ABSTRACT
Objective: To investigate the impact of coronavirus disease 2019 (COVID-19) specified preventive and control measures on the distribution and resistance transition of Pseudomonas aeruginosa (P. aeruginosa) in uninfected hospitalized patients during the pandemic. Methods: This retrospective study retrieved data from 316 P. aeruginosa isolates in the year pre-COVID-19 (n=131) pandemic and the year under COVID-19 specified preventive and control (post-pandemic year, n=185), compared the general characteristics, laboratory results, and antimicrobial susceptibility tests of P. aeruginosa between the two groups. Results: Compared with the pre-pandemic year, the isolation rate of P. aeruginosa (14.35% vs 22.31%, P<0.001) increased, while the rate of drug resistant P. aeruginosa decreased significantly (29.77% vs 19.45%, P<0.001) in the post-pandemic year; Prescription of ß-Lactams (30.5% vs 50.0%, P<0.01) also increased significantly. The resistance rates of P. aeruginosa isolates to ceftazidime (P<0.01), ciprofloxacin (P<0.01), and gentamicin (P<0.001) increased, whereas the resistance rates to piperacillin/tazobactam (P<0.01) and imipenem (P<0.05) decreased significantly. Conclusion: The COVID-19 specified preventive and control measures have influenced the distribution and resistance transition of P. aeruginosa, further verifications are needed in future research.
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Objective: To investigate the distribution and drug resistance of pathogens among hospitalized patients in the respiratory unit during the COVID-19 pandemic, analyze the risk factors of drug resistance, construct a risk prediction model. Methods: This study isolated 791 strains from 489 patients admitted to the Affiliated Hospital of Chengdu University, who were retrospectively enrolled between December 2019 and June 2021. The patients were divided into training and validation sets based on a random number table method (8:2). The baseline information, clinical characteristics, and culture results were collected using an electronic database and WHONET 5.6 software and compared between the two groups. A risk prediction model for drug-resistant bacteria was constructed using multi-factor logistic regression. Results: K. pneumoniae (24.78%), P. aeruginosa (17.19%), A. baumannii (10.37%), and E. coli (10.37%) were the most abundant bacterial isolates. 174 isolates of drug-resistant bacteria were collected, ie, Carbapenem-resistant organism-strains, ESBL-producing strains, methicillin-resistant S. aureus, multi-drug resistance constituting 38.51%, 50.57%, 6.32%, 4.60%, respectively. The nosocomial infection prediction model of drug-resistant bacteria was developed based on the combined use of antimicrobials, pharmacological immunosuppression, PCT>0.5 ng/mL, CKD stage 4-5, indwelling catheter, and age > 60 years. The AUC under the ROC curve of the training and validation sets were 0.768 (95% CI: 0.624-0.817) and 0.753 (95% CI: 0.657-0.785), respectively. Our model revealed an acceptable prediction demonstrated by a non-significant Hosmer-Lemeshow test (training set, p=0.54; validation set, p=0.88). Conclusion: K. pneumoniae, P. aeruginosa, A. baumannii, and E. coli were the most abundant bacterial isolates. Antimicrobial resistance among the common isolates was high for most routinely used antimicrobials and carbapenems. COVID-19 did not increase the drug resistance pressure of the main strains. The risk prediction model of drug-resistant bacterial infection is expected to improve the prevention and control of antibacterial-resistant bacterial infection in hospital settings.
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BACKGROUND: Sebaceous carcinoma (SC), a malignancy primarily characterized by aggressive growth, affects cutaneous tissues of the periocular region. Extraocular SC is extremely rare, especially in the extremities, as evidenced by only a handful of reported cases. CASE SUMMARY: A 65-year-old man presented with a rapidly enlarging swelling on the left inner thigh, which was initially misdiagnosed as a subcutaneous abscess. The lesion had appeared two months prior to admission. Clinical examination revealed a cauliflower-like swollen content, with an ulcerated and infected mass located on his left thigh. At the same time, we observed solitary nodular lesions in his lungs and brain, with biopsy pathology of the lung lesions found to be consistent with the mass in the thigh. The patient received chemotherapy comprising cis-platinum with fluorouracil, followed by targeted therapy with anlotinib hydrochloride and chemotherapy with vinorelbine, implantation of iodine-125 seeds in the thigh and pulmonary tumor. The initial stage intervention achieved partial remission. The efficacy of maintenance treatment was evaluated as stable disease after the first 5 cycles; however, the patient developed a new brain lesion after the sixth cycle of treatment, which resulted in progressive disease and he received whole brain gamma knife radiotherapy. CONCLUSION: We analyzed the clinical presentation, imaging features, pathology and treatment of a rare case of lung, brain and lymph node metastasis of SC located in the thigh. It is evident that cis-platinum combined with fluorouracil, vinorelbine combined with anlotinib hydrochloride may be an effective therapeutic regimen in advanced SC. However, brain metastatic lesions should receive early radiotherapy.
ABSTRACT
Visekriterijumsko kompromisno rangiranje (VIKOR) method is one of the commonly used multi criteria decision making (MCDM) methods for improving the quality of decision making. VIKOR has an advantage in providing a ranking procedure for positive attributes and negative attributes when it is used and examined in decision support. However, we noticed that this method may failed to support an objective result in medical field because most medical data have normal reference ranges (e.g., for normally distributed data: NRR ∈ [µ ± 1.96σ], this limitation shows a negative effect on the acceptance of it as an effective decision supporting method in medical decision making. This paper proposes an improved VIKOR method with enhanced accuracy (ea-VIKOR) to make it suitable for such data in medical field by introducing a new data normalization method taking the original distance to the normal reference range (ODNRR) into account. In addition, an experimental example was presented to demonstrate efficiency and feasibility of the ea-VIKOR method, the results demonstrate the ability of ea-VIKOR to deal with moderate data and support the decision making in healthcare care management. For this reason, the ea-VIKOR should be considered for use as a decision support tool for future study.
