How Lazy Are Pet Cats Really? Using Machine Learning and Accelerometry to Get a Glimpse into the Behaviour of Privately Owned Cats in Different Households.

Surprisingly little is known about how the home environment influences the behaviour of pet cats. This study aimed to determine how factors in the home environment (e.g., with or without outdoor access, urban vs. rural, presence of a child) and the season influences the daily behaviour of cats. Using accelerometer data and a validated machine learning model, behaviours including being active, eating, grooming, littering, lying, scratching, sitting, and standing were quantified for 28 pet cats. Generalized estimating equation models were used to determine the effects of different environmental conditions. Increasing cat age was negatively correlated with time spent active (p < 0.05). Cats with outdoor access (n = 18) were less active in winter than in summer (p < 0.05), but no differences were observed between seasons for indoor-only (n = 10) cats. Cats living in rural areas (n = 7) spent more time eating than cats in urban areas (n = 21; p < 0.05). Cats living in single-cat households (n = 12) spent more time lying but less time sitting than cats living in multi-cat households (n = 16; p < 0.05). Cats in households with at least one child (n = 20) spent more time standing in winter (p < 0.05), and more time lying but less time sitting in summer compared to cats in households with no children (n = 8; p < 0.05). This study clearly shows that the home environment has a major impact on cat behaviour.

The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Cats (Felis catus): A Validation Study.

Animal behaviour can be an indicator of health and welfare. Monitoring behaviour through visual observation is labour-intensive and there is a risk of missing infrequent behaviours. Twelve healthy domestic shorthair cats were fitted with triaxial accelerometers mounted on a collar and harness. Over seven days, accelerometer and video footage were collected simultaneously. Identifier variables (n = 32) were calculated from the accelerometer data and summarized into 1 s epochs. Twenty-four behaviours were annotated from the video recordings and aligned with the summarised accelerometer data. Models were created using random forest (RF) and supervised self-organizing map (SOM) machine learning techniques for each mounting location. Multiple modelling rounds were run to select and merge behaviours based on performance values. All models were then tested on a validation accelerometer dataset from the same twelve cats to identify behaviours. The frequency of behaviours was calculated and compared using Dirichlet regression. Despite the SOM models having higher Kappa (>95%) and overall accuracy (>95%) compared with the RF models (64-76% and 70-86%, respectively), the RF models predicted behaviours more consistently between mounting locations. These results indicate that triaxial accelerometers can identify cat specific behaviours.

Are there gender disparities in symptom presentation or triage of patients with chest discomfort at primary care out-of-hours services? An observational study.

OBJECTIVES: Previous hospital-based studies have suggested delayed recognition of acute coronary syndrome (ACS) in women. We wanted to assess differences in symptom presentation or triage among women and men who contacted primary care out-of-hours services (OHS) for chest discomfort. DESIGN: Retrospective observational study. SETTING: Primary care OHS. PARTICIPANTS: 276 women and 242 men with chest discomfort who contacted a primary care OHS in the Netherlands in 2013 and 2014. MAIN OUTCOME MEASURES: Differences between women and men regarding symptom presentation and urgency allocation. RESULTS: 8.4% women and 14.0% men had ACS. Differences in symptoms between patients with and without ACS were in general small, for both women and men. In women with ACS compared with women without ACS, mean duration of telephone calls was discriminative; 5.22 (SD 2.53) vs 7.26 (SD 3.11) min, p value=0.003. In men, radiation of pain (89.3% vs 54.9%, p value=0.011) was discriminative for ACS, and stabbing chest pain (3.7% vs 24.0%, p value=0.014) for absence of ACS . Women and men with chest discomfort received similar high urgency allocation (crude and adjusted OR after correction for ACS and age; 1.03 (95% CI 0.72 to 1.48) and 1.04 (95% CI 0.72 to 1.52), respectively). Women with ACS received a high urgency allocation in 22/23 (95.7%) and men with ACS in 30/34 (88.2%), p value=0.331. CONCLUSIONS: Discriminating ACS in patients with chest discomfort who contacted primary care OHS is difficult in both women and men. Women and men with chest discomfort received similar high urgency allocation.

Rapid Systematic Review of Normal Audiometry Results as a Predictor for Benign Paroxysmal Positional Vertigo.

