The importance of circulating tumor cells (CTC) is well recognized. However, the biological characteristics of CTC in the bloodstream have not yet been examined in detail, due to the limited number of CTC cell lines currently available. Thirty-nine CTC cell lines were reported by 2021. For successful cell culturing, these CTC cell lines were reviewed. Previous studies on short-term cultures of CTC also analyzed approaches for establishing the long-term culture of CTC. Negative selection, hypoxic conditions, three-dimensional conditions, and careful management are preferable for the long-term culture of CTC. However, the establishment of CTC cell lines is dependent on the specific characteristics of each cell type. Therefore, a method to establish CTC cell lines has not yet been developed. Further efforts are needed to resolve this issue.  The importance of circulating tumor cells (CTC) has recently been recognized. Although the cultivation of CTC is very promising as a preclinical model for human cancer, difficulties are associated with establishing CTC cell lines due to the low number of CTC in blood and the majority of these cells not being viable. Only a limited number of permanent cell lines of CTC are currently available and their management is more challenging than that of standard cell lines.CTC cell lines are important for examining the biology of CTC, but not for providing quick feedback to cancer patients. Furthermore, the translational relevance of cell lines is often questioned because prolonged cultures and multiple passages lead to phenotypes that are no longer representative of the original tumor in terms of cell epigenetics and gene expression profiles . TherefoWe previously established cancer cell lines from several tissue specimens, including esophageal and pancreatic cancer ,3. We idThe systematic detection of CTC was initially reported by other research groups. Racila et al.  used an The majority of conventional cancer cell lines generally proliferate on a two-dimensional (2D) environment, in contrast to the 3D environment of most CTC cell lines, such as spheroids or organoids. We established a small cell esophageal cancer cell line (TYUC-1) that actively grew with sphere formation in a standard culture flask from a tissue specimen . This exConcerning the approaches to long-term culture, we also examined the literature for short-term cultures. Our aim was to obtain a more detailed understanding of the characteristics of CTC and several methods that support the establishment of long-term cultures from short-term cultures. Based on previous findings and our own research, we herein discussed critical issues facing the development of CTC culture methodologies and proposed a rational strategy for the long-term culture of CTC.In this review, a long-term culture (CTC cell line) is defined as a culture period of longer than 1 year or continuous passages for more than 6 months. A short-term culture is defined as a culture period of longer than 1 week. We briefly discussed culture-related factors, such as methods for enriching CTC, the characteristics of CTC in the bloodstream, and stem cell biology. We excluded methods for ultra-short term (less than 1 week) cultures and expression assays because the focus of these methods is clinical evaluation, not long-term cultures. We mainly concentrated on the development of a reproducible protocol for establishing CTC cell lines. We also excluded details on the CTC capture system, clinical applications, genetic analyses, CTC-derived xenografts, and cultures of bone marrow disseminated tumor cells.Thirty-nine CTC cell lines were reported by 2021: 11 from colon cancer, 13 from breast cancer, 2 from prostate cancer, 2 from gastroesophageal cancer, 5 from small cell lung cancer, 2 from non-small cell lung cancer, and 4 from malignant melanoma. Establishment rates ranged between 1 and 17%, with most studies achieving a rate <10%  cells should survive in the blood because they negligibly express EpCAM. Therefore, EpCAM is not a standard marker for the establishment of cell lines. However, some cells express EpCAM and maintain epithelial characteristics. Additionally, many cells express stem cell markers, such as CD133, CD44, CD24, and ALDH1. These findings indicate that CTC have a mixed epithelial phenotype, an intermediate epithelial/mesenchymal phenotype, and stem cell-like properties.Stem cell concepts have been employed for long-term CTC cultures. CTC as non-adherent spheres may reflect the intrinsic properties of cancer stem cells that remain viable in the bloodstream after the loss of their attachment to the basement membrane . HoweverThe majority of CTC cell lines have been established using 6 to 18 mL of blood. A blood volume >10 mL is a suitable source for CTC cultures. Furthermore, since the establishment of cell lines was found to be slightly easier from patients with a high CTC count (>30/mL), a CTC count >300 is preferable for the success of cultures. However, a high CTC count only does not guarantee the establishment of a permanent cell line. Furthermore, a conventional CTC count is generally assessed using EpCAM(+) cells. Since EpCAM(−) CTC have been overlooked, CTC counts are not accurate. The most recent study on CTC used a 96 mL sample obtained from leukapheresis for a cell culture . Leukaph®). Although antibody-based approaches were employed in three studies, capture with antibodies against surface antigens is harmful, precluding its use for CTC cultures [An enrichment step for CTC is indispensable before cells are cultured. Viable CTC isolation may be divided into three types: antibody-based, physical property-based, and function-based approaches. The most successful enrichment method for long-term cultures is the negative selection method  has been used as the start medium. The culture medium is then changed to ordinal standard medium. The culture medium is important during the initial 2 weeks. The proliferation of most cells peaks after approximately 21 days. CTC with proliferative potential may then grow without special growth supplement, except for FCS .Epidermal growth factor (EGF) and basic fibroblast growth factor (FGF) are often listed in recipes for stem cell cultures. Recipes for serum-free CTC culture media mostly include proprietary B27 and N2 supplements, together with insulin, transferrin, and selenium. Y27632, a cell-permeable inhibitor of Rho-associated coiled-coil containing protein kinase, is also used. However, the optimal conditions for ex vivo CTC cultures have yet to be established.To appropriately culture CTC, it is important to understand the progression of CTC growth in each step. In this section, we focus on the growth progression of selected CTC cell lines.1.CTC-1, CTC2, and CTC3Zhang et al.  previous2.CTC-MCC-41In the case of CTC-MCC-41 cells, viable CTC were still observed after 4 days of culture in serum-reduced medium under hypoxic conditions. After 10 days, CTC started to proliferate and formed spheres. After a few weeks, the cell culture was switched to normoxic conditions. Single colonies from the CD45(−) population that appeared during the first week of the cell culture were transferred into a 24-well plate for further growth .3.BHGc7 and BHGc10BHGc cells initially grew as typical small spheroids that eventually showed the outgrowth of adherent tumor cells and shedding of apoptotic cell fragments . Prolife4.CTC-TJH-01During an ex vivo culture of CTC-TJH-01 cells, contaminating blood cells and a small number of CTC underwent rapid cell death. The majority of surviving CTC adhered to the plate, but remained dormant throughout the initial two weeks, suggesting the low in vitro clonogenic potential of CTC. CTC started to proliferate after two weeks, and the culture was switched back to normoxic conditions. CTC slowly proliferated, formed clusters, and then exhibited sustained proliferation ,20.5.CTC-ITB-01CTC-ITB-01 cells grew in parallel as adherent and non-adherent cell fractions with various cell sizes. Non-adherent cells grew from adherent cells. CTC showed an interchangeable adherent and non-adherent cell population similar to cancer stem cells .6.UWG01CTC and UWG02CTCIn the case of UWG01CTC and UWG02CTC cells, viable, relatively pure cultures were observed within 3 weeks and rapidly expanded. UWG01CTC is adherent and requires trypsinization for passaging; however, a loose adherent spheroid phenotype was induced in a hypoxic environment and serum-free media. In contrast, UWG02CTC grew in long mucinous, loosely aggregated, and weakly adherent strands that only required gentle mechanical dissociation for passaging .7.CTC-3CTC-3 cells began to proliferate and form clusters after 14 days of culture . HoweverSeveral conditions were used for the expansion of CTC. However, suitable conditions depended on the characteristics of each cancer cell. Furthermore, the optimal timing and culture conditions for CTC expansion have not yet been established. Representative cases are described below.1.CTC-1, CTC-2, and CTC-3Adherent plates were used during the first week with growth factor-enriched stem cell culture medium. The medium was changed to EpiCult-C medium between 8 and 21 days. Standard medium with 10% FCS was then used for maintenance .2.Mel-167, PEM-22, Mel-182, and PEM-78CTC were cultured in 24-well ultra-low attachment plates for between 4 and 8 weeks. A 3D fibrin Matrigel culture was then used, and this was followed by a switch to an anchorage-independent culture [3.CTC-MCC-41CTC were cultured in 24-well non-adherent plates for the 1st week. Cells were then transferred to new 24-well non-adherent plates in the 2nd week and maintained in a T25 flask .4.CTC-3A Matrigel-coated plate was used in the first 2 weeks, and cells were then transferred to a normal 6-well plate .To establish approaches to long-term