There are few studies providing a more comprehensive picture of advanced hybrid closed-loop (AHCL) systems in clinical practice. The aim was to evaluate the effects of the AHCL systems, Tandem
Theoretical frameworks have recommended organisational-level interventions to decrease employee withdrawal behaviours such as sickness absence and employee turnover. However, evaluation of such interventions has produced inconclusive results. The aim of this study was to investigate if mixed-effects models in combination with time series analysis, process evaluation, and reference group comparisons could be used for evaluating the effects of an organisational-level intervention on employee withdrawal behaviour.Monthly data on employee withdrawal behaviours (sickness absence, employee turnover, employment rate, and unpaid leave) were collected for 58 consecutive months (before and after the intervention) for intervention and reference groups. In total, eight intervention groups with a total of 1600 employees participated in the intervention. Process evaluation data were collected by process facilitators from the intervention team. Overall intervention effects were assessed using mixed-effects models with an AR (1) covariance structure for the repeated measurements and time as fixed effect. Intervention effects for each intervention group were assessed using time series analysis. Finally, results were compared descriptively with data from process evaluation and reference groups to disentangle the organisational-level intervention effects from other simultaneous effects.All measures of employee withdrawal behaviour indicated statistically significant time trends and seasonal variability. Applying these methods to an organisational-level intervention resulted in an overall decrease in employee withdrawal behaviour. Meanwhile, the intervention effects varied greatly between intervention groups, highlighting the need to perform analyses at multiple levels to obtain a full understanding. Results also indicated that possible delayed intervention effects must be considered and that data from process evaluation and reference group comparisons were vital for disentangling the intervention effects from other simultaneous effects.When analysing the effects of an intervention, time trends, seasonal variability, and other changes in the work environment must be considered. The use of mixed-effects models in combination with time series analysis, process evaluation, and reference groups is a promising way to improve the evaluation of organisational-level interventions that can easily be adopted by others.
Abstract Background In Sweden with about 10 million inhabitants, there are about one million primary ambulance missions every year. Among them, around 10% are assessed by Emergency Medical Service (EMS) clinicians with the primary symptom of dyspnoea. The risk of death among these patients has been reported to be remarkably high, at 11,1% and 13,2%. The aim was to develop a Machine Learning (ML) model to provide support in assessing patients in pre-hospital settings and to compare them with established triage tools. Methods This was a retrospective observational study including 6,354 patients who called the Swedish emergency telephone number (112) between January and December 2017. Patients presenting with the main symptom of dyspnoea were included which were recruited from two EMS organisations in Göteborg and Södra Älvsborg. Serious Adverse Event (SAE) was used as outcome, defined as any of the following:1) death within 30 days after call for an ambulance, 2) a final diagnosis defined as time-sensitive, 3) admitted to intensive care unit, or 4) readmission within 72 h and admitted to hospital receiving a final time-sensitive diagnosis. Logistic regression, LASSO logistic regression and gradient boosting were compared to the Rapid Emergency Triage and Treatment System for Adults (RETTS-A) and National Early Warning Score2 (NEWS2) with respect to discrimination and calibration of predictions. Eighty percent (80%) of the data was used for model development and 20% for model validation. Results All ML models showed better performance than RETTS-A and NEWS2 with respect to all evaluated performance metrics. The gradient boosting algorithm had the overall best performance, with excellent calibration of the predictions, and consistently showed higher sensitivity to detect SAE than the other methods. The ROC AUC on test data increased from 0.73 (95% CI 0.70–0.76) with RETTS-A to 0.81 (95% CI 0.78–0.84) using gradient boosting. Conclusions Among 6,354 ambulance missions caused by patients suffering from dyspnoea, an ML method using gradient boosting demonstrated excellent performance for predicting SAE, with substantial improvement over the more established methods RETTS-A and NEWS2.
Subsampling is commonly used to overcome computational and economical bottlenecks in the analysis of finite populations and massive datasets. Existing methods are often limited in scope and use optimality criteria (e.g., A-optimality) with well-known deficiencies, such as lack of invariance to the measurement-scale of the data and parameterisation of the model. A unified theory of optimal subsampling design is still lacking. We present a theory of optimal design for general data subsampling problems, including finite population inference, parametric density estimation, and regression modelling. Our theory encompasses and generalises most existing methods in the field of optimal subdata selection based on unequal probability sampling and inverse probability weighting. We derive optimality conditions for a general class of optimality criteria, and present corresponding algorithms for finding optimal sampling schemes under Poisson and multinomial sampling designs. We present a novel class of transformation- and parameterisation-invariant linear optimality criteria which enjoy the best of two worlds: the computational tractability of A-optimality and invariance properties similar to D-optimality. The methodology is illustrated on an application in the traffic safety domain. In our experiments, the proposed invariant linear optimality criteria achieve 92-99% D-efficiency with 90-95% lower computational demand. In contrast, the A-optimality criterion has only 46% and 60% D-efficiency on two of the examples.
