1,721,066 research outputs found
Qini Curves for Potential Impact Assessment of Risk Predictive Models Informing Intervention Policies
No full textObjectives: Predictive models in medicine help make decisions about which individual to treat with a given therapeutic or preventive intervention. Before being tested in large field studies and recommended for clinical adoption, it is important to evaluate not only their statistical accuracy but also the impact they may have when used to inform health intervention policies. We aim to provide simple methods for the potential impact assessment of health intervention policies based on predictive models. Methods: We propose an analytic framework based on Qini curves wherein prediction-based policies are analyzed on 2 impact endpoints: (1) the fraction of the population that would be selected for the intervention (coverage) and (2) the effect on the clinical outcomes of interest (disutility). The drivers of values are the disease prevalence, the predictive performance of the model, and the effectiveness of the intervention. Results: We present simple formulas for calculating coverage and disutility from either observational or randomized controlled data. We illustrate possible value measures arising from geometrical properties on the Qini plane: delta coverage and disutility, number needed to treat, and integrated difference between Qini curves. We show the applicability of the Qini analysis by providing examples about the prevention of falls in older adults and prevention of secondary cardiovascular events with pioglitazone. Conclusions: Coverage and disutility capture key value components of prediction-based policies. The method can be used for comparing models or tuning risk thresholds for managing trade-offs between conflicting objectives (eg, clinical benefits, side effects, and healthcare resources)
Quality Assessment and Morphological Analysis of Photoplethysmography in Daily Life
Full text linkThe photoplethysmographic (PPG) signal has been applied in various research fields, with promising results for its future clinical application. However, there are several sources of variability that, if not adequately controlled, can hamper its application in pervasive monitoring contexts. This study assessed and characterized the impact of several sources of variability, such as physical activity, age, sex, and health state on PPG signal quality and PPG waveform parameters (Rise Time, Pulse Amplitude, Pulse Time, Reflection Index, Delta T, and DiastolicAmplitude). We analyzed 31 24 h recordings by as many participants (19 healthy subjects and 12 oncological patients) with a wristband wearable device, selecting a set of PPG pulses labeled with three different quality levels. We implemented a Multinomial Logistic Regression (MLR) model to evaluate the impact of the aforementioned factors on PPG signal quality. We then extracted six parameters only on higher-quality PPG pulses and evaluated the influence of physical activity, age, sex, and health state on these parameters with Generalized Linear Mixed Effects Models (GLMM). We found that physical activity has a detrimental effect on PPG signal quality quality (94% of pulses with good quality when the subject is at rest vs. 9% during intense activity), and that health state affects the percentage of available PPG pulses of the best quality (at rest, 44% for healthy subjects vs. 13% for oncological patients). Most of the extracted parameters are influenced by physical activity and health state, while age significantly impacts two parameters related to arterial stiffness. These results can help expand the awareness that accurate, reliable information extracted from PPG signals can be reached by tackling and modeling different sources of inaccuracy
Proton therapy re-irradiation preserves health-related quality of life in large recurrent glioblastoma
No full textPurpose Proton therapy could minimize the risk of side effects and, therefore, reduce the possible detrimental effect on health-related quality of life (HRQOL) of re-irradiation. The aim of this study was to determine the effect of re-irradiation with active scanning proton therapy on recurrent glioblastoma (GBM) in terms of HRQOL scored by the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ)-C30 and EORTC Quality of Life Questionnaire Brain Cancer Module (QLQ-BN20). Methods Thirty-three patients with recurrent GBM were re-irradiated with active scanning proton therapy. Subscales within the EORTC QLQ-C30 include five functional scales, six single-item scales, and global QoL. The BN20 assessed visual disorders, motor function, communication deficit, various disease symptoms, treatment, toxicity, and future uncertainty. The patients completed the questionnaires before starting proton therapy, the last day of proton therapy, and at every follow-up visit until progression of disease. Results The treatment was associated with improvement or stability in most of the preselected HRQOL domains. Global health improved over time with a maximum difference of six points between baseline and 3-months follow-up. Social functioning and motor dysfunction improved over time with a maximum difference of eight and two points, respectively. We showed a non-significant decrease in cognitive and emotional functioning. Fatigue remained stable during the analysis such as the other preselected domains. Conclusions Re-irradiation with proton therapy is a safe and effective treatment in patients with recurrent glioblastoma. Proton therapy does not negatively effect on HRQOL, but rather it seems to preserve HRQOL until the time of disease progression
Assessing Gait in Parkinson’s Disease Using Wearable Motion Sensors: A Systematic Review
Full text linkAbstract: Parkinson’s disease (PD) is a progressive neurodegenerative disorder. Gait impairments are common among people with PD. Wearable sensor systems can be used for gait analysis by providing spatio-temporal parameters useful to investigate the progression of gait problems in Parkinson disease. However, various methods and tools with very high variability have been developed. The aim of this study is to review published articles of the last 10 years (from 2008 to 2018) concerning the application of wearable sensors to assess spatio-temporal parameters of gait in patients with PD. We focus on inertial sensors used for gait analysis in the clinical environment (i.e., we do not cover the use of inertial sensors to monitor walking or general activities at home, in unsupervised environments). Materials and Methods: Relevant articles were searched in the Medline database using Pubmed. Results and Discussion: Two hundred ninety-four articles were initially identified while searching the scientific literature regarding this topic. Thirty-six articles were selected and included in this review. Conclusion: Wearable motion sensors are useful, non-invasive, low-cost, and objective tools that are being extensively used to perform gait analysis on PD patients. Being able to diagnose and monitor the progression of PD patients makes wearable sensors very useful to evaluate clinical efficacy before and after therapeutic interventions. However, there is no uniformity in the use of wearable sensors in terms of: number of sensors, positioning, chosen parameters, and other characteristics. Future research should focus on standardizing the measurement setup and selecting which spatio-temporal parameters are the most informative to analyze gait in PD. These parameters should be provided as standard assessments in all studies to increase replicability and comparability of results
Tecnologie indossabili per il monitoraggio e la prevenzione delle cadute nell'anziano
No full textSe si prende in considerazione uno dei cosiddetti giganti geriatrici, appare evidente come le cadute costituiscano un pesante onere economico e sociale, determinando una significativa riduzione della qualità di vita nella popolazione anziana e/o patologica. Nel solo 2009, le cadute hanno determinato costi che variano tra lo 0,85 e lo 1,5 per cento delle spese sanitarie totali negli Stati Uniti, Australia, UE e Regno Unito (Heinrich et al., 2009). Le cadute hanno anche un impatto notevole sulle condizioni di salute generali di uno stato, dato che lo 81-98% delle fratture sono causate da cadute (Tinetti, 2003), e queste sono la principale causa di accessi al pronto soccorso in USA (Fuller, 2000).