culture, short-term culture was reviewed. Information that will contribute to the establishment of long-term cultures of CTC was obtained .We successfully performed a short-term culture of CTC using a magnetic cell sorting system (MACS) with anti-Human Epithelial Antigen (HEA) 125 in 1999 . We dete®). Few studies employed RBC cell lysis only. Under optimal conditions, the introduction of an RBC lysis step does not markedly affect the viability of the nucleated cell fraction [® (filtration using a porous polycarbonate membrane) selection [Similar to long-term cultures, most enrichment methods for cultures involve the negative selection method  [® detects cancer cells not only on the separating membrane, but also on the plastic bottom of the 6-well plate [The MetaCellof 8 μm) ,45,47,51ll plate . Membran2.On-chip cultureZhang et al.  describeKapeleris et al.  suggesteRegarding CD45, a significant population of “double positive” cells with hematopoietic and epithelial markers (CK+/CD45+) were detected in many patient samples . FurtherThus, most of the double positive cells may be the heterogeneous CTCs . From thKhoo et al.  maintainThe viability and morphology of expanded CTC were examined by LIVE/DEAD staining .Few studies have employed low attachment plates and hypoxic conditions. Therefore, short-term cultures are generally aimed at clinical use, not long-term cultures. Therefore, they are routinely conducted under adherent and normoxic conditions. Furthermore, most studies used simple standard culture medium with FCS.Tapered microwells are preferred over conventional cylindrical microwells for cluster formation. Cultures are sheltered within microwells with minimal disturbance during medium changes, which reduces shear stress and cell loss to allow for subsequent expansion . This meCarmona-Ule et al.  suggeste® was 65%. Despite the leukapheresis approach, CTC from only a few patients have been successfully cultured. A success rate of 100% (12/12 samples) was achieved by Ficoll-Paque selection [The success rate of cultures was previously reported to be more than 50%. The total success rate of MetaCellelection .The typical cell culture period is <14 days and few cells may be cultured for more than 3 months. Typical spheres were shown to increase in size over time from 7–21 days . Khoo etCarmona-Ule et al.  investigAlthough proliferation was not observed due to the challenging ex vivo growth environment, the maintenance of CTC viability was guaranteed for at least two months . EliasovKhoo et al.  showed tKhoo et al. ,63 demonThe morphological spheroid construct typically consists of three different presentable types: large-sized, cohesive round-shaped spheroids; small-sized cohesive irregular or round spheroids; and discohesive “grape-like” spheroids . Some sa® system, CTC grew through the membrane on the bottom of the well after 14 days in an in vitro culture exhibiting two different phenotypes, epithelial-like and stem cell-like. Stem cell-like cells formed visible cell clumps. Cells growing on the bottom showed a very plastic morphology [In the MetaCellrphology . CTC incrphology .Non-adherent blood cells provide a cushion layer that keeps CTC in suspension . CTC havAfter 1 week of a cell culture under hypoxic conditions, cells are generally switched to standard cell culture conditions. When 85% confluence is reached, growing cells are transferred to an ultra-low attachment 24-well plate . HoweverRegarding the establishment of CTC cell lines, the majority of cultured CTC lack the ability to proliferate. Therefore, 2 months is a sufficient time period to assess whether CTC exhibit the ability to continuously proliferate. Future studies that compare CTC lines with CTC that stop proliferating after a short-term culture will facilitate the development of optimal molecular conditions for the permanent survival and growth of CTC .Cell growth depends on the proliferative potential of each cell. Among 21 conventional esophageal cancer cell lines, the time after the initiation of the primary culture to the second culture varied from 2.5 weeks to 48 weeks, depending on cell growth patterns, such as the adhesion-dependent type, spheroid type, and mixed type. Adaptability to a new environment is also important [We previously reported the maintenance of several cancer cells in protein-free medium without other supplements for 2 years . These cBased on our experience and previous findings, we preliminarily performed a culture of CTC collected from patients with prostate cancer and renal cell carcinoma. We recently developed a CTC capture system . We firs®-pot (porous polycarbonate membrane selection) with Lymphoprep or negative selection using RosetteSep® CD45, CD36, or CD56. A short-term culture of CTC was successful for 16 out of 27 samples; however, expansion was very challenging to overcome. One reason is that we did not obtain a high number of viable CTC from patients because most had already received several courses of chemotherapy. The most important issue is that we have not yet established a specific protocol for individual carcinoma cells.We also used VIVANT-CELLRegarding double positive (CK+/CD45+) cells, we also detected these cells in the short-term culture of CTC. Some of these cells were large, polymorphic in shape and polynuclear . These mIn several trials, we developed three cell lines with the spontaneous immortalization of lymphocytes from different patients under conditions involving several growth supplements, such as Y-27632 and Z-VAD-fmk. We previously reported the incidental establishment of B-cell cell lines (ITSM), derived from EB virus spontaneous activation during an ordinary cell culture from pseudomyxoma peritonei . The lonCTC cell lines have unique intermediate characteristics between primary tumors and distant metastasis. CTC are not simple travelers in the bloodstream. Although most CTC are apoptotic and sensitive to stress, a few viable CTC survived.The CTC count of samples is the most important factor for the successful establishment of CTC cell lines. A CTC count of >100 cells/mL blood is favorable. Leukapheresis may be the most powerful method for enriching CTC; however, its success rate is low.There is currently no evidence to support the contribution of CD45+ cells to the success of long-term culture. However, a reconsideration of the parameters for CTC isolation is warranted .It is important to note that because optimal CTC culture conditions have yet to be defined, current research relies on stem cell culture methods that ensure maximal CTC expansion. Each protocol is designed based on a researcher’s own expertise and empirical results, and not by a rationale based on the biological features of CTC, which are presently not defined .Moreover, activated lymphocyte proliferation is transient, ending with programmed activation-induced cell death. Macrophages are the only cell type among peripheral blood mononuclear cells that are capable of surviving for weeks in culture. However, they are also a terminally differentiated cell type that is incapable of ex vivo proliferation .Other key technical limitations include the maintenance of the CTC phenotype and the composition of a stable population in culture. Previous studies suggested that normal human mesenchymal stem cells are prone to genomic changes and subsequent malignant transformation in long-term cultures .Therefore, the further optimization of CTC culture conditions and a more detailed understanding of the differences between non-adherent CTC cell lines and adherent cell lines as well as CTC cultures and patient samples is required .As described above, the recommended protocol for CTC cell lines is as follows . With caA careful review of previous studies suggested that CTC in long- and short-term cultures have different characteristics. Not only the culture method, but also the characteristics of CTC themselves affect the success of establishing CTC cell lines.In the circulation, CTC from any type of cancer encounter the same environment, which differs from their solid tissue location. Only cells with specific characteristics, such as stem cell-like cells, may survive in the bloodstream. CTC without these characteristics are protected by surrounding cells, such as leukocytes and platelets. However, it currently remains unclear whether only CTC with stem cell features are suitable long-term culture.Low attachment plates and hypoxic conditions may be the ideal conditions for the initial culture steps. A successful culture medium contains several growth factors and anti-apoptotic supplements. However, as described above, cancer cells have already acquired growth and survival autonomy. Therefore, CTC cultured in the presence of growth factors and small molecule inhibitors may not be ideal for modeling cancer and stromal interactions. The most important factors are the timing of CTC transfer to the next step and the careful management of medium changes.Short-term culture is the initial approach to long-term culture; however, prolonged protocols sometimes deviate from the main expansive route for establishing CTC cell lines. The success of short-term cultures is not always affected by a high number of CTC; however, long-term cultures generally require high numbers of CTC. Therefore, the culture method may only prolong cell survival; it does not affect permanent proliferative activity. The most important issue is obtaining CTC that are suitable for long-term culture; however, a method to identify these CTC has not yet been established.There is currently no gold standard method. The selection of an optimal method for each sample may be achieved through careful management and continued efforts. Extensive trials are needed to establish long-term cultures of CTC. Without these efforts, CTC that are suitable for expansion will not be identified.


