Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data  into European ancestry training datasets . The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups ( Most genetic studies have been done on European cohorts, which affects the efficacy of polygenic risk scores in non-European populations. Here, the authors demonstrate that a colorectal cancer PRS including Asian and European ancestries has improved performance over the European-centric PRS across racial and ethnic groups. Together with the detection of CRC at early stages, which dramatically improves prognosis, optimal screening has the potential for a major impact on CRC mortality. However, current screening programs are primarily age and family-history based and more refinement through risk-based screening recommendations could be instrumental in improving their effectiveness.Colorectal cancer (CRC) is a leading cause of cancer death, yet it is among the most preventable cancers via screening2. As such, we can utilize the polygenic risk structure to develop a polygenic risk score (PRS) to quantify an individual’s inherited risk of developing CRC. As the predictive performance improves, a PRS can become clinically useful as a risk stratification tool for targeted screening and chemoprevention. However, PRS built based on European ancestry data have sub-optimal performance in other ancestral populations3 because of differential linkage disequilibrium (LD) patterns and allele frequencies across racial and ethnic groups for disease risk variants of CRC9. The poor transferability of PRS across racial and ethnic groups has raised concern regarding whether its application in clinical practice may exacerbate existing health disparities7. As a result, there is a need to improve the accuracy of polygenic prediction across different racial and ethnic groups to maximize the clinical and public-health translational potential of PRS and enhance equity in precision medicine.Genetics plays a key role in the CRC development and, as for most cancers and other common diseases, the risk is polygenic11. As an alternative to developing ancestry-specific PRS, one may develop a single cross-ancestry PRS based on meta-analysis of genome-wide association studies (GWAS) across all available ancestral groups14. To our knowledge, there is no study of PRS for non-European ancestral populations for CRC. Here we consider two different approaches to PRS development, (1) ancestry-specific PRS using PRS-CSx15 based on ancestry-specific GWAS while leveraging cross-ancestry information and (2) single cross-ancestry Asian-European PRS using LDPred216 based on combined meta-analysis summary statistics and LD matrices across Asian and European ancestries. Using independent racially and ethnically diverse datasets, we evaluated the performance of these two PRS and compared them with a genome-wide PRS built using European-only GWAS data3 and a PRS based on 204 known CRC loci20. To facilitate understanding of its clinical utility, we used decision-curve analyses21 to assess the standardized net benefit for the model based on family-history and PRS and compared to the family-history-only model, as the latter is currently used to decide at what age screening starts.Developing ancestry-specific PRS requires sufficient sample sizes for each ancestral group; however, the sample sizes for non-European ancestry groups, while increasing, remain only a fraction of the sample size for European ancestry. Existing studies suggest that leveraging information from other ancestries can improve ancestry-specific PRSFor developing PRSs, we used GWAS summary statistics of 1,020,293 SNPs based on 21,731 cases and 47,444 controls of Asian and 78,473 cases and 107,143 controls of European ancestries. We evaluated the performance of the PRS in independent validation individual-level data sets including 12,025 Asian , 13,823 Black/African-American , 10,378 Latinx/Hispanic  and 118,756 non-Hispanic White  participants. More details about study participant characteristics for training and validation data sets are included in Table p-value < 4.5e−09, Table p-value = 6.0e−03). Despite lack of Black/African American and Hispanic individuals in deriving the PRS, the Asian-European PRS improved the AUC for Black/African American  and Hispanic individuals . The Asian-European PRS improved the AUC in all racial and ethnic groups compared to the known-loci PRS .The single cross-ancestry Asian-European PRS derived using the combined Asian-European GWAS meta-analysis summary statistics and LD matrices with LDpred2 improved the discriminatory accuracy in the Asian population compared to the European-centric PRS  in the non-Hispanic White population; however, it was significantly higher than the known-loci PRS . The ancestry-specific PRS-CSx is not relevant for Black/African American and Hispanic groups, because there were no GWAS for these groups included in the training datasets.The ancestry-specific PRS derived using PRS-CSx improved the discriminatory accuracy in the Asian population compared to the European-centric PRS (AUC = 0.64 vs. 0.59), though not statistically significant with 06 Table . The AUCp-value = 3.0e−03. However, we did not observe statistically significant differences in Asian individuals . Taken together, the single cross-ancestry Asian-European PRS using LDpred2 performs among the best in terms of AUC but with much narrower confidence intervals; hereafter we focus only on the single cross-ancestry Asian-European PRS. The ROC curves for the cross ancestry Asian-European PRS showed a similar pattern to the AUC for Asian, Black/African American, Hispanic, and non-Hispanic White participants  and hypothetically intervening on all or no people  to assess the clinical utility of using a model based on PRS and family-history to recommend an intervention (such as screening) for participants <50 years of age. We used the average 10-year risk of developing CRC at age 45 as the risk threshold, because the current CRC-screening guidelines