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This study aims to generate foundational metabolomic data of aqueous humor (AH) in healthy horses and donkeys, and to investigate potential changes or trends in the metabolomic profile associated with age, sex or ocular pathology in horses. The AH metabolomic fingerprint from 5 donkeys and 35 equine eyes (17 controls, 8 with cataracts, 6 with retinal disease and 4 with anterior chamber disease (ACD)) were analyzed using nuclear magnetic resonance (NMR) spectroscopy. A linear mixed-effects model, with individual horse as a random effect and group as a fixed effect, with multiple testing correction using the Benjamini–Hochberg false discovery rate (FDR) method was used to compare groups. The metabolomic profile of the donkeys and horse’s AH is very similar to that of other mammals. Threonine was higher in young horses (p = 0.04), and creatinine was elevated in males (p = 0.04). Compared with control groups, dimethyl sulfone was higher in the retina (p < 0.00) and cataract (p = 0.05) groups. Arginine (p = 0.05) and valine (p = 0.03) were lower in the retina group compared to controls. This study successfully characterized the AH metabolomic profile in healthy horses and donkeys and identified several metabolites that could be associated with ocular pathology, warranting further investigation to determine their potential as biomarkers of ocular disease.

Background Comparisons between smartphone ECG (SpECG) recordings obtained from the right and left sides of the thorax have not been reported in animals. Objectives To evaluate the screening potential of a SpECG obtained from both sides of the thorax and to compare the degree of agreement between the SpECG and a reference ECG (rECG) for measurement of the duration of baseline electrical deflections recorded over both sides of the thorax. Animals Fifty horses admitted to the equine hospital, university‐owned horses, and horses from an endurance riding facility. Methods Prospective observational study. Simultaneous rECG and SpECG tracings were recorded on both sides of the thorax and evaluated by 2 experienced observers for interpretability, presence and classification of arrhythmias, and duration of deflections of the electrical baseline. Results Fifteen (15/50) horses had spontaneous arrhythmias. Excellent agreement was found between the 2 devices (κ = 0.91; P < .001; 95% confidence interval [CI] 0.79‐1.03) for the detection of arrhythmias. Significantly more P waves were of diagnostic quality from the right side (20 versus 5, P = .001) and significantly more QRS and QT intervals were of diagnostic quality from the left side (66 versus 38 and 62 versus 34, respectively, P < .001). Substantial agreement for the duration of deflections between devices only was observed for the QRS interval (SpECG left). Conclusions and Clinical Importance It is important to obtain SpECG tracings from both sides of the thorax in horses. The SpECG is a useful stall‐sidearrhythmia‐screening tool for some common arrhythmias in horses.

Background In modern times, horses are utilized not only for labour and transportation purposes but also for recreational activities such as competition and pleasure riding. In these various pursuits, the role of vision plays a crucial role. Electroretinography is the most used test to diagnose diseases of the retinal outer segment. There is a wide variety of devices to perform the electroretinography differing one from each other in the corneal electrode and the light stimulation. The Koijman electrode has been tested in dogs but not in horses. The main purpose of this study was to compare electroretinography parameters from horses sedated with detomidine alone or in combination with butorphanol, during a standardized protocol using the Koijman electrode and RETI-port® system. Seven mares were allocated to the detomidine and detomidine plus butorphanol group in a randomised, controlled, crossover study. Friedman and Willcoxon-signed ranked tests were used to compare the electroretinogram parameters. A Student’s t-test was used to compare differences in the number of artefacts to valid values ratio obtained under both sedation protocols. Results Dark adaptation peaked after 16 min under scotopic conditions in both groups. No significant differences in electroretinogram parameters between groups were observed. During the mixed rod and cone response evaluation under scotopic conditions, all mares made a movement of the head resulting in a high number of artefacts. The detomidine plus butorphanol group showed a non-significant tendency to have fewer artefacts and a longer duration of sedation compared to the detomidine group. Conclusions Detomidine alone or combined with butorphanol may be suitable to use Koijman electrode and the RETI-port® to perform a standardized long protocol in horses with some adaptations.

