Prevalence of and risk factors for degenerative mitral valve disease in dogs attending primary-care veterinary practices in england

Background To date, epidemiological studies on degenerative mitral valve disease (DMVD) in dogs have largely reported referral caseloads or been limited to predisposed breeds. Analysis of primary‐care data to identify factors associated with DMVD would help clinicians identify high‐risk individuals and improve understanding. Objectives To estimate the prevalence of and identify risk factors for DMVD in dogs attending primary‐care veterinary practices in England. Animals Cases were identified within the electronic patient records of 111,967 dogs attending 93 practices. Four hundred and 5 dogs were diagnosed with DMVD (diagnosed cases) and a further 3,557 dogs had a heart murmur (HM) consistent with DMVD (possible cases). Methods Retrospective cross‐sectional study design. Prevalence was adjusted for the sampling approach. Mixed effects logistic regression models identified factors associated with DMVD. Results Prevalence estimates of diagnosed DMVD and HMs consistent with DMVD (both diagnosed and possible cases) were 0.36% (95% confidence interval [CI]: 0.29–0.45) and 3.54% (95% CI: 3.26–3.84) respectively. In the multivariable analysis, males had higher odds of diagnosed DMVD than did females (odds ratio [OR] 1.40, 95% CI: 1.12–1.74). Insured dogs had increased odds of DMVD compared with noninsured dogs (OR 3.56, 95% CI: 2.79–4.55) and dogs ≥20 kg had approximately half the odds of DMVD diagnosis compared with dogs(OR 0.51, 95% CI: 0.36–0.74). Strong associations between a DMVD diagnosis and individual breeds and age were identified. Conclusions and Clinical Importance Degenerative mitral valve disease was a common disorder in practice‐attending dogs. Knowledge of identified risk factors for DMVD could improve clinical diagnosis and direct future research

A simple prognostic index in acute heart failure

Background Rapid effective triage is integral to emergency care in patients hospitalized for heart failure, to guide the type and intensity of therapy. Several indexes and scores have been proposed to predict outcome; most of the them are complex and unfit to use at the bedside. Methods We propose a new prognostic index for in hospital mortality in acute heart failure. The index was built according to the formula; 220 – age – heart rate + systolic blood pressure – ( creatinine X 10). The index was tested in 1628 patients admitted for acute heart failure and enrolled, from November 2007 to December 2009, in the Italian Registry on Heart Failure Outcome ( IN-HF); a prospective, multicentre, observational study. Results The prognostic index was an independent predictor for in hospital mortality risk ( c statistic= 0.74) (p<0.0001), together with left ventricular ejection fraction (p= 0.001), Glycemia ( p= 0.019) and hemoglobin concentration (p = 0.002). Conclusion A simple prognostic index based on variables easily assessed can be useful to predict mortality in acute heart failure at the first arrival in hospital

Diagnosis of obstructive coronary artery disease using computed tomography angiography in patients with stable chest pain depending on clinical probability and in clinically important subgroups: meta-analysis of individual patient data

