SI had a higher prognostic value than the initial SI. Conclusion:Both initial SI and trajectory of SI were found to be independent factors that affect the prognosis of patients with sepsis. Therefore, in clinical treatment, we should closely monitor the basic vital signs of patients and arrive at appropriate clinical decisions on basis of their change trajectory.
10.3389/fmed.2022.898424
Analysis of the correlation between the longitudinal trajectory of SOFA scores and prognosis in patients with sepsis at 72 hour after admission based on group trajectory modeling.
Journal of intensive medicine
Background:To identify the distinct trajectories of the Sequential Organ Failure Assessment (SOFA) scores at 72 h for patients with sepsis in the Medical Information Mart for Intensive Care (MIMIC)-IV database and determine their effects on mortality and adverse clinical outcomes. Methods:A retrospective cohort study was carried out involving patients with sepsis from the MIMIC-IV database. Group-based trajectory modeling (GBTM) was used to identify the distinct trajectory groups for the SOFA scores in patients with sepsis in the intensive care unit (ICU). The Cox proportional hazards regression model was used to investigate the relationship between the longitudinal change trajectory of the SOFA score and mortality and adverse clinical outcomes. Results:A total of 16,743 patients with sepsis were included in the cohort. The median survival age was 66 years (interquartile range: 54-76 years). The 7-day and 28-day in-hospital mortality were 6.0% and 17.6%, respectively. Five different trajectories of SOFA scores according to the model fitting standard were determined: group 1 (32.8%), group 2 (30.0%), group 3 (17.6%), group 4 (14.0%) and group 5 (5.7%). Univariate and multivariate Cox regression analyses showed that, for different clinical outcomes, trajectory group 1 was used as the reference, while trajectory groups 2-5 were all risk factors associated with the outcome ( < 0.001). Subgroup analysis revealed an interaction between the two covariates of age and mechanical ventilation and the different trajectory groups of patients' SOFA scores ( < 0.05). Conclusion:This approach may help identify various groups of patients with sepsis, who may be at different levels of risk for adverse health outcomes, and provide subgroups with clinical importance.
10.1016/j.jointm.2021.11.001
Continuous sepsis trajectory prediction using tensor-reduced physiological signals.
Scientific reports
The quick Sequential Organ Failure Assessment (qSOFA) system identifies an individual's risk to progress to poor sepsis-related outcomes using minimal variables. We used Support Vector Machine, Learning Using Concave and Convex Kernels, and Random Forest to predict an increase in qSOFA score using electronic health record (EHR) data, electrocardiograms (ECG), and arterial line signals. We structured physiological signals data in a tensor format and used Canonical Polyadic/Parallel Factors (CP) decomposition for feature reduction. Random Forests trained on ECG data show improved performance after tensor decomposition for predictions in a 6-h time frame (AUROC 0.67 ± 0.06 compared to 0.57 ± 0.08, ). Adding arterial line features can also improve performance (AUROC 0.69 ± 0.07, ), and benefit from tensor decomposition (AUROC 0.71 ± 0.07, ). Adding EHR data features to a tensor-reduced signal model further improves performance (AUROC 0.77 ± 0.06, ). Despite reduction in performance going from an EHR data-informed model to a tensor-reduced waveform data model, the signals-informed model offers distinct advantages. The first is that predictions can be made on a continuous basis in real-time, and second is that these predictions are not limited by the availability of EHR data. Additionally, structuring the waveform features as a tensor conserves structural and temporal information that would otherwise be lost if the data were presented as flat vectors.
10.1038/s41598-024-68901-x
Platelet-to-Lymphocyte Ratio and In-Hospital Mortality in Patients With AKI Receiving Continuous Kidney Replacement Therapy: A Retrospective Observational Cohort Study.
