This work attempted to construct a new metal artifact reduction (MAR) framework in kilo-voltage (kV) computed tomography (CT) images by combining (1) deep learning and (2) multi-modal imaging, defined as MARTIAN . Most CNNs under supervised learning require artifact-free images to artifact-contaminated images for artifact correction. Mega-voltage (MV) CT is insensitive to metal artifacts, unlike kV CT due to different physical characteristics, which can facilitate the generation of artifact-free synthetic kV CT images throughout the first network (Network 1). The pairs of true kV CT and artifact-free kV CT images after post-processing constructed a subsequent network (Network 2) to conduct the actual MAR process. The proposed framework was implemented by GAN from 90 scans for head-and-neck and brain radiotherapy and validated with 10 independent cases against commercial MAR software. The artifact-free kV CT images following Network 1 and post-processing led to structural similarity (SSIM) of 0.997, and mean-absolute-error (MAE) of 10.2 HU, relative to true kV CT. Network 2 in charge of actual MAR successfully suppressed metal artifacts, relative to commercial MAR, while retaining the detailed imaging information, yielding the SSIM of 0.995 against 0.997 from the commercial MAR. Many of the patients have metal implanted for bilateral hip prosthesis, fixation of the spine, dental filling, etc. The high atomic materials yield severe streak artifacts, beam hardening, and photon starvation around those in the reconstructed CT images. The presence of metal artifacts mainly impairs both accuracy and efficiency of treatment planning as the metal-contaminated image could preclude accurate contouring and dose calculation in treatment planning, leading to the necessity of labor-intensive density override.Computed tomography (CT) is a viable imaging modality for radiation therapy, which is used to contour tumors and normal organs, perform treatment planning with dose calculation, and conduct image-guidance for the actual treatment. The tube voltage in generating X-ray ranges from 80 to 150 kV for planning fan-beam CT and cone-beam CT (CBCT) in general. It benefits from higher contrast due to the photoelectric effect prevalent in the kV energy range. However, the kV-CT should be substantially sensitive to materials with a high atomic number, such as metal25. The most widely used technique is to identify and remove the metal-associated region in projection (processed projection), finally replacing the missing region by linear interpolation, also called the in-painting method12. However, the linear interpolation in projection datais likely to neglect the edge information in metal trace, and introduce a new imaging artifact due to lack of smooth transition between the original and missing regions. To mitigate the transition effect throughout the linear interpolation, normalized MAR (NMAR) by employing prior image information was proposed17. It was hypothesized that normalizing the processed projection after removing the metal-associated region would be able to attain less modulated intensity distribution, which benefit for suppressing a possibility of introducing new imaging artifacts. It could reduce the drawback of the conventional linear interpolation, while the thresholding-based technique might still lead to wrong tissue classification in generating the prior image necessary for the relevant normalization. The iterative reconstruction applied to the processed projection was an alternative to it, which attempts to control the potential imaging artifacts by regularizing such edge-preserving transformations as total-variation, and wavelet with the statistically weighted projection information27. Due to highly complicated pattern of metal-driven artifacts in clinical cases, a single technique could not completely address the pitfalls thus far, so no standard technique has been built yet.In an effort to minimize the imaging artifacts, a variety of metal artifact reduction (MAR) algorithms have been developed29. Convolutional neural networks (CNN) are powerful deep learning techniques inimage processing35. Several studies have developed deep learning-based algorithms for MAR in the projection or image domain that focused on improving the accuracy of linear interpolation and normalization methods41. Applying the deep neural network to artifact correction including MAR has been considered challenging, as most of the currently available deep neural networks are subject to supervised learning that requires both metal artifact containing image (input) and metal artifact-free image (output) for the network training. A recent study suggested generating metal artifact-free digital phantom images from genuine kV CT images by manipulating the projection data of the metal-free CT images39. However, the digital phantom image could not completely simulate the actual metal artifacts occurring in the conventional kV CT image.Deep learning is a recent breakthrough that shows promising results in many diagnostic and therapeutic applicationsThis work presents a novel framework for MAR in kV CT image based on two-serial convolutional neural networks, named Metal Artifact Reduction throughout Two-step sequentIAl Networks (MARTIAN). To address the underlying challenge from the supervised learning system above, the principle of multi-modal imaging was used to generate artifact-free synthetic kV CT images from a different imaging modality (other than kV CT) that is robust to the metal artifact. To achieve this, mega-voltage (MV) CT was employed to generate artifact-free kV CT in the first deep neural network. The subsequent network used the resulting artifact-free image for actual artifact correction.As illustrated in Fig. Image translation is one of the most frequent applications of the CNNs, which can be also applied to generating the synthetic kV CT images from the MV CT images. As the MV CT is a metal-artifact-free imaging modality, the resulting translated images are likely to exclude metal artifacts. The generated artifact-free images could be the output images of the deep neural network against the metal-contaminated input images. This is the basic motivation of the proposed framework, consisting of the two-step sequential deep convolutional neural networks for MAR (MARTIAN), as illustrated in Fig. 45 was demonstrated to work out for image translation, the GAN architecture was applied to both networks. Figure 46. The generator had three layered down- and up-sampling blocks and 8-layered residual blocks at the bottleneck, while the discriminator had five layered down-sampling blocks, followed by a sigmoid function. Indeed, U-Net and its variations are currently the most popular. The network with skip-connection, generally used for U-Net, was not engaged in the generator as it did not fully suppress the streak artifact in the generated kV CT images. The CNN without skip-connection embedded in GAN architecture was more powerful in this application. All the learning tasks were performed with a 2D paired image dataset. The loss function of the generator was defined as a combination of adversarial loss of the conventional GAN, L1-loss, and structural similarity (SSIM) loss, as expressed in Eq.  is the generated image of the given image in GAN, so that G(M) is the predicted synthetic kV CT image.Two sequential networks technically conducted image translation. As the generative adversarial network (GAN)All research was performed in accordance with relevant guidelines and regulations. This study protocol was approved by the ethics committee/ institutional review board of the Yonsei University Severance Hospital, Korea (4-2022-0384), which waived the need for informed patient consent to the use of patient images. The patient cohort for training Networks 1 and 2 consisted of 90 brain and head-and-neck (HN) cancer cases who were treated by radiation therapy throughout two advanced TomoTherapy units . Of 90 scans, 72 scans had metal-contaminated kV CT slices. Network 1 trained the pairs of MV and kV CT image from 90 scans . Then, the trained Network 1 generated the synthetic kV CT images for 90 scans. Of 90 scans, Network 2 employed 72 scans containing metal-contaminated kV CT slices in network training. To validate the performance of Network 2 in charge of the actual MAR process, the additional 10 planning kV CT scans, including the MAR processed images provided by the Canon CT SEMAR algorithm, were applied to evaluating Network 2.3, and 0.76 × 0.76 × 4 mm3, respectively, where both MV and kV CT images had the same 2D matrix, 512 × 512. The kV CT images were resampled to the MV CT images, such that the MV and kV CT images have the same size and resolution. The paired MV and kV CT images passed through the rigid and cubic B-spline-based deformable image registration (DIR) to further enhance the similarity. The DIR was performed by an open-source software Plastimatch (http://www.plastimatch.org) with a three-layered multi-resolution approach, where mutual information (MI) and mean-squared-error (MSE) were used as image similarity metrics in DIR for multi-modal and monomodal images, respectively.We picked out the patient cases treated by TomoTherapy units that obtained MV CT images for image-guided radiotherapy. The frequency of MV CT acquisition varies, depending on treatment type, which were obtained in the first fraction of treatment in most cases. The MV CT scan at the first fraction of the treatment was adopted for training Network 1 to maximize the image similarity between the planning kV CT and the MV CT images. The planning kV CT scans were acquired by Canon Aquilion LB CT simulator . The voxel spacing of the planning CT and TomoTherapy MV CT was 1.06 × 1.06 × 3 mmAs stated above, the actual MAR processing was performed in Network 2, while Network 1 was no longer engaged. Hence, to succeed in the MAR procedure, the metal-artifact-free images generated from Network 1 had to be as similar to the given kV CT images as possible, except for the metal contaminations. To achieve the goal, we conducted pre-processing (excluding kV CT image with high intensity > 3000 HU)) and post-processing  in training and inferring Network 1. Some kV CT images in Network 1 had metal artifacts unlike the paired MV CT images, which generated undesirable synthetic kV CT images. As a pre-processing, Network 1 trained the 2D slices with the MV-kV CT pair of images whose maximum kV CT intensity in the Hounsfield Unit (HU) is less than or equal to 3000 HU. Then, inference of Network 1 encompassed the removed 2D kV CT image slices (> 3000HU). The generated kV CT images did not describe the high intensity regions (> 3000 HU), which were overridden by those in the corresponding synthetic kV CT voxels as a post-processing. Furthermore, additional DIR operation was executed for the synthetic and true kV CT images. Unlike the initial DIR for multi-modal MV and kV CT images, the additional DIR for mono-modal images was expected to further enhance the image similarity in the synthetic and true kV CT images used for training Network 2. The pre- and post-processing operations were specified in Fig. 000 HU) ahttp://www.tensorflow.org) and Python 3.6 (http://www.python.org) on a personal workstation with an accelerated GPU (Nvidia GPX Titan X). The input and output images of the deep neural network were 2D axial images with 512 × 512 voxels. The intensity of the original CT images defined as an integer in the Hounsfield Unit was normalized such that the intensity of the input of the network ranged from -1 to 1 for training. The Adam optimizer was used for training the network with a mini-batch size of 3 by a learning rate of 2 × 10–4. The number of epochs was set to be 100 for both Network 1 and Network 2, which was empirically determined based upon the loss function values from validation and test dataset combined with the learning rates. To increase the number of images in training the network, we augmented the paired MV and kV CT images (used for training phase) by flipping (left–right), rotating (randomly from −10° to 10°), and translation , and SSIM. The image similarity metrics were quantified in 2D slice as the images were trained in the 2D-based CNN. Also, the image quality of the generated metal-artifact-reduced images from Network 2 was assessed in two aspects in this work: degree of metal artifact removal in a qualitative manner, and preservation of detailed imaging information post-correction (tissue and tissue-bone contrasts) in a quantitative manner. The second aspect, preservation of image details such as imaging contrast and soft-tissue information, was quantitatively analyzed in MAE and SSIM for 2D slices with high maximum HU (> 3000), while not encompassing severe metal artifacts. The extent of metal artifact suppression, which is the first aspect, was visually investigated for 2D slices, including explicit metal-contaminated artifacts, compared against the true kV CT, and the artifact-corrected image from commercial software, provided by Canon CT SEMAR algorithm.Figures Figure Network 2 trained the convolutional neural network with pairs of two kV CT images: metal-contaminated, original kV CT and artifact-free synthetic, post-processed kV CT images. Figure The difficulty of quantitative evaluation for the MAR framework lies in the fact that no ground-truth kV CT image perfectly removed the metal-associated artifacts. The visual inspection helped look into how the proposed framework could remove the metal artifacts. It did not, however, proceed to confirm how the MAR framework affected the soft tissue or bone regions adjacent to the metal-associated elements. Thus, the quantitative analysis to see the effect of the MAR framework on the soft tissue and bone regions adjacent to the metal-associated elements was performed in the selected 2D slices with high-density (HU > 3000), but not containing severe metal artifacts such as dark bands and streak artifacts, as seen in the first row of Fig. 44. The direct MV CT to kV CT conversion discussed in such literatures relied on analytic approach that match the image intensity between MV and kV CT images. The resulting images could possess the disadvantages of the MV CT images, which are vulnerable to the scattering, followed by much less contrast than kV CT images. The learning-based method that generates the artifact-free kV CT image first from MV CT images may overcome these shortcomings. To our best knowledge, incorporating the MV CT into artificial intelligence (AI)-driven MAR has not been studied.Deep learning with a convolutional neural network has become a powerful computational tool for medical image processing, such as segmentation, reconstruction, and translation. However, stretching it to artifact correction remains challenging as most of the network architectures in CNN are categorized as supervised learning that demands the artifact-free image as an output in training a network for artifact correction. The notion of multi-modal imaging with MV CT was adopted for reducing metal artifacts in the kV CT images, expecting the different physical characteristics of each medical imaging modality to be beneficial for overcoming the underlying shortcoming of supervised learning. By getting MV CT images robust to metal artifacts involved in deep neural network, we constructed a new two-step sequential network, named MARTIAN. The first deep neural network (Network 1) successfully generated synthetic metal artifact-free kV CT images from MV CT images, which were plugged into the output images for the corresponding metal-contaminated original kV CT images in training the subsequent second network (Network 2). Bringing MV CT image for MAR of kV CT image is not a new idea. Several previous studies tried to reduce metal artifacts of the kV CT using the MV CT imagesThe framework of MARTIAN, where the second network in charge of actual artifact correction takes the synthetic images produced from the first network, was able to encounter challenges in the reliability of the artifact correction. Namely, the first network, Network 1, generating the synthetic kV CT from MV CT images may not be prepared for training Network 2 for the actual MAR. We specified the obstacles in two aspects. First, when training Network 1 in this work, the slices containing metal (high density) regions were excluded as those pairs of images did not offer any benefit in generating the artifact-free kV CT images. In contrast, the slices encompassing metal elements are required to conduct MAR.Intensity override easily solved this problem, in which the high-density region of generated synthetic kV CT images was overridden with the true-kV CT images when applying Network 1 to the metal-containing 2D slices. Second, before training Network 1, the DIR between MV and kV CT images was conducted. The multi-modal DIR does not generally guarantee registration accuracy, possibly leading to the sub-optimal image similarity in the generated synthetic images against the true kV CT. Thus, additional DIR process was performed with the synthetic, density-overridden kV CT and true kV CT images. The quantitative analysis of image similarity between the synthetic and true kV CT images demonstrated that the two extra processing was effective, which enlarged SSIM from 0.967 to 0.997 and lowered MAE from 56.6 HU to 10.2 HU.Network 2 conducted training with the metal-contaminated true kV CT images as input, and synthetic, additionally processed kV CT images as output. Importantly, the images employed in training Network 2 were not overlapped with those trained for Network 1. The trained Network 2 was tested with the ten independent testing cases. As stated, the performance of the proposed MAR strategy was evaluated in 1) degree of reduction of metal artifacts for 2D slices with explicit metal artifacts, and 2) preservation of detailed imaging information for 2D slices without explicit metal artifacts. Throughout quantitative analysis for the second criterion, compared against the commercial MAR, the resulting images from MARTIAN had very competent results in image similarity to the true kV CT. The slight compromise in image quality was expected as the network was inherently enforced to remove the unwanted metal artifacts. The network essentially emphasizing the elimination of metal artifacts would have affected any other regions than the metal-associated elements, finally mitigating the image contrast or details slightly. Regarding the first criterion, in the extent of metal artifact suppression, our proposed strategy was superior to the commercial software, Canon SEMAR. The commercial software controlled the metal artifacts well in many testing cases. The degree of reduction, however, was inconsistent, varying from case to case. The remaining steaking artifacts caused troubles in image quality at times. It was important to note that the proposed framework performed consistently, and eliminated the metal artifacts in kV CT more effectively, while not damaging the detailed soft-tissue information adjacent to the metal elements.The proposed work has a couple of limitations to be discussed. The verification of the proposed framework must be challenging as there were no ground-truth kV CT images due to the same reason that made the artifact correction challenging throughout supervised learning. Thus, the resulting artifact-free kV CT images were visually investigated against true kV CT images and MAR-processed images from the commercial software. The quantitative analysis was executed for the metal-artifact-free slices only. Second, all the frameworks developed in this work paid attention to the application of the brain or head-and-neck kV CT scans. Generally, the deep convolutional neural network is likely to conform to what types of data were used in training. Thus, site-specific training scheme would be required to extend this framework to the other body sites. Third, the performance of the deep neural network highly depends on the magnitude of data. This work employed 90 and 72 cases for training Network 1 and Network 2. The number of 2D slices available for Network 2 got more reduced. Though the number of samples was partially compensated by augmentation, it might have constrained the performance. To be able to enhance the accuracy and be clinically useful, the training data would have to be bigger.Comparing the proposed work to the previous deep learning-based MAR approach, which generates artificial artifact-contaminated images, would be interesting. It is believed that our proposed work would be the more compelling strategy for MAR in several aspects. The previous learning-based approach might not be able to describe non-linear and unpredicted features of the artifact distributions on the kV CT images. This strategy was possible by manipulating the sinogram of the artifact-free image, while it may not be achievable for the other types of medical imaging modalities. It implies that the approach is limited in expandability besides reality. Contrarily, our proposed model focused on generating artifact-free synthetic images from actual clinical data acquired by a different modality, while leaving the original kV CT images intact. Additional post-processing was conducted to further reduce the possible propagation of discrepancy between multi-modal images, yielding a high degree of image similarity. Given multi-modal images that are mutually beneficial in physical characteristics available, the two-step framework might be more powerful with respect to artifact correction of the medical images.MARTIAN was characterized as a combination of a deep convolutional neural network and the notion of multi-modal imaging for the purpose of metal artifact suppression in kV CT. MV CT images known to be robust against metal artifacts unlike kV CT were involved in the framework to produce artifact-free kV CT images, which were brought into the subsequent deep neural network for actual MAR. To make artifact correction more appropriate, the synthetic kV CT images throughout the first network were additionally processed, leading to enhancing image similarity to the true kV CT images. Training the second network with such pairs of metal contaminated and artifact-free kV CT images successfully suppressed metal artifacts in kV CT, which outperformed the commercial MAR software without compromising the image contrast and detailed information.


