RESUMEN
Background: Chlamydia psittaci (C. psittaci) is a pathogen that is seldom implicated in community-acquired pneumonia and is rarely linked to severe pneumonia. Reports of severe C. psittaci pneumonia accompanied by Guillain-Barre syndrome (GBS) are scarce. Tetracyclines are the preferred therapeutic approach for psittacosis. Omadacycline, a novel tetracycline, demonstrates strong antibacterial efficacy against typical bacteria and atypical pathogens, including C. psittaci. However, its application in the treatment of psittacosis pneumonia remains constrained. Case Presentation: A 77-year-old female patient was admitted to the hospital presenting with symptoms of fever, low back pain, and headache. The diagnosis of C. psittaci was established through the utilization of metagenomic next-generation sequencing (mNGS). Initial administration of moxifloxacin, meropenem, piperacillin-tazobactam, and doxycycline proved to be ineffective. Subsequent omadacycline leaded to the successful resolution of fever and dyspnea. However, after the endotracheal tube was removed, the patient experienced a rapid decline in symmetrical limb strength, leading to a diagnosis of GBS based on clinical manifestations, cerebrospinal fluid analysis, and electromyography. Following a 5-day course of immunoglobulin therapy and nutritional nerve treatment, the patient's condition ameliorated, culminating in an uncomplicated discharge. Conclusion: This case provides evidence supporting the potential use of omadacycline as a therapeutic option for the treatment of severe C. psittaci pneumonia. The utilization of mNGS technology is of paramount importance in the prompt identification of uncommon pathogens, including C. psittaci. Nevertheless, the occurrence of GBS should be taken into consideration when C. psittaci pneumonia is accompanied by symmetrical limb weakness. These findings have important implications for the diagnosis, treatment, and management of patients with C. psittaci pneumonia.
RESUMEN
BACKGROUND: Acute kidney injury (AKI) is a common complication in critically ill patients with sepsis and is often associated with a poor prognosis. We aimed to construct and validate an interpretable prognostic prediction model for patients with sepsis-associated AKI (S-AKI) using machine learning (ML) methods. METHODS: Data on the training cohort were collected from the Medical Information Mart for Intensive Care IV database version 2.2 to build the model, and data of patients were extracted from Hangzhou First People's Hospital Affiliated to Zhejiang University School of Medicine for external validation of model. Predictors of mortality were identified using Recursive Feature Elimination (RFE). Then, random forest, extreme gradient boosting (XGBoost), multilayer perceptron classifier, support vector classifier, and logistic regression were used to establish a prognosis prediction model for 7, 14, and 28 days after intensive care unit (ICU) admission, respectively. Prediction performance was assessed using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to interpret the ML models. RESULTS: In total, 2599 patients with S-AKI were included in the analysis. Forty variables were selected for the model development. According to the areas under the ROC curve (AUC) and DCA results for the training cohort, XGBoost model exhibited excellent performance with F1 Score of 0.847, 0.715, 0.765 and AUC (95% CI) of 0.91 (0.90, 0.92), 0.78 (0.76, 0.80), and 0.83 (0.81, 0.85) in 7 days, 14 days and 28 days group, respectively. It also demonstrated excellent discrimination in the external validation cohort. Its AUC (95% CI) was 0.81 (0.79, 0.83), 0.75 (0.73, 0.77), 0.79 (0.77, 0.81) in 7 days, 14 days and 28 days group, respectively. SHAP-based summary plot and force plot were used to interpret the XGBoost model globally and locally. CONCLUSIONS: ML is a reliable tool for predicting the prognosis of patients with S-AKI. SHAP methods were used to explain intrinsic information of the XGBoost model, which may prove clinically useful and help clinicians tailor precise management.
