Is Breast Background Parenchymal Enhancement on MRI Related to BI-RADS Score and Follow-Up Rate?

Objective: We investigated the correlations between background parenchymal enhancement (BPE) and MRI interpretations with respect to short-interval follow-ups and biopsy rates. Methods: All accessible MRI examinations from 128 women during a limited time period in 2016 were evaluated. A blinded radiologist visually categorized BPE as minimal, mild, moderate, or marked. A BI-RADS category was also assigned. We used descriptive statistics to report the findings and chi-square and Fisher’s exact tests to compare categories. Results: Prevalence of minimal, mild, moderate, and marked BPE was 14.1%, 43.0%, 32.0%, and 10.9%, respectively. The short-interval follow-up rates were 22.2%, 27.3%, 26.8%, and 7.1% in women with minimal, mild, moderate, and marked BPE, respectively. BPE was not associated with the short-interval follow-up rate (p-value = 0.477). Biopsy rates were 22.2%, 27.3%, 22.0%, and 57.1% in women with minimal, mild, moderate, and marked BPE, respectively. Although there was no significant relationship between biopsy rates and BPE levels (p-value = 0.095) in the total population, these two factors were significantly associated in premenopausal women (p-value = 0.023) and in women of 30 - 39 years (p-value = 0.001). Conclusion: Higher BPE does not correlate with short-interval follow-up rates, but appears to be related to biopsy rate, thus causing false-positives and unnecessary biopsy recommendations, particularly in younger, premenopausal women.

Minimum 10-year follow-up outcomes of arthroscopic Bankart’s repair with metallic anchors:Reliable results with low redislocation rates

BACKGROUND With stiff competition from alternative albeit more expensive counterparts,it has become important to establish the applicability of metallic anchors for shoulder instability in the modern era.This can be accomplished,in part,by analysing long-term outcomes.AIM To analyse minimum 10-year outcomes from 30 patients following arthroscopic anterior stabilisation using metallic anchors.METHODS Prospectively collected data from arthroscopic Bankart repairs performed using metal anchors during 2007P-2010 were retrospectively analysed in this singlesurgeon study.Comprehensive data collection included historical and clinical findings,dislocation details,operative specifics,and follow-up radiological and clinical findings including shoulder scores.The primary outcomes were patientreported scores(Constant,American Shoulder and Elbow Surgeons[ASES],and Rowe scores)and pain and instability on a visual analogue scale(VAS).RESULTS A 3% recurrence rate of dislocation was noted at the final follow-up.Total constant scores at 10 years postoperatively measured between 76 and 100(mean 89)were significantly better than preoperative scores(mean 62.7).Congruous improvements were also noted in the Rowe and ASES scores and VAS at the 10-year review.CONCLUSION Reliable long-term outcomes with metallic anchors in surgery for shoulder instability can be expected.Our results provide additional evidence of their continued,cost-effective presence in the modern scenario.

Low grade gastric mucosa associated lymphoid tissue lymphoma:Treatment strategies based on 10 year follow-up

AIM:To deduce strategic guidelines of gastric mucosa associated lymphoid tissue lymphoma (MALTOMA) by evaluating the long-term outcome of patients in respect to various treatment modalities. METHODS:A total of 55 patients with MALTOMA from May 1992 to August 2002 were retrospectively reviewed. RESULTS:Complete remission was obtained in 24 (82.8%) of 29 patients treated with anti Helicobacter pylori (Hpylori) regimen only.The duration to reach complete remission was 12 months (85 percentile,2-33 months).Five patients showed complete remission with radiation therapy (26-86 months).Two of them were Hpyloritreatment failure cases. CONCLUSION:Hpylorieradication is an effective primary treatment option for low grade MALTOMA and radiation therapy could be considered in patients with no evidence of Hpyloriinfection or who do not respond to Hpylorieradication therapy 12 months after successful eradication.

Patient satisfaction and follow-up adherence to glaucoma case management clinic in China

