The immunologic pathways activated during snakebite envenoming (SBE) are poorly described, and their association with recovery is unclear. The immunologic response in SBE could inform a prognostic model to predict recovery. The purpose of this study was to develop pre- and post-antivenom prognostic models comprised of clinical features and immunologic cytokine data that are associated with recovery from SBE. We performed a prospective cohort study in an academic medical center emergency department. We enrolled consecutive patients with Crotalinae SBE and obtained serum samples based on previously described criteria for the Surgical Critical Care Initiative (SC2i). We assessed a standard set of clinical variables and measured 35 unique cytokines using Luminex Cytokine 35-Plex Human Panel pre- and post-antivenom administration. The Patient-Specific Functional Scale (PSFS), a well-validated patient-reported outcome of functional recovery, was assessed at 0, 7, 14, 21 and 28 days and the area under the patient curve (PSFS AUPC) determined. We performed Bayesian Belief Network (BBN) modeling to represent relationships with a diagram composed of nodes and arcs. Each node represents a cytokine or clinical feature and each arc represents a joint-probability distribution (JPD).Twenty-eight SBE patients were enrolled. Preliminary results from 24 patients with clinical data, 9 patients with pre-antivenom and 11 patients with post-antivenom cytokine data are presented. The group was mostly female (82%) with a mean age of 38.1 (SD ± 9.8) years. In the pre-antivenom model, the variables most closely associated with the PSFS AUPC are predominantly clinical features. In the post-antivenom model, cytokines are more fully incorporated into the model. The variables most closely associated with the PSFS AUPC are age, antihistamines, white blood cell count (WBC), HGF, CCL5 and VEGF. The most influential variables are age, antihistamines and EGF. Both the pre- and post-antivenom models perform well with AUCs of 0.87 and 0.90 respectively.Pre- and post-antivenom networks of cytokines and clinical features were associated with functional recovery measured by the PSFS AUPC over 28 days. With additional data, we can identify prognostic models using immunologic and clinical variables to predict recovery from SBE. Snakebite envenoming (SBE) is a neglected tropical disease of global significance that primarily impacts low resource areas in low and middle income countries (LMICs), and snake-specific antivenoms are the cornerstone of therapy –8. SnakeDespite positive cost-effectiveness assessments of snake antivenoms, the cost to individual SBE victims can be beyond the financial means of many of those affected –23. ThisThe clinical courses of SBE patients are highly variable, even within a single envenoming species, which further contributes to uncertainty regarding both the value of administering antivenom and how much is necessary for an individual patient . UnfortuCrotalus horridus and Agkistrodon contortrix are known to have a large proportion of their venom composed of secretory phospholipase A2 (sPLA2), snake venom metalloproteases (SVMPs), and serine protease toxins. These toxins are of particular biologic importance and contribute to the venom-induced tissue injury, coagulopathy, and systemic symptoms seen in these SBEs  in the southeastern US in 2017 and 2019.Venomation Investigation Pilot to Expedite Recovery (SC2i - VIPER) based on the SC2i - Tissue and Data Acquisition Protocol (TDAP) . As the parent protocol has been previously described, we provide additional methods specific to the SC2i-VIPER sub-study that are necessary for reproducibility (Agkistrodon contortrix (copperhead) and Crotalus horridus (timber rattlesnake). Regionally transferred patients were eligible with the addition of the following potential Crotalinae species: Crotalus ademanteus (eastern diamondback rattlesnake), Sistrurus miliaris (pygmy rattlesnake) and Agkistrodon piscivorus (cottonmouth snake). Species identification was not required for enrollment, however the majority of investigator-identified species were copperhead snakes which predominate throughout the region. Exclusion criteria were pregnancy, prisoners, or inability to obtain consent during their stay for any reason. Informed consent was provided by a patient or proxy and the Duke University Institutional Review Board approved the study.We enrolled consecutive patients with acute snakebite envenoming (SBE) based on the Surgical Critical Care Initiative-encibility . We enroWe assessed clinical variables as per SC2i protocol which includes static pre-existing variables such as sex, age, comorbidities and dynamic clinical variables such as laboratory results, medications, and vital signs. Clinical data were collected from patients and by review of the electronic health record using standard operating procedures.Samples from SBE patients were collected as soon as possible following their enrollment, ideally before antivenom administration when possible, as well as after initial antivenom administration. Whole blood was collected and serum was isolated at the site for the SC2i Consortium Biorepository following consortium-wide standard operating procedures and Guidelines for Good Clinical Laboratory Practice (GGCLP), and then stored until use for these analyses. The methods of sample collection and preparation have been reported, but are briefly encapsulated here for clarity .Frozen serum samples were thawed on wet ice for filtering. The samples could have a single freeze-thaw cycle. They were mixed and 400 uL were transferred to a 1.5 mL tube and centrifuged at 12,000g for 15 minutes at 4 degrees Celsius. A 350uL sample was transferred into a 0.65um filter tube . They were centrifuged 12,000g for 30 minutes at 4 degrees Celsius. We then aliquoted 120uL of the filtered samples into clean tubes. We then flash froze samples in liquid nitrogen and stored in -80 degree Celsius freezer. The maximum time from freeze to thaw was less than one year.After filtering, samples were thawed and a 1:50 dilution of samples were used. We measured levels of 35 unique cytokines using Luminex Cytokine 35-Plex Human Panel  pre- and post-antivenom administration using standard cytokine preparation per kit instructions. A 1x wash preparation was created and used for all washes. Standard plates were prepared and 50ul of sample, standards, and incubation buffer were added to all wells and allowed to incubate for 2 hours. After washing, detection antibodies were added and incubated for one hour. After another wash, streptavidin-RPE was added and incubated for 30 minutes. These were washed again and beads were resuspended in wash buffer. For 35 plex analysis, we used Invitrogen Catalog# LHC6005M kits are from Thermo Fisher Scientific. The Comprehensive Human Cytokine Magnetic 35-Plex Panel provides reagents for the accurate, reproducible, and sensitive quantitation of human proteins including: EGF, Eotaxin, FGF-basic, G-CSF, GM-CSF, HGF, IFN-alpha, IFN-gamma, IL-1 beta, IL-1 alpha, IL-1RA, IL-2, IL-2R, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17A, IL-17F, IL-22, CXCL10, CCL2, CXCL9, CCL3, CCL4, CCL5, TNF-alpha, VEGF.The Patient-Specific Functional Scale (PSFS), a well-validated patient-reported outcome of functional recovery, was assessed at 0, 7, 14, 21 and 28 days. Individual patient PSFS recovery curves over time were assessed to determine the PSFS area under the patient curve (AUPC) over the course of 28 days. The median PSFS AUPC was determined. Patients with PSFS AUPC great than or equal to the median were considered to have good recovery and those less than the median defined poor recovery.For this preliminary analysis, we report baseline characteristics of all patients with available data. Continuous variables are summarized using mean, medians and interquartile range (IQR) as appropriate for the distribution of data.To account for the small sample size with high dimensionality of the data set, we performed Bayesian Belief Network (BBN) modeling using a machine learning technique to build directed acyclic graphs that represent relationships with a diagram composed of nodes and arcs. Each node represents a cytokine, chemokine or clinical feature and each arc represents a joint-probability distribution (JPD). The JPDs were determined in a step-wise fashion over multiple iterations. We report associations of this cytokine/chemokine/clinical feature model with the patient’s recovery (PSFS AUPC). We built the BBNs employing FasterAnalytics Version 7.0. A BBN starts with an observation, then calculates the relationships of various variables that are likely to impact that observation based on known probabilities (beliefs). BBNs are very useful for illustrating the differences in the immune systems of different patient groups since they are represented by acyclic directed graphs. These graphs can be overlaid on each other so that we can easily see where there are differences in the cytokine, chemokine and clinical feature joint probability distributions. In this case, BBN’s can 1) identify which relationships exist between variables 2) allow us to measure the magnitude of relationships (one variable on another) but also the number of relationships for any given variable 3) handle a large number of variables without large datasets 4) compare which relationships exist and how many relationships exist between the two groups of recovery (good and poor). These models also deal easily with missing data, which was anticipated in this cytokine data set.For the Luminex 35-plex kit, we built four Bayesian Belief Networks (BBN) for preliminary modeling using a Minimum Descriptive Lengths (MDL) of 0.01. The MDL measures the trade-off between goodness-of-fit and complexity of the model. Models with lower MDLs have more complexity and are more likely to be overfit. For each time point (pre- and post-antivenom), we built one model on the full data set and then models using leave-one-out. For each timepoint, we looked at the models and found which variables were first-degree associates of recovery. We used these variables in final modeling. If a variable was missing for more than 20% of records, we dropped the variable for the models with that kit.We used leave-one-out cross validation. We used the scores from each test case to build a receiver operating characteristic (ROC) and measure the area under the curve (AUC). We consider any AUC of 0.6 or above to be a good differentiator between good and poor recovery. We also provided statistics about the connectivity of the models.Twenty-eight snake envenoming patients were enrolled. Preliminary demographic data and PSFS AUPC is available for 24 patients with cytokine and chemokine data available from 9 pre-antivenom and 11 post-antivenom patients. The group was mostly female (82%) with a mean age of 38.1 (SD ± 9.8) years. The patients’ functional recovery as measured by the PSFS AUPC, for the 24 patients with available data, had a median of 0.73  and is represented graphically in The pre- and post-antivenom cytokines and chemokines were compared in The pre-antivenom individual cytokines and chemokines values were compared between good and poor functional recovery and are represented in In order to illustrate the effect antivenom has on the networks, the pre-and post-antivenom modeling networks are presented in Using a preliminary data set from patients