recommend that an average-risk individual start screening at age 45 years old. Using the GERA cohort, we estimated the 10-year risk to be 0.29% across all racial and ethnic groups. At this risk threshold, the risk model based on PRS, and family-history achieved 37.3% (95% CI: 23.8%–50.8%) of the maximum possible achievable utility. This was greater than the model based on family-history alone  for improving the prediction in under-represented groups, and our observation of the performance of these approaches could be generalized to other traits besides CRC. We found that both approaches performed similarly in Asian and non-Hispanic European individuals. Further, the cross-ancestry Asian-European PRS also improved risk prediction performance in Hispanic individuals and, to a smaller extent, in Black/African American individuals. We also show that we can correct this raw PRS for genetic ancestry and create a common distribution that can be used across racial and ethnic groups, avoiding the potential difficulty of using ancestry-specific PRS in admixed populations. Accordingly, our cross-ancestry Asian-European PRS has the potential to reduce health disparities between non-European ancestry populations and the European ancestry population.24 Their performance should thus be compared with risk factors currently used for risk stratification such as family-history in terms of cost effectiveness. In this paper, we performed a decision-curve analysis that has been used in cancer research for assessing the potential population impact of incorporating a risk prediction model into clinical practice26. The risk model that incorporates both the PRS and family-history achieves 37.3% of the maximum possible achievable utility for those 40–49 years old, significantly greater than 21.7% under the family-history-only model. Recently the US Preventive Services Task Force recommended lowering the age at screening initiation to 45 years for individuals at average risk27. However, given the substantial burden of additional approximately 22 million people becoming eligible for screening and the fact that CRC remains a rare event in younger individuals, there has been critique of the universal change to the initial screening age that, instead, emphasizes the importance of targeted screening based on an individual’s risk factors30. The results from the decision-curve analysis suggest that there is clinical utility to adding a PRS to the family-history-only model in risk stratification for CRC prevention. In decision curve analysis, we assumed the decision in question was whether an individual in the general population should undergo intervention , based on their risk. Overall, the model with the highest (standardized) net benefit is considered the “best” strategy in decision curve analysis. However, as argued in Kerr et al.21, decision curves cannot be used to choose a risk threshold, but it summarizes the costs and benefits of intervention of the risk model at different risk threshold. To fully evaluate the effectiveness of including PRS as part of risk stratification, a full decision analytic modeling that incorporates other aspects such as different screening methods, implementation factors, behavioral factors, and corresponding costs are warranted31.As there is growing interest in clinical use of PRS, it is important to point out that the purpose of PRS is not to identify CRC, but rather stratify individuals into different risk strata for which different levels of cancer preventive interventions may be devised.33 in clinical implementation of PRS shows the potential of PRS to effectively stratify the risk of diseases development and guide screening. BOADICEA v5 (as implemented in the CanRisk tool)32 already implements a 313-variant PRS of breast cancer and currently supports hundreds of thousands of women, doctors, and genetic counselors annually in >90 countries making treatment decisions. PRS-guided mammographic screening is also being tested in the WISDOM and PERSPECTIVE I&I studies33. GenoVA Study34 is a clinical trial in which patients and their primary care physicians receive a clinical PRS laboratory report on five diseases including CRC. MyOme implements a cross-ancestry risk score for breast cancer risk stratification35. As CRC has an effective screening intervention, it would be of great interest to explore implementation of PRS for guiding personal screening recommendations.Recent effortsThis study has several strengths. We brought together most of the globally available GWAS of CRC for Asian and European ancestry populations as our training data, which is an important factor for the improved performance of the proposed PRS. Further, we used multiple independent evaluation data sets that were not part of our training data nor GWAS discovery, providing an unbiased evaluation of the developed models. Moreover, the single cross-ancestral PRS derived in this study makes it easy to implement in any admixed population.36.The results of this investigation should be interpreted in the context of its limitations. The discriminatory accuracy remains lower in Latinx/Hispanic and particularly in Black/African American individuals due to their limited sample sizes in training data. Future studies more inclusive of these individuals are warranted for deriving PRS to enhance the discriminatory accuracy. Furthermore, we have not been able to evaluate the performance of these models in other racial and ethnic groups, including Alaskan Native, Native American and Pacific Islander individuals. Lastly, we expect to further improve risk prediction by combining the PRS with non-genetic risk factors such as obesity, diet, and aspirin use, as previously shown36. We anticipate that the continuous expansion of PRS development and validation to include more diverse populations and prospective evaluation of PRS-enhanced risk prediction model in clinical trials along with decreasing genotyping cost and adaptation of health care systems to accommodate genetic data