For this purpose, we classified the breathing patterns according to EEG signals using a machine learning technique and proposed a method to reduce the side effects of mouth breathing on brain function. Twenty subjects participated in this study, and each subject performed three different breathings: nose and mouth breathing and mouth breathing with O2 supply during a working memory task. The results showed that nose breathing guarantees normal O2 supply to the brain, but mouth breathing interrupts the O2 supply to the brain. Therefore, this comparative study of EEG signals using machine learning showed that one of the most important elements distinguishing the effects of mouth and nose breathing on brain function was the difference in O2 supply. These findings have important implications for the workplace environment, suggesting that special care is required for employees who work long hours in confined spaces such as public transport, and that a sufficient O2 supply is needed in the workplace for working efficiency.This study was to investigate the changes in brain function due to lack of oxygen (O Therefore, it is very important to understand and study breathing from an ergonomic perspective. Breathing methods are primarily divided into mouth and nose breathing. In some studies, mouth breathing has been known to be associated with side effects, such as facial shape deformation  and decrreathing .2 supply to the brain.It has been reported that breathing methods can affect brain functions in working memory , but mos2 supply, and it has already been confirmed that the performance for single- and multi-tasking was improved in a work environment with increased O2 supply to the brain [2 supply is very important to improve work efficiency and brain performance. Studies have also reported that the administration of high concentrations of O2 could lead to improvement in cognitive ability, such as a decrease in the reaction time during the 1-back task [2 in the working environment has a great influence on brain function.The performance of the brain is very sensitive to a reduced Ohe brain . In otheack task . TherefoIn a recent study that investigated the direct and immediate effects of breathing methods on brain function, EEG was primarily used to measure brain activity during the working memory task ,10. EEG Machine learning technology is receiving special attention as a method for effectively analyzing EEG data because it has the advantage of finding subtle patterns, even in vast amounts of data measured in various channel signals ,18,19,20In fact, machine learning has been applied in several EEG-based studies, such as EEG classification according to emotion ,12 and E2 supply to distinguish the types of breathing.In this study, we applied a machine learning technique to EEG data to distinguish the types of performed breathing by analyzing the changes in the brain oscillatory activities caused by breathing. Therefore, this study aimed to investigate the immediate and direct difference between nose and mouth breathing in brain activities, as well as the effects of additional OThis study was approved by the Institutional Review Board in Gachon University (IRB: 1044396-201706-HR-108-01). All EEG data were obtained from 20 subjects who voluntarily participated and provided written consent . To minimize the unnecessary effect of the measuring environment on the EEG data and to remove ambient noise, measurements were made in a soundproof room. The EEG electrodes were attached to the scalp in accordance with the 10–20 system. The average temperature of the data measurement environment was 22.6 ± 1.45 °C. The average humidity remained constant at 23.0 ± 2.1%.Measurements were performed with an EEG (LAXTHA QEEG-32FX) in which we obtained data from 32 channels, electrooculogram (EOG) from 2 channels, and electrocardiogram (ECG) from 1 channel. Using Telescan (LAXTHA), five types of EEG brain wave frequency bands: delta (δ: 0.5–4 Hz), theta (θ: 4–8 Hz), alpha (α: 8–13 Hz), beta (β: 13–30 Hz), and gamma (γ: 30–50 Hz) were sampled at 250 Hz. Data preprocessing was conducted using MATLAB-based EEGLAB (SCCN). The first 1 min out of the total 5 min of epoch measurement data was excluded , and onl2) during both the resting state and the n-back working memory tasks. Also, n-back tasks were performed with the Psytoolkit (www.psytoolkit.org (accessed on 25 February 2021)), and the working memory (WM) accuracy and response time were measured.To examine the EEG signal differences in working memory performance during respiration, the EEG signals were measured for 5 min each in the rest, 1-back, and 2-back task states. The subject’s brain waves were measured in two rest states with their eyes closed and open. The n-back task state consisted of a 1-back task and a 2-back task, representing two levels of working memory tasks. In the n-back task state, a number from 0 to 9 was presented to the subject for 0.5 s . Then, a2 supply. In addition, the tasks consisted of the rest state with eyes open and the n-back states during mouth breathing with O2  with random forest (RF). The EEG data used for learning consisted of 160 attributes (or features), that is, 5 frequencies  × 32 channels. LDARF extracted effective feature vectors from the EEG data by applying LDA based on supervised learning, and then the machine learning model was trained with the data in the RF. The LDA learned the distribution of EEG data and extracted the feature vectors that produced the optimal decision boundary. The dimension of the input vector was effectively reduced using this process. The next classification was performed by the RF classifier, which is an ensemble technique combining multiple decision trees. This was followed by bootstrap techniques using several decision trees, which allowed the classifier to achieve high generalization ability and minimize over-fitting even with a small amount of learning data. 2 supply, was mouth or nose breathing. In detail, cross-validation (CV) method is utilized for the classifier, in which CV divides the datasets into data folds, for example, 10-fold CV means dividing the entire datasets into 1 test fold and 9 training folds so that the 10 folds can be a test fold and 10 iterations can be performed to train the model. In this study, the repeated 10-fold CV estimator was applied to improve the accuracy of the datasets [For the machine learning training, each EEG was processed as a vector with 160 elements consisting of the relative spectral density power of five brain waves in 32 channels. To train the LDARF classifier to differentiate between mouth and nose breathing through brain waves, the signal recorded at rest with closed eyes was used. By preprocessing the EEG data obtained from the closed eyes rest state, the values of the five brain waves in the 32 channels were converted to a 160-dimensional vector. After the LDARF classifier trained the individual EEG data, it individually determined whether the EEG sample, especially mouth breathing with Odatasets . That isThe LDARF classifier was trained using only the EEG signal of closed eyes rest with mouth and nose breathing. Thus, the difference in brain waves between nose breathing and mouth breathing in a stable condition was extracted, and this was used to distinguish between the breathing methods. 2 supply during the four tasks: closed eyes rest, open eyes rest, 1-back, and 2-back. These tasks may have different O2 demands according to their difficulty. In 500 LDARF classifiers obtained through fifty 10-fold CVs, inferences to these EEG data were performed. In addition, the accuracy of each classifier was measured.The LDARF classifier could distinguish the signals between mouth and nose breathing in the closed eyes rest condition with high accuracy through learning with steady state EEG signals. Then, the inference was estimated using the EEG data obtained during mouth breathing with O2 saturation (SpO2) and behavioral performance (WM accuracy) were performed for examining the physiological effects according to breathing types . In addition, the WM accuracy did not differ significantly from each group . Therefore, due to their subtle effects on behavioral performance, machine learning analysis had to be used to investigate whether the breathing types could be detected in brain oscillatory activity.The analysis for Ong types . The O2 The performance of the LDARF classifier trained with the EEG data collected during mouth and nose breathing in the closed eyes rest condition was sufficiently high, as shown in 2 supply, which were obtained by applying the previously trained LDARF classifier to the EEG data of those tasks. In 2 supply during the rest with open eyes state. The accuracy of the classifier dropped from 98% for the rest state with closed eyes to 50% for rest with open and closed eyes and mouth breathing with O2 supply in the presence of gamma waves. When the LDARF classifier for mouth breathing was trained for O2 supply situations, the results were significantly less distinguishable, suggesting that the LDARF classifier was affected by the O2-related changes in the EEG signals used for distinguishing between mouth and nose breathing. Oxygen was thought to be more adequately supplied at higher levels of oxygen demand (1-back and 2-back working memory tasks) than in the rest state. These mouth breathings with O2 supply were determined to be nose breathing during 67% of the 1-back task and 75% of the 2-back task. Furthermore, when the O2 demand increased (from 1-back to 2-back working memory tasks), and sufficient O2 was supplied through the mouth, the EEG signal patterns might be more similar to that of nose breathing than mouth breathing. Therefore, the LDARF classifier determined it consistently as nose breathing, even though it was mouth breathing.The LDARF classifier could quantify which channels and brain waves were more important in distinguishing between mouth and nose breathing with respect to their relative spectral density power while performing dimension reduction through LDA. In terms of machine learning, LDARF could quantitatively calculate the feature importance for 160 input features, as shown in 2 supplied by mouth breathing. Unlike previous studies limited to the existing otolaryngology treatment for mouth breathing, this study aimed to confirm the necessity of additional O2 supply in a work environment that requires extensive brain function and to show that additional O2 supply can alleviate or prevent brain function decline caused by mouth breathing. The results showed no differences in behavioral performance depending on the breathing types, but differences in brain activity were clearly detectable through machine learning analysis.This study aimed to extract the characteristics of the breathing method from EEG signals using machine learning technology. Furthermore, this study demonstrated the direct and immediate changes in brain function due to lack of O2, with different O2 demands for different working memory tasks. Each inference was evaluated for accuracy. The complexity level of the inferences for the different states increased in the following order: rest with open eyes, 1-back, and 2-back with mouth breathing with O2 supply. Therefore, the O2 demand should increase with the level of complexity of the