The Prediction of Quality of Life by Frailty and Disability among Dutch Community-Dwelling People Aged 75 Years or Older.

The present study aimed to examine the prediction of quality of life by frailty and disability in a baseline sample of 479 Dutch community-dwelling people aged 75 years or older using a follow-up period of 8 years. Regarding frailty, we distinguish between physical, psychological, and social frailty. Concerning physical disability, we distinguish between limitations in performing activities in daily living (ADL) and instrumental activities in daily living (IADL). The Tilburg Frailty Indicator (TFI) and the Groningen Activity Restriction Scale (GARS) were used to assess frailty domains and types of disability, respectively. Quality of life was determined by the WHOQOL-BREF containing physical, psychological, social, and environmental domains. In our study, 53.9% of participants were woman, and the mean age was 80.3 years (range 75-93). The study showed that psychological frailty predicted four domains of quality of life and physical frailty three. Social frailty was only found to be a significant predictor of social quality of life and environmental quality of life. ADL and IADL disability proved to be the worst predictors. It is recommended that primary healthcare professionals (e.g., general practitioners, district nurses) focus their interventions primarily on factors that can prevent or delay psychological and physical frailty, thereby ensuring that people's quality of life does not deteriorate.

External Validation of Models for Predicting Disability in Community-Dwelling Older People in the Netherlands: A Comparative Study.

Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.

Predicting Disability Using a Nomogram of the Tilburg Frailty Indicator (TFI).

Disability is associated with lower quality of life and premature death in older people. Therefore, prevention and intervention targeting older people living with a disability is important. Frailty can be considered a major predictor of disability. In this study, we aimed to develop nomograms with items of the Tilburg Frailty Indicator (TFI) as predictors by using cross-sectional and longitudinal data (follow-up of five and nine years), focusing on the prediction of total disability, disability in activities of daily living (ADL), and disability in instrumental activities of daily living (IADL). At baseline, 479 Dutch community-dwelling people aged ≥75 years participated. They completed a questionnaire that included the TFI and the Groningen Activity Restriction Scale to assess the three disability variables. We showed that the TFI items scored different points, especially over time. Therefore, not every item was equally important in predicting disability. 'Difficulty in walking' and 'unexplained weight loss' appeared to be important predictors of disability. Healthcare professionals need to focus on these two items to prevent disability. We also conclude that the points given to frailty items differed between total, ADL, and IADL disability and also differed regarding years of follow-up. Creating one monogram that does justice to this seems impossible.

Bayesian Techniques in Predicting Frailty among Community-Dwelling Older Adults in the Netherlands.

Background Frailty 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. Objective We 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. Methods We 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). Results The 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 1800 euro increased the likelihood of frailty. Conclusions Although 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.

Prediction of COVID-19 Infections for Municipalities in the Netherlands: Algorithm Development and Interpretation.

BACKGROUND: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. OBJECTIVE: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. METHODS: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. RESULTS: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 µm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. CONCLUSIONS: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.

Nurses' Opinions on Frailty.

Nurses come into frequent contact with frail older people in all healthcare settings. However, few studies have specifically asked nurses about their views on frailty. The main aim of this study was to explore the opinions of nurses working with older people on the concept of frailty, regardless of the care setting. In addition, the associations between the background characteristics of nurses and their opinions about frailty were examined. In 2021, members of professional association of nurses and nursing assistants in the Netherlands (V&VN) received a digital questionnaire asking their opinions on frailty, and 251 individuals completed the questionnaire (response rate of 32.1%). The questionnaire contained seven topics: keywords of frailty, frailty domains, causes of frailty, consequences of frailty, reversing frailty, the prevention of frailty, and addressing frailty. Regarding frailty, nurses especially thought of physical deterioration and dementia. However, other domains of human functioning, such as the social and psychological domains, were often mentioned, pointing to a holistic approach to frailty. It also appears that nurses can identify many causes and consequences of frailty. They see opportunities to reverse frailty and an important role for themselves in this process.

Frailty at Risk Scale (FARS): development and application.

The aim of this cross-sectional study was to develop a Frailty at Risk Scale (FARS) incorporating ten well-known determinants of frailty: age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. In addition, a second aim was to develop an online calculator that can easily support healthcare professionals in determining the risk of frailty among community-dwelling older people. The FARS was developed using data of 373 people aged ≥ 75 years. The Tilburg Frailty Indicator (TFI) was used for assessing frailty. Multivariate logistic regression analysis showed that the determinants multimorbidity, unhealthy lifestyle, and ethnicity (ethnic minority) were the most important predictors. The area under the curve (AUC) of the model was 0.811 (optimism 0.019, 95% bootstrap CI = -0.029; 0.064). The FARS is offered on a Web site, so that it can be easily used by healthcare professionals, allowing quick intervention in promoting quality of life among community-dwelling older people.

