Identification of clinical subphenotypes of sepsis after laparoscopic surgery

Objective:Some patients exhibit septic symptoms following laparoscopic surgery,leading to a poor prognosis.Effective clinical subphenotyping is critical for guiding tailored therapeutic strategies in these cases.By identifying predisposing factors for postoperative sepsis,clinicians can implement targeted interventions,potentially improving outcomes.This study outlines a workflow for the subphenotype methodology in the context of laparoscopic surgery,along with its practical application.Methods:This study utilized data routinely available in clinical case systems,enhancing the applicability of our findings.The data included vital signs,such as respiratory rate,and laboratory measures,such as blood sodium levels.The process of categorizing clinical routine data involved technical complexities.A correlation heatmap was used to visually depict the relationships between variables.Ordering points were used to identify the clustering structure and combined with Consensus K clustering methods to determine the optimal categorization.Results:Our study highlighted the intricacies of identifying clinical subphenotypes following laparoscopic surgery,and could thus serve as a valuable resource for clinicians and researchers seeking to explore disease heterogeneity in clinical settings.By simplifying complex methodologies,we aimed to bridge the gap between technical expertise and clinical application,fostering an environment where professional medical knowledge is effectively utilized in subphenotyping research.Conclusion:This tutorial could primarily serve as a guide for beginners.A variety of clustering approaches were explored,and each step in the process contributed to a comprehensive understanding of clinical subphenotypes.

Predictive analytics with ensemble modeling in laparoscopic surgery:A technical note

Predictive analytics have been widely used in the literature with respect to laparoscopic surgery and risk stratification.However,most predictive analytics in this field exploit generalized linearmodels for predictive purposes,which are limited by model assumptionsdincluding linearity between response variables and additive interactions between variables.In many instances,such assumptions may not hold true,and the complex relationship between predictors and response variables is usually unknown.To address this limitation,machine-learning algorithms can be employed to model the underlying data.The advantage of machine learning algorithms is that they usually do not require strict assumptions regarding data structure,and they are able to learn complex functional forms using a nonparametric approach.Furthermore,two or more machine learning algorithms can be synthesized to further improve predictive accuracy.Such a process is referred to as ensemble modeling,and it has been used broadly in various industries.However,this approach has not been widely reported in the laparoscopic surgical literature due to its complexity in both model training and interpretation.With this technical note,we provide a comprehensive overview of the ensemble-modeling technique and a step-by-step tutorial on how to implement ensemble modeling.

Causal inference with marginal structural modeling for longitudinal data in laparoscopic surgery: A technical note

Causal inference prevails in the field of laparoscopic surgery.Once the causality between an intervention and outcome is established,the intervention can be applied to a target population to improve clinical outcomes.In many clinical scenarios,interventions are applied longitudinally in response to patients’conditions.Such longitudinal data comprise static variables,such as age,gender,and comorbidities;and dynamic variables,such as the treatment regime,laboratory variables,and vital signs.Some dynamic variables can act as both the confounder and mediator for the effect of an intervention on the outcome;in such cases,simple adjustment with a conventional regression model will bias the effect sizes.To address this,numerous statistical methods are being developed for causal inference;these include,but are not limited to,the structural marginal Cox regression model,dynamic treatment regime,and Cox regression model with time-varying covariates.This technical note provides a gentle introduction to such models and illustrates their use with an example in the field of laparoscopic surgery.

The application of artificial intelligence in the management of sepsis

Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide.Patients afflicted by severe sepsis or septic shock are customarily placed under intensive care unit(ICU)supervision,where a multitude of apparatus is poised to produce high-granularity data.This reservoir of high-quality data forms the cornerstone for the integration of AI into clinical practice.However,existing reviews currently lack the inclusion of the latest advancements.This review examines the evolving integration of artificial intelligence(AI)in sepsis management.Applications of artificial intelligence include early detection,subtyping analysis,precise treatment and prognosis assessment.AI-driven early warning systems provide enhanced recognition and intervention capabilities,while profiling analyzes elucidate distinct sepsis manifestations for targeted therapy.Precision medicine harnesses the potential of artificial intelligence for pathogen identification,antibiotic selection,and fluid optimization.In conclusion,the seamless amalgamation of artificial intelligence into the domain of sepsis management heralds a transformative shift,ushering in novel prospects to elevate diagnostic precision,therapeutic efficacy,and prognostic acumen.As AI technologies develop,their impact on shaping the future of sepsis care warrants ongoing research and thoughtful implementation.