Individualization of PEEP and tidal volume in ARDS patients with electrical impedance tomography: a pilot feasibility study.

BACKGROUND: In mechanically ventilated patients with acute respiratory distress syndrome (ARDS), electrical impedance tomography (EIT) provides information on alveolar cycling and overdistension as well as assessment of recruitability at the bedside. We developed a protocol for individualization of positive end-expiratory pressure (PEEP) and tidal volume (VT) utilizing EIT-derived information on recruitability, overdistension and alveolar cycling. The aim of this study was to assess whether the EIT-based protocol allows individualization of ventilator settings without causing lung overdistension, and to evaluate its effects on respiratory system compliance, oxygenation and alveolar cycling. METHODS: 20 patients with ARDS were included. Initially, patients were ventilated according to the recommendations of the ARDS Network with a VT of 6 ml per kg predicted body weight and PEEP adjusted according to the lower PEEP/FiO2 table. Subsequently, ventilator settings were adjusted according to the EIT-based protocol once every 30 min for a duration of 4 h. To assess global overdistension, we determined whether lung stress and strain remained below 27 mbar and 2.0, respectively. RESULTS: Prospective optimization of mechanical ventilation with EIT led to higher PEEP levels (16.5 [14-18] mbar vs. 10 [8-10] mbar before optimization; p = 0.0001) and similar VT (5.7 ± 0.92 ml/kg vs. 5.8 ± 0.47 ml/kg before optimization; p = 0.96). Global lung stress remained below 27 mbar in all patients and global strain below 2.0 in 19 out of 20 patients. Compliance remained similar, while oxygenation was significantly improved and alveolar cycling was reduced after EIT-based optimization. CONCLUSIONS: Adjustment of PEEP and VT using the EIT-based protocol led to individualization of ventilator settings with improved oxygenation and reduced alveolar cycling without promoting global overdistension. Trial registrationThis study was registered at clinicaltrials.gov (NCT02703012) on March 9, 2016 before including the first patient.

Assessing the variation in workload among general practitioners in urban and rural areas: An analysis based on SMS time sampling data.

OBJECTIVE: An important reason why general practitioners (GPs) are less inclined to work in rural areas is a perception of a higher workload. This study assesses the differences in the workloads of GPs in rural and urban areas. We used two definitions of rurality, one based on the number of addresses per square kilometre, and a second defined by the expected decline in population. METHODS: We collected time use data over 1 year by sending SMS text messages to Dutch GPs who each participated during a period of 1 week. This data was matched with those from GPs' registration and practice location. Data from 596 self-employed GPs were analysed using descriptive statistics and multiple regression analyses. RESULTS: In group practices, the patient list size of rural GPs was, on average, 231 patients more than those of urban GPs. They worked 3.5 more hours per week, with 2.6 more hours directly related to patients. A small significant relation was found between degree of urbanisation and the dependent variables list size and working hours. Working in a depopulation area had no significant effect on the workload indicators. Furthermore, GPs in group practices worked significantly fewer hours, and had smaller list sizes, than GPs in single-handed practices. CONCLUSION: The results show that the assumption of a higher workload in rural practices does not completely match the objective workload of GPs in these areas. Rural GPs have a higher workload in certain cases, but the type of a practice seems a more important determinant.

SMS text messaging to measure working time: the design of a time use study among general practitioners.

BACKGROUND: Measuring the working hours of general practitioners (GPs) is an important but complex task due to the effects of bias related to self-reporting, recall, and stress. In this paper we describe the deployment, feasibility, and implementation of an innovative method for measuring, in real time, GPs' working time, plus the response to the study. METHODS: A Short Message Service (SMS) application was developed which sent messages at random to GPs during their working week. Approximately nineteen GPs participated each week during a period of 57 weeks. The text messages asked if GPs were doing activities related to patients, directly, indirectly, or not at all, at the moment of sending. Participants were requested to reply by SMS. RESULTS: Approximately 27,000 messages were sent to 1051 GPs over more than one year. The SMS system was functioning 99.9% of the time. GPs replied to 94% of all the messages sent. Only a few participants dropped out of the study. The data was available in real time enabling the researchers to monitor the response and overall quality of the data each day. CONCLUSIONS: The SMS method offers advantages over other instruments of measurement because it allows a better response, ease of use and avoids recall bias. This makes it a feasible method to collect valid data about GPs working time.