with SBE, we identified distinct pre- and post-antivenom patterns of clinical features combined with cytokines and chemokines that are associated with recovery. These findings are important as they demonstrate the feasibility of using this technique to develop a more predictive prognostic model than currently exists. Ultimately this could result in a clinical decision support tool capable of determining thresholds to treat with antivenom or other novel therapeutics and/or informing the need for escalating therapies. This individually tailored therapy approach holds the potential to most efficiently improve patient outcomes from SBE while preserving treatment for those likely to benefit.The functional impact of SBE varies widely from patient to patient even within a given envenoming species. Specific SBE-related disabilities may impact different functions from work, to self-care, domestic life, social interactions, or civic life to name a few –64. By pOur technique has already been used to enhance the care of other disease states such as infection and injury. In 2019, Gelbard et al. developed models using clinical data, cytokines, chemokines and growth factors that predicted severe sepsis and organ space infections following laparotomy for abdominal trauma . This moBy expanding this technique to SBE, we have built on the prior work evaluating both the clinical features predicting severity and studies of the association of soluble biomarkers with severity , 65, 66.Bothrops asper SBE. Important associations were established with individual biomarkers as well as classes of immune response polarizations such as Th-1,Th-2, and Th17. However, their results are limited by the lack of an established relationship between acute tissue findings they evaluated, such as swelling, blisters, or superficial necrosis, with important patient-centered outcomes such as functional recovery . Previous work has shown a relationship between increasing TNF and SBE –74. Our Our preliminary findings are limited by the small sample size currently available for analysis. This resulted in some atypical demographic patterns such a female predominant study population; whereas, a similar SBE population in our geographic region is typically slightly male predominant . This alThis study is limited to only a few species of Crotalinae snakes and the predominant species is known to have a less severe clinical course than others. Yet, our data demonstrate a wide distribution of recovery as measured by PSFS AUPC over 28 days. The heterogeneity of the disease itself and the utility of our chosen outcome measure allows us to discriminate between patient’s recovery and develop robust prognostic models. This technique can be easily applied to other Crotalinae snake species. Lastly, as this is preliminary work, we have not used the models to determine thresholds to treat nor assessed clinical efficacy of implementing this application.Pre- and post-antivenom networks of cytokines/chemokines and clinical features were associated with functional recovery measured by the PSFS AUPC over 28 days. With additional patient data, we can identify prognostic models using immunologic and clinical variables to predict recovery from snake envenoming.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 Duke University School of Medicine Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.Grant funding for this research: United States Department of Defense. USUHS HT9404-13-1-0032 and USUHS HU001-15-2-0001.ES was employed by DecisionQ. CG receives grant funding from BTG Specialty Pharmaceuticals.The remaining 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.


The combination of ABO incompatibility and DRWR incompatibility was not an independent risk factor for biopsy-proven graft rejection ; however, it was an independent risk factor for pneumonia . The mortality rate due to infection was higher among patients with both ABO incompatibility and DRWR incompatibility than among patients with neither factor or with either factor alone. The combination of ABO incompatibility and DRWR incompatibility was an independent risk factor for graft and patient survival after LDKT, whereas neither factor alone significantly affected graft or patient survival. Thus, donor-recipient weight matching should be cautiously considered in LDKT with ABO incompatibility.The effect of donor-recipient weight mismatch is not well established in ABO-incompatible living donor kidney transplantation (LDKT). A total of 2584 LDKT patients in the Korean Organ Transplantation Registry were classified into four groups according to the presence or absence of ABO incompatibility and donor-recipient weight mismatch (donor-to-recipient weight ratio (DRWR) < 0.8). In a multivariable Cox analysis, the combination of ABO incompatibility and DRWR incompatibility ( Kidney transplantation (KT) is the best treatment for most patients with end-stage renal disease . Amidst Nephron mass is an important determinant of the fate and function of the kidney , as insun = 209), crossmatch-positive LDKT (n = 252), or lack of data (n = 5). The remaining 2584 LDKTs included 1941 ABOc LDKTs and 643 ABOi LDKTs. We defined DRWRi as a DRWR less than 0.8, which was near the cutoff for the 25th percentile among all LDKTs in the dataset; we defined DRWR compatibility (DRWRc) as a DRWR greater than 0.8 (n = 1570), ABOc-DRWRi (n = 371), ABOi-DRWRc (n = 519), and ABOi-DRWRi  who underwent KT in Korea between May 2014 and December 2018. Details of the KOTRY are described elsewhere [than 0.8 . For ourn = 124; .We retrieved data on the demographics of the recipients and donors, underlying kidney disease, donor-specific antibody (DSA) positivity, and induction agent. Serum creatinine was measured at the time of discharge, at 6 and 12 months, and annually thereafter. The estimated glomerular filtration rate (eGFR) was calculated from the serum creatinine level using the Modification of Diet in Renal Disease equation , which aThe causes of graft failure and patient death were recorded. We defined graft failure as a need for return to dialysis or re-transplantation. Data from patients who died with a functioning graft were censored in the analysis of graft failure. The first event of biopsy-proven rejection (BPAR) was recorded for each patient. The causative pathogens and infection sites were recorded for infections that required re-admission to the hospital after KT. The primary end point of the study was death-censored graft survival. Secondary end points were patient survival, BPAR, and infection.p-value was <0.10 in a univariable Cox analysis. A multivariable model for patient survival was established by stepwise regression to ensure statistical significance with limited number of events. The results were presented as hazard ratios (HRs) with 95% confidence intervals (CIs). All analyses were performed using standard software . p < 0.05 was considered statistically significant.Categorical variables were compared by Chi-square test or Fisher’s exact test and presented as the number (proportion). Continuous variables were compared by one-way analysis of variance (ANOVA) and presented as the mean ± standard deviation. Survival curves were generated using the Kaplan–Meier method and compared by log-rank test. Cox regression analysis was performed for outcomes adjusted with covariates for which the The demographics of the study sample are shown in 2) and ABOi-DRWRi (57.8 mL/min/1.73 m2) groups than in the ABOc-DRWRc (75.3 mL/min/1.73 m2) and ABOi-DRWRc (75.2 mL/min/1.73 m2) groups. This trend was maintained throughout the study period. The full results are provided in n = 23, 0.9%), the detailed causes of which are presented in p = 0.063, p = 0.028), together with other factors such as age and the number of HLA mismatches. The full results of the Cox regression analyses of death-censored graft survival are provided in During a mean follow-up of 36.2 ± 15.7 months, graft loss occurred in 50 (1.9%) patients. Rejection was the most common cause of graft loss , followed by unknown causes  and cardiovascular disease . The detailed causes of death are presented in p < 0.001, p = 0.008), along with other factors such as cardiovascular disease, unrelated donor, and XM-DSA+ (Thirty-one (1.2%) patients died during the study period. Infection was the most common cause of death , ABOi , and DRWRi  were not independent risk factors for BPAR , as were ABOi with DRWRc  and ABOi alone .We compared the incidences of infections that required hospitalization within 1 year after KT  and founAlthough the outcomes of ABOi LDKT have been inconsistent among different studies, patients who undergo ABOi LDKT have better long-term survival than those who wait for an extended period of time for an ABOc transplant from a deceased donor . FurtherABOi alone was not an independent risk factor for death-censored graft survival in our population, in contrast to the findings of a recent meta-analysis . One reap = 0.074), which could be due to the relatively small population and the short-to-medium duration of follow-up. Regardless, our results showed that DRWRi had a greater effect on graft survival in ABOi LDKT than in ABOc LDKT.Among the ABOi LDKT patients in our study, only those with DRWRi had lower death-censored graft survival rates compared with ABOc LDKT patients. Donor-recipient size mismatch is a well-known risk factor in KT ,27; howeIn the Kaplan–Meier analysis, patients without BPAR events during follow-up had equal graft survival regardless of ABOi or DRWRi. In contrast, among patients who experienced BPAR, the combination of ABOi and DRWRi was associated with lower death-censored graft survival, although the difference was not significant, probably because of the small sample size. The low graft survival after ABOi-DRWRi LDKT among patients who experienced BPAR might be due to low functional reserves in relatively small renal allografts, which were well demonstrated in a previous study . More poInterestingly, ABOi-DRWRi was the independent risk factor for patient survival while ABOi alone was not a significant risk factor in this study. High recipient BMI, which is characteristic of DRWRi, might affect patient survival; however, DRWRi alone did not significantly increase the risk of mortality. In fact, recent studies in the United States found that recipient BMI was not an independent risk factor for patient survival after KT . A possiOur study had some limitations. Because of the short-to-medium duration of follow-up, our results do not represent the long-term consequences of ABOi and DRWRi. The small sample size of the ABOi-DRWRi group was another limitation, which resulted in large confidence intervals of hazard for some outcomes. Additionally, detailed information, including the strength of anti-blood group antibody and desensitization protocols, was not included in the risk factor analysis because of data unavailability in the registry. Furthermore, potential confounders not controlled by the multivariable analysis because of the small number of events could be present, although we attempted to optimize the adjusted model to obtain statistical relevance.The combination of ABOi and DRWRi was an independent risk factor for graft and patient survival after LDKT, whereas neither factor alone significantly affected graft or patient survival. Thus, donor-recipient weight matching should be considered when selecting donors for ABOi LDKT.