Medical education has drastically transformed during the COVID-19 pandemic. Measures such as adopting telemedicine visits, minimizing the number of trainees on service, discontinuing external rotations, and converting in-person to online didactics have been broadly and swiftly implemented. While these innovations have promoted greater interconnectivity amongst institutions and made continuing medical education possible, international exchange programs in medical education are still largely disrupted. In response to the changing guidelines and restrictions necessitated by the COVID-19 pandemic, the authors used Kern’s six-step approach to design and implement a virtual curriculum to replace the in-person activities of the 2020–2021 Neurology Peru-Rochester exchange program (NeuroPro). Twenty-seven trainees participated in this virtual adaptation. The average daily attendance was ≥85% and the program was rated 9/10 on average in a feedback survey (63% response rate). The median percentage of correct answers during the pre-test was 64% and it increased to 79% during the post-test (P = 0.003). Virtual adaptation of international exchange programs in medical education is feasible to safely continue international collaborative efforts to promote symbiotic building of local expertise and cross-cultural exchange during the ongoing COVID-19 pandemic and beyond. Neurological disorders are the leading cause of disability worldwide. While nearly 80% of the neurological disease burden comes from low- and middle-income countries, high-income countries have about 70 times the neurological workforce as low- and middle-income countries . With thThe Neurology Peru-Rochester exchange program (NeuroPro) is an international exchange program established in 2019–2020 by institutional agreements between the University of Rochester School of Medicine and Dentistry (URSMD), Rochester, New York, the Universidad Nacional de San Agustin de Arequipa (UNSA), Arequipa, Peru, and the Universidad Nacional Mayor de San Marcos (UNMSM), Lima, Peru. The goals of the NeuroPro are to promote global equity in medical education, foster cross-cultural exchange and cultural humility, and engage in the symbiotic building of local expertise through cultural immersion and educational innovation. The NeuroPro was originally designed to be an annual two-week to four-week, in-person, international elective consisting of clinical and teaching components. The program is designed for 4–6 selected trainees and two faculty members from the URSMD to travel to Peru for two-week rotations , and 2–4When the COVID-19 pandemic began, institutions across the world had to reimagine training in order to continue medical education while minimizing risks. In response to the changing guidelines and restrictions necessitated by the pandemic, we used Kern’s six-step approach to design and implement a virtual curriculum to replace the 2020–2021 in-person activities of the NeuroPro: 1) problem identification and general needs assessment, 2) targeted needs assessment, 3) goals and measurable objectives, 4) educational strategies, 5) implementation, 6) evaluation and feedback [Participating institutions selected four last-year medical students and three neurology residents from the URSMD, eight last-year medical students and two neurology residents from the UNSA and 10 last-year medical students from the UNMSM. We obtained approval from the institutional review board of the UNSA.Adopting video visits, minimizing the number of trainees on in-person service, and converting in-person to online didactics are measures that have been broadly and swiftly implemented to continue medical education during the COVID-19 pandemic. Tele-education is non-inferior to in-person lectures and confers the benefit of allowing synchronous  and asynchronous exposure to a variety of educators and topics across institutions 6. While To ensure that the curriculum centered on the interests and needs of learners from participating institutions, planning meetings were held every two weeks after the trainee selection process was finalized. Topics of interest were proposed and selected during open discussions between participating faculty and trainees . Some ofAdditional meetings were held amongst participating faculty and trainees to define specific objectives and facilitate lesson planning for the identified topics of interest . To obtaThe virtual curriculum was designed to include four-hour synchronous activities per day over 10 days (two weeks). Based on the topics of interest , activitSynchronous virtual activities were held between March 1 and March 12, 2021, from 4–8 p.m. EST (3–7 p.m. Peru) using the platform Zoom. Selected materials and articles were shared electronically with trainees and faculty before each activity on a daily basis. To adapt to the diverse language skills of participants, a mixture of English and Spanish language was allowed for communication. Participants were encouraged to use the “raise hand” and Chat functions in Zoom to ask questions, make comments or share content during the program.Average attendance was 85% for the general segment and 90% for the neurology segment. We used the Wilcoxon signed-rank test to compare the median percentage of correct answers per question during the pre-test and post-test. This percentage increased from 64% during the pre-test (interquartile range 50–74%) to 79% during the post-test (interquartile range 61–86%) (P = 0.003). Seventeen of the 27 trainees completed the anonymous feedback survey (63% response rate). Average rating of the program was nine on a 1–10 point scale. The most frequently mentioned positive aspects were the live tele-neurology consultations, cross-cultural discussions surrounding healthcare delivery systems, and the opportunity to network with international faculty and trainees. The most frequently mentioned negative aspects were screen/Zoom fatigue and issues with internet connection.Virtual international programs in medical education have been piloted in various capacities 8. We folAnother limitation we encountered was the intermittent unreliability of internet connection for some participants. This issue was particularly troublesome during the live tele-neurology consultations. Utilizing stable and higher-speed internet connections facilitated by participating institutions could be considered for future programs. Virtual platforms that require less internet bandwidth for synchronous video capabilities could also be considered.We decided to use case-based learning presentations to cover most of the neurology topics. As previously demonstrated, these activities promoted engagement and facilitated discussions during this virtual program. The addition of skills workshops, simulations and standardized patient encounters in breakout rooms was feasible and provided the opportunity for live demonstrations that increased participant engagement. The concomitant use of the “raise hand” and Chat functions in Zoom also increased active participation during the program.The idea of universal adoption of tele-neurology has been recently embraced due to pandemic-related policy changes. Thus, we deliberately included live tele-neurology consultations to equip students with skills unique to tele-neurology visits. These activities were considered a strength by most participants as they provided real-world opportunities to consolidate knowledge while contrasting social determinants of health and health care delivery systems. While it is unclear the exact role that tele-neurology will play beyond the pandemic, it has been projected that there is likely to be a more permanent transition to tele-health. The ever-expanding digital infrastructure could allow this transition to be feasible for the growing field of global health .During the ongoing COVID-19 pandemic and beyond, international exchange programs in medical education could be adapted and implemented in a virtual fashion. Virtual modification of these programs is feasible to safely continue international collaborative efforts to promote symbiotic building of local expertise and cross-cultural exchange in medical education.The additional files for this article can be found as follows:10.5334/aogh.3663.s1Supplementary Material 1.Multiple choice questions used during the pre-test and post-test of the NeuroPro 2020–2021.10.5334/aogh.3663.s2Supplementary Material 2.Questions of the feedback survey used to evaluate the NeuroPro 2020–2021.


