Failure Database Research Articles

Probabilistic failure investigation of small diameter cast iron pipelines for water distribution

At present, a significant proportion of Australia’s ageing water infrastructure is composed of cast-iron pipes. The corrosion of cast-iron pipes is the main triggering factor for Australia’s water industry. It has been found that the failure mode of small diameter water mains (<300 mm external diameters) from Yarra Valley Water is mainly the longitudinal failure (hoop-stressed) instead of broken back failure, which is unexpected. Therefore, the purpose of this paper is to examine the failure mechanism of longitudinal failure of small diameter cast-iron pipes. The observational failure database provided by Yarra Valley Water show limited data availability. This has stimulated the focus of this paper: to examine the corrosion mechanism by utilising probabilistic inverse analysis for unknown physical parameters. Focusing on the likelihood of failure, this paper investigates the performance of small diameter cast-iron pipes by using statistical analysis based on the observational failure lifetime data within pipe cohorts. Background studies on the deterioration of cast-iron pipes due to corrosion are made to help set up the probabilistic physical modelling (PPM). Using the updated corrosion parameters, the lifetime probabilities of failure and the hazard rate of the cast iron model are derived from PPM. Last, it is found that the modelling results agree reasonably well with prediction curves of statistical data, indicating that the proposed method for pipe lifetime prediction is promising.

Evaluation of Empirical Approach for Gearbox Diagnosis in the Construction Equipment

A planetary gearbox is one of the most important components in rotating machinery. In construction equipment, it is mainly used for swing and traveling devices due to huge torque capability and good torque transmission. Unexpected failure of the planetary gearbox can cause increased idle time, unnecessary cost due to inability of the construction site and safety hazards such as an operator isolation in a remote area. For these reasons, the need for timely prediction of unexpected failure becomes increasingly important in the field of construction machinery. In general, studies on fault diagnosis and condition monitoring of planetary gearboxes have been carried out in fields such as aircraft, wind-turbines and power plants. Researches in the above-mentioned fields usually require high performance computing power, and the burden of cost for diagnosis is relatively small. In addition, construction machinery also faces difficulties due to various uncertainties such as uncertain operating conditions that affect the vibration characteristics of gearboxes.&#x0D; This study focuses on an approach to the vibration-based fault diagnosis methodology that can distinguish gear faults in a planetary gearbox in an excavator using vehicle-based test. First, we analyze the types of gear faults and the parts where failures occur mainly through the database of field failures. From this result, several fault types to be used in the experiment are selected, and an arbitrary fault is applied to gears. Secondly, since the vibration data is acquired directly from the excavator, the signal processing method that can remove the noise as much as possible and distinguish the fault is selected. Finally, optimized features are selected to minimize the uncertainty impacts that cannot be eliminated or unknown. Through this study, we confirmed the effect of the signal processing method which can be used in the planetary gearbox of the excavator as follows: 1) Several kinds of fault can be distinguished. 2) Faults and methodologies that can be distinguished in the constant speed condition 3) Faults and methodologies that can be distinguished in the transition speed condition.

Prediction of wind turbine generator failure using two‐stage cluster‐classification methodology

AbstractReducing wind turbine downtime through innovations surrounding asset management has the potential to greatly influence the levelised cost of energy (LCoE) for large wind farm developments. Focusing on generator bearing failure and vibration data, this paper presents a two‐stage methodology to predict failure within 1 to 2 months of occurrence. Results are obtained by building up a database of failures and training machine learning algorithms to classify the bearing as healthy or unhealthy. This is achieved by first using clustering techniques to produce subpopulations of data based on operating conditions, which this paper demonstrates can greatly influence the ability to diagnose a fault. Secondly, this work classifies individual clusters as healthy or unhealthy from vibration‐based condition monitoring systems by applying order analysis techniques to extract features. Using the methodology explained in the report, an accuracy of up to 81.6% correct failure prediction was achieved.