This study aims to investigate the relationship between RNs and hospital-based medical specialties staffing levels with inpatient COVID-19 mortality rates.We relied on data from AHA Annual Survey Database, Area Health Resource File, and UnitedHealth Group Clinical Discovery Database. In phase 1 of the analysis, we estimated the risk-standardized event rates (RSERs) based on 95,915 patients in the UnitedHealth Group Database Higher staffing levels of RNs and emergency medicine physicians were associated with lower COVID-19 mortality rates. Moreover, larger teaching hospitals located in urban settings had higher COVID-19 mortality rates. Finally, counties with greater social vulnerability, specifically in terms of housing type and transportation, and those with high infection rates had the worst patient mortality rates.Higher staffing levels are associated with lower inpatient mortality rates for COVID-19 patients. More research is needed to determine appropriate staffing levels and how staffing levels interact with other factors such as teams, leadership, and culture to impact patient care during pandemics. COVID-19 is a serious infection that spreads rapidly within a population and has placed tremendous strain on hospitals worldwide. According to the CDC, as of May 2022, around 83 million people were infected with COVID-19, and a million lost their lives in the U.S. alone . ComplicHospitals faced unprecedented circumstances and pressures during the COVID-19 pandemic, with documented variations in COVID-19 caseloads and occupancy rates . Few stuStrained capacity limited hospitals in their response to any "pandemic-associated surge" and contributed to mortality from COVID-19 . Data frStaffing capacity impacts the workload experienced by clinical staff which is associated with patient outcomes . StaffinStudies in the United States on COVID-19 patient outcomes were limited by the following factors (1) datasets containing minimal clinical information, (2) analysis that included a limited number of hospital-level variables, or (3) findings based on data from only one healthcare facility . More reWe used de-identified administrative claims from the UnitedHealth Group Clinical Discovery Database for both Medicare and commercial fully insured populations (including patients with administrative services only (ASO) accompanied by a daily record of hospital admissions due to primary or secondary diagnosis of COVID-19  along with the daily disposition status  available until December 31, 2020. To create the study population, we included all Medicare Advantage and commercially insured hospitalized patients, including data for ASO plan members where available in the database. Next, we restricted the sample to patients continuously enrolled for at least six months to capture better the claims-based historical comorbidities. We further confined the sample to patients who are older than eighteen and excluded any cases with missing patient-level risk factors. To accommodate two pandemic surges that happened in 2020, we excluded patients admitted before March 1, 2020 and after December 31, 2020. We included hospitals  only with medical provider numbers matched to the 2020 American Hospital Association (AHA) Annual Survey Database. Patients with readmission or a history of hospital transfer within 30-days of the initial admission were precluded to evade any potential misattribution of hospital-level outcomes; a sensitivity analysis relaxing this criterion was also performed. To improve the robustness of hospital-specific parameter estimates, we included sites with at least ten patients who were present in our database; see S5 Fig in The New York Times database.We used hospital‐level data from the 2020 AHA Annual Survey Database and county-level data from the 2019–2020 Area Health Resource File (AHRF). We obtained 2018 Social Vulnerability Index (SVI) from the Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry (CDC/ATSDR). We also obtained county-level cumulative COVID-19 case rates for March 1 to December 31, 2020 from To represent an outcome close to a 30-day hospital mortality rate, hospital’s risk standardized event rate (RSER) was used. This estimated measure reflects a composite of either inpatient mortality or referral to hospice within 30 days of hospitalization due to COVID-19—based on the National Quality Forum-approved hierarchical generalized linear models accounting for heterogeneity , communiThe primary objective of this paper was to study the association between hospital staffing levels and county-level variables with COVID-19 mortality rates. We captured the staffing levels of RNs  RNs divided by the number of hospital beds), hospitalists , medical/surgical intensivists , and emergency medicine physicians  in 2020.At the county level, we use the Social Vulnerability Index (SVI) to examine the relationship between county-level factors and COVID-19 mortality rate. The pandemic disproportionately impacted socially vulnerable populations since social and environmental factors influence a community’s ability to withstand disasters like pandemics . The CDCWe limited our study to general acute hospitals. According to Donabedian’s structure-process-outcomes framework, structural characteristics such as type of hospital ownership, staffing, and size impact the delivery of health services . TherefoHierarchical model is fitted to estimate the odds of mortality or referral to hospice adjusting for the selected covariates including Elixhauser based comorbidities, demographic variables (age and gender), status of transfer from nursing facility admission, the number of days between each hospital admission and March 1, 2020, and the volume of average patient admissions in 2020; the last variable is to adjust for the association between hospital volume and mortality rates. Variation among patients is accounted for through hospital specific random effects which are estimated via restricted maximum likelihood (REML). We report the adjusted odds ratios of each risk factor with 95% confidence intervals along with p-values based on Wald test statistics. Each hospital’s RSERs were calculated via recycled prediction where the idea is to take average over all patients’ predicted probabilities of experiencing events had each of them hypothetically been treated at each hospital; for details we refer to Asch, et al., George, et al. , Silber,We utilize the estimated RSER as the dependent variable in the subsequent model to investigate the association between hospital staffing levels and county-level variables with COVID-19 mortality rates. Since RSER is a proportion, we use beta regression , 40. We We analyzed data in two phases. In Phase 1, the patient-level analysis was based on 1,398 hospitals exploiting 95,915 patients. Descriptive statistics of the patient-level variables are stipulated in S2 Table in In Phase 2, we first removed hospitals that had missing values in county-level variables and left with 1,364 hospitals. Seven states  have each fewer than five hospitals with reported data, with a total of 19 hospitals. Since we had state-level fixed-effects in the model, we excluded hospitals from the seven states due to their small number of observations and obtained the final analysis set of 1,345 hospitals. In the final sample, only one hospital-level predictor, hospitalist staffing level (as will be detailed later), had missing values. The 243 missing values were replaced by the median value of that variable. Descriptive statistics of the hospital-level variables are summarized in The parameter estimates, p values, and the 90% and 95% confidence intervals based on non-parametric bootstrapping  for the hospital-level beta regression model are shown in Among the four staffing variables, higher levels of RNs  and emergency medicine physicians  staffing levels were associated with lower RSERs at the 10% and 5%  significance level respectively, suggesting that these two types of medical personnel helped to reduce COVID-19 mortality rate on average. Possible values for the true mortality rate decrease with respect to RN that were most compatible with our data, given our statistical model, ranged from -0.032 to -0.002 (90% CI).At the county level, urban counties had higher mean COVID-19 mortality rates than rural counties  signaling statistical significance at both 5% and 10% significance level. Higher SVI Theme 4 (housing type and transportation) value was associated with higher mortality rate, on average, at the 10% (but not 5%) significance level . Specifically, housing types of multi-unit structure, mobile home, and group quarter, which usually resulted in higher population density, and no personal vehicle for transportation contributed to higher SVI Theme 4 values .Regarding the covariates, for-profit hospitals had lower mortality rate, on average, compared to not-for-profit hospitals  signaling statistical significance at 10% but not at 5%; possible values for the true mortality rate decrease with respect to profit-status that were most compatible with our data, given our statistical model, ranged from -0.080 to -0.003 (90% CI).Hospitals having one or more ACGME accredited programs  tended to have higher mortality rates on average compared to hospitals not having such programs. As for hospital size, higher number of hospital beds  was associated with higher mortality rate; possible values for the true mortality rate increase that were most compatible with our data, given our statistical model, ranged from 0.003 to 0.056 (90% CI). Hospital baseline occupancy rate was positively and significantly correlated with COVID-19 mortality rate, indicating that hospitals with higher occupancy rates tended to have worse patient outcomes in terms of survival . Finally, hospitals located in counties with higher cumulative COVID-19 case rate had higher mortality rate  highlighting statistical significance at both 5% and 10% level of significance; possible values for the true mortality rate increase that were most compatible with our data, given our statistical model, ranged from 0.014 to 0.053 (90% CI).Based on the regression results, COVID-19 patients receiving treatment in larger not-for-profit teaching hospitals with lower RN and ER physicians staffing levels had the worst outcomes. Moreover, teaching hospitals and hospitals with higher occupancy had higher COVID-19 mortality rates. In terms of county characteristics, hospitals located in urban areas and counties with higher COVID-19 infection rates had a lower likelihood of surviving. Moreover, counties with greater social vulnerability, specifically in the Housing Type and Transportation theme, had higher mortality rates. Note that the strengths of association with mortality rate among RN, for-profit hospital status, and SVI theme 4, while were biologically implicative, were not as potent as other confounders. Bootstrapping provides better uncertainty quantification for point estimates, however this does not necessarily alleviate the effects of unmeasured confounders on an association model which may play a role in inferences.Staffing levels matter! The positive relationship between RN staffing levels and patient outcomes is well-documented in the literature , 41, 42.According to our analysis, ER physician staffing is also associated with better COVID-19 patient outcomes. Patients with severe COVID-19 infections are admitted to hospitals through the ER, where they are first treated and triaged by ER physicians. Therefore, ER physicians, particularly, are critical in the fight against morbidity and mortality from the novel coronavirus since they are the first point of contact. As Gaeta and Brennessel argue “physicians serve as a unique resource connecting a diverse patient population with emergent management of conditions” . EmergenDonabedian argued that hospital structural attributes impact patient outcomes . In addiAs for teaching hospitals, previous research offers conflicting findings on the relationship between teaching status and patient outcomes , 56. HoyGreater social vulnerability, COVID-19 infection rates, and urban settings were the county-level variables associated with worst RSERs. According to our findings, housing structures, crowded housing, and lack of transportation means, which result in greater social vulnerability, are associated with worst COVID-19 outcomes. Our finding is consistent with previous research that found "poor housing conditions and transport accessibility" are associated with higher COVID-19 case fatality . PreviouThis study is not without limitations. Our sample is limited to patients whose outcomes are reported in the UnitedHealth Group Clinical Discovery database which, for example, specifically does not include those patients who are insured via Medicaid. Moreover, since we do not have reliable race data for the commercially insured patient population, we used residential level information to determine the distribution of patients by race at the hospital level. In addition, while we include 2020 staffing levels, our data is annual and does not include specific staffing levels during surges in demand. While our study has strong observarions, we acknowledge that our study is rarely sufficient to establish robustness of our findings for which a thorough investigation is warrented.Our study is the first to examine the association between RN and physician staffing levels with hospital-level COVID-19 mortality and includes a large geographically diverse cohort over a significant time span. Based on our findings, staffing levels are key predictors of patient outcomes. While policymakers have focused on increasing bed capacity, the supply of personal protective equipment, and ventilators, emergency preparedness plans and future policies should also take into account RN and ER physicians staffing levels. More research is needed to determine appropriate staffing levels during pandemics and how staffing levels interact with other factors such as teams, leadership, and culture to impact patient care. This will help hospitals establish benchmarks, policies and plans to handle demand surge during pandemics, given the impact staffing levels have on patient outcomes.S1 File(DOCX)Click here for additional data file.