C-indices, calibration plots, and expected-to-observed ratios and compared to a non-clinical model, the Pooled Cohort Equation, Framingham CVD Risk Scores (FRS), PROCAM scores, and the Systematic Coronary Risk Evaluation (SCORE). Our non-clinical score included age, gender, waist circumference, smoking, hypertension, type 2 diabetes, CVD family history, and dietary parameters. C-indices consistently indicated good discrimination  comparable to established clinical scores . Additional clinical parameters slightly improved discrimination . Calibration plots indicated very good calibration with minor overestimation in the highest decile of predicted risk. The developed non-clinical 10-year CVD risk score shows comparable discrimination to established clinical scores, allowing assessment of individual CVD risk in physician-independent settings.Inclusion of clinical parameters limits the application of most cardiovascular disease (CVD) prediction models to clinical settings. We developed and externally validated a non-clinical CVD risk score with a clinical extension and compared the performance to established CVD risk scores. We derived the scores predicting CVD  in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort  using competing risk models and externally validated in EPIC-Heidelberg . Performance was assessed by  Prognostic CVD prediction models allow identifying individuals at high risk that are eligible for lifestyle interventions and preventive treatment by estimating individual CVD risk. Their development is largely focussed on applications in clinical settings to support treatment decisions as for example with the Systematic COronary Risk Evaluation (SCORE) and the Pooled Cohort Equations (PCE)5. However, as these evaluations require information from physical examinations (blood pressure) and blood tests (cholesterol), application of these scores is unfeasible in most physician-independent settings like self-assessment of individuals, health education campaigns, and step-wise screening procedures including a non-clinical stage. The few available non-clinical models to be used independently of physical examinations are limited in terms of study design, originating from case–control studies or high-risk cohorts7; short follow-ups and lack of equations to calculate absolute risks7; the endpoints, predicting only myocardial infarction (MI) or stroke8; or inclusion of dietary predictors on a nutrient level requiring assessment of a large variety of individual foods, thus hampering the applicability in practice9. We only identified one model allowing large-scale estimation of individual CVD risk based on non-clinical parameters10. However, despite established risk associations, the score does not include potentially informative dietary information11.Cardiovascular diseases (CVD) are a major public health burden12.Moreover, overlap in risk factor profiles of CVD and type 2 diabetes (T2D) offers the potential for combined risk assessment with only minor deviations in the required predictors, including dietary parameters. The German Diabetes Risk Score (GDRS) is a multiply validated non-clinical score to predict T2D and its extension for CVD risk prediction would enable simultaneous quantification of individual CVD and T2D risk in non-clinical settingsThus, we aimed to develop and externally validate a non-clinical risk score to predict 10-year CVD risk based on shared predictors with the GDRS and to compare its performance to the identified non-clinical and established clinical CVD risk scores. Furthermore, we developed a clinical extension with routinely available clinical predictors for step-wise screening approaches.Descriptive comparison of the unimputed and imputed data, including the proportion of missingness, is presented in the supplement (Supplementary Table (ST) The final non-clinical model included the predictors age, gender, waist circumference, smoking status, self-reported hypertension and T2D, CVD family history, and consumption of whole grain, red meat, coffee, high energy soft drinks, and plant oil. The clinical model additionally contained systolic and diastolic blood pressure, total and HDL cholesterol.The proportional hazards assumption was fulfilled for all included predictors. The supremum test for functional form was only significant for ‘CVD points’ in the clinical model. However, subsequent examination of the according restricted cubic splines did not indicate strong deviations from a linear function  Estimates derived by using Cox proportional hazards regression and the Fine and Gray model were overall comparable. However, comparison of the model performance indicated slightly better calibration of absolute risks by the Fine and Gray model compared to the Cox model in the upper risk range . As a coAdding statistically significant interaction terms or squared terms as well as deriving gender-specific equations of the Fine and Gray models did not improve overall performance relevantly .The final parameters used for absolute risk calculation based on the competing risk model are depicted in Table C-indices indicated good discrimination of both developed models in EPIC-Potsdam  0.736–0.832; clinical 0.796, 0.746–0.841) and EPIC-Heidelberg . The categorical Net-Reclassification-Improvement (NRI) suggested only slight improvement of risk category assignment by additional clinical parameters . Sensitivity and specificity in both cohorts are shown in the Competing risk-adjusted C-indices in EPIC-Potsdam  with blood lipids 0.781, 0.730–0.828) , while the derived clinical score still showed the highest C-index. C-indices of the non-clinical chronic metabolic disease (CMD) score were considerably lower in EPIC-Potsdam  and EPIC-Heidelberg .Comparison of the performance with established risk scores demonstrated that the two derived equations reached the highest The derived scores were well calibrated for the majority of individuals in the lower nine deciles of predicted risk while they slightly overestimated risk in the highest decile of predicted risk Fig. . ExpecteC-indices were consistently higher for women compared to men and for MI compared to stroke for both derived scores in EPIC-Potsdam and EPIC-Heidelberg . However, due to the limited number of fatal cases, estimates were imprecise.Additional appraisal of CVD mortality discrimination resulted in higher We derived and externally validated a non-clinical risk score predicting 10-year CVD risk with superior or comparable performance to established clinical CVD risk scores. Additional clinical parameters only slightly improved discrimination. Our results suggest that estimation of 10-year CVD risk based on the selected and easily obtainable non-clinical CVD risk factor information is feasible without loss of predictive accuracy compared to clinical models.14. Two meta-analyses, one based on 86 prospective studies, concluded that the PCE discriminates relatively well  and reported a prediction interval  covering the observations from our study samples16. A pooled analysis of two other German population-based cohort studies showed a C-index  comparable to our findings17. For the FRS including blood lipids, a meta-analysis of prospective studies reported a C-index of 0.719 (0.715–0.723), which is lower than in our cohorts16. For SCORE, the same meta-analysis suggested relatively good discrimination for all CVD events  and better discrimination for fatal events only 16. SCORE showed higher discriminatory ability in our samples when including all cases, while discrimination for fatal events was comparable. The PROCAM score for MI showed lower discrimination in other European validation studies than in our sample, with AUCs ranging from 0.55 to 0.7418.Other external validations of the CMD Score showed acceptable to good discrimination in an Iranian (areas under the receiver operating characteristic curve (AUC): men 0.71, 95%CI 0.66–0.75; women 0.81, 0.76–0.85) and an Australian population  which is comparable or higher than in our samples17. This could be related to deviations in the documented CVD incidence as a result of actual incidence differences in the studied populations and/or differences in the case identification and ascertainment procedure, potentially leading to systematically fewer or more identified cases. Additional inclusion of heart failure and angina in the FRS endpoint definition might explain the strong overestimation of risk detected in our samples.The PCE endpoint definition  is largely comparable to our definition. While the PCE was well calibrated in a German sample after recalibration, our study still suggests slight overestimation of the recalibrated equation19, these findings indicate that the established clinical CVD models performed mostly comparable or better in EPIC-Potsdam and EPIC-Heidelberg compared to other studies. This suggests that underestimation of the performance in our samples is unlikely.Despite minor heterogeneity across individual validation studies potentially related to deviations in the population characteristics and covariate structureSeveral features of our approach are worth mentioning. Firstly, and most importantly, the developed non-clinical risk score extends individual risk prediction to prevention settings that are not covered by existing clinical risk scores without loss of predictive precision. These include