"GOLD or lower limit of normal definition? a comparison with expert-based diagnosis of chronic obstructive pulmonary disease in a prospective cohort-study"

<p>Abstract</p> <p>Background</p> <p>The Global initiative for chronic Obstructive Lung Disease (GOLD) defines COPD as a fixed post-bronchodilator ratio of forced expiratory volume in 1 second and forced vital capacity (FEV1/FVC) below 0.7. Age-dependent cut-off values below the lower fifth percentile (LLN) of this ratio derived from the general population have been proposed as an alternative. We wanted to assess the diagnostic accuracy and prognostic capability of the GOLD and LLN definition when compared to an expert-based diagnosis.</p> <p>Methods</p> <p>In a prospective cohort study, 405 patients aged ≥ 65 years with a general practitioner's diagnosis of COPD were recruited and followed up for 4.5 (median; quartiles 3.9; 5.1) years. Prevalence rates of COPD according to GOLD and three LLN definitions and diagnostic performance measurements were calculated. The reference standard was the diagnosis of COPD of an expert panel that used all available diagnostic information, including spirometry and bodyplethysmography.</p> <p>Results</p> <p>Compared to the expert panel diagnosis, 'GOLD-COPD' misclassified 69 (28%) patients, and the three LLNs misclassified 114 (46%), 96 (39%), and 98 (40%) patients, respectively. The GOLD classification led to more false positives, the LLNs to more false negative diagnoses. The main predictors beyond the FEV1/FVC ratio for an expert diagnosis of COPD were the FEV1 % predicted, and the residual volume/total lung capacity ratio (RV/TLC). Adding FEV1 and RV/TLC to GOLD or LLN improved the diagnostic accuracy, resulting in a significant reduction of up to 50% of the number of misdiagnoses. The expert diagnosis of COPD better predicts exacerbations, hospitalizations and mortality than GOLD or LLN.</p> <p>Conclusions</p> <p>GOLD criteria over-diagnose COPD, while LLN definitions under-diagnose COPD in elderly patients as compared to an expert panel diagnosis. Incorporating FEV1 and RV/TLC into the GOLD-COPD or LLN-based definition brings both definitions closer to expert panel diagnosis of COPD, and to daily clinical practice.</p

Heart-type Fatty acid-binding protein in Acute Myocardial infarction Evaluation (FAME): Background and design of a diagnostic study in primary care

<p>Abstract</p> <p>Background</p> <p>Currently used biomarkers for cardiac ischemia are elevated in blood plasma after a delay of several hours and therefore unable to detect acute coronary syndrome (ACS) in a very early stage. General practitioners (GPs), however, are often confronted with patients suspected of ACS within hours after onset of complaints. This ongoing study aims to evaluate the added diagnostic value beyond clinical assessment for a rapid bedside test for heart-type fatty-acid binding protein (H-FABP), a biomarker that is detectable as soon as one hour after onset of ischemia.</p> <p>Methods</p> <p>Participating GPs perform a blinded H-FABP rapid bedside test (Cardiodetect^®) in patients with symptoms suggestive of ACS such as chest pain or discomfort at rest. All patients, whether referred to hospital or not, undergo electrocardiography (ECG) and venapunction for a plasma troponin test within 12–36 hours after onset of complaints. A final diagnosis will be established by an expert panel consisting of two cardiologists and one general practitioner (blinded to the H-FABP test result), using all available patient information, also including signs and symptoms. The added diagnostic value of the H-FABP test beyond history taking and physical examination will be determined with receiver operating characteristic curves derived from multivariate regression analysis.</p> <p>Conclusion</p> <p>Reasons for presenting the design of our study include the prevention of publication bias and unacknowledged alterations in the study aim, design or data-analysis. To our knowledge this study is the first to assess the diagnostic value of H-FABP <it>outside </it>a hospital-setting. Several previous hospital-based studies showed the potential value of H-FABP in diagnosing ACS. Up to now however it is unclear whether these results are equally promising when the test is used in primary care. The first results are expected in the end of 2008.</p

