BACKGROUND: Capillary refill time (CRT) is defined as the time taken for color to return to an external capillary bed after pressure is applied to cause blanching. Recent studies demonstrated the benefits of CRT in guiding fluid therapy for sepsis. However, lack of consistency among physicians in how to perform and interpret CRT has led to a low interobserver agreement for this assessment tool, which prevents its availability in sepsis clinical settings.
OBJECTIVE: To give physicians a concise overview of CRT and explore recent evidence on its reliability and value in the management of sepsis.
METHODS: A narrative review.
RESULTS: This narrative review summarizes the factors affecting CRT values, for example, age, sex, temperature, light, observation techniques, work experience, training level and differences in CRT measurement methods. The methods of reducing the variability of CRT are synthesized. Based on studies with highly reproducible CRT measurements and an excellent inter-rater concordance, we recommend the standardized CRT assessment method. The threshold of normal CRT values is discussed. The application of CRT in different phases of sepsis management is summarized.
CONCLUSIONS: Recent data confirm the value of CRT in critically ill patients. CRT should be detected by trained physicians using standardized methods and reducing the effect of ambient-related factors. Its association with severe infection, microcirculation, tissue perfusion response, organ dysfunction and adverse outcomes makes this approach a very attractive tool in sepsis. Further studies should confirm its value in the management of sepsis.
CONCLUSIONS: As a simple assessment, CRT deserves more attention even though it has not been widely applied at the bedside. CRT could provide nursing staff with patient\'s microcirculatory status, which may help to develop individualized nursing plans and improve the patient\'s care quality and treatment outcomes.

UNASSIGNED: Platelets play a critical role in hemostasis and inflammatory diseases. Low platelet count and activity have been reported to be associated with unfavorable prognosis. This study aims to explore the relationship between dynamics in platelet count and in-hospital morality among septic patients and to provide real-time updates on mortality risk to achieve dynamic prediction.
UNASSIGNED: We conducted a multi-cohort, retrospective, observational study that encompasses data on septic patients in the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The joint latent class model (JLCM) was utilized to identify heterogenous platelet count trajectories over time among septic patients. We assessed the association between different trajectory patterns and 28-day in-hospital mortality using a piecewise Cox hazard model within each trajectory. We evaluated the performance of our dynamic prediction model through area under the receiver operating characteristic curve, concordance index (C-index), accuracy, sensitivity, and specificity calculated at predefined time points.
UNASSIGNED: Four subgroups of platelet count trajectories were identified that correspond to distinct in-hospital mortality risk. Including platelet count did not significantly enhance prediction accuracy at early stages (day 1 C-indexDynamic  vs C-indexWeibull: 0.713 vs 0.714). However, our model showed superior performance to the static survival model over time (day 14 C-indexDynamic  vs C-indexWeibull: 0.644 vs 0.617).
UNASSIGNED: For septic patients in an intensive care unit, the rapid decline in platelet counts is a critical prognostic factor, and serial platelet measures are associated with prognosis.

UNASSIGNED: To investigate the potential prognostic value of mean blood glucose (MBG) in hospital for prognosis of COVID-19 adult patients in the intensive unit care unit (ICU).
UNASSIGNED: A single-site and retrospective study enrolled 107 patients diagnosed as COVID-19 from department of critical care medicine in the Second Xiangya Hospital between October 2022 and June 2023. Demographic information including glucose during ICU hospitalization, comorbidity, clinical data, types of medications and treatment, and clinical outcome were collected. The multivariate logistic and cox regression was used to explore the relationship between blood glucose changes and clinical outcomes of COVID-19 during ICU stay.
UNASSIGNED: In total, 107 adult patients confirmed with COVID-19 were included. Multivariate logistic regression results showed an increase in MBG was associated with ICU mortality rate. Compared with normal glucose group (MBG <= 7.8 mmol/L), the risk of ICU mortality, 7-day mortality and 28-day mortality from COVID-19 were significantly increased in high glucose group (MBG >7.8mmol/L).
UNASSIGNED: MBG level during ICU hospitalization was strongly correlated to all-cause mortality and co-infection in COVID-19 patients. These findings further emphasize the importance of overall glucose management in severe cases of COVID-19.

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts\' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

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UNASSIGNED: Echovirus type 11(E-11) can cause fatal haemorrhage-hepatitis syndrome in neonates. This study aims to investigate clinical risk factors and early markers of E-11 associated neonatal haemorrhage-hepatitis syndrome.
UNASSIGNED: This is a multicentre retrospective cohort study of 105 neonates with E-11 infection in China. Patients with haemorrhage-hepatitis syndrome (the severe group) were compared with those with mild disease. Clinical risk factors and early markers of haemorrhage-hepatitis syndrome were analysed. In addition, cytokine analysis were performed in selective patients to explore the immune responses.
UNASSIGNED: In addition to prematurity, low birth weight, premature rupture of fetal membrane, total parenteral nutrition (PN) (OR, 28.7; 95% CI, 2.8-295.1) and partial PN (OR, 12.9; 95% CI, 2.2-77.5) prior to the onset of disease were identified as risk factors of developing haemorrhage-hepatitis syndrome. Progressive decrease in haemoglobin levels (per 10 g/L; OR, 1.5; 95% CI, 1.1-2.0) and platelet (PLT) < 140 × 10⁹/L at early stage of illness (OR, 17.7; 95% CI, 1.4-221.5) were associated with the development of haemorrhage-hepatitis syndrome. Immunological workup revealed significantly increased interferon-inducible protein-10(IP-10) (P < 0.0005) but decreased IFN-α (P < 0.05) in peripheral blood in severe patients compared with the mild cases.
UNASSIGNED: PN may potentiate the development of E-11 associated haemorrhage-hepatitis syndrome. Early onset of thrombocytopenia and decreased haemoglobin could be helpful in early identification of neonates with the disease. The low level of IFN-α and elevated expression of IP-10 may promote the progression of haemorrhage-hepatitis syndrome.