Despite evidence suggesting an association between influenza infection and increased risk of acute myocardial infarction (AMI) in the older adult population (aged 65 years or above), little is known about its near-term risks in middle-aged adults (aged 45 to 64 years). This study aims to estimate the risks of and association between severe influenza infection requiring hospitalization and subsequent AMI within 12 months in middle-aged adults.This is a retrospective case-control analysis of territorywide registry data of people aged 45 to 64 years admitting from up to 43 public hospitals in Hong Kong during a 20-year period from January 1997 to December 2017. The exposure was defined as severe influenza infection documented as the principal diagnosis using International Classification of Diseases codes and non-exposure as hospitalization for orthopedic surgery. Logistic regression was used to analyze the risk of subsequent hospitalization for AMI within 12 months following the exposure.Among 30,657 middle-aged adults with an indexed hospitalization, 8,840 (28.8%) had an influenza-associated hospitalization. 81 (0.92%) were subsequently rehospitalized with AMI within 12 months after the indexed hospitalization. Compared with the control group, the risk of subsequent hospitalization for AMI was significantly increased . The association remained significant even after adjusting for potential confounders . Patients with a history of hypertension, but not those with diabetes mellitus, dyslipidemia or atrial fibrillation/flutter, were at increased risk .Subsequent hospitalization for AMI within 12 months following an indexed respiratory hospitalization for severe influenza increased nearly two-fold compared with the non-cardiopulmonary, non-exposure control. Recommendation of influenza vaccination extending to middle-aged adult population may be justified for the small but significant increased near-term risk of AMI. Influenza is a seasonal communicable disease caused by influenza A or B virus with tropism for the respiratory tract that transmits via direct contact with fomites, self-inoculation and the airborne route . Past inIn spite of early as well as late severe complications from influenza infection that resulted in hospitalizations and deaths , 4, its The Centers of Disease Control and Prevention indicated that persons aged 50 years or above belong to the priority groups for seasonal influenza vaccination because they are at higher risk for medical complications attributable to severe influenza than general population . PreviouAn electronic health record and clinical database system operated by the Hospital Authority that served up to 43 Hong Kong public hospitals was used to query discharge diagnoses and outcomes based on International Classification of Disease, Ninth Revision (ICD-9), codes. The relevant socio-demographic and clinical data were compiled on individuals aged 45–64 years (defined as ‘middle-aged’) who had been hospitalized during the period between January 1, 1997 and December 31, 2017. To protect patient privacy, the Hospital Authority had assigned an investigator-nonidentifiable unique alias code number to each individual before releasing the data file for this research. This study was approved by the Survey and Behavioral Research Ethics Committee of the Chinese University of Hong Kong with the wavier of informed consent because the data was deidentified.The exposed group included middle-aged adults who were hospitalized between January 1997 and December 2017 with the principal diagnosis of influenza  that constituted the indexed hospitalization. The non-exposed comparator group comprised middle-aged adults without a cardiopulmonary indication for hospitalization who were admitted with the principal diagnosis of severe orthopedic problems, including fracture of the neck of femur, fracture of the humerus, and fracture of tibia and fibula, that required surgical intervention. Patients admitted for orthopedic surgery were included in the non-exposed group as their conditions were usually independent of influenza or related problems.Patients’ information related to hospitalization for AMI in the first 12 months following index hospitalization was retrieved. The outcome of subsequent hospitalization for AMI was considered as positive if the patient had a principal diagnosis of new-onset AMI based on the ICD-9 code 410.Data were summarized and presented using appropriate descriptive statistics. Length of the indexed hospital stay was square root-transformed before subject to inferential analysis. Subsequent hospitalization for AMI within one year after discharge from the indexed hospitalization, the outcome of the study, was coded as a binary variable (Yes/No). To avoid computational convergence problems, logistic regression was used, instead of log-binomial regression, to estimate the risk ratio of subsequent one-year hospitalization for AMI of the exposed group  versus the non-exposed group . Unadjusted and adjusted logistic regression analyses were conducted with adjustment for demographics and medical conditions in the clinical history. Odds ratios (ORs), which yielded a good approximation of the risk ratio of a rare outcome, were calculated. Specifically, a hierarchical approach was used for the adjusted analyses using the following batches of potential confounders successively included in the adjusted models: (a) age, sex, length of the indexed hospital stay, and medical history of hypertension, diabetes mellitus, dyslipidemia, and atrial fibrillation or atrial flutter, and (b) whether or not the indexed hospitalization occurred after the H1N1 pandemic (April 2009) or in winter (November to March) and non-winter (April to October) seasons. We did not consider the time effect in the adjusted models because there was no notable trend or cycle in the incidence of subsequent hospitalization for AMI over the 20-year observation period. All the statistical analyses were performed using SAS 9.4  and all tests were 2-sided at a 5% level of significance.Of 30,657 middle-aged adults hospitalized during the study period, 8,840  had influenza and 21,817  identified as the non-exposed control had undergone orthopedic surgery. The median length of hospital stay was 3 and 7 days for the influenza and the control group, respectively. The control group had fewer cardiovascular comorbidities including hypertension, diabetes, dyslipidemia, and atrial fibrillation or atrial flutter compared with the influenza group .Within 12 months after the index respiratory hospitalization, a total of 81 (0.92%) patients were admitted to the hospital with new-onset AMI. Among those hospitalized for AMI, there was no significant difference in the length of the index hospitalization  compared to those without subsequent AMI hospitalization. The incidence rate of subsequent 12-month AMI hospitalization was significantly increased in older male patients with a history hypertension, diabetes mellitus, and dyslipidemia . The incAs shown in This study has leveraged the availability of a 20-year territorywide data set from the Hong Kong public healthcare system to examine the association between prior influenza infection and subsequent new-onset AMI hospitalization in middle-aged adults. The incidence of subsequent hospitalization for AMI in the first year after index hospitalization increased nearly two-fold in the exposed compared with the non-exposed control . Our findings suggest that influenza infection can heighten the subsequent 12-month risk of AMI in a small (0.92%) but significant proportion of middle-aged adults.Our results concur with previous studies reporting increased hospitalization for AMI during the seasonal influenza outbreak period  and an iPrevious studies have reported an epidemiological link between influenza infection and cardiovascular events predominantly in the older adult populations , 7, 10. While the viral pathogenic mechanisms remain poorly understood, Muscente and De Caterina recently provided a comprehensive review summarizing the multiple pathways including a heightened inflammatory response, altered vasomotor tone, endothelial dysfunction, platelet aggregation, and thrombus formation that could predispose to acute coronary syndrome and atherothrombosis in patients with atherosclerotic cardiovascular disease . AlthougInfluenza vaccination is the primary strategy to reduce the lethality and morbidity of influenza infection . InfluenThe use of an unabridged, 20-year territory-wide data set retrieved from an electronic health record and clinical database system is a major strength of this study. However, there are limitations including the possibility of unmeasured factors, or latent variables, that could affect our estimates despite best efforts in adjusting for potential confounders. Moreover, a variety of clinical and laboratory tests values and data were not accessible or available, including influenza virus type and serotype, uptake of influenza vaccines, and longitudinal inventory of medications, among others. Other environmental  factors that were not studied could affect the accuracy of interpretations, since both influenza epidemic and AMI exhibit seasonal patterns that may be modulated by the environment. Nevertheless, we have adjusted for seasonal effects  and other demographic and clinical factors, where appropriate. Another study limitation is our focus on an inpatient population with severe influenza infections that likely represented a minority of the territory’s population, as most infected individuals probably had mild or no symptoms and were not admitted into hospitals . The finThis territorywide study adds to a growing body of evidence that severe influenza infection requiring hospitalization increases subsequent risks for future AMI by nearly two-fold in middle-aged adults, particularly in patients with preexisting hypertension.