Infiltrating adipose tissue (inFAT) has been recently found to co-localize with scar in infarcted hearts and may contribute to ventricular arrhythmias (VAs), a life-threatening heart rhythm disorder. However, the contribution of inFAT to VA has not been well-established. We investigated the role of inFAT versus scar in VA through a combined prospective clinical and mechanistic computational study. Using personalized computational heart models and comparing the results from simulations of VA dynamics with measured electrophysiological abnormalities during the clinical procedure, we demonstrate that inFAT, rather than scar, is a primary driver of arrhythmogenic propensity and is frequently present in critical regions of the VA circuit. We determined that, within the VA circuitry, inFAT, as opposed to scar, is primarily responsible for conduction slowing in critical sites, mechanistically promoting VA. Our findings implicate inFAT as a dominant player in infarct-related VA, challenging existing paradigms and opening the door for unexplored anti-arrhythmic strategies. Despite advancements in anti-arrhythmic therapeutics and catheter ablation procedures2, VA prevalence and recurrence rates remain unacceptably high4, in part due to an incomplete understanding of the underlying substrate5. Thorough characterization of this substrate6 would improve existing, and inform novel, treatment strategies to decrease VA burden.Sudden cardiac death is a major leading cause of mortality in the world. Life-threatening VAs greatly increase the risk of sudden cardiac death, especially in patients with prior myocardial infarction8. Scar and fibrosis promote electrical wave conduction slowing and also uni-directional block7, creating a milieu conducive to arrhythmogenesis. Many clinical studies have used the visualization of ventricular scar on late gadolinium-enhanced cardiac magnetic resonance imaging (LGE-MRI) in localizing VA ablation targets11. However, these extensive efforts have failed to markedly improve VA recurrence rates, suggesting that scar characterization alone may be insufficient for identifying and eliminating VA.For decades, traditional dogma has maintained that disease-induced heterogeneous scarring and fibrosis infiltration in ventricles with ischemic (infarction) or non-ischemic cardiomyopathies forms the arrhythmia substrate14. However, it has remained an underappreciated aspect of post-infarct remodeling, and its clinical importance is ill-defined. Recent work from our group and others suggests that there is an association between inFAT and arrhythmia16. Clinically, inFAT is identifiable on contrast-enhanced computed tomography (CE-CT)18. However, because inFAT is intermingled with fibrosis13, the specific role of inFAT in VA propensity is difficult to discern. To date, no study has assessed the arrhythmogenic propensity of post-infarct inFAT versus scar in patients with ischemic cardiomyopathy or provided insight into whether scar and inFAT could synergistically combine to promote VA occurrence.inFAT is often observed in heart histological studies, penetrating into the myocardium and co-localizing with fibrosisHere we present a combined prospective clinical and personalized mechanistic computational study aimed at comprehensively characterizing the role of inFAT versus scar in post-infarct VAs. In this two-center study, CE-CTs and LGE-MRIs were acquired for the first time concurrently from enrolled post-infarct patients undergoing VA ablation procedure, so that inFAT and scar distributions could be simultaneously visualized. As imaging and intraprocedural electroanatomic mapping (EAM) alone cannot distinguish which type of remodeling—inFAT or scar—is responsible for the aberrant electrical behavior in the ventricles, a personalized computational approach was employed to discern the mechanistic roles of inFAT versus scar in arrhythmogenesis. For this purpose, novel hybrid CT-MRI 3D heart models were constructed from the imaging scans of each patient. Our results challenge preexisting paradigms about infarct-related VA and implicate inFAT as a dominant player in post-infarct arrhythmias, thus opening the door for new strategies to effectively mitigate a patient’s arrhythmic burden.P < 0.0005; Following the overview above, for each patient in the clinical study, first, 3D distributions of scar on LGE-MRI and of inFAT on CE-CT were reconstructed, using image processing, within the geometry of the patient’s ventricles. P < 0.0005) and in the mid anterior/anterolateral , mid septal , mid inferior/inferolateral  and basal anterior/anterolateral  regions. However, the amounts of inFAT and scar in the basal septum  and in the basal inferior/inferolateral regions  were not significantly correlated ; (2) scar that does not overlap with inFAT (ScarnoFat); and (3) inFAT that that does not overlap with scar (inFATnoScar). These three region definitions in the combined ventricular geometry are illustrated in Given these results, we classified the post-infarct remodeling into three regions in a single combined ventricular geometry: (1) inFAT and scar overlap  in inFATinScar  than in ScarnoFat , inFATnoScar  and tissue without remodeling   . Thus, inFATinScar encompasses most low-voltage areas and exhibits lower-voltage amplitudes than ScarnoFat and inFATnoScar.We first represented the distributions of bipolar and unipolar voltage amplitudes (BiV and UniV), which are the most commonly obtained electrical measurements from EAM1.86 mV) , middle.us 9.3%) , right. 20. Per patient, the amount of inFAT in ablation lesions correlated with the amount of scar in the lesions , suggesting that ablations, which primarily targeted sites with electrophysiological abnormalities conducive to VT, often ended up in inFATinScar. inScar and inFATnoScar regions both exhibited greater numbers of crowded isochrones (DZs) than did ScarnoFat and tissue without remodeling (3(2) and 3(2) versus 2(1) and 2(1), P < 0.0005)  (inScar and inFATnoScar) was positively associated with the presence of DZs; there was a stronger relationship between inFATinScar and DZs (odds ratio (OR) = 1.69 , P < 0.0005) than between inFATnoScar and DZs , P < 0.0005, Fisher’s exact test). An example of DZs localizing primarily to inFATinScar is shown in noFat OR = 0.45 , P < 0.0005; tissue without remodeling OR = 0.83 , P < 0.0005, Fisher’s exact test). These results indicate that regions with inFAT, whether overlapping with scar or not, were prone to slowed conduction. Thus, regions with inFAT exhibited the most substantial pro-arrhythmic abnormal electrophysiological properties.We next determined the localization, in the ventricular geometries, of the following clinical measurements: (1) clinical ablation lesions, delivered at locations in the substrate deemed most likely to terminate VT, and (2) deceleration zones (DZs), which are regions of electrophysiological abnormalities with crowding of isochrones  indicative of conduction slowing 0.0005) . DZs pris 13.8%) . The preThe measured electrophysiological abnormalities described above were next compared with the results from personalized computational modeling so that the separate contributions of scar and inFAT to arrhythmogenesis could be mechanistically dissected.21.To address this, we created three different ventricular heart models per patient: one reconstructed from LGE-MRI, another from CE-CT and a hybrid CT-MRI model; each model incorporated electrophysiological properties as described in the P > 0.05). P > 0.05; 36 versus 35 mid VTs, P > 0.05; 24 versus 23 apical VTs, P > 0.05)  . Thus, iP < 0.05) and in the inFAT-based substrate alone .We next investigated the VTs manifesting in the hybrid CT-MRI heart models, which are the complete representations of the post-infarct substrate (both inFAT and scar). These hybrid models allow us to determine whether there are interactions between the inFAT and scar distributions that affect arrhythmogenicity in the combined substrate. Rapid pacing induced a total of 140 VTs in the combined inFAT-and-scar substrate  across all heart models . There wP < 0.05). The total amount of scar was not significantly associated with the number of induced VTs (P > 0.05). This indicates that, for a given heart, increasing amounts of inFAT, rather than scar, result in an increased arrhythmic burden in the combined substrate.We then assessed whether the amount of inFAT or scar in all hearts was associated with the number of VTs induced in the hybrid CT-MRI models. In a multivariable Poisson regression model, inFAT, but not scar, was significantly associated with the total number of VTs induced in these models. For every 5.55-g increase in the total amount of inFAT, there was a 10% increase in the total number of VTs induced (22) encompassed both inFAT (2.89 ± 2.46 g) and scar (4.58 ± 3.17 g). Critical VT isthmuses, which represent the ideal ablation targets within the inner part of the circuit, more often consisted of inFATinScar (101/140 (72.1%) VTs) than ScarnoFat (22/140 (15.7%) VTs), inFATnoScar (13/140 (9.3%) VTs) or tissue without remodeling (4/140 (2.9%) VTs) (P < 0.0005). We next determined what were the types of regions where VTs perpetuated in the personalized heart models. First, we evaluated the hybrid CT-MRI model VTs that manifested in the combined inFAT-and-scar substrate. In these hybrid CT-MRI heart models, most VT circuits . These results indicate that regions of the scar with inFAT are more likely to harbor critical sites of the VT circuit than just scar alone. We also investigated the critical VT isthmuses in the LGE-based models. Although these models do not explicitly represent the inFAT distribution, we hypothesized that critical VT isthmuses in these models would localize to regions of the scar that overlapped with inFAT (when superimposed from CE-CT). We found that most critical isthmuses (66/95 (69.5%) VTs) contained scar and also overlapped with inFAT as superimposed from CE-CT .P < 0.0005) and DZs . In contrast, the amount of scar in these regions was not associated with the volume of ablated tissues  or DZs . Hence, in regions with greater VT propensity, the amount of inFAT, rather than scar, is a primary determinant of the amount of electrophysiological and conduction abnormalities.First, we examined whether the amount of inFAT or scar in the region encompassing the VT circuit in the hybrid CT-MRI heart models was associated with the volume of clinically measured pro-arrhythmic conduction abnormalities. Larger amounts of inFAT in regions that encompassed the VT circuits were associated with larger volumes of ablated tissue  and DZs . The same relationship was not true for the amount of scar in the region encompassing the VT circuit and the volumes of ablated tissue  or DZs . Collectively, these results indicate that, in regions with higher VT susceptibility, inFAT, but not scar, is the primary source of pro-arrhythmic conduction abnormalities.Next, we evaluated the association between LGE-based heart model VTs and EAM data. In these LGE-based models, larger amounts of inFAT (as superimposed from CE-CT) in the region encompassing the VT circuit were associated with a larger volumes of ablated tissue  overlapped. Within VT circuits in hybrid CT-MRI heart models, the overlap between inFAT and DZs occurred more within the common pathway (33.7%) and entrance (27.5%) than in the outer loop (11.1%) and exit (12.6%). Similarly, clinically ablations were more likely to overlap with inFAT in the common pathway (20.3%) and entrance (19.3%) than in the outer loop (9.7%) and exit (11.1%) of the model VT circuits.25. Hence, these results highlight how inFAT mechanistically promotes re-entrant VT by slowing conduction within critical sites of the VT circuit.In this first combined multi-center prospective clinical and personalized computational study, we