Quick outpatient diagnosis in small district or general tertiary hospitals: A comparative observational study.

While quick diagnosis units (QDUs) have expanded as an innovative cost-effective alternative to admission for workup, studies investigating how QDUs compare are lacking. This study aimed to comparatively describe the diagnostic performance of the QDU of an urban district hospital and the QDU of its reference general hospital.This was an observational descriptive study of 336 consecutive outpatients aged ≥18 years referred to the QDU of a urban district hospital in Barcelona (QDU1) during 2009 to 2016 for evaluation of suspected severe conditions whose physical performance allowed them to travel from home to hospital and back for visits and examinations. For comparison purposes, 530 randomly selected outpatients aged ≥18 years referred to the QDU of the reference tertiary hospital (QDU2), also in Barcelona, were included. Clinical and QDU variables were analyzed and compared.Mean age and sex were similar (61.97 (19.93) years and 55% of females in QDU1 vs 60.0 (18.81) years and 52% of females in QDU2; P values = .14 and .10, respectively). Primary care was the main referral source in QDU1 (69%) and the emergency department in QDU2 (59%). Predominant referral reasons in QDU1 and 2 were unintentional weight loss (UWL) (21 and 16%), anemia (14 and 21%), adenopathies and/or palpable masses (10 and 11%), and gastrointestinal symptoms (10 and 19%). Time-to-diagnosis was longer in QDU1 than 2 (12 [1-28] vs 8 [4-14] days; P 2 visits to be diagnosed were in general more likely to be males, to have UWL and adenopathies and/or palpable masses but less likely anemia, to undergo more examinations except endoscopy, and to be referred onward to specialist outpatient clinics.Despite some differences, results showed that, for diagnostic purposes, the overall performance and effectiveness of QDUs of urban district and reference general hospitals in evaluating patients with potentially serious conditions were similar. This study, the first to compare the performance of 2 hospital-based QDUs, adds evidence to the opportunity of producing standardized guidelines to optimize QDUs infrastructure, functioning, and efficiency

Device for negative pressure wound therapy in low-resource regions: open-source description and bench test evaluation

Background: Negative (vacuum) pressure therapy promotes wound healing. However, commercially available devices are unaffordable to most potential users in low- and middle-income countries (LMICs), limiting access to many patients who could benefit from this treatment. This study aimed to design and test a cheap and easy-to-build negative pressure device and provide its detailed open-source description, thereby enabling free replication. Methods: the negative pressure device was built using off-the-shelf materials available via e-commerce and was based on a small pump, a pressure transducer, and the simplest Arduino controller with a digital display (total retail cost ≤ 75 US$). The device allows the user to set any therapeutic range of intermittent negative pressure and has two independent safety mechanisms. The performance of the low-cost device was carefully tested on the bench using a phantom wound, producing a realistic exudate flow rate. Results: the device generates the pressure patterns set by the user (25-175 mmHg of vacuum pressure, 0-60 min periods) and can drain exudate flows within the clinical range (up to 1 L/h). Conclusions: a novel, low-cost, easy-to-build negative pressure device for wound healing displays excellent technical performance. The open-source hardware description provided here, which allows for free replication and use in LMICs, will facilitate the application and wider utilization of this therapy to patients

Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning

There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson's disease patients according to cognitive status using machine learning. Two independent subject samples were assessed with resting-state fMRI. The first (training) sample comprised 38 healthy controls and 70 Parkinson's disease patients (27 with mild cognitive impairment). The second (validation) sample included 25 patients (8 with mild cognitive impairment). The Brainnetome atlas was used to reconstruct the functional connectomes. Using a support vector machine trained on features selected through randomized logistic regression with leave-one-out cross-validation, a mean accuracy of 82.6% (p < 0.002) was achieved in separating patients with mild cognitive impairment from those without it in the training sample. The model trained on the whole training sample achieved an accuracy of 80.0% when used to classify the validation sample (p = 0.006). Correlation analyses showed that the connectivity level in the edges most consistently selected as features was associated with memory and executive function performance in the patient group. Our results demonstrate that connection-wise patterns of functional connectivity may be useful for discriminating Parkinson's disease patients according to the presence of cognitive deficits

Functional brain networks and cognitive deficits in Parkinson's disease

Abstract: Graph-theoretical analyses of functional networks obtained with resting-state functional mag-netic resonance imaging (fMRI) have recently proven to be a useful approach for the study of the sub-strates underlying cognitive deficits in different diseases. We used this technique to investigate whethercognitive deficits in Parkinson's disease (PD) are associated with changes in global and local networkmeasures. Thirty-six healthy controls (HC) and 66 PD patients matched for age, sex, and education wereclassified as having mild cognitive impairment (MCI) or not based on performance in the three mainlyaffected cognitive domains in PD: attention/executive, visuospatial/visuoperceptual (VS/VP), anddeclarative memory. Resting-state fMRI and graph theory analyses were used to evaluate network meas-ures. We have found that patients with MCI had connectivity reductions predominantly affecting long-range connections as well as increased local interconnectedness manifested as higher measures of cluster-ing, small-worldness, and modularity. The latter measures also tended to correlate negatively with cogni-tive performance in VS/VP and memory functions. Hub structure was also reorganized: normal hubsdisplayed reduced centrality and degree in MCI PD patients. Our study indicates that the topologicalproperties of brain networks are changed in PD patients with cognitive deficits. Our findings providenovel data regarding the functional substrate of cognitive impairment in PD, which may prove to havevalue as a prognostic marker