Development of a slope failure database for Alabama highways

Slope stability management systems (SSMSs) have been developed by multiple state transportation agencies to assess landslides adjacent to highways and aide in the effective allocation of resources for slope and/or roadway repairs. SSMSs catalog and analyze slope failures through the use of three main components: a landslide data collection system, a geographic information systems (GIS) database, and a hazard prioritization system. The components form a landslide management system used for the identification or prediction of landslide risk areas and the determination of landslide hazards to motorists. The Alabama Department of Transportation (ALDOT) has not previously employed a SSMS, which limits the ability of ALDOT engineers and geologists to effectively manage landslide hazards along highways. This paper discusses the development of a landslide collection system and database developed to improve the ability of ALDOT to identify common causes and locations of slope failures along Alabama highways. The collected data were converted into a GIS database, which allows for correlations between landslides and additional spatial data, such as average precipitation and surface geology. This paper presents a brief review of similar SMSSs, reviews the proposed database, and discusses preliminary observations. The data collected from this study can be used to identify common causes of landslides along Alabama highways and select efficient remediation options.

Framework for managing the operations and maintenance of wind farms

The operations and maintenance of a wind farm constitutes a considerable source of expenditures therefore a holistic framework is herein presented aiming at optimising their management. The principles upon which the framework was developed belongs to the asset management discipline oriented towards the wind energy sector. Besides, it is understood from a cross functional perspective which also embraces information management and business aspects. The framework comprehends different modules conceived with the following purposes: (i) creation of an integral and comprehensive failure database, (ii) modelling of the wind turbines’ reliability taking into account their operational context and (iii) defining of maintenance strategy within a life-cycle perspective. The practical aspects of the framework are demonstrated through a case study based on real-field data from the sector. Furthermore, the present research sets the foundation to consider all the proposed algorithms, processes and techniques within a Product-Service System approach, which entails important benefits for the OEMs an for the operators.

Model uncertainties and bias in SHEAR strength predictions of slender stirrup reinforced concrete beams

AbstractPrior studies point to high model uncertainty in shear prediction. Model factors for various shear prediction models are characterized here, based on a recent and well‐vetted database of shear failures. Characterization includes estimation of main statistical parameters; and importantly also correlation and regression analysis to assess consistency of model factors over ranges of design parameters. The aim of the study is to identify models suitable for use as general probabilistic model (GPM) in future reliability assessments. The Variable Strut Inclination Method (VSIM), shear level of approximation III of the fib Model Code 2010, and ACI 318 displayed high bias, variability and various correlations with shear input parameters which make them unsuitable choices for GPM. The compression chord capacity model and the modified compression field theory displayed low bias and variability with consistent model factors over the ranges of shear design parameters and are thus suitable as GPM. Concerns stem from observed inconsistency in capacity prediction by VSIM and size effect in ACI. Reliability assessment is advised.

Outcomes in Pediatric Hypertrophic Cardiomyopathy Patients with Reduced Systolic Ventricular Function