elucidate the role of inFAT versus scar in infarct-related VT propensity. Through imaging and intraprocedural EAM data, we discovered that inFAT exhibits greater pro-arrhythmic electrophysiological abnormalities than scar. Using personalized heart models, we demonstrate that inFAT, rather than scar, is the primary driver of substrate arrhythmogenic propensity and is frequently present together with scar in the VT isthmus, which is the ideal target for ablation therapy. Lastly, using a combined analysis of both the clinical EAM and mechanistic simulation data, we identify that inFAT, and not scar, is the primary source of arrhythmogenic conduction slowing that is present at critical sites of the VT circuitry. Our results redefine conventional ‘known’ paradigms about infarct-related arrhythmias and implicate inFAT as a major player in VT, thus opening the door for potential new anti-arrhythmic strategies.8. The canonical teaching is that heterogeneous scarring and electrical changes over time in the chronic infarct transform into the substrate necessary for VT. However, not all parts of the scar are necessarily arrhythmogenic26, and not all post-infarct patients will develop VT. inFAT is also present in the post-infarct substrate and develops ~3 years after infarction18, a timescale similar to how long it takes most VTs to manifest. Concordantly, inFAT seems to be present more often in patients with VT16. inFAT is not a passive bystander for several reasons. First, inFAT predicts arrhythmic burden independently of other important clinical factors. Specifically, inFAT has been shown to be an independent predictor of a composite outcome, including mortality and ventricular arrhythmias27 and VT recurrence28. In the current study, we demonstrated that larger amounts of inFAT predicted an elevated VT burden in hybrid CT-MRI models; in contrast, an increase in the amount of scar was not associated with increased VT burden. Second, inFAT is commonly present across sites critical for VT. In our study, we first uncovered how regions of inFAT, even in the absence of scar, were associated with greater conduction slowing. Furthermore, we demonstrated how most critical VT isthmuses in both hybrid CT-MRI and in LGE-based heart models were in locations with inFAT, meaning that regions with inFAT have an enhanced susceptibility for VT. This finding is consistent with previous studies that demonstrated that critical VT sites were often in the vicinity of inFAT28. Thus, inFAT is not a passive bystander but plays a major role in infarct-related VT.Our study challenges prevailing paradigms for infarct-related VT. The infarct scar distribution has long been considered the major source of pro-arrhythmic structural remodeling in the substrate for VT29 that result in conduction slowing and repolarization abnormalities31. Specifically in the ventricles, myocardial tissue in the vicinity of inFAT exhibits slowed conduction speed32 and electrogram abnormalities28. Consistently, we discovered that inFAT exhibits smaller voltage amplitudes and more conduction slowing than scar, indicating that inFAT is likely to be more pro-arrhythmic than scar. Furthermore, we identified that, within heart model VT circuits, larger amounts of inFAT, but not scar, were significantly associated with larger volumes of clinical EAM-defined conduction abnormalities. These conduction abnormalities overlapped with inFAT specifically at the VT entrance and common pathway, which are critical sites for ablation. It is well-established that conduction slowing at these specific regions of the circuit are essential for maintenance of re-entrant VT25. Hence, inFAT mechanistically promotes re-entrant VT by slowing conduction in the critical components of the circuit.Our study further establishes a mechanism by which inFAT promotes re-entrant VTs. Cardiac adipose tissue can exhibit pro-inflammatory and paracrine effects8. Paracrine effects of adipokines secreted by inFAT are also another possible mechanism of conduction slowing. However, no data currently exist on the secretome profile of inFAT. Epicardial adipose tissue (EAT), a tissue type that is distinct from inFAT, is well-known to be metabolically active and secretes a number of adipokines33. These adipokines have been established to alter myocyte electrophysiology and cause conduction slowing in atrial tissues35. Although EAT and inFAT are separate entities, there may be some similarities in the adipokines secreted by both tissue types. Studies are needed to investigate the relationship between EAT and inFAT in the post-infarcted ventricles. A third possibility that has been hypothesized is electrotonic coupling between adipocytes and myocytes, which could, in theory, alter myocyte excitability via changes to sodium channel inactivation and increase risk of spontaneous depolarizations34. However, there has been no proof of adipocyte–myocyte heterocellular coupling in intact hearts. Hence, we would hypothesize that inFAT causes conduction slowing through two primary mechanisms: formation of a structural barrier to conduction and release of paracrine factors that alter ventricular myocyte electrophysiology.inFAT could promote conduction slowing through three primary mechanisms: (1) structural changes to myocardial architecture, (2) paracrine effects and (3) adipocyte–myocyte electrical coupling. Adipocytes are non-excitable, and their presence alone will directly alter the normal myocardial architecture. This altered myocardial structure disrupts normal electrical propagation and will slow conduction26. However, our study implies that regions with inFAT are likely to harbor critical VT sites. Thus, time and effort could be devoted to mapping these specific arrhythmogenic areas with inFAT instead of the entire infarct. In addition, knowledge of how the inFAT is distributed would also affect how ablations are delivered. Adipose tissue is a known inhibitor of ablation lesions formed by radiofrequency energy. If critical VT sites are found deep within the inFAT distribution, then more aggressive ablation strategies should be employed at these areas, involving either more lesions delivered or more advanced ablation techniques36. From a non-procedural perspective, we envision new strategies that prioritize decreasing the extent of inFAT in post-infarct VT patients. Reduction of cardiac adiposity is possible via medical therapies and may work synergistically with current standard-of-care treatments for VT. For instance, pharmacological agents such as sodium-glucose co-transporter 2 (SGLT2) inhibitors can reduce the amount of cardiac adiposity38 and have been shown to decrease arrhythmic events in randomized controlled trials40. Decreasing inFAT would eliminate substrate available to sustain VTs and, thus, reasonably result in decreased arrhythmia burden.We envision several potential advancements to clinical management resulting from our findings. From a procedural perspective, non-invasive identification of inFAT on imaging could help shorten procedure times and improve ablation efficacy. Intraprocedural EAM of the post-infarct substrate is a labor-intensive process that does not always successfully elucidate the critical VT sites, because not all regions with electrical abnormalities identified on EAM are necessarily arrhythmogenicIn conclusion, we present, to our knowledge, the first combined prospective clinical and personalized computational study that assesses the role of inFAT versus scar in infarct-related VT. We demonstrate that inFAT, as opposed to scar, is a primary determinant of arrhythmic burden and mechanistically promotes VT. These findings redefine conventional wisdom regarding infarct-related VT pathogenesis, implicating inFAT as a new, important player. We envision that this new knowledge will motivate novel, patient-specific therapeutic strategies that target inFAT to better address infarct-related VT burden.Patients were enrolled in a prospective, multi-center study at two institutions approved by our institutional review boards (IRBs) (IRB study 831270). Written informed consent was obtained from all patients. Inclusion criteria included history of myocardial infarction, history of VT and having undergone VT ablation. All patients enrolled in the study had cardiac CE-CT and LGE-MRI imaging acquired. Furthermore, all patients received a substrate-based ablation  LGE-MRI images at a resolution of 1.3 × 1.3 × 5–8 mm and six 3D LGE-MRI images at a resolution of 1.3 × 1.3 × 2.5 mm were also obtained, using a 1.5-T MRI scanner and the same protocol as previously described41. All images were resampled into short axis at an isotropic resolution of 0.35 × 0.35 × 0.35 mm for ease of processing and segmentation. Myocardium in both CE-CT and LGE-MRI images were semi-automatically segmented as reported in previous publications43. In brief, landmark control points were placed along the left ventricular endocardium and epicardium. From these points, 3D endocardial and epicardial surfaces were automatically generated using the variational implicit method44. For all images, overlying EAT was carefully excluded from the segmentation. CE-CT images were acquired, using the same protocol as described previously42. Two separate subranges between −180 HU and −50 HU (dense inFAT) and between −50 HU and −5 HU (fat–myocardium admixture) were identified as done in our previous study42. All lead and device artifacts were identified by thresholding and were removed from analysis the same as in our previous study42. For this study, the term inFAT refers to both dense inFAT and fat–myocardium admixture.inFAT was identified as hypoattenuation in the range of −180 Hounsfield units (HU) to −5 HU43. All voxels with intensity values >50% of the maximal intensity in the myocardium were labeled as dense scar, and voxels with intensity values between 35% and 50% of the maximal intensity were labeled as gray zone. Device, respiratory and motion artifacts were manually identified, annotated and removed from analysis. All regions affected by artifacts on CT or MRI were considered to be non-injured myocardium. In this study, we defined ‘scar’ to be both dense scar and gray zone. All volumes in this study were converted to mass by using a scaling factor of 1.055 g cm−3 to estimate myocardial tissue density45.Dense scar and gray zone were identified on LGE-MRI imaging using the full-width half-maximum method as done in our previous studies42. Using open-source Visualization Toolkit system software, EAM surfaces and ablation lesion locations were translated and rotated until they conformed to the heart geometry. The median number of points across EAM surfaces was 13,016 . Points on the EAM surface geometry were projected to the closest point along the heart endocardial or epicardial surface, depending on whether the mapping was performed from the endocardium or epicardium, respectively. All points on the EAM surface geometry that were projected to a point on the heart surface >5 mm away were removed from analysis due to potential for mis-registration. The registration error between EAM surface and ventricular heart geometries was 2.27 ± 1.39 mm .To compare the inFAT and scar distributions with clinical procedural data, EAM surface geometries and clinical ablation locations were registered to the patient’s ventricular heart geometry via a rigid transformation, as described in our previous publication1, and critical VT sites are often found in areas with low voltage. Ablations were selectively delivered to areas with abnormal electrograms that ultimately resulted in non-inducibility of VT, and DZs have been mechanistically linked to critical components of the VT circuitry20.Due to the hemodynamic instability of all patients in this cohort, which is often the case in patients with ischemic heart disease, ablation