AIM:To assess glaucoma patient satisfaction and follow-up adherence in case management and identify associated predictors to improve healthcare quality and patient outcomes.METHODS:In this cross-sectional study,a total of 119 patients completed a Patient Satisfaction Questionnaire-18 and a sociodemographic questionnaire.Clinical data was obtained from the case management system.Follow-up adherence was defined as completing each follow-up within±30d of the scheduled time set by ophthalmologists during the study period.RESULTS:Average satisfaction scored 78.65±7,with an average of 4.39±0.58 across the seven dimensions.Age negatively correlated with satisfaction(P=0.008),whilst patients with follow-up duration of 2 or more years reported higher satisfaction(P=0.045).Multivariate logistics regression analysis revealed that longer follow-up durations were associated with lower follow-up adherence(OR=0.97,95%CI,0.95-1.00,P=0.044).Additionally,patients with suspected glaucoma(OR=2.72,95%CI,1.03-7.20,P=0.044)and those with an annual income over 100000 Chinese yuan demonstrated higher adherence(OR=5.57,95%CI,1.00-30.89,P=0.049).CONCLUSION:The case management model proves effective for glaucoma patients,with positive adherence rates.The implementation of this model can be optimized in the future based on the identified factors and extended to glaucoma patients in more hospitals.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

Pulse rate estimation based on facial videos:an evaluation and optimization of the classical methods using both self-constructed and public datasets

Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.

Dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates

Interactive holography offers unmatched levels of immersion and user engagement in the field of future display.Despite of the substantial progress has been made in dynamic meta-holography,the realization of real-time,highly smooth interactive holography remains a significant challenge due to the computational and display frame rate limitations.In this study,we introduced a dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates.To our knowledge,this is the first reported practical dynamic interactive metasurface holographic system.We spa-tially divided the metasurface device into multiple distinct channels,each projecting a reconstructed sub-pattern.The switching states of these channels were mapped to bitwise operations on a set of bit values,which avoids complex holo-gram computations,enabling an ultra-high computational frame rate.Our approach achieves a computational frame rate of 800 kHz and a display frame rate of 23 kHz on a low-power Raspberry Pi computational platform.According to this methodology,we demonstrated an interactive dynamic holographic Tetris game system that allows interactive gameplay,color display,and on-the-fly hologram creation.Our technology presents an inspiration for advanced dynamic meta-holography,which is promising for a broad range of applications including advanced human-computer interaction,real-time 3D visualization,and next-generation virtual and augmented reality systems.

INTERFACE BEHAVIOR AND DECAY RATES OF COMPRESSIBLE NAVIER-STOKES SYSTEM WITH DENSITY-DEPENDENT VISCOSITY AND A VACUUM

In this paper,we study the one-dimensional motion of viscous gas near a vacuum,with the gas connecting to a vacuum state with a jump in density.The interface behavior,the pointwise decay rates of the density function and the expanding rates of the interface are obtained with the viscosity coefficientμ(ρ)=ρ^(α)for any 0<α<1;this includes the timeweighted boundedness from below and above.The smoothness of the solution is discussed.Moreover,we construct a class of self-similar classical solutions which exhibit some interesting properties,such as optimal estimates.The present paper extends the results in[Luo T,Xin Z P,Yang T.SIAM J Math Anal,2000,31(6):1175-1191]to the jump boundary conditions case with density-dependent viscosity.

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.

Experimental study on the effect of unloading rate on gneiss rockburst

Rockburst are often encountered in tunnel construction due to the complex geological conditions.To study the influence of unloading rate on rockburst,gneiss rockburst experiments were conducted under three groups of unloading rates.A high-speed photography system and acoustic emission(AE)system were used to monitor the entire process of rockburst process in real-time.The results show that the intensity of gneiss rockburst decreases with decrease of unloading rate,which is manifested as the reduction of AE energy and fragments ejection velocity.The mechanisms are proposed to explain this effect:(i)The reduction of unloading rate changes the crack propagation mechanism in the process of rockburst.This makes the rockbursts change from the tensile failure mechanism at high unloading rate to the tension-shear mixed failure mechanism at low unloading rate,and more energy released in the form of shear crack propagation.Then,less strain energy is converted into kinetic energy of fragments ejection.(ii)Less plate cracking degree of gneiss has taken shape due to decrease of unloading rate,resulting in the destruction of rockburst incubation process.The enlightenments of reducing the unloading rate for the project are also described quantitatively.The rockburst magnitude is reduced from the medium magnitude at the unloading rate of 0.1 MPa/s to the slight magnitude at the unloading rate of 0.025 MPa/s,which was judged by the ejection velocity.

A new era of mutation rate analyses: Concepts and methods

The mutation rate is a pivotal biological characteristic,intricately governed by natural selection and historically garnering considerable attention.Recent advances in high-throughput sequencing and analytical methodologies have profoundly transformed our understanding in this domain,ushering in an unprecedented era of mutation rate research.This paper aims to provide a comprehensive overview of the key concepts and methodologies frequently employed in the study of mutation rates.It examines various types of mutations,explores the evolutionary dynamics and associated theories,and synthesizes both classical and contemporary hypotheses.Furthermore,this review comprehensively explores recent advances in understanding germline and somatic mutations in animals and offers an overview of experimental methodologies,mutational patterns,molecular mechanisms,and driving forces influencing variations in mutation rates across species and tissues.Finally,it proposes several potential research directions and pressing questions for future investigations.