operation; a previous report [2 increases, and O2 supplied in high concentrations is used more efficiently.In this study, inferences were performed using the EEG data from the condition with additional supply of Os report  has conc2 was supplied through the mouth, the discriminator, with increasing complexity of the task, increasingly identified mouth breathing as nose breathing.This study has shown that machine learning using EEG signals can sufficiently determine the immediate and direct effects of oral and nasal breathing on cognitive function. In addition, mouth breathing affected the working memory performance due to the possible lack of oxygen as the task difficulty increased, but when additional O2 supply more effectively reduced the characteristic differences between EEG signals in nose and mouth breathing. These results support the fact that the main difference between the EEG signals in nose and mouth breathing may be due to the extent of O2 supply to the brain [Moreover, in complex tasks that require more oxygen from the brain, Ohe brain  and thathe brain ,4,5. Furhe brain ,28,29.A variety of machine learning methods are used in EEG studies: random forests, as well as support vector machines, have been used by researchers to analyze EEG signals and perform dimensionality reduction through principal component analysis ,14,15,22Mouth breathing is very common in adolescents and causes morphological deformation of the face ,5. It ha2 supply is provided during mouth breathing, it is recognized as nose breathing, at least by the brain waves. This means that brain function degradation, such as working memory performance reduction, can be improved through additional O2 supply. Prior research has thoroughly investigated differences in brain regions where changes in brain function occur due to breathing. However, there has been no research to show whether the distinction could be made based on the type of breathing. Once machine learning models become more advanced and can distinguish and analyze EEG signals of different breathing conditions effectively, it will be possible to predict the effect of breathing changes on the brain and to prevent breathing habits that damage brain function. Even in the field of cognitive ability, such as working memory tasks related to breathing, the machine learning model can be a very effective approach to analyze EEG signals. This will also allow the field of cognitive science to customize personalized treatment plans and initial interventions for patients based on predictions from EEG signals. Personalized treatment plans can have a significant effect on clinical management transitions [The results showed that if additional Onsitions .2 supply could affect brain performance. Similar to this result, other studies have also shown improved work efficiency in working environments where O2 supply was increased [2 [2 demand were tested through inference using the LDARF classifier. However, only the data from the condition of mouth breathing with an additional supply of O2 were examined, whereas the effect of O2 supplied by the nose should also be examined. Further improvements to the generalized performance of LDARF should be made because the data were obtained from a limited number of subjects, although the generalized performance was strictly verified with 50 10-fold CVs. Further research should be conducted to investigate how O2 supply will help improve work efficiency in various working environments and why there had no differences in cognitive performance in the current design. In order to draw definite conclusions, participants, such as patients with long-term mouth breathing or cognitive impairment, should be also included in future studies. In addition, the LDA and RF classifiers were used in this study. In future studies, EEG signals need to be analyzed using different types of machine learning models, such as support vector machine, principal component analysis, and independent component analysis. Also, it is necessary to determine which machine learning models are effective in analyzing EEG signals for a specific task. Moreover, considering that the ensemble technique of combining different models in machine learning is superior to one model-based method, the study of the application of the ensemble technique is also of great importance. Furthermore, this study could provide the basis for future research, including the effects of insufficient O2 supply on structural brain development and academic performance in children, the effects on cerebral activity while further supplying O2 supply to the brain, the effects of gas exchange in the lungs, and/or the respiratory effects of degenerative brain diseases such as vascular dementia, Alzheimer’s, or Parkinson’s disease.The present study showed that the management of Oncreased . An increased [2 . Oxygen


The cornea is avascular, which makes it an excellent model to study matrix protein expression and tissue stiffness. The corneal epithelium adheres to the basement zone and the underlying stroma is composed of keratocytes and an extensive matrix of collagen and proteoglycans. Our goal was to examine changes in corneas of 8- and 15-week mice and compare them to 15-week pre-Type 2 diabetic obese mouse. Nanoindentation was performed on corneal epithelium in situ and then the epithelium was abraded, and the procedure repeated on the basement membrane and stroma. Confocal imaging was performed to examine the localization of proteins. Stiffness was found to be age and obesity dependent. Young’s modulus was greater in the epithelium from 15-week mice compared to 8-week mice. At 15 weeks, the epithelium of the control was significantly greater than that of the obese mice. There was a difference in the localization of Crb3 and PKCζ in the apical epithelium and a lack of lamellipodial extensions in the obese mouse. In the pre-Type 2 diabetic obese mouse there was a difference in the stiffness slope and after injury localization of fibronectin was negligible. These indicate that age and environmental changes incurred by diet alter the integrity of the tissue with age rendering it stiffer. The corneas from the pre-Type 2 diabetic obese mice were significantly softer and this may be a result of changes both in proteins on the apical surface indicating a lack of integrity and a decrease in fibronectin. One area of great interest is understanding if the changes that occur to tissues and cells with disease or age reflect physical changes. The result of mechanical forces can be seen in endothelial vasculature , bone reThe cornea provides a superb tissue to study changes in stiffness in epithelium, basement membrane proteins and stromal matrix proteins as it is accessible, and its properties can be readily examined. This tissue is constantly subjected to a wide range of mechanical stimuli. Response to the basement membrane is critical as changes in the composition of the basement membrane have been shown to mediate cell signaling associated with cell adhesion and migration ,7. OtherThe unwounded corneal epithelium consists of 5–7 layers of cells and the basal cells adhere to the basement membrane through structures called hemidesmosomes that are comprised of several large proteins that contact collagen anchoring fibrils in the anterior stroma. The stroma is comprised of an array of collagens and proteoglycans that permit the light to pass unaltered to the retina. Upon injury fibronectin is released and present transiently ,15. In aHere we investigate if the change in stiffness of corneal epithelium, basement membrane zone and stroma are altered either with age or in an obese mouse that is a model for a Type 2 pre-diabetes . Previously, investigators demonstrated that the DiO mice had impaired skin and corneal wound healing and a change in the regulation of an ionotropic receptor, P2X7 ,18.In our current study we found a significant increase in epithelial stiffness with age and an age matched decrease in the DiO epithelium. In contrast, there is a greater percent change in stiffness in the corneal stroma (anterior to posterior) in the 8-week mice compared to the 15-week mice. Furthermore, there is little if any change in stiffness in the stroma in the 15-week DiO mice. To understand these changes, we examined the polarity protein, Crumbs3, that is associated with tight junctions in the epithelium. Together our results demonstrate that age and environment impact the integrity of the cornea.Anti-fibronectin monoclonal mouse antibodies  were purchased from Sigma-Aldrich . Rhodamine phalloidin (Lot# 1842723) was purchased from Invitrogen by Thermo Fisher Scientific . Alexa Fluor 488 goat anti-mouse was obtained from Invitrogen by Thermo Fisher Scientific . VectaSHIELD with 4′,6-diamidino-2-phenylindole (DAPI) was purchased from Vector Labs .The research protocol conformed to the standards of the Association for Research in Vision and Ophthalmology for the Use of Animals in Ophthalmic Care and Vision Research and the Boston University Institutional Animal Care and Use Committee (IACUC). Control and DiO mice were obtained from Jackson Laboratory . Control mice were maintained on the diet, D1425OB , while the DiO mice were fed a high fat diet, D12492 for 15 weeks . The mice exhibited obesity (40.8 g versus 32.4 g at 16 weeks), an elevated HbA1c, mild hyperglycemia, significantly elevated triglycerides, impaired glucose tolerance, and blood glucose for DiO that remained elevated at 120 min. In addition, no difference in bone mineral density in DiO mice was detected. Prior to delivery, body weight, blood glucose and other measurements were performed as described ,8,9.2 for different time points. After incubation, the eyes were fixed with 4% paraformaldehyde for 30 min at room temperature. The tissue was then dissected, leaving an intact scleral rim, and cut into radial wedges.All eyes were examined under a microscope prior to experiments to make sure there were no ocular injuries prior to debridement. Experiments were performed ex vivo and all animals were sacrificed prior to measurements or injury. To examine localization of proteins, a debridement wound was made with a dull laser blade following demarcation with a 1.5 mm-diameter trephine in the central cornea . After wCorneal epithelium and stromal stiffnesses were measured with a Piumananoindenter system . To perform the measurements the head was immobilized. The eye was immersed in phosphate buffered saline (PBS) and the probe was lowered onto the center of the cornea. The probe had a tip radius of 26 μm and stiffness of 4.4 N/m. Epithelium measurements were taken through 10 μm indentations.To examine the basement membrane and stroma, the epithelium was abraded. Measurements were taken using various indentation depths, ranging from 1 μm to 17 μm. The Young’s modulus of the sample was calculated using the built-in PIUMA software based on the Hertzian contact mechanics model, assuming cornea tissue is perfectly incompressible with a Poisson ratio of 0.5. The loading curves were fit to the following equation by the software:v/v Triton X-100 in PBS and blocked with 4% bovine serum albumin (BSA) solution in PBS for 1 h for indirect immunofluorescence. Samples were incubated in anti-fibronectin solution , or anti-laminin-5 (γ2 chain)  at 4 °C overnight, washed with