Structural correlates of facial emotion recognition deficits in Parkinson's disease patients

The ability to recognize facial emotion expressions, especially negative ones, is described to be impaired in Parkinson's disease (PD) patients. Previous neuroimaging work evaluating the neural substrate of facial emotion recognition (FER) in healthy and pathological subjects has mostly focused on functional changes. This study was designed to evaluate gray matter (GM) and white matter (WM) correlates of FER in a large sample of PD. Thirty-nine PD patients and 23 healthy controls (HC) were tested with the Ekman 60 test for FER and with magnetic resonance imaging. Effects of associated depressive symptoms were taken into account. In accordance with previous studies, PD patients performed significantly worse in recognizing sadness, anger and disgust. In PD patients, voxel-based morphometry analysis revealed areas of positive correlation between individual emotion recognition and GM volume: in the right orbitofrontal cortex, amygdala and postcentral gyrus and sadness identification; in the right occipital fusiform gyrus, ventral striatum and subgenual cortex and anger identification, and in the anterior cingulate cortex (ACC) and disgust identification. WM analysis through diffusion tensor imaging revealed significant positive correlations between fractional anisotropy levels in the frontal portion of the right inferior fronto-occipital fasciculus and the performance in the identification of sadness. These findings shed light on the structural neural bases of the deficits presented by PD patients in this skill

Resting-state frontostriatal functional connectivity in Parkinson's disease-related apathy

Background: One of the most common neuropsychiatric symptoms in PD is apathy, affecting between 23 and 70% of patients and thought to be related to frontostriatal dopamine deficits. In the present study, we assessed functional resting-state frontostriatal connectivity and structural changes associated with the presence of apathy in a large sample of PD subjects and healthy controls, while controlling for the presence of comorbid depression and cognitive decline. Methods: Thirty-one healthy controls (HC) and 62 age, sex and education-matched PD patients underwent resting-state functional MRI. Apathy symptoms were evaluated with the Apathy Scale (AS). The 11 Beck Depression Inventory-II items that measure dysphoric mood symptoms as well as relevant neuropsychological scores were used as nuisance factors in connectivity analyses. Voxel-wise analyses of functional connectivity between frontal lobes (limbic, executive, rostral motor and caudal motor regions), striata (limbic, executive, sensorimotor regions) and thalami were performed. Subcortical volumetry/shape analysis and fronto-subcortical voxel-based morphometry were performed to assess structural changes. Results: Twenty-five PD patients were classified as apathetic (PD-A) (AS>13). PD-A patients showed functional connectivity reductions compared with HC and with non-apathetic patients (PD-NA), mainly in left-sided circuits, and predominantly involving limbic striatal and frontal territories. Similarly, severity of apathy negatively correlated with connectivity in these circuits. No significant effects were found in structural analyses. Conclusions: Our results indicate that the presence of apathy in PD is associated with functional connectivity reductions in frontostriatal circuits, predominating in the left hemisphere and mainly involving its limbic components

Differential diagnosis between Parkinson's disease and essential tremor using the smartphone's accelerometer

Background: The differential diagnosis between patients with essential tremor (ET) and those with Parkinson's disease (PD) whose main manifestation is tremor may be difficult unless using complex neuroimaging techniques such as 123I-FP-CIT SPECT. We considered that using smartphone's accelerometer to stablish a diagnostic test based on time-frequency differences between PD an ET could support the clinical diagnosis. Methods: The study was carried out in 17 patients with PD, 16 patients with ET, 12 healthy volunteers and 7 patients with tremor of undecided diagnosis (TUD), who were re-evaluated one year after the first visit to reach the definite diagnosis. The smartphone was placed over the hand dorsum to record epochs of 30 s at rest and 30 s during arm stretching. We generated frequency power spectra and calculated receiver operating characteristics curves (ROC) curves of total spectral power, to establish a threshold to separate subjects with and without tremor. In patients with PD and ET, we found that the ROC curve of relative energy was the feature discriminating better between the two groups. This threshold was then used to classify the TUD patients. Results: We could correctly classify 49 out of 52 subjects in the category with/without tremor (97.96% sensitivity and 83.3% specificity) and 27 out of 32 patients in the category PD/ET (84.38% discrimination accuracy). Among TUD patients, 2 of 2 PD and 2 of 4 ET were correctly classified, and one patient having PD plus ET was classified as PD. Conclusions: Based on the analysis of smartphone accelerometer recordings, we found several kinematic features in the analysis of tremor that distinguished first between healthy subjects and patients and, ultimately, between PD and ET patients. The proposed method can give immediate results for the clinician to gain valuable information for the diagnosis of tremor. This can be useful in environments where more sophisticated diagnostic techniques are unavailable