Abstract Objectives Telehealth and home spirometry feasibility for children has been established, but their impact on cystic fibrosis (CF) disease progression remains unassessed. We aimed to evaluate the effects of telehealth and home spirometry on CF disease progression and care. Methods Children with CF aged 5–17 years from all Swedish CF centers were provided with home spirometers. A minimum of two in‐person visits were replaced with telemedicine visits and participants were instructed to conduct home spirometry before visits. Linear mixed‐effects models were used to compare annual CF disease trajectories during the intervention period and prepandemic period (1 January 2019 to 28 February 2020). Participants and caregivers completed study questionnaires. Results A total of 59 individuals completed the study over a mean (SD) period of 6.8 (1.4) months, made 3.1 (1.0) physical visits and 2.2 (0.6) telehealth visits per patient year during the study period. The mean difference (95% CI) between the intervention and prepandemic period progression rate for FEV 1 %, lung clearance index and BMI were −0.4 (−1.3 to 0.5, p = 0.39), 0.11 (−0.07 to 0.28, p = 0.25) and −0.02 (−0.13 to 0.08, p = 0.70), respectively. There were no major shifts in the incidence of airway pathogens, sputum cultures, or antibiotics use between the periods ( p > 0.05). The intervention did not increase stress. Almost all participants and caregivers expressed a desire to continue with home spirometry and telemedicine. Conclusion Combining telehealth and physical visits with access to home spirometry demonstrated comparable effectiveness as exclusively in‐person care with enhanced flexibility and personalization of CF care.
Use of the glucagon-like peptide 1 receptor agonist liraglutide has been shown to reduce weight. Different types of anthropometric measurements can be used to measure adiposity. This study evaluated the effect of liraglutide on sagittal abdominal diameter, waist circumference, waist-to-hip ratio and adiponectin levels in people with type 2 diabetes (T2D) treated with multiple daily insulin injections (MDI).In the multicentre, double-blind, placebo-controlled MDI-liraglutide trial, 124 individuals with T2D treated with MDI were randomized to either liraglutide or placebo. Basal values of weight, waist circumference, waist-to-hip ratio, sagittal abdominal diameter and adiponectin were compared with measurements at 12 and 24 weeks after randomization.Baseline-adjusted mean weight loss was 3.8 ± 2.9 kg greater in liraglutide than placebo-treated individuals (p < 0.0001). Waist circumference was reduced by 2.9 ± 4.3 cm and 0.2 ± 3.6 cm in the liraglutide and placebo groups, respectively, after 24 weeks (baseline-adjusted mean difference: 2.6 ± 4.0 cm, p = 0.0005). Corresponding reductions in sagittal abdominal diameter were 1.1 ± 1.7 cm and 0.0 ± 1.8 cm (baseline-adjusted mean difference: 1.1 ± 1.7 cm, p = 0.0008). Hip circumference was reduced in patients randomized to liraglutide (baseline-adjusted mean difference between treatment groups: 2.8 ± 3.8 cm, p = 0.0001), but there was no significant difference between the groups in either waist-to-hip ratio (baseline-adjusted mean difference: 0.0 ± 0.04 cm, p = 0.51) or adiponectin levels (baseline-adjusted mean difference: 0.8 ± 3.3 mg L-1, p = 0.17). Lower HbA1c and mean glucose levels measured by masked continuous glucose monitoring at baseline were associated with greater effects of liraglutide on reductions in waist circumference and sagittal abdominal diameter.In patients with T2D, adding liraglutide to MDI may reduce abdominal and hip obesity to a similar extent, suggesting an effect on both visceral and subcutaneous fat. Liraglutide had greater effects on reducing abdominal obesity in patients with less pronounced long-term hyperglycaemia but did not affect adiponectin levels.
Abstract Liraglutide is associated with blood pressure reduction in patients with type 2 diabetes. However, it is not known whether this blood pressure reduction can be predicted prior to treatment initiation, and to what extent it correlates with weight loss and with improved glycemic control during follow‐up. We analyzed data from a double‐blind, placebo‐controlled trial, in which 124 insulin‐treated patients with type 2 diabetes were randomized to liraglutide or placebo. We evaluated various baseline variables as potential predictors of systolic blood pressure (SBP) reduction, and evaluated whether changes in SBP correlated with weight loss and with improved glycemic control. A greater reduction in SBP among liraglutide‐treated patients was predicted by higher baseline values of SBP ( P < 0.0001) and diastolic blood pressure ( P = 0.012), and by lower baseline values of mean glucose measured by continuous glucose monitoring (CGM; P = 0.044), and serum fasting C‐peptide ( P = 0.015). The regression coefficients differed significantly between the liraglutide group and the placebo group only for diastolic blood pressure ( P = 0.037) and mean CGM ( P = 0.021). During the trial period, SBP reduction correlated directly with change in body weight and BMI, but not with change in HbA1c. We conclude that patients with lower mean CGM values at baseline responded to liraglutide with a larger reduction in SBP, and that improved HbA1c during follow‐up was not associated with reductions of SBP. Our data suggest that some patients with type 2 diabetes may benefit from liraglutide in terms of weight and SBP reduction.