procedures were performed with substrate-based EAM, meaning that the VT circuit could not be intraprocedurally mapped. Because of this, even though all patients were non-inducible for VT by the end of the ablation procedure, ablation lesions were not necessarily delivered only to sites critical for VT re-entry. Hence, locations of multiple measures indicative of abnormal electrophysiological activity on EAM  were identified, because these locations represent areas with pro-arrhythmic, abnormal electrophysiological properties that are known to be present at critical VT sites. Voltage abnormalities are the most commonly obtained clinical measurement; they are a hallmark of EAM46. LVZs and MVZs on EAM were defined using both BiV  and UniV  cutoffs that were previously reported in the literature19. inFAT or scar was defined to be present at an EAM point if it comprised at least 10% of the volume within a 1-cm radius. inFATinScar was defined as regions with both inFAT and scar. ScarnoFat was defined as regions that had scar but no inFAT, whereas inFATnoScar was defined as regions that had inFAT but no scar. Tissue without remodeling was defined as tissue without inFAT and scar.For each EAM point projected to the heart surface of the ventricular geometry, the volume of inFAT and scar was computed within a radius of 1 cm from the EAM point to account for remodeling in the intramural myocardium. Because of the potential for noisy, non-physiological measurements, a threshold was applied for both BiV and UniV to limit the range of values considered for analysis. Specifically, BiV and UniV were capped at 1.5 mV and 8.3 mV, respectively, for analysis, which corresponds to cutoffs used for voltage amplitudes of normal tissue in previous studies3), whereas lesions in gray zone were estimated to encompass a radius of 3.07 mm (volume: 121 mm3) and in dense scar a radius of 2.50 mm (volume: 66 mm3), corresponding to volumes reported previously in experimental literature47. As for dense inFAT and fat–myocardium admixture, there has not been a clear characterization of how radiofrequency ablation penetrates dense inFAT versus fat–myocardium admixture. Limited numerical experiments suggest that thermal lesions successfully created by radiofrequency ablation may be similar between inFAT and scar48. Hence, we assumed lesions in dense inFAT and fat–myocardium admixture to be the same as the values reported for scar and gray zone, respectively.To assess the overlap of clinical ablation lesions with inFAT and scar, lesion volumes were estimated in all ventricular geometries. Clinical ablations were estimated to burn tissue at a certain radius from the myocardial surface, depending on the type of tissue present at that location. Lesions centered on non-injured myocardium were estimated to encompass a radius of 4.56 mm from the myocardial surface  starting from the earliest to the latest activation. The average total activation time across EAMs was 264.4 ± 109.7 ms . Because each isochrone represents a fixed unit of time, more closely spaced isochrones indicate less distance traveled and, hence, a slower conduction speed. To quantify isochronal crowding for each point on the EAM geometry, the number of different isochrones present within a 1-cm radius on the surface EAM geometry was computed. From this, DZs, which are regions of isochronal crowding that have previously been mechanistically linked to critical VT sites20, were defined as EAM points with ≥5 isochrones within a 1-cm radius.Activation maps acquired during sinus rhythm were also analyzed. Following a previously validated clinical methodologyBecause the arrhythmogenic substrate is inherently 3D and not 2D, DZs reflect electrical propagation through the intramural myocardium, not only along the surface. Thus, to account for the 3D propagation, DZs were estimated to reflect the myocardium within a 1-cm radius of the EAM point. These DZ volumes were then compared with the inFAT and scar distributions.49. This mesh resolution was chosen to maximize computational efficiency and to maintain a sufficient size appropriate for simulating wave propagation, as reported in previous publications50. Furthermore, a previous study indicated that a resolution of ~400 μm is sufficient for avoiding artificial conduction block due to numerical artifacts and accurately identifying the locations of re-entrant activity49. Fiber orientations, specific to each individual heart model, were assigned to each computational mesh on a per-element basis, as reported in previous publications43, using a validated rule-based method51.From the reconstructed ventricular geometries, finite-element, tetrahedral meshes were generated  with ~5 million nodes with an average resolution of ~390 μm to conduct computational simulations using a previously described meshing procedure52. An illustration is shown in For all heart geometries, mesh node coordinates were defined using the universal ventricular coordinate (UVC) system, as described in a previous studyUsing the UVC system, the inFAT and scar distributions were assessed. Definitions for the anatomical regions are presented in For CT-based heart models, the patient-specific inFAT distribution from CE-CT was incorporated into the ventricular geometry. Similarly, for LGE-based heart models, scar from LGE-MRI was incorporated into the ventricular geometry. Mesh elements in each heart model geometry were each labeled with different tags corresponding to the regions identified on imaging. In the CT-based heart models, there were three region tags: non-injured myocardium, fat–myocardium admixture and dense inFAT. In LGE-based heart models, the three region tags were: non-injured myocardium, gray zone and dense scar.Hybrid CT-MRI heart models were created by combining the inFAT and scar distributions. The scar distribution from the LGE-based heart models was projected onto the CT-based heart model geometry because CT imaging has a higher resolution and, thus, represents the myocardial structure more accurately. First, the UVCs of mesh elements tagged as dense scar or gray zone on LGE-based heart models were identified. Then, the corresponding UVCs on the CT-based heart models were identified, thus giving each mesh element two distinct tags from each of the CT-based and LGE-based heart models.A summary of how the seven distinct regions were defined in these hybrid CT-MRI models is shown in 53. Dense inFAT, dense scar and combined dense inFAT-and-scar were modeled as non-conducting insulators, as done in previous works56. Because there is no proof of fibroblast–myocyte or adipocyte–myocyte coupling in intact hearts, heterocellular coupling was not represented in the models57. The ionic currents of fat–myocardium admixture, gray zone and fibrofatty infiltrated myocardium regions were adjusted as done in previous works56. A previous study demonstrated that representing these admixture regions with a heterogeneous structural composition  did not appreciably impact VT location58. Specifically, the ionic currents were scaled according to the experimental literature describing infarct border zone: the peak INa was reduced by 62%59; ICaL was reduced by 69%60; IKr was reduced by 70%; and IKs was reduced by 80%61.Ionic properties of the non-injured myocardium were modeled with the ten Tusscher human ventricular myocyte model42. In gray zone regions, transverse conductivity was decreased by 90% to reflect remodeling of gap junctions in infarct border zone62. In fat–myocardium admixture, gap junction alterations15, decreased conduction velocity32 and abnormal electrogram signals28 are present, similarly to electrophysiological changes in the infarct border zone63, but the extent of such changes remains unknown. In the absence of such data, the electrophysiological properties of fat–myocardium admixture were approximated using the same parameters as reported in our previous publication42.Tissue conductivities for non-injured myocardium in all heart models were assigned a value of 0.08 and 0.00889 S/m in the longitudinal and transverse directions, respectively, the same as used in previous studies15, which results in greater conduction slowing. Furthermore, a clinical study found that regions of wall thinning (suggestive of scarring) with inFAT had lower voltage amplitude and prolonged electrogram durations than regions of wall thinning without inFAT, suggesting that these regions exhibit greater electrophysiological abnormalities. Hence, to encapsulate this apparent increased conduction abnormality, we empirically decreased both the longitudinal and transverse conductivities in regions of fibrofatty infiltrated myocardium by an additional 50%, corresponding to an estimated decrease in conduction velocity of 25%.Detailed data regarding conduction properties in fibrofatty infiltrated regions in the infarcted myocardium are non-existent. However, regions of concomitant fibrosis and fatty infiltration exhibit greater gap junction alterations than regions of fibrosis or adipose alone43 was applied from multiple sites to induce all possible VTs in each heart model. Each pacing was delivered to a 1-mm3 volume of myocardium. Six stimuli were delivered at 450-ms basic cycle length followed by up to four successive premature stimuli until VT induction. Pacing was delivered from 17 sites, one for each American Heart Association (AHA) segment, as done in a previous work43, to comprehensively identify all possible arrhythmias in the patient-specific substrate. Within each AHA segment, pacing sites were projected to be adjacent to the closest inFAT in CT-based heart models and scar in LGE-based heart models to maximize the likelihood of VT induction. For hybrid CT-MRI heart models, the same pacing sites in CT-based heart models were used because the ventricular geometry was the same. In all heart models, if a particular pacing site failed to capture, the stimulus site was manually shifted to nearby conducting tissue. This process was repeated until the stimulus successfully captured. All simulations were performed using Cardiac Arrhythmia Research Package software (https://carp.medunigraz.at/) at a temporal resolution of Δt = 25 μs.A previously validated rapid pacing protocol64, demonstrating excellent correspondence between model VT predictions and clinical targets.The approach to personalized image-based modeling of VT, as described above, was previously validated with clinical data in multiple studies43. The locations of all VT exit sites were manually selected by visualizing the 3D activation map and choosing the point in a software called meshalyzer . VTs were classified as being in the basal, mid or apical myocardium based on the location of the VT exit site ; the second (gray) and third (red) isochrones reflect the outer loop. The VT entrance is defined as the fourth isochrone (orange), and the fifth (yellow) through eighth (blue) isochrones represent the VT common pathway. The critical isthmus is defined as the sixth (green) and seventh (cyan) isochrones.To define the VT circuit using the UVC system, a window around each VT exit site of 0.4 units for the apicobasal axis, 72° for the rotational axis and the entire myocardium for the transmural axis were used. These values were determined manually and selected to include the entire VT circuit. An illustration of this VT circuit definition is shown in At the location of each hybrid CT-MRI model VT circuit, the total amounts of inFAT and scar were measured. The amount of inFAT for VT circuits in LGE-based heart models was measured by superimposing the inFAT distribution from the corresponding CE-CT. To validate whether the conduction properties of computational model VT circuits were representative of that of patient VT circuits, we also measured the total cycle length of each VT  were used to assess whether the amounts of inFAT and/or scar within a VT circuit (in grams) were associated with the volume of clinical ablations and DZs.To determine the association between the amount of inFAT and scar and the number of VTs induced in the hybrid CT-MRI heart models, a multivariable Poisson regression model was used. To assess differences in arrhythmogenicity between the inFAT-based and scar-based substrates, the total number of VTs in CT-based and LGE-based heart models was assessed with an unpaired Further information on research design is available in the Nature Research Reporting Summary linked to this article.supplementary materials