PBS, and incubated in Alexa-Fluor-conjugated secondary antibodies  for 1 hr. Samples were washed with PBS, then incubated in rhodamine phalloidin  for 20 min.Tissues were permeabilized with 0.1% Tissues were mounted using VectaSHIELD antifade mounting medium with DAPI during image acquisition. Tissues were placed apical side down on a glass coverslip bottom petri dish and flattened by placing another glass coverslip on top of the tissue. Tissues were imaged using a 40× oil immersion objective. The gain and laser intensity were set according to a secondary control sample and remained constant throughout the experiment. The pinhole was maintained at 1 airy unit for all sample images. The slice interval for all z-stack images was 1 μm.Values were presented as the mean ± standard error of the mean (SEM). Statistical significance was determined by the Wilcoxon rank-sum test  using the MATLAB function.The corneal epithelium is typically a very stable structure due to tight junctions that are located in the apical epithelium that prevent growth factors and other molecules from penetrating, binding to their receptors, and activating signaling cascades .p < 0.01) greater than the epithelium from an 8-week mouse. At the equivalent age, the epithelium was significantly  greater than an age matched DiO mouse cornea. Here the background of the mice are similar and the DiO mouse is fed a high fat diet for 15 weeks.In the following experiments we compared mice of 8 and 15 weeks with a naturally occurring murine obesity model (pre-Type 2 diabetic mice) to examine changes in stiffness in epithelium and the stroma. Both eyes from five 8-week control mice, five 15-week control mice, and 2 DiO 15-week-old mice were used. Previously, we demonstrated that corneal epithelial wound repair is impaired in corneas from the obese mice . These rSince there was a major difference in stiffness in the control and DiO epithelium, we examined localization of a polarity protein, Crumbs3 (Crb3), which is associated with epithelial tight junctions and recruits tight junction proteins, such as ZO-1, to tight junction structures. Crumbs3 has been shown to maintain apical-basal polarity, apical stability, cell adhesion, and epithelial integrity  and is rTissue was stained for Crb3 and PKCζ and counter stained with rhodamine phalloidin and DAPI and an image is shown of the most apical image and of a basal image. Both Crb3 and PKCζ are present in apical images but localization is different. The control tissue shows punctate staining of Crb3 b and PKCWhen the control tissue was treated with PNGase we found the major form of Crb3 at 38kDa. Likewise, when the DiO tissue was treated with PNGase the major form of Crb3 shifted to 27kDa, indicating that Crb3 is more highly glycosylated. The major 38kDa form is also present .We abraded the corneas, removed the epithelium, and indented at different depths to examine the differences in corneal basement membrane and stromal stiffness. We have shown previously using electron microscopy that the basement membrane is intact . Data waWe examined for changes in fibronectin as it is known to be transiently localized in the stroma and wound edge of control corneas. In the control cornea we found that fibronectin was present at the wound edge (arrows). In addition, we detected fibronectin along the stromal nerves in the control corneas (arrowheads) where it is presumably secreted by the nerves a. The stp < 0.05) [The cornea is unique because it serves an essential function as the strongest refracting surface of the eye while also maintaining an impermeable barrier between the eye and the external environment. We have shown that changes in stiffness occur with age as well as on obese mice (DiO) that are a model of pre-Type II diabetes. Previously, we demonstrated that corneas of DiO mice heal significantly slower than corneas from control C57BL6 mice . Our dat < 0.05) .We demonstrated that the stiffness of epithelium was age dependent and diet dependent for age matched animals. As the epithelium of the DiO animals was not as stiff we reflected on the changes in integrity of the epithelium and stained for polarity proteins that are typically seen in the apical epithelium. We examined unwounded and wounded corneas as planar polarity can be initiated by several different cues, including changes in growth factors and/or the extracellular matrix . The cueDifferences in stiffness were also detected in the basement membrane and stroma with a change in the percent change of stiffness through the stroma. All the measurements were made in the central cornea. The reason for the change in stiffness is not well understood . showed In a recent review, McKay et al., stated that collagen cross-linking provides the strength for maintaining the integrity of the cornea and that aging and diabetes are associated with an increase in collagen cross-linking . HoweverOther investigators hypothesized that the increase was associated with diabetes, and found instead that there was no association of lysyl oxidase (LOX) with diabetes or obesity in their cohort . While wOur studies propose that the impaired wound healing in the DiO cornea could reflect the difference in stiffness or a change in the matrix molecules such as fibronectin. In the control the fibronectin was detected along the stromal nerves and at the leading edge of control compared to the DiO corneas. A reduction in fibronectin was detected in corneas subjected to hypoxia, where healing was delayed compared to control ,22,29. I


This cohort study examines the association of individual transitional care program components with all-cause 30-day inpatient or observation stay readmission among patients discharged from the hospital after treatment for heart failure. What are the associations between individual components of a real-world transition care program for heart failure (HF-TCP) with all-cause 30-day inpatient or observation stay readmission?In a cohort study of 26 128 patients with heart failure, receipt of a home health visit within 2 days of discharge and a 7-day case manager call were not associated with lower rates of readmission. Although completion of a 7-day clinic visit with a physician or a nurse practitioner was marginally associated with lower readmission risk, the HF-TCP as a whole was not associated with lower 30-day readmission rates.These findings suggest that continuous evaluation and refinement of existing clinical programs are critical. Prompted by null findings from several care transition trials and practice changes for heart failure in recent years, leaders from a large integrated health care system aimed to reassess the outcomes of its 10-year multicomponent transitional care program for heart failure (HF-TCP).To examine the association of the individual HF-TCP components and their bundle with the primary outcome of all-cause 30-day inpatient or observation stay readmissions.This retrospective cohort study included patients enrolled in the HF-TCP during an inpatient encounter for heart failure at 13 Kaiser Permanente Southern California hospitals from January 1, 2013, to October 31, 2018, who were followed up from discharge until 30 days, readmission, or death. Data were analyzed from May 7, 2019, to May 1, 2020, with additional review from September 2 to October 1, 2020.Patients received 1 home health visit or telecare (telephone) visit from a registered nurse within 2 days of hospital discharge, a heart failure care manager call within 7 days, and a clinic visit with a physician or a nurse practitioner within 7 days.Multivariable proportional hazards regression models were used to estimate the probability of 30-day readmission for those who received the individual or bundled HF-TCP components compared with those who did not.A total of 26 128 patients were included; 57.0% were male, and the mean (SD) age was 73 (13) years. The 30-day readmission rate was 18.1%. Both exposure to a home health visit within 2 days of discharge  and a 7-day heart failure case manager call  compared with no visit or call were not associated with a lower rate of readmission. Completion of a 7-day clinic visit was associated with a lower readmission rate  compared with no clinic visit. There were no synergistic effects of all 3 components compared with clinic visit alone .This study found that HF-TCP as a whole was not associated with a reduction in 30-day readmission rates, although a follow-up clinic visit within 7 days of discharge may be helpful. These findings highlight the importance of continuous quality improvement and refinement of existing clinical programs. Nevertheless, findings from more recent intervention studies tend to have weak to null effects on readmissions.8 For instance, a recent large-scale pragmatic study of a multicomponent care transition intervention for 2500 Canadian patients with heart failure across 10 hospitals using a stepped-wedge design  conducted from 2015 to 2016 found no reductions in all-cause readmission or emergency department visits at 30 days or 3 months compared with usual care.8A meta-analysis of 53 trials of transitional care services with more than 12 000 patients with heart failurePrompted by null findings from recent trials of care transition for heart failure as well as substantive changes in standard care transition practices in recent years and suspected implementation drift, clinical and operational leaders from a large integrated health care system, Kaiser Permanente Southern California (KPSC), aimed to reassess the value of its multicomponent transitional care program for heart failure (HF-TCP) nearly 10 years after its initial implementation. The purpose of this analysis was to examine the association of the individual HF-TCP components and the bundled service with the primary outcome of all-cause 30-day inpatient or observation stay readmission.STROBE) reporting guideline for cohort studies.This study used a retrospective cohort design and was approved by the KPSC institutional review board, which waived the need for informed consent for review of medical records. This report follows the Strengthening the Reporting of Observational Studies in Epidemiology . Only the first index hospitalization for heart failure was included in the analysis. Patients were followed up from discharge until 30 days, readmission, or death.11 but adapted to the local system context and resources. The overall goal of the HF-TCP was to ensure that patients safely transition from hospital to home by addressing gaps in care, meeting patients’ emerging needs in a timely manner, and empowering patients (with the assistance of their family as appropriate) to engage in self-care. Patients admitted for heart failure who during their hospitalization agreed to enroll in the HF-TCP received a 1-time home health visit (HHV) or telecare (telephone) visit from a registered nurse within 2 days of discharge from the hospital, a call from a heart failure care manager within 7 days, and a physician or nurse practitioner clinic visit in primary or specialty care within 7 days. The HHV could be provided by KPSC registered nurses or contracted to external, non-KPSC home health agencies. The registered nurses received basic training on heart failure management from the clinical program leaders but were not specialists. The HHV