BackgroundTo explore the correlations between SAA, CRP, and clinical indices of patients with acutely exacerbated chronic obstructive pulmonary disease (AECOPD).MethodsA total of 120 patients with AECOPD and another 120 with remitted COPD were enrolled in an AECOPD group and a COPD remission group, respectively. Meanwhile, 120 healthy subjects were included as a control group. SAA, CRP, PCT, Fbg, IL‐8, IL‐6, TNF‐α, and IP‐10 levels were detected. FEV1 and FEV1/FVC were measured.ResultsCompared with control group, the serum levels of SAA, CRP, PCT, Fbg, IL‐8, IL‐6, TNF‐α, and IP‐10 significantly increased in COPD remission group (P < 0.05). The levels of AECOPD group significantly exceeded those of COPD remission group (P < 0.05). The levels of AECOPD patients with different GOLD grades were significantly different (P < 0.05). AECOPD group had significantly lower FEV1 and FEV1/FVC than those of COPD remission group (P < 0.05). The CAT score of AECOPD patients was (18.41 ± 2.55) points. The levels of SAA, CRP, PCT, Fbg, IL‐8, IL‐6, TNF‐α, and IP‐10 were negatively correlated with FEV1 and FEV1/FVC, and positively correlated with CAT score. The area under receiver operating characteristic curve of SAA was largest (0.931). The cutoff values for SAA, CRP, PCT and Fbg were 18.68 mg/L, 14.70 mg/L, 0.39 μg/L, 3.91 g/L, 0.46 μg/L, 24.17 μg/L, 7.18 mg/L, and 83.19 ng/L, respectively.ConclusionsSerum levels of SAA, CRP, PCT, Fbg, IL‐8, IL‐6, TNF‐α, and IP‐10 in AECOPD patients were elevated, which may undermine pulmonary functions. SAA can be used as an effective index for AECOPD diagnosis and treatment.

Early assessment of acute pancreatitis (AP) severity is key to its treatment. The present study aimed to explore the role of microRNAs (miRNAs/miRs) combined with inflammatory factors in determining AP severity. For this, serum pro-inflammatory cytokines [tumor necrosis factor (TNF)-α, interleukin (IL)-1, IL-6, IL-8 and IL-10)] and miRNAs [ Homo sapiens (hsa)-miR-548d-5p, hsa-miR-126-5p and hsa-miR-130b-5p] were detected in patients with mild AP (MAP), severe AP (SAP) and recurrent AP (RAP). High expression of IL-10, TNF-α, hsa-miR-126-5p, hsa-miR-548d-5p and hsa-miR-130b-5p was able to distinguish SAP from MAP and RAP (P<0.05). Multifactorial binary logistic regression analysis indicated that IL-1/IL-6 combined with hsa-miR-126-5p/hsa-miR-548d-5p had a significant influence on AP and AP severity (P<0.05). Receiver operating characteristic analysis revealed that IL-1 combined with hsa-miR-126-5p [area under the curve (AUC), 0.926; sensitivity, 90.0%; specificity, 86.7%, P<0.001] and IL-6 combined with hsa-miR-126-5p (AUC, 0.952; sensitivity, 93.3%; specificity, 90.0%; P<0.001) were able to better distinguish MAP from SAP than IL-1/IL-6 combined with hsa-miR-548d-5p, lipase, and amylase. IL-1 or IL-6 combined with hsa-miR-548d-5p (AUC, 0.924; sensitivity, 83.3%; specificity, 93.3%; P<0.001) were able to better distinguish SAP from RAP than IL-1/IL-6 combined with hsa-miR-126-5p, lipase, and amylase. IL-1 combined with hsa-miR-126-5p (AUC, 0.926; sensitivity, 90.0%; specificity, 86.7%; P<0.001) and IL-6 combined with hsa-miR-126-5p (AUC, 0.952; sensitivity, 93.3%; specificity, 90.0%; P<0.001) were able to better differentiate between MAP and RAP than IL-1/IL-6 combined with hsa-miR-548d-5p, lipase, and amylase. These results demonstrated that the combined detection of serum IL-6 and hsa-miR-126-5p may be useful for the early prediction of AP classification.

BackgroundCommunity-acquired pneumonia (CAP) is an extraordinarily heterogeneous illness, both in the range of responsible pathogens and the host response. Metagenomic next-generation sequencing (mNGS) is a promising technology for pathogen detection. However, the clinical application of mNGS for pathogen detection remains challenging.MethodsA total of 205 patients with CAP admitted to the intensive care unit were recruited, and broncho alveolar lavage fluids (BALFs) from 83 patients, sputum samples from 33 cases, and blood from 89 cases were collected for pathogen detection by mNGS. At the same time, multiple samples of each patient were tested by culture. The diagnostic performance was compared between mNGS and culture for pathogen detection.ResultsThe positive rate of pathogen detection by mNGS in BALF and sputum samples was 89.2% and 97.0%, which was significantly higher (P &lt; 0.001) than that (67.4%) of blood samples. The positive rate of mNGS was significantly higher than that of culture (81.0% vs. 56.1%, P = 1.052e-07). A group of pathogens including Mycobacterium abscessus, Chlamydia psittaci, Pneumocystis jirovecii, Orientia tsutsugamushi, and all viruses were only detected by mNGS. Based on mNGS results, Escherichia coli was the most common pathogen (15/61, 24.59%) of non-severe patients with CAP, and Mycobacterium tuberculosis was the most common pathogen (21/144, 14.58%) leading to severe pneumonia. Pneumocystis jirovecii was the most common pathogen (26.09%) in severe CAP patients with an immunocompromised status, which was all detected by mNGS only.ConclusionmNGS has higher overall sensitivity for pathogen detection than culture, BALF, and sputum mNGS are more sensitive than blood mNGS. mNGS is a necessary supplement of conventional microbiological tests for the pathogen detection of pulmonary infection.

