Developing new hospital-at-home models based on Comprehensive Geriatric Assessment: Implementation recommendations by the Working Group on Hospital-at-Home and Community Geriatrics of the Catalan Society of Geriatrics and Gerontology

Last decade, the Government of Catalonia have urged an integrated care strategy for planning the care model to older populations living with frailty, multimorbidity and advanced illnesses. Based on international evidence that was reviewed by a group of experts from the Catalan Society of Gerontology and Geriatrics, we summarised some recommendation to adapt hospital-at-home care to older populations in our system. We defined Comprehensive Geriatric Assessment (CGA) hospital-at-home (HaH) as a specialised home hospitalisation service formed by interdisciplinary teams, characterised by using the clinical methodology of CGA, and by adapting geriatric units’ protocols for the provision of person-centred care at home. Main benefits of CGA-HaH in these populations are: response to heath crises according to individualised care plans based on the situational diagnosis carried out by Primary Care teams; provision of a comprehensive health and social approach tailored to the complexity of cases and situations; and adaptation of multipurpose hospitalisation, by working on different person-centred care, aspects, such as caregivers support on care provision, focusing on function or home adaptation.     

Prevalence of transthyretin cardiac amyloidosis in elderly patients diagnosed with heart failure

ObjectiveThere is increased interest in studying ATTR-CA, a pathology that primarily affects patients of geriatric age and is frequently underdiagnosed. We aim to establish the prevalence of ATTR-CA in a cohort of patients with a history of HFpEF and to describe its characteristics.MethodsWe conducted a prospective observational study. Patients ≥75 years, clinical history of HFpEF, atrial dilation ≥34 ml/m² and left ventricular wall thickening >13 mm, were included. Demographic and analytical parameters were collected, and a comprehensive geriatric assessment was performed, along with a transthoracic echocardiogram and cardiac scintigraphy. Finally, telephone follow-up was carried out at 6 and 12 months.Results50 patients were recruited, mean age 86 ± 6 years, 54% women. Age and functional class (I–II vs. III–IV) were factors associated with presenting with ATTR-CA. Patients with positive scintigraphy had a median time to admission of 5.2 months (confidence interval [CI] 95% 0–10.9), while in those with negative scintigraphy, it was 12.2 months (95% CI 11.7–12.8); log-rank: p = 0.064. Patients with positive scintigraphy had a median time to the combined endpoint (death and readmission) of 1.9 months (95% CI 0–6.1), and patients with negative scintigraphy of 11.9 months (95% CI 11.7–12); log-rank: p = 0.027.ConclusionsATTR-CA appears to be a prevalent etiology in elderly patients within the spectrum of HFpEF. Patients with a diagnosis of ATTR-CA had a shorter time to admission for HF and the combined event of death and admission than patients with a negative result on scintigraphy.     

Perfil clínico y mortalidad a 90 días de los pacientes centenarios atendidos en servicios de urgencias hospitalarios

ObjectivesTo determine the clinical profile and to develop a model to predict 90-day mortality in centenarian patients attended in emergency departments (ED).MethodologyThis was an observational, retrospective, multicentre cohort study including patients > 99 years attended in 5 ED in the Community of Madrid from January to December 2012. Demographic variables were recorded, as well as, comorbidities, cognitive, functional, social basal status, geriatric syndromes, acute episode, and hospital and social resources use, and 90-day mortality.ResultsThe study included 209 patients aged 101 years (SD 1.7) of whom 161 (77.0%) were female. Sixty four (32.5%) had severe comorbidity (Charlson index ≥ 3), 101 (49.8%) on multiple medication, 100 (52.6%) had cognitive impairment, 82 (42.3%) had severe functional dependence, 85 (40.7%) were institutionalised, and 190 (94.5%) had a geriatric syndrome. Dyspnoea (26.8%), followed by falls (12.4%) were the most common causes of attendance. One hundred and eighteen (56.5%) were admitted, and 58 out of 174 (33.3%) died in the first 90 days. The model to predict 90-day overall mortality included male sex (OR 2.42 95% CI = 0.97-6.04; P = .059), emergency care in the previous 3 months (OR 4.08 95% CI = 1.26-13.16; P = .019) and the hospitalization by index event (OR 8.63 95% CI = 3.25-22.9; P < .001) and this model had an area under ROC curve of 0.776 (95% CI = 0.70-0.85; P < .001).ConclusionsCentenarian patients attended in ED had a significant frailty and one in three cases died in the first 90 days after being attended, and this was associated with male sex, emergency care in the previous 3 months, and hospitalisation.     