and prediction algorithm will bring closer the implementation of PRS in clinical practice.Advances in PRS development have promoted the use of PRS-enhanced models to determine and stratify disease risk, which could improve disease prevention and management through screening and early detection. Our cross-ancestry Asian-European PRS, built upon data on both Asian and European ancestry individuals, improves the PRS performance in Asian, Black/African, and Latinx/Hispanic individuals considerably. Combining PRS and other CRC-associated risk factors such as lifestyle/environmental risk factors and high penetrance genes will likely further improve the prediction performanceGCST90129505 , totaling 100,204 CRC cases and 154,587 controls. Comprehensive details on the participants, genotyping and standard quality control (QC) procedures are summarized in Supplementary Data To develop polygenic risk scores (PRS) across population, we used the genome-wide association study (GWAS) summary statistics of 1,020,293 SNPs based on 78,473 cases and 107,143 controls of European (EUR) and 21,731 cases and 47,444 controls of Asian ancestries from GWAS catalog under accession code 39; Minority GWAS African American study (MG-AA)40; Hispanic Colorectal Cancer Study (HCCS)41; Multiethnic Cohort study (MEC); Cancer Prevention Study II (CPSII)42; Basque-colon cohort (BCC); and Electronic Medical Records and Genomics (eMERGE) study. Racial and ethnic identification in these studies were self-reported. In total, there were 12,025 Asian , 13,823 Black/African-American , 10,378 Latinx/Hispanic  and 118,756 non-Hispanic White  participants. None of these samples was included in the training data sets for model building. More details about study participant characteristics are included in Table We evaluated the performance of each of the developed PRS in the Genetic Epidemiology Research on Adult Health and Aging Cohort (GERA) cohort; Minority GWAS Japanese study (MG-JPN)CRC status (Yes/No) was determined from cancer-registry data. Family-history of CRC (>=1 first-degree relatives with CRC), was ascertained through baseline study questionnaire or electronic medical records at study entry.We compared two different approaches for PRS development using (1) ancestry-specific PRS using PRS-CSx that integrates genome-wide Asian and European summary statistics and LD matrices; (2) single cross-ancestry PRS using LDpred2 that combine genome-wide Asian and European summary statistics and a weighted LD matrix with weight defined as the proportion of participants from each ancestry in the summary statistics. Figure 15 derives ancestry-specific PRS while leveraging GWAS summary statistics from other ancestral groups. We first obtained ancestry-specific PRS using ancestry-specific GWAS summary statistics and LD matrix for Asian and non-Hispanic White participants based on ~1M genome-wide SNPs, respectively, while leveraging GWAS from the other ancestral group. We denoted these PRS by PRSAsian and PRSEuropean, respectively. We then improved ancestry-specific PRS by taking a weighted sum of these PRSs to predict CRC of respective ancestral group. To derive PRS for the Asian population, we calculated a weighted sum of PRSAsian and PRSEuropean (α1 PRSEuropean + β1 PRSAsian) and obtained α1 and β1 from a logistic regression model using the MG-JPN study. Similarly, to derive PRS for the European population, we calculated a weighted sum of PRSAsian and PRSEuropean (α2 PRSEuropean + β2 PRSAsian), where α2 and β2 were obtained based on the pooled BCC and CPSII studies.PRS-CSx16, we combined the summary statistics from the Asian and European GWAS using the inverse variance weighted estimator43 and combined the LD matrices, as the weighted sum of the Asian and European-specific LD matrices with the weights proportional to the sample sizes of the Asian and European individuals in the combined summary statistics.To derive the single cross-ancestry PRS using LDpred23 and a known-loci PRS consisting of 204 independently CRC-associated variants based on GWAS of European and Asian ancestries20  in each of the racial and ethnic groupsp-values for comparisons across various subgroups using 500 bootstrap samples.We obtained the bootstrapped-based standard error (se), 95% confidence intervals (CI) (1.96* se) and two-sided 46. We used the 1000 Genome dataset to estimate the ancestry adjustment following the approach in Khera et al.46. Specifically, we derived principal components (PCs) based on 343,662 ancestry informative SNPs with little overlapped (0.3%) with SNPs used in PRS development. To correct for the mean and variance differences between ancestry groups, we fit two linear regression models to predict the mean and variance of PRS based on the first four PCs. To correct for the raw PRS distribution in our data set, we first calculated the PCs using the same loadings for the top 4 PCs from the 1000 Genome data set. We then obtained the ancestry-adjusted PRS for each individual by subtracting the predicted mean based on the 4 PCs from the individual’s raw PRS and then divided it by the predicted standard deviation based on the 4 PCs. Additional adjustments are needed for data sets with different imputation panels. The ancestry adjusted PRS is computed as given below:As the PRS distributions were different across racial and ethnic groups due to different allele frequencies, we used a modified trans-ancestry adjustment of PRS to align the PRS distributionsWe estimated the OR and 95% CI of CRC-risk associated per SD change in PRS by logistic regression model, overall and stratified by family history and age. For each racial and ethnic group, we estimated the AUC and OR by study and combined the estimates using the inverse variance weighted estimator. In addition, we estimated OR stratified by family history of 1st degree relative with CRC  and age . All analyses were adjusted for age, sex, and top 4 principal components of ancestry.We binned PRS into 5% strata and defined the reference group as