Occipital condylar fractures (OCFs) causing delayed onset lower cranial nerve paralysis (LCNPs) are rare. We present a 7‐year‐old Friesian horse with delayed onset dysphagia caused by vagus nerve (CNX) paralysis and suspicion of glossopharyngeal nerve (CNIX) paralysis developed several days after a minor head injury. Endoscopic examination revealed right laryngeal hemiplegia and intermittent dorsal displacement of the soft palate. An area of submucosal hemorrhage and bulging was appreciated over the dorsal aspect of the medial compartment of the right guttural pouch. Radiological examination of the proximal cervical region showed rotation of the atlas and the presence of a large bone fragment dorsal to the guttural pouches. Occipital condyle fracture with delayed onset cranial nerve paralysis was diagnosed. Delayed onset cranial nerve paralysis causing dysphagia might be a distinguishable sign of OCF in horses. Delayed onset dysphagia after head injury should prompt equine clinicians to evaluate the condition of the atlanto‐occipital articulation and skull base.

A six-year-old female goat was presented to the veterinary teaching hospital of the University of the West Indies with a history of progressive hind-limb paresis lasting two weeks. The doe developed a grade 6/6 holosystolic murmur during hospitalisation. Echocardiography revealed vegetative growths attached to cusps of the mitral and aortic valves. There was an accelerated aortic flow at 2.9 m/s and aortic insufficiency. The aortic vegetation was prolapsing into the left ventricle during diastole, causing it to contact the septal mitral valve leaflet. A diagnosis of mitral and aortic vegetative endocarditis, with a mitral kissing vegetation and mild aortic stenosis, was reached. The patient was placed on broad-spectrum antimicrobials. A short-term follow-up showed no resolution of clinical signs, and the animal eventually died. Post-mortem examination showed severe vegetative, fibrino-necrotic, aortic and mitral valve lesions. The goat also had a severe fibrino-suppurative mastitis. Histopathology confirmed the lesions to be vegetative endocarditis.

Background The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results The database included a total of 2022 patients (mean age 64 [IQR 5–71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10–17]) and SOFA score (5 [IQR 3–7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. Conclusion The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice .

Around one-third of patients diagnosed with COVID-19 develop a severe illness that requires admission to the Intensive Care Unit (ICU). In clinical practice, clinicians have learned that patients admitted to the ICU due to severe COVID-19 frequently develop ventilator-associated lower respiratory tract infections (VA-LRTI). This study aims to describe the clinical characteristics, the factors associated with VA-LRTI, and its impact on clinical outcomes in patients with severe COVID-19. This was a multicentre, observational cohort study conducted in ten countries in Latin America and Europe. We included patients with confirmed rtPCR for SARS-CoV-2 requiring ICU admission and endotracheal intubation. Only patients with a microbiological and clinical diagnosis of VA-LRTI were included. Multivariate Logistic regression analyses and Random Forest were conducted to determine the risk factors for VA-LRTI and its clinical impact in patients with severe COVID-19. In our study cohort of 3287 patients, VA-LRTI was diagnosed in 28.8% [948/3287]. The cumulative incidence of ventilator-associated pneumonia (VAP) was 18.6% [610/3287], followed by ventilator-associated tracheobronchitis (VAT) 10.3% [338/3287]. A total of 1252 bacteria species were isolated. The most frequently isolated pathogens were Pseudomonas aeruginosa (21.2% [266/1252]), followed by Klebsiella pneumoniae (19.1% [239/1252]) and Staphylococcus aureus (15.5% [194/1,252]). The factors independently associated with the development of VA-LRTI were prolonged stay under invasive mechanical ventilation, AKI during ICU stay, and the number of comorbidities. Regarding the clinical impact of VA-LRTI, patients with VAP had an increased risk of hospital mortality (OR [95% CI] of 1.81 [1.40–2.34]), while VAT was not associated with increased hospital mortality (OR [95% CI] of 1.34 [0.98–1.83]). VA-LRTI, often with difficult-to-treat bacteria, is frequent in patients admitted to the ICU due to severe COVID-19 and is associated with worse clinical outcomes, including higher mortality. Identifying risk factors for VA-LRTI might allow the early patient diagnosis to improve clinical outcomes. Trial registration: This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable.