We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health–supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors.In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI.We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each.SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager. We provide the clinical rationale for each analysis along with pointers to the workflow of the SlicerDMRI tools used. We begin by demonstrating a conventional diffusion tensor imaging analysis followed by advanced multifiber and automated tractography analyses. We give two examples of how SlicerDMRI, used within the platform of 3D Slicer, enables multimodal image analysis for neurosurgical planning and guidance research.RelevanceOne key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections and insight into the tissue microstructure that surrounds brain tumors. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.18 The most popular measures used in neurosurgical patients are derived from modeling water diffusion using tensor mathematics (diffusion tensor imaging [DTI]19). These measures include fractional anisotropy (FA) and mean diffusivity (MD). In neurosurgical patients, alterations in FA and MD can reflect combinations of increased water content from edema and/or tumor infiltration.20dMRI also provides quantitative microstructure measures. These measures allow the assessment of changes of tissue microstructure caused by tumors and the effects of treatment.Brain mapping using dMRI requires sophisticated computational processing to enable visualization and quantification, and multiple software packages exist for this purpose. In fact, several packages are US Food and Drug Administration (FDA) approved and purpose-built for neurosurgical brain mapping, neuronavigation, and radiation therapy planning. However, these dedicated packages do not make cutting-edge developments in dMRI analysis available to clinicians or are specifically designed for research needs. Rather, they are designed to provide robust results within the constraints imposed by routine clinical settings and FDA approval, where there is relatively little time to analyze the data of an individual patient and where the operators of the navigation systems are typically residents with limited training in dMRI.21) includes dMRI visualization, interactive and automated tractography, tissue microstructure modeling, and quantitative analyses. SlicerDMRI can import dMRI data, including clinical Digital Imaging and Communications in Medicine (DICOM) images, research formats of Neuroimaging Informatics Technology and Nearly Raw Raster Data, and the new DICOM tractography format compatible with multiple software platforms.22 The open-source codebase of SlicerDMRI can be extended as needed to fit the needs of a specific research project.SlicerDMRI is a suite of clinician-accessible software tools with a strong focus on the needs of clinical researchers. SlicerDMRI has an average of > 200 downloads per month. The main functionality of SlicerDMRI .27 The data were acquired at Brigham and Women’s Hospital. The use of the data was approved by the Partners Healthcare institutional review board, and informed consent was obtained from the patients before scanning. All data analyses in the current work were performed retrospectively for research purposes only.Retrospective imaging data sets from two neurosurgical patients were selected to illustrate two different clinical scenarios: a high-grade glioma displacing the corticospinal tracts (CSTs), with significant surrounding edema, and a high-grade glioma abutting the arcuate fasciculus (AF). Preservation of these tracts is critical for voluntary motor movements (CSTs)2, six baselines, and a matrix of 112 × 112 with 66 axial slices. Functional MRI (fMRI) was acquired as the patient performed motor and language tasks as clinically indicated, and data were processed using FDA-approved software . Ultrasound data were acquired with the BK5000 system  with a slice thickness of 0.5 mm coregistered to the T1-weighted anatomic volume using the Curve navigation system  and extracted using OpenIGTLink28 and 3D Slicer.All magnetic resonance data were acquired on a 3T MAGNETOM Prisma MRI scanner  with a 20-channel head coil. Anatomic imaging included volumetric T2 weighted and gadolinium-enhanced T1 weighted. dMRI was acquired with a repetition time of 3,500 ms and echo time of 58 ms with a flip angle of 90°, a voxel size of 2 × 2 × 2 mm, a b value of 1,000 seconds/mmA 69-year-old, right-hand-dominant man with a recurrent left-sided frontoparietal glioma initially presented with a diffuse large fronto-insular infiltrative pattern lesion. A biopsy sample was found to have a WHO grade 2 astrocytoma IDH1 wild type, which was treated with chemoradiation. Follow-up imaging at 3 years revealed progression in the left-side perirolandic area, which did not respond to salvage systemic therapy. Radiographic progression and clinical deterioration (motor weakness) led to the decision to pursue surgical resection for debulking and tissue diagnosis. Image-guided left frontal craniotomy was performed with intraoperative motor- and somatosensory-evoked potentials and cortical and subcortical mapping. Gross total resection of enhancing tumor (now grade 4) was accomplished with stable motor function immediately postoperatively and improved motor function relative to baseline at 1-week follow-up.A 49-year-old, right-hand-dominant man who presented with secondarily generalized seizures and word-finding difficulties was found to have a left-sided temporoparietal tumor immediately adjacent to posterior language areas by fMRI. He underwent image-guided craniotomy and resection of tumor with gross total resection of the lesion. Pathology demonstrated high-grade glioma, WHO grade 4.1 DTI enables scalar measurements such as FA and MD19 and single-fiber tractography that accounts for one fiber tract per voxel.5 In this section, we give two clinically relevant example applications using DTI analysis in SlicerDMRI for neurosurgical planning research.DTI, which models water diffusion using a tensor model, is the most widely used method for analyzing dMRI data.The analysis of scalar measurements from DTI can give insight into tissue microstructure differences between the tumor and surrounding tissues. SlicerDMRI provides the DiffusionTensorEstimation module to calculate DTIs from diffusion-weighted image sequences. The 3D Slicer Volumes module allows visualization of diffusion tensors with multiple options, including both two-dimensional (2D) and three-dimensional (3D) displays.SlicerDMRI supports computation of quantitative diffusion measures, such as FA and MD, on the basis of the estimated diffusion tensors using the DiffusionTensorScalarMaps module.SlicerDMRI, together with other modules in the 3D Slicer core, enables quantification of region-specific diffusion measures. This can be done using 3D fiducials to define regions of interest (ROIs) and Data Probe to view diffusion measures from an ROI. This process can also be performed with the SegmentEditor module that enables manual drawing of an ROI and the LabelStatistics module that calculates statistics for different ROIs.DTI tractography can identify patient-specific fiber tracts for visualization. This can inform surgical planning to minimize morbidity associated with disruption of critical white matter connections. 5 Tractography seeding using two clinically relevant approaches is provided: seeding from an interactively visualized 3D object that can be manipulated by the user and seeding from an ROI that is stored as a label map, or mask. These two seeding strategies are provided in the TractographySeeding and TractographyROISeeding modules. Tractography visualization is performed using the TractographyDisplay module, where multiple tract visualization modes are provided .SlicerDMRI supports single-fiber DTI tractography that performs fiber tracking by following the principal direction of the estimated diffusion tensors.SlicerDMRI provides multiple tools to enable quantification of tract diffusion and geometric measures. For example, the TractographyMeasurement module allows for computation of multiple statistics  of diffusion-derived values of the fiber points along a tract. The TractographyToMask and LabelStatistics modules together enable the measurement of tract volume, and the WhiteMatterAnalysis package enables computation of the fiber length distribution of a tract.Advanced analyses in SlicerDMRI include more sophisticated mathematical modeling of the white matter tissue, which enables depiction of crossing fiber tracts, and automated identification of critical fiber tracts, which enables rapid, standardized assessment of patient-specific neuroanatomy.29 because of the effects of edema and infiltration. DTI tractography is also limited to model only one fiber tract per voxel, despite that there are tens to hundreds of thousands of axons packed per square millimeter.30 DTI tractography cannot reconstruct fiber tracts that cross one another,33 such as the CST and AF. Modern multifiber tractography approaches can map a more-complex fiber architecture  to better trace important anatomic fiber tracts.34Conventional DTI tractography is often unable to fully trace fiber tracts in the peritumoral region35 which provides an expanded range of multifiber models, including multitensor and multicompartment models. UKF tractography is more sensitive than standard single-tensor tractography, particularly in the presence of crossing fibers and peritumoral edema.38 Thus, UKF can better match the established understanding of surgical neuroanatomy than traditional DTI tractography. However, it should be noted that such comparisons are difficult because of the lack of ground truth, and tractography validation and standardization remain an open problem.40SlicerDMRI supports advanced multifiber unscented Kalman filter (UKF) tractography,41 and neurite orientation dispersion and density imaging,42 are available during UKF tractography fiber tracking.SlicerDMRI provides the InteractiveUKF and UKFTractography modules for fiber seeding from an interactively modifiable 3D object and an ROI mask, respectively. Advanced tissue modeling options, such as free water modeling43 Using a combination of machine learning and expert anatomic annotation, we created an anatomically curated white matter atlas.44 The atlas contains 58 deep white matter tracts, including major long-range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections. This atlas is released as an open source as part of SlicerDMRI.45 Using this atlas and our accompanying open-source software, we have demonstrated automatic identification of fiber tracts in patients with brain tumors, despite the challenges of edema and tract displacement as a result of mass effect.46Conventional identification of critical fiber tracts requires expert manual interpretation, which is time consuming and has variable results across experts.48 The 3D Slicer Ruler function can be used to measure the distance of the tracts to other image data  displayed in the 3D Viewer.SlicerDMRI provides the WhiteMatterAnalysis Python package to perform automated tract identification using a data-driven fiber clustering pipeline. A one-step batch script is provided to run the pipeline for identification of fiber tracts according to the anatomically curated white matter atlas.v 1.5 hours on a data set containing 1 million fibers).49 This fast computation can motivate the use of automated analyses for intraoperative mapping of white matter connections.In addition to WhiteMatterAnalysis, in the near future, SlicerDMRI will also support fast tractography segmentation