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.
MULTIFILE
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.
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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