Purpose Our aim was to determine the transplant-free survival for pediatric hypertrophic cardiomyopathy (HCM) patients with decreased left ventricular (LV) systolic function and identify risk factors for change in function. Methods There were 177 patients (<18 years old) identified in our centre's Heart Failure Database diagnosed with HCM between January 1, 2001 and July 1, 2017. Patients with at least mildly reduced LV systolic function on quantitative and/or qualitative assessment were compared to those with normal function. Changes in ejection fraction (EF) over time for the entire HCM cohort were analyzed using a linear regression model adjusted for repeated measures and risk factors associated with change were identified. Results Reduced LV function was observed in 31 patients (17.5%) of which 19 (61%) were male and 22 (71%) had idiopathic HCM. Median age at diagnosis was 1.2 years (IQR 0.06-10.2) and at follow-up was 6.0 years (IQR 1.2-14.6). The 7-year transplant-free survival for patients with reduced systolic function was 41% (95% CI 30-52%) compared with 92% (95% CI 90-94%) for patients with normal function (p<.0001). Factors associated with a decline in EF were increased LV posterior wall thickness z-score and intensive care unit (ICU) admission. LV outflow tract obstruction was associated with an increase in EF (Table 1). Conclusion Pediatric HCM patients with reduced LV systolic function have a significantly worse transplant-free survival than those with normal LV function. The presence of increased LV posterior wall thickness or admission to the ICU are independent predictors of worsening LV function over time. Our aim was to determine the transplant-free survival for pediatric hypertrophic cardiomyopathy (HCM) patients with decreased left ventricular (LV) systolic function and identify risk factors for change in function. There were 177 patients (<18 years old) identified in our centre's Heart Failure Database diagnosed with HCM between January 1, 2001 and July 1, 2017. Patients with at least mildly reduced LV systolic function on quantitative and/or qualitative assessment were compared to those with normal function. Changes in ejection fraction (EF) over time for the entire HCM cohort were analyzed using a linear regression model adjusted for repeated measures and risk factors associated with change were identified. Reduced LV function was observed in 31 patients (17.5%) of which 19 (61%) were male and 22 (71%) had idiopathic HCM. Median age at diagnosis was 1.2 years (IQR 0.06-10.2) and at follow-up was 6.0 years (IQR 1.2-14.6). The 7-year transplant-free survival for patients with reduced systolic function was 41% (95% CI 30-52%) compared with 92% (95% CI 90-94%) for patients with normal function (p<.0001). Factors associated with a decline in EF were increased LV posterior wall thickness z-score and intensive care unit (ICU) admission. LV outflow tract obstruction was associated with an increase in EF (Table 1). Pediatric HCM patients with reduced LV systolic function have a significantly worse transplant-free survival than those with normal LV function. The presence of increased LV posterior wall thickness or admission to the ICU are independent predictors of worsening LV function over time.

First-line HIV treatment failures in non-B subtypes and recombinants: a cross-sectional analysis of multiple populations in Uganda

BackgroundOur understanding of HIV-1 and antiretroviral treatment (ART) is strongly biased towards subtype B, the predominant subtype in North America and western Europe. Efforts to characterize the response to first-line treatments in other HIV-1 subtypes have been hindered by the availability of large study cohorts in resource-limited settings. To maximize our statistical power, we combined HIV-1 sequence and clinical data from every available study population associated with the Joint Clinical Research Centre (JCRC) in Uganda. These records were combined with contemporaneous ART-naive records from Uganda in the Stanford HIVdb database.MethodsTreatment failures were defined by the presence of HIV genotype records with sample collection dates after the ART start dates in the JCRC database. Drug resistances were predicted by the Stanford HIVdb algorithm, and HIV subtype classification and recombination detection was performed with SCUEAL. We used Bayesian network analysis to evaluate associations between drug exposures and subtypes, and binomial regression for associations with recombination.ResultsThis is the largest database of first-line treatment failures (n=1724) in Uganda to date, with a predicted statistical power of 80% to detect subtype associations at an odds ratio of ge 1.2. In the subset where drug regimen data were available, we observed that use of 3TC was associated with a higher rate of first line treatment failure, whereas regimens containing AZT and TDF were associated with reduced rates of failure. In the complete database, we found limited evidence of associations between HIV-1 subtypes and treatment failure, with the exception of a significantly lower frequency of failures among A/D recombinants that comprised about 7% of the population. First-line treatment failure was significantly associated with reduced numbers of recombination breakpoints across subtypes.ConclusionsExpanding access to first-line ART should confer the anticipated public health benefits in Uganda, despite known differences in the pathogenesis of HIV-1 subtypes. Furthermore, the impact of ART may actually be enhanced by frequent inter-subtype recombination in this region.

Study of ECG Signals based on Gender and Heart Abnormalities

Appropriate QRS detection of the ECG signal is extremely important for determining the variations in the heart rate. The two most famous algorithms for the QRS detection are Murthy and Rangaraj and Pan and Tompkin. Using these two algorithms, comparison of results for five different datasets i.e., MIT-BIH arrhythmia database, European ST-T database, the MIT-BIH Noise stress test database, the BIDMC congestive heart failure database and intracardiac atrial fibrillation database was done in MATLAB and comparative analysis was obtained on the basis of age, sex and heart rate (above 95 and below 60). Murthy and Rangaraj algorithm is the methodology which is derivative-based and provides results without any delay and gives reduced noise and sharp peak output. On the other hand, Pan and Tompkin algorithm performs the analysis of slope, width and amplitude of QRS complexes and is found to have higher accuracy than other real-time methods.