using deep learning techniques. This will be provided in the DeepWMA package. While achieving visually similar tract identification results to WhiteMatterAnalysis, DeepWMA performs much faster by leveraging deep learning and graphics processing unit computation , is already being used for surgical planning57 and may be ideal for children or patients who are unable to perform a task. A prospective multi-institutional study to analyze fMRI, rsfMRI, and multifiber DTI techniques is needed.Preserving function during brain tumor operations is associated with increased quality of life and overall survival.Anatomic fiber tracts identified using the SlicerDMRI WhiteMatterAnalysis tool are displayed together with functional activations computed from the fMRI data . Visualization of the multimodal data can be performed in either 2D mode (to show the intersection of 3D objects such as tracts and activations on a 2D image) or 3D mode through the 3D graphical user interface .58 The only commercially available option for intraoperative reregistration of a brain is intraoperative MRI, which is expensive, requires nonferromagnetic instruments, and adds up to 1 hour of operative time per scan. Because ultrasound resolution has improved, intraoperative ultrasound is increasingly used for updated real-time imaging.59 This allows the surgeon to better estimate the effect of brain deformation while also obtaining a real-time navigated view. Ongoing work is addressing quantification of this brain deformation such that the preprocedure magnetic resonance data, including tractography, can be updated to match the current brain configuration, thus improving the use of tractographic information to augment intraoperative surgical decision making.61Another consideration during intraoperative identification of function is brain shift, which is the deformation of the brain that occurs during craniotomy and resection. As brain shift worsens during an operation, preoperative imaging becomes less reliable.Anatomic tracts from presurgical dMRI data are computed using the SlicerDMRI WhiteMatterAnalysis tool and are registered to the presurgical anatomic T1-weighted image. Intraoperative ultrasound data are overlaid on the presurgical MRI data for an integrated visualization.62 SlicerDMRI is open source, so all code is available.63 User and software development support for SlicerDMRI is available through the 3D Slicer forums.64 SlicerDMRI has been used successfully for research with dozens of dMRI protocols from various vendors, with appropriate approvals from institutional review boards for each intended research use.SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager, an app store for distribution of software extensions to 3D Slicer, with installation instructions and tutorials provided.In conclusion, SlicerDMRI provides open-source and clinician-accessible software tools for dMRI research. In this work, we demonstrate SlicerDMRI functionality as an informatics tool to enable end-to-end dMRI analysis. We provide examples that demonstrate the use of SlicerDMRI to retrospectively analyze data from brain tumor resection.


Virtual care, the use of videoconferencing technology to connect with patients, has become critical in providing continuing care for patients during the current COVID-19 pandemic. Virtual care has now been adopted by health care providers across the spectrum, including physicians, residents, nurse practitioners, nurses, and allied health care professionals. Virtual care is novel and nuanced compared to in-person care. Most of the health care providers who are delivering or expected to deliver virtual care have little to no prior experience with it. The nuances of virtual care involve regulatory standards, platforms, technology and troubleshooting, patient selection, etiquette, and workflow, all of which comprise critical points in the provision of health care. It is important to consistently deliver high-quality, equitable, and professional virtual care to inspire patients with the trust they need to continue follow-up of their care in these difficult times. We have been adopting virtual care in our clinical practice for over two years. In partnership with Canada Health Infoway, we have assembled a primer for virtual care that can serve as a guide for any health care provider in Canada and globally, with the goal of providing seamless transitions between in-person and virtual care. As part of the response to the COVID-19 pandemic, health care organizations across Canada have cancelled elective and nonurgent clinics as a measure to reduce the risk of exposing patients to COVID-19 -7. VirtuVirtual care is novel and nuanced compared to in-person care. Virtual Care has significant benefits to patients, health care systems and society at large by offering patient-centered care -16. VirtThe Division of Neurology at Queen's University/Kingston Health Sciences Center has been an early adopter of virtual care for ambulatory services since 2016 for stroke, epilepsy, and sleep medicine, and it has an active virtual care research program ,26. LeveAt this time, no rigorous standalone virtual care guidelines have been established by licensing bodies in Canada . Some guFurther information on medicolegal liability can be obtained from the Canadian Medical Protective Association (CMPA) ,38. NonpSecure messaging, secure email, and secure video conferencing are some of the most used virtual care modalities. These services can be delivered on multiple platforms and devices. The platforms are broadly divided into regulated and unregulated categories. Regulated platforms are those that comply with Canada's federal Personal Information Protection and Electronics Document Act (PIPEDA), as well as provincial and territorial privacy laws that apply to health or medical records, such as Ontario's Personal Health Information Protection Act (PHIPA). A comprehensive list of regulated platforms in Canada is available ,36. UnreFor the virtual care provider, the choice of platform may depend on the features offered, such as secure messaging, video conferencing, patient portals, integration into electronic health records (EHRs), and integration with other remote monitoring devices and applications. Before finalizing a decision on a platform, we recommend that practitioners seek clearance from their own organization's privacy and security officer if available. Each organization and each individual provider will have a unique set of circumstances, resources, and abilities. It is essential for each provider to develop familiarity with the platform selected by their organization. The platforms currently used at Queen's University are the Ontario Telemedicine Network and Reacts .It is important to ensure that the appropriate precautions are taken by the physician and the patient to ensure the privacy of health care information . For addThe equipment (or hardware) required for delivering virtual care is widely available and includes smartphones, tablets, laptop computers, and desktop computers. Ideally, all these devices should have built-in or peripheral hardware for video conferencing . The network (bandwidth) requirements must be confirmed before use, although most of these platforms enable the quality of the video conference to be adjusted to low bandwidth. In general, the faster and more reliable the internet connection, the better the experience will be for the provider and the patient.The choice of the device used by the provider depends on multiple factors, including the physical location of the provider at the time of the virtual video visit. Although virtual visits can be conducted from any internet-enabled device, we suggest using a laptop or a desktop computer with a built-in camera and speaker. These devices typically provide the highest quality internet connection, a large screen, and a better speaker and microphone setup. However, this is highly dependent on the technology available to the patient, as modern-day tablets and smartphones have high-quality built-in cameras and speakers. Perhaps most importantly, the patient and provider should be familiar with their respective devices in case troubleshooting is required.It is essential to identify when virtual care is most appropriate for patients. Virtual care is an effective alternative to in-person care for many but not all medical conditions and clinical scenarios. Some general guidelines on patient selection and screening are provided in table 1 . Given tIt is mandatory in all jurisdictions to document all health care encounters as part of the patient's health care record. Provincial and territorial medical regulatory authorities and the CMPA consider appropriate documentation to be an essential communication skill and a core component of physicians' professional best practice.Although some virtual care platforms integrate the video visit into the provider's EHR, patient portal, or hospital information system, this practice is not yet commonplace. Thus, two parallel workflows often coexist for the provider: the first for the virtual care platform, and the second for the provider's medical record system.This “double workflow” ensures that the virtual care encounter details  are captured in the patient's health record.It is necessary practice to discuss consent with the patient at the time they sign up for virtual care, covering subjects such as the inherent risks related to privacy and security as well as the limitations of virtual care . There aThe limitations of virtual care should be made clear to the patient. It should be clear that when the clinician cannot see the patient physically, there is inherent risk of missing physical findings. This is only exacerbated by the potential for unstable network connections or poor quality/malfunctioning equipment. It should also be made clear to patients that with this in mind, should there be a requirement for in-person assessment, there may a delay relative to the traditional method of seeing the patient in-person. The option of seeing a clinician in person should always be given to the patient and should always be suggested by the clinician if they feel that the virtual visit was insufficient for the patient’s care or may even have caused harm.Generic consent forms are readily available on the web through many of the resources referenced in this paper ,37,39, aIt is crucial to gauge the degree of a patient's or caregiver's comfort level with using technology before proposing the use of virtual care. Some health conditions can affect motor strength, coordination, language, vision, and cognition. All these challenges can adversely affect a patient’s ability to use personal internet-enabled devices and participate in virtual care, even if the patient is proficient with technology. In addition, it is important to recognize that not all patients will have easy access to the ideal technology for virtual care, including video capability and a sufficiently fast internet connection. We have found that modern mobile phones are capable and ubiquitous but may have poor video and/or audio quality depending on the internet connection. Some patients have chosen to use audio-only telephone visits instead. As we transition to virtual care, we need to be keenly aware of patient-readiness for this transition. Although audio-only visits are shown to be useful, an unexpected change to an audio-based visit from a planned video-based visit may result in slight inconvenience at best and a delay in essential visual assessment and therefore care at worst. In cases of communication breakdown, an in-person visit is often suggested.With the patient's consent, providers should explore the option of having a patient's family member or friend assist with