Present-day Upper-crustal Strain Rate Field in Southeastern Tibet and its Geodynamic Implications:Constraints from GPS Measurements with ABIC Method

The Earth’s surface kinematics and deformation are fundamental to understanding crustal evolution.An effective research approach is to estimate regional motion field and deformation fields based on modern geodetic networks.If the discrete observed velocity field is obtained,the velocity related fields,such as dilatation rate and maximum shear strain rate,can be estimated by applying varied mathematical approaches.This study applied Akaike's Bayesian Information Criterion(ABIC)method to calculate strain rate fields constrained by GPS observations in the southeast Tibetan Plateau.Comparison with results derived from other three methods revealed that our ABIC-derived strain rate fields were more precise.The maximum shear strain rate highlighted the Xianshuihe–Xiaojiang fault system as the main boundary for the outward migration of material in southeastern Tibet,indicating rotation of eastern Tibet material around the eastern Himalaya rather than whole extrusion along a fixed channel.Additionally,distinct dilatation rate patterns in the northeast and southwest regions of the fault system were observed.The northeast region,represented by the Longmenshan area,exhibited negative dilatational anomalies;while the southwest region,represented by the Jinsha River area north of 29°N,displayed positive dilatational anomalies.This indicates compression in the former and extension in the latter.Combined with deep geophysical observations,we believe that the upper and lower crusts of the Jinsha River area north of 29°N are in an entire expanding state,probably caused by the escape-drag effect of material.The presence of a large,low-viscosity region south of 29°N may not enable the entire escape of the crust,but instead result in a differential escape of the lower crust faster than the upper crust.

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

One-year follow-up study of Helicobacter pylori eradication rate with ^(13)C-urea breath test after 3-d and 7-d rabeprazole-based triple therapy

AIM: To investigate the long-term role of a 3-d rabeprazole-based triple therapy in patients with Helicobacter pylori (H pylori)-infected active peptic ulcers. METHODS: We prospectively studied 115 consecutive patients with H pylori-infected active peptic ulcers. H pylori infection was confirmed if any two of H pylori DNA, histology, and rapid urease test were positive. Patients were assigned to either an open-labeled 3-d course of oral amoxicillin 1 000 mg b.i.d., clarithromycin 500 mg b.i.d., and rabeprazole 20 mg b.i.d., or 7-d course of oral amoxicillin 1 000 mg b.i.d., clarithromycin 500 mg b.i.d., and rabeprazole 20 mg b.i.d. Subsequently, all patients received oral rabeprazole 20 mg once daily until the 8th wk. Three months after therapy, all patients were followed-up endoscopically for the peptic ulcer, H pylori DNA, histology, and rapid urease test. One year after therapy, H pylori infection was tested using the 13C-urea breath test. RESULTS: The ulcer healing rates 3 mo after therapy were 81.0% vs 75.4% for the 3-d and 7-d groups [intention-to-treat (ITT) analysis, P=0.47] respectively, and 90.4% vs 89.6% for the 3-d and 7-d groups [per-protocol (PP) analysis, P=0.89] respectively. The eradication rates 3 mo after therapy were 75.9% vs 73.7% for the 3-d and 7-d groups (ITT, P=0.79) respectively, and 84.6% vs 87.5% for the 3-d and 7-d groups (PP, P=0.68) respectively. One year after therapy, seventy-five patients returned to receive the 13C-urea breath test, and the eradication rates were 78.4% vs 81.6% in 3-d and 7-d groups (PP, P=0.73) respectively. CONCLUSION: Our study showed the eradication rates against H pylori infection 3 and 12 mo after triple therapy were not different between the 3-d and 7-d rabeprazole-based groups. Therefore, the 3-d rabeprazole-based triple therapy may be an alternative treatment for peptic ulcers with H pylori infection.

