Wells Syndrome with Generalized Oedema.

Wells syndrome or eosinophilic cellulitis is a rare and relapsing skin disease which lacks systemic involvement. A skin biopsy is needed to establish a diagnosis. Several precipitating factors have been proposed but no proven causative link has been found. On the other hand, Churg-Strauss syndrome, also known as eosinophilic granulomatosis with polyangiitis (EGPA), an auto-immune disease, is associated with multiorgan, including cutaneous manifestations. We report a case with overlapping features of Wells and Churg-Strauss syndrome, suggesting that these syndromes could be part of the same nosological entity. LEARNING POINTS: Biopsies are essential to establish a diagnosis in unusual cases of cellulitis.COVID-19 vaccination is a putative trigger for Wells syndrome.There is significant overlap between Wells and Churg-Strauss syndromes.

Machine Learning for Antibiotic Resistance Prediction: A Prototype Using Off-the-Shelf Techniques and Entry-Level Data to Guide Empiric Antimicrobial Therapy.

OBJECTIVE: In the era of increasing antimicrobial resistance, the need for early identification and prompt treatment of multi-drug-resistant infections is crucial for achieving favorable outcomes in critically ill patients. As traditional microbiological susceptibility testing requires at least 24 hours, automated machine learning (AutoML) techniques could be used as clinical decision support tools to predict antimicrobial resistance and select appropriate empirical antibiotic treatment. METHODS: An antimicrobial susceptibility dataset of 11,496 instances from 499 patients admitted to the internal medicine wards of a public hospital in Greece was processed by using Microsoft Azure AutoML to evaluate antibiotic susceptibility predictions using patients' simple demographic characteristics, as well as previous antibiotic susceptibility testing, without any concomitant clinical data. Furthermore, the balanced dataset was also processed using the same procedure. The datasets contained the attributes of sex, age, sample type, Gram stain, 44 antimicrobial substances, and the antibiotic susceptibility results. RESULTS: The stack ensemble technique achieved the best results in the original and balanced dataset with an area under the curve-weighted metric of 0.822 and 0.850, respectively. CONCLUSIONS: Implementation of AutoML for antimicrobial susceptibility data can provide clinicians useful information regarding possible antibiotic resistance and aid them in selecting appropriate empirical antibiotic therapy by taking into consideration the local antimicrobial resistance ecosystem.

Using Machine Learning to Predict Antimicrobial Resistance of Acinetobacter Baumannii, Klebsiella Pneumoniae and Pseudomonas Aeruginosa Strains.

Hospital-acquired infections, particularly in ICU, are becoming more frequent in recent years, with the most serious of them being Gram-negative bacterial infections. Among them, Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa are considered the most resistant bacteria encountered in ICU and other wards. Given the fact that about 24 hours are usually required to perform common antibiotic resistance tests after the bacteria identification, the use of machine learning techniques could be an additional decision support tool in selecting empirical antibiotic treatment based on the sample type, bacteria, and patient's basic characteristics. In this article, five machine learning (ML) models were evaluated to predict antimicrobial resistance of Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. We suggest implementing ML techniques to forecast antibiotic resistance using data from the clinical microbiology laboratory, available in the Laboratory Information System (LIS).

Diuretic-Resistant Ascites Following Laparoscopic Surgery in a Patient With Chronic Myeloid Leukemia on Imatinib Therapy.

Imatinib mesylate is a tyrosine kinase inhibitor with high efficacy in the treatment of chronic myeloid leukemia (CML). Although fluid retention is a common adverse effect of imatinib, it rarely necessitates discontinuation of therapy. Isolated ascites has not been reported as a complication of imatinib therapy in patients with CML. Here, we report the case of a 72-year-old male with CML on imatinib (600 mg daily), who developed ascites two weeks after a laparoscopic hernia repair with intraperitoneal placement of a nylon mesh. The ascites was resistant to diuretic therapy and required repeated large-volume paracentesis. Discontinuation of imatinib resulted in arrest of ascites production, but reintroduction of the drug at the same dose two weeks later was rapidly followed by recurrence of ascites requiring further therapeutic paracenteses. It was postulated that peritoneal inflammation had resulted in increased capillary permeability, which was further augmented by imatinib via inhibition of platelet-derived growth factor receptor (PDGFR), a tyrosine kinase known to play a significant physiological role in the regulation of interstitial fluid pressure and capillary permeability. The possibility of developing ascites after abdominal surgery should be considered in patients receiving imatinib or related PDGFR inhibitors. In such cases, perioperative interruption of tyrosine kinase therapy might be indicated.

Using Machine Learning Algorithms to Predict Antimicrobial Resistance and Assist Empirical Treatment.