facilitating a virtual visit . PatientIt is also helpful to provide some tips for the patient to prepare for the virtual care visit. The patient’s clothing should allow a reasonable range of motion if the provider requests physical maneuvers during the virtual visit. If the patient requires a hearing or visual aid, these should be readily available. For patients with hearing, visual, or cognitive challenges, it is often more challenging to see and hear via a screen and speakers, in contrast to an in-person visit. Patients requiring mobility aids should keep these devices close by and may benefit from the presence of a nondisabled individual to assist them as necessary.Many health care providers are not familiar with how their patients will perceive them in a virtual visit. With this in mind, it is useful to be aware of the etiquette that is unique to virtual care ,43. The Due to technical limitations, auditory and visual information can be lost during a virtual consultation. At the beginning of the session, both cameras should be directed so that the faces and shoulders of both the patient and the health care provider are framed. Remember that slower, broader movements decrease visual blurring, and obscuring the face and mouth should be avoided. The physician should be mindful that gazing at the patient's image on the screen can make it seem as though the physician is looking down at them. When possible, the physician should look into the camera.During the session, the interviewer must recognize the importance of clear communication. Virtual visits require communication adjustments. Physicians should speak slowly and use nonmedical terms for maximum clarity. They should be prepared for lags in communication for technical reasons. Recognizing these technical limitations reinforces the need for speaking clearly. It may be helpful for the interviewer to check in with the patient to see if they can hear and understand their messaging. Reiterating and summarizing what a patient has said may also be helpful. In addition, clear signposting by the interviewer can indicate the progress and direction of the session. For example, the interviewer could say, “I am now going to summarize what I understand of your history, then proceed to the physical exam.”The primary sensory modalities available in a virtual visit are limited to auditory and visual. However, touch is a standard method of communication during an in-person visit, especially during the physical examination. When requesting that patients perform physical examination maneuvers during a virtual visit, adjustments are required. The clinician should clearly state the movement that the patient is asked to perform. They should consider documenting the absent physical examination maneuvers that would have typically been included but were not feasible due to the virtual nature of the consultation. This suggests, at a minimum, that these steps have been considered. Also, the clinician should inform the patient before they navigate to another screen to look at laboratory or imaging data. When possible, screen-sharing modes should be used to show patients their images or laboratory results.Last but certainly not least, kindness and compassion are paramount in a virtual visit. Listening carefully to a patient's concerns, taking time to clarify their statements, and following up on their questions are vital skills, regardless of what form of medical care is being offered.Preparing one's virtual visit environment is vital for both patients and providers. Clinical administrative staff play a key role. Prior to the appointment, the staff should send a message to the patient with recommendations on setting up for a visit. Some recommendations are relevant to patients and virtual visit providers alike:The room should be reasonably private, with minimal potential for distractions. It may be helpful to notify other members of the household or building that space will be used privately for the duration of the appointment. If possible, adequate lighting, presenting oneself in an uncluttered room, and ensuring the visibility of one's face and shoulders are ideal. Background should be reduced to a minimum. This may include distancing from a louder area . We recommend posting a notice at the entrance to the room indicating that a confidential meeting is occurring. Personal communication devices  should be switched to silent mode to avoid distraction.If other parties are involved in the meeting, such as trainees, family members, and friends, they should be introduced prior the beginning of the meeting. As with standard in-office visits, the people who are involved in the meeting should be transparent and within the control of the patient and physician.Other recommendations are more relevant to patients and can be tailored to their medical conditions and clinical scenarios :For some clinical specialties, it may be helpful for the patient's room to have enough space to allow them to stand and move away from the camera, so their entire body is visible.It is helpful if the camera is high-definition (720p or higher) and wide-angle. The room should have a comfortable yet easily movable chair available for the patient to rest.If the consultation requires equipment, the patient should have an easily accessible area to place these materials.A high-quality internet connection within the selected room is also essential. A wired connection is ideal, but if only Wi-Fi is available, the room should not be too far from the wireless router to ensure a reliable connection.During the presession preparation, attention must be paid to the equipment, environment, and external appearance. In addition to checking the equipment and network, it should be ensured that the general guidelines laid out in the above section with regard to etiquette and the environment are followed. The patient's information or EHR should be available within reach of where the video visit is happening.A good habit is to check the platform appointment software to confirm that the patient or patients have been booked as expected. If there is an issue with connecting to the patient, it is always useful to have an alternative means of communication. Ideally, this will be a telephone number; however, email or instant messaging can be used. Most platforms allow the patient to join and wait in a virtual “room.” It is recommended to mute the microphone before the patient enters the appointment.The first thing to do when the patient logs in is to confirm that both parties’ cameras and microphones are working correctly and troubleshoot as necessary. In all first visits, it should be expected that there will be delays due to technological issues. Technical factors play a significant role in one’s ability to communicate effectively with a patient. It is vital for the medical professional to be familiar with the platform they are using, as it is not uncommon to have to walk through teaching a patient how to modify camera or audio settings. It is essential to explain the steps in a simple and organized fashion.Formal introductions can be made next, in which it is essential to obtain consent for the virtual visit and establish the identity of the patient. The ID verification step may not be necessary for clinical scenarios in which the patients are well known to the interviewer . Announcing who is involved in the meeting for both parties is useful, as some participants can be virtual and others can be physical but not visible (off-camera).The history will likely feel very similar to an in-person visit. It is useful to be clear and intentional with questions, as information can easily be lost through the virtual connection. The physical examination will be a different experience, as traditional medical training in conducting examinations anticipates that the patient will be physically present. When the correct group of patient, diagnosis, and visit purpose are selected, a virtual physical examination is equivalent to an in-person visit examination. Investigations can be reviewed with the patient, including sharing the actual results and images directly via screen sharing. If discussions with colleagues or other staff are necessary, the patient may be notified and muted. At the end of the appointment, as with most appointments, the clinician should discuss and establish a plan. Most relevant in this context is whether a follow-up appointment will be a virtual visit or in-person visit. Because the patient will not be taking any items  with them, it is crucial to verify details with the patient .If orders and prescriptions are integrated into the EHR, clinicians can follow their routine workflow. Otherwise, orders and prescriptions must be sent post-encounter . It should be ensured that follow-up arrangements have been made. Finally, the encounter can be documented by clinicians as per typical workflow , clearly identifying the visit as an eVisit or virtual visit.Before the virtual video visit (presession):Check that the device, video conferencing equipment, and network are working properlyEnsure that the camera and microphone are positioned properlyEnsure that the room is well lit and does not contain any distractions and that only professional objects are visibleEnsure privacy during a virtual video visit:Mute your mobile phone or place it on vibrateClose the office door and place a “Do Not Disturb” or “Doing Video Visits” signEnsure beforehand that you have access to the patient charts, results, and any other health informationMute the microphone until the videoconference startsEnsure that the patient’s telephone number is available should it be necessaryDuring the virtual video visit:Confirm that both parties’ videoconferencing technology is working and optimized for sound and videoIntroduce yourself and bring your photo ID close to the camera for the patient to seeAsk the patient to show a valid photo ID Obtain verbal consent for the virtual visitAsk the patient to ensure their room is well lit and has enough space for movementAsk the patient to ensure privacy during the virtual visit:Mobile phone(s) are placed on mute or vibrateDoors are closed and indicate “Do Not Disturb”Ask the patient to adjust their position so they are framed adequately within the camera viewEnsure that the patient has all objects and tools necessary for the full assessmentIntroduce any other health care providers or learners present in the roomAfter finishing the virtual video visit:Ensure that all reports and clinical documentation are completeEnsure that the planned investigations and medication prescriptions are orderedEnsure that follow-up plans have been madeVirtual care will become a reality in many future medical practices, and the COVID-19 pandemic is serving as a catalyst for this impending change. Virtual care is effective at removing barriers to access, especially to specialists who are typically located in larger centers, for the remarkably distributed rural and remote patients across Canada. As we have outlined, there are important nuances in the application of virtual care that help ensure patients receive at least equivalent care, or in some ways, more effective care than that received in the traditional in-person format. Many clinical organizations are working toward establishing baseline principles, guidelines, and protocols to guide Canadian physicians. This paper is intended to contribute to an emerging Canadian body of knowledge and to support optimal professional practice in an emerging and rapidly evolving field. Most importantly, virtual care should not fragment care. It should support both high-quality care, the foundation of which is an established trust-based physician-patient relationship, and continuity of care.