PRS in the 40–60% stratum. The expected OR for a PRS stratum is the ratio of the within-stratum geometric average of individuals’ model-based OR, defined as exponent of individuals’ PRS times log (OR), between that stratum and the reference stratum. We estimated the observed OR estimates and its 95% CI by fitting a logistic regression model with CRC disease status as outcome and a binary variable with 1 indicating a specific stratum and 0 indicating the reference stratum, adjusting for age, sex, and first four principal components.21, to assess the potential clinical impact of the risk prediction models on recommended interventions . For a given risk threshold, the NB was defined asw was the odds at the threshold, sensitivity was the proportion of cases above the risk threshold based on the model, specificity was the proportion of controls below the risk threshold based on the model, and p was the disease probability at the landmark time. As it was difficult to interpret NB itself, we followed the approach proposed by Kerr et al.21 to calculate sNB, i.e., dividing NB by the maximum NB, which is achieved when sensitivity = 1 and specificity = 1. Hence, the sNB was equal toThe decision-curve analysis was performed by calculating the standardized net benefit (sNB), defined as the net benefit divided by the maximum possible net benefitsNB = 0.4, it means that the risk model achieves 40% of the maximum possible achievable utility.It provided some sense of magnitude of sNB on a percent scale and was interpreted as the relative utility that has maximum value of 1. For example, if 47, we denote rt be the risk threshold and I(t) the cumulative incidence of developing CRC for an individual by time t in the presence of competing risks, here, death. Further, we define z = 1 to indicate that an individual is at high risk if their predicted t-year risk from the model is greater than or equal to rt and z = 0 otherwise. We chose the landmark time t = 10 years. At each rt, we calculated the number of true and false positives, TPrt and FPrt, byTPrt/TPrt=0 and the false-positive rate was calculated as FPrt/FPrt=0. We also calculated the reduction in the number of false positives per 100 patients as22: /{rt/(1− rt)) × 100. We compared the model based on PRS and family history with the model based on family history alone, as well as two hypothetical extreme scenarios: intervention  for all and intervention for none. We calculated the sNB under the competing risks framework48, where the observational time is the minimum of time to CRC, time to death, and time at last observation, and the disease status is 1 if the study participant had CRC, 2 if the participant died (competing event), and 0 otherwise. We plotted decision-curves of sNB at the 10-year landmark time vs. risk threshold for age at study entry 40–49 and 50–59 years old, because average-risk individuals in these age groups are recommended to start CRC screening.To calculate the NB in the presence of competing risks51. A two-sided p-value < 0.05 is considered statistically significant.We performed the analyses using R version 4.0.0Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


The short-term case-fatality rate was higher in septic patients and this difference persisted at 6 months . Furthermore, age older than 75 years, Barthel index <40 and healthcare-associated UTI were also associated with the middle-term case-fatality rate. In conclusion, the detrimental impact of sepsis is maintained on the middle-term prognosis of elderly patients with UTI. Age, functional status and healthcare-associated UTIs also play significant roles in shaping patient outcomes.Urinary tract infection (UTI) is a common condition that predominantly affects elderly people, who are particularly susceptible to developing sepsis. Previous studies have indicated a detrimental effect of sepsis on short-term outcomes in elderly patients with UTI, but there is a lack of data about the middle-term prognosis. The aim of this study was to investigate the influence of sepsis on the middle-term prognosis of patients aged 65 years or older with complicated community-acquired UTIs. A prospective observational study of patients admitted to a hospital with UTI. We conducted a comparison of epidemiological and clinical variables between septic and nonseptic patients with UTI, as well as their 6-month case-fatality rate. A total of 412 cases were included, 47.8% of them with sepsis. Septic patients were older (83 vs. 80 years,  Urinary tract infections (UTIs) are a prevalent condition affecting individuals of all ages, with particular significance among elderly patients . UTI mosSepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, is a common disorder in elderly patients with complicated UTIs . The preIdentifying the site of infection may be critical for the prognosis. The Surviving Sepsis Campaign (SSC) advises immediate evaluation of patients with severe sepsis or septic shock to identify potential sites of infection suitable for source control. Once diagnosed, it is recommended that source control procedures be performed within 12 h . HoweverAlthough urinary sepsis has been considered to have a better prognosis than sepsis from other sources ,17, seveExamining the middle-term outcomes of elderly patients admitted to hospitals with complicated UTIs, with or without sepsis, is essential for enhancing clinical management strategies and optimizing patient care. The aim of this study was to investigate the influence of sepsis on the medium-term prognosis, as assessed by the 6-month case-fatality rate of a series of patients aged 65 years or older admitted to a hospital with complicated community-acquired UTI.Cohort prospective observational study of patients aged 65 years or older consecutively admitted to an internal medicine ward at a university hospital, diagnosed with complicated community-acquired UTI, from January 2017 to December 2021. The diagnosis of UTI was initially created by the attending physician in the hospital