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.
DOCUMENT
Achtergrond: De Two-Minute Step Test (TMST) is een meetinstrument gericht op het beoordelen van uithoudingsvermogen. Verscheidene psychometrische eigenschappen van de TMST-NL (Nederlands vertaalde versie) zijn onderzocht bij intramuraal wonende ouderen. De gevoeligheid voor verandering en de responsiviteit is bij deze patiëntenpopulatie nog niet vastgesteld. Doel: Het vaststellen van de gevoeligheid voor verandering en de responsiviteit (Minimal Clinical Important Difference) van de TMST-NL bij intramuraal wonende ouderen. Design: Prospectief responsiviteitsonderzoek.Methode: De onderzoekspopulatie bestond uit intramuraal wonende ouderen. Deelnemers hebben twee meetmomenten (T0 en T1) ondergaan waartussen ze drie maanden fysiotherapie gericht op uithoudingsvermogen ontvingen. Om de gevoeligheid van verandering te meten werd de distributie methode gebruikt waarbij de correlatie met de 6-minuten wandeltest (6MWT) werd getoetst. Via de anker methode met de Receiver Operating Characteristic (ROC) curve werd de MCID bepaald.Metingen voor het aerobe uithoudingsvermogen werden verricht met de TMST-NL en de 6-minuten wandeltest (6MWT). De Global Rating of Change (GRC) en de Borg Category-Ratio10 (BORG-CR10) werden gebruikt als subjectieve vragenlijsten om verandering van de gezondheidssituatie en vermoeidheid te meten.Resultaten: Intramurale ouderen (N=50) met een gemiddelde (SD) leeftijd van 83,96 jaar (6,96) zijn geïncludeerd. De correlatie tussen de verschilscores van de TMST-NL en de 6MWT over de deelnemerspopulatie die T1 ook hebben afgerond (N= 36) kwam uit op r=0.51 (P <0.05). Vanuit de ROC curve werd een MCID van 8,50 stappen berekend. De AUC-waarde was 0,74 (95% CI 0,54-0,94; P =0.02). Conclusie: De TMST-NL is gevoelig voor verandering en responsief bij intramuraal wonende ouderen. Echter doordat de MCID binnen de minimale meetfout (MDC) valt moeten de resultaten voor individuele evaluatie bij deze doelgroep met voorzichtigheid worden geïnterpreteerd.
DOCUMENT
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.
DOCUMENT
CC-BY-NC-NDSTUDY DESIGN:prospective cohort study.OBJECTIVE:To analyze responsiveness and minimal clinically important change (MCIC) of the US National Institutes of Health (NIH) minimal dataset for chronic low back pain (CLBP).SUMMARY OF BACKGROUND DATA:The NIH minimal dataset is a 40-item questionnaire developed to increase use of standardized definitions and measures for CLBP. Longitudinal validity of the total minimal dataset and the subscale Impact Stratification are unknown.METHODS:Total outcome scores on the NIH minimal dataset, Dutch Language Version, were calculated ranging from 0-100 points with higher scores representing worse functioning. Responsiveness and MCIC were determined with an anchor based method, calculating the area under the receiver operating characteristics (ROC) curve (AUC) and by determining the optimal cut-off point. Smallest detectable change (SDC) was calculated as a parameter of measurement error.RESULTS:In total 223 patients with CLBP were included. Mean total score on the NIH minimal dataset was 44 ± 14 points at baseline. The total outcome score was responsive to change with an AUC of 0.84. MCIC was 14 points with a sensitivity of 72% and specificity 82%, and SDC was 23 points. Mean total score on Impact Stratification (scale 8-50) was 34.4 ± 7.4 points at baseline, with an AUC of 0.91, an MCIC of 7.5 with a sensitivity 96% of and specificity of 78%, and an SDC of 14 points.CONCLUSION:The longitudinal validity of the NIH minimal dataset is adequate. An improvement of 14 points in total outcome score and 7.5 points in Impact Stratification can be interpreted as clinically important in individual patients. However, MCIC depends on baseline values and the method that is chosen to determine the optimal cut-off point. Furthermore, measurement error is larger than the MCIC. This means that individual change scores should be interpreted with caution.LEVEL OF EVIDENCE:4This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal
MULTIFILE
BACKGROUND: Physical activity (PA) levels might be a simple overall physical function indicator of recovery in acutely hospitalized older adults; however it is unknown which amount and level of PA is associated with recovery. Our objective was to evaluate the amount and level of post discharge PA and its optimum cut-off values associated with recovery among acutely hospitalized older adults and stratified for frailty.METHODS: We performed a prospective observational cohort study including acutely hospitalized older adults (≥ 70 years). Frailty was assessed using Fried's criteria. PA was assessed using Fitbit up to one week post discharge and quantified in steps and minutes light, moderate or higher intensity. The primary outcome was recovery at 3-months post