Despite widespread interest in the use of virtual  visits for ambulatory patient care during the COVID-19 pandemic, studies examining their adoption during the pandemic by race, sex, age, or insurance are lacking. Moreover, there have been limited evaluations to date of the impact of these sociodemographic factors on the use of telephone versus video visits. Such assessments are crucial to identify, understand, and address differences in care delivery across patient populations, particularly those that could affect access to or quality of care.The aim of this study was to examine changes in ambulatory visit volume and type  by patient sociodemographics during the COVID-19 pandemic at one urban academic medical center.We compared volumes and patient sociodemographics  for visits during the first 11 weeks following the COVID-19 national emergency declaration  to visits in the corresponding weeks in 2019. Additionally, for visits during the COVID-19 study period, we examined differences in visit type  by sociodemographics using multivariate logistic regression.Total visit volumes in the COVID-19 study period comprised 51.4% of the corresponding weeks in 2019 . Although patient sociodemographics between the COVID-19 study period in 2020 and the corresponding weeks in 2019 were similar, 60.5%  of the visits were virtual, compared to 0% in 2019. Of the virtual visits, 61.2%  were video based, and 38.8%  were telephone based. In the COVID-19 study period, virtual (vs in-person) visits were more likely among patients with race categorized as other (vs White) and patients with Medicare  insurance and less likely for men, patients aged 0-17 years, 65-74 years, or ≥75 years (compared to patients aged 18-45 years), and patients with Medicaid insurance or insurance categorized as other. Among virtual visits, compared to telephone visits, video visits were more likely to be adopted by patients aged 0-17 years (vs 18-45 years), but less likely for all other age groups, men, Black (vs White) patients, and patients with Medicare or Medicaid  insurance.Virtual visits comprised the majority of ambulatory visits during the COVID-19 study period, of which a majority were by video. Sociodemographic differences existed in the use of virtual versus in-person and video versus telephone visits. To ensure equitable care delivery, we present five policy recommendations to inform the further development of virtual visit programs and their reimbursement. The COVID-19 pandemic has significantly altered the landscape of health care delivery. One of the major changes resulting from the pandemic has been the rapid adoption of virtual  visits and other telemedicine programs that facilitate health care services via health care information technologies to accommodate necessary reductions in in-person care . A majorUnfortunately, access to virtual visits may not be equitable in the United States. Differential access to the internet and devices and differences in health literacy may leave patients without the ability to attend video visits. Thus, those patients may only be able to participate in telephone visits if they are unable to attend in-person visits. Surveys by the Pew Research Center in 2019 found lower rates of internet usage and smartphone ownership among people ages ≥65 years compared to younger adults ,5. When There is already existing evidence that other recent innovations in health care technology may exacerbate differences in health care access. For example, patient portal use, which has the potential to improve the quality and efficiency of health care delivery, differs with respect to race, insurance, and neighborhood broadband internet access . One stuDespite widespread interest in the use of virtual visits for ambulatory patient care during the COVID-19 pandemic, few studies to date have evaluated the adoption of ambulatory virtual visits during the pandemic by age, race, sex, or insurance . The stuIn this study, we aimed to (1) assess changes in visit volume, type, and patient sociodemographics from the start of the COVID-19 national emergency to the end of May 2020, compared to the same weeks in 2019; and (2) elucidate differences in the use of ambulatory virtual visits (as compared to in-person visits) and, for those using virtual visits, the use of video visits (compared to telephone visits) by age, sex, race, and insurance. We hypothesize that (1) total visit volumes decreased and virtual visits increased during the COVID-19 pandemic, while patient sociodemographics remained similar between the two time periods; and (2) patients who utilized in-person visits during the COVID-19 study period were more likely to be younger than patients who utilized virtual visits, and of those using virtual visits, patients utilizing video visits were more likely to be younger, White, and have commercial insurance than patients utilizing telephone visits -23.The University of Chicago Medical Center (UCMC) is the flagship institution of University of Chicago Medicine, and includes 5 multispecialty faculty ambulatory practice sites in Chicago, IL, and the surrounding area, with over 600,000 encounters per year. UCMC began offering virtual visits in March 2020 in response to the widespread shelter-in-place orders at the city, state, and regional level due to the COVID-19 pandemic. Telephone visits began during the week of March 15, 2020. Video visits began with a pilot program in the hematology/oncology, pediatrics, psychiatry, gastroenterology, and obstetrics/gynecology practices on March 26, 2020, followed by a broad roll-out to all ambulatory faculty clinics on April 6, 2020. All practices used a HIPAA -compliant Zoom platform to enable video visits, which was not integrated into the institution’s electronic health record system (Epic) during the evaluated time period.Immediately after the City of Chicago and State of Illinois shelter-in-place orders were enacted, patients with previously scheduled in-person office visits were contacted and given the option to either reschedule or convert their appointment to a virtual visit. If a patient agreed to a virtual visit, a video visit was encouraged. Patients scheduled for video visits were sent the following through the patient portal or email: a Zoom link for the video visit; a brief prevideo visit checklist followed by more detailed instructions describing the technical requirements to participate in the video visit; and a link to a video highlighting methods to best prepare for the video visit and a demonstration of what to expect. If the patient was unable or unwilling to participate in a video visit, a telephone visit was scheduled, and they were told to expect a call from their provider at the scheduled appointment time. Patients reaching out to schedule new virtual visits were also preferentially offered video visits but were given the opportunity to schedule a telephone visit as well in accordance with their preferences. The availability of virtual visits was marketed widely to our patient population through our patient portal, marketing emails, and our health system’s internet home page. Beginning on May 1, 2020, patients were given the option to begin self-scheduling video visits (but not telephone visits) through the patient portal.All adult and pediatric outpatient clinic visits occurring in UCMC faculty practice locations from March 15 to May 31, 2019, and March 15 to May 31, 2020, were included. The type of outpatient clinic visit was classified as in-person or virtual, and virtual visits were further classified as telephone or video, based on the scheduled visit type for all completed visits. Patient sociodemographic data were examined for each visit, including age, sex, race, and insurance. Age was categorized into 5 groups: 0-17 years, 18-45 years, 46-64 years, 65-74 years, and ≥75 years. Patients were grouped into 3 racial categories: White, Black, and other . Insurance was categorized as Medicare (including Medicare-Medicaid Alignment Initiative), Medicaid, commercial, or other. The data were extracted from the institution’s electronic health record data warehouse. This project received a formal determination of Quality Improvement according to institutional policy. As such, this initiative was not reviewed by the Institutional Review Board.First, we used descriptive statistics to examine weekly and overall visit volumes during the study period, which were the 11 weeks following the COVID-19 national emergency declaration , compared to visit volumes in the corresponding weeks of the 2019 calendar year. Next, we examined visit type  and patient sociodemographics  associated with the visit and compared these characteristics to those visits occurring during the same date range in 2019. Last, we examined differences in ambulatory visit type  by patient sociodemographics  for visits occurring during the COVID-19 study period.P≤.001. To estimate the association between patient sociodemographics and visit type , we performed logistic regression. Results were similar between unadjusted and adjusted analyses; only adjusted analyses are presented. Data were analyzed using RStudio, version 3.6.3 .Data were summarized with chi-square tests where appropriate. Because of the large sample size, statistical significance was set at In the week of March 15-21, 2020, the ambulatory visit volume dropped to 34% of visit volumes when compared to the same week in 2019  and reached a nadir of 20.8% of 2019 levels  in the following week. By the week of May 24-30, 2020, the ambulatory visit volume had rebounded to 81.8% of the volume of the same week in 2019 . Total visit volumes from March 15 to May 31, 2020, were 51.4% of 2019 volumes .Virtual ambulatory visits increased from 0 to 48,475 visits between March 15 to May 31, 2020, and comprised 60.5% of total ambulatory visit volume, with the remaining 39.5%  conducted in person . Among vIn unadjusted analyses, there were statistically significant differences between those who received in-person and virtual visits for all sociodemographics examined . In adjuIn unadjusted analyses, there were statistically significant differences across all sociodemographics examined except sex between those using telephone versus video visits . In adjuTotal visit volumes in the COVID-19 study period were approximately half of that in 2019, although patient sociodemographics were similar. Recovery of clinic volumes after the escalation of the pandemic was largely driven by virtual ambulatory care, which comprised over 60%  of total ambulatory clinic volumes from March 15 through May 31, 2020, a majority of which were video visits. Children, adults ≥65 years, men, and patients with Medicaid coverage were less likely to have virtual visits, whereas patients with Medicare coverage were more likely to have virtual visits compared to patients with commercial insurance coverage. For those who attended virtual