typically included medication reconciliation and patient-family education, training, and reinforcement on heart failure self-care behaviors such as fluid intake, sodium restriction, weight monitoring, and implementation of a flexible diuretic plan if appropriate. The telecare calls were conducted by KPSC home health registered nurses and were offered to patients if they declined an HHV. This call included assessing common heart failure symptoms; addressing questions patients might have about their medications, diet, and self-care; and connecting patients to heart failure clinicians as needed. Care manager calls occurred within 7 days of discharge and similarly focused on medication management, self-adjusted diuretic dosing, education on signs and symptoms of an exacerbation, and where to call with concerns. Heart failure care managers were registered nurses, nurse practitioners, or pharmacists. The clinic visits were dedicated postdischarge follow-up visits12 with primary care physicians, cardiologists, or cardiology nurse practitioners; these visits focused on medication review and optimization, follow-up on pending test results, and referrals to other medical or social services as needed. Program leaders engaged in ongoing quality, process improvement activities that included audit and feedback of process measures, standard approaches used across other clinical programs.The HF-TCP was broadly implemented across KPSC in 2010, modeled after core elements of evidence-based care transition models13 Heart failure medications were obtained from pharmacy records in the year before the index hospitalization. Ejection fraction was obtained from echocardiograms closest to the index hospitalization. The laboratory acute physiology score,14 a proxy for severity of hospitalization, and LACE  readmission risk score15 were calculated based on data from the index hospitalization. The primary outcome was all-cause 30-day inpatient or observation stay readmission from clinical and claims records.Sociodemographic and clinical characteristics were obtained from the electronic medical record system. The Charlson Comorbidity Index was calculated based on all available diagnostic codes.16 to estimate the subdistribution hazard of 30-day all-cause inpatient and observation stay readmissions for those who received the HF-TCP components compared with those who did not at any point during the follow-up period. The 3 HF-TCP components  were included as independent factors associated with readmission. Death was treated as a competing risk, and the end of the study period was treated as a censoring event. Visits and telephone calls were modeled as time-dependent events to account for immortal time bias17; if a patient had more than 1 HHV or telecare visit, care manager call, or clinic visit, then the date of the first contact was used. Variables that were identified as clinically relevant were included in the models. The remainder were optimized using model fit statistics (Akaike information criterion and Schwarz bayesian criterion); variables that did not improve fit and whose inclusion did not appreciably (>10%) affect the estimates were dropped. Covariates in the final model included sociodemographic characteristics , clinical details , medications , and other relevant characteristics . We also examined the association of the HF-TCP components with outcomes in relevant patient subgroups: ejection fraction (≤40% or >40%), ambulatory vs nonambulatory, CKD stage (<4 or ≥4), and a combination of these groups. A 2-way interaction term between the HF-TCP components and patient subgroup was used to determine whether the association of the HF-TCP components varied based on patient subgroups. All analyses were conducted using SAS, version 9.4 . A 2-sided P < .05 was considered statistically significant.Data were analyzed from May 7, 2019, to May 1, 2020, with additional review performed from September 2 to October 1, 2020. Descriptive statistics were used to describe the study sample by exposure to the HF-TCP components. We used the proportional hazards models of Fine and GrayA total of 26 128 patients with heart failure were eligible for inclusion in the study, of whom 43.0% were female and 57.0% were male, with a mean (SD) age of 73 (13) years . A totalTwo-thirds of the encounters (66.1%) were followed by an HHV  or telecare call (5214 [20.0%]) within 2 days of discharge. Readmissions were less likely among patients who had an KPSC HHV (1436 [17.3%]), non-KPSC HHV (926 [17.6%]), or a telecare call (793 [15.2%]) compared with those without any contact from the HF-TCP within 2 days of discharge (1807 [20.4%]). Over time, KPSC HHVs became less common (2251 [45.4%] of encounters in 2013 vs 762 [20.6%] in 2018), whereas non-KPSC HHVs (797 [14.2%] vs 1282 [34.7%]) and telecare calls (290 [5.2%] vs 1125 [30.4%]) increased. In adjusted models, exposure to an HHV or telecare call within 2 days of discharge was not associated with a lower rate of readmission  compared with no contact. Home health visits by KPSC registered nurses were not different from non-KPSC HHVs  or telecare calls  .Most patients received a care manager call (87.6% of encounters), although receipt of the call was not associated with lower readmission in adjusted models . When the care manager call was coupled with the HHV or telecare call, this bundle was marginally associated with higher readmission compared to the HHV or telecare call alone  .Most patients completed a visit with a physician or nurse practitioner (72.5%). In adjusted models, this component was associated with a lower risk of readmission  compared with no clinic visit within 7 days. The combination of a clinic visit plus HHV or telecare call was not associated with lower readmission compared to a clinic visit alone . Similarly, there were no synergistic effects of all 3 bundled components compared with the clinic visit alone .P = .002 for interaction), for patients who had a combination of an ejection fraction of 40% or less and CKD stage of less than 4  compared with patients who did not meet this criteria , and for patients who had a combination of these ejection fraction and CKD criteria and were ambulatory  compared with those who did not meet these criteria . There were no other meaningful interactions across these clinical subgroups on the care manager calls, clinic visits, or all 3 components combined  vs an ejection fraction of greater than 40%   and readmission rates (18.1%) in this study sample compared with a 7.6% mortality rate from the Get With the Guidelines-Heart Failure Registry18 and the national heart failure 30-day readmission rate of 21.5% from the Centers for Medicare & Medicaid Services, which leaves little room for further improvements.This evaluation of a real-world multicomponent transitional care program for patients hospitalized for heart failure showed that the HF-TCP as a whole was not associated with a reduction in all-cause 30-day inpatient or observation stay readmission, although one of the program components, a follow-up clinic visit within 7 days of discharge, may be helpful; the HHV with a registered nurse and a heart failure care manager telephone call conducted within 2 and 7 days of discharge, respectively, were not associated with lower readmission. These results, although disappointing, were not surprising given findings from recent studies.12 as well as other studies of patients with heart failure specifically,20 and aligns with current reimbursement policies for transitional care management, although those who are well enough to complete a clinic visit may be less likely to be readmitted to the hospital. The rate of 7-day clinic follow-up in our study (72.5%) is rather impressive compared with other published studies reporting rates of postdischarge follow-up visits of approximately one-third.22 This may, however, reflect the fact that patients who agree to participate in the HF-TCP are likely to be more adherent to care recommendations compared with the general population with heart failure. Greater use of telehealth going forward could facilitate more robust and seamless handoffs to outpatient care, especially for vulnerable, older patients with functional limitations and/or transportation challenges.22The association between completing a follow-up visit and lower readmission is consistent with an earlier study in a large cohort of patients on the medicine service,The challenge with evaluating the effects of the heart failure care manager calls was that 87.6% of patients received at least 1 call, leaving a very small control group that was not exposed to a call for comparison. Given that postdischarge calls are now an expected standard practice for medium- to high-risk patients that is reimbursed by private and public payers, future research is best directed at ensuring high-quality, effective interactions during these brief calls to maximize value for patients, families, health systems, and payers.7 which is simply not scalable nor sustainable for systems that are responsible for managing a large population of patients with heart failure.The lack of an association from the 1-time HHV with a nurse was disappointing but not surprising, because program leaders had been concerned about the quality of the interactions between nonspecialist home health nurses and patients and families for education and training on medications and self-care behaviors. It can be extremely challenging to provide ongoing training and maintain quality assurance with a large home care workforce that typically experiences high turnover and chronic staffing shortages. Our experience with implementing nursing HHVs at scale is very different from that of previous, relatively small-scale efficacy studies of nurse HHVs. For instance, a meta-analysis of 53 trials of transitional care services with more than 120 000 patients with heart failure reported that nurse HHVs were associated with a 35% reduction in all-cause readmission. These studies often included multiple visits by highly trained heart failure nurses,7 may have contributed to the null effects. Moreover, the home health nurses were not as well integrated into the heart failure care management team as the specialized nurses in published studies.7 Greater efforts to improve handoff communications and coordination across the inpatient, home health, and outpatient care teams may improve outcomes, as well as consideration for instituting additional home visits for higher-risk patients.It is important to note that although the home health nurses conducted these visits, the visits were not considered standard, Medicare-certified home visits with the regulatory requirements for patients to be homebound and having a skilled need and did not come with the extensive documentation. The fact that a single HHV was provided in this clinical program as opposed to the multiple visits in more ideal grant-funded research studies25 have had mixed results, and more rigorous investigation is required to determine which patient subgroups would benefit most from ongoing surveillance.However, our subgroup analyses do offer insights into how the 1-time nurse HHV could be refined to target a subset of patients further upstream who might have less severe heart failure, fewer comorbidities, and nonsevere functional impairment, wherein