OBJECTIVE: To evaluate whether absence of hearing loss on pure-tone audiometry (PTA) is reliable as a diagnostic test for predicting benign paroxysmal positional vertigo (BPPV) in adult patients with vertigo. DATA SOURCES: PubMed, Embase, and the Cochrane Library. METHODS: A systematic literature search was conducted on December 10, 2013. Relevant publications were selected based on title, abstract, and full text. Selected articles were assessed for relevance and risk of bias using predetermined criteria. Prevalence and the positive and negative predictive value (PPV and NPV) were extracted. RESULTS: Of 603 retrieved publications, 1 article with high relevance and moderate risk of bias was included. In this study, the prevalence of BPPV was 28%. The PPV of hearing loss assessed by PTA was 31% (95% CI, 17-49) and the NPV was 73% (95% CI, 61-83). The absence of hearing loss on PTA decreased the risk of BPPV by 1%. CONCLUSION AND RECOMMENDATION: There is insufficient high-quality evidence regarding the diagnostic value of the absence of hearing loss, assessed by PTA, for predicting BPPV in adult patients with vertigo.

Time-frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures.

PURPOSE: A definite diagnosis of psychogenic nonepileptic seizures (PNES) usually requires in-patient video-electroencephalography (EEG) monitoring. Previous research has shown that convulsive psychogenic nonepileptic seizures (PNES) demonstrate a characteristic pattern of rhythmic movement artifact on the EEG. Herein we sought to examine the potential for time-frequency mapping of data from a movement-recording device (accelerometer) worn on the wrist as a diagnostic tool to differentiate between convulsive epileptic seizures and PNES. METHODS: Time-frequency mapping was performed on accelerometer traces obtained during 56 convulsive seizure-like events from 35 patients recorded during in-patient video-EEG monitoring. Twenty-six patients had PNES, eight had epileptic seizures, and one had both seizure types. The time-frequency maps were derived from fast Fourier transformations to determine the dominant frequency for sequential 2.56-s blocks for the course of each event. KEY FINDINGS: The coefficient of variation (CoV) of limb movement frequency for the PNES events was less than for the epileptic seizure events (median, 17.18% vs. 52.23%; p < 0.001). A blinded review of the time-frequency maps by an epileptologist was accurate in differentiating between the event types, that is, 38 (92.7%) of 41 and 6 (75%) of 8 nonepileptic and epileptic seizures, respectively, were diagnosed correctly, with seven events classified as "nondiagnostic." Using a CoV cutoff score of 32% resulted in similar classification accuracy, with 42 (93%) of 45 PNES and 10 (91%) of 11 epileptic seizure events correctly diagnosed. SIGNIFICANCE: Time-frequency analysis of data from a wristband movement monitor could be utilized as a diagnostic tool to differentiate between epileptic and nonepileptic convulsive seizure-like events.

Routine polysomnography in an epilepsy monitoring unit.

Up to 13% of patients with epilepsy have moderate or severe sleep-disordered breathing, in particular obstructive sleep apnea (OSA), a disorder associated with reduced quality of life, worsened seizure control, and increased cardiovascular morbidity and mortality. Combining video-EEG monitoring with polysomnography (VPSG) provides the opportunity to diagnose clinically significant OSA as well as relate the occurrence of seizures and the epilepsy diagnosis to the presence and severity of sleep-disordered breathing. We have established routine VPSG in our inpatient video-EEG monitoring unit and present our findings in 87 patients. Clinically significant sleep-disordered breathing was diagnosed in 19 of 87 (22%) patients. Patients with psychogenic non-epileptic seizures (PNES) had poorer sleep quality compared to patients with epilepsy and those with neither diagnosis, whereas the prevalence of clinically significant sleep-disordered breathing in patients with PNES (29%) did not differ significantly compared to patients with epilepsy (21%) and those with neither diagnosis (22%). The differences in sleep quality are not explained by differences in body mass index (BMI) or anti-epileptic drug (AED) effects.

Xanthogranulomatous pyelonephritis presenting as a pseudotumour in a 5-year-old boy.

Xanthogranulomatous pyelonephritis (XGPN) is a rare, severe and atypical form of chronic pyelonephritis. It is characterised by destruction of the renal parenchyma and replacement with a chronic inflammatory infiltrate and lipid-laden macrophages resulting in a non-functional kidney. The authors report a case of a 5-year-old boy presented with a history of abdominal pain, malaise, anorexia and weight loss for 2 months. Physical examination revealed a large flank mass and the child was directed to the oncology unit on suspicion of renal tumour. Based on clinical examination and imaging, the presumptive diagnosis of XGPN of the left kidney was made. A left transperitoneal nephrectomy was performed and the histology confirmed the diagnosis. Although rare, XGPN is a clinically important entity that should be considered in the differential diagnosis of an atypical-appearance renal mass in paediatric age.