visits, children were more likely to have video visits, while adults ≥46 years, men, Black patients, and patients with Medicare or Medicaid coverage were less likely to have video visits.The sociodemographic differences in virtual visits we identified are in line with prior research. For example, prior research found that women were more likely than men to shelter in place due to concerns about the risk of COVID-19 infection for themselves and their family; this would make virtual visits a more appealing visit type for women . AdditioThe sociodemographic differences in virtual (vs in-person) visits and video (vs telephone) visits illustrate the digital divide . The patThe shift in the delivery of ambulatory care through virtual visits was incentivized by the new virtual reimbursement policies from CMS and private insurance companies. The significant contribution of virtual visits to overall ambulatory visit volumes is likely to continue once the COVID-19 pandemic has ended. The volume of virtual ambulatory visits at UCMC has continued to grow even after the end of the study period, indicating sustained interest in virtual visits likely due to continued safety concerns related to the pandemic, ongoing reimbursement for these services, and physician and patient satisfaction with this new option for care delivery ,28. Givemaintaining reimbursement parity between video and telephone visits. Second, given the rapid growth and early success of virtual visits, and the role they will likely play in blended models of care, legislation that makes virtual visit reimbursement permanent is essential to allow for the long-term investment by health care systems and providers needed to improve the virtual visit infrastructure and experience. Third, government insurers and specialty societies should collaborate to establish guidance to help distinguish ambulatory care best suited for virtual versus in-person care. Fourth, quality improvement initiatives should be undertaken at medical institutions to support and improve access to and usability of video visits in populations encountering the greatest barriers to its use. Last, advocacy for policy changes and more universal broadband access are essential to help close the digital divide experienced by our most vulnerable patient populations, which would help address the differential access to virtual visits described in this study.The results of this study and our review of the virtual visit landscape has prompted us to offer five recommendations . First, Maintain reimbursement parity between video and telephone visitsPass legislation making virtual visit reimbursement permanentEstablish guidance to distinguish ambulatory care best suited for virtual versus in-person carePerform quality improvement initiatives to improve access to and usability of video visits in vulnerable populationsAdvocate for policy changes and universal broadband access to close the digital divideOur study has limitations. First, this study only examined a single medical center and was a retrospective analysis; despite this, the diversity of the patient population examined in our study enabled our analysis of ambulatory virtual visit use. Second, our study only examined a limited set of variables, which were used as surrogates for the social determinants of health described in this paper, such as access to broadband internet, health literacy, tech literacy, education, and income, and did not examine virtual and video visit use by ethnicity due to limited data availability. Third, this area of clinical practice and study is rapidly changing and will likely continue to change rapidly over the next few months to years. Further studies at other medical institutions should be conducted to confirm our findings and examine additional sociodemographic variables. Future analyses of ambulatory virtual visits should also investigate patient satisfaction and outcomes by patient visit type , given the differences in reimbursement by visit type category, and whether ambulatory virtual visits increase the geographic area served by academic medical centers or medical institutions with subspecialty care, as already suggested by limited data .The COVID-19 pandemic has drastically changed the health care delivery landscape largely due to the growth of ambulatory virtual visits, which have rapidly become a vital component of health care delivery. Given the differential use of these technologies by age, sex, race, and insurance, these changes also risk perpetuating and even exacerbating existing disparities in health care access and quality, especially if reimbursement policies do not sufficiently account for these differences and the digital divide remains unaddressed.


Hints and Kinks, we discussed the role of causal inference in tasks of health services research (HSR) using examples from health system interventions  as a health system intervention.In a previous would not have been implemented. Note that we never observe one of the two situations, because either the HM is implemented or not, but not both. We now introduce a formal notation for causal inference which allows us to mathematically define a causal effect.We introduced counterfactuals as hypothetical outcomes which are actually not observed in a real-world setting Hernán . We usedYnoHM) together with the outcome under the HM (denoted as YHM). The superscripts denote the counterfactual outcomes we can formalize, but which are actually not observed: Only YHM can be observed if the HM is implemented. An average causal effect in the study population can then be defined by the risk difference Probability(YHM  = 1)–Probability(YnoHM  = 1), abbreviated as RDCausal. Note that we could also use other risk measures, for example a relative risk, for the definition of a causal effect. The choice of the used effect measure depends on the research question because the underlying scale  influences its final interpretation  if the HM had not been implemented . For example, the patient with ID 5 was treated in the HM region with no observed hospital readmission (YObserved = 0). The observed outcome is equal to the counterfactual outcome in the HM region (YObserved = YHM = YHM = 0). Note that if this patient would have been treated in the control region, he or she would have had a readmission (YnoHM  = 1). Because this patient is actually only observed in the HM region, one will never observe the outcome of the control region (YnoHM is missing). The mathematical notation for counterfactuals might be initially confusing, yet it is a necessary component for a causal inference framework.An important question remains: How can we assess an effect measure based on outcomes which are actually not observed? One could compare the outcomes in the region with HM to outcomes in a 'control' region with no HM. Table Causal is zero, because Probability(YHM = 1) = 3/5 and Probability(YnoHM  = 1) = 3/5. Thus, the HM does not reduce hospital readmissions.What is the average causal effect in the study population from Table associational effect measure generally compares risks in subsets of a study population by conditioning on certain study characteristics  and the second expression 1/3 . Thus, RDAssociational is equal to 0–1/3 = –1/3, i.e., the risk of hospital readmissions in the HM region is lower compared to the risk in the control region.An see Fig.  (Hernán see Fig. . In the mentclass2pt{minimCausal and the associational effect RDAssociational leads to the famous ‘association is not causation’ adage. Likely because of this adage, many researchers in HSR avoid any causal terminology, especially when they use ‘only’ observational data (Hernán The difference between the derived causal effect RDa Hernán . They arHow can researchers integrate ‘causality’ in HSR? Our above introduced components of a framework for causal inference is the backbone for modern causal inference. Modern causal inference allows for inference which mimics a situation as if patients would have been assigned by random allocation, despite using an observational study design. Topics for recent calls of causal inference approaches in HSR include, for example, comparative effectiveness research, payment scheme evaluations, health care utilization or the use of simulation studies (see Table Hints and Kinks, we introduced components for a principled framework for causal inference in HSR. Because ‘causal inference’ is conceptually different from ‘description’ or ‘modeling’, HSR needs the integration of a causal inference framework which includes a specific notation, definitions and analysis techniques to extend the traditional tasks of ‘description’ and ‘modeling’. Public health decision-making which solely relies on associational effect measures might lead to inappropriate decisions because questions about optimal decision-making are inherently causal. We plea that students and researchers in the field of HSR are aware of the different available frameworks to successfully address ‘description’, ‘modeling’ and ‘causal inference’, depending on the intended research question.In the present


Hints and Kinks, we explain why a solidly principled causal inference framework should be integrated into the tasks of HSR.In a recent issue of the American Journal of Public Health, Hernán and other colleagues strongly plea for causal thinking in scientific research where the research question investigates consequences of decisions and interventions Ahern . Hernán Table The first example in Table The second example in Table would not have been implemented, i.e., outcomes which are actually never observed. Counterfactuals allow to mathematically define a causal effect, which is conceptually different from an associational effect. In a follow-up Hint and Kinks, we continue on the introduction of a principled framework for causal inference in HSR (Moser et al. Unfortunately, public health decisions on interventions or policies are often only based on ‘descriptive’ and ‘modeled’ results, without the integration of a solidly principled causal inference framework. Despite both, ‘traditional’ and ‘causal inference’, approaches have their proper legitimation in the investigation of specific research questions, many researchers and students in the field of HSR are not trained to the notions of causal inference. Modern causal inference identifies effects of interventions by using the concept of counterfactuals, which are a key component of a causal inference framework Hernán . A countAs Hernán and others, we believe that causal reasoning plays an important role in HSR to support public health decision making. This can only be done by using an explicit ‘causal inference’ framework, besides the traditional tasks of ‘description’ and ‘modeling’. None of these tasks should be viewed superior to another, but each should be adequately chosen, related to the intended research question (Hernán et al.