Simulation-based design optimization of houses with low grid dependency

There is significant growth in the utilization of renewable energy in the built environment. Due to the intermittent nature of most renewable energy sources, energy mismatch problems between on-site generation and demand both in hourly and seasonal levels are unavoidable. This problem is more significant in Northern latitudes, as in summer there is high solar availability despite low or no electricity demand for cooling and in winter the solar availability is low when there is a high demand for heating. In addition, energy-pricing policies are leading to less or no Photovoltaic (PV) feed-in-tariffs in the near future and/or even providing incentives to uphold self-consumption. Therefore, it is important to enhance the energy flexibility potential of a building to improve utilization of on-site generated energy.\u3cbr/\u3eIn this study, a performance optimization of various residential building designs with differences in energy demand, on-site energy generation and storage sizes is carried out considering future policy scenarios. The objective is to minimize the dependency to the nearby energy grid and maximize the self-consumption. To achieve this, a performance-based design support framework is proposed and demonstrated using a case study

Simulation-based comparison of robustness assessment methods to identify robust low-energy building designs

Uncertainties in occupant behaviour and climate change can have a large influence on future building performance, especially in low-energy buildings. These uncertainties cause performance variations resulting in deviations between actual operation compared to the predicted performance in the design phase. Therefore, performance robustness assessment of these buildings should consider uncertainties and should be included in the design phase to ensure the intended performance in the future. The probability of occurrences of these uncertainties are usually unknown and hence, scenarios are essential to assess the performance robustness of buildings. However, studies on robustness assessment using scenarios in the building performance context are limited. Therefore, in this work, scenario analysis is combined with various robustness assessment methods from other fields, and these methods are compared using a case study for different decision makers such as homeowners and policymakers.\u3cbr/\u3e\u3cbr/\u3eThe max-min and the best-case and worst-case methods lead to conservative robust designs and can be used when a risk-free approach is indispensable in decision-making. The minimax regret method leads to less conservative robust designs and can be used where a decision maker can accept a certain range of performance variation

Estimating the influence of occupant behavior on building heating and cooling energy in one simulation run

\u3cp\u3eEnergy performance contracting (EPC) aims at guaranteeing a specified level of energy savings in the built environment for a client. Among the building energy performance uncertainties that hinder EPC, occupant behavior (OB) plays a major role. For this reason, energy service companies (ESCOs) may be interested in including OB-related clauses in their contracts. The inclusion of such a clause calls for an efficient, easy-to-implement method to provide a first estimate of the potential effect of various aspects of OB on building cooling and heating energy demand. In contrast with common sensitivity analysis approaches based on a high number of scenarios, a novel simulation method requiring only a single simulation run for both heating and cooling seasons is presented here. The estimate is provided by evaluating the newly developed impact indices (II) based on the results obtained by means of the simulation run. A set of 16 building variants differing in floor height, climate, construction vintage and equipment and lighting power density was investigated to test the method. All II were calculated for the 16 building variants. In order to verify their significance, the results of a one-at-a-time sensitivity analysis mimicking simplified variations in occupant behavior (OB) were plotted against the II. The R\u3csup\u3e2\u3c/sup\u3e values were above 0.9 when evaluating the effect of equipment use, lights use, and occupant presence, confirming the significance of the developed II. For blind use and temperature setpoint setting, the R\u3csup\u3e2\u3c/sup\u3e values were ca. 0.85. Subsequently, the method was applied to an existing office building in Delft, The Netherlands, to evaluate its potential for EPC. This study confirms the high variability of the effect of OB on heating and cooling energy demand according to the case at hand. The developed method is useful for practitioners to evaluate the potential effect of OB on a given design in a time-effective manner.\u3c/p\u3

Occupant behavior in building energy simulation: towards a fit-for-purpose modeling strategy