Our results revealed an urban–rural divide in patients’ engagement in follow-up attendance and blood glucose monitoring, which suggested the need for different educational approaches tailored to the local context to enhance diabetes care.Regular follow-up attendance in primary care and routine blood glucose monitoring are essential in diabetes management, particularly for patients at higher cardiovascular (CV) risk. We sought to examine the regularity of follow-up attendance and blood glucose monitoring in a primary care sample of type 2 diabetic patients at moderate-to-high CV risk, and to explore factors associated with poor engagement. Cross-sectional data were collected from 2130 patients enrolled in a diabetic retinopathy screening programme in Guangdong province, China. Approximately one-third of patients (35.9%) attended clinical follow-up <4 times in the past year. Over half of patients (56.9%) failed to have blood glucose monitored at least once per month. Multivariable logistic regression analysis showed that rural residents (adjusted odds ratio [aOR] = 0.420, 95% confidence interval [CI] = 0.338–0.522,  Prevention and control of diabetes and its complications represents a formidable public health challenge worldwide. Current estimates suggest that nearly 130 million people are living with diabetes in China, which accounts for 12.8% of the Chinese adult population [The pathogenesis and progressive nature of diabetes requires patients’ inputs to continuous care, regular monitoring of blood glucose, and adherence to tailored medications to control hyperglycaemia. It is therefore particularly important to ensure the routine delivery of effective and affordable healthcare services to diabetic patients. According to the Chinese national standards for delivering basic public service (third edition), people diagnosed with T2DM are provided with regular follow-up care and fasting plasma glucose monitoring on a free-of-charge basis at primary care facilities . PatientRegular follow-up care and routine blood glucose monitoring are essential in diabetes management. The results of blood glucose monitoring are important information for physicians to provide appropriate treatment regimens with tailored lifestyle advice to achieve glycaemic control and prevent disease progression ,6,7. A lExisting studies reported barriers to follow-up attendance at primary care facilities among Chinese T2DM patients. A cross-sectional study conducted in eastern China found that determinants of infrequent follow-up visits included lower household income, absence of health insurance, and lack of telephone communication and community outreach services . A multiHowever, there is limited evidence from studies conducted among T2DM patients at moderate to high CV risk, who account for a large proportion of diabetic patients seen in primary care practice and are more likely to experience major adverse outcomes. We therefore sought to examine the regularity of follow-up attendance and blood glucose monitoring in a primary care sample of T2DM patients at moderate to high CV risk, and to explore factors associated with patients’ poor engagement in routine diabetes care.This was a cross-sectional study conducted in an urban–rural fringe in Guangdong province, southern China. The study was part of a larger project on the screening for diabetic retinopathy (DR) in collaboration with the Zhongshan Ophthalmic Center, Sun Yat-Sen University. The screening cohort was originally designed to evaluate the prevalence, incidence, and progression rate of DR in T2DM patients. Diabetic patients enrolled in family doctor teams for routine diabetes management at local primary care facilities were invited for ophthalmoscopic exams. A semi-structured questionnaire was used to collect information on follow-up attendance and blood glucose monitoring among diabetic patients in the 2019 wave of DR screening.Data were collected at community and township health centres in the primary care settings. All subjects were interviewed face-to-face by trained clinical staff during on-site examination for DR screening. All completed questionnaires were scrutinised by a senior supervisor on a daily basis as a quality control measure. Information were gathered using a semi-structured questionnaire which included: (1) the patient’s basic information, including age, gender, place of residence, education level, marital status, and living relationships; (2) routine lifestyle behaviours, including smoking and drinking status; (2) disease-related information, including duration of diabetes, use of glucose-lowering medications, presence of comorbidity, as well as awareness of adverse consequences of diabetes complications; (4) health services utilisation, including follow-up attendance and blood glucose monitoring; and (5) the patient’s self-rated health scores.Study subjects were drawn from one community health centre and nine township health centres in an urban–rural fringe. Patients with T2DM at moderate to high (including very high) CV risk who participated in the 2019 wave of DR screening were included in the present study. Diabetes was diagnosed as having a fasting plasma glucose level ≥ 7.0 mmol/L or glycated haemoglobin (HbA1c) ≥ 6.5% . The pre2). Blood pressure (BP) was measured in a seated position by routinely validated automatic sphygmomanometers. The arm with the higher BP values was obtained following the standard clinical operating procedure.Weight and height were measured by trained medical staff according to a standardised protocol. Body weight was measured with light clothing. Body mass index (BMI) was calculated as weight in kilograms divided by squared height in meters  for delivering basic public health services , in the 2 [Independent variables included demographics, such as age (<65 or ≥65 years), gender , place of residence , marital status , living relationships , education level ; routine lifestyle behaviours including smoking status  and regular drinking ; and disease-related variables including duration of diabetes (<10 or ≥10 years), use of glucose-lowing medications (yes or no), the presence of comorbidity (yes or no). Healthcare-related variables included venues for blood glucose testing , self-rated health scores , overweight or obese (defined as BMI ≥ 24 kg/m2 ) and poot-tests or chi-square tests, where appropriate, were used to compare the characteristics of urban vs. rural patients. Univariate logistic regression analysis was performed to explore the relationship between each independent variable and frequency of follow-up attendance and blood glucose monitoring, respectively. Variables that were significant were further entered into the multivariable binary logistic regression model. Data analysis was conducted using IBM SPSS Statistics 26. A p-value less than 0.05 was considered statistically significant.Double-entry verification was performed in EpiData 3.1 . Descriptive statistics were used to describe subject characteristics, frequency of follow-up attendance and the regularity of blood glucose monitoring. Independent All participants provided written informed consent. Data anonymisation was carried out by removing all patient identifiers from the dataset before data analysis. Ethics approval was granted by ethics committees at Zhongshan Ophthalmic Center, Sun Yat-Sen University (Ref: 2017KYPJ094) in accordance with the Declaration of Helsinki 2013.The study enrolled a total of 2130 eligible patients, of which, 885 were from urban areas and 1245 were from rural areas. Over half of patients were under 65 years of age (51.5%) and were female (55.6%). Less than one-fifth (16.2%) of participants had no formal education. The prevalence of active smoking (17.7%) and regular drinking (12.6%) was low. More than half of participants were overweight or obese. A substantial proportion of patients were diagnosed with diabetes within the past 10 years (78.7%), reported the presence of comorbidity (81.1%), and received glucose-lowering medications (82.3%). More than half (58.8%) of patients had poor awareness of adverse consequences of diabetes complications. We found that 37.9% of patients had their blood glucose tested only at primary care facilities. When compared to urban patients, rural subjects were younger, had less formal education with a higher prevalence of active smoking and regular drinking, had higher self-rated health scores, and had shorter duration of diagnosed disease with a poor awareness of adverse consequences of diabetes complications, but were as likely as urban patients to develop diabetes comorbidity .Of 2130 participants in total, 64.1% attended clinical follow-up at the community health centres or township health centres at least four times in the past year, while slightly more than one-third of patients did not achieve the target frequency of follow-up attendance for T2DM. With respect to the frequency of blood glucose monitoring, 13.6% of patients tested their blood glucose at least 1 to 2 times a week, and 51.3% of patients tested 1 to 2 times a quarter. We found a small proportion of patients who tested their blood glucose only when not feeling well and patients who did not have blood glucose tested in the past year. Overall, nearly half (43.1%) of patients were able to have their blood glucose tested once or more per month .p < 0.05), which were further entered in the multivariable analysis. In the further analysis, patients who received glucose-lowering medications  or had blood glucose tested only at primary care facilities  were more prone to have a higher regularity of follow-up attendance compared to their counterparts. However, patients who lived in rural areas , had poor awareness of adverse consequences of diabetes complications  with self-rated health scores ≥3  were less likely to attend clinical follow-up at least 4 times in the past year , which were further entered in the multivariable analysis. In the further analysis, the following factors remained significantly associated with having blood glucose monitored once or more per month: age ≥ 65 years , living in rural areas , having formal education , having a diagnosis of diabetes for over 10 years , poor awareness of adverse consequences of diabetes complications , use of glucose-lowering medications , and having blood glucose tested only at primary care facilities   .We found that approximately one-third of patients (35.9%) attended clinical follow-up <4 times in the past year, and that slightly over half of patients (56.9%) failed to monitor their blood glucose at least once per month. Multivariable logistic regression analysis showed that place of residence, awareness of consequences of diabetes complications, use of glucose-lowering medications, and venue for testing blood glucose were factors associated with the regularity of both follow-up attendance and blood glucose monitoring. Moreover, current smoking and having better self-rated health status were negatively associated with the attainment of the recommended frequency target for follow-up visits at primary care facilities. Patients aged 65 years or older, having an education level of primary school and above, or having a diagnosed diabetes for over 10 years