emergency department and subsequently confirmed by the physician in charge after admission. This confirmation was based on a thorough assessment of the patient’s medical history, physical examination, as well as laboratory and microbiological diagnostic procedures. Patients were categorized into two groups based on the presence or absence of sepsis. The primary outcome measure was a 6-month case-fatality rate. Cases with a negative urine culture or a clinical syndrome compatible with any other condition, after being reviewed by two independent researchers, were excluded, as well as nosocomial or UTI cases transferred from the intensive care unit (ICU). Standard clinical care for UTI and sepsis was implemented without any modifications in its management. The flowchart of the inclusion of 412 cases of complicated community-acquired urinary sepsis is shown in This study was approved by the Clinical Research Ethics Committee of the Doctor Peset University Hospital  and followed the STROBE statement. All patients provided their signed consent to participate in the study.The researchers extracted epidemiological and clinical variables from the electronic medical record using a predefined data collection form . PatientThe definition of a complicated urinary tract infection involved the identification of pyuria and bacteriuria through urinalysis using microscopy. Additionally, at least one of the following criteria needed to be met: (i) presence of cystitis symptoms  accompanied by fever or other signs of systemic illness, such as chills, rigors or acute mental status changes; (ii) experiencing flank pain and/or costovertebral tenderness; (iii) in the absence of localizing symptoms and after ruling out other potential causes, fever or sepsis were considered indications of complicated urinary tract infection. Community-onset healthcare-associated UTI (HCA-UTI) was defined as a urinary tract infection that originated in the community and met any of the following criteria: (I) hospital admission within the 90 days prior to the current hospital admission; (II) receiving antimicrobial therapy within the 90 days prior to the current hospital admission; or (III) residence in a nursing home . CommuniUrinary sepsis was diagnosed according to sepsis-3 criteria  and idenClinical symptoms and signs were directly obtained through patient interviews and physical examinations. Comorbidities included were diabetes mellitus , cognitive impairment , chronic kidney disease (defined by a decreased glomerular filtration rate of less than 60 mL/min according to CKD-EPI equation), chronic obstructive pulmonary disease , active or previous cancer and indwelling urinary catheter. Fever was considered present if the patient reported a temperature of ≥38 °C at home or if it was measured in the emergency department. Laboratory assessments included coagulation testing, a complete blood count and blood chemistry analysis .The Charlson comorbidity index was used as a measure of total comorbidity burden, considering a Charlson comorbidity index equal to or greater than 3 as indicative of significant comorbidity. The score assigned to each subgroup of diseases was as follows: 6 points for AIDS/HIV, metastasic solid tumor and any tumor or malignancy (including metastasic disease); 3 points for diabetes with end-organ damage and moderate or severe liver disease; 2 points for diabetes with complications, moderate or severe renal disease, hemiplegia or paraplegia, leukemia or lymphoma; and 1 point for myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, connective tissue disease , peptic ulcer disease and mild liver disease .Inadequate empirical antimicrobial therapy (IEAT) was considered the occurrence of an infection that was not effectively treated at the time when the causative micro-organism and its antibiotic susceptibility were known. This included the absence of antimicrobial agents directed at a specific class of micro-organisms, and the administration of an antimicrobial agent to which the micro-organism responsible for the infection was resistant . Multidr®® VIRTUO™, which is an automated system for the culture and detection of micro-organisms in blood. Micro-organisms isolated from positive blood cultures were identified using the Bruker MALDI Biotyper system  and the drug sensitivity and resistance tests. The DxM MicroScan WalkAway microbiology system , and the VITEK 2 system (Biomerieux) are two systems using microbroth dilution methods according to a combination of CLSi and EUCAST rules.Microbiological data were collected through urine and blood cultures, as well as susceptibility testing. This encompassed the identification of bacteremia, the determination of the causative agents of UTI through culture isolation, the assessment of resistance patterns exhibited by the isolated micro-organisms, and identification of cases involving polymicrobial infections. The microbial identifications of the urine cultures were created using the Bruker MALDI Biotyper system , and for the drug sensitivity and resistance tests, the DxM MicroScanWalkAway microbiology system  was used. This is a microbroth dilution method based on a combination of CLSI and EUCAST rules. Blood cultures were obtained in the emergency department and were processed using the BacT/ALERTQuantitative variables were compared using the Mann–Whitney U-test. Qualitative variables were compared with the chi-square test and Fisher’s exact test. Kaplan–Meier method was used to compare survival rates between patients with and without sepsis. Cox proportional hazards regression was used to evaluate the significance of the observed differences between the survival curves. Multivariate analysis was performed using logistic regression, considering an α significance level of 0.05 for all tests. The statistical package SPSS version 22 from IBM for Windows was used for the statistical analysis.This study was approved by the Clinical Research Ethics Committee of the Doctor Peset University Hospital  and followed