Cognitive impairment and resting-state network connectivity in Parkinson's disease

Previous functional MRI studies have revealed changes in the default-mode network (DMN) in Parkinson's disease (PD). The purpose of this work was to evaluate changes in the connectivity patterns of a set of cognitively relevant, dynamically interrelated brain networks in association with cognitive deficits in PD using resting-state functional MRI. Sixty-five non-demented PD patients and 36 matched healthy controls (HC) were included. Thirty-four percent of PD patients were classified as having mild cognitive impairment (MCI) based on performance in the three mainly-affected cognitive domains in Parkinson's disease (attention/executive, visuospatial/visuoperceptual and declarative memory). Data-driven analyses through independent-component analysis (ICA) was used to identify the DMN, the dorsal attention network (DAN) and the bilateral frontoparietal networks (FPN), which were compared between groups using a dual-regression approach. Additional seed-based analyses using a-priori defined regions of interest were used to characterize local changes in intra and inter-network connectivity. ICA results revealed reduced connectivity between the DAN and right frontoinsular cortical regions in MCI patients, which correlated with worse performance in attention/executive functions. The DMN, on the other hand, displayed increased connectivity with medial and lateral occipito-parietal regions in MCI patients; these increases correlated with worse visuospatial/visuoperceptual performance. In line with data-driven results, seed-based analyses mainly revealed reduced within-DAN, within-DMN and DAN-FPN connectivity, as well as increased DAN-DMN coupling in MCI patients. Our findings demonstrate differential connectivity changes affecting the networks evaluated, which we hypothesize to be related to the pathophysiological bases of different types of cognitive impairment in PD

Differentiation of multiple system atrophy subtypes by gray matter atrophy

Background and purpose: Multiple system atrophy(MSA) is a rare adult-onset synucleinopathy that can be divided in two subtypes depending on whether the prevalence of its symptoms is more parkinsonian or cerebellar (MSA-P and MSA-C, respectively). The aim of this work is to investigate the structural MRI changes able to discriminate MSA phenotypes. Methods: The sample includes 31 MSA patients (15 MSA-C and 16 MSA-P) and 39 healthy controls. Participants underwent a comprehensive motor and neuropsychological battery. MRI data were acquired with a 3T scanner (MAGNETOM Trio, Siemens, Germany). FreeSurfer was used to obtain volumetric and cortical thickness measures. A Support Vector Machine (SVM) algorithm was used to assess the classification between patients' group using cortical and subcortical structural data. Results: After correction for multiple comparisons, MSA-C patients had greater atrophy than MSA-P in the left cerebellum, whereas MSA-P showed reduced volume bilaterally in the pallidum and putamen. Using deep gray matter volume ratios and mean cortical thickness as features, the SVM algorithm provided a consistent classification between MSA-C and MSA-P patients (balanced accuracy 74.2%, specificity 75.0%, and sensitivity 73.3%). The cerebellum, putamen, thalamus, ventral diencephalon, pallidum, and caudate were the most contributing features to the classification decision (z > 3.28; p < .05 [false discovery rate]). Conclusions: MSA-C and MSA-P with similar disease severity and duration have a differential distribution of gray matter atrophy. Although cerebellar atrophy is a clear differentiator between groups, thalamic and basal ganglia structures are also relevant contributors to distinguishing MSA subtypes. Keywords: cognition; cortical thickness; machine learning; multiple system atrophy; neuroimaging

Cognitive Behavioral Therapy Plus a Serious Game as a Complementary Tool for a Patient With Parkinson Disease and Impulse Control Disorder: Case Report

Background: Impulse control disorders (ICDs) are commonly developed among patients who take dopamine agonist drugs as a treatment for Parkinson disease (PD). Gambling disorder and hypersexuality are more frequent in male patients with PD, with a prevalence over 4% in dopamine agonists users. Although impulsive-compulsive behaviors are related to antiparkinsonian medication, and even though ICD symptomatology, such as hypersexuality, often subsides when the dopaminergic dose is reduced, sometimes ICD persists in spite of drug adjustment. Consequently, a multidisciplinary approach should be considered to address these comorbidities and to explore new forms of complementary interventions, such as serious games or therapies adapted to PD. Objective: The aim of this study is to present the case of a patient with ICD (ie, hypersexuality) triggered by dopaminergic medication for PD. A combined intervention was carried out using cognitive behavioral therapy (CBT) for ICD adapted to PD, plus an intervention using a serious game-e-Estesia-whose objective is to improve emotion regulation and impulsivity. The aim of the combination of these interventions was to reduce the harm of the disease. Methods: After 20 CBT sessions, the patient received the e-Estesia intervention over 15 sessions. Repeated measures, before and after the combined intervention, were administered to assess emotion regulation, general psychopathology, and emotional distress and impulsivity. Results: After the intervention with CBT techniques and e-Estesia, the patient presented fewer difficulties to regulate emotion, less emotional distress, and lower levels of impulsivity in comparison to before the treatment. Moreover, the frequency and severity of the relapses also decreased. Conclusions: The combined intervention-CBT and a serious game-showed positive results in terms of treatment outcomes