Reducing work pressure and IT problems and facilitating IT integration and audit & feedback help adherence to perioperative safety guidelines: a survey among 95 perioperative professionals

Background: To improve perioperative patient safety, guidelines for the preoperative, peroperative, andpostoperative phase were introduced in the Netherlands between 2010 and 2013. To help the implementation ofthese guidelines, we aimed to get a better understanding of the barriers and drivers of perioperative guidelineadherence and to explore what can be learned for future implementation projects in complex organizations.Methods: We developed a questionnaire survey based on the theoretical framework of Van Sluisveld et al. forclassifying barriers and facilitators. The questionnaire contained 57 statements derived from (a) an instrument formeasuring determinants of innovations by the Dutch Organization for Applied Scientific Research, (b) interviewswith quality and safety policy officers and perioperative professionals, and (c) a publication of Cabana et al. Thetarget group consisted of 232 perioperative professionals in nine hospitals. In addition to rating the statements on afive-point Likert scale (which were classified into the seven categories of the framework: factors relating to theintervention, society, implementation, organization, professional, patients, and social factors), respondents wereinvited to rank their three most important barriers in a separate, extra open-ended question.Results: Ninety-five professionals (41%) completed the questionnaire. Fifteen statements (26%) were considered tobe barriers, relating to social factors (N = 5), the organization (N = 4), the professional (N = 4), the patient (N = 1),and the intervention (N = 1). An integrated information system was considered an important facilitator (70.4%) aswell as audit and feedback (41.8%). The Barriers Top-3 question resulted in 75 different barriers in nearly allcategories. The most frequently reported barriers were as follows: time pressure (16% of the total number ofbarriers), emergency patients (8%), inefficient IT structure (4%), and workload (3%).Conclusions: We identified a wide range of barriers that are believed to hinder the use of the perioperative safetyguidelines, while an integrated information system and local data collection and feedback will also be necessary toengage perioperative teams. These barriers need to be locally prioritized and addressed by tailored implementationstrategies. These results may also be of relevance for guideline implementation in general in complex organizations.Trial registration: Dutch Trial Registry: NTR3568.Keywords: Guideline adherence, Implementation, Implementation barriers, Implementation facilitators, Patientsafety, Perioperative car

Increased adherence to perioperative safety guidelines associated with improved patient safety outcomes:a stepped-wedge, cluster-randomised multicentre trial

Background: National Dutch guidelines have been introduced to improve suboptimal perioperative care. A multifaceted implementation programme (IMPlementatie Richtlijnen Operatieve VEiligheid [IMPROVE]) has been developed to support hospitals in applying these guidelines. This study evaluated the effectiveness of IMPROVE on guideline adherence and the association between guideline adherence and patient safety. Methods: Nine hospitals participated in this unblinded, superiority, stepped-wedge, cluster RCT in patients with major noncardiac surgery (mortality risk >= 1%). IMPROVE consisted of educational activities, audit and feedback, reminders, organisational, team-directed, and patient-mediated activities. The primary outcome of the study was guideline adherence measured by nine patient safety indicators on the process (stop moments from the composite STOP bundle, and timely administration of antibiotics) and on the structure of perioperative care. Secondary safety outcomes included in-hospital complications, postoperative wound infections, mortality, length of hospital stay, and unplanned care. Results: Data were analysed for 1934 patients. The IMPROVE programme improved one stop moment: 'discharge from recovery room' (+16%; 95% confidence interval [CI], 9-23%). This stop moment was related to decreased mortality (-3%; 95% CI, -4% to -1%), fewer complications (-8%; 95% CI, -13% to -3%), and fewer unscheduled transfers to the ICU (-6%; 95% CI, -9% to -3%). IMPROVE negatively affected one other stop moment - 'discharge from the hospital' - possibly because of the limited resources of hospitals to improve all stop moments together. Conclusions: Mixed implementation effects of IMPROVE were found. We found some positive associations between guideline adherence and patient safety (i.e. mortality, complications, and unscheduled transfers to the ICU) except for the timely administration of antibiotics

The association between palliative care team consultation and hospital costs for patients with advanced cancer: An observational study in 12 Dutch hospitals

Background: Early palliative care team consultation has been shown to reduce costs of hospital care. The objective of this study was to investigate the association between palliative care team (PCT) consultation and the content and costs of hospital care in patients with advanced cancer. Material and Methods: A prospective, observational study was conducted in 12 Dutch hospitals.