An Intelligent Learning System Based on Random Search Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection

Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there is a paucity of human experts and equipments. Hence, different researchers have developed different intelligent systems for automated detection of heart failure. However, most of these methods are facing the problem of overfitting i.e. the recently proposed methods improved heart failure detection accuracy on testing data while compromising heart failure detection accuracy on training data. Consequently, the constructed models overfit to the testing data. In order, to come up with an intelligent system that would show good performance on both training and testing data, in this paper we develop a novel diagnostic system. The proposed diagnostic system uses random search algorithm (RSA) for features selection and random forest model for heart failure prediction. The proposed diagnostic system is optimized using grid search algorithm. Two types of experiments are performed to evaluate the precision of the proposed method. In the first experiment, only random forest model is developed while in the second experiment the proposed RSA based random forest model is developed. Experiments are performed using an online heart failure database namely Cleveland dataset. The proposed method is efficient and less complex than conventional random forest model as it produces 3.3% higher accuracy than conventional random forest model while using only 7 features. Moreover, the proposed method shows better performance than five other state of the art machine learning models. In addition, the proposed method achieved classification accuracy of 93.33% while improving the training accuracy as well. Finally, the proposed method shows better performance than eleven recently proposed methods for heart failure detection.

Reliability study of Kolo Creek Gas Turbine Power Station

Equipment failures such as unbalanced rotating elements, steam-induced vibration, misalignment of turbine shaft and rotor malfunction create production, safety, operational and environmental problems in a thermal power plant. This paper assessed the reliability and availability status of Kolo Creek Gas Turbine Power Station. The method of reliability evaluation was based on the collection of historical operational failure database of two turbines with total installed capacity of 20 MW over a period of seven years (1995 – 2001) and the analysis of the data was carried out by applying the reliability indices model. The result of the analysis indicated reliability and availability values of 10% and 70.5% respectively over the period under consideration. These values are very low due to too frequent failures and downtimes arising from lack of maintenance culture. It is necessary to improve the reliability indices of the power plant by employing well planned and routine maintenance of equipment as well as training and retraining of technical human resources of the major equipment.Keywords: Failure, Downtime, Maintenance, Reliability, Availability, Indices

Calculation methods for the gas pipeline failure rate

Determination of gas pipeline failure rates is essential for quantitative risk assessment and integrity management of gas pipelines. To this end, the functional relationship between failure rate and failure probability is established by reliability theory, considering that the two terms are often confused in the literature. Then, existing methodologies for the estimation of failure rates are divided into three categories, namely methods based on historical failure data statistics, methods based on expert knowledge and experience, and methods based on structural reliability analysis. Finally, characteristics and applicability of the three methods are compared. We find three essential aspects to consider in determination of the choice of method in practical engineering: (1) operation and management level of gas suppliers, (2) features or merits of detection technologies of pipelines, and (3) integrity of pipeline failure databases. Considering that external interference as a cause of failure belongs to a type of extreme failure load, it would be appropriate to adopt methods based on historical failure statistics to determine failure rates. On the contrary, when historical data are relatively scarce or the pipeline detection technology is advanced, it is advisable to use methods based on structural reliability analysis to obtain the failure rates. In this study, the calculation model and procedures for the method based on historical failure data statistics are established. Furthermore, the so-called bathtub curve of pipeline failure rates is obtained by methods based on structural reliability analysis.

Prevalence of underlying gastrointestinal malignancies in iron-deficient heart failure.