self-assessment of individuals, health education campaigns, and step-wise screening procedures with a non-clinical stage. Secondly, the inclusion of selected GDRS parameters  in the non-clinical score allows simultaneous risk assessment of CVD and T2D with only a few additional parameters. Thirdly, our non-clinical score contains several lifestyle risk factors, modifiable and easily to be obtained, including dietary information. As effect sizes and directions of the modifiable predictors are in line with previous evidence compare , the sco21.There are several strengths to our study. We based our analyses on physician-verified cases, reducing false-positive case assignment to a minimum. The application of the World Health Organization (WHO) Monitoring trends and determinants in cardiovascular disease (MONICA) criteria in the derivation cohort facilitates reproduction in other cohorts based on a standardised outcome definition. Harmonised data collection and procession methods between the EPIC centres in Potsdam and Heidelberg enabled us to fully rebuild the prediction model for external validation without regression or substitution of predictors that could be unavailable in other cohorts. Relevant sample sizes and case numbers in both cohorts  allowed the derivation of robust estimates, to perform sensitivity analyses, and to examine the performance in subgroups22. Secondly, we used the non-clinical score points as one predictor for the clinical score instead of individually modelling its risk factors. This approach may have diminished performance improvement. However, post-hoc re-estimation of the clinical model including the non-clinical risk factors individually showed that C-index increased only by 0.001, suggesting negligible loss of discriminatory ability. Thirdly, heterogeneous outcome definitions of the composite endpoint CVD may have hampered performance comparison with other risk scores, especially calibration. Finally, as we developed and validated our scores in German adults, generalisability to other populations with differences in case-mix and deviations in predictor and outcome assessment remains unclear.However, there are some limitations. Firstly, due to the case-cohort design, the proportion of missingness was high for most biomarkers. However, it has been shown that multiple imputation is a valid approach to handle missing data for absolute risk estimationshttps://drs.dife.de/) and a paper questionnaire will be developed.To conclude, we developed and externally validated a non-clinical risk score predicting 10-year CVD risk based on shared predictors with a validated T2D risk score with comparable or superior performance to established clinical CVD risk scores. It can be used independently of physical examinations and includes a variety of modifiable risk factors supporting both, risk assessment and subsequent counselling for preventive lifestyle modifications, e.g., through an online calculator. The models will be implemented in the online tool of the GDRS  and Heidelberg . The data was collected from 1994 to 2012. Detailed information on recruitment and follow-up procedures is described elsewhereIn both cohorts, participants with prevalent CVD, non-verifiable, silent events, stroke cases with prior brain cancer, meninges, or leukaemia, and with missing follow-up information were excluded. Exclusively in EPIC-Potsdam, we excluded individuals with ‘possible’ events according to the WHO MONICA criteria. Exclusively in EPIC-Heidelberg, we excluded participants with events only indicated by a death certificate but without further sources suggesting an event. The analysis sample in EPIC-Potsdam contained 25,993 participants for the full follow-up, including 684 overall CVD cases , 383 myocardial infarctions (MI), and 315 stroke cases and after 10 years 584 overall CVD , 324 MI, and 269 stroke cases. Non-CVD death was documented for 2312 participants (8.9%) during the full follow-up and 847 participants (3.3%) within the first 10 years. The respective analysis sample in EPIC-Heidelberg contained 23,529 participants, including 692 overall CVD , 370 MI and 345 stroke cases after 10 years of follow-up details: . Non-CVD25. Waist circumference, systolic and diastolic blood pressure were measured by trained personnel at baseline examination  codes: I21 acute MI, I63.0–I63.9 ischemic stroke, I61.0–I61.9 intracerebral haemorrhage, I60.0–I60.9 subarachnoid haemorrhage, I64.0–I64.9 unspecified stroke). In both cohorts, events were systematically detected via self-report of a diagnosis, information of death certificates, and reports by local hospitals or treating physicians. If an event was indicated by the aforementioned sources, treating physicians were contacted for diagnosis verification, occurrence date, and diagnostic details. Only events with physician–verified diagnoses were considered as incident CVD cases. In EPIC-Potsdam, physician-verified cases were additionally ranked into ‘definite’, ‘probable’, and ‘possible’ events by two trained physicians based on the WHO MONICA criteria for MI and an adapted version for stroke details: .28.We applied multiple imputation by chained equations (m = 10) to handle missing values in predictor candidates and parameters needed to derive other scores for comparison 27,28.29. Only parameters that were consistent in regards to effect size and direction with available meta-analyses or large-scale studies remained in the model. For the identification of CVD-specific predictor candidates, the literature was screened for established non-clinical and routinely available clinical CVD risk factors. To derive the non-clinical score, we considered candidates with regard to anthropometric measures, gender, CVD family history, self-reported prevalent diseases, medication, weight history, and dietary information as the main focus. The final selection of the predictors was based on the following criteria: performance improvement, assumed availability in physician-independent settings or routine care, consistency with previous evidence, and robustness of the association. Different predictor candidate combinations were added to the previously identified shared predictors from the GDRS to assess the independence and robustness of the associations. For the clinical extension, we used the score points of the non-clinical score as one predictor and subsequently added clinical candidates with regard to blood pressure measurements, blood pressure or lipid-lowering medication, blood lipid concentrations , and HbA1c. Predictor candidates meeting the previously defined criteria were included in the final scores.Data of the EPIC-Potsdam cohort  was used for score derivation. We used the predictors of the GDRS in the first step and assessed their association with CVD using Cox proportional hazard regression in each imputed set separately30. The proportional hazards assumption was assessed by visual inspection of the Schoenfeld residuals.Linearity assumptions of the risk associations were examined by deriving Martingale residuals and performing supremum tests for functional form32.Even though previous studies have commonly used Cox proportional hazards regression models for absolute risk predictions, including the PCE and FRS, non-CVD mortality is considered a competing risk event for the analysis of CVD endpoints. Despite a limited proportion of non-CVD mortality events in EPIC-Potsdam (3.3% during the first 10 years of follow-up), we additionally used Fine and Gray models accounting for competing risks, calculated absolute risks, assessed the model performance, and compared it to the performance of the Cox proportional hazards modelsIn a final step, we additionally considered squared terms and multiplicative interaction terms of the selected predictors with gender and age and, if statistically significantly associated with the outcome, added them to the model and re-evaluated the performance. To assess the potential benefit of modelling gender-stratified equations, we re-estimated the models in men and women separately and compared their performance.β estimates of the final models were rounded, multiplied by 100 and the following equation including the subdistribution baseline survival S0 and mean values 33:35. To quantify the discrimination of the scores, we calculated C-indices by using a bootstrap approach dividing each imputed set into 10 random subsets and adjusting for competing risks38. Calibration was assessed with calibration plots and expected-to-observed ratios. The calibration of the CMD score, SCORE, and both PROCAM scores was not evaluated due to differences in the predicted time frame or in the endpoint definitions . Potential changes in risk group assignment between the derived non-clinical and clinical score were assessed using the NRI with previously implemented risk groups 2. Sensitivity and specificity were calculated based on the aforementioned risk cut-offs.We evaluated the performance of the generated scores in EPIC-Potsdam and for external validation in EPIC-Heidelberg censored at 10 years of follow-up and compared it to the performance of established CVD risk scores. Namely the non-clinical CMD risk score, the for Germany recalibrated PCE, two FRS including blood lipids or BMI, the ESC SCORE, and two PROCAM Scores predicting MI or stroke .Supplementary Information 1.Supplementary Information 2.