Behavioural and cognitive interventions remain credible approaches in addressing loneliness and depression. There was a need to rapidly generate and assimilate trial-based data during COVID-19.We undertook a parallel pilot RCT of behavioural activation  for depression and loneliness . We also assimilate these data in a living systematic review (PROSPERO CRD42021298788) of cognitive and/or behavioural interventions.Participants (≥65 years) with long-term conditions were computer randomised to behavioural activation (n=47) versus care as usual (n=49). Primary outcome was PHQ-9. Secondary outcomes included loneliness . Data from the BASIL-C19 trial were included in a metanalysis of depression and loneliness.The 12 months adjusted mean difference for PHQ-9 was −0.70 (95% CI −2.61 to 1.20) and for loneliness was −0.39 (95% CI −1.43 to 0.65).The BASIL-C19 living systematic review  found short-term reductions in depression (standardised mean difference (SMD)=−0.31, 95% CI −0.51 to −0.11) and loneliness . There were few long-term trials, but there was evidence of some benefit .We delivered a pilot trial of a behavioural intervention targeting loneliness and depression; achieving long-term follow-up. Living meta-analysis provides strong evidence of short-term benefit for loneliness and depression for cognitive and/or behavioural approaches. A fully powered BASIL trial is underway.Scalable behavioural and cognitive approaches should be considered as population-level strategies for depression and loneliness on the basis of a living systematic review. Older people with long-term conditions have been impacted by COVID-19 pandemic restrictions and have experienced social isolation. In turn, this puts them at risk for depression and loneliness, and these are bad for health and well-being. Psychosocial approaches, such as behavioural activation, could be helpful.Trial-based evidence is needed to demonstrate if it is possible to address the onset, or mitigate the impact, of loneliness and depression.There are a few studies of brief psychosocial interventions to mitigate depression and loneliness, and it is important to know how emerging trial-based data adds to existing evidence.There was preliminary evidence that levels of loneliness were reduced at 3 months when behavioural activation was offered.At longer-term (12-month) follow-up, there was a potential positive impact.When Behavioural Activation in Social Isolation data were assimilated into a living systematic review there is clear evidence of impact of brief psychological interventions on depression and loneliness in the short term. More research into the longer-term impact is needed.Cognitive and/or behavioural interventions show evidence of benefit which will be useful for policy-makers in offering support to people who are socially isolated.This research knowledge will be useful once the COVID-19 pandemic has passed, since loneliness is common in older populations and effective scalable solutions will be needed to tackle this problem.As new trial-based data emerges, our living meta-analysis will be updated since this is an area of active research.The mental health of the population deteriorated during the COVID-19 pandemic.Social isolation, social disconnectedness, perceived isolation and loneliness are known to be linked to common mental health problems, such as depression in older people.The need for research to mitigate the psychological impacts of COVID-19, particularly loneliness, was highlighted as a priority,Behavioural activation (BA) is an evidence-based psychological treatment that explores how physical inactivity, avoidance and low mood are linked and result in a reduction of valued activity.In this paper, we present the long-term 12-month) results of the BASIL-C19 trial : a pilot randomised controlled trial (RCT) of manualised BA, adapted specifically to be delivered at scale and remotely  for older adults who became socially isolated as a consequence of COVID-19. The long-term (12-month outcomes) complement the already-published short-term (up to 3 months) outcomes of the BASIL-C19 trial.-month reResearch into loneliness is a rapidly evolving area, and therefore, we present the short-term and long-term results of the BASIL-C19 trial alongside all available randomised data in a prospective evidence synthesis and cumulative meta-analysis. We adopted the method of a ‘living systematic review’ which is a form of evidence synthesis that is continually updated, incorporating relevant new evidence as it becomes available.Existing reviews in this area are conventional systematic reviewsThe BASIL-C19 pilot RCT was the first and only mental health trial adopted by the National Institute for Health and Care Research (NIHR) Urgent Public Health (UPH) programme (adopted on 28 May 2020).Inclusion criteria: Based on the Academy of Medical Sciences definition of multimorbidity,Exclusion criteria: Older adults who had cognitive impairment , bipolar disorder/psychosis/psychotic symptoms, alcohol or drug dependence, in the palliative phase of illness, had active suicidal ideation, were currently receiving psychological therapy or were unable to speak or understand English.Potentially eligible participants were telephoned and those who expressed an interest in the study were contacted by a member of the research team to determine eligibility, obtain consent and collect baseline data. Interested patients could also complete an online consent form or contact the study team directly.Eligible and consenting participants were randomised 1:1 to BA intervention or usual care using simple randomisation via an automated computer data entry system, administered remotely by the York Trials Unit, University of York. Participants, GPs, study clinicians or BSWs were not blinded to treatment allocation. Outcome assessment was by self-report, and study researchers facilitating the telephone-based outcome assessment were blind to treatment allocation.The intervention (BA within a collaborative care framework) has been described elsewhereBA pays particular attention to the function the behaviour holds for an individual and that reinforcement is determined functionally. An important consequence of this view is the idea of functional equivalence. A specific form of a behaviour may have served a particular function for a person. However, that behaviour may no longer be possible due to physical health problems or COVID-19 lockdown. In this situation, an aim of treatment was to identify a functionally equivalent behaviour that is different and therefore still possible despite physical changes or shielding, but which may serve the same function for a person.Intervention participants were offered up to eight sessions over a period of 4–6 weeks delivered by trained BSWs, accompanied by a BASIL Behavioural Activation booklet.Sessions were delivered by BSWs remotely via telephone or video call, according to participant preference. The first session was scheduled to last approximately 1 hour, with subsequent sessions lasting approximately 30 min.Participants in the control group received usual care as provided by their current National Health Service (NHS) and/or third sector providers. In addition, control participants were ‘signposted’ to reputable sources of self-help and information, including advice on how to keep mentally and physically well  guideline. The flow of participants through the pilot trial is shown in a CONSORT flow diagram . DiffereDifferent covariance structures were applied to the model. An unstructured covariance pattern for the correlation between the observations for a participant over time was specified in the final model based on Akaike’s information criterion .An estimate of the difference between treatment groups in all outcome measures was extracted from the models for the 12-month time point, and overall, with a 95% CI as preliminary estimates of effect, but this pilot trial was not powered to show efficacy. Model assumptions were checked as follows: the normality of the standardised residuals was visually assessed using a QQ plot, and homoscedasticity by means of a scatter plot of the standardised residuals against fitted values. No concerning deviations were noted.Using all available trial data to February 2022, we incorporated studies from an earlier CochraneWe searched PubMed, EMBASE, PsycINFO from inception to February 2022 using the MetaPsy database, and also scrutinised the bibliography of two recent systematic reviews in this area to identify additional studies , of which 80 (83.3%) completed the 12-month follow-up and valid scores were available for 79 (82.3%) see .The mean age of randomised participants was 74 years (SD 5.5) and most were white . Nearly two-thirds of the sample were female  , and theEighty randomised participants (83.3%) completed the 12-month follow-up and valid primary and secondary outcome data were available for 79 (82.3%) participants (one participant commenced the questionnaire but then felt too unwell to continue and did not complete any of the outcome measures). At 12 months, unadjusted between-group mean differences was in the direction of the intervention for the Patient Health Questionnaire-9 (PHQ-9), Generalised Anxiety Disorder-7 (GAD-7), De Jong Social Loneliness and the Short Form-12 (SF-12) Mental Component Score (MCS), and usual care for De Jong total and the Emotional Loneliness subscale, and the Short Form-12 (SF-12) Physical Component Score (PCS). The point estimate adjusted mean difference between groups in the PHQ-9 indicated lower severity in the intervention group at 12 months , with an overall difference of −0.41 (95% CI −1.65 to 0.83) across all time points. The width of confidence intervals included benefit, harm and no overall effect. The adjusted mean difference for the total De Jong Gierveld score indicated lower severity in the intervention group at 12 months , with an overall difference of −0.32 (95% CI −0.97 to 0.34) across all time points. The direction of effect in long-term follow-up was consistent, though the majority were non-significant  and the We identified 12 studies (including BASIL-C19) that evaluated cognitive or behavioural interventions and reported either loneliness or depression outcomes (or both)  tool2=64.3%). Four studies assessed loneliness in the long term (≥12 months) and there was some evidence of benefit . Nine studies assessed depression in the short term, and there was strong evidence of benefit . Four studies assessed depression in the long term, at 12+ months, and although favouring cognitive and/or behavioural interventions the 95% CI was wider due to fewer studies reporting at this time point . No studies