As the search for effective treatments for COVID-19 continues, the high mortality rate among critically ill patients in Intensive Care Units (ICU) presents a profound challenge. This study explores the potential benefits of traditional Chinese medicine (TCM) as a supplementary treatment for severe COVID-19. A total of 110 critically ill COVID-19 patients at the Intensive Care Unit (ICU) of Vulcan Hill Hospital between Feb., 2020, and April, 2020 (Wuhan, China) participated in this observational study. All patients received standard supportive care protocols, with a subset of 81 also receiving TCM as an adjunct treatment. Clinical characteristics during the treatment period and the clinical outcome of each patient were closely monitored and analysed. Our findings indicated that the TCM group exhibited a significantly lower mortality rate compared with the non-TCM group (16 of 81 vs 24 of 29; 0.3 vs 2.3 person/month). In the adjusted Cox proportional hazards models, TCM treatment was associated with improved survival odds (P < 0.001). Furthermore, the analysis also revealed that TCM treatment could partially mitigate inflammatory responses, as evidenced by the reduced levels of proinflammatory cytokines, and contribute to the recovery of multiple organic functions, thereby potentially increasing the survival rate of critically ill COVID-19 patients.
Humans ; COVID-19 ; Medicine, Chinese Traditional ; SARS-CoV-2 ; Critical Illness ; Treatment Outcome

OBJECTIVE: To develop a mortality prediction model for critically ill patients based on multidimensional and dynamic clinical data collected by the hospital information system (HIS) using random forest algorithm, and to compare the prediction efficiency of the model with acute physiology and chronic health evaluation II (APACHE II) model. METHODS: The clinical data of 10 925 critically ill patients aged over 14 years old admitted to the Third Xiangya Hospital of Central South University from January 2014 to June 2020 were extracted from the HIS system, and APACHE II scores of the critically ill patients were extracted. Expected mortality of patients was calculated according to the death risk calculation formula of APACHE II scoring system. A total of 689 samples with APACHE II score records were used as the test set, and the other 10 236 samples were used to establish the random forest model, of which 10% (n = 1 024) were randomly selected as the validation set and 90% (n = 9 212) were selected as the training set. According to the time series of 3 days before the end of critical illness, the clinical characteristics of patients such as general information, vital signs data, biochemical test results and intravenous drug doses were selected to develope a random forest model for predicting the mortality of critically ill patients. Using the APACHE II model as a reference, receiver operator characteristic curve (ROC curve) was drawn, and the discrimination performance of the model was evaluated through the area under the ROC curve (AUROC). According to the precision and recall, Precision-Recall curve (PR curve) was drawn, and the calibration performance of the model was evaluated through the area under the PR curve (AUPRC). Calibration curve was drawn, and the consistency between the predicted event occurrence probability of the model and the actual occurrence probability was evaluated through the calibration index Brier score. RESULTS: Among the 10 925 patients, there were 7 797 males (71.4%) and 3 128 females (28.6%). The average age was (58.9±16.3) years old. The median length of hospital stay was 12 (7, 20) days. Most patients (n = 8 538, 78.2%) were admitted to intensive care unit (ICU), and the median length of ICU stay was 66 (13, 151) hours. The hospitalized mortality was 19.0% (2 077/10 925). Compared with the survival group (n = 8 848), the patients in the death group (n = 2 077) were older (years old: 60.1±16.5 vs. 58.5±16.4, P < 0.01), the ratio of ICU admission was higher [82.8% (1 719/2 077) vs. 77.1% (6 819/8 848), P < 0.01], and the proportion of patients with hypertension, diabetes and stroke history was also higher [44.7% (928/2 077) vs. 36.3% (3 212/8 848), 20.0% (415/2 077) vs. 16.9% (1 495/8 848), 15.5% (322/2 077) vs. 10.0% (885/8 848), all P < 0.01]. In the test set data, the prediction value of random forest model for the risk of death during hospitalization of critically ill patients was greater than that of APACHE II model, which showed by that the AUROC and AUPRC of random forest model were higher than those of APACHE II model [AUROC: 0.856 (95% confidence interval was 0.812-0.896) vs. 0.783 (95% confidence interval was 0.737-0.826), AUPRC: 0.650 (95% confidence interval was 0.604-0.762) vs. 0.524 (95% confidence interval was 0.439-0.609)], and Brier score was lower than that of APACHE II model [0.104 (95% confidence interval was 0.085-0.113) vs. 0.124 (95% confidence interval was 0.107-0.141)]. CONCLUSIONS The random forest model based on multidimensional dynamic characteristics has great application value in predicting hospital mortality risk for critically ill patients, and it is superior to the traditional APACHE II scoring system.
Female ; Male ; Humans ; Aged ; Adult ; Middle Aged ; Adolescent ; Critical Illness ; Hospitalization ; Length of Stay ; APACHE ; Hospital Information Systems