a brief home-based interaction might have positive effects, but this targeted approach will need to be tested. Remote biometric monitoring may offer some promise for transitional care from a scaling perspective; however, published trials26 and recommendations from professional societies on the need to integrate palliative care with heart failure care,27 very limited data exist regarding the effectiveness of primary or secondary palliative care services for heart failure.28 Only a few small studies with mixed findings regarding the effect on proximal patient-centered outcomes such as quality of life are available. None of the heart failure palliative care studies31 thus far have been powered to detect reductions in use of acute care services. Interestingly, the PACT-HF study,8 which was not framed as a palliative care intervention but had many palliative care elements, reported significant improvements in measures such as perception of discharge preparedness, quality of care transition, and quality of life, although there were no reductions in use of services.Finally, despite the high mortality rate 12 months after hospitalization for patients with heart failure8 The follow-up duration of 30 days was short, although the HF-TCP was designed to be a time-limited intervention. We did not correct for multiple comparisons that could have generated significant findings by chance alone. Finally, omission of unmeasured confounders, such as exposure to other care transition interventions, adherence to heart failure treatment, and socioeconomic risks, may have also biased some of the findings.There are several notable limitations with this study. The analytical cohort represents only patients who agreed to participate in the HF-TCP. Although this limits the generalizability of the findings to the broad patient population with heart failure that was discharged from Kaiser Permanente hospitals to home, the findings likely represent the best-case scenario in terms of intervention effects for patients who were receptive to the program. We were unable to measure the content and heterogeneity in the implementation quality for each program component within and across the 13 sites and could only rely on whether an encounter occurred; the overstretched checklist home care culture may have prioritized task completions over quality, and thus the null findings for the HHV in particular could reflect a failure in implementation rather than a lack of effectiveness. The HF-TCP did not routinely capture other patient-centered outcomes , and thus we do not know whether the program had a positive effect on these important, proximal measures.We found that a multicomponent transitional care program for patients with heart failure, as implemented in the real world, was not associated with a reduction in 30-day all-cause inpatient or observation stay readmission, although 1 program component, a follow-up clinic visit with a physician or nurse practitioner within 7 days of discharge, may be helpful; the nurse HHV and a heart failure care manager telephone call conducted within 2 and 7 days of discharge, respectively, were not associated with lower readmission rates. These findings highlight the importance of ongoing evaluation, continuous improvement, and refinement of existing clinical programs for any learning health care system to maximize the value of its services, especially for a challenging clinical condition such as heart failure.


Increasing the coverage of community-based treatment of childhood pneumonia (CCM) is part of the strategy to improve child survival, increase life-expectancy at birth and promote equity in Ethiopia. However, full coverage of CCM has not been reached in any regions of the country. There are no sub-national cost-effectiveness analyses available to inform decision makers on the most equitable scale up strategy.Our first objective is to estimate the sub-national cost-effectiveness and the interindividual inequality impacts of scaling up CCM coverages to 90% in each region. Our second objective is to explore the costs, health effects, and geographical inequality impacts associated with three scale-up scenarios promoting different policy-aims: maximizing health, reducing geographical inequalities, and achieving 90% universal coverage.We used Markov modelling to estimate the sub-national cost-effectiveness of CCM in each region. All data were collected through literature review and adjusted to the region-specific proportions of the rural population. Health effects were modeled as life years gained and under-five deaths averted. Interindividual and geographical inequality impacts were measured by the GINI index applied to health. In scenario analysis we explored three different scale-up strategies: 1) maximizing health by prioritizing the regions where the intervention was the most cost-effective, 2) reducing geographical inequalities by prioritizing the regions with high baseline under-five mortality rate (U5MR), and 3) universal upscaling to 90% coverage in all the regions.The regional incremental-cost effectiveness ratio (ICER) of scaling up the intervention coverage varied from 26 USD per life year gained in Addis to 199 USD per life year gained in the Southern Nations, Nationalities, and Peoples’ region. Universal upscaling of CCM in all regions would cost about 1.3 billion USD and prevent about 90,000 under-five deaths. This is less than 15,000 USD per life saved and translates to an increase in life expectancy at birth of 1.6 years across Ethiopia. In scenario analysis, we found that prioritizing regions with high U5MR is effective in reducing geographical inequalities, although at the cost of fewer lives saved as compared to the health maximizing strategy.Our model results illustrate a trade-off between maximizing health and reducing health inequalities, two common policy-aims in low-income settings. Universal health coverage (UHC) is achieved when the entire population has access to essential medical services and can access these services without being exposed to financial hardship . AchieviEthiopia has achieved considerable improvements in population health and child survival over the past decade. However, many preventable deaths are still occurring, and some regions seem to be left behind in the progress that has been made. In 2016, the under-five mortality rate ranged from 39 deaths per 1000 live births in Addis Ababa to 125 deaths per 1000 live births in Afar [kebele (community). Two health extension workers are typically stationed at each local health posts serving a population of 5000 people on average. Integrated community case management (iCCM) and community-based treatment of pneumonia (CCM) in children less than 5 years of age were included as part of the essential services provided by the health extension workers in 2010. However, only 29.2% of rural children and 59.1% of urban children with symptoms of pneumonia sought treatment in 2016 [The Health Extension Program was launched by the government in 2004 with the aim of increasing coverage of primary health care services in Ethiopia. The Health Extension Program consists of local, government salaried women who do preventive work and provide basic curative services in their assigned Despite efforts to reach rural and poor population with high impact interventions through the Health Extension Program, evidence suggests that inequalities in important child health indicators have increased . DistancWhen progressing towards UHC, policy makers need to consider both the inequality impacts and the cost-effectiveness of prioritizing different strategies. Previous studies have already shown that pneumonia treatment is pro-poor and provides protection from financial risk , 7. It hThe regions of Ethiopia are diverse in terms of influential factors such as available infrastructure, wealth, local epidemiology, background mortality and baseline coverage of pneumonia treatment. These factors are likely to affect both the costs and the expected benefits of providing pneumonia treatment at the community level, and we believe that data on regional cost-effectiveness would inform policy planning more effectively than national averages alone . Our firWe used Markov modeling in the software program TreeAge to model health and economic impacts of scaling up coverage of CCM from baseline to a target coverage of 90% in each of Ethiopia’s eleven major regions. Each regional model was populated with 2016 region-specific data on cost per treatment, incidence of childhood pneumonia, background mortality, and baseline coverage of the intervention. Untreated case fatality rate and treatment effect were assumed to be similar in all the regions. Region-specific inputs are displayed in Table baseline and target coverage. The branches within the arms represent the probabilities that individuals within the modeled 2016 birth cohort would progress through the health events labeled at each branch. The CCM intervention is present during only the first five cycles. At the end of each cycle, one proportion of the cohort moves back to the “Alive and well” status, whereas the other proportion moves to the “Death” status. Those who returned to the “Death” status are removed from the model, whereas each return to the “Alive and well” status is counted as one life year completed . The DHS 2016 estimated the prevalence of pneumonia by asking mothers of children under 5 years of age whether their child had experienced clinical symptoms of pneumonia during the 2 weeks prior to the interview. Clinical symptoms are defined as cough accompanied by short, rapid breathing that was chest-related, and/or difficult breathing that was chest-related [We used the following formula to estimate the annual incidence of pneumonia among children below the age of five:Incidence = prevalence/duration of disease The estimated incidence of pneumonia varied from 0.453 cases per year in the Amhara region to 0.040 cases per year in the Harari region Table . Our calAge-specific mortality rates among adults affect the incremental life years gained by reducing U5MRs, and since U5MR is an acknowledged predictor of general population health, the large regional inequalities in U5MRs are likely to be reflected in mortality among older age groups. However, only life tables representing national averages were available for age-specific mortality rates among adults and children older than 5 years of age. Therefore, we modeled adult mortality rates based on the assumption that the U5MR is associated with mortality among older age groups of the same population. In practice, we selected Ethiopian abridged life tables from the 2015 UN world population prospect  and matcThere are no census data from 2016. Without taking into account the effect of still births, we used regional fertility rates and data on the total number of women in each region to yield a rough estimate of the number of births in 2016 . We usedData on effectiveness of the treatment were collected from a previously published systematic review of studies assessing CCM of pneumonia in developing countries . The revData on treatment costs were collected through literature review. We did not encounter any data on regional cost per treatment. However, previous studies indicate that there are significant differences in costs of providing community health services in different geographical contexts , 20. TheAverage cost per treatment = (45 ∗ x) + (45 ∗ 7.2 ∗ y),where x is the proportion with urban residency while y is the proportion with rural residency.One study from Kenya showed that it was 7.2 times more expensive to provide community health services in rural areas compared to urban areas . We assuThe studies we relied on to estimate the costs per treatment costed the intervention from a providers’ perspective. Cost items classified as personnel costs, capital costs or supply costs were included. These were further divided into patient care costs and overhead costs . In our We modeled effects of scaling up coverage of CCM as life expectancy gains for children less than 5 years of age. The children who recovered from pneumonia were assumed to continue to live with the same health risks as the overall population. We did not apply disability weights to the effect measure as we were primary interested in mortality reduction afforded by the intervention. Hence, the incremental effects of the intervention represent gains in life expectancy at birth. We half-cycle corrected and discounted the effects at a 3% rate. The incremental cost-effectiveness ratios were calculated by dividing incremental costs by incremental life years gained.The Gini coefficient is a measure of inequality , represented by a number between 0 and 1 where 0 represents absolute equality in the distribution of a chosen variable, and 1 represents a situation in which all of the chosen variable belong to one individual. Gini coefficients can be used to describe inequalities in life expectancy between individuals or population groups . We usedIn the regional scenario analysis, we explored three possible objectives: health maximization, reducing geographic inequality, and universal scale-up. The first two scenarios have lower costs and could be possible pathways to universal scale-up. We estimated incremental costs, reduction in national U5MR, incremental effects, and interindividual GINI impacts of 1) maximizing health by scaling up to 90% coverage in the six regions where the intervention is the most cost-effective, 2) reducing geographic inequality by scaling up to 90% coverage in the three regions with the highest under five mortality rates, and 3) universal scale-up to 90% coverage in all regions. For all scenarios, we estimated expected health impacts across Ethiopia by adding the weighted averages of the effects observed in each region.In total, scaling up coverage of CCM to 90% in all regions would decrease the Ethiopian U5MR from 67 to 52 deaths per 1000 live births. Figure Reduced U5MR translates as increased life expectancy. Scaling-up treatment coverage to 90% in all regions would increase the average life expectancy at birth from 63.18 to 64.73 years, a 1.55 years gain. At a regional level, the incremental effects of scaling up the intervention coverage to 90% varied from 0.13 life years gained in Harari to 1.93 life years gained in Oromia  of scaling up coverage ranged from 26.15 USD per life year gained in Addis to 195.80 USD per life year gained in SNNP region Fig. .Fig. 4IIncreasing the coverage of pneumonia treatment would decrease U5MR and alter the survival curves, translating as reduced interindividual inequalities. Table Table in Table Universal scale-up to 90% coverage in all regions would substantially alter the survival curves and decrease interindividual inequalities in life expectancy Table . The heaAccording to the final report of the WHO Consultative Group on Equity and Universal Health Coverage, cost-effectiveness, priority to the worse off, and financial risk protection are important concerns for evaluating which health care interventions should be ranked first in a universal health care package . They alOur results on incremental cost effectiveness ratio of scaling up pneumonia treatment coverage in the eleven major regions are comparable to those of published literature, in which we found incremental cost effectiveness ratios (ICERs) ranging from 26,6 to 208 USD per DALY averted , 26. GeoThere are several data limitations and weaknesses in this study. The first limitation is our estimation of costs per treatment in rural compared to urban areas. The study we relied on for these calculations was small and undertaken in Kenya which has a different strategy for delivery of community-based services . HoweverThe second limitation is that we assumed the untreated case fatality rate of pneumonia was similar across all regions of Ethiopia. This may not be the case. Malnutrition, coinfection with HIV and low birth weight are some of the factors that influence the severity of pneumonia infections in children . The preWe based our estimations of yearly incidence of pneumonia on data from the Demographic and Health survey 2016 . There aA third limitation is that we did not include costs of demand generation, although evidence suggests that demand side barriers are major causes of sustained low intervention coverage , 30. To The cost-effectiveness threshold is the upper ICER to be considered cost-effective within a given health budget. Investing in health interventions that are above this threshold would draw resources away from more cost-effective interventions and would reduce total population health . If we aConcerns about reducing inequalities may justify giving priority to investment in regions where the health interventions are proven less cost-effective than in other regions where the interventions are more cost-effective . This trPneumonia is among the major causes of under-five mortality in Ethiopia and increasing treatment coverage effectively reduces the under-five mortality rate in all eleven regions. This translates to decreased interindividual inequalities and increased life expectancy at birth. Despite large regional variations in incremental cost-effectiveness ratios, our results indicate that pneumonia treatment remains cost-effective across Ethiopia. However, scaling up treatment coverage to 90% in all the regions simultaneously would require substantial resources and, in practice, the scale-up is more likely to happen gradually. Prioritizing the intervention scale-up in the regions with the highest baseline U5MRs would reduce geographical inequalities in life expectancy; however, this would save fewer lives as compared to the health maximizing strategy. Of course, several other scale-up strategies are possible. Our results highlight the challenging trade-off between two common policy aims for health budgeting in low-income countries with geographical diversity: heath maximization and inequality reduction.


Delay in detection of breast cancer may worsen tumour characteristics, with progression of tumour size and a higher risk of metastatic lymph nodes. The purpose of this study was to investigate delayed breast cancer diagnosis after repeated recall for the same mammographic abnormality at screening.This was a retrospective study performed in two cohorts of women enrolled in a mammography screening programme in the Netherlands. All women aged 50−75 who underwent biennial screening mammography either between January 1, 1997 and December 31, 2006 (cohort 1) or between January 1, 2007 and December 31, 2016 (cohort 2) were included.P = 0.29). Most delays were caused by incorrect BI-RADS classifications after recall (74.2%). An increase in mean tumour size was seen when comparing sizes at initial false-negative recall and at diagnosis of breast cancer (P < 0.001).The cohorts showed no difference in proportions of women with delayed breast cancer diagnosis of at least 2 years (2.2% versus 2.8%, The proportion of women with a long delay in breast cancer confirmation following repeated recall at screening mammography has not decreased during 20 years of screening. These delays lead to larger tumour size at detection and may negatively influence prognosis. Previous studies have mostly focused on shorter diagnostic delays in symptomatic patients and very limited data are available on this subject in asymptomatic, screen-detected breast cancers.8 Ciatto et al.9 found that 1.4% of women experienced a delay of at least 2 years in their breast cancer confirmation after repeated recall for a screen-detected abnormality. Several causes for a delayed breast cancer diagnosis after recall at screening have been reported, including an improper classification of mammographic abnormalities at subsequent clinical breast imaging, communication errors between physicians, and sample errors at biopsy.9Delay in the detection of breast cancer remains an important topic in health care, both for its frequency of occurrence and its possible negative effects on survival.6 However, the impact of these improvements on the proportion of women who experience a long delay in breast cancer diagnosis after recall at screening mammography is unknown. In the current study, we focused on women who experienced a delay of at least 2 years in their confirmation of breast cancer following a repeated recall for the same mammographic abnormality at biennial screening mammography in the period from 1997 to 2016. We investigated this delay over time, comparing two screening cohorts of 10 years each, and specified the radiological and tumour characteristics of the cancers with a diagnostic delay. Finally, we determined the causes of diagnostic delay and assessed whether the hospitals that handled recall varied in the number of women who experienced a delay in their breast cancer confirmation.The last decade is characterised by substantial changes in the diagnostic management of (a)symptomatic breast disease, including the introduction of multidisciplinary meetings and new imaging modalities , an intensified use of advanced imaging modalities (breast MRI), a further replacement of fine-needle biopsy by (vacuum-assisted) large core biopsy, and an increased sub-specialisation of physicians in breast care.12 In brief, women are personally invited by letter to attend the programme, with about 80% of the women accepting the invitation. Women who are being treated for breast cancer and those who still receive clinical oncologic follow-up after breast cancer treatment usually do not attend the screening programme.This is a retrospective observational study, based on a prospective database of women aged 50−75 years attending the biennial breast cancer screening programme conducted in the south of the Netherlands. Characteristics of this programme have been reported previously.A consecutive series of 283,479 screen-film mammography screens  were included between January 1, 1997 and December 31, 2006 and this series is labelled cohort 1. A second consecutive series of 534,177 screen-film and full-field digital mammography (FFDM) screens  were included between January 1, 2007 and December 31, 2016 and this series is labelled cohort 2.Screen-film mammograms were obtained with commercially available units  and dedicated mammography screens were utilised . Dedicated film was used  to process mammography screens and processing was done by extended-cycle dedicated processing. During 2009/2010, screen-film mammography in the southern screening region of the Netherlands was gradually replaced by FFDM. All digital mammograms were obtained with a Lord Selenia