OBJECTIVE: To determine whether coronary computed tomography angiography (CTA) should be performed in patients with any clinical probability of coronary artery disease (CAD), and whether the diagnostic performance differs between subgroups of patients. DESIGN: Prospectively designed meta-analysis of individual patient data from prospective diagnostic accuracy studies. DATA SOURCES: Medline, Embase, and Web of Science for published studies. Unpublished studies were identified via direct contact with participating investigators. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Prospective diagnostic accuracy studies that compared coronary CTA with coronary angiography as the reference standard, using at least a 50% diameter reduction as a cutoff value for obstructive CAD. All patients needed to have a clinical indication for coronary angiography due to suspected CAD, and both tests had to be performed in all patients. Results had to be provided using 2×2 or 3×2 cross tabulations for the comparison of CTA with coronary angiography. Primary outcomes were the positive and negative predictive values of CTA as a function of clinical pretest probability of obstructive CAD, analysed by a generalised linear mixed model; calculations were performed including and excluding non-diagnostic CTA results. The no-treat/treat threshold model was used to determine the range of appropriate pretest probabilities for CTA. The threshold model was based on obtained post-test probabilities of less than 15% in case of negative CTA and above 50% in case of positive CTA. Sex, angina pectoris type, age, and number of computed tomography detector rows were used as clinical variables to analyse the diagnostic performance in relevant subgroups. RESULTS: Individual patient data from 5332 patients from 65 prospective diagnostic accuracy studies were retrieved. For a pretest probability range of 7-67%, the treat threshold of more than 50% and the no-treat threshold of less than 15% post-test probability were obtained using CTA. At a pretest probability of 7%, the positive predictive value of CTA was 50.9% (95% confidence interval 43.3% to 57.7%) and the negative predictive value of CTA was 97.8% (96.4% to 98.7%); corresponding values at a pretest probability of 67% were 82.7% (78.3% to 86.2%) and 85.0% (80.2% to 88.9%), respectively. The overall sensitivity of CTA was 95.2% (92.6% to 96.9%) and the specificity was 79.2% (74.9% to 82.9%). CTA using more than 64 detector rows was associated with a higher empirical sensitivity than CTA using up to 64 rows (93.4% v 86.5%, P=0.002) and specificity (84.4% v 72.6%, P<0.001). The area under the receiver-operating-characteristic curve for CTA was 0.897 (0.889 to 0.906), and the diagnostic performance of CTA was slightly lower in women than in with men (area under the curve 0.874 (0.858 to 0.890) v 0.907 (0.897 to 0.916), P<0.001). The diagnostic performance of CTA was slightly lower in patients older than 75 (0.864 (0.834 to 0.894), P=0.018 v all other age groups) and was not significantly influenced by angina pectoris type (typical angina 0.895 (0.873 to 0.917), atypical angina 0.898 (0.884 to 0.913), non-anginal chest pain 0.884 (0.870 to 0.899), other chest discomfort 0.915 (0.897 to 0.934)). CONCLUSIONS: In a no-treat/treat threshold model, the diagnosis of obstructive CAD using coronary CTA in patients with stable chest pain was most accurate when the clinical pretest probability was between 7% and 67%. Performance of CTA was not influenced by the angina pectoris type and was slightly higher in men and lower in older patients. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42012002780

Building Prediction Models for Dementia: The Need to Account for Interval Censoring and the Competing Risk of Death

Indiana University-Purdue University Indianapolis (IUPUI)Context. Prediction models for dementia are crucial for informing clinical decision making in older adults. Previous models have used genotype and age to obtain risk scores to determine risk of Alzheimer’s Disease, one of the most common forms of dementia (Desikan et al., 2017). However, previous prediction models do not account for the fact that the time to dementia onset is unknown, lying between the last negative and the first positive dementia diagnosis time (interval censoring). Instead, these models use time to diagnosis, which is greater than or equal to the true dementia onset time. Furthermore, these models do not account for the competing risk of death which is quite frequent among elder adults. Objectives. To develop a prediction model for dementia that accounts for interval censoring and the competing risk of death. To compare the predictions from this model with the predictions from a naïve analysis that ignores interval censoring and the competing risk of death. Methods. We apply the semiparametric sieve maximum likelihood (SML) approach to simultaneously model the cumulative incidence function (CIF) of dementia and death while accounting for interval censoring (Bakoyannis, Yu, & Yiannoutsos, 2017). The SML is implemented using the R package intccr. The CIF curves of dementia are compared for the SML and the naïve approach using a dataset from the Indianapolis Ibadan Dementia Project. Results. The CIF from the SML and the naïve approach illustrated that for healthier individuals at baseline, the naïve approach underestimated the incidence of dementia compared to the SML, as a result of interval censoring. Individuals with a poorer health condition at baseline have a CIF that appears to be overestimated in the naïve approach. This is due to older individuals with poor health conditions having an elevated risk of death. Conclusions. The SML method that accounts for the competing risk of death along with interval censoring should be used for fitting prediction/prognostic models of dementia to inform clinical decision making in older adults. Without controlling for the competing risk of death and interval censoring, the current models can provide invalid predictions of the CIF of dementia

The Kimberley assessment of depression of older Indigenous Australians: prevalence of depressive disorders, risk factors and validation of the KICA-dep scale