Multi-drug-resistant (MDR) infections and their devastating consequences constitute a global problem and a constant threat to public health with immense costs for their treatment. Early identification of the pathogen and its antibiotic resistance profile is crucial for a favorable outcome. Given the fact that more than 24 hours are usually required to perform common antibiotic resistance tests after the sample collection, the implementation of machine learning methods could be of significant help in selecting empirical antibiotic treatment based only on the sample type, Gram stain, and patient's basic characteristics. In this paper, five machine learning (ML) algorithms have been tested to determine antibiotic susceptibility predictions using simple demographic data of the patients, as well as culture results and antibiotic susceptibility tests. Implementing ML algorithms to antimicrobial susceptibility data may offer insightful antibiotic susceptibility predictions to assist clinicians in decision-making regarding empirical treatment.

Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece.

Hospital-acquired infections, particularly in the critical care setting, have become increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. Time is critical to identifying bacteria and their resistance to antibiotics due to the critical health status of patients in the intensive care unit (ICU). As common antibiotic resistance tests require more than 24 h after the sample is collected to determine sensitivity in specific antibiotics, we suggest applying machine learning (ML) techniques to assist the clinician in determining whether bacteria are resistant to individual antimicrobials by knowing only a sample's Gram stain, site of infection, and patient demographics. In our single center study, we compared the performance of eight machine learning algorithms to assess antibiotic susceptibility predictions. The demographic characteristics of the patients are considered for this study, as well as data from cultures and susceptibility testing. Applying machine learning algorithms to patient antimicrobial susceptibility data, readily available, solely from the Microbiology Laboratory without any of the patient's clinical data, even in resource-limited hospital settings, can provide informative antibiotic susceptibility predictions to aid clinicians in selecting appropriate empirical antibiotic therapy. These strategies, when used as a decision support tool, have the potential to improve empiric therapy selection and reduce the antimicrobial resistance burden.

Using Microbiological Data Analysis to Tackle Antibiotic Resistance of Klebsiella Pneumoniae.

Optimal antibiotic use for the treatment of nosocomial infections plays a central role in the effort to control the rapidly increasing prevalence of multidrug-resistant bacteria. Antibiotic selection should be based on accurate knowledge of local susceptibility rates. Traditional methods of resistance reporting, which are in routine use by microbiology laboratories could be enhanced by using statistically significant results. We present a method of reporting based on antibiotic susceptibility data analysis which offers an accurate tool that reduces clinician uncertainty and enables optimization of the antibiotic selection process.

Case Report: Clinically amyopathic dermatomyositis presenting acutely with isolated facial edema.

A 45-year-old Asian man presented with acute-onset periorbital and facial edema associated with pyrexia. Muscle weakness was absent. Initial laboratory investigations showed an inflammatory reaction, while screening for infections was negative. Serum muscle enzyme levels were normal. He was hospitalized and treated empirically with antibiotics and corticosteroids, pending the result of facial skin and muscle biopsy. He showed a good clinical and laboratory response but an attempt to discontinue corticosteroids led to a prompt relapse of facial edema and pyrexia, associated with rising laboratory indices of inflammation. Biopsy findings were typical of dermatomyositis. Reintroduction of corticosteroid treatment resulted in complete clinical and laboratory remission. Facial edema as the sole clinical manifestation of dermatomyositis is extremely rare. There have been no previous reports of isolated facial edema in the setting of acute, clinically amyopathic dermatomyositis in adults. A high level of suspicion is required to make the diagnosis in the absence of myopathy and the hallmark cutaneous manifestations of the disease (heliotrope rash, Gottron papules).

Steroid-induced hypocalcaemia with tetany in a patient with hypoparathyroidism.

Although glucocorticoids have a known negative effect on calcium balance, they do not normally cause clinically significant hypocalcaemia. A young woman with post-surgical hypoparathyroidism developed symptomatic hypocalcaemia on two occasions following treatment with intravenous hydrocortisone for allergic reactions. Oral calcium and vitamin D supplementation could not prevent the development of hypocalcaemia. She was treated successfully with intravenous calcium gluconate infusions and discontinuation of glucocorticoids. In patients with hypoparathyroidism, impaired parathyroid hormone response to steroid-induced negative calcium balance may result in severe symptomatic hypocalcaemia requiring hospitalisation.

Intentional overdose with tinzaparin: management dilemmas.