reported medium term (≥6 to <12 month) data. In all analyses, the level of between-study heterogeneity was low to moderate.When we pooled data for cognitive and/or behavioural interventions, all twelve studies assessed loneliness in the short term (≥6 months) and there was strong evidence of benefit for cognitive and/or behavioural interventions .For loneliness as an outcome, we found that although the effect estimate was larger in working age adults  than in studies in older adult populations , differences between subgroups were not statistically significant . The effect estimate for loneliness was larger in studies using loneliness-specific intervention  compared with interventions using generic interventions  and the difference between subgroups was statistically significant . The effect estimate for depression was also larger in studies using loneliness-specific intervention  compared with interventions using generic interventions , but the difference between subgroups was not statistically significant .For depression as an outcome, we found that the effect estimates were similar in working age adults  compared with studies in older adult populations  and differences between subgroups were not statistically significant , but not for short term depression (Egger’s test p=0.76).The BASIL-C19 trial is an external pilot trial, designed to test an adapted behavioural intervention and to refine trial procedures before undertaking a full-scale trial. To our knowledge, this is one of only a small number of trials undertaken during COVID-19 to mitigate the psychological impact of the pandemic and its restrictions.We have previously reported the short-term outcomes where there was a statistically significant benefit in reducing loneliness,Research to date has shown behavioural approaches to be highly effective in the treatment of depression among older peopleOur pilot trial was also undertaken rapidly and during the COVID-19 pandemic in early 2020; the time elapsed between the onset of the pandemic and the recruitment of the first participant was less than 3 months. We chose to study the impact of a plausible psychosocial intervention to mitigate depression and loneliness in an at-risk population of older people with multiple LTCs. It is also important that interventions to tackle the higher rates of depression and loneliness in all age groups are also developed and evaluated.The BASIL-C19 trial was not designed or powered to detect effectiveness, and a fully powered pragmatic trial , is now underway to test for robust effects in important secondary outcomes such as loneliness with the benefit of greater statistical precision.The COVID-19 pandemic prompted a number of studies to understand the impacts of COVID-19,We also emphasise that we have used, for the first time, the technique of ‘living systematic review’ to describe the impact of cognitive and/or behavioural interventions in addressing depression and loneliness in the face of social isolation. This will be updated in line with future and emerging trial-based evidence. The use of this technique was accelerated in many domains of health during the COVID-19 pandemic,It is not clear on the basis of the living systematic review whether behavioural or cognitive approaches are equally effective, and more trials-based research is needed to understand this. The broader literature shows the equivalence, in terms of effectiveness, of behavioural vs cognitive treatment modalities in treating depression,


Pediatric inflammatory multisystem syndrome (PIMS) is a life-threatening complication in pediatric patients with SARS-CoV-2 infection. An increase in the association of gastrointestinal symptoms and the presence of PIMS has been observed. The objective of this study was to analyze whether pediatric patients with COVID-19, who debut with gastrointestinal symptoms, have a higher risk of developing PIMS.An observational, analytical and retrolective study was carried out with a review of the records of patients diagnosed with COVID-19. Demographic, clinical and laboratory variables were recorded.A total of 248 patients who met the selection criteria were included. Of Those 40% were female, with a mean age of 7 +/- 5.8 years. Gastrointestinal symptoms were the initial presentation in 103 patients, with vomiting being the most frequent symptom, followed by abdominal pain and diarrhea. In total 52 patients developed PIMS, 30 of whom presented with gastrointestinal symptoms. A RR of 1.57 (97% CI of 1.17–2.11) was found for the presentation of PIMS in patients positive for SARS-CoV-2 who present with gastrointestinal symptoms.There is an increased risk of developing pediatric multisystem inflammatory syndrome when there are gastrointestinal symptoms in pediatric patients with COVID-19. SARS-CoV-2 infection has spread throughout the world and affects patients of all ages. The proportion of symptomatic pediatric patients is lower than that of the adult population. Until recently it was thought that children had a short course of the disease without complications; however, a group of children with severe multisystem disease known as pediatric inflammatory multisystem syndrome (PIMS or MIS-C), characterized by fever and multiple organ dysfunction were found to be associated with infection SARS-CoV-2 and development of COVID-19 . SimilarHospital Infantil de México Federico Gómez (HIMFG) from April 27th, 2020, to May 9th, 2021. All pediatric patients who had a positive test for SARS-CoV-2  were included, those patients who did not have laboratory results for acute phase reactants during their hospitalization were excluded. PIMS/MIS-C was defined according to the CDC criteria, which include patients under 21 years of age with fever >38.0, with involvement of two or more organs or systems, with elevated acute phase reactants (ESR/CRP/Ferritin); which were determined as high according to the values established by the hospital laboratory (HIMFG); as well as not having another disease that explains the patient's symptoms  with 95% confidence intervals (CIs) were computed for significant categorical variables. Predictive factors for MIS-C were identified using a multiple logistic regression model including variables that were significant in univariate analysis. A value of P < 0.05 was considered as significant. Given the retrospective nature of the study, informed consent was not required.All data were analyzed using IBM SPSS Statistics for Windows, Version 21.0 . Normally distributed variables are summarized as the mean and standard deviation, data from skewed distributions are shown as the median (range), and categorical variables are summarized as frequency and percentages. A (two-tailed) n = 57; 55%), followed by abdominal pain  and diarrhea . In addition, 41 patients (39%) presented more than one gastrointestinal symptom at the time of admission.A total of 278 patient records were reviewed, excluding 30 files that didn't complete our inclusion criteria. Ultimately 248 patients were included in the study. All of them had a positive PCR test for SARS-CoV-2 (we didn't identified the strain). Of these 113 were female (40%), with a mean age of 7 years (SD: +/- 5.76). The main demographic data can be found in A total of 52 patients (21%) met the criteria established by the CDC for PIMS/MIS-C during their hospitalization, observing 16 patients (30%) with PIMS/MIS-C diagnosis upon admission and 36 patients (70%) that developed PIM/MIS-C during hospitalization; of these, 30 patients (57%) debuted with gastrointestinal symptoms. Univariate and multivariate logistic regression analysis revealed that patients with positive RT-PCR for SARS-CoV-2 who debut with gastrointestinal symptoms  were significantly associated with PIMS/MIS-C. It is worth mentioning that no deaths or other adverse events were found among the patients studied. The comparison of patient's characteristics, comorbidities and biometric laboratory results can be found in Cause of admission was variable. Most patients were admitted due to complications of previous illness; COVID-19 related complications was the second most common cause of admission. With 68 patients (27.4%) admitted due to hematologic/oncologic related complications . While 54 patients (21.7%) were admitted because of COVID-19 related illness, having 16 patients admitted due to suspicion of PIMS/MIS-C (29%) and 38 patients (71%) admitted due to COVID-19 pneumonia. Other causes of admission were surgical related  in 28 patients (11.2%), previous renal disease related in 20 patients (8%), cardiac disease decompensation in 9 patients (3.6%), and other less frequent . The diagnosis of some surgical cause at admission was associated with a lower risk of developing PIMS regardless of having or not having gastro-intestinal manifestations.Although it wasn't possible to determine the strain of SARS-CoV-2 in our institution (HIMFG) until after our study was finished (October 2021), based on epidemiological data we can assume our patients were infected with the Delta strain of the SARS-CoV-2 virus.At the start of the COVID-19 pandemic very little was known about the SARS-CoV-2 virus, little by little we have gained new knowledge to answer the questions regarding this new virus, however, as the knowledge about this disease increases, the more questions we have. COVID-19 severe disease and associated mortality has been frequent in older adults and in patients with comorbidities, such as: diabetes mellitus, cardiovascular disease, chronic lung disease, obesity. All of which are risk factors that were identified from very early stages of the pandemic for higher morbidity and mortality, even for readmission –12. SomeIn general, it is considered that SARS Cov2 infection in the pediatric population is usually asymptomatic or has few symptoms and for that the true prevalence of infection in children is not known . HoweverEven though only few studies have described the presentation of gastrointestinal symptoms initially associated with PIMS/MIS-C, their results agree with ours, finding a high incidence of gastrointestinal symptoms in patients positive for SARS-CoV-2 who present or develop subsequently PIMS/MIS-C , supportAlthough there is not enough evidence to prove that outpatients with GI symptoms are at higher risk of developing PIMS/MIS-C, we do recommend keeping them under observation, being the general recommendation to continue with constant communication and suggest follow-up visits or serial laboratory tests, especially during the sub-acute period of the disease, which is when PIMS/MIS-C mostly develops.P-value of statistical significance to correlate with PIMS/MIS-C; it should not be interpreted