Lukas Koenen

Background: Granulomatosis with polyangiitis, formerly known as Wegener granulomatosis, is a necrotizing vasculitis with granulomatous inflammation that belongs to the class of antineutrophil cytoplasmic antibodies-positive diseases. It occurs in a localized and a systemic form and may present with a variety of symptoms. Involvement of the upper respiratory tract is very common, while neurologic, endocrinological, and nephrological dysfunction may occur. Case presentation: We describe the case of a 29-year-old Central European male patient presenting with severe bilateral sensorineural hearing loss, otorrhea, and one-sided facial nerve paralysis. The patient was unsuccessfully treated with i.v. antibiotics at another hospital in Berlin, and tympanic tubes were inserted. After presentation to our emergency room, he was hospitalized and further diagnostics started. Increased fluid intake and 12 kg weight gain over the last months were reported. The patient was diagnosed with granulomatosis with polyangiitis and diabetes insipidus. The patient’s condition improved after treatment with rituximab. Discussion: A comprehensive PubMed search of all articles with granulomatosis with polyangiitis and diabetes insipidus was conducted to assess which combination of symptoms occurs simultaneously and whether other parts of the pituitary are commonly involved. The 39 selected articles, describing 61 patients, showed that ear–nose–throat involvement occurred most commonly, in 71% of cases. Of patients, 59% had involvement of the anterior pituitary gland, while true panhypopituitarism occurred in 13% of cases. Only one case report featured the same set of symptoms as described herein. Conclusion: Granulomatosis with polyangiitis is a highly variable disease, commonly involving the upper airways, but that may present with symptoms solely related to the pituitary gland. Clinicians should have a low threshold to investigate for granulomatosis with polyangiitis in patients with therapy-resistant otorrhea. Patients may present with a complex set of symptoms, and integrating different specialists when additional symptoms occur may lead to faster diagnosis.

Objectives: The total laryngectomy is one of the most standardized major surgical procedures in otolaryngology. Several studies have proposed the Clavien-Dindo classification (CDC) as a solution to classifying postoperative complications into 5 grades from less severe to severe. Yet more data on classifying larger patient populations undergoing major otolaryngologic surgery according to the CDC are needed. Predicting postoperative complications in clinical practice is often subject to generalized clinical scoring systems with uncertain predictive abilities for otolaryngologic surgery. Machine learning offers methods to predict postoperative complications based on data obtained prior to surgery. Methods: We included all patients (N = 148) who underwent a total laryngectomy after diagnosis of squamous cell carcinoma at our institution. A univariate and multivariate logistic regression analysis of multiple complex risk factors was performed, and patients were grouped into severe postoperative complications (CDC ≥ 4) and less severe complications. Four different commonly used machine learning algorithms were trained on the dataset. The best model was selected to predict postoperative complications on the complete dataset. Results: Univariate analysis showed that the most significant predictors for postoperative complications were the Charlson Comorbidity Index (CCI) and whether reconstruction was performed intraoperatively. A multivariate analysis showed that the CCI and reconstruction remained significant. The commonly used AdaBoost algorithm achieved the highest area under the curve with 0.77 with high positive and negative predictive values in subsequent analysis. Conclusions: This study shows that postoperative complications can be classified according to the CDC with the CCI being a useful screening tool to predict patients at risk for postoperative complications. We provide evidence that could help identify single patients at risk for complications and customize treatment accordingly which could finally lead to a custom approach for every patient. We also suggest that there is no increase in complications with patients of higher age.