Kidney medicine
Rationale & Objective:The platelet-to-lymphocyte ratio (PLR) is a marker of inflammation and a predictor of mortality in a variety of diseases. However, the effectiveness of PLR as a predictor of mortality in patients with severe acute kidney injury (AKI) is uncertain. We evaluated the association between the PLR and mortality in critically ill patients with severe AKI who underwent continuous kidney replacement therapy (CKRT). Study Design:Retrospective cohort study. Setting & Participants:A total of 1,044 patients who underwent CKRT in a single center, from February 2017 to March 2021. Exposures:PLR. Outcomes:In-hospital mortality. Analytical Approach:The study patients were classified into quintiles according to the PLR values. A Cox proportional hazards model was used to investigate the association between PLR and mortality. Results:The PLR value was associated with in-hospital mortality in a nonlinear manner, showing a higher mortality at both ends of the PLR. The Kaplan-Meier curve revealed the highest mortality with the first and fifth quintiles, whereas the lowest mortality occurred with the third quintile. Compared with the third quintile, the first (adjusted HR, 1.94; 95% CI, 1.44-2.62; < 0.001) and fifth (adjusted HR, 1.60; 95% CI, 1.18-2.18; = 0.002) quintiles of the PLR group had a significantly higher in-hospital mortality rate. The first and fifth quintiles showed a consistently increased risk of 30- and 90-day mortality rates compared with those of the third quintile. In the subgroup analysis, the lower and higher PLR values were predictors of in-hospital mortality in patients with older age, of female sex, and with hypertension, diabetes, and higher Sequential Organ Failure Assessment score. Limitations:There may be bias owing to the single-center retrospective nature of this study. We only had PLR values at the time of initiation of CKRT. Conclusions:Both the lower and higher PLR values were independent predictors of in-hospital mortality in critically ill patients with severe AKI who underwent CKRT.
10.1016/j.xkme.2023.100642
Temperature Trajectory Subphenotypes Correlate With Immune Responses in Patients With Sepsis.
Critical care medicine
OBJECTIVES:We recently found that distinct body temperature trajectories of infected patients correlated with survival. Understanding the relationship between the temperature trajectories and the host immune response to infection could allow us to immunophenotype patients at the bedside using temperature. The objective was to identify whether temperature trajectories have consistent associations with specific cytokine responses in two distinct cohorts of infected patients. DESIGN:Prospective observational study. SETTING:Large academic medical center between 2013 and 2019. SUBJECTS:Two cohorts of infected patients: 1) patients in the ICU with septic shock and 2) hospitalized patients with Staphylococcus aureus bacteremia. INTERVENTIONS:Clinical data (including body temperature) and plasma cytokine concentrations were measured. Patients were classified into four temperature trajectory subphenotypes using their temperature measurements in the first 72 hours from the onset of infection. Log-transformed cytokine levels were standardized to the mean and compared with the subphenotypes in both cohorts. MEASUREMENTS AND MAIN RESULTS:The cohorts consisted of 120 patients with septic shock (cohort 1) and 88 patients with S. aureus bacteremia (cohort 2). Patients from both cohorts were classified into one of four previously validated temperature subphenotypes: "hyperthermic, slow resolvers" (n = 19 cohort 1; n = 13 cohort 2), "hyperthermic, fast resolvers" (n = 18 C1; n = 24 C2), "normothermic" (n = 54 C1; n = 31 C2), and "hypothermic" (n = 29 C1; n = 20 C2). Both "hyperthermic, slow resolvers" and "hyperthermic, fast resolvers" had high levels of G-CSF, CCL2, and interleukin-10 compared with the "hypothermic" group when controlling for cohort and timing of cytokine measurement (p < 0.05). In contrast to the "hyperthermic, slow resolvers," the "hyperthermic, fast resolvers" showed significant decreases in the levels of several cytokines over a 24-hour period, including interleukin-1RA, interleukin-6, interleukin-8, G-CSF, and M-CSF (p < 0.001). CONCLUSIONS:Temperature trajectory subphenotypes are associated with consistent cytokine profiles in two distinct cohorts of infected patients. These subphenotypes could play a role in the bedside identification of cytokine profiles in patients with sepsis.
10.1097/CCM.0000000000004610
Distinct immune profiles and clinical outcomes in sepsis subphenotypes based on temperature trajectories.
