Acute spontaneous subdural hematoma caused by skull metastasis of hepatocellular carcinoma: case report.

BACKGROUND: Skull and intracranial metastases from hepatocellular carcinoma (HCC) have seldom been reported. A skull metastasis of HCC with a tumor bleeding resulting in spontaneous subdural hematoma (SDH) is extremely unusual. We report the first case of acute spontaneous SDH in a 69-year-old woman who presented with acute onset of headache, because of tumor bleeding caused by skull metastasis of HCC. CASE PRESENTATION: A 69-year-old woman was referred to our hospital because of progressive headache, nausea, and vomiting for 3 days. Brain computed tomography (CT) performed in the emergency department (ED) revealed a left temporal SDH with a slight mass effect and a small left temporal bone erosion. Tri-phasic abdominal CT demonstrated a large right lobe liver tumor compatible with HCC. She experienced progressive deterioration of consciousness in the intensive care unit. Follow-up CT showed an enlargement of the SDH. An emergency craniotomy for hematoma evacuation and removal of skull tumor was performed. She regained consciousness and had no neurological deficits during the postoperative course. Pathological examination of the skull specimen indicated metastasis of a HCC. CONCLUSION: Patients with acute SDH without a history of head injury are rarely encountered in the ED. Metastatic carcinoma with bleeding should be included as a differential diagnosis for acute spontaneous SDH. Before an operation for SDH, the possibility of metastatic lesion of the skull should be considered in the surgical planning and the origin of malignancy should be sought.

Risk Factors and Outcomes of Community-Acquired Carbapenem-Resistant Klebsiella pneumoniae Infection in Elderly Patients.

The increasing prevalence of carbapenem-resistant Klebsiella pneumoniae (CRKP) infections is a global concern. Elderly patients have a diminished immune response and functional reserve, and are thus more vulnerable to bacterial infection. This study aimed to investigate the risk factors and outcomes in elderly patients with community-acquired CRKP infections. We performed a retrospective cohort study in a tertiary medical center between 1 January 2021, and 31 December 2021. All elderly patients who visited the emergency department during this period with culture-positive K. pneumoniae were enrolled, and their baseline demographics, laboratory profiles, management strategies, and outcomes were recorded and analyzed. We identified 528 elderly patients with K. pneumonia infection, and the proportion of patients with CRKP infection was 10.2% (54/528). Recent intensive care unit (ICU) admission and prior carbapenem use are independent risk factors for CRKP infection in elderly patients. Compared to patients with carbapenem-sensitive K. pneumoniae infection, those with CRKP infection had a significantly higher risk of adverse outcomes, including ICU care, respiratory failure, septic shock, and 90-day mortality. CRKP infection was also identified as an independent risk factor for 90-day mortality. Clinicians should be aware of the increasing prevalence of CRKP infections in elderly patients and judiciously choose appropriate antibiotics for these patients.

Prediction of 30-Day Mortality Using the Quick Pitt Bacteremia Score in Hospitalized Patients with Klebsiella pneumoniae Infection.

Purpose: Klebsiella pneumoniae is an important causative pathogen of nosocomial infections, resulting in poor prognosis owing to its hypervirulence and antibiotic resistance. A simplified quicker version of the Pitt bacteremia score (PBS) (qPitt) for acute illness severity measurement was developed recently. The goal of this study was to explore the prognostic value of qPitt in patients with K. pneumoniae infection. Patients and Methods: Demographic information and management strategies were retrospectively collected from the records of all adult patients who visited the emergency department between January 1, 2021, and December 31, 2021, with culture-positive K. pneumoniae. The qPitt score was calculated based on: temperature <36°C, systolic blood pressure ≤90 mmHg or vasopressor administration, respiratory rate ≥25 times/min or need of mechanical ventilation, altered mental status, and cardiac arrest event. The 30-day mortality prediction abilities of the qPitt were compared with the PBS, the sequential organ failure assessment (SOFA), and the quick sequential organ failure assessment (qSOFA) using receiver operating characteristic curves. Results: Data from 867 patients (57.8% men) with a mean age of 66.9 were compiled. The 30-day mortality rate of the enrolled patients was 13.4%, and the area under the curve (AUC) of the scoring systems were as follows: SOFA, 0.91 (95% confidence interval [CI]=0.89-0.93), qPitt, 0.87 (95% CI=0.84-0.89), PBS, 0.87 (95% CI=0.85-0.89), and qSOFA, 0.73 (95% CI=0.70-0.76). The AUC of qPitt was significantly higher than that of qSOFA (p<0.01) and similar to that of PBS (p=0.65).The qPitt also demonstrated excellent mortality discrimination ability in non-bacteremic patients, AUC= 0.85 (95% CI=0.82-0.88). Conclusion: The qPitt revealed excellent 30-day mortality prediction ability and also predicted mortality in non-bacteremic patients with K. pneumoniae infection. Clinicians can use this simplified scoring system to stratify patients earlier and initiate prompt treatment in high-risk patients.

Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography.

Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography.

Comparing Mortality Risk Predictive Ability of Different Scoring Systems in Cirrhotic Patients with Bacteremia.

Patients with liver cirrhosis and bacteremia have substantially higher risk of mortality and morbidity. Our study aimed to investigate scoring systems that can predict the mortality risk in patients with cirrhosis and bacteremia. A single-center, retrospective cohort study was performed among adult patients who visited the emergency department from January 2015 to December 2018. All patients diagnosed with liver cirrhosis and bacteremia were enrolled and divided into survivor and nonsurvivor groups for comparison based on their 30-day in-hospital mortality event. The Pitt bacteremia score (PBS), model for end-stage liver disease (MELD) score, Child-Pugh score, and quick sequential Organ Failure Assessment (qSOFA) score were calculated and compared using the area under the receiver operating characteristic (AUROC) curves. A total of 127 patients (survivor: 86; nonsurvivor: 41) were eligible for this study. Compared with the nonsurvivor group, patients in the survivor group had significantly lower MELD score (22 ± 7 vs. 29 ± 5, p < 0.001), lower proportion of high qSOFA (score ≥ 2) (23.3% vs. 51.2%, p < 0.01), and high PBS (score ≥ 4) (7.0% vs. 34.1%, p < 0.001) category. There was also a significantly different distribution in Child-Pugh classification between the two groups (p < 0.01). The survivor group had significantly lower proportion of acute-on-chronic liver failure (27.9% vs. 68.3%, p < 0.001) and fewer number of organ failures (p < 0.001). In comparison of the discriminative ability in mortality risk prediction, PBS (AUROC = 0.83, 95% CI = 0.75-0.90, p < 0.001) and MELD scores (AUROC = 0.78, 95% CI = 0.70-0.86, p < 0.001) revealed a better predictive ability than Child-Pugh (AUROC = 0.69, 95% CI = 0.59-0.70, p < 0.01) and qSOFA scores (AUROC = 0.65, 95% CI = 0.54-0.75, p < 0.01). PBS and MELD scores both demonstrated a superior ability of predicting mortality risk in cirrhotic patients with bacteremia.