Estimating regression parameters and degree of dependence for multivariate failure time data

Multivariate failure time data are frequently encountered in longitudinal studies when subjects may experience several events or when there is a grouping of individuals into a cluster. To take into account the dependence of the failure times within the unit (the individual or the cluster) as well as censoring, two multivariate generalizations of the Cox proportional hazards model are commonly used. The marginal hazard model is used when the purpose is to estimate mean regression parameters, while the frailty model is retained when the purpose is to assess the degree of dependence within the unit. We propose a new approach based on the combination of the two aforementioned models to estimate both these quantities. This two-step estimation procedure is quicker and more simple to implement than the EM algorithm used in frailty models estimation. Simulation results are provided to illustrate robustness, consistency, and large-sample properties of estimators. Finally, this method is exemplified on a diabetic retinopathy study in order to assess the effect of photocoagulation in delaying the onset of blindness as well as the dependence between the two eyes blindness times of a patient.     

Testing the correlation for clustered categorical and censored discrete time-to-event data when covariates are measured without/with errors

In the analysis of clustered categorical data, it is of common interest to test for the correlation within clusters, and the heterogeneity across different clusters. We address this problem by proposing a class of score tests for the null hypothesis that the variance components are zero in random effects models, for clustered nominal and ordinal categorical responses. We extend the results to accommodate clustered censored discrete time-to-event data. We next consider such tests in the situation where covariates are measured with errors. We propose using the SIMEX method to construct the score tests for the null hypothesis that the variance components are zero. Key advantages of the proposed score tests are that they can be easily implemented by fitting standard polytomous regression models and discrete failure time models, and that they are robust in the sense that no assumptions need to be made regarding the distributions of the random effects and the unobserved covariates. The asymptotic properties of the proposed tests are studied. We illustrate these tests by analyzing two data sets and evaluate their performance with simulations.     

A semiparametric model for the analysis of recurrent-event panel data

In many longitudinal studies, interest focuses on the occurrence rate of some phenomenon for the subjects in the study. When the phenomenon is nonterminating and possibly recurring, the result is a recurrent-event data set. Examples include epileptic seizures and recurrent cancers. When the recurring event is detectable only by an expensive or invasive examination, only the number of events occurring between follow-up times may be available. This article presents a semiparametric model for such data, based on a multiplicative intensity model paired with a fully flexible nonparametric baseline intensity function. A random subject-specific effect is included in the intensity model to account for the overdispersion frequently displayed in count data. Estimators are determined from quasi-likelihood estimating functions. Because only first- and second-moment assumptions are required for quasi-likelihood, the method is more robust than those based on the specification of a full parametric likelihood. Consistency of the estimators depends only on the assumption of the proportional intensity model. The semiparametric estimators are shown to be highly efficient compared with the usual parametric estimators. As with semiparametric methods in survival analysis, the method provides useful diagnostics for specific parametric models, including a quasi-score statistic for testing specific baseline intensity functions. The techniques are used to analyze cancer recurrences and a pheromone-based mating disruption experiment in moths. A simulation study confirms that, for many practical situations, the estimators possess appropriate small-sample characteristics.     

Semiparametric Analysis for Recurrent Event Data with Time-Dependent Covariates and Informative Censoring

Summary .  Recurrent event data analyses are usually conducted under the assumption that the censoring time is independent of the recurrent event process. In many applications the censoring time can be informative about the underlying recurrent event process, especially in situations where a correlated failure event could potentially terminate the observation of recurrent events. In this article, we consider a semiparametric model of recurrent event data that allows correlations between censoring times and recurrent event process via frailty. This flexible framework incorporates both time-dependent and time-independent covariates in the formulation, while leaving the distributions of frailty and censoring times unspecified. We propose a novel semiparametric inference procedure that depends on neither the frailty nor the censoring time distribution. Large sample properties of the regression parameter estimates and the estimated baseline cumulative intensity functions are studied. Numerical studies demonstrate that the proposed methodology performs well for realistic sample sizes. An analysis of hospitalization data for patients in an AIDS cohort study is presented to illustrate the proposed method.