Intensive care medicine
PURPOSE:Sepsis is a heterogeneous syndrome. Identification of sepsis subphenotypes with distinct immune profiles could lead to targeted therapies. This study investigates the immune profiles of patients with sepsis following distinct body temperature patterns (i.e., temperature trajectory subphenotypes). METHODS:Hospitalized patients from four hospitals between 2018 and 2022 with suspicion of infection were included. A previously validated temperature trajectory algorithm was used to classify study patients into temperature trajectory subphenotypes. Microbiological profiles, clinical outcomes, and levels of 31 biomarkers were compared between these subphenotypes. RESULTS:The 3576 study patients were classified into four temperature trajectory subphenotypes: hyperthermic slow resolvers (N = 563, 16%), hyperthermic fast resolvers (N = 805, 23%), normothermic (N = 1693, 47%), hypothermic (N = 515, 14%). The mortality rate was significantly different between subphenotypes, with the highest rate in hypothermics (14.2%), followed by hyperthermic slow resolvers 6%, normothermic 5.5%, and lowest in hyperthermic fast resolvers 3.6% (p < 0.001). After multiple testing correction for the 31 biomarkers tested, 20 biomarkers remained significantly different between temperature trajectories: angiopoietin-1 (Ang-1), C-reactive protein (CRP), feline McDonough sarcoma-like tyrosine kinase 3 ligand (Flt-3l), granulocyte colony stimulating factor (G-CSF), granulocyte-macrophage colony stimulating factor (GM-CSF), interleukin (IL)-15, IL-1 receptor antagonist (RA), IL-2, IL-6, IL-7, interferon gamma-induced protein 10 (IP-10), monocyte chemoattractant protein-1 (MCP-1), human macrophage inflammatory protein 3 alpha (MIP-3a), neutrophil gelatinase-associated lipocalin (NGAL), pentraxin-3, thrombomodulin, tissue factor, soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and vascular cellular adhesion molecule-1 (vCAM-1).The hyperthermic fast and slow resolvers had the highest levels of most pro- and anti-inflammatory cytokines. Hypothermics had suppressed levels of most cytokines but the highest levels of several coagulation markers (Ang-1, thrombomodulin, tissue factor). CONCLUSION:Sepsis subphenotypes identified using the universally available measurement of body temperature had distinct immune profiles. Hypothermic patients, who had the highest mortality rate, also had the lowest levels of most pro- and anti-inflammatory cytokines.
10.1007/s00134-024-07669-0
Influence of systolic blood pressure trajectory on in-hospital mortality in patients with sepsis.
BMC infectious diseases
BACKGROUND:Numerous studies have investigated the mean arterial pressure in patients with sepsis, and many meaningful results have been obtained. However, few studies have measured the systolic blood pressure (SBP) multiple times and established trajectory models for patients with sepsis with different SBP trajectories. METHODS:Data from patients with sepsis were extracted from the Medical Information Mart for Intensive Care-III database for inclusion in a retrospective cohort study. Ten SBP values within 10 h after hospitalization were extracted, and the interval between each SBP value was 1 h. The SBP measured ten times after admission was analyzed using latent growth mixture modeling to construct a trajectory model. The outcome was in-hospital mortality. The survival probability of different trajectory groups was investigated using Kaplan-Meier (K-M) analysis, and the relationship between different SBP trajectories and in-hospital mortality risk was investigated using Cox proportional-hazards regression model. RESULTS:This study included 3034 patients with sepsis. The median survival time was 67 years (interquartile range: 56-77 years). Seven different SBP trajectories were identified based on model-fit criteria. The in-hospital mortality rates of the patients in trajectory classes 1-7 were 25.5%, 40.5%, 11.8%, 18.3%, 23.5%, 13.8%, and 10.5%, respectively. The K-M analysis indicated that patients in class 2 had the lowest probability of survival. Univariate and multivariate Cox regression analysis indicated that, with class 1 as a reference, patients in class 2 had the highest in-hospital mortality risk (P < 0.001). Subgroup analysis indicated that a nominal interaction occurred between age group and blood pressure trajectory in the in-hospital mortality (P < 0.05). CONCLUSION:Maintaining a systolic blood pressure of approximately 140 mmHg in patients with sepsis within 10 h of admission was associated with a lower risk of in-hospital mortality. Analyzing data from multiple measurements and identifying different categories of patient populations with sepsis will help identify the risks among these categories.