AimsAnaemia and iron deficiency (ferritin level < 100 or 100–300 μg/L with transferrin saturation < 20%) are prevalent in heart failure. Mechanistically, iron deficiency is linked to poor intestinal uptake, increased intestinal loss, and chronic inflammation. However, the prevalence of underlying gastrointestinal malignancies is not established in iron‐deficient heart failure with or without anaemia.Methods and resultsPatients followed up in a single‐centre, heart failure database with baseline registration of haemoglobin and iron status were retrospectively evaluated. The proportion of patients undergoing upper and lower gastrointestinal endoscopy between inclusion and censoring was determined. Afterwards, the prevalence of biopsy that confirmed intestinal malignancies in relation to baseline iron and haemoglobin status was determined. Anaemia was defined as a haemoglobin level <12 g/dL, and iron deficiency according to the aforementioned criteria. Of the 1197 patients in the database, 699 (59%) patients underwent full endoscopic workup over a mean follow‐up of 50 ± 27 months. A total of 50 intestinal malignancies were identified (n = 42, 84%, in iron‐deficient vs. n = 8, 16%, non‐iron‐deficient patients; P < 0.001). The prevalence of intestinal malignancies was non‐statistically different in iron‐deficient patients with anaemia (n = 12/129, 9.3%) or without anaemia (n = 30/287, 10.5%; P = 0.551). The prevalence was much lower in patients without iron deficiency with anaemia (n = 5/83, 6%) or without anaemia (n = 3/200, 1.5%). In patients with iron deficiency but without anaemia (a group in which the role of endoscopic workup is less established), ferritin levels carried an inverse diagnostic capacity in detecting patients with an underlying malignancy (area under the curve = 0.741, P < 0.001). A ferritin level < 56 μg/L had the best acuity, detecting malignancies with a sensitivity of 80% and a specificity of 71%.ConclusionsEndoscopic evaluation is warranted in heart failure patients with iron‐deficient anaemia given the high prevalence of underlying intestinal malignancies, as advised by gastroenterology guidelines. However, additional research is needed assessing the best approach to patients with iron deficiency without anaemia, given the high occurrence of intestinal malignancies in these patients. A lower ferritin level could potentially help stratify the need for an endoscopic workup in these patients.

Wind Turbine Public Safety Risk, Direct and Indirect Health Impacts

Wind turbines are often perceived as benign. This can be attributed to the population majority dwelling in urban locations distant from most wind turbines. Society may understate the risk to individuals living near turbines due to an overstatement of the perceived benefits of turbines, and an understatement of the risk of injury from falling turbine parts, or shed ice. Flaws in risk calculation may be attributed to a less than fully developed safety culture. Indications of this are the lack of a comprehensive industry failure database, and safety limits enabling the industry growth, but not protective of the public. A comprehensive study of wind turbine failures and risks in the Canadian province of Ontario gives data to enable validation of existing failure models. Failure probabilities are calculated, to show risk on personal property, or in public spaces. Repeated failures, and inadequate safety separation show public safety is not currently assured. A method of calculating setbacks from wind turbines to mitigate public risk is shown. Wind turbines with inadequate setbacks can adversely impact public health both directly from physical risk and indirectly by irritation from loss of safe use of property. Physical public safety setbacks are separate from larger setbacks required to prevent irritation from noise and other stressors, particularly when applied to areas of learning, rest and recuperation. The insights provided by this paper can assist the industry to enhance its image and improve its operation, as well as helping regulators set safety guidelines assuring protection of the public.

Marine diesel engine failure simulator based on thermodynamic model

The diesel engine is a widely used machine in naval sector both as a propeller and auxiliary generator sets, being the most critical equipment of vessel platform. Therefore, diesel engine reliability optimization has a transcendental impact on vessel availability, safety and life cycle costs.This article describes the development of a 4-stroke high speed marine diesel engine failure simulator used in military and civil vessels as the main engine of small patrols and yachts and as an auxiliary genset for larger vessels. Failure simulator is based on a one-dimensional thermodynamic model developed in AVLBoost©, adjusted and validated with experimental data from a real engine in a test bench.The developed model is able to reproduce with confinable results the effect of a large number of typical thermodynamic failures, the behavior of the engine and the effects over several parameters measured and non-measured. Therefore, it is possible to obtain the engine response to failures without having to provoke them in real engine and also it is possible to know the symptoms of one failure before this failure becomes dangerous for the correct behavior of the engine. In addition, this paper exposes a methodology which allows to obtain a failure simulator of any marine diesel engine. This simulator is able to identify diesel engine symptoms under failure condition and build a reliable failure database for diagnosis purposes.