OBJECTIVE: This study aims to develop an accurate and simplified scoring system based on the national early warning score (NEWS) to predict the mortality of intensive care unit (ICU) patients.
METHODS: The information of patients was retrieved from the Medical Information Mart for Intensive Care (MIMIC)-III and -IV databases. The Modified National Early Warning Score (MNEWS) of the patients was calculated. The discrimination ability of the MNEWS, acute physiology and chronic health scoring system II (APACHE II), and original NEWS systems in predicting patients\' mortality was evaluated using area under the receiver operating characteristic (AUROC) analysis. The DeLong test was used to estimate the receiver operating characteristic curve. The Hosmer-Lemeshow goodness-of-fit test was then applied to evaluate the calibration of the MNEWS.
RESULTS: In total, 7275 ICU patients from the MIMIC-III and -IV databases were included in the derivation cohort and 1507 ICU patients from Xi\'an Medical University were included in the validation cohort. In the derivation cohort, the nonsurvivors had significantly higher MNEWSs than the survivors (12.5 ± 3.4 vs 8.8 ± 3.4, P < 0.05). MNEWS and APACHE II both had a better performance than the NEWS in predicting hospital mortality and 90-day mortality. The optimal cutoff of MNEWS was 11. Patients with an MNEWS ≥ 11 had significantly shorter survival than those having an MNEWS of <11. Furthermore, MNEWS had a high calibration ability in predicting hospital mortality of ICU patients (χ2 = 6.534 and P = 0.588) by the Hosmer-Lemeshow test. This finding was confirmed in the validation cohort.
CONCLUSIONS: MNEWS is a simple and accurate scoring system for evaluating the severity and predicting the outcomes of ICU patients.

Central venous catheter (CVC)-related thrombosis (CRT) is a known complication in critically ill patients. However, its clinical significance remains unclear. The objective of the study was to evaluate the occurrence and evolution of CRT from CVC insertion to removal.
A prospective multicenter study was conducted in 28 intensive care units (ICUs). Duplex ultrasound was performed daily from CVC insertion until at least 3 days after CVC removal or before patient discharge from the ICU to detect CRT and to follow its progression. CRT diameter and length were measured and diameter > 7 mm was considered extensive.
The study included 1262 patients. The incidence of CRT was 16.9% (95% confidence interval 14.8-18.9%). CRT was most commonly found in the internal jugular vein. The median time from CVC insertion to CRT onset was 4 (2-7) days, and 12% of CRTs occurred on the first day and 82% within 7 days of CVC insertion. CRT diameters > 5 mm and > 7 mm were found in 48% and 30% of thromboses. Over a 7-day follow-up, CRT diameter remained stable when the CVC was in place, whereas it gradually decreased after CVC removal. The ICU length of stay was longer in patients with CRT than in those without CRT, and the mortality was not different.
CRT is a frequent complication. It can occur as soon as the CVC is placed and mostly during the first week following catheterization. Half of the thromboses are small but one-third are extensive. They are often non-progressive and may be resolved after CVC removal.

Interest in critical care-related artificial intelligence (AI) research is growing rapidly. However, the literature is still lacking in comprehensive bibliometric studies that measure and analyze scientific publications globally.
The objective of this study was to assess the global research trends in AI in intensive care medicine based on publication outputs, citations, coauthorships between nations, and co-occurrences of author keywords.
A total of 3619 documents published until March 2022 were retrieved from the Scopus database. After selecting the document type as articles, the titles and abstracts were checked for eligibility. In the final bibliometric study using VOSviewer, 1198 papers were included. The growth rate of publications, preferred journals, leading research countries, international collaborations, and top institutions were computed.
The number of publications increased steeply between 2018 and 2022, accounting for 72.53% (869/1198) of all the included papers. The United States and China contributed to approximately 55.17% (661/1198) of the total publications. Of the 15 most productive institutions, 9 were among the top 100 universities worldwide. Detecting clinical deterioration, monitoring, predicting disease progression, mortality, prognosis, and classifying disease phenotypes or subtypes were some of the research hot spots for AI in patients who are critically ill. Neural networks, decision support systems, machine learning, and deep learning were all commonly used AI technologies.
This study highlights popular areas in AI research aimed at improving health care in intensive care units, offers a comprehensive look at the research trend in AI application in the intensive care unit, and provides an insight into potential collaboration and prospects for future research. The 30 articles that received the most citations were listed in detail. For AI-based clinical research to be sufficiently convincing for routine critical care practice, collaborative research efforts are needed to increase the maturity and robustness of AI-driven models.

Since the advent of critical care in the twentieth century, the core elements that are the foundation for critical care systems, namely to care for critically ill and injured patients and to save lives, have evolved enormously. The past half-century has seen dramatic advancements in diagnostic, organ support, and treatment modalities in critical care, with further improvements now needed to achieve personalized critical care of the highest quality. For critical care to be even higher quality in the future, advancements in the following areas are key: the physical ICU space; the people that care for critically ill patients; the equipment and technologies; the information systems and data; and the research systems that impact critically ill patients and families. With acutely and critically ill patients and their families as the absolute focal point, advancements across these areas will hopefully transform care and outcomes over the coming years.