Fatigue in patients with chronic disease:results from the population-based Lifelines Cohort Study

(1) To evaluate the prevalence of severe and chronic fatigue in subjects with and without chronic disease; (2) to assess to which extent multi-morbidity contributes to severe and chronic fatigue; and (3) to identify predisposing and associated factors for severe and chronic fatigue and whether these are disease-specific, trans-diagnostic, or generic. The Dutch Lifelines cohort was used, including 78,363 subjects with (n = 31,039, 53 ± 12 years, 33% male) and without (n = 47,324, 48 ± 12 years, 46% male) ≥ 1 of 23 chronic diseases. Fatigue was assessed with the Checklist Individual Strength-Fatigue. Compared to participants without a chronic disease, a higher proportion of participants with ≥ 1 chronic disease were severely (23% versus 15%, p < 0.001) and chronically (17% versus 10%, p < 0.001) fatigued. The odds of having severe fatigue (OR [95% CI]) increased from 1.6 [1.5–1.7] with one chronic disease to 5.5 [4.5–6.7] with four chronic diseases; for chronic fatigue from 1.5 [1.5–1.6] to 4.9 [3.9–6.1]. Multiple trans-diagnostic predisposing and associated factors of fatigue were found, explaining 26% of variance in fatigue in chronic disease. Severe and chronic fatigue are highly prevalent in chronic diseases. Multi-morbidity increases the odds of having severe and chronic fatigue. Several trans-diagnostic factors were associated with fatigue, providing a rationale for a trans-diagnostic approach

Bacterial biogeography of adult airways in atopic asthma

Abstract Background Perturbations to the composition and function of bronchial bacterial communities appear to contribute to the pathophysiology of asthma. Unraveling the nature and mechanisms of these complex associations will require large longitudinal studies, for which bronchoscopy is poorly suited. Studies of samples obtained by sputum induction and nasopharyngeal brushing or lavage have also reported asthma-associated microbiota characteristics. It remains unknown, however, whether the microbiota detected in these less-invasive sample types reflect the composition of bronchial microbiota in asthma. Results Bacterial microbiota in paired protected bronchial brushings (BB; n = 45), induced sputum (IS; n = 45), oral wash (OW; n = 45), and nasal brushings (NB; n = 27) from adults with mild atopic asthma (AA), atopy without asthma (ANA), and healthy controls (HC) were profiled using 16S rRNA gene sequencing. Though microbiota composition varied with sample type (p < 0.001), compositional similarity was greatest for BB-IS, particularly in AAs and ANAs. The abundance of genera detected in BB correlated with those detected in IS and OW (r median [IQR] 0.869 [0.748–0.942] and 0.822 [0.687–0.909] respectively), but not with those in NB (r = 0.004 [− 0.003–0.011]). The number of taxa shared between IS-BB and NB-BB was greater in AAs than in HCs (p < 0.05) and included taxa previously associated with asthma. Of the genera abundant in NB, only Moraxella correlated positively with abundance in BB; specific members of this genus were shared between the two compartments only in AAs. Relative abundance of Moraxella in NB of AAs correlated negatively with that of Corynebacterium but positively with markers of eosinophilic inflammation in the blood and BAL fluid. The genus, Corynebacterium, trended to dominate all NB samples of HCs but only half of AAs (p = 0.07), in whom abundance of this genus was negatively associated with markers of eosinophilic inflammation. Conclusions Induced sputum is superior to nasal brush or oral wash for assessing bronchial microbiota composition in asthmatic adults. Although compositionally similar to the bronchial microbiota, the microbiota in induced sputum are distinct, reflecting enrichment of oral bacteria. Specific bacterial genera are shared between the nasal and the bronchial mucosa which are associated with markers of systemic and bronchial inflammation.https://deepblue.lib.umich.edu/bitstream/2027.42/144218/1/40168_2018_Article_487.pd

Fatigue and physical disability in patients with multiple sclerosis: a structural equation modeling approach

Although fatigue is one of the most common and disabling symptoms in patients with multiple sclerosis (MS), its pathogenesis is still poorly understood and it is difficult to treat. The aim of the current study was to test the assumptions of a cognitive-behavioral model that explains fatigue and physical disability in MS patients, by comparing this approach with a more traditional biomedical approach. Structural equation modeling was applied to a sample of 262 MS patients. Neither the cognitive-behavioral, nor the biomedical model showed an adequate fit of our data. The modification indices supported an integration of both models, which showed a better fit than those of the separate models. This final model, is notable for at least three features: (1) fatigue is associated with depression and physical disability, (2) physical disability is associated with disease severity and fatigue-related fear and avoidance behavior, and (3) catastrophic interpretations about fatigue, fueled by depression, mediated the relationship between fatigue and fatigue-related fear and avoidance behavior. Our results suggest that an integrated approach, including the modification of catastrophic thoughts about fatigue, would be beneficial in the treatment of fatigue in MS patients

Air pollution exposure estimation using dispersion modelling and continuous monitoring data in a prospective birth cohort study in the Netherlands

Previous studies suggest that pregnant women and children are particularly vulnerable to the adverse effects of air pollution. A prospective cohort study in pregnant women and their children enables identification of the specific effects and critical periods. This paper describes the design of air pollution exposure assessment for participants of the Generation R Study, a population-based prospective cohort study from early pregnancy onwards in 9778 women in the Netherlands. Individual exposures to PM10 and NO2 levels at the home address were estimated for mothers and children, using a combination of advanced dispersion modelling and continuous monitoring data, taking into account the spatial and temporal variation in air pollution concentrations. Full residential history was considered. We observed substantial spatial and temporal variation in air pollution exposure levels. The Generation R Study provides unique possibilities to examine effects of short- and long-term air pollution exposure on various maternal and childhood outcomes and to identify potential critical windows of exposure

Association of Cardiometabolic Multimorbidity With Mortality.

IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy. RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity