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Objective:To analyze the clinical features of patients with invasive Klebsiella pneumoniae liver abscess syndrome (IKLAS). Methods:The clinical data of 12 patients diagnosed as IKLAS in Zhuzhou Central Hospital from January 2020 to January 2023 were retrospectively analyzed.Results:Among 12 patients there were 6 males and 6 females with an mean age of 65.3±12.2 years (49-90). Nine patients were complicated with type 2 diabetes. The main clinical manifestations were fever ( n=9), chill ( n=6), shiver ( n=4), nausea and vomiting ( n=2), upper abdominal pain ( n=2), fatigue and anepithymia ( n=2), cough and expectoration ( n=1), disturbance of consciousness ( n=1) and hemoptysis ( n=1). The leukocyte count was increased in 8 cases, lymphocyte count decreased in 10 cases, and platelets count decreased in 3 cases. C-reactive protein and procalcitonin levels were elevated, while serum albumin levels were lowered in all patients. The alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were increased in 7 cases each. Liver abscess was located in the right lobe in 8 cases, in the left lobe in 1 cases, and in both lobes in 3 cases. There were 7 patients with single abscess, and 5 patients with multiple abscesses. The etiology was confirmed by liver pus culture ( n=10) and blood culture ( n=5), respectively. The main sites of invasion were lung and blood stream ( n=10 and n=5, respectively). The majority of Klebsiella pneumoniae isolates were antibiotic sensitive strains and the overall drug resistance rate was relatively low. All patients were given antibiotics, and 10 of them also received liver abscess puncture drainage. After treatment, 11 patients were discharged, and 1 died of septic shock. Conclusions:Patients with IKLAS exhibit diverse clinical symptoms, most patients are complicated with diabetes, and the main sites of invasion are in the lungs and blood stream. Timely diagnosis, active screening of extrahepatic infection sites, effective drainage of abscess and appropriate antibiotic treatment can improve the survival of patients.
RESUMO
Objective:To develop a risk prediction model for early cardiac arrest in emergency sepsis utilizing a machine learning algorithm to enhance the quality and efficiency of patient treatment.Methods:This study focused on patients with sepsis who received treatment at the emergency room of the First Medical Center of Chinese PLA General Hospital from January 1, 2020 to June 1, 2023. The basic clinical characteristics such as vital signs and laboratory results were collected. Patients who fulfilled the specified inclusion criteria were allocated randomly into a training group and a testing group with a ratio of 8:2. A CatBoost model was constructed using Python software, and the prediction efficiency of the model was assessed by calculating the area under the receiver operating characteristic curve (AUC). Furthermore, the performance of the model was compared to that of other widely employed clinical scores.Results:This study included a cohort of 2 131 patients diagnosed with sepsis, among whom 449 experienced cardiac arrest. The CatBoost model demonstrated an AUC of 0.760, surpassing other scores. Notably, the top 10 predictors in the model were identified as age, lactate, interleukin -6, oxygen saturation, albumin, N-terminal pro-B-type natriuretic peptide, potassium, sodium, creatinine, and platelets.Conclusions:The utilization of this machine learning algorithm-based prediction model offers a more precise basis for predicting cardiac arrest in emergency sepsis patients, thereby potentially improving the treatment efficacy for this disease.