Elapid snake envenomation in horses: 52 cases (2006-2016)

Background: Snake envenomation is a cause of morbidity and mortality in domestic animals worldwide. The clinical features of crotalid snake (pit viper) envenomation are widely reported and well described in horses but elapid snake envenomation is poorly characterised. Objectives: To describe the presentation, clinical and laboratory findings, treatment and outcome of horses with a diagnosis of elapid snake envenomation in Australia. Study design: Retrospective case series. Methods: Medical records of horses with a diagnosis of elapid snake envenomation (2006-2016) at several university and private veterinary practices were reviewed. Inclusion criteria comprised one or more of the following: 1) observed snakebite, 2) positive snake venom detection kit (SVDK) result, 3) appropriate clinical response to treatment with antivenom or 4) supportive post-mortem findings. Results: Fifty-two cases met the inclusion criteria. Most cases (94%) demonstrated clinical signs of neurotoxicity, characterised by generalised neuromuscular weakness. Associated neurologic signs included staggering gait, muscle fasciculations, recumbency, mydriasis, ptosis and tongue paresis. Concurrent clinically important conditions included rhabdomyolysis (50%) and haemolysis (19%). Of 18 urine samples evaluated with a SVDK, only three (17%) were positive. Overall survival was favourable (86%) among 49 horses who received antivenom. Eighteen surviving horses (43%) required more than one vial of antivenom. Main limitations: Possible cases within the searchable database were not included if horses died acutely or responded to symptomatic treatment without receiving antivenom. Conclusions: Elapid snake envenomation is primarily a syndrome of neuromuscular weakness. Supportive anamnesis or an obvious bite site is rarely encountered. In endemic areas, this diagnosis should be considered for horses with generalised neuromuscular weakness, altered mentation, rhabdomyolysis and/or haemolysis; especially during spring and summer months. Diagnostic suspicion is best confirmed by response to treatment with antivenom

Diagnostic approaches, aetiological agents and their associations with short-term survival and laminitis in horses with acute diarrhoea admitted to referral institutions

First published: 20 November 2023. OnlinePublBackground: An international description of the diagnostic approaches used in different institutions to diagnose acute equine diarrhoea and the pathogens detected is lacking. Objectives: To describe the diagnostic approach, aetiological agents, outcome, and development of laminitis for diarrhoeic horses worldwide. Study design: Multicentre retrospective case series. Methods: Information from horses with acute diarrhoea presenting to participating institutions between 2016 and 2020, including diagnostic approaches, pathogens detected and their associations with outcomes, were compared between institutions or geographic regions. Results: One thousand four hundred and thirty-eight horses from 26 participating institutions from 4 continents were included. Overall, aetiological testing was limited (44% for Salmonella spp., 42% for Neorickettsia risticii [only North America], 40% for Clostridiodes difficile, and 29% for ECoV); however, 13% (81/633) of horses tested positive for Salmonella, 13% (35/262) for N. risticii, 9% (37/422) for ECoV, and 5% (27/578) for C. difficile. C. difficile positive cases had greater odds of non-survival than horses negative for C. difficile (OR: 2.69, 95%CI: 1.23–5.91). In addition, horses that were positive for N. risticii had greater odds of developing laminitis than negative horses (OR: 2.76, 95%CI: 1.12–6.81; p = 0.029). Main limitations: Due to the study's retrospective nature, there are missing data. Conclusions: This study highlighted limited diagnostic investigations in cases of acute equine diarrhoea. Detection rates of pathogens are similar to previous reports. Nonsurvival and development of laminitis are related to certain detected pathogens.Diego E. Gomez, Luis G. Arroyo, Angelika Schoster, David L. Renaud, Jamie J. Kopper, Bettina Dunkel, David Byrne, The MEDS group: Anna Mykkanen ... Gustavo Ferlini Agne ... et al

A Machine Learning Algorithm to Identify Patients at Risk of Unplanned Subsequent Surgery After Intramedullary Nailing for Tibial Shaft Fractures

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.Orthopaedics, Trauma Surgery and Rehabilitatio