Metadata Correction: A Comparison of Different Modeling Techniques in Predicting Mortality With the Tilburg Frailty Indicator: Longitudinal Study.

[This corrects the article DOI: 10.2196/31480.].

A Comparison of Different Modeling Techniques in Predicting Mortality With the Tilburg Frailty Indicator: Longitudinal Study.

BACKGROUND: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. OBJECTIVE: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. METHODS: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. RESULTS: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable "difficulty in walking" was important for all models. CONCLUSIONS: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of "gender" and "age" variables. External validation is a necessary step before applying the prediction models in a new setting.

The Added Value of Art for the Well-Being of Older People with Chronic Psychiatric Illnesses and Dementia Living in Long-Term Care Facilities, and on the Collaboration between Their Caregivers and Artists.

This study sought to provide insight into how art activities influence the well-being of long-term care residents, and how artists and caregivers collaborate in offering these activities. In two long-term care facilities for people with dementia and one for older people with chronic psychiatric disorders, an uncontrolled pre- and post-test study was conducted using a mixed-method design. Forty-six residents participated in the study. Three art activities-(a) dance, (b) music and movement, and (c) visual arts-were studied and co-created with the residents and executed by artists and caregivers together in eight to ten weeks. The Face expression scale (FACE) was used to examine the extent to which participating in the art activity influenced resident mood. Qualitative data were collected via group discussions with artists, caregivers, residents, and an informal caregiver. The results indicated that participating in an art activity positively influenced resident mood (p < 0.000). p-values for the three art activities were: p < 0.000 for dance, p = 0.048 for music and movement, and p = 0.023 for visual arts. The qualitative data revealed that joining an art activity provided a positive effect, increased social relationships, and improved self-esteem for residents. The collaboration between artists and caregivers stimulated creativity, beauty, and learning from each other, as well as evoking emotions.

The Development of Multidimensional Frailty Over Seven Years A longitudinal study among Dutch community-dwelling older people using the Tilburg Frailty Indicator.

PURPOSE: To examine the development of multidimensional frailty, including physical, psychological and social components, over a period of seven years. To determine the effects of sociodemographic factors (gender, age, marital status, education, income) on the development of frailty. METHODS: This longitudinal study was conducted in sample of 479 community-dwelling people aged ≥ 75 years living in the municipality of Roosendaal, the Netherlands. The Tilburg Frailty Indicator (TFI), a self-report questionnaire, was used to collect data about frailty. Frailty was assessed annually. RESULTS: Frailty increased significantly over seven years among the people who completed the entire TFI all years (n = 121), the average score was 3.75 (SD 2.80) at baseline and 5.05 (SD 3.18) after seven years. Regarding frailty transitions, most participants remained unchanged from their baseline status. The transition from non-frail to frail was present in 8.3% to 12.6% of the participants and 5.1% to 10.7% made a transition from frail to non-frail. Gender (woman), age (≥80 years), marital status (not married/cohabiting), high level of education, and incomes from €601-€1800 were significantly associated with a higher frailty score. CONCLUSION: This study showed that multidimensional frailty, assessed with the TFI, increased among Dutch community-dwelling people aged ≥75 years using a follow-up of seven years. Gender, age, marital status, education, and income were associated with frailty transitions. These findings provide healthcare professionals clues to identify people at increased risk of frailty, and target interventions which aim to prevent or delay frailty and its adverse outcomes, such as disability and mortality.

The prediction of mortality by quality of life assessed with the WHOQOL-BREF: a longitudinal analysis at the domain and item levels using a seven-year follow-up period.

PURPOSE: To determine the predictive value of quality of life for mortality at the domain and item levels. METHODS: This longitudinal study was carried out in a sample of 479 Dutch people aged 75 years or older living independently, using a follow-up of 7 years. Participants completed a self-report questionnaire. Quality of life was assessed with the WHOQOL-BREF, including four domains: physical health, psychological, social relationships, and environment. The municipality of Roosendaal (a town in the Netherlands) indicated the dates of death of the individuals. RESULTS: Based on mean, all quality of life domains predicted mortality adjusted for gender, age, marital status, education, and income. The hazard ratios ranged from 0.811 (psychological) to 0.933 (social relationships). The areas under the curve (AUCs) of the four domains were 0.730 (physical health), 0.723 (psychological), 0.693 (social relationships), and 0.700 (environment). In all quality of life domains, at least one item predicted mortality (adjusted). CONCLUSION: Our study showed that all four quality of life domains belonging to the WHOQOL-BREF predict mortality in a sample of Dutch community-dwelling older people using a follow-up period of 7 years. Two AUCs were above threshold (psychological, physical health). The findings offer health care and welfare professionals evidence for conducting interventions to reduce the risk of premature death.

Interrater agreement and reliability of clinical tests for assessment of patients with shoulder pain in primary care.

Background: There is limited information about the agreement and reliability of clinical shoulder tests. Objectives: To assess the interrater agreement and reliability of clinical shoulder tests in patients with shoulder pain treated in primary care. Methods: Patients with a primary report of shoulder pain underwent a set of 21 clinical shoulder tests twice on the same day, by pairs of independent physical therapists. The outcome parameters were observed and specific interrater agreement for positive and negative scores, and interrater reliability (Cohen's kappa (κ)). Positive and negative interrater agreement values of ≥0.75 were regarded as sufficient for clinical use. For Cohen's κ, the following classification was used: <0.20 poor, 0.21-0.40 fair, 0.41-0.60 moderate, 0.61-0.80 good, 0.81-1.00 very good reliability. Participating clinics were randomized in two groups; with or without a brief practical session on how to conduct the tests. Results: A total of 113 patients were assessed in 12 physical therapy practices by 36 physical therapists. Positive and negative interrater agreement values were both sufficient for 1 test (the Full Can Test), neither sufficient for 5 tests, and only sufficient for either positive or negative agreement for 15 tests. Interrater reliability was fair for 11 tests, moderate for 9 tests, and good for 1 test (the Full Can Test). An additional brief practical session did not result in better agreement or reliability. Conclusion: Clinicians should be aware that interrater agreement and reliability for most shoulder tests is questionable and their value in clinical practice limited.

Prediction of Mortality by the Tilburg Frailty Indicator (TFI).

OBJECTIVE: To predict mortality with the Tilburg Frailty Indicator (TFI) in a sample of community-dwelling older people, using a follow-up of 7 years. DESIGN: Longitudinal. SETTING AND PARTICIPANTS: 479 Dutch community-dwelling people aged 75 years or older. MEASUREMENTS: The TFI, a self-report questionnaire, was used to collect data about total, physical, psychological, and social frailty. The municipality of Roosendaal (a town in the Netherlands) provided the mortality dates. RESULTS: Total, physical, and psychological frailty predicted mortality, with unadjusted hazard ratios of 1.295, 1.168, and 1.194, and areas under the receiver operating characteristic curves of 0.664, 0.671, and 0.567, respectively. After adjustment for age and gender, the areas under the curves for total, physical, and psychological frailty were 0.704, 0.702, and 0.652, respectively. Analyses using individual components of the TFI show that difficulty in walking and unexplained weight loss predict mortality. CONCLUSIONS AND IMPLICATIONS: This study has shown the predictive validity of the TFI for mortality in community-dwelling older people. Our study demonstrated that physical and psychological frailty predicted mortality. Of the individual TFI components, difficulty in walking consistently predicted mortality. For identifying frailty, using the integral instrument is recommended because total, physical, psychological, and social frailty and its components have proven their value in predicting adverse outcomes of frailty, for example, increase in health care use and a lower quality of life.

The Prediction of Mortality by Disability Among Dutch Community-Dwelling Older People.