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without nonalcoholic fatty liver

BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear regression(MLR)to identify risk factors for decreased estimated glomerular filtration rate(eGFR).However,medical research is increasingly relying on emerging machine learning(Mach-L)methods.The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD(NAFLD+,NAFLD-)and to rank their importance.AIM To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD.METHODS A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort,accounting for 32 independent variables including demographic,biochemistry and lifestyle parameters(independent variables),while eGFR was used as the dependent variable.Aside from MLR,three Mach-L methods were applied,including stochastic gradient boosting,eXtreme gradient boosting and elastic net.Errors of estimation were used to define method accuracy,where smaller degree of error indicated better model performance.RESULTS Income,albumin,eGFR,High density lipoprotein-Cholesterol,phosphorus,forced expiratory volume in one second(FEV1),and sleep time were all lower in the NAFLD+group,while other factors were all significantly higher except for smoking area.Mach-L had lower estimation errors,thus outperforming MLR.In Model 1,age,uric acid(UA),FEV1,plasma calcium level(Ca),plasma albumin level(Alb)and T-bilirubin were the most important factors in the NAFLD+group,as opposed to age,UA,FEV1,Alb,lactic dehydrogenase(LDH)and Ca for the NAFLD-group.Given the importance percentage was much higher than the 2nd important factor,we built Model 2 by removing age.CONCLUSION The eGFR were lower in the NAFLD+group compared to the NAFLD-group,with age being was the most important impact factor in both groups of healthy Chinese women,followed by LDH,UA,FEV1 and Alb.However,for the NAFLD-group,TSH and SBP were the 5th and 6th most important factors,as opposed to Ca and BF in the NAFLD+group.

Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

Readmission rate and early complications in patients undergoing total knee arthroplasty:A retrospective study

BACKGROUND Total knee arthroplasty(TKA)can improve pain,quality of life,and functional outcomes.Although uncommon,postoperative complications are extremely consequential and thus must be carefully tracked and communicated to patients to assist their decision-making before surgery.Identification of the risk factors for complications and readmissions after TKA,taking into account common causes,temporal trends,and risk variables that can be changed or left unmodified,will benefit this process.AIM To assess readmission rates,early complications and their causes after TKA at 30 days and 90 days post-surgery.METHODS This was a prospective and retrospective study of 633 patients who underwent TKA at our hospital between January 1,2017,and February 28,2022.Of the 633 patients,28 were not contactable,leaving 609 who met the inclusion criteria.Both inpatient and outpatient hospital records were retrieved,and observations were noted in the data collection forms.RESULTS Following TKA,the 30-day and 90-day readmission rates were determined to be 1.1%(n=7)and 1.8%(n=11),respectively.The unplanned visit rate at 30 days following TKA was 2.6%(n=16)and at 90 days was 4.6%(n=28).At 90 days,the unplanned readmission rate was 1.4%(n=9).Reasons for readmissions included medical(27.2%,n=3)and surgical(72.7%,n=8).Unplanned readmissions and visits within 90 days of follow-up did not substantially differ by age group(P=0.922),body mass index(BMI)(P=0.633),unilateral vs bilateral TKA(P=0.696),or patient comorbidity status(30-day P=0.171 and 90-day P=0.813).Reoperation rates after TKA were 0.66%(n=4)at 30 days and 1.15%(n=8)at 90 days.The average length of stay was 6.53 days.CONCLUSION In this study,there was a low readmission rate following TKA.There was no significant correlation between readmission rate and patient factors such as age,BMI,and co-morbidity status.

Loss to Specialized Cardiology Follow-Up in Adults Living with Congenital Heart Disease

Background:Much has been written about the loss to follow-up in the transition between pediatric and adult Congenital Heart Disease(CHD)care centers.Much less is understood about the loss to follow-up(LTF)after a successful transition.This is critical too,as patients lost to specialised care are more likely to experience mor-bidity and premature mortality.Aims:To understand the prevalence and reasons for loss to follow-up(LTF)at a large Australian Adult Congenital Heart Disease(ACHD)centre.Methods:Patients with moderate or highly complex CHD and gaps in care of>3 years(defined as LTF)were identified from a comprehensive ACHD data-base.Structured telephone interviews examined current care and barriers to clinic attendance.Results:Overall,407(22%)of ACHD patients(n=1842)were LTF.The mean age at LTF was 31(SD 11.5)years and 54%were male;311(76%)were uncontactable.Compared to adults seen regularly,lost patients were younger,with a greater socio-economic disadvantage,and had less complex CHD(p<0.05 for all).We interviewed 59 patients(14%).The top 3 responses for care absences were“feeling well”(61%),losing track of time(36%),and not needing fol-low-up care(25%).Conclusions:A large proportion of the ACHD population becomes lost to specialised cardiac care,even after a successful transition.This Australian study reports younger age,moderate complexity defects,and socio-economic disadvantage as predictive of loss to follow-up.This study highlights the need for novel approaches to patient-centered service delivery even beyond the age of transition and resources to maintain patient engagement within the ACHD service.