FFDM system , with a 70 µm pixel size and a 232 × 286 mm field of view.14 Women with a BI-RADS 1  or 2 (benign findings) were not recalled. Women with a BI-RADS 0, 4 or 5 finding were referred to a dedicated breast unit of a hospital for further analysis. BI-RADS category 3 (probably benign findings) is not used in the Dutch screening programme, as short-term follow-up is not available in the screening setting.The examinations were obtained by specialised screening mammography radiographers. Since the start of FFDM screening in 2009/2010, two-view mammography  and cranio-caudal (CC) view) of each breast was performed in first as well as subsequent screens. In the era of screen-film screening, all women who attended the programme for the first time received two-view mammography, whereas subsequent screens comprised a routine MLO view of each breast and additional CC views if indicated. All screening mammograms were then independently double read by certified breast cancer screening radiologists. Since the start of our screening programme in 1995, all previous screens were always available for comparison at the time of assessment of a new screening examination. Screening radiologists categorised abnormal mammographic findings into one of the following categories: suspicious mass, suspicious calcifications, suspicious mass combined with calcifications, asymmetry, architectural distortion, or other abnormalities not otherwise categorised. In the FFDM screening period only, each recall was also classified according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and the radiologists annotated each recalled mammographic abnormality on a drawing which was part of the recall report.The diagnostic workup in the vast majority of recalled women (98%) was performed in six regional hospitals in the south of the Netherlands, whereas the remainder underwent additional examinations at various other hospitals. After physical examination by a surgical oncologist or dedicated breast nurse, additional breast imaging (when needed) was performed at the radiology department. The screening mammogram was first reassessed by a radiologist and the screening mammograms were routinely available. Additional mammographic projections were obtained at the discretion of the radiologist. Breast ultrasonography was used for the additional characterisation of mammographic abnormalities and palpable breast lesions, as guidance for biopsy and for target or second look purposes following breast MRI. Breast MRI gradually became available in the hospitals from 1998 while whole breast ultrasound was performed in only one of the six hospitals. Digital breast tomosynthesis was introduced in 2011 and available in all but one of the hospitals in the later years of inclusion. Fine-needle aspiration biopsy (FNAB) and percutaneous core needle biopsy (CNB) were available in each hospital from the beginning of our study and FNAB was gradually replaced by CNB in the 1990s. Vacuum-assisted stereotactic core needle biopsy (SCNB) was introduced in 2000 and was available in each hospital from 2004. Multidisciplinary meetings for the discussion of recalled women were gradually implemented in the hospitals.16 For all cancers treated by neo-adjuvant therapy (either chemotherapy or endocrine therapy), the initial tumour size was derived from breast imaging  prior to the start of this therapy.During a minimum of 2-year follow-up, clinical data and data from diagnostic breast imaging, biopsy specimen and surgical procedures were collected of all recalled women by one of the screening radiologists (L.E.M.D.) and several radiology residents. The radiologist then entered these data in a database that had been constructed for quality assurance of the screening programme in the south of the Netherlands. Breast cancers were categorised into ductal carcinoma in situ (DCIS) and invasive cancers. Lobular carcinoma in situ was considered a non-malignant lesion. The TNM classification (6th and 7th edition) was used for malignant lesions.In this study, we focused on women diagnosed with breast cancer after a second recall for a mammographic abnormality, which had been considered benign at workup after the first  recall. Therefore, the delay in breast cancer diagnosis in these women was at least 2 years. We limited our study to these delays because this allowed us to review the initial and the subsequent screening mammograms to measure tumour size and calculate progression. As we had only access to the radiology reports and the screening mammograms and not to the mammographic images obtained after recall, we could not determine tumour sizes at the clinical mammograms of women with a diagnostic delay of less than 2 years following recall.The database for quality assurance was used to identify women who had been recalled twice between January 1997 and January 2017 and two radiologists  then determined independently whether or not the second recall concerned the same lesion for which a woman had been recalled previously. Discrepant observations between the two radiologists were solved by consensus. For all cases in which both recalls concerned the same mammographic abnormality and the second recall resulted in confirmation of invasive breast cancer, the two radiologists measured the diameter of the suspicious abnormality both at the initial, false-negative recall, and at the second, true-positive recall. These measurements were also done in an independent fashion, followed by consensus reading. Both reviewers knew they reassessed cases characterised by a delay in breast cancer diagnosis. We restricted the reassessment of tumour sizes on the screening mammograms to cohort 2 as, contrary to cohort 1, almost all screening examinations of cohort 2 consisted of two-view mammography which better enabled the radiologists to determine lesion size. Like the screening radiologists, the two radiologists had the availability of all previous screening rounds at the time they reviewed women with a possible delay in cancer diagnosis. To explore the causes of the diagnostic delays, the radiologists reviewed the clinical data  and assessed whether a delay was due to an erroneous BI-RADS classification assignment at workup, a false-negative biopsy result, a patient related delay, or other causes.Ethical approval by our local Institutional Review Board was not required for this observational follow-up study, according to the Dutch Central Committee on Research involving Human Subjects (CCMO). Prior to their participation in the programme, women were asked for permission to use their data for the evaluation of the screening programme and scientific purposes.P value < 0.05 was considered to indicate a statistically significant difference. P values were two-sided. Whenever applicable , the Fisher’s exact test was used.Descriptive statistics were performed using Statistical Package for Social Science 23.0 . The chi-square test was used to test for differences between cohorts with respect to categorical data and for differences between hospitals with respect to delayed breast cancer diagnoses in cohort 2. A T test was used to compare the mean tumour size of invasive cancers in cohort 2 at the false-negative assessment after initial recall with the mean tumour size at subsequent (second) recall when breast cancer was finally confirmed. To test for differences in median delay between the two cohorts, the Mann−Whitney U test was used. Similarity in distributions of both cohorts with respect to delays was tested and comparable distributions were found. Data in cohort 2 were missing in <0.5% of the patients, which in most cases involved the lack of oestrogen or progesterone receptor status due to an insufficient tissue sample. In the analysis of the categorical data, we treated the missing numbers as a separate category to allow a complete case analysis.The dependent samples P < 0.001, Table P < 0.001) and a lower positive predictive value (PPV) of recall (23.2% versus 41.2%).The recall rate was significantly higher in cohort 2  versus 1.2% , P = 0.29). The median delay in cohort 1 was 26.0 months (range 24−97 months), whereas the median delay in cohort 2 was 25.0 months (range 24−97 months). The difference in median delay of the two cohorts was not statistically significant (P = 0.12). In cohort 1, 12 out of the 32 women (37.5%) with a diagnostic delay had their breast cancer confirmed at the next biennial screen, 2 years after their first, false-negative recall. In the remaining 20 women, breast cancer was finally confirmed after a second recall that took place at least 4 years after the first recall. In cohort 2, 66.3% of women (65/98) with a diagnostic delay had their breast cancer confirmed at the next biennial screen .Focusing on a delay in breast cancer diagnosis of at least 2 years after the first recall, 2.2% (32/1421 women) who were diagnosed with breast cancer in cohort 1 experienced this delay, compared to 2.8%  in cohort 2 .Sufficient data in cohort 2 were available to compare the tumour characteristics of breast cancers with or without a diagnostic delay. A smaller proportion of DCIS was found among breast cancers with a delayed diagnosis (9.2% (9/98) versus 20.8% (710/3414), P = 0.33). Invasive cancers also showed a similar histological grade distribution in both groups, with a majority of them classified Bloom & Richardson grade I or II (87.6% (78/89) versus 87.2% (2358/2703), P = 0.19). The majority of invasive breast cancers were lymph node negative in both groups, although there was a slightly larger number of lymph-node-negative cancers in case of a delayed diagnosis (84.3% (75/89) versus 74.9% (2023/2703), P = 0.14). We observed no significant differences in oestrogen-receptor, progesterone-receptor and Her2/Neu receptor status, as well as triple-negative receptor status, between the two groups. Finally, the type of surgical therapy (breast conserving surgery or mastectomy) was also similar for both groups (P = 0.74).Tumour size of invasive cancers was comparable for both groups, with a majority of cancers sized between 10 and 20 mm  was the most frequent cause of a diagnostic delay after the first recall, followed by an erroneous BI-RADS classification given by the clinical radiologists after the first recall . The latter women received a BI-RADS 1  or BI-RADS 2 (benign) classification without additional biopsy. Another seven lesions (21.9%) were classified as BI-RADS 3 at initial assessment and follow-up consisted of imaging only. These seven lesions proved to be malignant after the second recall of these lesions at a subsequent screening round. In one case the surgeon did not follow the radiologists’ advice for additional biopsy and one woman refused biopsy of a suspicious mass. In cohort 2, a majority of the diagnostic delays  was attributed to an erroneous BI-RADS classification after the first recall. A false-negative biopsy after the first recall was observed in 18 cancers (18.4%) with a delayed diagnosis. In 17 of these women , the false-negative biopsy was due to a sample error as repeated biopsy at subsequent recall 2 years later yielded proof of malignancy. In one case, the patient refused a repeated biopsy of a lesion that had grown at follow-up. Another six lesions (6.1%) were classified as BI-RADS 3 at initial assessment and one patient (1.0%) underwent excision of a lesion other than the recalled one and the latter lesion proved malignant after the second recall. An example of delayed breast cancer diagnosis due to previous false negative recall is shown in Fig. P < 0.001, Table P < 0.001).The tumour size in patients with a delayed diagnosis of invasive breast cancer was measured, both at the screening mammogram that initiated the first, false-negative recall and at the screening mammogram that resulted in a repeated recall for the same lesion and the confirmation of breast cancer. The average tumour size was 10.2 mm range, 3−53 mm) at the first recall, compared to 17.3 mm  at the second recall  were widely implemented after 2005 (cohort 2).9 These three studies, however, used different definitions for diagnostic delay. A majority of women had a diagnostic delay of 3−12 months, with only 4, 9 and 10 women respectively with a delay of at least 2 years. We found median delays of 26 and 25 months in the two study cohorts. Our lower proportion of 2.2−2.8% of diagnostic delays on average can be explained by the fact that we merely focused on delays of at least 2 years (until the next screening round) as these delays will probably have a more significant impact on survival and thus being more clinically relevant than shorter delays. Inclusion of shorter delay intervals would obviously increase and probably more than double our number of women with a delay in their breast cancer confirmation, but these delays were outside of the scope of the current study.A few studies have previously reported on delayed breast cancer confirmation after recall at screening mammography, with 4.1−6.5% of women experiencing a diagnostic delay.Most delays in cohort 2, which mainly comprised digital screening mammograms, were due to an incorrect interpretation of radiological findings after the first false-negative recall.20 This type of cancer may result in an infinite diagnostic delay as it may lead to recall and be overlooked, only to have vanished at a later screening and therefore go undetected. On the other hand, these cancers may be difficult to diagnose and may be the ones that are subject to delayed diagnosis. Over-diagnosis and subsequent over-treatment of cancers detected at screening mammography is another issue to be taken into account when dealing with diagnostic delays. Over-diagnosed breast cancers are cancers that would never have become clinical when not detected at screening and would never lead to death. While an infinite delay for these cancers is desirable, they may actually be difficult to diagnose and thus be included as cancers with a delay in diagnosis—and from a healthcare system view, we might well think that such delay should be avoided. Finally, we did not include interval cancers that were missed at the latest screen in this study as having a delayed diagnosis, since they do not participate in the next screening round. All in all, it is difficult to assess whether estimates from this study are likely to under- or over-estimate the actual occurrence of delay in diagnosis.23The exact impact of a delay in breast cancer confirmation on patient survival is a controversial topic, with studies yielding heterogeneous outcomes. Little is known on the natural history of breast cancer and researchers have raised the possibility of spontaneous breast cancer regression.24 We found a decrease in mean delay from 45 months to 33.8 months between the two cohorts and the majority of delays in cohort 2 were diagnosed at the next biennial screen, 2 years after the first, false-negative recall. However, it is likely that the delay of 33.8 months is somewhat flattered as cohort 2 had a shorter overall follow-up period.Most studies focus on delayed diagnosis of symptomatic breast cancer and breast cancer in younger women, which differs from the asymptomatic breast cancers found at screening mammography in women aged over 50.25 Moreover, the high inter-observer variation for the classification of microcalcifications at mammography may also encourage radiologists to rather biopsy than follow-up these lesions.27The differences in tumour histology between cancers with or without a diagnostic delay in our study may have a multi-factorial explanation. Only a few diagnostic delay cases comprised DCIS. Improvement in biopsy techniques have made biopsy of calcifications faster, easier, and provides considerable amounts of tissue for pathological analysis.30 Over the past two decades, screening mammography and clinical breast imaging have been subject to many changes, with the aim to improve the accuracy and efficiency of breast radiology. Despite the availability and proven benefit of state-of-the-art imaging, specialised outpatient clinics for breast care, specialised breast nurses and multidisciplinary team meetings and the continuous improvement of biopsy techniques, a delayed confirmation of breast cancer following recall at screening mammography continues to be a point of concern in the Netherlands and probably in other countries as well. An Italian biennial screen-film breast screening programme, conducted from 1992 to 2001, also reported a diagnostic delay in breast cancer diagnosis of more than 2 years in 2.1% of confirmed breast cancers, comparable to the 2.2% we observed in the era of screen-film screening.We observed a shift in the distribution of mean tumour size among cancers with a long diagnostic delay. This phenomenon emphasises the need of a swift confirmation of breast cancer in order to prevent a further progression of the disease, as is stressed in other studies as well.35 In contrast to the screening mammography programme, the Dutch nationwide screening programmes for cervical cancer and large bowel cancer have embedded a quality assurance protocol with respect to structured assessment after recall. Quality assurance is partially based on accreditation, e.g., only accredited endoscopists are allowed to perform diagnostic colonoscopy after a positive screening result. Also, cervical smears are assessed in only a handful of accredited laboratories. Screening radiologists who are involved in the Dutch breast cancer screening programme have to obtain an accreditation by a national training programme prior to their employment as screening radiologists; they have to attend breast radiology courses at regular intervals and screening radiologists receive feedback on their performance continuously. These kinds of quality assurance measures are not mandatory at all for radiologists who perform the radiological assessment after recall. We found a worrisome variation in the proportions of delayed breast cancer cases among the hospitals handling the recalls in our screening region. Differences among hospitals may not be explained by the type of hospital  or case load, but probably by the experience of the clinical breast radiologists, as the lowest proportion of breast cancer delays was observed in a smaller non-teaching hospital. Moreover, there are similar diagnostic pathways due to national guidelines and no notable differences in diagnostic imaging capabilities. To our knowledge, no (recent) data are available on differences in diagnostic delay of breast cancer diagnosis in teaching versus non-teaching hospitals. Earlier studies on difference in breast cancer survival between teaching and non-teaching hospitals showed no statistically significant differences, although these results have to be interpreted with care as data on delayed diagnosis is not reported in these studies.39Similar to screening mammography, screening programmes for other diseases have also shown benefit in terms of a reduction in cancer-related morbidity and mortality.43 The use of breast assessment centres has also been studied and might be the first step towards centralisation of breast cancer care in the Netherlands.24 However, due to high patient volumes and concerns for the impact on demographics, centralisation of care in a limited number of hospitals in the whole country does not seem feasible. Nevertheless, cooperation between hospitals on a regional scale could prove beneficial, with implementation of adequate quality assurance, accreditation of radiologists handling referrals, and regular audits to continuously improve breast care in these hospitals.46To reduce the variations in diagnostic errors and delay an accreditation, similar to the one that is applied to screening radiologists, may be beneficial for radiology departments assessing recalled women and it could help to improve diagnostic accuracy, reduce diagnostic errors and prevent unnecessary delays. Perhaps, centralisation of breast care may also further improve the current standard of breast cancer diagnosis and treatment. Studies have shown that centralisation of complex and high-risk treatment in accredited hospitals may have a beneficial effect on the treatment outcome and survival.There are several strengths and limitations to this study. To our knowledge, this is the first study focusing on long delays in breast cancer confirmation after recall for a screen-detected mammographic abnormality. A large series of consecutive screening mammograms were included during two decades of screening mammography, with an almost complete follow-up of recalled women. On the other hand, certain outcome measures should be interpreted with care due to the relatively small sample size of patients with a delayed breast cancer diagnosis. Also, screening mammography programmes are constantly subject to changes and the design of the programmes and the assessment of recalled women differ between countries. These parameters influence diagnostic accuracy (both positively and negatively) and may limit extrapolation of our findings to other screening programmes.We cannot rule out the rare occasion that a lesion with a delayed confirmation of malignancy was in fact precancerous rather than cancerous at the time of the initial assessment. However, the majority of delayed cases showed suspicious mammographic characteristics that should have been biopsied at initial workup. Moreover, in women with benign biopsy outcome at the initial workup, sampling errors will likely have caused the diagnostic delays as only benign pathological findings and no high-risk lesions, that are associated with an increased breast cancer risk in the future , were reported at this workup.In summary, we found that the proportion of women who experience a long delay in their breast cancer confirmation following recall at screening mammography did not decrease during 20 years of screening, despite improvements in diagnostic modalities, biopsy procedures and multidisciplinary approaches. These delays worsen the tumour size and may negatively influence prognosis of survival. Over-estimation of the negative impact on survival of a delayed breast cancer diagnosis may be due to over-diagnosed breast cancers and further study is needed on this subject. Further study is also necessary to determine whether accreditation of hospitals and/or centralisation of breast care may lower the number of diagnostic delays, especially in hospitals with larger proportions of women facing a diagnostic delay. We suggest that quality assurance not only covers the screening mammography programmes, but also the hospitals handling the workup of recalled women.