Background: Previous studies found that 40-60% of the sarcoidosis patients suffer from small fiber neuropathy (SFN), substantially affecting quality of life. SFN is difficult to diagnose, as a gold standard is still lacking. The need for an easily administered screening instrument to identify sarcoidosis-associated SFN symptoms led to the development of the SFN Screening List (SFNSL). The usefulness of any questionnaire in clinical management and research trials depends on its interpretability. Obtaining a clinically relevant change score on a questionnaire requires that the smallest detectable change (SDC) and minimal important difference (MID) are known. Objectives: The aim of this study was to determine the SDC and MID for the SFNSL in patients with sarcoidosis. Methods: Patients with neurosarcoidosis and/or sarcoidosis-associated SFN symptoms (N=138) included in the online Dutch Neurosarcoidosis Registry participated in a prospective, longitudinal study. Anchor-based and distribution-based methods were used to estimate the MID and SDC, respectively. Results: The SFNSL was completed both at baseline and at 6-months’ follow-up by 89/138 patients. A marginal ROC curve (0.6) indicated cut-off values of 3.5 points, with 73% sensitivity and 49% specificity for change. The SDC was 11.8 points. Conclusions: The MID on the SFNSL is 3.5 points for a clinically relevant change over a 6-month period. The MID can be used in the follow-up and management of SFN-associated symptoms in patients with sarcoidosis, though with some caution as the SDC was found to be higher.
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Background: Pain assessment is a necessary step in pain management in older people in palliative care. In older people, pain assessment can be challenging due to underreporting and atypical pain manifestations by other distressing symptoms. Anxiety, fatigue, loss of appetite, nausea, insomnia, dyspnoea, and bowel problems correlate with pain in palliative care patients. Insight into these symptoms as predictors may help to identify the underlying presence of pain. This study aimed to develop and test a prediction model for pain in community-dwelling frail older people in palliative care. Methods: In this cross-sectional observational study, community-care nurses from multiple organizations across the Netherlands included eligible patients (life expectancy < 1 year, aged 65+, community-dwelling and frail). The outcome pain and symptoms were assessed by means of the Utrecht Symptom Diary. Also, demographic and illness information, including relevant covariates age, sex and living situation, was collected. Multivariable logistic regression and minimum Akaike Information Criterion(AIC) were used for model development and Receiver Operating Characteristics(ROC)-analysis for model performance. Additionally, predicted probability of pain are given for groups differing in age and sex. Results: A total of 157 patients were included. The final model consisted of insomnia(Odds Ratio[OR] = 2.13, 95% Confidence Interval[CI] = 1.01–1.30), fatigue(OR = 3.47, 95% CI = 1.11–1.43), sex(female)(OR = 3.83, 95% CI = 2.11–9.81) and age(OR=-1.59, 95% CI = 0.92–1.01) as predicting variables. There is an overall decreasing trend for age, older persons suffer less from pain and females have a higher probability of experiencing pain. Model performance was indicated as fair with a sensitivity of 0.74(95% CI = 0.64–0.83) and a positive predictive value of 0.80(95% CI = 0.70–0.88). Conclusions: Insomnia and fatigue are predicting symptoms for pain, especially in women and younger patients. Further testing of the model in external cohorts is needed before clinical adoption.