as a lack of utility for the diagnosis of PIMS, but rather as the criteria established for the diagnosis itself suggests, the elevation alone of this biometric factors does not correlate with the diagnosis of PIMS/MIS-C. Therefore, always consider the whole criteria  for said diagnosis.We also found that procalcitonin levels were higher in those who developed PIMS. A recent study indicates that a rise in PCT above 0.2 ng/mL is associated with an elevated risk in all-cause mortality, the need for non-invasive ventilation, and a longer duration of mechanical ventilation . A meta-As we have stated before, even though GI symptoms and elevation of procalcitonin have positive statistical significance, both factors alone cannot establish the diagnosis of PIMS, however, both variables can be used to establish risk assessment in order to determine which patients require close supervision in a hospital setting.It is important to mention that even though our institution is a national reference center for the management of pediatric patients with COVID-19, in addition to the fact that many of the patients of our pediatric population had comorbidities typical of a highly specialized hospital, a considerable proportion of the pediatric population with COVID-19  are treated elsewhere. Therefore, the results obtained cannot necessarily be extrapolated to the entire pediatric population.In conclusion, PIMS/MIS-C is a serious and potentially fatal complication of SARS-CoV-2 infection that occurs in the pediatric population. Based on our results where we observed an association between the presence of gastrointestinal (GI) symptoms in pediatric patients with COVID-19 and the subsequent development of PIMS/MIS-C, we can conclude that our hypothesis of a higher risk of developing PIMS-MIS-C in children with COVID-19 and GI symptoms is correct.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.CJ-E participated with the conception or design of the work, data collection, data analysis and interpretation, drafting the article, and final approval of the version to be published. RV-F participated with the design of the work, data analysis and interpretation, drafting the article, critical revision of the article, and final approval of the version to be published.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In biomedical studies, survival data with a cure fraction (the proportion ofsubjects cured of disease) are commonly encountered. The mixture cure andbounded cumulative hazard models are two main types of cure fraction models whenanalyzing survival data with long-term survivors. In this article, in theframework of the Cox proportional hazards mixture cure model and boundedcumulative hazard model, we propose several estimators utilizingpseudo-observations to assess the effects of covariates on the cure rate and therisk of having the event of interest for survival data with a cure fraction. Avariable selection procedure is also presented based on the pseudo-observationsusing penalized generalized estimating equations for proportional hazardsmixture cure and bounded cumulative hazard models. Extensive simulation studiesare conducted to examine the proposed methods. The proposed technique isdemonstrated through applications to a melanoma study and a dental data set withhigh-dimensional covariates. On the other hand, in adental study,2the cured subjects are the teeth that underwent proper periodontal treatments andcan last a lifetime. In general, it is difficult to identify the cured subjects, buttheir presence is signaled by a leveling of the Kaplan-Meier (KM) survival curve atthe end of the follow-up, for example, 3 Even if the cure fraction is accounted for, the dental studyposts additional challenges on high-dimensionality. More than 50 predictors relevantto decision making in periodontal treatments are potential risk factors affectingthe teeth’ survival. Motivated to tackle these emerging and challenging scientificquestions, we propose a new estimating procedure using a pseudo-observationsapproach for the statistical inference on the parameters of interest and extend theproposed methods to regularized regression.In the time-to-event analyzes, it is usually assumed that all subjects willeventually experience the event of interest if the follow-up period is sufficientlylong. However, in many research fields, including biomedical, genetic, and socialstudies, some subjects may never experience the event of interest in their lifetime.These subjects are referred to as the cured or nonsusceptible subjects. For example,in a melanoma progression study,4 The MC model assumes that the population consists of twotypes of patients, a cured group in which the patients are not at risk ofexperiencing the event and a susceptible group in which they eventually experiencethe event. The MC model consists of two components, incidence andlatency. The former indicates whether the subject issusceptible, and the latter represents the time to the event when the patient is inthe susceptible group. For the incidence component, a logisticregression model is often used to describe the covariate effects on the curefraction. In contrast, parametric and semiparametric models have been proposed forthe latency component to describe the underlying failure timedistribution of susceptible subjects. Among those, Weibull model,4 generalized 5 Cox proportional hazards (PH)model,6 and accelerated failure time model7 have been studied.Two types of cure models have been popular in the literature, with most emphasis onthe mixture cure (MC) model.8 The idea was first introducedby Yakovlev et al.9 in biological considerations, in which cancer recurrence ispromoted by the number of carcinogenic cells and disease progression. Thus, theparameters specified in the BCH model bear exact biological meaning. Treating thecarcinogenic cell counts as latent and nuisance, the BCH model is suitable for anysurvival data types as long as it is reasonable to assume the data have a curefraction.10 Incorporating covariates into the BCH model modifies the curefraction and introduces a long-term covariate effect on thesurvival. The BCH model has a PH structure through the long-termeffect parameter. Tsodikov et al.11 further incorporatedcovariates to the baseline survival function through another PH structure,introducing a short-term covariate effect on survival. Thetwo-component BCH model of Tsodikov et al.11 is termed the PHPH model. TheMC model and the PHPH model consist of different covariate effects, each providingunique clinical interpretations.The second type of cure model is the bounded cumulative hazard (BCH) model, alsoknown as the promotion time cure model, which models the survival times through animproper survival function, e.g Tsodikov12 to model the transitionprobabilities in multi-state models. Since then, the pseudo-observations approachhas been applied to many settings in survival analysis, including survivalestimates,13 the restricted mean survival times,14 thecumulative incidence function,15 the relative survivalfunction,16 the illness-death model with interval-censored data,17 and thecausal inference for recurrent event data.18 Large sample properties havealso been thoroughly investigated.1921 However, thepseudo-observations approach has not been applied to the analysis of survival datawith a cure fraction.Existing estimating procedures for the MC model and the PHPH model usually involveupdating the parameters via expectation-maximization (EM) algorithms to account forlatent variables, for example, cure fraction, in the likelihood. However, theseapproaches could be computationally expensive in high-dimensional data or when thebootstrap method is used in variance estimation. Thus, estimating procedures such asthe pseudo-observations approach that does not rely on the EM algorithm are moreappealing for practical use. The concept of the pseudo-observations approach is tocreate pseudo values for the quantities of interest at individual levels using theanalogy of leave-one-out cross-validation. These pseudo values are then treated ascomplete data where standard methods can be conveniently applied. Specifically, forsubject 22 adaptive LASSO (ALASSO),23 and smoothly clipped absolutedeviation (SCAD).24 In the Cox MC models, Liu et al.25 used LASSO and SCAD penaltiesto select variables based on penalized likelihood functions. Masud et al.26 performed thevariable selection based on the ALASSO penalty while considering the linear andnonlinear effects in both components. Masud et al.27 further utilized the ALASSOpenalty to the Cox MC and the BCH models. In both works, an EM algorithm was adoptedto estimate the parameters, and the bootstrap resampling procedure was used toobtain standard error estimates. Their works can be computationally intensive inhigh-dimensional data. The penalized BCH model of Masud et al.27 has limitedapplication as it requires additional information on the latent carcinogenic cellcounts, making their approach not applicable for survival data without similarbiological interpretations. Moreover, the short-term covariateeffect is not considered in their approach, limiting the understanding of thecovariate impact on the timing of disease occurrence.Regularization and variable selection are commonly used in high-dimensional dataanalysis, but it has been less studied for cure models. Regularized proceduresminimize an objective function that consists of a penalty function to reflectsparsity. Some of the popular penalty functions are the least absolute shrinkage andselection operator (LASSO),28 to perform variable selection. The proposed work closes the gapon variable selection in cure rate models with pseudo-observations techniques. Theproposed approaches are attractive in several aspects. First, pseudo-observationscan be straightforwardly used as complete outcomes for the generalized linear model(GLM) without indication of censoring. Second, the proposed estimating proceduresare computationally efficient and faster in running time than standard approachesthat adopt the EM algorithm for estimation and bootstrapping for standard errors asthe unknown regression parameters are estimated via the generalized estimatingequations (GEE) approach with corresponding variance estimates obtained by sandwichestimators. Third, unlike the estimation and variable selection of regressionparameters in the incidence and latency componentof the MC model27 or the short-term andlong-term effects of the BCH model27 are performed simultaneouslywithin one model, our proposed methods using pseudo-observations can perform theestimation and variable selection separately in each component of the MC model oreach