Severity was assessed for side effects related to sleep, somatic anxiety, gastrointestinal function, and sexual dysfunction. Analysis of covariance was used to assess the relation between these side effects and ratings of relevant HDRS-17-derived outcome parameters. Side effects related to sleep, somatic anxiety and sexual dysfunction significantly and exclusively associated with higher scores on HDRS-17 items measuring the corresponding domains. Side effects related to gastrointestinal function associated with higher HDRS-17 item scores on all assessed domains. Treatment outcome was significantly related to side effect severity when assessed using HDRS-17-sum (beta 0.32 (0.074), p < 0.001), but not when the HDRS-6-sum-score (beta 0.035 (0.043), p = 0.415) or the depressed mood item (beta 0.007 (0.012), p = .527) were used as effect parameters. That some HDRS-17 items co-vary with common antidepressant side effects suggests some of these adverse events are counted twice, potentially leading to an underestimation of antidepressant efficacy.The Hamilton Depression Rating Scale (HDRS-17) measures symptoms that may overlap with common antidepressant side effects , thus making it possible that side effects of antidepressant treatment are erroneously rated as symptoms of depression, and vice versa. This study uses patient-level data from previously conducted antidepressant treatment trials to assess whether side effect ratings co-vary with HDRS-17 ratings. Data from all HDRS-17-rated, industry-sponsored pre- and post-marketing trials ( This has since long been suggested not to be the case for the effect parameter most widely used in trials of antidepressants, namely the sum of the 17 items of the Hamilton Depression Rating Scale (HDRS-17-sum)6. One potential drawback of the HDRS-17-sum as a measure of depression severity is that some of the items included with the intention to quantify depressive severity may also capture common antidepressant side effects (SEs)8.It is desirable that a rating scale accurately reflects the underlying severity of the condition it aims to depict, as well as how this severity is influenced by treatment9. Among the SEs frequently reported in relation to their use are insomnia, sweating, nausea, constipation, diarrhoea, loss of appetite, ejaculation difficulties, anorgasmia, loss of libido, and weight loss12. As the HDRS-17 includes three items pertaining to insomnia (items 4–6), one for gastrointestinal symptoms (item 12), one for genital symptoms (item 14), and one for weight loss (item 16)13, there is potential for the SEs of SSRIs and SNRIs to be erroneously accounted for as symptoms of depression. Similarly, the somatic anxiety item of the HDRS-17 (item 11), which includes complaints such as dry mouth, micturition difficulties, palpitations, etc., may also reflect common antidepressant SEs. If at hand, such a mechanism would lead to an underestimation of the antidepressant efficacy of these compounds14. Conversely, it is possible that the antidepressant efficacy of compounds with sedative or appetite increasing effects may be erroneously overestimated due to reduced ratings of sleep disturbance or weight loss15.Selective serotonin reuptake inhibitors (SSRIs) and serotonin and noradrenaline reuptake inhibitors (SNRIs) are the most commonly prescribed drugs for the treatment of major depression17. In these studies, however, we did not analyse SE reports. In the present study, we follow up on these findings by including data on SEs related to sleep, somatic anxiety, gastrointestinal function, or sexual dysfunction. We thus aimed to assess, (i) whether there are significant positive associations between specific SE ratings and ratings on HDRS-17 items that overlap in content with these SEs , (ii) whether such associations also impact the HDRS-17 sum-score, and (iii) whether outcome measures like the unidimensional HDRS-6 subscale and the HDRS-17 depressed mood item, which display little overlap with common antidepressant SEs, are less influenced by SE severity3.We have previously reported that the HDRS-17 items most likely to reflect common SSRI SEs either do not separate active treatment from placebo, or show significantly larger improvements in placebo-treated patients than in SSRI-treated patients19 were acquired through ClinicalStudyDataRequest.com. Ten of these were included in the FDA Approval Package for duloxetine and five were post-marketing trials. We confirmed that we had access to all relevant trials by searching the FDA Approval Package for duloxetine, the ClinicalStudyDataRequest website, Eli Lilly’s Clinical Study Results portal and Clinicaltrials.gov . All adverse events present at endpoint in at least five patients were considered. Due to a low prevalence of weight loss-related adverse events (1.2%), this group was merged with the gastrointestinal function group. Adverse events not considered to belong to any of these five groups were excluded. For each SE group, a severity score was calculated by adding up the severities of all included SEs . Overall SE severity was computed by summing the severity scores for all four SE groups.Adverse events were classified according to their lowest level Medical Dictionary for Regulatory Activities (MedDRA) term21. Since SEs may lead to treatment discontinuation, and since subjects dropping out early have had less time to improve, including drop-outs in the analyses could result in associations between SE ratings and symptom ratings related to time rather than to content overlap between SE and HDRS-17 ratings. To avoid this confounder, the primary analyses were based on the observed cases (OC) population including only patients with an observation at study endpoint , duloxetine and SSRI-treated patients were analysed togetherTo confirm that the complaints reported as adverse events were at least partly actual SEs of the active treatment, we first assessed whether the included adverse events were more common among patients allocated to duloxetine or SSRI than to placebo by contrasting severity scores for all four SE groups individually, and for overall SE severity, using unequal variance t-tests. We additionally compared the active treatment and placebo groups with respect to the percentage of patients presenting with at least one relevant SE using the chi-square test of independence.We then used analysis of covariance (ANCOVA) to assess whether specific SE groups associated with particular HDRS-17 items in a predictable manner . For these analyses, only patients receiving active treatment (duloxetine or SSRI) were included. All models included study as a fixed effect and baseline severity on the corresponding dependent variable as a covariate. The dependent variables were (i) the sum of the three sleep items , (ii) the somatic anxiety item (HDRS-17 item 11), (iii) the gastrointestinal symptoms item (HDRS-17 item 12), and (iv) the genital symptoms item (HDRS-17 item 14). For each model, the severity of the four SE groups  were included as covariates.We then assessed whether overall SE severity  predicted endpoint scores on three HDRS-based outcome parameters: (i) HDRS-17-sum, (ii) the sum rating of the six items included in the unidimensional HDRS-6 subscale (HDRS-6-sum) , and (iii) the depressed mood item. This was done to explore, (i) whether any observed item-level associations significantly impact the sum of all HDRS-17 items, and (ii) whether such an influence may be reduced by using outcome measures showing less content overlap with SEs. As for the individual SE groups, this was done using ANCOVA models including actively treated patients only, but using the overall SE severity term rather than the four individual SE group severity terms. Prompted by a statistically significant association between overall SE severity and outcome when measured using HDRS-17-sum, but not HDRS-6-sum or depressed mood, we also assessed the relation between overall SE severity and outcome on the complementary set of HDRS items not included in the HDRS-6 (non-HDRS-6-sum). We also analysed HDRS-17-sum based remission rates (HDRS-17-sum endpoint score ≤7) using logistic regression. The logistic model used a binary distribution with a logit link and was otherwise identical to the ANCOVA models described above.All analyses were repeated on the ITT population, with missing values imputed using last observation carried forward (LOCF) methodology; patients randomised to treatment but not having a post-baseline HDRS-17 evaluation were however not included. To assess whether the presence of SEs may be confounded with time under treatment when analysing the ITT population, we also ran an ANCOVA model on this population with time (week) of the last observation as the dependent variable. The model included protocol as a fixed factor, and baseline HDRS-17 and overall SE severity as covariates.All analyses were carried out through remote desktop access to the Clinical Trial Data Transparency environment using SAS Software version 9.4 . All statistical tests were two-tailed. Since most hypothesis tests are not independent—the OC population is a subgroup of the ITT population, HDRS-17 subscales and items are nested within the HDRS-17, and SE categories are nested within the overall SE category—we did not correct for multiple testing.Details on the included studies are presented in the supplement . While in total 41% of the actively treated sample achieved remission, this was the case for 44% of those with an SE severity score of zero, as compared to 41%, 32%, 37%, and 34%, of those with an overall SE severity score of one, two, three, or ≥ four, respectively.The positive association between overall SE severity and HDRS-17-sum was mirrored by a negative association between overall SE severity and HDRS-17-based remission rates (beta = −0.091 (0.027) and p < 0.0001). While in total 35% of the active treatment sample achieved remission, this was the case for 39% of those with an overall SE severity score of zero, as compared to 36%, 28%, 31%, and 22%, of those with a score of one, two, three, or ≥ four, respectively. As hypothesised, overall SE severity was negatively associated with time under treatment in the ITT population (beta −0.16 (0.018), p < .001, and Supplementary figure Sensitivity analyses of the ITT population were in line with those of the OC population  that the severity of a priori defined SEs, as expected, is higher at endpoint in patients receiving active treatment than in patients receiving placebo, (ii) that the severity of each individual SE group associates positively with corresponding HDRS item in patients given active treatment, and (iii) that there were no associations (in the OC population), or more modest associations (in the ITT population), between efficacy and SEs, when efficacy was measured using the HDRS-6 subscale or the depressed mood item.22, and further suggest that the antidepressant efficacy of SSRIs and SNRIs may have been consistently underestimated in previous meta-analyses using HDRS-17-sum as effect parameter25.As a follow-up analysis, we looked at the association between SEs and outcome measured by the sum of the HDRS items not included in the HDRS-6 subscale Fig. . These aSince antidepressant SEs tend to be most impactful during the first days or weeks of treatment, we assumed that there may be confounding between SEs and time under treatment in the ITT population and hence considered the observed cases population as primary. This assumption was supported by the observation of an inverse relation between overall SE severity and time under treatment  have also demonstrated increased sensitivity32. To what extent