This study aimed to develop a culturally acceptable and valid scale to assess depressive symptoms in older Indigenous Australians, to determine the prevalence of depressive disorders in the older Kimberley community, and to investigate the sociodemographic, lifestyle and clinical factors associated with depression in this population. Methods Cross-sectional survey of adults aged 45 years or over from six remote Indigenous communities in the Kimberley and 30% of those living in Derby, Western Australia. The 11 linguistic and culturally sensitive items of the Kimberley Indigenous Cognitive Assessment of Depression (KICA-dep) scale were derived from the signs and symptoms required to establish the diagnosis of a depressive episode according to the DSM-IV-TR and ICD-10 criteria, and their frequency was rated on a 4-point scale ranging from ‘never’ to ‘all the time’ (range of scores: 0 to 33). The diagnosis of depressive disorder was established after a face-to-face assessment with a consultant psychiatrist. Other measures included sociodemographic and lifestyle factors, and clinical history. Results The study included 250 participants aged 46 to 89 years (mean±SD = 60.9±10.7), of whom 143 (57.2%) were women. The internal reliability of the KICA-dep was 0.88 and the cut-point 7/8 (non-case/case) was associated with 78% sensitivity and 82% specificity for the diagnosis of a depressive disorder. The point-prevalence of a depressive disorder in this population was 7.7%; 4.0% for men and 10.4% for women. Heart problems were associated with increased odds of depression (odds ratio = 3.3, 95% confidence interval = 1.2,8.8). Conclusions The KICA-dep has robust psychometric properties and can be used with confidence as a screening tool for depression among older Indigenous Australians. Depressive disorders are common in this population, possibly because of increased stressors and health morbidities

The relations of metabolic syndrome to anxiety and depression symptoms in children and adults

Metabolic syndrome is a cluster of five factors (elevated systolic blood pressure, elevated blood glucose, elevated triglycerides, large waist circumference, and decreased HDL) that are related to a greater chance of heart disease, stroke, and diabetes. There is evidence that metabolic syndrome is correlated with depression, but the directionality and mechanism is unclear. There is also dispute in the literature as to whether there is a correlation with anxiety and metabolic syndrome. In this study, levels of depression and anxiety determined from questionnaires and interviews (Adult Self Report, Child Behavior Checklist, Kiddie Schedule for Affective Disorders and Schizophrenia-Present and Lifetime, and the Composite International Diagnostic Interview) were compared with the five factors of metabolic syndrome in 100 three-person families. In children and adolescents, elevated triglycerides were predictive of elevated depressive behavior above the age of 12.68 (pppp \u3c .05 respectively). Additionally, a lower SES, older age, greater anxious behavior, and being male were all predictive of greater overall metabolic risk. Results implicate an age-moderated difference in how metabolic factors affect depression in children, possibly having a mechanism coinciding or affected by puberty. In adults, the directionality seems to reverse, with the anxious behavior having an effect on the metabolic syndrome factor, possibly related to stress and inflammation. Further research is needed to study these mechanisms and elucidate the connections between the disorders

A survey of validity and utility of electronic patient records in a general practice

Objective: To develop methods of measuring the validity and utility of electronic patient records in general practice. Design: A survey of the main functional areas of a practice and use of independent criteria to measure the validity of the practice database. Setting: A fully computerised general practice in Skipton, north Yorkshire. Subjects: The records of all registered practice patients. Main outcome measures: Validity of the main functional areas of the practice clinical system. Measures of the completeness, accuracy, validity, and utility of the morbidity data for 15 clinical diagnoses using recognised diagnostic standards to confirm diagnoses and identify further cases. Development of a method and statistical toolkit to validate clinical databases in general practice. Results: The practice electronic patient records were valid, complete, and accurate for prescribed items (99.7%), consultations (98.1%), laboratory tests (100%), hospital episodes (100%), and childhood immunisations (97%). The morbidity data for 15 clinical diagnoses were complete (mean sensitivity=87%) and accurate (mean positive predictive value=96%). The presence of the Read codes for the 15 diagnoses was strongly indicative of the true presence of those conditions (mean likelihood ratio=3917). New interpretations of descriptive statistics are described that can be used to estimate both the number of true cases that are unrecorded and quantify the benefits of validating a clinical database for coded entries. Conclusion: This study has developed a method and toolkit for measuring the validity and utility of general practice electronic patient records