OBJECTIVE: To predict mortality by disability in a sample of 479 Dutch community-dwelling people aged 75 years or older. METHODS: A longitudinal study was carried out using a follow-up of seven years. The Groningen Activity Restriction Scale (GARS), a self-reported questionnaire with good psychometric properties, was used for data collection about total disability, disability in activities in daily living (ADL) and disability in instrumental activities in daily living (IADL). The mortality dates were provided by the municipality of Roosendaal (a city in the Netherlands). For analyses of survival, we used Kaplan-Meier analyses and Cox regression analyses to calculate hazard ratios (HR) with 95% confidence intervals (CI). RESULTS: All three disability variables (total, ADL and IADL) predicted mortality, unadjusted and adjusted for age and gender. The unadjusted HRs for total, ADL and IADL disability were 1.054 (95%-CI: [1.039;1.069]), 1.091 (95%-CI: [1.062;1.121]) and 1.106 (95%-CI: [1.077;1.135]) with p-values <0.001, respectively. The AUCs were <0.7, ranging from 0.630 (ADL) to 0.668 (IADL). Multivariate analyses including all 18 disability items revealed that only "Do the shopping" predicted mortality. In addition, multivariate analyses focusing on 11 ADL items and 7 IADL items separately showed that only the ADL item "Get around in the house" and the IADL item "Do the shopping" significantly predicted mortality. CONCLUSION: Disability predicted mortality in a seven years follow-up among Dutch community-dwelling older people. It is important that healthcare professionals are aware of disability at early stages, so they can intervene swiftly, efficiently and effectively, to maintain or enhance the quality of life of older people.

Prolonging the duration of post-infusion scalp cooling in the prevention of anthracycline-induced alopecia: a randomised trial in patients with breast cancer treated with adjuvant chemotherapy.

PURPOSE: Scalp cooling as a method to reduce the incidence of chemotherapy-induced alopecia (CIA) is increasingly used in daily practice worldwide. However, in patients treated with 5-fluorouracil, epirubicin and cyclophosphamide (FEC), scalp cooling fails in 48-67% of patients. This study investigated the efficacy of extended duration of post-infusion scalp cooling in breast cancer patients treated with this regimen. METHODS: In this prospective multi-centre randomised study, 102 patients with early breast cancer treated with adjuvant FEC chemotherapy were randomly assigned in a 1:1 ratio to a post-infusion cooling time of 90 or 150 min. The primary endpoint was the need to wear a wig or other head covering to mask visible hair loss. RESULTS: Sixteen out of 48 patients (33%) treated with 90 min of post-infusion cooling did not need any head covering, compared with 21 out of 46 patients (45%) treated with 150 min of post-infusion cooling (p = 0.2). WHO grades 2-3 (moderate-complete) alopecia were reported more often in patients treated with 90-min post-infusion cooling time (n = 25/51 (49%) versus n = 17/51 (33%); p = 0,02). Scalp cooling was well-tolerated (mean Visual Analogue Score 7.4) and only three patients (3%) stopped due to intolerance during treatment. CONCLUSIONS: Extending the duration of 90-min post-infusion scalp cooling to 150 min in patients treated with adjuvant FEC chemotherapy was well-tolerated but did not significantly diminish the need for head covering. However, grades 2-3 alopecia was seen less often with prolonged post-infusion scalp cooling.

Gender differences in retention rate of tumor necrosis factor alpha inhibitor treatment in ankylosing spondylitis: a retrospective cohort study in daily practice.

AIM: To assess gender differences in ankylosing spondylitis (AS) patients in relation to tumor necrosis factor alpha inhibitor (TNFi) drug survival and occurrence of adverse events in daily practice in a large peripheral hospital. METHOD: Retrospective data were collected from AS patients treated with etanercept, infliximab and adalimumab between January 2004 and January 2014. Kaplan-Meier survival curves were conducted to describe the drug survival and occurrence of adverse events in time. RESULTS: Overall, 122 AS patients (60.7% male) were included over a 10-year time period, with a mean treatment period of 51 months (1-127 months). In total, 21 (17.2%) patients stopped the TNFi, mainly due to inefficacy (52.4%). Female patients showed a significant shorter treatment period compared to males (33.4 vs. 44.9 months). In addition, female patients switched more between TNFi compared to males (26.9% vs. 16.3%) and had a significantly higher risk at developing infections compared to male patients (26% vs.19%). CONCLUSION: Females stayed on the same TNFi for a significantly shorter period compared to males (33.4 vs. 44.9 months) and the most important reason to stop or switch the drug was inefficacy. Moreover, females seemed to be more prone to infections during TNFi treatment than males.

Exercise Electrocardiogram Neither Predicts Nor Excludes Coronary Artery Disease in Women with Low to Intermediate Risk.