Assessing the precision of a time-sampling-based study among GPs: balancing sample size and measurement frequency.

BACKGROUND: Our research is based on a technique for time sampling, an innovative method for measuring the working hours of Dutch general practitioners (GPs), which was deployed in an earlier study. In this study, 1051 GPs were questioned about their activities in real time by sending them one SMS text message every 3 h during 1 week. The required sample size for this study is important for health workforce planners to know if they want to apply this method to target groups who are hard to reach or if fewer resources are available. In this time-sampling method, however, standard power analyses is not sufficient for calculating the required sample size as this accounts only for sample fluctuation and not for the fluctuation of measurements taken from every participant. We investigated the impact of the number of participants and frequency of measurements per participant upon the confidence intervals (CIs) for the hours worked per week. METHODS: Statistical analyses of the time-use data we obtained from GPs were performed. Ninety-five percent CIs were calculated, using equations and simulation techniques, for various different numbers of GPs included in the dataset and for various frequencies of measurements per participant. RESULTS: Our results showed that the one-tailed CI, including sample and measurement fluctuation, decreased from 21 until 3 h between one and 50 GPs. As a result of the formulas to calculate CIs, the increase of the precision continued and was lower with the same additional number of GPs. Likewise, the analyses showed how the number of participants required decreased if more measurements per participant were taken. For example, one measurement per 3-h time slot during the week requires 300 GPs to achieve a CI of 1 h, while one measurement per hour requires 100 GPs to obtain the same result. CONCLUSIONS: The sample size needed for time-use research based on a time-sampling technique depends on the design and aim of the study. In this paper, we showed how the precision of the measurement of hours worked each week by GPs strongly varied according to the number of GPs included and the frequency of measurements per GP during the week measured. The best balance between both dimensions will depend upon different circumstances, such as the target group and the budget available.

Age-related differences in working hours among male and female GPs: an SMS-based time use study.

BACKGROUND: In several countries, the number of hours worked by general practitioners (GPs) has decreased, raising concern about current and impending workforce shortages. This shorter working week has been ascribed both to the feminisation of the workforce and to a younger generation of GPs who prefer more flexible working arrangements. There is, however, limited insight into how the impact of these determinants interact. We investigated the relative importance of differences in GPs' working hours in relation to gender, age, and employment position. METHODS: An analysis was performed on real-time monitoring data collected by sending SMS text messages to 1051 Dutch GPs, who participated during a 1-week time use study. We used descriptive statistics, independent sample t-tests, and one-way ANOVA analysis to compare the working time of different GP groups. A path analysis was conducted to examine the difference in working time by gender, age, employment position, and their combinations. RESULTS: Female GPs worked significantly fewer hours than their male peers. GPs in their 50s worked the highest number of hours, followed by GPs age 60 and older. GPs younger than 40 worked the lowest number of hours. This relationship between working hours and age was not significantly different for women and men. As shown by path analysis, female GPs consistently worked fewer hours than their male counterparts, regardless of their age and employment position. The relationship between age and working hours was largely influenced by gender and employment position. CONCLUSIONS: The variation in working hours among GPs can be explained by the combination of gender, age, and employment position. Gender appears to be the most important predictor as the largest part of the variation in working hours is explained by a direct effect of this variable. It has previously been reported that the difference in working hours between male and female GPs had decreased over time. However, our findings suggest that gender remains a critical factor for variation in time use and for policy instruments such as health workforce planning.