Coronavirus disease 2019 (COVID-19), the most hectic pandemic of the era, is increasing exponentially and taking thousands of lives worldwide. This study aimed to assess the prevalence of pre-existing comorbidities among COVID-19 patients and their mortality risks with each category of pre-existing comorbidity.To conduct this systematic review and meta-analysis, Medline, Web of Science, Scopus, and CINAHL databases were searched using pre-specified search strategies. Further searches were conducted using the reference list of the selected studies, renowned preprint servers , and relevant journals’ websites. Studies written in the English language included if those were conducted among COVID-19 patients with and without comorbidities and presented survivor vs non-survivor counts or hazard/odds of deaths or survivors with types of pre-existing comorbidities. Comorbidities reported in the selected studies were grouped into eight categories. The pooled likelihoods of deaths in each category were estimated using a fixed or random-effect model, based on the heterogeneity assessment. Publication bias was assessed by visual inspection of the funnel plot asymmetry and Egger’s regression test. Trim and Fill method was used if there any publication bias was found.A total of 41 studies included in this study comprised of 27 670 samples. The most common pre-existing comorbidities in COVID-19 patients were hypertension (39.5%), cardiovascular disease (12.4%), and diabetes (25.2%). The higher likelihood of deaths was found among COVID-19 patients who had pre-existing cardiovascular diseases (odds ratio (OR) = 3.42, 95% confidence interval (CI) = 2.86-4.09), immune and metabolic disorders , respiratory diseases , cerebrovascular diseases , any types of cancers , renal , and liver diseases .This study provides evidence that COVID-19 patients with pre-existing comorbidities had a higher likelihood of death. These findings could potentially help health care providers to sort out the most susceptible COVID-19 patients by comorbidities, take precautionary measures during hospitalization, assess susceptibility to death, and prioritize their treatment, which could potentially reduce the number of fatalities in COVID-19. The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a virus from the large coronavirus family, was first detected on December 31st, 2019 in Wuhan, China, and has now been deemed a global pandemic by the World Health Organization (WHO). The virus is highly transmissible  [The virus is equally transmissible in all ages; however, people who are now in critical conditions or who have died were more likely to be in the elderly population and had one or more morbidities -11. CommThis systematic review and meta-analysis was conducted by following the Preferred Reporting for Systematic Review and Meta-Analysis (PRISMA) consensus statement . StudiesFour databases: Medline, Web of Science, Scopus, and CINAHL, were searched using pre-specified search strategies for each database and concluded on May 01, 2020. The search strategy consists of keywords on COVID-19 disease , pre-existing morbidity , and patients’ survival status  combined using the Boolean operators . Details of the search strategies are presented in Table S1-S4 in the All peer-reviewed and preprint (not-peer-reviewed) studies met the pre-specified inclusion criteria were included in this study.Studies were included if they met the following inclusion criteria: (i) conducted for the hospitalized patients infected with COVID-19 with or without pre-existing comorbidities, (ii) presented survivor and non-survivor counts following COVID-19 among patients with or without pre-existing morbidity or presented hazard/risk/odds ratio of deaths or survival following COVID-19 with the types of morbidities, and (iii) published in the English language. Studies without complete information but met our inclusion criteria were included in the narrative review.Studies were excluded if COVID-19 was reported among pregnant women or children (aged <18 years) because there are additional conditions or morbidities existing in these groups, such as pregnancy complications. We also excluded papers written in languages other than English. Additionally, we excluded review papers, correspondence, viewpoints, editorials, commentaries, and studies where no information related to previous morbidities were reported.All articles identified through the specified search strategy that were compiled and screened in Endnote version X9 . Two investigators  independently screened the included articles based on inclusion and exclusion criteria. Initially, the titles and abstracts of the included studies were screened for a full-text review. Finally, the full-text of each of the selected studies were reviewed by the same investigators. Any conflict of interest faced by the investigators was reported to the senior authors (MNK and MIK) and solve based on the group discussion.A data extraction form was designed, trialed, and modified to extract information from the selected studies. Two authors (MMAK and MGM) used the pre-designed form to extract information independently. The following information was extracted: study location, design, sample size, study population characteristics , and survivor vs non-survivor counts among COVID-19 patients with or without specific comorbidities. If available, the odds/risk/hazard ratio of deaths among COVID-19 patients with comorbidities were extracted against the types of morbidity. Disagreements reported in data extraction were reviewed and solved by the corresponding and senior authors (MNK and MIK). The Modified Newcastle-Ottawa scale, as part of the data extraction strategy, was used to assess the quality of selected studies . The sca2I statistics) was moderate (50%-74%) or high (≥75%), the pooled estimates of ORs were computed using the random-effects model [One or more pre-existing comorbidities among COVID-19 patients reported in the selected studies were grouped into eight broad categories based on the type of comorbidities. These were cardiovascular system diseases , immune and metabolic disorders , respiratory system diseases , acute respiratory distress syndrome, tuberculosis, etc.), cancer , cerebrovascular diseases , renal system diseases , liver system diseases , and gastrointestinal system diseases . The case definition of these comorbidities is presented in ts model . Subgrouts model . When evts model . Stata sA total of 247 articles were identified from the databases searched, and the additional 15 articles were identified by checking the reference list of the selected articles and the selected journals’ websites (A summary of the 41 selected articles is represented in Distribution of the type of morbidity presented in 2I = 81.2%) than the patients who had no cardiovascular diseases. The odds of death among COVID-19 patients with immune and metabolic disorders were also found to be 246% higher  than among COVID-19 patients without such disorders. The incidence of COVID-19 among people with respiratory diseases increases mortality risk around two times  more than COVID-19 patients without respiratory system diseases. Similarly, we found higher mortality risk among COVID-19 patients who had pre-existing any types of cancers  and cerebrovascular system diseases  more than their counterparts. Moreover, the incidence of COVID-19 among patients with pre-existing renal disease and chronic liver disease increased mortality risk by about three times  more and one and half times  more, respectively compared to the COVID-19 patients who did not have such comorbidities.The pooled ORs of deaths for each category of pre-existing comorbidities among COVID-19 patients, publication bias, and Trim and Fill estimates are presented in We found evidence of publication bias for the four categories of pre-existing comorbidities: cardiovascular diseases, cerebrovascular diseases, renal system diseases, and liver diseases  for cardiovascular diseases (81.2%) and respiratory diseases (75.7%). To examine the sources of heterogeneity, we conducted stratified analysis across types of comorbidities, study design , sample size , age of the total sample (divided based on mean age and classified as ≤60 years and >60 years), and age at death (divided based on mean age and classified as ≤70 vs >70) , diabetes (25.2%), and cardiovascular disease (12.4%). The likelihood of death was higher among COVID-19 patients who had comorbidities like cardiovascular diseases, cerebrovascular diseases, respiratory diseases, renal diseases, immune and metabolic disorders, hepatic diseases, and cancer. This evidence will guide physicians to take precautionary measures, which could reduce the number of fatalities following a secondary infection with COVID-19.Among the total positive COVID-19 cases included in this systematic review, around 43% had one or more pre-existing comorbidities, mostly cardiovascular diseases, and immune and metabolic disorders. These underlying diseases are increasing the risk of death among COVID-19 patients. Recent reviews have confirmed that COVID-19 infection among patients with cardiovascular diseases, and immune and metabolic disorders, particularly, having hypertension and diabetes elevate the risk of infection severity, ICU admission, and the risk of death . The undPooled likelihoods in this study provide evidence of higher deaths among COVID-19 patients who had pre-existing chronic respiratory diseases or any type of cancer- as found in other reviews . The chrThis study also suggests that patients with cerebrovascular, liver, and renal diseases are more vulnerable to mortality following the secondary incidence of COVID-19 than patients who do not have such diseases. The results are comparable to deaths among previously reported SARS patients . DiseaseThis study has several strengths and limitations that should be reported. To our knowledge, this is the first study of its kind that summarizes all morbidities among COVID-19 patients that lead to death using data from globally representative studies. Moreover, comorbidities reported among COVID-19 patients were classified into board groups based on their characteristics, and the likelihood of death was estimated separately for each group. This evidence will inform health care providers about the risk of death among COVID-19 patients with different groups of pre-existing comorbidities. Thus, they will be able to take precautionary measures early targeting to prevent deaths. However, this study reported the odds of death for COVID-19 patients with one pre-existing comorbidity only. Many COVID-19 patients may have multi-morbidities (COVID-19 with pre-existing two or more comorbidities), and they may have a higher risk of death. The studies included in this review considered each morbidity separately; for instance, if COVID-19 patients had both hypertension and diabetes, they were included in both groups. None of the included studies considered COVID-19 patients with two or more comorbidities together; therefore, we failed to provide the likelihood of deaths for higher risk patients. Moreover, the percentage distribution of morbidity among COVID-19 patients estimated in this study could be different from the actual situation depending upon the counts in the included studies. However, as we included all available studies conducted worldwide, therefore, any distortion is likely to be random. In addition, the likelihoods presented in this study were mostly unadjusted (31 of the 35 articles included) as they were calculated from the extracted raw data. This may overestimate or underestimate the actual likelihood of deaths in COVID-19 patients because age and other socio-demographic characteristics are potential confounders of their deaths, which should be adjusted for unbiased estimates. Despite these limitations, this study is unique and beneficial for health care providers to handle COVID-19 patients with pre-existing comorbidities.About 43% of the sample included in this systematic review had one or more pre-existing comorbidities and had COVID-19 as a secondary infection. The most common pre-existing comorbidities were hypertension, diabetes, and cardiovascular disease. The likelihood of death was higher among COVID-19 patients who had pre-existing cardiovascular and cerebrovascular diseases, respiratory diseases, renal diseases, immune and metabolic disorders, liver diseases, and any types of cancer. These findings will help health care providers to sort COVID-19 patients by comorbidities, take precautionary measures during hospitalization, assess susceptibility to death, and prioritize their treatment. These could potentially reduce the number of fatalities from secondary infection with COVID-19 disease.


