Tristan Kölsche

Objective: Stroke is a common risk among patients with cancer, and the odds of a fatal stroke are estimated to be twice those of the general population. Due to the exclusion of cancer patients from major intravenous thrombolysis and thrombectomy trials, existing literature consists largely of small case series lacking sufficient statistical power. We aimed to evaluate the safety profile of acute stroke therapies in patients with active cancer, stratified by cancer localization, using a nationwide administrative dataset. Methods: This retrospective, large-scale cohort study utilized data from the German Federal Statistical Office (DESTATIS). We analyzed all inpatient cases of acute ischemic stroke receiving recanalization therapy (intravenous thrombolysis and endovascular thrombectomy) in Germany. The primary endpoints were in-hospital mortality and safety outcomes (intracranial bleeding, subarachnoid hemorrhage, and acute anemia). To control for baseline characteristics, adjusted odds ratios (aOR) were calculated using multivariable logistic regression. Results: We analyzed 154,333 patients receiving intravenous thrombolysis (2482 with and 151,851 without cancer) and 39,534 receiving endovascular thrombectomy (1580 with and 37,954 without cancer). In the thrombolysis cohort, patients with cancer had significantly higher rates of in-hospital death (10.88% vs. 6.26%; OR 1.83; p < 0.001) and intracranial bleeding (5.76% vs. 4.58%; OR 1.27; p = 0.005). Similarly, in the thrombectomy cohort, active cancer was associated with increased in-hospital mortality (28.10% vs. 20.00%; OR 1.56; p < 0.001) and subarachnoid hemorrhage (6.14% vs. 4.67%; OR 1.33; p = 0.007). After multivariable adjustment, stratification revealed highly heterogeneous complication profiles depending heavily on the specific cancer subtype. Conclusions: This large-scale analysis demonstrates that while mortality and specific bleeding risks are generally elevated in stroke patients with malignancies, these risks depend strongly on the cancer subtype. These findings advocate for a tailored, risk-based approach to recanalization therapy, weighing individual complication profiles rather than a general exclusion of patients with cancer from acute stroke therapies.

Background: Acute confusional migraine (ACM) is a rare migraine variant, primarily documented in paediatric populations. Its presentation in geriatric patients is extremely rare and can mimic acute ischemic stroke. Case: We report on an 84-year-old male presenting with acute hemicrania followed by confusion and dysarthria. Despite initial systemic thrombolysis for suspected stroke, symptoms only resolved after sleep. Extensive diagnostics were unremarkable, leading to the diagnosis of ACM based on a 20-year history of stereotyped attacks. Conclusion: This case represents the oldest reported patient with ACM to date. It highlights the importance of including ACM in the differential diagnosis of stroke mimics in the elderly to avoid unnecessary interventions.

The RELY-CD study investigated the long-term clinical response to botulinum neurotoxin type A in cervical dystonia within a multicenter, real-world setting. This retrospective study focused on patients treated with complex-free (incobotulinumtoxinA) and complex-containing (onabotulinumtoxinA and abobotulinumtoxinA) BoNT/A formulations over an up to 10-year period. The novel dose–effect parameter “DEff” was introduced to quantify the relationship between dose adjustments and clinical outcomes, enabling the identification of partial treatment failures. The primary endpoint was a comparison of a clinically meaningful worsening in DEff in treatment year 7 compared to year 2 between complex-free and complex-containing botulinum neurotoxin type A. The RELY-CD study provides unique insights into long-term treatment patterns, clinical resistance phenomena, and the implications of formulation differences on treatment outcomes, addressing a critical gap in the literature on real-world botulinum neurotoxin type A application. The study methodology, including the definition and calculation of the novel DEff, as well as clinical baseline characteristics, are presented.