Validation and Recalibration of Seattle Heart Failure Model in Japanese Acute Heart Failure Patients

BackgroundPrecise risk stratification in heart failure (HF) patients enables clinicians to tailor the intensity of their management. The Seattle Heart Failure Model (SHFM), which uses conventional clinical variables for its prediction, is widely used. We aimed to externally validate SHFM in Japanese HF patients with a recent episode of acute decompensation requiring hospital admission. Methods and ResultsSHFM was applied to 2470 HF patients registered in the West Tokyo Heart Failure and National Cerebral And Cardiovascular Center Acute Decompensated Heart Failure databases from 2006 to 2016. Discrimination and calibration were assessed with the use of the c-statistic and calibration plots, respectively, in HF patients with reduced ejection fraction (HFrEF; <40%) and preserved ejection fraction (HFpEF; ≥40%). In a perfectly calibrated model, the slope and intercept would be 1.0 and 0.0, respectively. The method of intercept recalibration was used to update the model. The registered patients (mean age 74 ± 13 y) were predominantly men (62%). Overall, 572 patients (23.2%) died during a mean follow-up of 2.1 years. Among HFrEF patients, SHFM showed good discrimination (c-statistic = 0.75) but miscalibration, tending to overestimate 1-year survival (slope = 0.78; intercept = −0.22). Among HFpEF patients, SHFM showed modest discrimination (c-statistic = 0.69) and calibration, tending to underestimate 1-year survival (slope = 1.18; intercept = 0.16). Intercept recalibration (replacing the baseline survival function) successfully updated the model for HFrEF (slope = 1.03; intercept = −0.04) but not for HFpEF patients. ConclusionsIn Japanese acute HF patients, SHFM showed adequate performance after recalibration among HFrEF patients. Using prediction models to tailor the care for HF patients may improve the allocation of medical resources.

Corroboration of sparse failure data through Bayesian estimation for natural gas cross country pipelines: A case study with reference to the Indian scenario

The rising demand for Natural Gas (NG) in India, a developing country, necessitates adequate, uninterrupted and safe transmission and distribution of the gas to consumers at various points within the country. Though pipelines are considered as the safest mode of transportation, the failure of a flammable and highly pressurized natural gas pipeline can cause fatalities and extensive damage to properties. A reliable estimation of occurrence probabilities of ‘actual’ natural gas pipeline incident scenarios helps to evaluate the safety management and control system existing in the country. Unfortunately, India does not maintain any quantitative failure data base of the natural gas pipeline incidents due to lack of operational history. This paper outlines the use of Bayesian logic for corroborating the existing sparse and imprecise occurrence proportion of natural gas pipeline incident scenarios obtained from the incident analysis reports. This is done by incorporating the specific unreported Piped Natural Gas (PNG) incident scenario frequency per year. Posterior occurrence probability estimates of the incident scenarios lie between the sparse prior proportions obtained from the published incident analysis reports and the likelihood proportion of specific PNG incidents happening in India per year. The newly generated data through the Bayesian estimation therefore can be considered more reliable and sensible and are also validated like such data available in the developed countries. The posterior estimates also necessitate more improved safety management and control system in each City Gas Distribution (CGD) entity within the country to reduce specific failure cause-consequences.

Availability Performance Analysis of Thermal Power Plants

This case study presents the availability evaluation method of thermal power plants for conducting performance analysis in Indian environment. A generic availability model has been proposed for a maintained system (thermal plants) using reliability block diagrams and fault tree analysis. The availability indices have been evaluated under realistic working environment using inclusion exclusion principle. Four year failure database has been used to compute availability for different combinatory of plant capacity, that is, full working state, reduced capacity or failure state. Availability is found to be very less even at full rated capacity (440 MW) which is not acceptable especially in prevailing energy scenario. One of the probable reason for this may be the difference in the age/health of existing thermal power plants which requires special attention of each unit from case to case basis. The maintenance techniques being used are conventional (50 years old) and improper in context of the modern equipment, which further aggravate the problem of low availability. This study highlights procedure for finding critical plants/units/subsystems and helps in deciding preventive maintenance program.