The method was selective and linear over a concentration range of 0.5–50 ng/mL. The method was validated for sensitivity, accuracy, precision, extraction recovery, matrix effect, and stability in porcine whole blood and lung tissue homogenates, and all values were within acceptable ranges. The method was applied to a pharmacokinetic study to quantitate sirolimus levels in porcine blood and its distribution in lung tissue following the application of stents in the porcine coronary arteries. It enabled the quantification of sirolimus concentration until 2 and 14 days in blood and in lung tissue, respectively. This method would be appropriate for both routine porcine pharmacokinetic and bio-distribution studies of sirolimus formulations.Sirolimus is a hydrophobic macrolide compound that has been used for long-term immunosuppressive therapy, prevention of restenosis, and treatment of lymphangioleiomyomatosis. In this study, a simple and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) was developed and validated for the simultaneous determination of sirolimus in both porcine whole blood and lung tissue. Blood and lung tissue homogenates were deproteinized with acetonitrile and injected into the LC-MS/MS system for analysis using the positive electrospray ionization mode. The drug was separated on a C18 reversed phase column with a gradient mobile phase (ammonium formate buffer (5 mM) with 0.1% formic acid and acetonitrile) at 0.2 mL/min. The selected reaction monitoring transitions of  Streptomyces hygroscopicus in Easter Island + at m/z 931.5 and 809.5, respectively, which are frequently observed in ammonia-mediated ionization mass spectra of compounds with oxygen groups  in their structures [m/z 864.4 for sirolimus and m/z 756.5 for IS, which is in accordance with previous findings [m/z 864.4 of sirolimus was likely formed due to the separation of an -OCH3 and two H2O, whereas the product ion at m/z 756.5 of the IS was potentially the result of three H2O separation. The declustering potential, collision energy, and collision cell exit potential were optimized to obtain maximum responses of sirolimus and the IS (m/z 931.5 → 864.4 for sirolimus and m/z 809.5 → 756.5 for the IS. An ammonium formate solution (5 mM) was employed as a mobile phase component to induce the ammonium adducts of sirolimus and the IS. Several mobile phase compositions and reverse phase columns were tested to produce peaks of sirolimus and the IS with adequate retention time and separation. Peaks of sirolimus and IS were not appropriately observed with isocratic modes; thus, gradient modes were used. Finally, a mixture of ammonium formate buffer (5 mM) with 0.1% formic acid (A) and acetonitrile (B) in gradient mode  and a SynergiTM 4 μm polar-RP 80A column were used to obtain optimized peaks of sirolimus and the IS.The chemical structures of sirolimus and ascomycin  are depicted in ructures . In the findings ,34. The d the IS . ThereafTypical chromatograms of blank porcine whole blood/lung tissue, zero calibrators, a sirolimus standard, and a porcine sample from the pharmacokinetic studies are shown in 2 = 0.99317 ± 0.00150  least squares regression analysis), where y, x, and R refer to peak area ratio (sirolimus/IS), sirolimus concentration in whole blood, and the correlation coefficient, respectively. The calibration curves (peak area ratio versus concentration) for sirolimus in porcine lung tissue were linear over a range of 0.5–50 ng/mL with the developed analytical conditions. Using the weighted (1/x) least squares regression analysis, the calibration equation for sirolimus was y = (0.05056 ± 0.00356) x + (0.00003 ± 0.00553), with R2 = 0.99792 ± 0.00162 , where y, x, and R refer to peak area ratio (sirolimus/IS), sirolimus concentration in lung tissue, and the correlation coefficient, respectively. The weighted (1/x) least squares regression analysis demonstrated the acceptable precision and accuracy of the corresponding calculated concentrations at each level for both whole blood and lung tissue. The precision was 4.33–13.26% for whole blood and 1.66–10.52% for lung tissue, whereas the accuracy was 90.42–107.63% for whole blood and 94.06–102.29% for lung tissue. Thus, the response (sirolimus/IS peak ratio) was directly proportional to the whole blood/lung tissue concentration of sirolimus, indicating the linearity of the LC-MS/MS assay in the range of 0.5–50 ng/mL.Under the developed analytical conditions, the calibration curves for sirolimus in porcine whole blood were linear over a concentration range of 0.5–50 ng/mL. The calibration equation for sirolimus was y = (0.05227 ± 0.00373) x + (0.00766 ± 0.00251), with RThe lower limit of quantification (LLOQ) for the LC-MS/MS assay was established at a concentration of 0.5 ng/mL. In porcine whole blood, the LLOQ showed an accuracy of 95.25% and a precision of 14.14%, whereas, in porcine lung tissue, the LLOQ showed an accuracy of 110.79% and a precision of 1.85% . The sigQuality control (QC) samples were analyzed in five replicates over five different days to determine the intra-day and inter-day accuracy/precision. Four levels of sirolimus in porcine whole blood/lung tissue were investigated, including LLOQ (0.5 ng/mL), low QC , middle QC , and high QC . The extraction recovery was evaluated by comparing the peak responses of sirolimus QC samples and the IS with those of blanks spiked with the analytes after extraction at the same concentrations. The extraction recovery values were similar for all levels of QC within the same type of sample matrix . They raThe matrix effect was investigated in three different porcine lung tissue samples . The peaThe relative matrix effect  was assessed by a direct comparison of the peak areas among samples in the same QC level (set 1). For porcine whole blood samples, the precision of set 1 was in the range 0.77–8.43%, which was comparable with that of set 2. For lung tissue samples, the precision ranged from 1.15% to 6.91%, slightly narrower than the values from set 2. These results indicate that there was no significant effect of the matrix for the analysis of sirolimus and IS in porcine whole blood and lung tissue samples using the developed LC-MS/MS method.At short-term storage conditions , stock solutions of sirolimus (50 ng/mL) and the IS (1 ng/mL) showed the stability of 100.80% and 101.58% compared to fresh-prepared solutions, respectively. After long-term storage at −80 °C for 3 months, the stability was 101.26% for sirolimus and 95.91% for the IS. These data indicate that sirolimus and the IS are stable in the stock solution during both short- and long-term storage.The stability of sirolimus was investigated in porcine whole blood and lung tissue at three different QC  levels over various typical handling and storage conditions . The posCarry-over study was conducted to measure residual analyte from a preceding sample that remains in the analytical instrument. Obtained results for carry-over of sirolimus and IS are shown in max) was 7.79 ng/mL, and the Tmax was 2.26 hr. The systemic exposure up to the last time point  was 156.9 ng/mL·h, and the AUCinf was 164.8 ng/mL·h , ascomycin , formic acid, and ammonium formate were purchased from Sigma-Aldrich . HPLC-grade acetonitrile and water were supplied by Honeywell Burdick & Jackson . All other reagents were of analytical grade and were used without any further purification.TM 4 μm polar-RP 80A column  equipped with a SecurityGuardTM column (4.0 mm × 3.0 mm) was utilized to optimally separate sirolimus and the IS from endogenous substances of porcine whole blood and lung tissue. The mobile phase was a mixture of ammonium formate buffer (5 mM) with 0.1% formic acid (A) and acetonitrile (B). Sample separation was conducted using a flow rate of 0.2 mL/min and an 8.5-min gradient condition . Temperatures of the column and the autosampler were maintained at 30 °C and 4 °C, respectively. The injection volume was 10 μL for whole blood and 5 μL for tissue samples. The SRM transitions of m/z 931.5 → 864.4 and m/z 809.5 → 756.5 were applied for sirolimus and IS, respectively. Mass data were acquired using Analyst software version 1.5.2 . Data analysis was processed using SCIEX OS offline software version 1.6 . The working parameters of the LC-MS/MS system are listed in Sirolimus was analyzed using an LC-MS/MS system consisting of an AB SCIEX Triple Quad 5500 mass spectrometer  equipped with a turbo ion spray interface in positive ionization mode, an Agilent LC 1200 Binary pump system , and a CTC analytics autosampler . Ascomycin was used as an IS. A Synergiw/w) cold phosphate-buffered saline (PBS) was added to process the homogenization. Tissues