BackgroundSepsis-induced cardiomyopathy significantly increased the mortality of patients with sepsis. The diagnostic criteria for septic cardiomyopathy has not been unified, which brings serious difficulties to clinical treatment. This study aimed to provide evidence for the early identification and intervention in patients with sepsis by clarifying the relationship between the ultrasound phenotype of septic cardiomyopathy and the prognosis of patients with sepsis.MethodsThis was a multicenter, prospective cohort study. The study population will consist of all eligible consecutive patients with sepsis or septic shock who meet the Sepsis 3.0 diagnostic criteria and were aged ≥18 years. Clinical data and echocardiographic measurements will be recorded within 2 h, at the 24th hour, at the 72nd hour, and on the 7th day after admission. The prevalence of each phenotype will be described as well, and their association with prognosis will be analyzed statistically.DiscussionTo achieve early recognition, prevent reinjury, achieve precise treatment, and reduce mortality in patients with sepsis, it is important to identify septic cardiac alterations and classify the phenotypes at all stages of sepsis. First, there is a lack of studies on the prevalence of each phenotype in Chinese populations. Second, each phenotype and its corresponding prognosis are not clear. In addition, the prognosis of patients with normal cardiac ultrasound phenotypes vs. those with suppressed or hyperdynamic cardiac phenotypes is unclear. Finally, this study was designed to collect data at four specific timing, then the timing of occurrence, duration, changes over time, impact to outcomes of each phenotype will probably be found. This study is expected to establish a standard and objective method to assess the ultrasound phenotype of septic cardiomyopathy due to its advantages of visualization, non-invasiveness and reproducibility, and to provide more precise information for the hemodynamic management of septic patients. In addition, this research will promote the clinical application of critical care ultrasound, which will play an important role in medical education and make ultrasound the best method to assess cardiac changes in sepsis.Trial registrationhttps://clinicaltrials.gov/ct2/show/NCT05161104, identifier NCT05161104.

Background A growing body of research suggests that the use of computerized decision support systems can better guide disease treatment and reduce the use of social and medical resources. Artificial intelligence (AI) technology is increasingly being used in medical decision-making systems to obtain optimal dosing combinations and improve the survival rate of sepsis patients. To meet the real-world requirements of medical applications and make the training model more robust, we replaced the core algorithm applied in an AI-based medical decision support system developed by research teams at the Massachusetts Institute of Technology (MIT) and IMPERIAL College London (ICL) with the deep deterministic policy gradient (DDPG) algorithm. The main objective of this study was to develop an AI-based medical decision-making system that makes decisions closer to those of professional human clinicians and effectively reduces the mortality rate of sepsis patients. Methods We used the same public intensive care unit (ICU) dataset applied by the research teams at MIT and ICL, i.e., the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) dataset, which contains information on the hospitalizations of 38,600 adult sepsis patients over the age of 15. We applied the DDPG algorithm as a strategy-based reinforcement learning approach to construct an AI-based medical decision-making system and analyzed the model results within a two-dimensional space to obtain the optimal dosing combination decision for sepsis patients. Results The results show that when the clinician administered the exact same dose as that recommended by the AI model, the mortality of the patients reached the lowest rate at 11.59%. At the same time, according to the database, the baseline mortality rate of the patients was calculated as 15.7%. This indicates that the patient mortality rate when difference between the doses administered by clinicians and those determined by the AI model was zero was approximately 4.2% lower than the baseline patient mortality rate found in the dataset. The results also illustrate that when a clinician administered a different dose than that recommended by the AI model, the patient mortality rate increased, and the greater the difference in dose, the higher the patient mortality rate. Furthermore, compared with the medical decision-making system based on the Deep-Q Learning Network (DQN) algorithm developed by the research teams at MIT and ICL, the optimal dosing combination recommended by our model is closer to that given by professional clinicians. Specifically, the number of patient samples administered by clinicians with the exact same dose recommended by our AI model increased by 142.3% compared with the model based on the DQN algorithm, with a reduction in the patient mortality rate of 2.58%. Conclusions The treatment plan generated by our medical decision-making system based on the DDPG algorithm is closer to that of a professional human clinician with a lower mortality rate in hospitalized sepsis patients, which can better help human clinicians deal with complex conditional changes in sepsis patients in an ICU. Our proposed AI-based medical decision-making system has the potential to provide the best reference dosing combinations for additional drugs.