The role of the clinical departments for understanding patient heterogeneity in one-year mortality after a diagnosis of heart failure: A multilevel analysis of individual heterogeneity for profiling provider outcomes.

PurposeTo evaluate the general contextual effect (GCE) of the hospital department on one-year mortality in Swedish and Danish patients with heart failure (HF) by applying a multilevel analysis of individual heterogeneity.MethodsUsing the Swedish patient register, we obtained data on 36,943 patients who were 45–80 years old and admitted for HF to the hospital between 2007 and 2009. From the Danish Heart Failure Database (DHFD), we obtained data on 12,001 patients with incident HF who were 18 years or older and treated at hospitals between June 2010 and June2013. For each year, we applied two-step single and multilevel logistic regression models. We evaluated the general effects of the department by quantifying the intra-class correlation coefficient (ICC) and the increment in the area under the receiver operating characteristic curve (AUC) obtained by adding the random effects of the department in a multilevel logistic regression analysis.ResultsOne-year mortality for Danish incident HF patients was low in the three audit years (around 11.1% -13.1%) and departments performed homogeneously (ICC ≈1.5% - 3.5%). The discriminatory accuracy of a model including age and gender was rather high (AUC≈ 0.71–0.73) but the increment in AUC after adding the department random effects into these models was only about 0.011–0.022 units in the three years.One-year mortality in Swedish patients with first hospitalization for heart failure, was relatively higher for 2007–2009 (≈21.3% - 22%) and departments performed homogeneously (ICC ≈ 1.5% - 3%). The discriminatory accuracy of a model including age, gender and patient risk score was rather high (AUC≈ 0.726–0.728) but the increment in AUC after adding the department random effects was only about 0.010–0.017 units in the three years.ConclusionUsing the DHFD standard benchmark for one-year mortality, Danish departments had a good, homogeneous performance. In reference to literature, Swedish departments had a homogeneous performance and the mortality rates for patients with first hospitalization for heart failure were similar to those reported since 2000. Considering this, if health authorities decide to further reduce mortality rates, a comprehensive quality strategy should focus on all Swedish hospitals. Yet, a complementary assessment for the period after the study period is required to confirm whether department performance is still homogeneous or not to determine the most appropriate action.

Gender Differences in Presentation, Treatment, and In-Hospital Outcome of Patients Admitted With Heart Failure Complicated by Atrial Fibrillation (from the Get With the Guidelines—Heart Failure [GWTG-HF] Registry)

Almost 25% of patients with heart failure (HF) have coexisting atrial fibrillation (AF), the latter of which may increase morbidity and mortality. Despite the high prevalence of HF with concomitant AF, this subgroup of patients remains understudied. This study examines gender differences in presentation, treatment and in-hospital outcome of patients with HF and AF. The Get With the Guidelines-Heart Failure (GWTG-HF) database enrolled 6,496 patients with HF who presented to Cooper University Hospital from 2005 to 2012. Twenty-four percent (1,561 patients) had concomitant AF. Pearson chi-square tests and the Student T-tests were used to compare patient characteristics by gender. Multivariate logistic regression was used to predict in-hospital mortality. Six hundred sixty-nine (42.8%) patients with HF and AF were women. Women were older (p <0.001), had a higher ejection fraction (p <0.001), had systolic hypertension (p <0.001), and were more likely to have health insurance (p <0.001). Despite a higher CHADS2 score in women (p = 0.007), there was no gender difference in percent of anticoagulation medications prescribed before admission. Women were less likely to present with dizziness, lightheadedness, or syncope, and were more likely to be compliant with medications and diet recommendations before admission. Despite differences in presentation, co-morbidities, and therapy, in-hospital mortality was similar between men and women. Decreased appetite or early satiety predicted in-hospital mortality in women, whereas age, chest pain on admission, and decreased appetite or early satiety predicted in-hospital mortality in men. In conclusion, women presenting with HF complicated by AF clinically differ from men, but despite these differences, both groups shared similar symptom presentation and in-hospital mortality rates.