AIM: The value of exercise electrocardiogram (ExECG) in symptomatic female patients with low to intermediate risk for significant coronary artery disease (CAD) has been under debate for many years, and nondiagnostic or even erroneous test results are frequently encountered. Cardiac-CT may be more appropriate to exclude CAD in women. This study compares the results of ExECGs with those of cardiac-CTs, performed within a time frame of 1 month in an all-comers female chest pain population. PATIENTS AND METHODS: Five hundred fifty-one consecutive female patients from a patient registry were included. ExECGs were negative in 324 (59%), positive in 14 (3%), and nondiagnostic in 213 (39%) patients. CAD was revealed by cardiac-CT in 57% of the women with negative ExECG. No signs of CAD were present on cardiac-CT in 64% of the women with a positive ExECG. Cardiac-CT showed presence of CAD in 268/551 (49%) patients, of whom 56/268 (21%) was diagnosed with ≥50% stenosis. The ExECG of the latter group was negative in 26 (46%), inconclusive in 29 (52%), and positive in 1 (2%). Considering ≥50% stenosis at cardiac-CT as the reference, sensitivity, specificity, PPV, and NPV of ExECG for the present population were 3.7%, 95.7%, 7.1%, and 91.7%, respectively. Similar diagnostic performance was calculated when considering ≥70% stenosis at cardiac-CT as the reference. CONCLUSION: ExECG failed to detect CAD in more than half of this cohort and in almost half of women with >50% stenosis at cardiac-CT. Importantly, no CAD was detected by cardiac-CT in 64% of women with a positive ExECG. ExECG is therefore questionable as a diagnostic strategy in women with low-to-intermediate risk of CAD, although prospective studies are warranted to determine whether replacing ExECG by cardiac-CT provides better prognoses.

In patients undergoing fast track total knee arthroplasty, addition of buprenorphine to a femoral nerve block has no clinical advantage: A prospective, double-blinded, randomized, placebo controlled trial.

BACKGROUND: Several adjuvants have been proposed to prolong the effect of peripheral nerve blocks, one of which is buprenorphine. In this randomized double blinded placebo controlled trial we studied whether the addition of buprenorphine to a femoral nerve block prolongs analgesia in patients undergoing total knee arthroplasty in a fast track surgery protocol. METHODS: The treatment group (B) was given an ultrasound-guided femoral nerve block with ropivacaine 0.2% and 0.3mg buprenorphine. We choose to use 2 control groups. Group R was given a femoral nerve block with ropivacaine 0.2% only. Group S also received 0.3 mg buprenorphine subcutaneously. Only patients with a successful block were enrolled in the study. RESULTS: We found no difference in our primary outcome parameter of time to first rescue analgesic. We found lower opioid use and better sleep quality the first postoperative night in patients receiving buprenorphine perineurally or subcutaneously. Buprenorphine did not lead to any significant change in pain or mobilization. We found a high overall incidence of nausea and vomiting. CONCLUSION: In patients undergoing total knee arthroplasty, in the setting of a fast track surgery protocol, the addition of buprenorphine to a femoral nerve block did not prolong analgesia.

Dementia and delirium, the outcomes in elderly hip fracture patients.

BACKGROUND: Delirium in hip fractured patients is a frequent complication. Dementia is an important risk factor for delirium and is common in frail elderly. This study aimed to extend the previous knowledge on risk factors for delirium and the consequences. Special attention was given to patients with dementia and delirium. METHODS: This is a retrospective cohort study performed in the Amphia Hospital, Breda, the Netherlands. A full electronic patient file system (Hyperspace Version IU4: Epic, Inc., Verona, WI, USA) was used to assess data between January 2014 and September 2015. All patients presented were aged ≥70 years with a hip fracture, who underwent surgery with osteosynthesis or arthroplasty. Patients were excluded in case of a pathological or a periprosthetic hip fracture, multiple traumatic injuries, and high-energy trauma. Patient and surgical characteristics were documented. Postoperative outcomes were noted. Delirium was screened using Delirium Observation Screening Scale and dementia was assessed from medical notes. RESULTS: Of a total of 566 included patients, 75% were females. The median age was 84 years (interquartile range: 9). Delirium was observed in 35%. Significant risk factors for delirium were a high American Society of Anesthesiology score, delirium in medical history, functional dependency, preoperative institutionalization, low hemoglobin level, and high amount of blood transfusion. Delirium was correlated with a longer hospital stay (P=0.001), increased association with complications (P<0.001), institutionalization (P<0.001), and 6-month mortality (P<0.001). Patients with dementia (N=168) had a higher delirium rate (57.7%, P<0.001) but a shorter hospital stay (P<0.001). There was no significant difference in the 6-month mortality between delirious patients with (34.0%) and without dementia (26.3%). CONCLUSION: Elderly patients with a hip fracture are vulnerable for delirium, especially when the patient has dementia. Patients who underwent an episode of delirium were at increased risk for adverse outcomes.