\u3cp\u3eOccupant behavior is nowadays acknowledged as a main source of discrepancy between predicted and actual building performance; therefore, researchers attempt to model occupants' presence and adaptive actions more realistically. Literature shows a proliferation of increasingly complex, data-based models that well fit the cases analyzed. However, the actual use of these models by practitioners is very limited. Moreover, simpler models might be preferable, depending on the aim of investigation. The present study proposes shifting the focus to fit-for-purpose modeling, in which the most appropriate model for a specific case is characterized by the lowest complexity, while preserving its validity with respect to the aim of the simulation. A number of steps are taken to achieve this shift in focus. The existing models are presented according to complexity. The available inter-comparison studies are critically reviewed. Subsequently, a list of parameters that affect the choice of an appropriate modeling strategy is presented as a first attempt to derive guidelines and generate a framework for investigation. To support such claims the effect of some of the listed parameters is evaluated in a case study. The main conclusion to be drawn is that determining the best complexity for occupant behavior modeling is strongly case specific.\u3c/p\u3

Integrating robustness indicators into multi-objective optimization to find robust optimal low-energy building designs

\u3cp\u3eUncertainties can have a large influence on building performance and cause deviations between predicted performance and performance during operation. It is therefore important to quantify this influence and identify robust designs that have potential to deliver the desired performance under uncertainties. Generally, robust building designs are identified by assessing the performance of multiple design configurations under various uncertainties. When exploring a large design space, this approach becomes computationally expensive and infeasible in practice. Therefore, we propose a simulation framework based on multi-objective optimization and sampling strategies to find robust optimal designs at low computational costs. The genetic algorithm parameters of optimization are fine tuned to further enhance the computational efficiency. Furthermore, a modified fitness function is implemented to use minimax regret robustness method in the optimization loop. The implemented simulation framework can save up to 94–99% of computational time compared to full factorial approach, while identifying the same robust designs.\u3c/p\u3

Optimal balance between energy demand and onsite energy generation for robust net zero energy buildings considering future scenarios

Net-zero energy buildings have usually very low energy demand, and consequently heating ventilation and air conditioning (HVAC) systems are designed and controlled to meet this low energy demand. However, a number of uncertainties in the building use, operation and external conditions such as climate change and occupant behaviour can influence the energy demand. Considering these variations, the currently designed net zero energy buildings will not always be able to provide the required indoor environmental quality. A proper balance between energy demand, HVAC and renewable energy systems needs to be investigated to meet the performance requirements over the building life span. Therefore, in this work, performance optimization of various net zero energy building designs, with different energy demand and onsite energy balance, is carried out under uncertainties due to future scenarios to minimize the performance variation across all future scenarios. The design with optimal performance and low performance variation across all scenarios is identified as the most preferred robust design solution

A methodology for performance robustness assessment of low-energy buildings using scenario analysis

Uncertainties in building operation and external factors such as occupant behavior, climate change, policy changes etc. impact building performance, resulting in possible performance deviation during operation compared to the predicted performance in the design phase. Multiple low-energy building configurations can lead to similar optimal performance under deterministic conditions, but can have different magnitudes of performance deviation under these uncertainties. Low-energy buildings must be robust so that these uncertainties do not result in significant variations in energy use, cost and comfort. However, these uncertainties are rarely considered in the design of low-energy buildings and hence, the decision making process may result in designs that are sensitive to uncertainties and might not perform as intended. Therefore, to reduce this sensitivity, performance robustness assessment of low-energy buildings considering uncertainties should be assessed in the design phase. The probability of occurrences of these uncertainties are usually unknown and hence, scenarios are essential to assess the performance robustness of buildings. Therefore, a non-probabilistic robustness assessment methodology, based on scenario analysis, is developed to identify robust designs. Maximum performance regret calculated using the minimax regret method is used as the measure of performance robustness. In this approach, the preferred robust design is based on optimal performance and performance robustness.\u3cbr/\u3e\u3cbr/\u3eThe proposed methodology is demonstrated using a case study with a policymaker as the decision maker. The proposed methodology can be used by designers and consultants to aid decision makers in the design phase to identify robust low-energy building designs that deliver preferred performance in the future operation