discharge. ROC-curve analyses were used to determine cut-off values and area under the curve (AUC), and logistic regression analyses to calculate odds ratios (ORs).RESULTS: The analytic sample included 174 participants with a mean (standard deviation) age of 79.2 (6.7) years of whom 84/174 (48%) were frail. At 3-months, 109/174 participants (63%) had recovered of whom 48 were frail. In all participants, determined cut-off values were 1369 steps/day (OR: 2.7, 95% confidence interval [CI]: 1.3-5.9, AUC 0.7) and 76 min/day of light intensity PA (OR: 3.9, 95% CI: 1.8-8.5, AUC 0.73). In frail participants, cut-off values were 1043 steps/day (OR: 5.0, 95% CI: 1.7-14.8, AUC 0.72) and 72 min/day of light intensity PA (OR: 7.2, 95% CI: 2.2-23.1, AUC 0,74). Determined cut-off values were not significantly associated with recovery in non-frail participants.CONCLUSIONS: Post-discharge PA cut-offs indicate the odds of recovery in older adults, especially in frail individuals, however are not equipped for use as a diagnostic test in daily practice. This is a first step in providing a direction for setting rehabilitation goals in older adults after hospitalization.
DOCUMENT
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
LINK
BACKGROUND: Differentiating mild cognitive impairment (MCI) from dementia is important, as treatment options differ. There are few short (<5 min) but accurate screening tools that discriminate between MCI, normal cognition (NC) and dementia, in the Dutch language. The Quick Mild Cognitive Impairment (Qmci) screen is sensitive and specific in differentiating MCI from NC and mild dementia. Given this, we adapted the Qmci for use in Dutch-language countries and validated the Dutch version, the Qmci-D, against the Dutch translation of the Standardised Mini-Mental State Examination (SMMSE-D).METHOD: The Qmci was translated into Dutch with a combined qualitative and quantitative approach. In all, 90 participants were recruited from a hospital geriatric clinic (25 with dementia, 30 with MCI, 35 with NC). The Qmci-D and SMMSE-D were administered sequentially but randomly by the same trained rater, blind to the diagnosis.RESULTS: The Qmci-D was more sensitive than the SMMSE-D in discriminating MCI from dementia, with a significant difference in the area under the curve (AUC), 0.73 compared to 0.60 (p = 0.024), respectively, and in discriminating dementia from NC, with an AUC of 0.95 compared to 0.89 (p = 0.006). Both screening instruments discriminated MCI from NC with an AUC of 0.86 (Qmci-D) and 0.84 (SMMSE-D).CONCLUSION: The Qmci-D shows similar,(good) accuracy as the SMMSE-D in separating NC from MCI; greater,(albeit fair), accuracy differentiating MCI from dementia, and significantly greater accuracy in separating dementia from NC. Given its brevity and ease of administration, the Qmci-D seems a useful cognitive screen in a Dutch population. Further study with a suitably powered sample against more sensitive screens is now required.
DOCUMENT
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
DOCUMENT
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.
LINK
Background While low back pain occurs in nearly everybody and is the leading cause of disability worldwide, we lack instruments to accurately predict persistence of acute low back pain. We aimed to develop and internally validate a machine learning model predicting non-recovery in acute low back pain and to compare this with current practice and ‘traditional’ prediction modeling. Methods Prognostic cohort-study in primary care physiotherapy. Patients (n = 247) with acute low back pain (= one month) consulting physiotherapists were included. Candidate predictors were assessed by questionnaire at baseline and (to capture early recovery) after one and two weeks. Primary outcome was non-recovery after three months, defined as at least mild pain (Numeric Rating Scale > 2/10). Machine learning models to predict non-recovery were developed and internally validated, and compared with two current practices in physiotherapy (STarT Back tool and physiotherapists’ expectation) and ‘traditional’ logistic regression analysis. Results Forty-seven percent of the participants did not recover at three months. The best performing machine learning model showed acceptable predictive performance (area under the curve: 0.66). Although this was no better than a’traditional’ logistic regression model, it outperformed current practice. Conclusions We developed two prognostic models containing partially different predictors, with acceptable performance for predicting (non-)recovery in patients with acute LBP, which was better than current practice. Our prognostic models have the potential of integration in a clinical decision support system to facilitate data-driven, personalized treatment of acute low back pain, but needs external validation first.
MULTIFILE