Background: Large language models (LLMs) have demonstrated impressive performances in various medical domains, prompting an exploration of their potential utility within the high-demand setting of emergency department (ED) triage. This study evaluated the triage proficiency of different LLMs and ChatGPT, an LLM-based chatbot, compared to professionally trained ED staff and untrained personnel. We further explored whether LLM responses could guide untrained staff in effective triage. Objective: This study aimed to assess the efficacy of LLMs and the associated product ChatGPT in ED triage compared to personnel of varying training status and to investigate if the models’ responses can enhance the triage proficiency of untrained personnel. Methods: A total of 124 anonymized case vignettes were triaged by untrained doctors; different versions of currently available LLMs; ChatGPT; and professionally trained raters, who subsequently agreed on a consensus set according to the Manchester Triage System (MTS). The prototypical vignettes were adapted from cases at a tertiary ED in Germany. The main outcome was the level of agreement between raters’ MTS level assignments, measured via quadratic-weighted Cohen κ. The extent of over- and undertriage was also determined. Notably, instances of ChatGPT were prompted using zero-shot approaches without extensive background information on the MTS. The tested LLMs included raw GPT-4, Llama 3 70B, Gemini 1.5, and Mixtral 8x7b. Results: GPT-4–based ChatGPT and untrained doctors showed substantial agreement with the consensus triage of professional raters (κ=mean 0.67, SD 0.037 and κ=mean 0.68, SD 0.056, respectively), significantly exceeding the performance of GPT-3.5–based ChatGPT (κ=mean 0.54, SD 0.024; P<.001). When untrained doctors used this LLM for second-opinion triage, there was a slight but statistically insignificant performance increase (κ=mean 0.70, SD 0.047; P=.97). Other tested LLMs performed similar to or worse than GPT-4–based ChatGPT or showed odd triaging behavior with the used parameters. LLMs and ChatGPT models tended toward overtriage, whereas untrained doctors undertriaged. Conclusions: While LLMs and the LLM-based product ChatGPT do not yet match professionally trained raters, their best models’ triage proficiency equals that of untrained ED doctors. In its current form, LLMs or ChatGPT thus did not demonstrate gold-standard performance in ED triage and, in the setting of this study, failed to significantly improve untrained doctors’ triage when used as decision support. Notable performance enhancements in newer LLM versions over older ones hint at future improvements with further technological development and specific training.

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) of still unclear etiology. In recent years, the search for biomarkers facilitating its diagnosis, prognosis, therapy response, and other parameters has gained increasing attention. In this regard, in a previous meta-analysis comprising 22 studies, we found that MS is associated with higher nitrite/nitrate (NOx) levels in the cerebrospinal fluid (CSF) compared to patients with non-inflammatory other neurological diseases (NIOND). However, many of the included studies did not distinguish between the different clinical subtypes of MS, included pre-treated patients, and inclusion criteria varied. As a follow-up to our meta-analysis, we therefore aimed to analyze the serum and CSF NOx levels in clinically well-defined cohorts of treatment-naïve MS patients compared to patients with somatic symptom disorder. To this end, we analyzed the serum and CSF levels of NOx in 117 patients (71 relapsing–remitting (RR) MS, 16 primary progressive (PP) MS, and 30 somatic symptom disorder). We found that RRMS and PPMS patients had higher serum NOx levels compared to somatic symptom disorder patients. This difference remained significant in the subgroup of MRZ-negative RRMS patients. In conclusion, the measurement of NOx in the serum might indeed be a valuable tool in supporting MS diagnosis.

BACKGROUND Large language models (LLMs) have demonstrated impressive performances in various medical domains, prompting an exploration of their potential utility within the high-demand setting of emergency department (ED) triage. This study evaluated the triage proficiency of different LLMs and ChatGPT, an LLM-based chatbot, compared to professionally trained ED staff and untrained personnel. We further explored whether LLM responses could guide untrained staff in effective triage. OBJECTIVE This study aimed to assess the efficacy of LLMs and the associated product ChatGPT in ED triage compared to personnel of varying training status and to investigate if the models’ responses can enhance the triage proficiency of untrained personnel. METHODS A total of 124 anonymized case vignettes were triaged by untrained doctors; different versions of currently available LLMs; ChatGPT; and professionally trained raters, who subsequently agreed on a consensus set according to the Manchester Triage System (MTS). The prototypical vignettes were adapted from cases at a tertiary ED in Germany. The main outcome was the level of agreement between raters’ MTS level assignments, measured via quadratic-weighted Cohen κ. The extent of over- and undertriage was also determined. Notably, instances of ChatGPT were prompted using zero-shot approaches without extensive background information on the MTS. The tested LLMs included raw GPT-4, Llama 3 70B, Gemini 1.5, and Mixtral 8x7b. RESULTS GPT-4–based ChatGPT and untrained doctors showed substantial agreement with the consensus triage of professional raters (κ=mean 0.67, SD 0.037 and κ=mean 0.68, SD 0.056, respectively), significantly exceeding the performance of GPT-3.5–based ChatGPT (κ=mean 0.54, SD 0.024; P<.001). When untrained doctors used this LLM for second-opinion triage, there was a slight but statistically insignificant performance increase (κ=mean 0.70, SD 0.047; P=.97). Other tested LLMs performed similar to or worse than GPT-4–based ChatGPT or showed odd triaging behavior with the used parameters. LLMs and ChatGPT models tended toward overtriage, whereas untrained doctors undertriaged. CONCLUSIONS While LLMs and the LLM-based product ChatGPT do not yet match professionally trained raters, their best models’ triage proficiency equals that of untrained ED doctors. In its current form, LLMs or ChatGPT thus did not demonstrate gold-standard performance in ED triage and, in the setting of this study, failed to significantly improve untrained doctors’ triage when used as decision support. Notable performance enhancements in newer LLM versions over older ones hint at future improvements with further technological development and specific training.