10.1186/s12879-023-08054-w
Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.
Critical care (London, England)
BACKGROUND:Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear. METHODS:This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation. RESULTS:Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87-9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69-7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41-2.94). These associations were similar on external validation. CONCLUSIONS:These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.
10.1186/s13054-024-04935-x
Development and validation of novel sepsis subphenotypes using trajectories of vital signs.
Intensive care medicine
PURPOSE:Sepsis is a heterogeneous syndrome and identification of sub-phenotypes is essential. This study used trajectories of vital signs to develop and validate sub-phenotypes and investigated the interaction of sub-phenotypes with treatment using randomized controlled trial data. METHODS:All patients with suspected infection admitted to four academic hospitals in Emory Healthcare between 2014-2017 (training cohort) and 2018-2019 (validation cohort) were included. Group-based trajectory modeling was applied to vital signs from the first 8 h of hospitalization to develop and validate vitals trajectory sub-phenotypes. The associations between sub-phenotypes and outcomes were evaluated in patients with sepsis. The interaction between sub-phenotype and treatment with balanced crystalloids versus saline was tested in a secondary analysis of SMART (Isotonic Solutions and Major Adverse Renal Events Trial). RESULTS:There were 12,473 patients with suspected infection in training and 8256 patients in validation cohorts, and 4 vitals trajectory sub-phenotypes were found. Group A (N = 3483, 28%) were hyperthermic, tachycardic, tachypneic, and hypotensive. Group B (N = 1578, 13%) were hyperthermic, tachycardic, tachypneic (not as pronounced as Group A) and hypertensive. Groups C (N = 4044, 32%) and D (N = 3368, 27%) had lower temperatures, heart rates, and respiratory rates, with Group C normotensive and Group D hypotensive. In the 6,919 patients with sepsis, Groups A and B were younger while Groups C and D were older. Group A had the lowest prevalence of congestive heart failure, hypertension, diabetes mellitus, and chronic kidney disease, while Group B had the highest prevalence. Groups A and D had the highest vasopressor use (p < 0.001 for all analyses above). In logistic regression, 30-day mortality was significantly higher in Groups A and D (p < 0.001 and p = 0.03, respectively). In the SMART trial, sub-phenotype significantly modified treatment effect (p = 0.03). Group D had significantly lower odds of mortality with balanced crystalloids compared to saline (odds ratio (OR) 0.39, 95% confidence interval (CI) 0.23-0.67, p < 0.001). CONCLUSION:Sepsis sub-phenotypes based on vital sign trajectory were consistent across cohorts, had distinct outcomes, and different responses to treatment with balanced crystalloids versus saline.
10.1007/s00134-022-06890-z
Sepsis-associated acute kidney injury in the intensive care unit: incidence, patient characteristics, timing, trajectory, treatment, and associated outcomes. A multicenter, observational study.
Intensive care medicine
PURPOSE:The Acute Disease Quality Initiative (ADQI) Workgroup recently released a consensus definition of sepsis-associated acute kidney injury (SA-AKI), combining Sepsis-3 and Kidney Disease Improving Global Outcomes (KDIGO) AKI criteria. This study aims to describe the epidemiology of SA-AKI. METHODS:This is a retrospective cohort study carried out in 12 intensive care units (ICUs) from 2015 to 2021. We studied the incidence, patient characteristics, timing, trajectory, treatment, and associated outcomes of SA-AKI based on the ADQI definition. RESULTS:Out of 84,528 admissions, 13,451 met the SA-AKI criteria with its incidence peaking at 18% in 2021. SA-AKI patients were typically admitted from home via the emergency department (ED) with a median time to SA-AKI diagnosis of 1 day (interquartile range (IQR) 1-1) from ICU admission. At diagnosis, most SA-AKI patients (54%) had a stage 1 AKI, mostly due to the low urinary output (UO) criterion only (65%). Compared to diagnosis by creatinine alone, or by both UO and creatinine criteria, patients diagnosed by UO alone had lower renal replacement therapy (RRT) requirements (2.8% vs 18% vs 50%; p < 0.001), which was consistent across all stages of AKI. SA-AKI hospital mortality was 18% and SA-AKI was independently associated with increased mortality. In SA-AKI, diagnosis by low UO only, compared to creatinine alone or to both UO and creatinine criteria, carried an odds ratio of 0.34 (95% confidence interval (CI) 0.32-0.36) for mortality. CONCLUSION:SA-AKI occurs in 1 in 6 ICU patients, is diagnosed on day 1 and carries significant morbidity and mortality risk with patients mostly admitted from home via the ED. However, most SA-AKI is stage 1 and mostly due to low UO, which carries much lower risk than diagnosis by other criteria.