severity assessments based on other depression rating scales are impacted by the presence of SEs remains to be investigated.This report focuses on the 17-item version of the Hamilton Depression Rating Scale , but there are many other instruments available to assess the severity of depression33. While this reasoning applies for any scale aiming to measure overall well-being, HDRS and other depression rating scales are supposed to specifically measure depressive severity. Not least for future drug development, it is important to be able to distinguish drugs that are highly effective against depression, but provoke SEs in some patients, from those that are highly tolerable but only modestly effective34. In this context, it is unfortunate that pharmaceutical companies and regulatory authorities still favour rating instruments with demonstrable shortcomings in this respect4.One could argue that SEs impact the overall well-being of the patient and that a rating scale including them provides a more accurate reflection of the overall usefulness of a drug than a scale not capturing unwanted SEs26, the low prevalence of weight loss-related SEs may reflect underreporting. Third, the association between adverse events and treatment response may also have been attenuated by the fact that both response and side effects should be influenced in the same direction by factors for which we did not control, such as inter-individual differences in dose, compliance, and drug metabolism. Fourth, due to the exploratory nature of this study we opted for a simple analysis scheme in which ‘mild’, ‘moderate’ and ‘severe’ SEs, irrespective of type, were translated into 1, 2 and 3 severity points, respectively, and then added together and analysed as linear predictors. This transformation, however, is questionable since severity categories are unlikely to be linearly spaced and since SEs likely differ in their association with HDRS-17 ratings. Thus, while we do believe that the results are strong enough to suggest a positive relation between SEs and (some) HDRS-17-derived outcome parameters, we do not assume this association to be linear. Fifth, early treatment emergent SEs35, as well as early worsening in symptoms associated with depression 36, have been shown to predict lower remission rates. For feasibility reasons we, however, restricted our analyses to efficacy and SE reports collected at the endpoint visit (or last post-baseline visit in the ITT population); assessing to what extent SEs that appear early in treatment but remit prior to endpoint visit affect response trajectories over time was thus beyond the scope of this project. Sixth, the impact of SE “double counting” likely varies between studies due to differences in rater training, retention rates and adverse event recording practices. Seventh, this is a secondary analysis of data collected during the clinical development of duloxetine. While potentially a strength that these studies were not aimed at assessing the extent to which SEs and efficacy ratings may co-vary, hence limiting expectancy effects, the results should be replicated in studies directly aimed at assessing these issues, as well as in studies of other classes of antidepressants and outcome measures.This study has a number of limitations. First, the symptoms that may be captured by HDRS-17 item 11 (somatic anxiety) are many and diverse. While we used an inclusive definition for the somatic anxiety-related SE group, being more (or even less) restrictive with regard to which SEs to include may strengthen or weaken the association between this SE group and the various outcome parameters. Second, there may exist SEs of active treatment of sufficient intensity to influence HDRS-17 ratings, but not of sufficient impact as to merit an adverse event report (and/or vice versa). This should likely work to attenuate the observed associations between reported SEs and HDRS-17 ratings. Relatedly, since the HDRS-17 separates between loss of appetite (HDRS-17 item 12) and weight loss (HDRS-17 item 16), we originally intended to include weight loss as a separate SE category, but opted not to do so after noting that SEs coded as ‘weight loss’ had low prevalence at endpoint (1.2%). Given the well-known short-term weight decreasing properties of drugs inhibiting serotonin reuptake4.In summary, while depressive severity as measured using the HDRS-17 depressed mood item or the HDRS-6 subscale is not impacted by common antidepressant SEs, the ratings of a number of other items included in the HDRS-17 do co-vary with SE ratings. These associations are predictable in the sense that side effects belonging to a particular domain  primarily associates with HDRS-17 item ratings of the same domain. SEs may hence impact the assessment of efficacy when HDRS-17-sum is used as effect parameter, which suggests that studies and meta-analyses using the HDRS-17 as effect parameter may have underestimated the efficacy of SNRIs and SSRIs when used for the treatment of depression. These findings underline that it is high time for the HDRS-17 to be superseded by alternative measures of depression severity in drug trialsSupplementary materials


Drug-resistant TB (DR-TB) care shifted from centralized to decentralized care in Tanzania in 2015. This study explored whether DR-TB training and mentoring supported healthcare workers’ (HCWs) DR-TB care performance.This mixed study assessed HCWs’ DR-TB care knowledge, the training quality, and the mentoring around 454 HCWs who were trained across 55 DR-TB sites between January 2016 and December 2017. Pre- and post-training tests, end-of-training evaluation, supervisor’s interviews, DR-TB team self-assessment and team focus group discussion were conducted among trained HCWs. Interim and final treatment results of the national central site and the decentralized sites were compared.HCW’s knowledge increased for 15–20% between pre-training and post-training. HCWs and supervisors perceived mentoring as most appropriate to further develop their DR-TB competencies. Culture negativity after 6 months of treatment was similar for the decentralized sites compared to the national central site, 81% vs 79%, respectively, whereas decentralized sites had less loss to follow-up (0% versus 3%) and fewer deaths (3% versus 12%). Delays in laboratory results, stigma, and HCWs shortage were reported the main challenges of decentralized care.Training and mentoring to provide DR-TB care at decentralized sites in Tanzania improved HCWs’ knowledge and skills in DR-TB care and supported observed good interim and final patient treatment outcomes despite health system challenges.The online version contains supplementary material available at 10.1186/s12960-021-00600-4. TB remains a major cause of morbidity and mortality in Tanzania. The prevalence of multidrug resistant tuberculosis (MDR-TB) in Tanzania was estimated at 1.0% among new TB patients and 4.1% among retreatment patients in 2017  highlighImplementation Framework for Expanded Decentralization of MDR-TB Services in Tanzania . Th. Th16]. The DR-TB treatment outcomes were assigned by a team of physicians working at Kibong’oto Infectious Diseases Hospital based on the patient’s progress , and mycobacterial culture results. The treatment outcomes were recorded as cured, treatment completed, died, treatment failure, lost to follow-up or not evaluated as adapted from WHO DR-TB definitions . FavorabThe interviews for HCWs were conducted at the one national central site  and 14 decentralized sites which were selected purposefully to include sites with high patient volumes, treating 286/327 (87%) of patients in 2016 and 2017 when the study was conducted. Treatment outcomes of 327 DR-TB patients were analyzed for 2016 and 2017 to explore how training supported the level of care in the decentralized sites.This study included 454 HCWs from 93 DR-TB initiation sites that were trained between January 2016 and December 2017 by a team of 23 national trainers. Outcomes of patients enrolled in the year prior to training (2015) were used for comparison. The performance of 55 sites out of 93 sites was assessed as these were providing DR-TB care by December 2017. DR-TB teams from 15 sites were interviewed in a focus group discussion, 14 of which completed the self-assessment questionnaire and 14 had their supervisors interviewed to collect qualitative information about DR-TB teams’ performance on the job  .Quarterly supervision was provided by a team of four people from national, regional and district-level staff. During the supervision, the identified gaps were mentored on site. The next quarter supervision team also made a follow-up of previous visit action points before starting a new supervision.For qualitative data collection, we used semi-structured interviews for DR-TB team self-assessment and supervisors and a tool for DR-TB team focus group discussions interview. Qualitative data were collected on HCWs performance and the relevance of the training and mentoring one year after the training was conducted to the 15 DR-TB sites. All available members of the DR-TB teams in 15 sites participated in the focus group discussion interviews at the DR-TB health facility. The interviewer (BS) was an independent researcher not involved in DR-TB care, training or mentoring. BS interviewed and facilitated focus group discussions, making use of standardized tools . We used a composite variable for the final outcome whereby favorable outcomes were a combination of cure and treatment completion rates and unfavorable outcomes comprised death, lost to follow-up, not evaluated and treatment failure  to 55 sites from January 2016 to December 2017. Decentralized DR-TB sites are distributed all over the country, with a concentration in the Eastern and Northern part of the country. Most sites are in urban areas where most patients are found  were male, 132 (32%) were regional and district TB program coordinators, 33 (8%) TB direct observed treatment (DOT) nurses, 43 (10%) laboratory staff, 73 (17%) other clinicians, 78 (18%) other nurses, 39 (9%) pharmacists, and 28 (7%) social workers , nurses (7), and laboratory staff (4). Eleven out of 21 trainers, participated in this study and were; clinicians (6), laboratory staff (3) and nurses (2).p < 0.001  decentralized sites had high proportion of culture conversion (72%) compared to centralized site (50%) in 2016, however there was no difference in the 2 month culture conversion between central and decentralized sites in 2017. After 6 months of treatment, culture conversion was comparable between decentralized and centralized sites in 2016 and 2017. For final outcomes, cure rates were higher for decentralized (86%) than KIDH (58–69%) in 2016 and 2017. Likewise, deaths and loss to follow-up were lower for decentralized sites compared to KIDH in the same years Table .Table 3I2) was a significant predictor of unfavorable outcomes among patients at the national central site  similarly to HIV-coinfection among decentralized treated patients . Older ages [31–50 years  & > 50 year  and centralized treatment delivery (aOR 2.9 p = 0.006) were associated with unfavorable outcomes.Comparing predictors of unfavorable outcomes in decentralized vs centralized site in 2016 and 2017 Table , we founThe trained HCWs of the 15 DR-TB sites qualified the training course as comprehensive, relevant, good, practical and helpful and that it did not focus on clinical topics only. The course gave them confidence and skills to provide treatment to DR-TB patients. Five DR-TB teams mentioned that the “New Drugs and Regimens” session was confusing. Teams suggested regular updates on (the frequent) changes in DR-TB care and train new staff members which are needed due to frequent staff rotation.  