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.
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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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Aims and objectives: To examine the predictive properties of the brief Dutch National Safety Management Program for the screening of frail hospitalised older patients (VMS) and to compare these with the more extensive Maastricht Frailty Screening Tool for Hospitalised Patients (MFST-HP). Background: Screening of older patients during admission may help to detect frailty and underlying geriatric conditions. The VMS screening assesses patients on four domains (i.e. functional decline, delirium risk, fall risk and nutrition). The 15-item MFST-HP assesses patients on three domains of frailty (physical, social and psychological). Design: Retrospective cohort study. Methods: Data of 2,573 hospitalised patients (70+) admitted in 2013 were included, and relative risks, sensitivity and specificity and area under the receiver operating characteristic (AUC) curve of the two tools were calculated for discharge destination, readmissions and mortality. The data were derived from the patients nursing files. A STARD checklist was completed. Results: Different proportions of frail patients were identified by means of both tools: 1,369 (53.2%) based on the VMS and 414 (16.1%) based on the MFST-HP. The specificity was low for the VMS, and the sensitivity was low for the MFST-HP. The overall AUC for the VMS varied from 0.50 to 0.76 and from 0.49 to 0.69 for the MFST-HP. Conclusion: The predictive properties of the VMS and the more extended MFST-HP on the screening of frailty among older hospitalised patients are poor to moderate and not very promising. Relevance to clinical practice: The VMS labels a high proportion of older patients as potentially frail, while the MFST-HP labels over 80% as nonfrail. An extended tool did not increase the predictive ability of the VMS. However, information derived from the individual items of the screening tools may help nurses in daily practice to intervene on potential geriatric risks such as delirium risk or fall risk.
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The aim of this study was to assess the predictive ability of the frailty phenotype (FP), Groningen Frailty Indicator (GFI), Tilburg Frailty Indicator (TFI) and frailty index (FI) for the outcomes mortality, hospitalization and increase in dependency in (instrumental) activities of daily living ((I)ADL) among older persons. This prospective cohort study with 2-year follow-up included 2420 Dutch community-dwelling older people (65+, mean age 76.3±6.6 years, 39.5% male) who were pre-frail or frail according to the FP. Mortality data were obtained from Statistics Netherlands. All other data were self-reported. Area under the receiver operating characteristic curves (AUC) was calculated for each frailty instrument and outcome measure. The prevalence of frailty, sensitivity and specifcity were calculated using cutoff values proposed by the developers and cutoff values one above and one below the proposed ones (0.05 for FI). All frailty instruments poorly predicted mortality, hospitalization and (I)ADL dependency (AUCs between 0.62–0.65, 0.59–0.63 and 0.60–0.64, respectively). Prevalence estimates of frailty in this population varied between 22.2% (FP) and 64.8% (TFI). The FP and FI showed higher levels of specifcity, whereas sensitivity was higher for the GFI and TFI. Using a different cutoff point considerably changed the prevalence, sensitivity and specifcity. In conclusion, the predictive ability of the FP, GFI, TFI and FI was poor for all outcomes in a population of pre-frail and frail community-dwelling older people. The FP and the FI showed higher values of specifcity, whereas sensitivity was higher for the GFI and TFI.
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Abstract Aims: To lower the threshold for applying ultrasound (US) guidance during peripheral intravenous cannulation, nurses need to be trained and gain experience in using this technique. The primary outcome was to quantify the number of procedures novices require to perform before competency in US-guided peripheral intravenous cannulation was achieved. Materials and methods: A multicenter prospective observational study, divided into two phases after a theoretical training session: a handson training session and a supervised life-case training session. The number of US-guided peripheral intravenous cannulations a participant needed to perform in the life-case setting to become competent was the outcome of interest. Cusum analysis was used to determine the learning curve of each individual participant. Results: Forty-nine practitioners participated and performed 1855 procedures. First attempt cannulation success was 73% during the first procedure, but increased to 98% on the fortieth attempt (p<0.001). The overall first attempt success rate during this study was 93%. The cusum learning curve for each practitioner showed that a mean number of 34 procedures was required to achieve competency. Time needed to perform a procedure successfully decreased when more experience was achieved by the practitioner, from 14±3 minutes on first procedure to 3±1 minutes during the fortieth procedure (p<0.001). Conclusions: Competency in US-guided peripheral intravenous cannulation can be gained after following a fixed educational curriculum, resulting in an increased first attempt cannulation success as the number of performed procedures increased.
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Achtergrond Het is bekend dat gestructureerde instrumenten voor taxatie van het kortetermijnrisico bijdragen aan het voorspellen van fysiek agressief gedrag bij patiënten in de acute psychiatrie. Doel Onderzoeken of de Brøset Violence Checklist (BVC), een instrument voor de inschatting van fysieke agressie op korte termijn, kan bijdragen aan het voorspellen van fysieke agressie-incidenten binnen de forensische psychiatrie en onderzoeken hoe het gebruik van de BVC wordt ervaren. Methode Tweemaal per 24 uur op min of meer vaste momenten werd voor alle patiënten die in 2019 verbleven op een crisisafdeling binnen een forensisch psychiatrisch centrum een BVC-score geregistreerd. De totaalscores van de BVC werden vervolgens gerelateerd aan fysieke agressie-incidenten. Daarnaast werden focusgroepen en interviews gehouden met sociotherapeuten om de ervaringen met het gebruik van de BVC te onderzoeken. Resultaten Uit de analyse kwam een significante voorspellende waarde van de BVC-totaalscore naar voren (AUC = 0,69; p < 0,01). Bovendien ervoeren de sociotherapeuten de BVC als gebruikersvriendelijk en weinig tijdsintensief. Conclusie De BVC heeft toegevoegde waarde voor de forensische psychiatrie. Dit geldt met name voor patiënten bij wie de primaire diagnose géén persoonlijkheidsstoornis betreft.
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A local operating theater ventilation device to specifically ventilate the wound area has been developed and investigated. The ventilation device is combined with a blanket which lies over the patient during the operation. Two configurations were studied: Configuration 1 where HEPA-filtered air was supplied around and parallel to the wound area and Configuration 2 where HEPA-filtered air was supplied from the top surface of the blanket, perpendicular to the wound area. A similar approach is investigated in parallel for an instrument table. The objective of the study was to verify the effectiveness of the local device. Prototype solutions developed were studied experimentally (laboratory) and numerically (CFD) in a simplified setup, followed by experimental assessment in a full scale mock-up. Isothermal as well as non-isothermal conditions were analyzed. Particle concentrations obtained in proposed solutions were compared to the concentration without local ventilation. The analysis procedure followed current national guidelines for the assessment of operating theater ventilation systems, which focus on small particles (<10 mm). The results show that the local system can provide better air quality conditions near the wound area compared to a theoretical mixing situation (proof-of-principle). It cannot yet replace the standard unidirectional downflow systems as found for ultraclean operating theater conditions. It does, however, show potential for application in temporary and emergency operating theaters
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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: 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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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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