Septic shock is the most severe subset of sepsis, with a mortality rate of 30%-50%. In recent years, its treatment strategy has been shifting from a “one-size-fits-all” bundled approach to a “personalized” treatment model. This review, based on the core key links in the diagnosis and treatment of septic shock, proposes the EFSP framework for early key treatment strategies for septic shock: Early recognition and referral (E), Fast diagnosis and assessment (F), Standardized treatment (S), and Personalized treatment (P). It aims to provide clinicians with the latest progress in diagnosis and treatment, as well as practical guidance.

Sepsis is a life-threatening clinical syndrome with a high mortality rate globally. Chronic comorbidities are important factors influencing the clinical course and prognosis of sepsis. Chronic comorbidities are highly prevalent among sepsis patients, and those with chronic diseases exhibit relatively distinct physiological characteristics in immune response and vascular function. Moreover, patients with chronic comorbidities face higher mortality and poorer quality of life. In terms of treatment, uncertainty remains regarding the management of sepsis in patients with different types of comorbidities, particularly concerning anti-infection treatment, fluid resuscitation, and multiorgan support. Meanwhile, active and continuous post-ICU management is of great value. This review summarizes the disease burden and current research status of sepsis in patients with chronic comorbidities, aiming to provide a theoretical basis and reference for future mechanistic researches and precision treatment.

Sepsis is a highly heterogeneous syndrome with persistently high incidence and mortality. Patients with underlying chronic diseases have worse outcomes due to reduced organ reserve capacity. Fluid resuscitation, a key component of sepsis bundle therapy, has shown efficacy in certain populations; however, standardized resuscitation strategies are often limited in patients with chronic heart failure, chronic lung disease, or chronic kidney disease, and may even exacerbate organ injury. Organ reserve capacity, reflecting the body's compensatory ability under stress, is an important reference for individualized resuscitation strategies. For patients with heart failure, fluid resuscitation should be guided by precise assessment of fluid responsiveness; for those with chronic lung disease, treatment should balance respiratory support and lung protection to avoid fluid overload and ventilator-related injury; for patients with chronic kidney disease, early initiation of renal replacement therapy and careful fluid management are essential. Individualized resuscitation based on organ reserve capacity has the potential to improve outcomes in septic patients with chronic comorbidities. Therefore, this study aims to investigate the clinical value of organ reserve capacity in fluid resuscitation decision-making among patients with sepsis and underlying chronic diseases, in order to inform the development of more precise and safer individualized resuscitation strategies across different chronic disease populations.

Prolonged mechanical ventilation is a major cause of poor prognosis in patients with chronic diseases and has become a significant public health issue. The risk factors for prolonged mechanical ventilation in chronic disease patients are complex and constantly changing. Due to the lack of intelligent prediction models, it is difficult to identify high-risk populations in advance and provide precise interventions, resulting in high incidence and mortality rates of prolonged mechanical ventilation among these patients. By automatically extracting and integrating multidimensional organ functional features-including respiratory, neurological, cardiac, diaphragmatic, and endocrine metrics-an adaptive, optimized predictive model is constructed to form an intelligent phenotype prediction system that balances universality with specificity. Based on this, modular and clinical phenotype-oriented intervention strategies are developed. Furthermore, by leveraging high-frequency data feedback and virtual reality technology, an intelligent respiratory rehabilitation platform is built to cover both invasive and non-invasive support. The establishment of a full-cycle clinical pathway, extending from initial respiratory support to long-term rehabilitation, creates a "monitoring-feedback-intervention" closed loop, driving the transformation of the respiratory rehabilitation system from a traditional experience-based model toward a new, data-driven, individualized, and intelligent paradigm.

Ventilator dependence has become a critical challenge in the field of critical care medicine. Among patients with chronic diseases, who often have multiple underlying systemic conditions, the incidence of ventilator dependence, weaning failure, and mortality are significantly higher than those in the general population, along with substantial medical resource consumption. Existing intervention strategies suffer from insufficient systematization, lack of individualization, and weak predicative capability. Based on multidimensional clinical data and AI technology, this paper proposes a full-chain precision intervention framework consisting of four components: risk identification, stratified intervention, dynamic monitoring and follow-up management. By integrating standardized data collection, construction of explainable AI models, phenotype-oriented modular interventions, and multicenter validation, it enables closed-loop management from risk prediction to clinical practice, providing theoretical and practical guidance for improving prognosis of ventilator-dependent patients with chronic diseases and optimizing medical resource allocation.

Ventilator dependence is a common clinical challenge in the ICU. Mechanically ventilated patients with comorbidities are more likely to develop ventilator dependence, which is closely associated with poor prognosis and a significant healthcare burden. This review focuses on populations with major chronic diseases, affecting different systems, including the respiratory, cardiovascular, neuromuscular, and endocrine systems, as well as chronic kidney disease. It summarizes the current epidemiology, population characteristics, and high-risk factors for ventilator dependence in these populations. The goal is to provide a basis for more refined subgroup analysis, exploration of multi-dimensional indicators, and ultimately the achievement of early and precise identification and intervention for specific chronic disease populations.