to good (10 supervisors) concluding that trained HCWs had developed their DR-TB knowledge during the training , Bangladesh (21), South Africa (9) and Indonesia (14) confirm that quality training, mentoring and supervision of frontline workers are essential to provide quality (DR-) TB care.Our study findings are in line with those from Kenya, South Africa and Ethiopia which also adopted TB ambulatory care models –22. In aHCWs DR-TB knowledge to initiate, monitor, and provide treatment to patients increased between pre-training and post-training implying the added value of the DR-TB training package. After the training and mentoring, HCWs acknowledged that they had skills to initiate DR-TB treatment and provide quality care with good treatment outcomes. These results were discussed and endorsed in a meeting with the PMDT technical working group. Studies in other countries also showed that the treatment outcomes of decentralized care are comparable to centralized care, but the risk for defaulting is lower at the decentralized sites because of the patient centered approach of the mode of treatment , 9, 23.Decentralization was undertaken in a phased manner and it took time for health facilities to get ready (after implementing a readiness assessment checklist and/or renovations to TB clinic/wards), updating the national and regional supply chain system (N95 respirators and medicines), HCWs training, on-job mentoring and supervision. Trained HCWs, trainers and supervisors mentioned that the DR-TB training was important to prepare HCWs for the decentralized DR-TB care. The practical and interactive training methods, allowing HCWs to practice and ask questions, have facilitated trained HCWs’ learning . Other sTo ensure up-to-date quality trainings, National TB Programs need to evaluate their training courses systematically on HCWs’ knowledge, job performance and program outcome, and frequently update their training packages based on these evaluation results, new guidelines and practices. Evaluation of HCWs performance and patient outcomes at the facility level often does not take place because of lack of priority, funding, experience, and tools , 15, 25.All stakeholders acknowledged that mentoring and supervision were important to reinforce HCWs’ performance after the training. In comparison to training, mentoring and supervision have the additional value of focusing on skills building, addressing HCWs’ gaps, and repeating the key information shared in the training, and therefore reinforcing learning , 14, 24.Tanzania has chosen for a strongly decentralized model of DR-TB care, to provide care close to patients’ homes. However, at the beginning, the number of DR-TB patients was limited leading to DR-TB initiation centers with few patients only. There was a fear that HCWs in these sites would not build their DR-TB experience and risk losing the knowledge and skills they had been trained in. Monitoring the quality of DR-TB care in these small-scale DR-TB initiation centers needs extra attention and on-job HCWs mentoring is crucial. It might even be more effective to choose for on-job mentorship approach first (when a site has a patient) followed later by a comprehensive training. A dialogue is needed among the main stakeholders in DR-TB care, on the appropriate level of DR-TB care decentralization and investments needed for HCWs development and infrastructure.Trained DR-TB HCWs at the decentralized sites mentioned several bottlenecks that hindered their performance. To provide quality decentralized DR-TB care, these bottlenecks need to be addressed. Training, mentoring and supervision alone are not enough and need to be part and parcel of health systems strengthening interventions, to be effective , 10.A limitation of this study was that it was not designed to compare treatment outcomes between sites with and without DR-TB training, for which a cluster randomized trial would have been the preferred approach. This would, however, raise ethical concerns considering the necessity of highly trained HCWs to provide quality DR-TB care. As such this study does not provide a causative link between a HCWs training program and HCWs behavior nor treatment outcomes, but it does support the merit of providing a standardized training package combined with on the job mentoring. Another limitation, due to budget constraints, was that patients were not interviewed to know how they perceive the quality of the DR-TB care to explain the variability of their treatment outcomes. In addition, the qualitative data were collected 1 year after the training which might have introduced recall and information bias. Only 11 out of 23 trainers assessed the quality of the training and the training package which could bias the results. Four trainers were supervisors as well which could have biased the results of the self-assessment.The mixed study findings displayed the quality of DR-TB training necessary to build the, HCWs’ knowledge and skills to support the HCW’s performance and the DR-TB treatment results. The qualitative data have given additional insights in HCWs and their supervisors’ perceptions on the quality of DR-TB care at decentralized sites that would not have been possible using a quantitative study only.In conclusion, the success of decentralization in Tanzania is the outcome of investment in programmatic management in DR-TB, the enhanced cohort review process and capacity building. Therefore, we recommend NTPs to invest consistently in HCWs capacity building enabling HCWs to provide quality DR-TB care and implement innovations in DR-TB diagnosis and treatment. The focus should be on the day-to-day mentoring, which will contribute to flexible and continuous capacity building of HCWs in DR-TB care. The recently developed DR-TB Quality Improvement Tool (Additional file Additional file 1. Supplement file.


The management of hypertensive disorders of pregnancy (HDP) during hospitalization requires an accurate blood pressure (BP) measurement, mainly by invasive intra-arterial reading. Nevertheless, little is known about the precision of non-invasive (NI) central BP measurements in HDP. We aimed to assess the accuracy of NI central BP assessment in comparison to invasive BP measurement in HDP.This cross-sectional study included all patients with HDP that were admitted to university hospitals for high BP control, from December 2018 till December 2019, and 10 healthy matched non-hypertensive controls. Patients were compared for demographic, anthropometric, and echocardiographic data. In all subjects, invasive BP assessment was done by radial arterial cannulation and NI assessment of BP was performed by an oscillometric automated device (Mobil-O-Graph); the comparison was done after initial control of BP.p = 0.5) and diastolic BP  among the total studied population. A strong positive correlation was found between NI central and invasive SBP . HDP was associated with an increase in arterial stiffness, left ventricular diastolic dysfunction, and complications.One hundred patients were included and divided into 3 groups (pre-existing hypertension (HTN), gestational HTN, and pre-eclampsia). There was no statistically significant difference between NI central and invasive methods in measuring both systolic BP (SBP) . Hypertensive disorders of pregnancy (HDP), an umbrella that includes pre-existing and gestational hypertension, pre-eclampsia, and eclampsia, complicate up to 10% of pregnancies and represent a significant cause of maternal and perinatal morbidity and mortality [The definition of hypertension (HTN) in pregnancy is based only on office  BP values [systolic BP (SBP) ≥ 140 mmHg and/or diastolic BP (DBP) ≥ 90 mmHg]. However, the pathophysiology of HTN in pregnancy leading to a significant difference between central and peripheral hemodynamics all through pregnancy, thus necessitating the use of other methods to follow up HDP , 3. BracInvasive blood pressure measurement via arterial cannulation, commonly from a major artery, is considered to be the most accurate method of blood pressure measurement in those patients that need intensive care admission for control of high BP during pregnancy . HoweverIn this study on pregnant women with different types of HTN, we studied the accuracy of non-invasive central blood pressure assessment by an oscillometric automated device (Mobil-O-Graph) in comparison to the standard invasive blood pressure measurement.This cross-sectional study was conducted between the 1st of December 2018 to the 1st of December 2019 at a university hospital. The study was approved by our university institutional review board (RD no. = 17101032) and complied with the Declaration of Helsinki. Written informed consent was obtained from all patients .We included all pregnant women admitted to our university women’s health hospital during this period and diagnosed to have HDP that necessitate hospital management by anti-hypertensive medications. Exclusion criteria were patients with a history of cardiac diseases, chronic kidney disease, endocrine diseases such as hyperthyroidism, and conditions that prevent arterial cannulation as severe bleeding disorders or peripheral arterial disease. One hundred seventeen patients were recruited; 17 patients were excluded . Another group of 10 healthy non-hypertensive non-pregnant women, with matched age, weight, and height, were also included in the study as a control group to validate BP measurements’ accuracy, both invasive and central non-invasive techniques. We divided our patients into 3 groups according to recent 2018 ESC guidelines of HTN [All patients were subjected to a full medical and an obstetric history taking and clinical examination, including BP measurement by 3 different methods and echocardiographic examination. Exclusion of albuminuria was done at the time of hospital admission as routine work to all participants using the dipstick method owing to its sensitivity, convenience, and being widely available.A)Office blood pressure measurement using sphygmomanometer: According to ESC guidelines of hypertension 2018, BP was measured in the sitting position  with an appropriately sized arm cuff at heart level and using Korotkoff V for DBP, and an average of 3 readings was done [B)Non-invasive central BP monitoring in a quiet, temperature-controlled examination room: Three measurements with a 2-min break between them were taken to all patients in a sitting position with an adequately sized cuff. The Mobil-O-Graph 24 h NG  with inbuilt ARC Solver  and its blood pressure detection unit is validated according to the British Hypertension Society and European Society of Hypertension recommendations , 10. AlgC)Invasive blood pressure measurement through percutaneous radial artery cannulation by 20-gauge Teflon cannula under aseptic conditions after palpation of the artery as a guide. Cannulation was done by a single experienced operator. The arterial cannula was connected to a disposable tubing system and flushed frequently with a heparinized 0.9% saline solution. The tubing liquid was connected to a transducer . The transducer was kept horizontally at the level of the patient’s 4th intercostal anterior axillary line, and the radial access was at the same height of the cuff. We perform zeroing by opening the transducer to the atmospheric pressure and electronically zeroing the system. We recorded the arterial blood pressure signals by using a bedside monitor .In the blood pressure assessment in all studied groups, BP measurement was done after control of initial blood pressure