Risk stratification and survival time of patients with gram-negative bacillary pneumonia in the intensive care unit.

Introduction: Pneumonia is a common infection in the intensive care unit (ICU), and gram-negative bacilli are the most common bacterial cause. The purpose of the study was to investigate the risk factors for 30-day mortality in patients with gram-negative bacillary pneumonia in the ICU, construct a predictive model, and stratify patients based on risk to assess their short-term survival. Methods: Patients admitted to the ICU with gram-negative bacillary pneumonia at Fujian Medical University Affiliated First Hospital between January 2018 and September 2020 were selected. Patients were divided into deceased and survivor groups based on whether death occurred within 30 days. Multifactorial logistic regression analysis was used to identify independent risk factors for 30-day mortality in these patients, and a predictive nomogram model was constructed based on these factors. Patients were categorized into low-, medium-, and high-risk groups according to the model's predicted probability, and Kaplan-Meier survival curves were plotted to assess short-term survival. Results: The study included 305 patients. Lactic acid (odds ratio [OR], 1.524, 95% CI: 1.057-2.197), tracheal intubation (OR: 4.202, 95% CI: 1.092-16.169), and acute kidney injury (OR:4.776, 95% CI: 1.632-13.978) were identified as independent risk factors for 30-day mortality. A nomogram prediction model was established based on these three factors. Internal validation of the model showed a Hosmer-Lemeshow test result of X2=5.770, P=0.834, and an area under the ROC curve of 0.791 (95% CI: 0.688-0.893). Bootstrap resampling of the original data 1000 times yielded a C-index of 0.791, and a decision curve analysis indicated a high net benefit when the threshold probability was between 15%-90%. The survival time for low-, medium-, and high-risk patients was 30 (30, 30), 30 (16.5, 30), and 17 (11, 27) days, respectively, which were significantly different. Conclusion: Lactic acid, tracheal intubation, and acute kidney injury were independent risk factors for 30-day mortality in patients in the ICU with gram-negative bacillary pneumonia. The predictive model constructed based on these factors showed good predictive performance and helped assess short-term survival, facilitating early intervention and treatment.

Basophils absence predicts poor prognosis and indicates immunosuppression of patients in intensive care units.

Immune cells and immunity are associated with the prognosis of patients with critical illness. Here, medical records retrospectively extracted from the Medical Information Mart for Intensive Care IV were used for screening an immune-related biomarker in intensive care units (ICU) patients and applied for validating the identified indicator in septic patients. In this work, the count of innate immune cells, basophils, harbored a superior role in predicting ICU patients' prognosis compared with those of other blood immune cells (OR 0.013, 95% CI 0.001, 0.118, P < 0.001). Importantly, basophils absence during ICU stay was positively correlated with the 28-day mortality of ICU patients and served as an independent predictor of ICU patients' prognosis (OR 3.425, 95% CI 3.717-3.165, P < 0.001). Moreover, the association between critical illness progression, poor outcome, and basophils absence was verified in septic patients. Subsequent investigations revealed the positive relationship between basophils absence and immunosuppression, and suggested the potential of basophils-mediated immunity in predicting the 28-day mortality of ICU patients. Collectively, we identify basophils absence during ICU stay as a novel and unfavorable indicator for evaluating the prognosis of ICU patients and recognizing a branch of ICU patients potentially suitable for intensified treatment and immunoenhancement therapy.

Carbapenem-resistant gram-negative bacterial infection in intensive care unit patients: Antibiotic resistance analysis and predictive model development.

In this study, we analyzed the antibiotic resistance of carbapenem-resistant gram-negative bacteria (CR-GNB) in intensive care unit (ICU) patients and developed a predictive model. We retrospectively collected the data of patients with GNB infection admitted to the ICU of the First Affiliated Hospital of Fujian Medical University, who were then divided into a CR and a carbapenem-susceptible (CS) group for CR-GNB infection analysis. Patients admitted between December 1, 2017, and July 31, 2019, were assigned to the experimental cohort (n = 205), and their data were subjected to multivariate logistic regression analysis to identify independent risk factors for constructing the nomogram-based predictive model. Patients admitted between August 1, 2019, and September 1, 2020, were assigned to the validation cohort for validating the predictive model (n = 104). The Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve analysis were used to validate the model's performance. Overall, 309 patients with GNB infection were recruited. Of them, 97 and 212 were infected with CS-GNB and CR-GNB, respectively. Carbapenem-resistant Klebsiella pneumoniae (CRKP), carbapenem-resistant Acinetobacter baumannii (CRAB) and carbapenem-resistant Pseudomonas aeruginosa (CRPA) were the most prevalent CR-GNB. The multivariate logistic regression analysis results of the experimental cohort revealed that a history of combination antibiotic treatments (OR: 3.197, 95% CI: 1.561-6.549), hospital-acquired infection (OR: 3.563, 95% CI: 1.062-11.959) and mechanical ventilation ≥ 7 days (OR: 5.096, 95% CI: 1.865-13.923) were independent risk factors for CR-GNB infection, which were then used for nomogram construction. The model demonstrated a good fit of observed data (p = 0.999), with an area under the ROC curve (AUC) of 0.753 (95% CI: 0.685-0.820) and 0.718 (95% CI: 0.619-0.816) for the experimental and validation cohort, respectively. The decision curve analysis results suggested that the model has a high practical value for clinical practice. The Hosmer-Lemeshow test indicated a good fit of the model in the validation cohort (p-value, 0.278). Overall, our proposed predictive model exhibited a good predictive value in identifying patients at high risk of developing CR-GNB infection in the ICU and could be used to guide preventive and treatment measures.

Clinical characteristics of non-ICU hospitalized patients with coronavirus disease 2019 and liver injury: A retrospective study.

BACKGROUND & AIMS: Coronavirus disease 2019 (COVID-19) has raised world concern for global epidemic since December, 2019. Limited data are available for liver function in COVID-19 patients. We aimed to investigate the risk factors related to liver injury in the COVID-19 patients. METHODS: A retrospective study was performed in non-ICU Ward at Jinyintan Hospital from February 2, 2020 to February 23, 2020. Consecutively confirmed COVID-19 discharged cases were enrolled. The clinical characteristics of patients with liver injury and without liver injury were compared. RESULTS: A total of 79 COVID-19 patients were included. 31.6%, 35.4% and 5.1% COVID-19 patients had elevated levels of alanine transaminase (ALT), aspartate aminotransferase (AST) and bilirubin respectively. Median value of ALT, AST and bilirubin for entire cohort was 36.5 (17.5 ~ 71.5) U/L, 34.5 (25.3 ~ 55.3) U/L and 12.7 (8.1 ~ 15.4) mmol/L respectively. There were no significant differences in age, previous medical history and symptoms between the two groups. Males were more likely to have liver injury when infected with COVID-19 (P < .05); compared with patients without liver injury, patients with liver injury had increased levels of white blood cell counts, neutrophils, CRP and CT score (P < .05) and had a longer length of stay (P < .05). Logistic regression analyses suggested that the extent of pulmonary lesions on CT was a predictor of liver function damage (P < .05). CONCLUSIONS: Liver injury is common in non-ICU hospitalized COVID-19 patients. It may be related to systemic inflammation. Intense monitoring and evaluation of liver function in patients with severe pulmonary imaging lesions should be considered.