the STROBE statement. All patients provided their signed consent to participate in the study.Out of the 1198 cases of UTI diagnosed at admission, 412 cases were analyzed, 47.8% of them with sepsis. The median age was 81 years, with 97.6% of the patients having high comorbidity (Charlson ≥ 3), and genders were similarly distributed . Diabetes mellitus, chronic kidney disease and dementia (33%) were the more common comorbidities see .Escherichia coli , Klebsiella pneumoniae , Enterococcus faecalis  and Pseudomonas aeruginosa  were the most common micro-organisms isolated in the urine cultures, with no differences between septic and nonseptic patients; there were also no differences in UTI due to multidrug-resistant bacteria , polymicrobial UTI  or IEAT . In patients with a urinary catheter, the micro-organisms identified in the urine culture were as follows: Escherichia coli (35.6%), Klebsiella pneumonia (17.2%), Pseudomonas aeruginosa (25.3%), Proteus mirabilis (3.4%) and Enterococcus faecalis (19.5%). Both Pseudomonas and Enterococcus were more prevalent in patients with a urinary catheter compared to patients without urinary catheterization.p < 0.001 in septic and nonseptic patients, respectively), but it was not associated with sex. Septic patients, as might be expected, presented positive qSOFA (≥2 points) and septic shock in more cases (p < 0.001). Several clinical characteristics, such as altered mental status , respiratory rate ≥ 22 bpm  and systolic blood pressure < 100 mmHg , were more common in septic patients. However, acute pyelonephritis was more frequent in nonseptic patients , and fever was frequent in both septic and nonseptic patients without any statistically significant differences.Sepsis was associated with age , as well as 30-day case-fatality rates  and 6-month case-fatality rates . The higher case-fatality rate observed in septic patients was maintained over the 6-month follow-up (p = 0.007), respectively. Furthermore, patients with sepsis had a longer hospital stay .The in-hospital case-fatality rate was 9.5%, and the post-discharge case-fatality rate increased over time to 26% at 6 months . In-hospollow-up , and thep = 0.013), while ceftriaxone (47.7 vs. 45.9%) and quinolones (7.4% vs. 5.3%) had no statistically significant differences.The antibiotics are more frequently empirically used in patients who died, and those who did not die within 6 months of the follow-up were ceftriaxone (43 vs. 47.5%), meropenem (23.4 vs. 18.4%) and quinolones (8.4 vs. 4.9%), with no statistically significant differences between groups. From these antibiotics, only meropenem showed a difference in IEAT . Interestingly, comorbidity or IEAT were not related to the middle-term case-fatality rate.Risk factors for 6-month case-fatality rates were analyzed, choosing among known risk factors for mortality, such as age, comorbidity , dependeThe results of this study provide evidence that sepsis in elderly patients with UTI has lasting effects beyond the hospitalization period, leading to a worse medium-term prognosis, as indicated by a higher case-fatality rate at 6 months. Additionally, age, functional status, as measured by the Barthel index, and healthcare-associated UTI were identified as important factors influencing patient outcomes. Understanding these factors is crucial for improving post-discharge care and optimizing patient outcomes.There were no differences in microbiological factors, such as micro-organisms cultured in urine and blood, or the appropriateness of empirical antimicrobial therapy between septic and nonseptic patients. While these factors would indeed have a greater influence on short-term prognosis , it is iThe use of the 28-day case-fatality rate as an endpoint for clinical studies may lead to inaccurate inferences . In thisThe Barthel index was used to measure the individual’s functional independence and ability to perform activities of daily living . In thisOur findings show that age is an independent risk factor for mortality in complicated UTIs, both with and without sepsis, which is consistent with many other previous studies on sepsis from different sources ,39,40. IAnother finding from this study was the significant association between healthcare-associated UTIs and an increased mid-term case-fatality rate. Contrary to the recommendations for pneumonia, in which the classification as “healthcare-associated pneumonia” is discouraged due to the potential risks of inappropriate use of broad-spectrum antibiotics without clear benefits in identifying more severe patients ,43, healThese findings emphasize the need for tailored post-discharge care to address the specific needs of elderly patients with complicated UTIs, particularly those who experienced sepsis ,38. StraThe present study has several limitations. Firstly, it was conducted at a single center, which may limit the generalizability of the findings to a broader population. Secondly, this study focused solely on the 6-month case-fatality rate without considering other clinical outcomes such as cognitive impairment or the performance status of the patients. Despite these limitations, the study provides valuable insights into the influence of sepsis on middle-term outcomes with the strength of rigorous patient selection, all of whom had urinary tract infections as the sole source of sepsis.In conclusion, our study highlights the detrimental impact of sepsis on medium-term prognosis, as reflected by the increased case-fatality rate at 6 months in elderly patients with complicated UTI. Age, functional status and healthcare-associated UTIs also play significant roles in shaping patient outcomes. These findings underscore the importance of implementing targeted interventions and improving post-discharge care to optimize patient outcomes. Future research should further investigate the specific mechanisms underlying the observed associations and evaluate the effectiveness of tailored interventions in improving outcomes for elderly patients with UTI and sepsis.