on the 3rd or 4th day of hospital admission by 3 different methods including:P = 0.001).The BP measurements using the three mentioned methods were conducted by 2 different clinicians (A.G and M.K) in 2 different occasions using the standardized measuring procedures and reported their reading separately to the data collecting hub nurse. The inter-observer agreement was calculated with weighted Kappa statistics and showed good agreement  was performed on all patients before discharge from the hospital. ECHO was performed in the left lateral decubitus position using VIVID S5 instrument, GE Medical Systems, Horten, Norway, based on the recommendations of the European Association of Echocardiography (EAE) and American Society of Echocardiography (ASE) . Assessmt test. Analysis of variance (ANOVA) test was used to compare differences between more than two groups. The inter-observer agreement was calculated with weighted Kappa statistics. Correlations were done by Spearman correlation coefficient test. All P values are two-tailed, and statistical significance was defined if P < 0.05. Our analyses were performed with SPSS version 22.0 statistical software .We presented the categorical variables as counts and percentages then compared by Pearson chi-square analysis or Fisher’s exact test. We tested the normal distribution of our continuous data by Kolmogorov-Smirnov test. We presented continuous and normally distributed data as mean ± SD and were compared by unpaired The current study examined 117 patients for eligibility. Seventeen patients were excluded . Finally, the study included 100 pregnant participants and 10 healthy controls (non-hypertensive and non-pregnant women) who were seen and examined at a Women Health Hospital and a University Heart Hospital. We divided our patients into 3 main groups, defined according to 2018 ESC guidelines of hypertension [The mean age of the study population was 29.16 ± 6.6 years with no significant difference between all 3 groups. The lowest number of previous pregnancies was seen in the pre-eclampsia group (2 ± 2) as shown in Table p = 0.5) and DBP  among the total studied population. The same non-significant difference was noted among the studied groups, including the control, as shown in Table Results showed no significant difference between non-invasive central and invasive methods in measuring both SBP . Moreover, noninvasive central DBP and invasive DBP showed moderate positive correlation  as shown in Fig. Using Spearman correlation test, a strong positive correlation was found between noninvasive central SBP and invasive SBP (Table p < 0.001 for AIx and 0.03 for PWV) in the control group, as shown in Fig. Both are considered indicators of arterial stiffness and were recorded from the non-invasive oscillometric device ( Mobil-O-Graph). Both parameters were compared between the three different hypertensive groups and the control group. AIx and PWV were significantly lower , passed their pregnancy smoothly without complications. most complications were noticed in the pre-eclampsia group including (intrauterine growth retardation (IUGR) = eight patients, oligohydramnios = 4 patients, and HELLP syndrome = 13 patients). Eclampsia was found in 4 patients in the pre-existing hypertension group and 6 cases in the gestational hypertension group.Little is known about the accuracy of non-invasive central blood pressure by an oscillometric automated device (Mobil-O-Graph) to the invasive blood pressure measurement by arterial cannulation conducted on the same population in hypertensive pregnant women.Key findings of our study are as follows: (1) there was no difference between non-invasive BP readings by oscillometric automated devices (Mobil-O-Graph) and invasive BP readings, and (2) hypertension in pregnancy was associated with an increase in arterial stiffness, left ventricular diastolic dysfunction, and complications.Regarding the main aim of the study, to assess the accuracy of non-invasive central BP measurements in comparison to invasive BP measurement, results showed no statistically significant difference between SBP and DBP measurements by the two methods between the different studied groups indicating that central blood pressure measured non-invasively by the oscillometric automated device (Mobil-O-Graph) is accurate as of the invasive assessment of blood pressure, and the strong positive correlation between non-invasive central blood pressure measurements and invasively measured both systolic BP and diastolic BP confirm this conclusion.r = 0.763, p < 0.001) and diastolic  central blood pressures and non-invasive BP readings.In agreement with our results, Gotzmann and colleagues  conducter = 0.89) and women (r = 0.917).In the same context, another study, by Sanchez and colleagues , in 20 sr= 0.899, p < 0.001) central blood pressure in agreement with our results.Another study, by Weber and colleagues , includeOf note, central hemodynamics recorded by the oscillometric device (Mobil-O-Graph) uses in the current study showed that AIx and PWV were higher in the pre-existing hypertension group with a significant difference when compared to non-hypertensive control.p value 0.001, and the PWV values were higher in the preeclamptic groups but with a non-significant difference.Concordant to our results, Franz and colleagues  conductep value < 0.05.Furthermore, in Elvan-Tasšpinar and colleagues’  study ofDespite being used routinely, sphygmomanometer BP has many limitations, as it does not represent real-life BP, in addition to the discrepancy between brachial BP and central BP with its predictive value for cardiovascular events . CentralIn our study, we found a significant difference between sphygmomanometer and central methods in both SBP and DBP readings in patients with HDP, including pre-eclampsia. This was in concordance with Langenegger et al. who found discordance between readings collected by direct intra-arterial monitoring and peripheral methods by both manual and automated devices, and they concluded that invasive central BP monitoring is mandatory in patients with severe pre-eclampsia .Regarding diastolic dysfunction, we found that diastolic dysfunction was more frequent in patients with HDP, including pre-eclampsia. In concordance with these results, Guirguis et al. found more frequent diastolic dysfunction in patients with pre-eclampsia. Furthermore, we demonstrated that other variants of HDP were also associated with diastolic dysfunction .The invasive maneuver used  was refused by some patients, failed in other ones, and was an obstacle. We depended on radial cannulation  rather than advancing catheters into the aorta for accurate central BP assessment for comparison. The small number of non-pregnant, non-hypertensive matched the control due to logistic reasons. Lack of BP measurement, using the oscillometric automated device (Mobil-O-Graph), before and after BP control, could elucidate its role in an emergency. In this study, we did not evaluate the effect of different drugs used in HDP on BP control centrally or on pregnancy outcomes.Non-invasive measurement of central BP using oscillometric automated devices is as accurate as of the invasive methods, and it is a practical, safe method in pregnant women with hypertensive disorders. HDP was associated with an increase in arterial stiffness, left ventricular diastolic dysfunction, and complications.


In turn, affecting the cholesterol homeostasis has emerged as novel broad-spectrum antiviral strategy. On the other hand, the innate immune system is similarly regulated by the lipid and stimulated by its derivatives. This certainly requires attention in the design of antiviral strategies aiming to decrease cellular cholesterol, as evidence accumulates that withdrawal of cholesterol hampers innate immunity. Secondly, there are exceptions to the rule of the abovementioned virus-induced metabolic shift toward cholesterol anabolism. It therefore is of interest to dissect underlying regulatory mechanisms, which we aimed for in this minireview. We further collected evidence for intracellular cholesterol concentrations being less important in viral life cycles as compared to the spatial distribution of the lipid. Various routes of cholesterol trafficking were found to be hijacked in viral infections with respect to organelle-endosome contact sites mediating cholesterol shuttling. Thus, re-distribution of cellular cholesterol in the context of viral infections requires more attention in ongoing research. As a final aim, a pan-antiviral treatment could be found just within the transport and re-adjustment of local cholesterol concentrations. Thus, we aimed to emphasize the importance of the regulatory roles the endosomal system fulfils herein and hope to stimulate research in this field.Cholesterol has gained tremendous attention as an essential lipid in the life cycle of virtually all viruses. These seem to have developed manifold strategies to modulate the cholesterol metabolism to the side of lipid uptake and  While this may be true for various viruses, some examples demonstrated that cholesterol depletion actually behaves provirally . These ER-resident proteins are maintained in a complex together with insulin-induced gene 1 (INSIG1), SREBP cleaving-activating protein (SCAP), and sterols  liver X receptors (LXR) and (ii) farnesoid X receptors (FXR). The former is activated by sterols and oxysterols  is a way to rid a cell from the lipid specifically in liver tissue. The expression of both rate-limiting enzymes, namely, the ER-resident cholesterol 7 alpha-hydroxylase (CYP7A1) or the mitochondrial sterol 27-hydroxylase (CYP27A1), is induced by LXR . Once pr ABCB11;  or the o ABCB11; . Both LX ABCB11; . As a li ABCB11; . In turnvia esterification and subsequent intracellular storage. This catalytic process is mediated by the ER-resident acyl-coenzyme A:cholesterol acyltransferase (ACAT), which is activated by cholesterol  being sensed by pathogen recognition receptors (PRRs). Here, toll-like receptors (TLRs) represent PRRs being either present on the PM or within endosomes majorly recognizing nucleic acids . They shvia diverse mechanisms . The latter produces an oxysterol, 25-hydroxycholesterol, which has been described as broadly antiviral chanisms  against,families . In genefamilies .These infamilies , which bfamilies . This cofamilies . Furtherfamilies . Opposinfamilies . Effectifamilies . More reThe inflammasome is a multiprotein complex and a prime mediator for inflammatory processes and pyroptosis. Upon establishment of cellular stress or exposure to pathogens and subsequent PAMP-sensing, different subunit combinations of the complex assemble. In its core, homo-oligomers of either pro-caspase 1 or 8 are found. These may then be followed by an accessory proteins connecting the inner core with the outer subunits mediating specificity toward the activating agent. These outer subunits are represented by NLR family CARD domain-containing protein 4 (NLRC4), NLR family pyrin domain-containing 3, 6, or 7 (NLRP3/6/7), interferon-inducible protein AIM2 (AIM2), gamma-interferon-inducible protein Ifi-16 (IFI16), or pro-caspases 11, 4, or 5. As a result upon activation, the inner caspases proteolytically activate interleukins 1β and 18 (IL1β/18), mediating inflammation, or gasdermin D, initiating pyroptosis . Interesvia two further routes: (i) vesicles originating from the trans-Golgi network . Both clustering and activity hereof is enhanced by cholesterol , which also is capable of binding cholesterol, has been described to facilitate the cellular entry of Lassa virus .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.