effect of the BCH model. Specifically, once the pseudo-observations for thequantity of interest for each component of the MC model or each effect of the BCHmodel are generated for each subject, they can be modeled with standard methods likeGLMs. The GEE and PGEE estimating methods can be applied for parameter estimationand variable selection. Finally, the proposed regression estimators can be easilyimplemented via standard statistical software.In this article, we develop new estimating procedures based on thepseudo-observations approach for both the MC and the BCH models. We further extendthe proposed method by adopting the penalized generalized estimating equations(PGEE) approachThe remainder of the article is organized as follows. The MC model and the BCH modelare reviewed in Section 2. The construction of pseudo-observations is described inSection 3. Inference procedure, model diagnosis, and variable selection arepresented in Section 4. Comprehensive simulation results are reported in Section 5.The analysis of two real datasets is provided in Section 6. Concluding remarks aregiven in Section 7. Asymptotic properties and additional simulation results areprovided in the online Supplemental Materials.Let incidence component latencycomponent, we model the conditional survival function via the Cox proportionalhazards model6The MC model expresses the population survival function asonmodelπ(X)=P(Y=ds modelλ(t|Z)=λ029 and the covariates have a long-term effectbecause 8 A common choice of 8 extends the improper PHmodel by adding a short-term effect by incorporating covariatesinto 11 has the form11When the cure fraction is the only parameter ofinterest, it makes no difference which model formulation (Suppose itten asS(t)=exp{30 in which the cure rate 31 and 12 one can define thepseudo-observations for subject 32 through the connectionbetween models  quationsU=∑12We denote To estimate rvations  forsubjGLM withg2(E[I(Ti). Model  when g21,…,ξtH}U(ψ)=∑i=1short-term and long-term covariate effectscan be estimated separately. To estimate the long-term effect Under PHPH model , theshoof model , which m^NPi viaU=∑θ^NPi in  and 10)short-ter and ςHU(Θ)=∑i=1Pi(t) in  and 12)short-tergeepack.35 We adopt the approximatejackknife variance estimates,36 which is available in thegeese function. Note that adopting a sandwich estimator might lead toinconsistent and upward biased results for variance estimation; however, thishas an insignificant impact in practical applications.20 We follow the idea ofpseudo-residuals33 to assess thegoodness-of-fit for (All estimators obtained from solving estimating equations mentioned in Sections4.1 and 4.2 can be using the geese function in the R package-fit for  and 15)geepack.3or model  and the or model . The ide28Specifically, for the incidence component ofthe PHMC model, we penalize the GEE in SS28Specifxtending (22)S(ψ)long-term effect of the PHPH model, we extend . The results of PGEE with independentstructure tend to have slightly higher TP and FP and lower MSE than that of the PGEEwith exchangeable and AR(1) correlation structure. However, the performances areclose when the sample size is large and with 40% censoring rate, indicating thatlittle advantage gains while specifying complicated correlation structure amongpseudo-observations for variable selection. When the censoring rate is 60%, the TPdecreases and the MSE increases for all the presented methods. Compared to theresults based on LASSO and ALASSO, our proposed PGEE performs reasonably inidentifying important variables with short-term and long-term effects. In thiscase, we set long-term and short-term effects basedon different sample sizes. The definition of the MSE for thelong-term and short-term effects is similar tothat of the incidence and latency componentsmentioned in the above paragraph. We observe that the two constructions ofpseudo-observations yield similar results. For each sample size To investigate variable selection performance under the PHPH model, we generate thecovariate vector following the PHMC model’s configuration to specify the1 which is available from theR package smcure.38 The primary objective isto determine whether the high dose interferon alpha-2b (IFN) regimen inpostoperative adjuvant therapy would lead to a significantly prolonged intervalof relapse-free for melanoma. The event of interest is the relapse of melanoma.The interested covariates include treatment  rate. The estimatedcure rates based on rmodels  and 15)α^NPPO a on GLMs  and 19)α^NPPO a2The dataset contains dentalrecords from 5336 patients with periodontal disease collected between August2007 and March 2013. In this work, the outcome of interest is the time to thefirst tooth loss due to periodontal reasons for each patient, yielding acensoring rate of 74.1%. The data analysis includes a total of 44 risk factors,whose detailed descriptions can be found in Tables 3 and 4 in Calhoun etal.2 The length of the follow-up was 5.7 years, and the lastevent occurred at 5.37 years for both molar and non-molar groups. There were 35and 20 teeth censored between the last event and the end of the study for themalor group and non-molars group, respectively. 30 with a We apply the proposed PGEE approaches to a dental dataset from the CreightonUniversity School of Dentistry.27 implemented in the R packageintsurv.39In the variable selectionprocedure, the molar variable’s coefficient is not penalized since the variableeffect is the research of interest. latency, the PGEE tends to identify more risk factors inpredicting tooth survival time. The mobility score (mobil), decayed surfaces new(decayed_new) and filled tooth (filled_tooth) are three common selected factorsrelated to the tooth survival time based on PGEE and LASSO. Under the PHPH modelassumption, our proposed PGEE approach suggests that factors plaque score(plaque), bleeding on probing (bleed_ave), plaque index (plaque_ave), filledsurfaces (filled_sum), recurrent decayed surfaces (decay_recur_sum), decayed andfilled surfaces (dfs_sum), number of decayed teeth (decayed_tooth_sum) and ageat baseline (baseline_age) are importantly associated with thelong-term effect. Moreover, 20 factors are identified inassociation with the short-term effect, indicating losing thetooth more rapidly. Interestingly, we observe that 15 variables are bothselected by the PGEE in the latency component of the PHMC modeland the short-term effect of the PHPH model even thoughcovariates are interpreted differently in the two models.As the study aims to identify which factors are associated with tooth loss, theproposed PGEE provides a logical tool for risk factor screening. We perform thevariable selection procedure in both the PHMC model and the PHPH model. Underthe PHMC model, we apply the proposed PGEEs in  and 22)22)with PGEEs in  and 24)π^KMi anThis article extends the pseudo-observations approach to the context ofright-censored survival data with a cure fraction for two popular cure models, theMC model and the BCH model. Several estimators for the regression parameters relatedto the cure rate and the risk of experiencing the event are proposed under the PHMCmodel and the PHPH model. The proposed methods allow researchers to estimate thecovariate effects separately and identify essential factors associated with the curerate and the risk of failure. Simulation studies show that the proposed methodsperform reasonably under finite sample sizes. We also demonstrate the proposedmethodology on two real applications involving survival data with a curefraction.long-term effects and twodifferent regression estimators short-term effects. In thesimulation studies, each of the pairs of estimators performs similarly. However,from the computing time point of view, we recommend that researchers considerestimators based on KM estimator as its computing times is fast. On the other hand,as we showed in the simulation studies, our proposed estimators based onpseudo-observations under both MC and BCH models have larger ESE and SEE than thosefrom the usual MC and BCH models. This efficiency loss is commonly seen in thepseudo-observations literature.40 As pointed out by a referee,the efficiency loss is a limitation for our proposed pseudo-observations approach onthe cure models when the number of covariates is small, like our real data analysisin Section 6.1. However, modeling pseudo-observations constitutes a general andstraightforward approach to simplify survival analysis. In our applications, thepseudo-observations approach brings several advantages. First, it is more flexibleand feasible for parameter estimation and variable selection when the number ofcovariates is large. The estimating procedure can be performed separately for eachcomponent in the MC model and each effect in the BCH model. Second, the computingtime based on the pseudo-observations approach is faster than standard approachesthat use the EM algorithm for estimation and bootstrapping for standard errors.Finally, pseudo-residuals can be applied for model diagnosis via residual plots.In this work, for the MC model, we propose two different incidence regressionestimators short-term effect to the baseline survivalfunction via a proportional hazard model. The proposed PHPH model can be extended toaccommodate non-proportional hazard assumptions by considering an exponentialtransformation or an accelerated failure time model in theshort-term effect.8On the other hand, we appliedthe pseudo-observations approach to the PHMC model. Another commonly used MC modelis the accelerated failure time MC (AFTMC) model,7 in which the logisticregression model is considered to model the cure status in the incidence componentand the AFT model, geepack35 only provides commonly seenlink functions. Second, when the survival distribution Our PHPH model links the rvations  and 7)  short-te41 In this work, we only reportthe selected coefficient estimates from the penalized approach for the dental dataanalysis. In practice, one might adopt a two-stage approach in which the inferenceis based on the selected model to obtain standard errors.27The validity of inferencesbased on the penalization and the goodness-of-fit assessments will be studied infuture work.We note some limitations of our proposed work for variable selection. Based on oursimulation studies, the TP rate seems to be low for the incidence component of theMC model when Click here for additional data file.Supplemental material, sj-pdf-1-smm-10.1177_09622802221108579 for Analysis ofsurvival data with cure fraction and variable selection: A pseudo-observationsapproach by Chien-Lin Su, Sy Han Chiou, Feng-Chang Lin and Robert W Platt inStatistical Methods in Medical Research