Crimean-Congo Hemorrhagic Fever (CCHF) is a viral tick-borne disease with a high mortality rate. It is endemic in Kazakhstan. The Kyzylorda region reported higher-than-expected cases from March to July 2022 (15 compared to 10 in the previous year). We conducted an investigation to identify additional cases, determine risk factors, and assess CCHF knowledge and practices.We conducted a case-control study in July 2022. We defined cases as PCR-confirmed or hospitalized with CCHF symptoms. The control group was made up of regional contacts including neighbors and household members at a 1:2 ratio. We reviewed medical records from April to July. We conducted face-to-face interviews using structured questionnaires. Blood collected from the control group was tested with PCR and ELISA for detection of IgM and IgG antibodies. We also collected ticks from domestic animals for PCR-testing. We used logistic regression to assess factors associated with CCHF.We identified 38 cases  and 71 controls. Cases were mostly male (74%) with mean age of 45 (range 17-81); 27% died. Top patient symptoms (n=38) were weakness (100%), headache (97%), fever (84%) and loss of appetite (55%). Risk factors for CCHF were being male , animal husbandry , crop production , tick contact , tick bite , contact with tick blood , ticks on the body or clothing  and having been in places with high tick risk . The control group were more likely to have awareness of CCHF, know that they can protect themselves from CCHF, refrained from visiting forests and rural areas, used repellents, and taken protective measures to reduce ticks in domestic animals. Of 55 controls tested: one was IgG positive. Of 163 ticks tested, one was PCR-positive.Exposure to activities that are high risk for exposure to ticks increased odds of CCHF. Improved awareness of prevention to exposure from ticks, CCHF associated risk factors, and the importance of seeking early treatment is needed. This can be achieved by increasing population awareness and educating medical providers.All Authors: No reported disclosures


Some infants undergoing newborn screening (NBS) tests have inconclusive sweat chloride test (SCT) results that lead to the designation of Cystic Fibrosis Screen Positive, Inconclusive Diagnosis/CFTR-related metabolic syndrome (CFSPID/CRMS). Some proportion of them transition to a CF diagnosis, but no predictive markers can stratify which are at risk for this transition. We report single-center outcomes of children with CRMS.We retrospectively identified all infants born in Alabama from 2008 through 2020 referred to our CF Center with an elevated immunoreactive trypsinogen level (IRT) associated with a cystic fibrosis transmembrane conductance regulator (CFTR) mutation (IRT+/DNA+) who had at least one SCT result documented. Infants were classified per established guidelines as Carrier, CRMS, or CF based on the IRT+/DNA+ and SCT results. The electronic health record was reviewed for follow-up visits until the children received a definitive diagnosis (to carrier or CF) according to current diagnostic guidelines for CF, or through the end of the 2020 year.P group, a greater proportion of children in the CRMS-CF group cultured bacteria like methicillin-resistant Staphylococcus aureus, Stenotrophomonas maltophilia, and Pseudomonas aeruginosa, had smaller weight-for-height percentiles and remained smaller over time despite slightly greater growth.Of the 1,346 infants with IRT+ and at least 1 CFTR mutation identified (IRT+/DNA+), 63 (4.7%) were designated as CRMS. Of these infants, 12 (19.1%) transitioned to Carrier status (CRMS-Carrier), 40 (63.5%) of them remained CRMS status (CRMS-Persistent) and 11 (17.5%) of them transitioned to a diagnosis of CF (CRMS-CF). Of the 11 children in the CRMS-CF group, 4 (36%) had an initial SCT 30–39 mmol/L, 4 (36%) had an initial SCT 40–49 mmol/L and 3 (27%) had an initial SCT 50–59 mmol/L. These children also had higher initial sweat tests and greater yearly increases in sweat chloride values than others with CRMS. We found that in comparison to children in the CRMS-Infants with an inconclusive diagnosis of CF should continue to receive annual care and management given their potential risk of transition to CF. Further research is needed to assess whether certain phenotypic patterns, clinical symptoms, diagnostic tests or biomarkers could better stratify these children. Cystic fibrosis (CF) is an autosomal