were kept in the ice-bath during the homogenization process, and tissue homogenates were stored at −20 °C until use.Blank lung tissue and pharmacokinetic experimental lung tissue samples were homogenized using the KT 30 homogenizer . Lung tissues were accurately weighed, and four-fold  was appropriately diluted with acetonitrile to obtain sets of working standard solutions (5–500 ng/mL). All the stock and working standard solutions were stored at −80 °C until experimental analysis. Standard samples were prepared in whole blood by spiking 90 μL of blank porcine blood with 10 μL of each working standard solution. Similarly, standard samples in porcine lung tissues were prepared by spiking 15 μL of each working standard solution into 135 μL of blank lung tissue homogenate. The final sirolimus concentrations in blood and lung tissue homogenate were 0.5, 1, 2, 5, 10, 20, and 50 ng/mL. Extraction of the drug and IS was performed using a protein precipitation method as previously described ,39. To eThe LC-MS/MS method for sirolimus analysis was validated according to the bioanalytical method validation guidelines of the US FDA in 2018, as previously described ,40.Blank whole blood and lung tissue homogenates from six or three animals, respectively, were spiked with the IS only or with both sirolimus and IS. Chromatograms of blank blood/lung tissue, blood/lung tissue spiked with the IS only, and blood/lung tissue spiked with both sirolimus and IS were then compared for selectivity of the assay.2 values of calibration curves were used to evaluate the linearity of the assay, using the criteria of ≥ 0.990. Accuracy and precision were evaluated during linearity runs using the deviation from the nominal concentration (RE%) and the CV%, respectively. Criteria of the accuracy and precision of the assay were set within ± 15%, except for the LLOQ, which were set at ± 20%.The linearity of the assay was assessed using the standard samples prepared with sirolimus blood and lung tissue homogenates over the concentration range of 0.5–50 ng/mL. Calibration curves were constructed using the peak area ratios of sirolimus and IS by weighted (1/x) linear regression analysis. RThe LOD was determined by the equation: LOD = 3.3 σ/m, where σ is the standard deviation of the intercept of the regression line, and m is the slope of the calibration curve. LOD was also experimentally determined using a signal-to-noise ratio of ≥ 3. The LLOQ, which is the lowest concentration of sirolimus that can be quantitatively determined with a precision of less than or equal to 20% and accuracy between 80% and 120%, was determined with a signal-to-noise ratio criterion of ≥ 10.QC samples at four different concentrations of 0.5 (LLOQ), 1.5 (LQ), 15 (MQ), and 40 (HQ) ng/mL were used to assess accuracy and precision. For intra-day accuracy and precision, five replicates of QC samples were analyzed within one day. The QC samples were analyzed in five replicates over five different days to examine the inter-day data. The acceptance criteria were set at below ± 15% of RE and within ± 15% of CV for accuracy and precision, respectively, except at the LLOQ, which were set at ± 20%.The extraction recovery of the analyte was assessed to ensure that the sample extraction process is efficient and reproducible. Peak areas of extracted samples at LQ, MQ, and HQ concentrations with those of blanks spiked with sirolimus post-extraction were compared. The matrix effect was evaluated using three sources of blank whole blood or lung tissue homogenates matrix to determine whether the endogenous components of whole blood or lung tissue affect the ionization of sirolimus and IS. To determine the absolute matrix effect, mean peak areas of post-deproteinization samples (set 1) with those of neat solutions of compounds (set 2) in acetonitrile at equivalence concentrations were compared. The variability in peak areas from set 1, expressed as precision , was determined and considered as the relative matrix effect.The stability of stock solutions of sirolimus and IS was assessed by comparing the peak response of freshly prepared acetonitrile solution with that of the solution stored at room temperature for 4 h or at −80 °C for 3 months of sirolimus (50 ng/mL) and IS (1 ng/mL). The stability of QC samples in porcine whole blood  was investigated in terms of short- and long-term stability, post-preparative stability, and freeze–thaw stability. Short-term stability was assessed by keeping QC samples in laboratory condition (room temperature ~25 °C) for 4 h, and long-term stability was evaluated with QC samples stored at −80 °C for 3 months. The post-preparative stability of processed samples stored in an autosampler at 4 °C was evaluated after 24 h. Freeze–thaw stability was tested over three repeated cycles, whereby QC samples were frozen at −80 °C and then thawed at room temperature. Samples were considered stable at the test conditions if the intensity of stored samples differed by less than 15% from that of freshly prepared samples.Carry-over of sirolimus and IS was assessed by analyzing blank samples after the highest sirolimus standard samples . Then, standard samples at LLOQ (0.5 ng/mL) were also analyzed after the blank samples. The experiment was repeated three times for both blood and lung tissue samples. Carry-over in the blank samples should not be greater than 20% of the sirolimus response at the LLOQ and 5% of the response for the IS.In this study, a total of 27 male Defined Health Status pigs were utilized  . Animals were randomly divided into nine groups following the sacrifice time point . As a form of randomization, animals were assigned to the study in order of their implantation. All animals were handled under the National Institutes of Health guideline, and Animal Care and Use Committee policies of the Yonsei University Cardiovascular Product Evaluation Center . TM scaffolds  were implanted in 24 animals, two each. Three animals were used as blank controls. The scaffold is coated with a single layer of poly  (PDLLA), sirolimus (0.9 μg/mm2), and phosphorylcholine polymer. The total amount of sirolimus carried by the two PhoslineTM scaffolds per animal is 326 μg.For the scaffold implantation, anesthesia was induced with a mixture of 0.02–0.04 mg/kg of atropine, 2.0–8.0 mg/kg of azaperone, 0.1–1.0 mg/kg of xylazine, 5.0 mg/kg of alfaxalone, and 1.0–3.0 mg/kg of tramadol (via intramuscular injection), and maintained with a mixture of approximately 2% isoflurane in oxygen . About 200 IU/kg of heparin was administered intravenously before selective catheterization for the scaffold implantation. A total of 48 PhoslineImplantation was performed in two of the coronary arteries  per animal, as the anatomy allowed. The interventionalist decided on the suitability of the vessels, based on angiographic images of the native vessels. The scaffold was introduced into the artery by advancing the scaffolded balloon catheter through the guide catheter and over the guidewire to the deployment site. Four radiopaque markers at each end of the scaffold facilitated precise placement. After implantation, animals received 100 mg of aspirin and 75 mg of clopidogrel daily until termination.n = 24 at 0, 5 min, and 1 h; n = 21 at 2, 4, and 6 h; n = 18 at 1 d; n = 15 at 2 and 3 d; and n = 12 at 7 d. Then, the blood samples were treated with ethylenediaminetetraacetic acid (EDTA). After sacrifice, lung tissues were explanted and washed with PBS. All tissues and whole blood samples were stored at −80 °C until use.Blood samples were obtained at pre-implantation, post-implantation, and at other time points depending on each group until sacrifice . Thus, the numbers of blood samples were as follows: In this study, an LC-MS/MS bioanalytical assay method was successfully developed and validated for the determination of sirolimus levels in both porcine whole blood and lung tissue. The sample preparation required a simple protein precipitation step for sirolimus extraction. This method exhibited adequate selectivity, linearity, sensitivity, accuracy, and precision. The drug and IS were stable during typical handling and processing conditions. The method was successfully applied for pharmacokinetic and bio-distribution studies of sirolimus following the application of stents in porcine coronary arteries. To the best of our knowledge, this is the first report describing the development and validation of an LC–MS/MS assay of sirolimus in both porcine whole blood and lung tissues. This method can be applied in pharmacokinetic and bio-distribution studies to quantitate sirolimus levels in blood, as well as its distribution to various organs.