Partner randomized controlled trial: study protocol and coaching intervention

<p>Abstract</p> <p>Background</p> <p>Many children with asthma live with frequent symptoms and activity limitations, and visits for urgent care are common. Many pediatricians do not regularly meet with families to monitor asthma control, identify concerns or problems with management, or provide self-management education. Effective interventions to improve asthma care such as small group training and care redesign have been difficult to disseminate into office practice.</p> <p>Methods and design</p> <p>This paper describes the protocol for a randomized controlled trial (RCT) to evaluate a 12-month telephone-coaching program designed to support primary care management of children with persistent asthma and subsequently to improve asthma control and disease-related quality of life and reduce urgent care events for asthma care. Randomization occurred at the practice level with eligible families within a practice having access to the coaching program or to usual care. The coaching intervention was based on the transtheoretical model of behavior change. Targeted behaviors included 1) effective use of controller medications, 2) effective use of rescue medications and 3) monitoring to ensure optimal control. Trained lay coaches provided parents with education and support for asthma care, tailoring the information provided and frequency of contact to the parent's readiness to change their child's day-to-day asthma management. Coaching calls varied in frequency from weekly to monthly. For each participating family, follow-up measurements were obtained at 12- and 24-months after enrollment in the study during a telephone interview.</p> <p>The primary outcomes were the mean change in 1) the child's asthma control score, 2) the parent's quality of life score, and 3) the number of urgent care events assessed at 12 and 24 months. Secondary outcomes reflected adherence to guideline recommendations by the primary care pediatricians and included the proportion of children prescribed controller medications, having maintenance care visits at least twice a year, and an asthma action plan. Cost-effectiveness of the intervention was also measured.</p> <p>Discussion</p> <p>Twenty-two practices (66 physicians) were randomized (11 per treatment group), and 950 families with a child 3-12 years old with persistent asthma were enrolled. A description of the coaching intervention is presented.</p> <p>Trial registration</p> <p>ClinicalTrials.gov identifier <a href="http://www.clinicaltrials.gov/ct2/show/NCT00860834">NCT00860834</a>.</p

Physiological responses during ascent to high altitude and the incidence of acute mountain sickness.

Acute mountain sickness (AMS) occurs when there is failure of acclimatisation to high altitude. The aim of this study was to describe the relationship between physiological variables and the incidence of AMS during ascent to 5300 m. A total of 332 lowland-dwelling volunteers followed an identical ascent profile on staggered treks. Self-reported symptoms of AMS were recorded daily using the Lake Louise score (mild 3-4; moderate-severe ≥5), alongside measurements of physiological variables (heart rate, respiratory rate (RR), peripheral oxygen saturation (SpO2 ) and blood pressure) before and after a standardised Xtreme Everest Step-Test (XEST). The overall occurrence of AMS among participants was 73.5% (23.2% mild, 50.3% moderate-severe). There was no difference in gender, age, previous AMS, weight or body mass index between participants who developed AMS and those who did not. Participants who had not previously ascended >5000 m were more likely to get moderate-to-severe AMS. Participants who suffered moderate-to-severe AMS had a lower resting SpO2 at 3500 m (88.5 vs. 89.6%, p = 0.02), while participants who suffered mild or moderate-to-severe AMS had a lower end-exercise SpO2 at 3500 m (82.2 vs. 83.8%, p = 0.027; 81.5 vs. 83.8%, p 5000 m (OR 2.740, p-value 0.003) predicted the development of moderate-to-severe AMS. The Xtreme Everest Step-Test offers a simple, reproducible field test to help predict AMS, albeit with relatively limited predictive precision

Risk Prediction for Acute Kidney Injury in Acute Medical Admissions in the UK

Background Acute Kidney Injury (AKI) is associated with adverse outcomes; identifying patients who are at risk of developing AKI in hospital may lead to targeted prevention. This approach is advocated in national guidelines but is not well studied in acutely unwell medical patients. We therefore aimed to undertake a UK-wide study in acute medical units (AMUs) with the following aims: to define the proportion of acutely unwell medical patients who develop hospital-acquired AKI (hAKI); to determine risk factors associated with the development of hAKI; and to assess the feasibility of using these risk factors to develop an AKI risk prediction score. Methods In September 2016, a prospective multicentre cohort study across 72 UK AMUs was undertaken. Data were collected from all patients who presented over a 24-hour period. Chronic dialysis, community-acquired AKI (cAKI) and those with fewer than two creatinine measurements were subsequently excluded. The primary outcome was the development of h-AKI. Results 2,446 individuals were admitted to the AMUs of the 72 participating centres. 384 patients (16%) sustained AKI of whom 287 (75%) were cAKI and 97 (25%) were hAKI. After exclusions, 1,235 participants remained in whom chronic kidney disease (OR 3.08, 95% CI 1.96-4.83), diuretic prescription (OR 2.33, 95% CI 1.5-3.65), a lower haemoglobin concentration and an elevated serum bilirubin were independently associated with development of hAKI. Multivariable model discrimination was moderate (c-statistic 0.75), and this did not support the development of a robust clinical risk prediction score. Mortality was higher in those with hAKI (adjusted OR 5.22; 95% CI 2.23-12.20). Conclusion AKI in AMUs is common and associated with worse outcomes, with the majority of cases community acquired. The smaller proportion of hAKI cases, only moderate discrimination of prognostic risk factor modelling and the resource implications of widespread application of an AKI clinical risk score across all AMU admissions suggests that this approach is not currently justified. More targeted risk assessment or automated methods of calculating individual risk may be more appropriate alternatives