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Background: Healthcare providers’ attitudes and beliefs can influence how patients with persistent musculoskeletal pain are treated. A biopsychosocial approach is more effective than a purely biomedical approach. Ensuring healthcare professionals have appropriate pain science education (PSE) is essential for successful treatment outcomes. Objective: To validate the Spanish version of the Knowledge and Attitudes of Pain (KNAP-SP) questionnaire among Spanish physiotherapists and students and analyze its psychometric properties. Methods: From May to October 2022, two independent teams adapted the KNAP questionnaire from English to both European and Hispanic-Spanish. A cross-sectional validation study was conducted with 517 physiotherapists examining internal consistency (Cronbach’s alpha), structural validity (exploratory factor analysis), and construct validity (hypothesis testing). Longitudinal analyses assessed test–retest reliability (intraclass correlation coefficient [ICC2,1; n = 63]) and responsiveness following a PSE intervention using Receiver Operating Characteristic (ROC) curve analysis and hypothesis testing (n = 70). Results: The KNAP-SP showed strong internal consistency [overall α coefficient = 0.86; domain 1 (α = 0.82); domain 2 (α = 0.70)], explaining 32.3% of the variance. Construct validity was supported by 75% of the hypotheses. Test–retest reliability was high (ICC2,1 = 0.84). KNAP-SP’s responsiveness was confirmed by ROC analysis (area under the curve [AUC] = 0.87 [95% CI: 0.79–0.96, p-value
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Objective To develop and internally validate a prognostic model to predict chronic pain after a new episode of acute or subacute non-specific idiopathic, non-traumatic neck pain in patients presenting to physiotherapy primary care, emphasising modifiable biomedical, psychological and social factors. Design A prospective cohort study with a 6-month follow-up between January 2020 and March 2023. Setting 30 physiotherapy primary care practices. Participants Patients with a new presentation of non-specific idiopathic, non-traumatic neck pain, with a duration lasting no longer than 12 weeks from onset. Baseline measures Candidate prognostic variables collected from participants included age and sex, neck pain symptoms, work-related factors, general factors, psychological and behavioural factors and the remaining factors: therapeutic relation and healthcare provider attitude. Outcome measures Pain intensity at 6 weeks, 3 months and 6 months on a Numeric Pain Rating Scale (NPRS) after inclusion. An NPRS score of ≥3 at each time point was used to define chronic neck pain. Results 62 (10%) of the 603 participants developed chronic neck pain. The prognostic factors in the final model were sex, pain intensity, reported pain in different body regions, headache since and before the neck pain, posture during work, employment status, illness beliefs about pain identity and recovery, treatment beliefs, distress and self-efficacy. The model demonstrated an optimism-corrected area under the curve of 0.83 and a corrected R2 of 0.24. Calibration was deemed acceptable to good, as indicated by the calibration curve. The Hosmer–Lemeshow test yielded a p-value of 0.7167, indicating a good model fit. Conclusion This model has the potential to obtain a valid prognosis for developing chronic pain after a new episode of acute and subacute non-specific idiopathic, non-traumatic neck pain. It includes mostly potentially modifiable factors for physiotherapy practice. External validation of this model is recommended.
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BACKGROUND: Visceral obesity is associated with the metabolic syndrome. The metabolic risk differs per ethnicity, but reference values for visceral obesity for body composition analyses using Computed Tomography (CT) scans in the Caucasian population are lacking. Therefore, the aim of this study was to define gender specific reference values for visceral obesity in a Caucasian cohort based upon the association between the amount of visceral adipose tissue (VAT) and markers of increased metabolic risk.METHODS: Visceral Adipose Tissue Area Index (VATI cm 2/m 2) at the level of vertebra L3 was analyzed using CT scans of 416 healthy living kidney donor candidates. The use of antihypertensive drugs and/or statins was used as an indicator for increased metabolic risk. Gender specific cut-off values for VATI with a sensitivity ≥80% were calculated using receiver operating characteristic (ROC) curves. RESULTS: In both men and women who used antihypertensive drugs, statins or both, VATI was higher than in those who did not use these drugs (p ≤ 0.013). In males and females respectively, a value of VATI of ≥38.7 cm 2/m 2 and ≥24.9 cm 2/m 2 was associated with increased metabolic risk with a sensitivity of 80%. ROC analysis showed that VATI was a better predictor of increased metabolic risk than BMI (area under ROC curve (AUC) = 0.702 vs AUC = 0.556 in males and AUC = 0.757 vs AUC = 0.630 in females). CONCLUSION: Gender and ethnicity specific cut-off values for visceral obesity are important in body composition research, although further validation is needed. This study also showed that quantification of VATI is a better predictor for metabolic risk than BMI.
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Abstract Aim Screening is one of the most important ways for early frailty detection that contributes to its prevention and timely treatment. The aim of this study was to determine the diagnostic value of the Persian version of the Tilburg Frailty Indicator (P-TFI) in the frailty screening. Method This is a diagnostic test accuracy study that uses known group method. It was designed based on a STARD statement and performed on 175 elderly people in the City of Kashan, Iran. The subjects were selected among older people available in health centers affiliated to Kashan University of Medical Sciences using purposive sampling. Data analysis was carried out using SPSS v16. Descriptive statistics were used to describe the characteristics of the research subjects. Independent t-test was used to determine the ability of the P-TFI to discriminate frail and non-frail individuals, and to evaluate the cut-off point and instrument accuracy, the receiver operating characteristic (ROC) curve was used. The best cut-off point was determined among the proposed points using Youden index. At the determined cut-off point, the diagnostic value parameters of the P-TFI (sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, accuracy, and diagnostic odds ratio) were calculated and their range was estimated with 95 % confidence interval. Findings A total of 74.3 % of the sample was male and their mean age was 68.6 ± 54.44 years. The area under the ROC curve was calculated 0.922, indicating high accuracy of the instrument. The sensitivity and specificity of this instrument at the cut-off point of 4.5 were 0.95 and 0.86, respectively. Positive and negative predictive values were calculated 0.68 and 0.98, respectively, and the accuracy of the instrument was reported to be 0.88. Conclusion The P-TFI can be used as a sensitive and accurate instrument, which is highly applicable to screen frailty in older people.
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Abstract Background One of the most problematic expression of ageing is frailty, and an approach based on its early identification is mandatory. The Sunfrail-tool (ST), a 9-item questionnaire, is a promising instrument for screening frailty. Aims • To assess the diagnostic accuracy and the construct validity between the ST and a Comprehensive Geriatric Assessment (CGA), composed by six tests representative of the bio-psycho-social model of frailty; • To verify the discriminating power of five key-questions of the ST; • To investigate the role of the ST in a clinical-pathway of falls’ prevention. Methods In this retrospective study, we enrolled 235 patients from the Frailty-Multimorbidity Lab of the University-Hospital of Parma. The STs’ answers were obtained from the patient’s clinical information. A patient was considered frail if at least one of the CGAs’ tests resulted positive. Results The ST was associated with the CGA’s judgement with an Area Under the Curve of 0.691 (CI 95%: 0.591–0.791). Each CGA’s test was associated with the ST total score. The five key-question showed a potential discriminating power in the CGA’s tests of the corresponding domains. The fall-related question of the ST was significantly associated with the Short Physical Performance Battery total score (OR: 0.839, CI 95%: 0.766–0.918), a proxy of the risk of falling. Discussion The results suggest that the ST can capture the complexity of frailty. The ST showed a good discriminating power, and it can guide a second-level assessment to key frailty domains and/or clinical pathways. Conclusions The ST is a valid and easy-to-use instrument for the screening of frailty.
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Abstract BackgroundFrailty is a syndrome that is defined as an accumulation of deficits in physical, psychological, and social domains. On a global scale, there is an urgent need to create frailty-ready healthcare systems due to the healthcare burden that frailty confers on systems and the increased risk of falls, healthcare utilization, disability, and premature mortality. Several studies have been conducted to develop prediction models for predicting frailty. Most studies used logistic regression as a technique to develop a prediction model. One area that has experienced significant growth is the application of Bayesian techniques, partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. ObjectiveWe compared ten different Bayesian networks as proposed by ten experts in the field of frail elderly people to predict frailty with a choice from ten dichotomized determinants for frailty. MethodsWe used the opinion of ten experts who could indicate, using an empty Bayesian network graph, the important predictors for frailty and the interactions between the different predictors. The candidate predictors were age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. The ten Bayesian network models were evaluated in terms of their ability to predict frailty. For the evaluation, we used the data of 479 participants that filled in the Tilburg Frailty indicator (TFI) questionnaire for assessing frailty among community-dwelling older people. The data set contained the aforementioned variables and the outcome ”frail”. The model fit of each model was measured using the Akaike information criterion (AIC) and the predictive performance of the models was measured using the area under the curve (AUC) of the receiver operator characteristic (ROC). The AUCs of the models were validated using bootstrapping with 100 repetitions. The relative importance of the predictors in the models was calculated using the permutation feature importance algorithm (PFI). ResultsThe ten Bayesian networks of the ten experts differed considerably regarding the predictors and the connections between the predictors and the outcome. However, all ten networks had corrected AUCs 0.700. Evaluating the importance of the predictors in each model, ”diseases or chronic disorders” was the most important predictor in all models (10 times). The predictors ”lifestyle” and ”monthly income” were also often present in the models (both 6 times). One or more diseases or chronic disorders, an unhealthy lifestyle, and a monthly income below 1,800 euro increased the likelihood of frailty. ConclusionsAlthough the ten experts all made different graphs, the predictive performance was always satisfying (AUCs 0.700). While it is true that the predictor importance varied all the time, the top three of the predictor importance consisted of “diseases or chronic disorders”, “lifestyle” and “monthly income”. All in all, asking for the opinion of experts in the field of frail elderly to predict frailty with Bayesian networks may be more rewarding than a data-driven forecast with Bayesian networks because they have expert knowledge regarding interactions between the different predictors.
