Association of fluid balance trajectories with clinical outcomes in patients with septic shock:A prospective multicenter cohort study

Background:Septic shock has a high incidence and mortality rate in Intensive Care Units(ICUs).Earlier intravenous fluid resuscitation can significantly improve outcomes in septic patients but easily leads to fluid overload(FO),which is associated with poor clinical outcomes.A single point value of fluid cannot provide enough fluid information.The aim of this study was to investigate the impact of fluid balance(FB)latent trajectories on clinical outcomes in septic patients.Methods:Patients were diagnosed with septic shock during the first 48 h,and sequential fluid data for the first 3 days of ICU admission were included.A group-based trajectory model(GBTM)which is designed to identify groups of individuals following similar developmental trajectories was used to identify latent subgroups of individuals following a similar progression of FB.The primary outcomes were hospital mortality,organ dysfunction,major adverse kidney events(MAKE)and severe respiratory adverse events(SRAE).We used multivariable Cox or logistic regression analysis to assess the association between FB trajectories and clinical outcomes.Results:Nine hundred eighty-six patients met the inclusion criteria and were assigned to GBTM analysis,and three latent FB trajectories were detected.64(6.5%),841(85.3%),and 81(8.2%)patients were identified to have decreased,low,and high FB,respectively.Compared with low FB,high FB was associated with increased hospital mortality[hazard ratio(HR)=1.63,95%CI 1.22–2.17],organ dysfunction[odds ratio(OR)=2.18,95%CI 1.22–3.42],MAKE(OR=1.80,95%CI 1.04–2.63)and SRAE(OR=2.33,95%CI 1.46–3.71),and decreasing FB was significantly associated with decreased MAKE(OR=0.46,95%CI 0.29–0.79)after adjustment for potential covariates.Conclusion:Latent subgroups of septic patients followed a similar FB progression.These latent fluid trajectories were associated with clinical outcomes.The decreasing FB trajectory was associated with a decreased risk of hospital mortality and MAKE.

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

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(P<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(P<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.

A High-order Internal Model Based Iterative Learning Control Scheme for Discrete Linear Time-varying Systems

In this paper, an iterative learning control algorithm is proposed for discrete linear time-varying systems to track iterationvarying desired trajectories. A high-order internal model(HOIM) is utilized to describe the variation of desired trajectories in the iteration domain. In the sequel, the HOIM is incorporated into the design of learning gains. The learning convergence in the iteration axis can be guaranteed with rigorous proof. The simulation results with permanent magnet linear motors(PMLM) demonstrate that the proposed HOIM based approach yields good performance and achieves perfect tracking.

Eight-year trajectories of malalignment progression in symptomatic knee osteoarthritis

Background: Although various therapies have been developed to treat malalignment in osteoarthritic knees, the pattern of malalignment progression is still unclear. This study aimed to identify homogeneous subgroups with distinct trajectories of malalignment progression in subjects with symptomatic knee osteoarthritis (KOA) and to determine corresponding risk factors.Methods: Eight-year follow-up (from 2004 to 2012) data on 1252 participants with symptomatic KOA from the Osteoarthritis Initiative were included. Varus/valgus angle progression was characterized by group-based trajectory models. Time-varying covariates were introduced into the model to investigate how they affected trajectories. Multinomial logistic regression for trajectory group membership was applied to ascertain risk factors.Results: Five subgroups were identified. Participants in the varus worsening trajectory (n = 166) or valgus worsening trajectory (n = 118) proceeded to worsen malalignment over time. The neutral trajectory (n = 378), varus stable trajectory (n = 328), and valgus stable trajectory (n = 262) maintained close to the initial varus/valgus angle over 8 years. Higher baseline Kellgren and Lawrence grade (odds ratio [OR] = 4.35,P < 0.001 for varus;OR= 3.85,P < 0.001 for valgus) and "severe" baseline malalignment (OR = 13.57,P < 0.001 for varus;OR = 23.04,P < 0.001 for valgus) were risk factors for worsening trajectories. The cutoff point of the baseline varus/valgus angle to discriminate between stable or worsening trajectory was -4.5° for varus and 3.6° for valgus.Conclusions: This study identified the malalignment progression pattern - minor malalignment (-4.5° to +3.6°) tends to remain stable, while major baseline malalignment is likely to progress. This provides a reference for therapy to prevent malalignment from deteriorating and emphasizes the necessity of determining the trigger factors for malalignment onset.