10.1007/s00134-023-07138-0
Associations between serum albumin level trajectories and clinical outcomes in sepsis patients in ICU: insights from longitudinal group trajectory modeling.
Frontiers in nutrition
Background:Sepsis triggers a strong inflammatory response, often leading to organ failure and high mortality. The role of serum albumin levels in sepsis is critical but not fully understood, particularly regarding the significance of albumin level changes over time. This study utilized Group-based Trajectory Modeling (GBTM) to investigate the patterns of serum albumin changes and their impact on sepsis outcomes. Methods:We conducted a retrospective analysis on ICU patients from West China Hospital (2015-2022), employing GBTM to study serum albumin fluctuations within the first week of ICU admission. The study factored in demographics, clinical parameters, and comorbidities, handling missing data through multiple imputation. Outcomes assessed included 28-day mortality, overall hospital mortality, and secondary complications such as AKI and the need for mechanical ventilation. Results:Data from 1,950 patients revealed four serum albumin trajectories, showing distinct patterns of consistently low, increasing, moderate, and consistently high levels. These groups differed significantly in mortality, with the consistently low level group experiencing the highest mortality. No significant difference in 28-day mortality was observed among the other groups. Subgroup analysis did not alter these findings. Conclusion:The study identified four albumin trajectory groups in sepsis patients, highlighting that those with persistently low levels had the worst outcomes, while those with increasing levels had the best. Stable high levels above 30 g/L did not change outcomes significantly. These findings can inform clinical decisions, helping to identify high-risk patients early and tailor treatment approaches.
10.3389/fnut.2024.1433544
Association between serum magnesium trajectory and in-hospital mortality in hospitalized patients with sepsis: an analysis of the MIMIC-IV database.
Magnesium research
This study aimed to investigate the association between serum magnesium trajectory and risk of in-hospital mortality in intensive care unit (ICU) patients with sepsis. Adult sepsis patients who had complete data on serum magnesium at ICU admission (at 0, 12, 24, 36 and 48 hours after ICU admission) based the 2012-2019 Medical Information Mart for Intensive Care IV (MIMIC-IV) database were included in this retrospective cohort study. Serum magnesium trajectories were identified using K-means cluster analysis. The multivariable Cox proportional-hazards model was used to evaluate the association between magnesium level at different time points or magnesium trajectory and in-hospital mortality. A total of 2,270 patients with sepsis were enrolled, and in-hospital mortality occurred in 716 (31.54%). Three trajectories were identified: a high-level declining trajectory, normal-level stable trajectory, and low-level rising trajectory. Among the magnesium levels at different time points, a higher serum magnesium level only at ICU admission (0h) (hazard ratio [HR] = 1.13, 95% confidence interval [CI]: 1.03-1.23) was associated with an increased risk of in-hospital mortality. Compared with the normal-level stable trajectory group, patients in the low-level rising trajectory group (HR = 0.82, 95%CI: 0.70-0.97) had a reduced risk of in-hospital mortality, but no association with in-hospital mortality was found in patients in the high-level declining trajectory group (p=0.812). Conclusion: Sepsis patients with a low-level, rising magnesium trajectory may have a reduced risk of in-hospital mortality.
10.1684/mrh.2023.0520