Il rischio di una caduta aumenta con l'età (Mathers e Weiss, 1998); le cadute rappresentano l'eziologia primaria di morte accidentale in soggetti con più di 65 anni, e anche il tasso di mortalità associato aumenta notevolmente con l'età, con picchi pari al 70% delle morti accidentali nelle persone di 75 anni di età (Fuller, 2000). I principali costi associati tendono quindi a verificarsi in gruppi di età più avanzata e a seguito di fratture (si veda il capitolo di Luca Cristofolini in questo volume), un problema che si aggrava ulteriormente con l’invecchiamento della popolazione (Hamacher et al., 2011).
Risulta quindi chiaro l’interesse nell’individuazione di metodi efficaci, che consentano di identificare i soggetti a rischio e di mettere a punto interventi clinici/riabilitativi capaci di ridurre tale rischio. Purtroppo, però, questo risulta tutt’altro che semplice dato che, in base alle valutazioni epidemiologiche, il rischio di caduta ha una natura multifattoriale e può essere il risultato di quadri clinici e condizioni ambientali molto diversi da un soggetto all’altro. Sia la stabilità posturale che quella motoria sono il risultato dell’azione concorrente di diverse risorse funzionali
The improvement of turning ability is a key objective for fall-risk reduction in individuals with impaired dynamic stability
No full textTurning difficulty is a sign of balance instability and may be indicative of elevated fall risk. Features extracted from the 90° turn suggest that this turn type is the most unstable type of turn in older adults with compromised balance control. Since the 90° turn is also the most common type of turn executed during activities of daily living, we recommend targeting movement strategies specific to 90° turning during therapeutic intervention. Specific neuro-rehabilitation strategies to improve/optimize turning ability in individuals with compromised stability may significantly contribute to fall-risk reduction. The adoption of quantitative tools for the assessment and monitoring of turning quality is advisable
Risk Prediction Model for Late Life Depression: Development and Validation on Three Large European Datasets
Full text linkAssessing the risk to develop a specific disease is the first step towards prevention, both at individual and population level. The development and validation of Risk Prediction Models (RPMs) is the norm within different fields of medicine but still underused in psychiatry, despite the global impact of mental disorders. In particular, there is a lack of RPMs to assess the risk of developing depression, the first worldwide cause of disability and harbinger of functional decline in old age. We present DRAT-up, the first prospective RPM to identify late life depression among community-dwelling subjects aged 60 to 75. The development of DRAT-up was based on appraisal of relevant literature, extraction of robust risk estimates and integration into model parameters. A unique feature is the ability to estimate risk even in the presence of missing values. To assess the properties of DRAT-up a validation study was conducted on three European cohorts, namely ELSA, InCHIANTI and TILDA, with 20206, 1359, and 3124 eligible samples, respectively. The model yielded accurate risk estimation in the three datasets from a small number of predictors. The Brier scores were 0.054, 0.133, and 0.041, while the values of Area Under the Curve (AUC) were 0.761, 0.736, and 0.768, respectively. Sensitivity analyses suggest robustness to missing values: setting any individual feature to unknown caused Brier scores to increase of 0.004, and AUCs to decrease of 0.045 in the worst cases. DRAT-up can be readily used for clinical purposes and to aid policy making in the field of mental health
Wearable-based Sit-to-Stand transfers in Older Adults: in-lab and home-based assessments
No full textThe ability to complete sit-to-stand (STS) transfers has a significant impact on a person's functional mobility. Wearable sensors allows for objective, long-term monitoring of STS transfers under home-based conditions. However, despite some recent initiatives assessing STS transfers in home-based settings, the majority of STS transfer algorithms have primarily been used for assessment during the execution of prescribed activities in a lab setting. More research is also required to comprehend the distinctions between in-lab and home-based measurements, as well as their relationship to clinical outcomes. The current study compared STS features identified in 20 older adults performing activities in-lab and at home. Our results showed that duration, peak acceleration, and smoothness of STS transfers performed in the lab versus at home differed significantly. The duration of STS transfers at home was 30% longer on average than in the lab. Furthermore, because they are natural rather than instructed actions as in lab settings, the number of STS per hour was significantly lower in home-based conditions. Understanding the distinctions highlighted is critical for the successful incorporation of home-based mobility assessments into prospective clinical trials
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