BACKGROUND: Low-molecular-weight heparin (LMWH) is increasingly being prescribed for prophylaxis and treatment of thromboembolic diseases. Despite the fact that its therapeutic use is considered to be safe, it can be complicated by major hemorrhage and, in contrast to unfractionated heparin, it can only partially be neutralized by protamine. Recent reports of LMWH overdose illustrate the need for a consensus on its management. OBJECTIVES: To describe a case of self-poisoning with a very large dose of tinzaparin and discuss management options in patients with LMWH overdose. CASE REPORT: A 69-year-old woman was brought to the Emergency Department 2 h after injecting herself with 280,000 IU of tinzaparin subcutaneously in an attempt to commit suicide. Despite an unrecordable activated partial thromboplastin time (APTT > 180 s) and prolonged prothrombin time, there was no evidence of active bleeding. She was given an intravenous infusion of 100 mg protamine sulfate and was admitted to the intensive care unit, where further infusions of protamine were administered. Normalization of the APTT occurred 40-50 h post admission, reflecting normal tinzaparin clearance rather than neutralization by protamine. No hemorrhagic complications occurred during her hospitalization except for prolonged bleeding from venipuncture sites. CONCLUSION: In this case of massive tinzaparin overdose, conventional doses of protamine failed to rapidly normalize the deranged coagulation parameters. The favorable clinical outcome suggests that, regardless of the LMWH amount injected, no active treatment is needed in the absence of hemorrhage. This is in accordance with the limited published data concerning cases of overdose with other LMWHs.

Acute adrenal failure associated with primary antiphospholipid syndrome and homozygosity for MTHFR C677T.

A 47-year-old woman with a recent clinical diagnosis of acute cholecystitis developed acute adrenal failure. Initial computed tomograms of the abdomen showed bilateral adrenal swelling which evolved into bilateral adrenal hemorrhage on repeat examination a few days later. Laboratory investigations revealed a previously undiagnosed primary antiphospholipid syndrome and homozygosity for the MTHFR C677T mutation with hyperhomocysteinemia. This case highlights the protean clinical manifestations of adrenal vascular accidents and the need for a thorough search for underlying prothrombotic states in this setting.

Extragastrointestinal stromal tumors of the omentum: review apropos of a case with a novel gain-of-function KIT mutation.

INTRODUCTION: We describe a case of extragastrointestinal stromal tumor (EGIST) of the omentum harboring a novel KIT mutation and review the literature on omental EGISTs, emphasizing on molecular genetic data. CASE PRESENTATION: A large EGIST arising from the lesser omentum was incidentally diagnosed in a 68-year-old man during emergency laparotomy for intra-abdominal hemorrhage following a car accident. The tumor was composed of CD117+/CD34+ spindle-shaped cells with low mitotic activity. Analysis of tumor DNA revealed a heterozygous duplication of 30 bp in exon 11 of KIT. Nine months after R0 resection, positron emission tomography showed abnormal uptake in the upper abdomen. The patient was treated with imatinib mesylate and is alive and well 2 years after the operation. DISCUSSION: Omental EGISTs remain silent despite a large tumor size. They are diagnosed at a median age of 65 years and show low proliferative activity in the majority (about 80%) of cases. Although the median follow-up period of published cases is only 20 months, mortality appears to be low after R0 resection and is expected to decrease further following the recent introduction of imatinib therapy for high-risk tumors. Accumulating molecular genetic data may lead to improved prognostic classification and patient management.

Familial multiple endocrine neoplasia type I: the urologist is first on the scene.

OBJECTIVE: To study the urological manifestations of familial multiple endocrine neoplasia type 1 (MEN-1). PATIENTS AND METHODS: The study included 26 adults (median age 38.5 years, range 18-80) from two unrelated MEN-1 pedigrees. In 15 of the patients the diagnosis was confirmed by genetic analysis, while in the rest the diagnosis was based on clinical criteria combined with genealogy data. RESULTS: Urolithiasis associated with primary hyperparathyroidism was present in 65% of MEN-1 patients and in 77% of those who were symptomatic. In 68% of patients complications of urolithiasis (renal/ureteric colic, urinary tract infection) were the presenting clinical manifestations of MEN-1, whereas in 50% they constituted the only clinical manifestation of the syndrome. The mean time from the onset of symptoms of urolithiasis to the diagnosis of the polyendocrinopathy was 17.2 years. Initial failure to recognize the presence of MEN-1 in patients with primary hyperparathyroidism led to conservative parathyroid surgery, with subsequent relapse of the hyperparathyroidism, requiring re-operation. Serious renal morbidity included one case of pyonephrosis necessitating nephrectomy. While urolithiasis was a cardinal clinical manifestation of MEN-1, there was otherwise considerable phenotypic polymorphism, even among patients bearing the same MEN1 gene mutation. CONCLUSION: In patients with familial MEN-1 the complications of urolithiasis are the commonest presenting clinical manifestations and the cause of significant morbidity. In the presence of a family history of renal stones, appropriate investigations may lead to the timely diagnosis of this important, albeit rare, disorder.