With the increasing burden of chronic diseases, the population dependent on mechanical ventilation continues to grow, while traditional rehabilitation management faces challenges such as fragmented data, discontinuous care pathways, and poor adherence. Focusing on the need for integrated in-hospital and out-of-hospital management for patients with chronic disease who are dependent on mechanical ventilation, this article discusses the development and implementation of an intelligent respiratory rehabilitation platform. Based on its application in multiple scenarios, its role in optimizing rehabilitation pathways and improving continuity of care is analyzed. Current challenges, including data interoperability, standardization, the depth of intelligent application, and digital adaptation of both healthcare professionals and patients, are also discussed, in order to provide a reference for related practice.

The rising prevalence of chronic diseases has led to a significant increase in the number of patients with chronic conditions requiring mechanical ventilation, making ventilator dependence an increasingly prominent clinical issue. The status of underlying chronic disease serves as a pivotal determinant in the development and progression of ventilator dependence. In patientwith chronic diseases, ventilator dependence involves complex multisystem interactions, including respiratory, cardiovascular, neuromuscular, and metabolic systems, often presenting with phenotypic overlap and dynamic evolution. The development of an artificial intelligence-driven predictive system—centered on chronic disease profiles and integrating multidimensional data such as baseline chronic disease status, real-time physiological parameters, imaging features, biomarkers, laboratory indices, and therapeutic responses—holds significant promise for reducing the incidence of ventilator dependence and promoting the advancement of critical care medicine toward precision diagnosis and treatment. This article discusses the core role of chronic disease status, phenotypic diversity of ventilator dependence, and AI applications, aiming to provide theoretical basis and practical insights for establishing a precision prediction system for ventilator dependence centered on chronic diseases.

Quality improvement in intensive care medicine relies on a synergistic framework composed of quality registries, benchmarking, and homogenization. Quality registries establish real-world databases through systematic data collection, providing the foundation for continuous quality surveillance. Benchmarking compares performance across institutions to identify gaps and optimize clinical practice. Homogenization standardizes diagnostic and therapeutic protocols to ensure broad applicability of improvement measures. Together, these elements form a closed-loop management cycle of "data collection – gap identification – standard implementation." This synergistic pathway preserves the economies of scale inherent in standardization while supporting individualized, precise interventions. By translating real-world research into clinical practice innovation, it narrows quality disparities among healthcare facilities and ultimately advances high-quality, outcome-oriented intensive care medicine, continually optimizing clinical processes and improving patient prognosis. This article systematically elaborates on the theoretical basis, technical approach and practical cases of this framework, and also explores its future development direction.

To investigate the factors influencing the 28-day prognosis in patients with sepsis-associated severe thrombocytopenia and to develop and validate a clinical prognostic model.

Patients from the Medical Information Mart for Intensive Care (MIMIC-Ⅳ) v2.2 database were selected as the training cohort, and patients from the emergency intensive care unit (eICU) Collaborative Research Database served as the validation cohort. Patients diagnosed with sepsis-associated severe thrombocytopenia were included. Demographic data, vital signs, laboratory parameters, chronic comorbidities, treatment measures, and 28-day survival outcomes were collected. The least absolute shrinkage and selection operator (LASSO) and Bayesian information criteria were used to select variables for the prognostic model, and the model was externally validated using the eICU database.

1489 patients were included in the training cohort. Eight variables were selected: age, acute kidney injury stage, urine output, minimum blood urea nitrogen level, maximum heart rate, maximum international normalized ratio, minimum partial thromboplastin time, and the presence of liver disease. The C-index was 0.714 for the training cohort and 0.64 for the validation cohort. In the training cohort, the area under the curve for the 7-, 14-, and 28-day survival probabilities were 0.723, 0.733, and 0.736, respectively.

Key factors affecting the prognosis of patients with sepsis-associated severe thrombocytopenia were identified through multivariate analysis. A prognostic model incorporating eight variables was developed, which demonstrated good predictive performance for the prognosis of these patients.

This retrospective cohort study, utilizing the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database, aimed to investigate the impact of platelet transfusion on prognosis of patients with sepsis-associated severe thrombocytopenia.

Patients diagnosed with sepsis-associated severe thrombocytopenia were identified from the MIMIC-Ⅳ database. Based on whether they received platelet transfusion, they were divided into transfusion and non-transfusion groups (PLT≤50×10⁹/L: 708 cases; PLT≤20×10⁹/L: 214 cases). Prognostic variables were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression. A Multivariable Cox proportional hazards model was constructed to assess the association between platelet transfusion and mortality, with hazard ratios (HRs) with 95% confidence intervals (CIs) calculated.