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RATIONALE: Currently there is no consensus on protein requirements for obese older adults during weight loss. Here we explore the potential use of a new method for assessment of protein requirements based on changes in appendicular muscle mass during weight loss.METHODS: 60 obese older adults were subjected to 13 wk weight loss program, including hypocaloric diet and resistance training. Assessment of appendicular muscle mass was performed by DXA at baseline and after 13 wk challenge period, and the difference calculated as muscle mass change. Protein intake (g/kg body weight and g/kg fat free mass (FFM)) at 13wks was used as marker of protein intake during 13 wk period. 30 subjects received 10 times weekly 20 g protein supplement throughout the 13 week hypocaloric phase which is included in the calculation of total protein intake. Receiver operating characteristic (ROC) curve analysis was used to explore the optimal cutoff point for protein intake (g/kg) versus increase in appendicular muscle mass of more than 250 g over 13 wks (y/n). Subsequently, logistic regression analysis was performed for protein intake cutoff and muscle mass accretion, adjusted for sex, age, baseline BMI, and training compliance.RESULTS: ROC curve analysis provided a protein intake level per day of 1.2 g/kg bw and 1.9 g/kg FFM as cutoff point. Presence of muscle mass accretion during 13 wk challenge period was significantly higher with protein intake higher than 1.2 g/kg bw (OR 5.4, 95%CI 1.4-20.6, p = 0.013) or higher than 1.9 g/kg FFM (OR 8.1, 95%CI 2.1-31.9, p = 0.003). Subjects with a protein intake higher than 1.2 g/kg had significantly more often muscle mass accretion, compared to subjects with less protein intake (10/14 (72%) vs 15/46 (33%), p = 0.010). For 1.9 g/kg FFM this was 70% vs 28% (p = 0.002).CONCLUSION: This exploratory study provided a level of at least 1.2 g/kg body weight or 1.9 g/kg fat free mass as optimal daily protein intake for obese older adults under these challenged conditions of weight loss, based on muscle mass accretion during the challenge.TRIAL REGISTRATION: Dutch Trial Register under number NTR2751.
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Objective: This exploratory study investigated to what extent gait characteristics and clinical physical therapy assessments predict falls in chronic stroke survivors. Design: Prospective study. Subjects: Chronic fall-prone and non-fall-prone stroke survivors. Methods: Steady-state gait characteristics were collected from 40 participants while walking on a treadmill with motion capture of spatio-temporal, variability, and stability measures. An accelerometer was used to collect daily-life gait characteristics during 7 days. Six physical and psychological assessments were administered. Fall events were determined using a “fall calendar” and monthly phone calls over a 6-month period. After data reduction through principal component analysis, the predictive capacity of each method was determined by logistic regression. Results: Thirty-eight percent of the participants were classified as fallers. Laboratory-based and daily-life gait characteristics predicted falls acceptably well, with an area under the curve of, 0.73 and 0.72, respectively, while fall predictions from clinical assessments were limited (0.64). Conclusion: Independent of the type of gait assessment, qualitative gait characteristics are better fall predictors than clinical assessments. Clinicians should therefore consider gait analyses as an alternative for identifying fall-prone stroke survivors.
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