were more likely to have their blood glucose monitored once and above per month. Our results exhibited an urban–rural divide in patients’ engagement in follow-up attendance and blood glucose monitoring.In China, the management of T2DM has been integrated as part of the national basic public health service delivery . Follow-Multivariable analysis revealed that rural residence, current smoking, higher self-rated health scores, lack of awareness of adverse consequences of diabetes complications, absence of drug use, and having mixed venues for blood glucose monitoring were factors associated with less regularity of follow-up attendance among study subjects. The results were consistent with our expectations and existing literature which shows that poor primary care gate-keeping is more common in deprived neighbourhoods . A systeRoutine blood glucose monitoring helps provide timely information on blood glucose level of diabetic patients, in addition to diet, exercise, and medication adherence profiles, to inform evidence-based clinical decision-making. Fasting plasma glucose monitoring is recommended for patients with T2DM on a quarterly basis in primary care diabetes management . In our Earlier findings from a cross-sectional survey in eastern China suggested that diabetic patients who lived in urban areas, had a higher education level, had the presence of physician-diagnosed diabetes for over 10 years, and had insulin injection tended to achieve a higher frequency of blood glucose monitoring . This waThe high rate of uncontrolled blood glucose reported in a large-scale survey in China  has poseContinuous education on diabetes self-management to facilitate the knowledge and skills required for diabetes care can increase diabetes awareness and improve glycaemic control ,42, and The target subjects in this study consisted of T2DM patients at moderate to high CV risk, who are commonly encountered in daily primary care but are often struggling with barriers to engage in regular follow-up attendance and routine blood glucose monitoring. A semi-structured questionnaire was used to collect a broad range of information, including the patient’s demographics, routine lifestyle behaviours, disease-related information, healthcare utilisation, and self-reported health status through face-to-face interviews by trained medical practitioners. However, this study has some limitations that should be considered. First, we did not have an electronic health record system where data on follow-up attendance and blood glucose monitoring can be directly retrieved. Thus, recall bias may arise from self-reporting despite rigorous quality control. Secondly, the awareness of adverse consequences of diabetes complications was ascertained based on a single question. This may limit the extent to which our results are comparable with other studies due to inconsistent methodology. Third, subjects in this study were drawn from the register of diabetic management in primary care settings. Patients with T2DM who were not enrolled may not be captured in our study. Last but not least, we were not able to make inferences about causality or temporal ordering of events due to the cross-sectional nature of the study. Future research using data collected from computerised health records will help to provide longitudinal evidence for optimising diabetes care.In the present study, our findings suggested that follow-up care delivered in primary care was not adequately utilised by Chinese patients with T2DM who were at moderate to high CV risk, and that patients’ engagement in blood glucose monitoring was suboptimal overall. Living in rural areas and having poor awareness of diabetes complications acted as significant barriers to both follow-up attendance and blood glucose monitoring. This would require dedicated and continuous educational inputs from primary care practitioners to increase awareness of adverse consequences of diabetes complications in daily clinical practice. Our results revealed a significant urban–rural divide in patients’ engagement in regular follow-up attendance and blood glucose monitoring, which suggested the need for different educational approaches tailored to the local socio-economic context to strengthen the capacity of diabetes care.


COVID-19 and tuberculosis (TB) represent global threats to the public health system. The impact of COVID-19 on TB results in a reduction in the number of notified TB cases, delayed diagnosis and treatment, and increased case fatality and mortality rates. The aim of the study was to analyze the TB/COVID-19 co-infected cohort in Serbia as a low-burden country and compare it to the global TB/COVID-19 cohort.A retrospective analysis was done on 53 TB and COVID-19 co-infected patients treated in COVID hospital “Batajnica” in Belgrade and Special Hospital for Pulmonary Diseases “Ozren” Sokobanja in the period from 6 March 2020 to 1 April 2022. A comparative analysis with the global cohort published recently was also performed.TB/COVID-19 cohort in Serbia included significantly fewer migrants and diabetes cases, but more cases with chronic respiratory diseases compared to the global. Descriptive analysis of TB cases in the Serbian TB/COVID-19 cohort showed fewer cases diagnosed with sputum smear and Gene Xpert/HAIN, fewer EPTB and mono-resistant cases, and more cases diagnosed with solid culture, unilateral pulmonary infiltrate , and bilateral pulmonary infiltrate (no cavities) compared to TB/COVID-19 cases worldwide. Nasal congestion and fever were more common COVID-19 symptoms in the global cohort. Radiology was more commonly used for the diagnosis of COVID-19 in Serbia. Typical bilateral ground opacities were less common among Serbian patients. Serbian patients spent fewer days in the hospital and achieved a higher PCR conversion rate and TB treatment success rate.The Serbian TB/COVID-19 cohort achieved a higher treatment success rate compared to the global cohort. Encouraging vaccination against SARS-CoV-2 for people with a current or past TB disease, as well as rapid diagnosis and targeted treatment of TB in highly specialized pulmonology institutions, presents key points to avoid excessive morbidity and mortality. Tuberculosis (TB) presents a global public health threat with 10 million cases and 1.3 million deaths annually . This epA retrospective analysis of 53 patients with active TB co-infected with COVID-19 and treated in COVID hospital “Batajnica” in Belgrade and Special Hospital for Pulmonary Diseases “Ozren” Sokobanja from 6 March 2020 to 1 March 2022 was performed. During the study period, we enrolled all patients of any age who were diagnosed and notified in these hospitals with active TB and COVID-19 simultaneously.The data were obtained from medical records and include demographic data, laboratory, radiological, and clinical status at TB diagnosis and COVID-19 diagnosis, treatment, and follow-up. Follow-ups were done every month for each patient during the treatment period. After the treatment period, follow-ups were performed every 3 and 6 months, and some patients are still being followed up. On average, six to seven follow-ups were made per patient.All data were compared to global TB and COVID-19 cohort publicly available data. Serbia has given its contribution to the global cohort with the first six patients .t-tests, and categorical variables were compared using chi-squared or Fisher exact test.Statistical analysis includes descriptive statistical analysis of all patients with details of TB and COVID-19 in our cohort. Variables were summarized using frequencies and percentages. Continuous variables with normal distribution were compared using The demographical, epidemiological, and clinical characteristics of the 53 patients with TB/COVID-19 are shown in Among study participants, 9 patients (17.0%) had previously been treated for TB, while 19 (35.9%) had both diseases diagnosed at the same time and ~9 (16.9%) in the same week. As shown in The SARS-CoV-2 laboratory confirmation was available in all Serbian patients in a higher percentage compared to global as shown in via high-flow nasal cannula and later noninvasive ventilation was applied in one patient . Antiviral drugs including favipiravir, molnupiravir, and remdesivir were used in 33/53 (62.2%) patients. Favipiravir  and remdesivir  were used in our study in statistically significantly more cases than among the patients from the global cohort. Immunomodulatory treatment was applied in 23/53 (43.4%) patients; anticoagulation therapy in statistically significantly more cases in our study  than in 94/336 (27.9%) globally; and azithromycin in statistically significantly fewer cases  in our study compared to global .All the patients with COVID-19 and TB in Serbia were hospitalized during anti-COVID treatment. The average duration of hospitalization among our patients was statistically significantly lower (11.6 days) compared to the global cohort (31.0). Pulse oximetry was performed in all patients, with an average oxygen saturation of 95.4%. Oxygen support The Serbian cohort achieved a statistically significantly better PCR conversion rate (100%) compared to the global cohort (57.2%), and TB treatment outcomes compared to the global cohort, namely, a treatment success rate of 98.1% compared to 88.9%.Our study described and compared the findings of TB/COVID-19 co-infected patients in the Serbian cohort with a global cohort of 767 patients from 172 centers in 34 countries.®), Gam-COVID-Vac (Sputnik V), and ChAdOk1 nCoV-19 (AstraZeneca)  .We found differences in microbiological confirmation of the TB cases among our patients and the global cohort. Although Gene Xpert/HAIN is widely used in other countries, according to national and international guidelines, culture is used as the gold standard for TB diagnosis in low-TB burden countries . For thoThe Serbian TB cohort reached a high treatment success rate due to prompt diagnosis and therapy, as well as a low frequency of mono-resistant and an absence of multidrug-resistant TB. A positive trend in TB declining in the past decade can be reversed by COVID-19, resulting in the reduction of tuberculosis testing and access to tuberculosis-related health services. Reports show that TB deaths would return to those seen in 2013 and estimated TB mortality could increase up to 20% between 2020 and 2025The most common symptoms reported in both cohorts were dry cough, fever, and shortness of breath. Results correspond to global COVID-19 dataSimilar to symptoms, radiological findings overlapped in both COVID-19 and TB. The radiological findings of active tuberculosis and COVID-19 are often similar. Isolated upper-lobe pulmonary infiltrates are suggestive of TB, and lower infiltrates of other bacterial infections . Data shM. tuberculosis-specific CD4+ T cells in peripheral blood was found in patients with COVID-19, suggesting increased susceptibility to progression to active tuberculosis  and male gender have an important influence on the morbidity of COVID-19/TB co-infected persons. Encouraging vaccination against SARS-CoV-2 for people with a current or past TB disease, as well as rapid diagnosis and targeted treatment of TB in highly specialized pulmonology institutions, presents key points to avoid excessive morbidity and mortality.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Special Hospital for Pulmonary Diseases Ozren Sokobanja. The participants provided their written informed consent to participate in the study.TA-V and MS: concept and design and interpretation of the data. MS: statistical analysis. TA-V: drafting the manuscript. TA-V, MS, GA, MJ, AR-S, and JV: critical revision of the manuscript for important intellectual content. All authors have read and agreed to the published version of the manuscript.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.