A total of 1,914 patients with sepsis-associated severe thrombocytopenia were included (PLT≤50×10⁹/L). Patients in the transfusion group had higher mortality rates (35.20%) and more requirements for organ support therapies (mechanical ventilation, renal replacement therapy, and extracorporeal membrane oxygenation usage rates: 57.10%, 13.80%, and 1.41%, respectively) than those in the non-transfusion group (23.10%, 38.30%, 8.60%, and 0.25%, respectively), with all differences being statistically significant (P<0.01). Multivariable Cox regression analysis showed that platelet transfusion did not significantly improve 28-days mortality at either PLT threshold (PLT≤50×10⁹/L: HR=1.10, 95% CI: 0.90-1.36, P=0.346; PLT≤20×10⁹/L: HR=0.94, 95% CI: 0.61-1.44, P=0.774). Age, white blood cell count, prothrombin time, and activated partial thromboplastin time were identified as independent predictors of poor prognosis, whereas, higher albumin levels were associated with improved outcomes.

Platelet transfusion strategies based on current thresholds (≤50×10⁹/L or ≤20×10⁹/L) did not demonstrate significant improvement in 28-day survival for patients with sepsis and severe thrombocytopenia in this retrospective analysis. These findings warrant validation through prospective studies.

To analyze the current research status and development trends in the field of diaphragm dysfunction and diaphragm-protective ventilation in mechanically ventilated patients.

Literature was retrieved from the Web of Science Core Collection database on January 9, 2025, covering studies published between 2005 and 2024. CiteSpace 6.4 R1 software was used to visualize and analyze the countries/regions, authors, keywords, and research hotspots of the included studies.

A total of 682 articles were included in the analysis. The top three countries by publication volume were the United States (200 articles), Canada (95 articles), and France (94 articles). The most prolific authors were Powers Scott K (55 articles), Martin Dres (34 articles), and Heunks Leo (34 articles). The five most frequently mentioned keywords were "diaphragm dysfunction" (520 times), "mechanical ventilation" (434 times), "ultrasound" (173 times), "frailty" (124 times), and "intensive care unit" (119 times). Burst analysis revealed that recent research emphasizes concepts such as "diaphragm thickening fraction", "critical care", and "invasive mechanical ventilation". Co-citation analysis identified the work of Levine et al. (2008) on diaphragm atrophy mechanisms and Goligher et al. (2018) on clinical translation as pivotal references.

Research on diaphragm-protective ventilation in mechanically ventilated patients is moving from mechanistic exploration to optimizing clinical strategies. However, there are limitations, including the incompleteness of the databases used and a lack of multicenter clinical validation. Future directions should focus on integrating intelligent monitoring technologies, advancing studies on molecular mechanism, and promoting individualized ventilation strategies through interdisciplinary collaboration to achieve synergistic protection of both the lungs and diaphragm.

Sarcopenia is a syndrome characterized by progressive and generalized loss of skeletal muscle mass and strength, leading to numerous adverse clinical consequences. It is particularly prevalent among ICU patients, often resulting in various poor prognoses, which has gradually garnered the attention of ICU physicians. The assessment methods for sarcopenia include muscle strength measurement, physical performance evaluation, and the measurement of muscle quantity or quality. Given the limited feasibility of muscle strength measurement and physical performance in critically ill patients, the assessment of sarcopenia in critically ill patients primarily focuses on measuring muscle mass or quantity. Various methods can be employed, including dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), computed tomography (CT), magnetic resonance imaging (MRI), ultrasonography (US), sarcopenia index (SI), and other techniques. Each method has its own characteristics, advantages and disadvantages for critically ill patients. We aims to provide necessary references for the clinical evaluation and scientific research of sarcopenia in critically ill patients.

The decline of skeletal muscle mass is one of the serious complications of sepsis. It is characterized by the weakend muscle strength, reduced fiber cross-sectional area and dismished muscle quantity. It manifests as flaccid weakness in the limbs, particularly in the proximal limb muscles. Meanwhile, it will affect the prognosis of patients, especially elderly patients, leading to their reduced quality of life and life safety risk. The anabolic resistance in sepsis patients leads to the disruption of the balance between protein synthesis and breakdown, manifested as increased protein catabolism and decreased anabolism, which is one of the main reasons for the decline of skeletal muscle mass. At present, the relevant research on skeletal muscle catabolism in sepsis is relatively well estabished, and protein-anabolic resistance has become an issue of increasing concern. Therefore, we review the correlative mechanisms between protein-anabolic resistance and the decline of skeletal muscle mass in sepsis, aiming to provide theoretical basis and solutions for the clinical prevention and treatment of the skeletal muscle loss in sepsis patients.