Contralateral hip fractures and other osteoporosis-related fractures in hip fracture patients: Incidence and risk factors. An observational cohort study of 1,229 patients

Purpose: To report risk factors, 1-year and overall risk for a contralateral hip and other osteoporosis-related fractures in a hip fracture population. Methods: An observational study on 1,229 consecutive patients of 50 years and older, who sustained a hip fracture between January 2005 and June 2009. Fractures were scored retrospectively for 2005-2008 and prospectively for 2008-2009. Rates of a contralateral hip and other osteoporosis- related fractures were compared between patients with and without a history of a fracture. Previous fractures, gender, age and ASA classification were analysed as possible risk factors. Results: The absolute risk for a contralateral hip fracture was 13.8 %, for one or more osteoporosis-related fracture( s) 28.6 %. First-, second- and third-year risk for a second hip fracture was 2, 1 and 0 %. Median (IQR) interval between both hip fractures was 18.5 (26.6) months. One-year incidence of other fractures was 6 %. Only age was a risk factor for a contralateral hip fracture, hazard ratio (HR) 1.02 (1.006-1.042, p = 0.008). Patients with a history of a fracture (33.1 %) did not have a higher incidence of fractures during follow-up (16.7 %) than patients without fractures in their history (14 %). HR for a contralateral hip fracture for the fracture versus the non-fracture group was 1.29 (0.75-2.23, p = 0.360). Conclusion: The absolute risk of a contralateral hip fracture after a hip fracture is 13.8 %, the 1-year risk was 2 %, with a short interval between the 2 hip fractures. Age was a risk factor for sustaining a contralateral hip fracture; a fracture in history was not

Informal modernism - spontaneous building in Mexico City

Die überall aufragenden Betonstützen der unfertigen Selbstbau-Häuser sind in Mexiko-Stadt zum Symbol des spontanen Bauens geworden. Gleichzeitig haben die chaotischen Hüttensiedlungen längst einem routinierten Selbsthilfe-Städtebau Platz gemacht, der das Wohnungsproblem informell, aber gut organisiert und im großen Stil angeht. So kann man auch von einer improvisierten oder "informellen Moderne" sprechen, die sich die Menschen überall dort geschaffen haben, wo die formelle Stadtplanung und Wohnungsversorgung versagt oder auf halbem Wege stecken geblieben ist.The looming concrete columns of unfinished self-help-houses in Mexico City have become a symbol of spontaneous building worldwide. At the same time, the chaotic clusters of miserable huts have long since made way for a routinized self help urbanism, which approaches the housing problem informally, yet well organized and on a large scale. Thus one may speak of an improvised or "informal modernism" that people have created everywhere, where formal city planning and housing has either failed or gotten stuck midway

Tool support

Enterprise architecture, by nature, requires the interconnection and accumulation of large amounts of information from various sources. An enterprise modelling language, such as the one introduced in Chap. 5, can only be successful if supported by adequate tooling. Visualisation and analysis of architectures, as described in Chaps. 8 and 9, respectively, can hardly be carried out by hand and require tools as well. In this chapter, we outline the current state of the art in enterprise archite

Validity of Early Outcomes as Indicators for Comparing Hospitals on Quality of Stroke Care

Background Insight into outcome variation between hospitals could help to improve quality of care. We aimed to assess the validity of early outcomes as quality indicators for acute ischemic stroke care for patients treated with endovascular therapy (EVT). Methods and Results We used data from the MR CLEAN (Multicenter Randomized Controlled Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) Registry, a large multicenter prospective cohort study including 3279 patients with acute ischemic stroke undergoing EVT. Random effect linear and proportional odds regression were used to analyze the effect of case mix on between‐hospital differences in 2 early outcomes: the National Institutes of Health Stroke Scale (NIHSS) score at 24 to 48 hours and the expanded thrombolysis in cerebral infarction score. Between‐hospital variation in outcomes was assessed using the variance of random hospital effects (tau2). In addition, we estimated the correlation between hospitals' EVT‐patient volume and (case‐mix–adjusted) outcomes. Both early outcomes and case‐mix characteristics varied significantly across hospitals. Between‐hospital variation in the expanded thrombolysis in cerebral infarction score was not influenced by case‐mix adjustment (tau 2=0.17 in both models). In contrast, for the NIHSS score at 24 to 48 hours, case‐mix adjustment led to a decrease in variation between hospitals (tau 2 decreases from 0.19 to 0.17). Hospitals' EVT‐patient volume was strongly correlated with higher expanded thrombolysis in cerebral infarction scores (r=0.48) and weakly with lower NIHSS score at 24 to 48 hours (r=0.15). Conclusions Between‐hospital variation in NIHSS score at 24 to 48 hours is significantly influenced by case‐mix but not by patient volume. In contrast, between‐hospital variation in expanded thrombolysis in cerebral infarction score is strongly influenced by EVT‐patient volume but not by case‐mix. Both outcomes may be suitable for comparing hospitals on quality of care, provided that adequate adjustment for case‐mix is applied for NIHSS score

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke : Potential Value of Machine Learning Algorithms

Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables. Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ≥ 2b) and functional independence (modified Rankin Scale ≤2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed. Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53-0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77-0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88-0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00-0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge). Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome

Estimation of treatment effects in observational stroke care data: comparison of statistical approaches

Introduction: Various statistical approaches can be used to deal with unmeasured confounding when estimating treatment effects in observational studies, each with its own pros and cons. This study aimed to compare treatment effects as estimated by different statistical approaches for two interventions in observational stroke care data. Patients and methods: We used prospectively collected data from the MR CLEAN registry including all patients (n = 3279) with ischemic stroke who underwent endovascular treatment (EVT) from 2014 to 2017 in 17 Dutch hospitals. Treatment effects of two interventions – i.e., receiving an intravenous thrombolytic (IVT) and undergoing general anesthesia (GA) before EVT – on good functional outcome (modified Rankin Scale ≤2) were estimated. We used three statistical regression-based approaches that vary in assumptions regarding the source of unmeasured confounding: individual-level (two subtypes), ecological, and instrumental variable analyses. In the latter, the preference for using the interventions in each hospital was used as an instrument. Results: Use of IVT (range 66–87%) and GA (range 0–93%) varied substantially between hospitals. For IVT, the individual-level (OR ~ 1.33) resulted in significant positive effect estimates whereas in instrumental variable analysis no significant treatment effect was found (OR 1.11; 95% CI 0.58–1.56). The ecological analysis indicated no statistically significant different likelihood (β = − 0.002%; P = 0.99) of good functional outcome at hospitals using IVT 1% more frequently. For GA, we found non-significant opposite directions of points estimates the treatment effect in the individual-level (ORs ~ 0.60) versus the instrumental variable approach (OR = 1.04). The ecological analysis also resulted in a non-significant negative association (0.03% lower probability). Discussion and conclusion: Both magnitude and direction of the estimated treatment effects for both interventions depend strongly on the statistical approach and thus on the source of (unmeasured) confounding. These issues should be understood concerning the specific characteristics of data, before applying an approach and interpreting the results. Instrumental variable analysis might be considered when unobserved confounding and practice variation is expected in observational multicenter studies