OBJECTIVE: To establish a new emergency dynamic score (EDS) method based on modified early warning score (MEWS) combined with clinical symptoms, rapidly available examination results and bedside examination data in the emergency department, and to observe its applicability and feasibility in the clinical application of emergency department. METHODS: A total of 500 patients admitted to the department of emergency of Xing'an County People's Hospital from July 2021 to April 2022 were selected as research objects. After admission, EDS and MEWS scores were performed first, and then acute physiology and chronic health evaluation II (APACHE II) was performed retrospectively, and the prognosis of patients was followed up. The difference of short-term mortality in patients with different score segments of EDS, MEWS and APACHE II were compared. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the prognostic value of various scoring methods in critically ill patients. RESULTS: The mortality of patients in different score groups of each scoring method increased with the increase of the score value [The mortality of 0-1, 2-3, 4-5, 6-7 and ≥ 8 of MEWS were 1.9% (3/159), 2.9% (6/208), 12.4% (11/89), 29.0% (9/31) and 61.5% (8/13), respectively. The mortality of EDS stage 1 weighted MEWS score 0-3, 4-6, 7-9, 10-12 and ≥ 13 were 0 (0/49), 3.2% (8/247), 6.6% (10/152), 31.9% (15/47) and 80.0% (4/5), respectively. The mortality of EDS stage 2 clinical symptom score 0-4, 5-9, 10-14, 15-19, ≥ 20 were 0 (0/13), 0.4% (1/235), 3.6% (6/165), 26.2% (17/65), 59.1% (13/22), respectively. The mortality of EDS stage 3 rapid test data score 0-6, 7-12, 13-18, 19-24 and ≥ 25 were 0 (0/16), 0.6% (1/159), 4.6% (6/131), 13.7% (7/51) and 65.0% (13/20), respectively. The mortality of patients with APACHE II score 0-6, 7-12, 13-18, 19-24 and ≥ 25 were 1.9% (1/53), 0.4% (1/277), 4.6% (5/108), 34.2% (13/38) and 70.8% (17/24), respectively, all P < 0.01]. When the MEWS score was more than 4, the specificity was 87.0%, the sensitivity was 67.6%, and the maximum Youden index was 0.546, which was the best cut-off point. When the weighted MEWS score of EDS in the first stage was greater than 7, the specificity of predicting the prognosis of patients was 76.2%, the sensitivity was 70.3%, and the maximum Youden index was 0.465, which was the best cut-off point. When clinical symptom score of EDS in the second stage was more than 14, the specificity and sensitivity of predicting the prognosis of patients were 87.7% and 81.1%, respectively, and the maximum Youden index was 0.688, which was the best cut-off point. When the third stage rapid test of EDS reached 15 points, the specificity of predicting the prognosis of patients was 70.9%, and the sensitivity was 96.3%, and the maximum Youden index was 0.672, which was the best cut-off point. When APACHE II score was higher than 16, the specificity was 87.9%, the sensitivity was 86.5%, and the maximum Youden index was 0.743, which was the best cut-off point. ROC curve analysis showed that: EDS score in the stage 1, 2 and 3, MEWS score and APACHE II score can predict the short-term mortality risk of critically ill patients. The area under the ROC curve (AUC) and 95% confidence interval (95%CI) were 0.815 (0.726-0.905), 0.913 (0.867-0.959), 0.911 (0.860-0.962), 0.844 (0.755-0.933) and 0.910 (0.833-0.987), all P < 0.01. In terms of the differential ability to predict the risk of death in the short-term, the AUC in the second and third stages of EDS were highly close to APACHE II score (0.913, 0.911 vs. 0.910), and significantly higher than MEWS score (0.913, 0.911 vs. 0.844, both P < 0.05). CONCLUSIONS EDS method can dynamically evaluate emergency patients in stages, and has the characteristics of fast, simple, easy to obtain test and inspection data, which can facilitate emergency doctors to evaluate emergency patients objectively and quickly. It has strong ability to predict the prognosis of emergency patients, and is worth popularizing in emergency departments of primary hospitals.
Humans ; Research Design ; Critical Illness ; Retrospective Studies ; Hospitals ; Emergency Service, Hospital

Patients with critical illness are at the high risk of venous thromboembolism (VTE), and the older the patient, the higher the incidence of VTE. Despite its poor prognosis, VTE is preventable. At present, although there are many consensus or guidelines on the prevention of VTE at home and abroad, there is still a lack of consensus or guidelines for the prevention of VTE in elderly patients with critical illness. In order to standardize the prevention of VTE in elderly patients with critical illness in China, Expert consensus on the prevention of venous thromboembolism for elderly patients with critical illness in China (2023) was developed by Critical Care Medicine Division of Chinese Geriatric Society and Zhejiang Provincial Clinical Research Center for Critical Care Medicine. Members of the working group consulted relevant domestic and foreign guidelines, integrated evidence-based medical evidence and clinical experience, formed the draft consensus, submitted it to the expert group for discussion for many times, revised it for many times, and finally sent it to the expert group in the form of electronic questionnaire, and the expert gave a comprehensive score according to the theoretical basis, scientific nature and feasibility of the consensus items. The recommendation strength of each item was determined, and 21 recommendations were finally formed to provide reference for the prevention of VTE in elderly patients with critical illness.
Humans ; Aged ; Venous Thromboembolism/prevention & control* ; Critical Illness ; Consensus ; China ; Anticoagulants

Objectives: Among critically ill patients, there is usually impairment of the hypothalamic-pituitary- adrenal axis, leading to a condition known as critical illness-related corticosteroid insufficiency (CIRCI). The aims of this investigation are to determine the incidence of and characterize CIRCI among patients with COVID-19 as well as to analyze the outcomes of these critically ill patients. Methodology: This is a single-center, retrospective, cohort study that investigated the occurrence of CIRCI among critically ill patients infected with COVID-19. Results: In this cohort, there were 145 COVID-19 positive patients with refractory shock included, which reflects that 22.94% of the COVID-19 admissions have probable CIRCI. Patients who were given corticosteroids were found to have statistically significant longer median days on ventilator (p= 0.001). However, those on the corticosteroid arm were at higher risk of morbidity and mortality and a greater proportion of patients with organ dysfunction. Multivariable logistic regression analysis revealed that SOFA score was a significant predictor of mortality in CIRCI (p=0.013). Conclusion CIRCI has a unique presentation among COVID-19 patients because of the presence of a high level of inflammation in this life-threatening infection. It possibly is a harbinger of markedly increased risk of mortality in these patients.
adrenal insufficiency ; COVID-19 ; critical illness ; shock

Humans ; Critical Illness/therapy* ; Critical Care

We wished to establish an expert consensus on late stage of critical care (CC) management. The panel comprised 13 experts in CC medicine. Each statement was assessed based on the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) principle. Then, the Delphi method was adopted by 17 experts to reassess the following 28 statements. (1) ESCAPE has evolved from a strategy of delirium management to a strategy of late stage of CC management. (2) The new version of ESCAPE is a strategy for optimizing treatment and comprehensive care of critically ill patients (CIPs) after the rescue period, including early mobilization, early rehabilitation, nutritional support, sleep management, mental assessment, cognitive-function training, emotional support, and optimizing sedation and analgesia. (3) Disease assessment to determine the starting point of early mobilization, early rehabilitation, and early enteral nutrition. (4) Early mobilization has synergistic effects upon the recovery of organ function. (5) Early functional exercise and rehabilitation are important means to promote CIP recovery, and gives them a sense of future prospects. (6) Timely start of enteral nutrition is conducive to early mobilization and early rehabilitation. (7) The spontaneous breathing test should be started as soon as possible, and a weaning plan should be selected step-by-step. (8) The waking process of CIPs should be realized in a planned and purposeful way. (9) Establishment of a sleep-wake rhythm is the key to sleep management in post-CC management. (10) The spontaneous awakening trial, spontaneous breathing trial, and sleep management should be carried out together. (11) The depth of sedation should be adjusted dynamically in the late stage of CC period. (12) Standardized sedation assessment is the premise of rational sedation. (13) Appropriate sedative drugs should be selected according to the objectives of sedation and drug characteristics. (14) A goal-directed minimization strategy for sedation should be implemented. (15) The principle of analgesia must be mastered first. (16) Subjective assessment is preferred for analgesia assessment. (17) Opioid-based analgesic strategies should be selected step-by-step according to the characteristics of different drugs. (18) There must be rational use of non-opioid analgesics and non-drug-based analgesic measures. (19) Pay attention to evaluation of the psychological status of CIPs. (20) Cognitive function in CIPs cannot be ignored. (21) Delirium management should be based on non-drug-based measures and rational use of drugs. (22) Reset treatment can be considered for severe delirium. (23) Psychological assessment should be conducted as early as possible to screen-out high-risk groups with post-traumatic stress disorder. (24) Emotional support, flexible visiting, and environment management are important components of humanistic management in the intensive care unit (ICU). (25) Emotional support from medical teams and families should be promoted through"ICU diaries"and other forms. (26) Environmental management should be carried out by enriching environmental content, limiting environmental interference, and optimizing the environmental atmosphere. (27) Reasonable promotion of flexible visitation should be done on the basis of prevention of nosocomial infection. (28) ESCAPE is an excellent project for late stage of CC management.
Humans ; Consensus ; Critical Care/methods* ; Intensive Care Units ; Pain/drug therapy* ; Analgesics/therapeutic use* ; Delirium/therapy* ; Critical Illness

Humans ; Critical Illness ; Multiple Organ Failure/etiology*

Humans ; Critical Illness ; Critical Care ; Intensive Care Units ; Surveys and Questionnaires

Child ; Humans ; Critical Illness/rehabilitation*