recessive genetic disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene . Early iNBS programs across the world use a variety of algorithms for screening. Most begin with the measurement of immunoreactive trypsinogen (IRT), which is produced by the pancreas and elevated in the blood of infants with CF . In AlabAdvent of the NBS has led to the identification of asymptomatic infants found to have a positive NBS with at least one mutation and a SC concentration between 30 and 59 mmol/L, who are considered to have an inconclusive diagnosis and may be designated to have CF Screen Positive, Inconclusive Diagnosis (CFSPID), also known as CFTR-related metabolic syndrome (CRMS) –15. RepeThe state of Alabama uses an IRT/DNA protocol that involves a 2-step process of measuring IRT followed by screening for cystic fibrosis transmembrane regulator (CFTR) mutations on a limited DNA panel for any infants with an IRT value >96th percentile on that designated day ) of one P). Children designated CRMS are followed by pediatric pulmonologists with CF expertise in a separate clinic space than that of children followed in our CF clinics.This study was approved by the UAB Institutional Review Board (#300005123). We retrospectively reviewed the charts of all newborns born in Alabama between the years 2008 to 2020 referred to our center for evaluation of cystic fibrosis. Initial newborn screening results, including IRT value and mutation screening results, were collected for each of these infants. Data collected from available charts for any follow-up visits through January 2021 included self-reported race/ethnicity, sex, first quantifiable SCT, CFTR genetic results, microbiology results from sputum swabs, stool elastase, lung function, weight, and height measures. Infants initially designated CRMS were sub-classified by final diagnosis when data collection ended. A child transitioned to carrier status (CRMS-Carrier) with normalized SCT and if only one CFTR mutation identified. If a child met criteria for CF diagnosis [4] with repeat SCT concentration resulted >60 mmol/L, expanded genetics revealed a second CF-causing mutation, development and persistence of clinical symptoms, or with re-classification of their VCC mutation to a CF-causing mutation (determined by CFTR2 project database ), then tp-value of <0.05 was considered statistically significant. We compared initial diagnoses  and CRMS subgroups  using analysis of variance. Tukey's adjustment for multiple comparisons was completed for each comparison. Anthropomorphic trajectories and SCT trajectories are modeled using multilevel linear mixed models with a random intercept. Change over time is modeled as age. Statistical analysis was performed using STATA software  and GraphPad Prism .Descriptive analyses ) were used to describe patient demographics, clinical characteristics, and outcomes. Results are presented as means +/− standard deviation (SD) or standard error of the mean (SEM) where noted. For all analyses, a From 2008 to 2020, there were 1,411 infants identified through NBS with elevated IRT (IRT+) and referred to our CF Center. Sixty-one infants with no CFTR mutation identified on screening genetics and a normal SCT did not undergo any further evaluation, and were therefore excluded from the study. Four infants had insufficient quantities on all SCT attempts in the record and were excluded from the study. The remaining 1,346 infants with IRT+ and ≥1 CFTR mutation identified (IRT+/DNA+) had at least one SCT result documented. Of these 1,346 infants, 1,170 infants (86.9%) had an initial SC concentration <30 mmol/L, 51 infants (3.8%) had an intermediate SC concentration between 30 and 59 mmol/L and 125 infants (9.3%) had an initial SC concentration ≥ 60 mmol/L .p < 0.001 for IRT; p < 0.0001 for SCT) or Carrier . There was also a statistically significant difference seen in children with initial designations of CRMS compared to Carrier for IRT values (p = 0.0281) and first SCT value (p < 0.0001).Characteristics for all infants who presented with IRT+/DNA+ and confirmatory SCT are shown in P), and 11 (18%) of them transitioned to a diagnosis of CF (CRMS-CF). All 12 CRMS-Carrier infants had an initial SC value in the intermediate range. All 12 of these infants had a repeat SCT that normalized to <30 mmol/L. Average IRT values for infants classified as CRMS-Carrier was 67.4 ng/ml. Of the 40 infants who remained CRMS status by the end of the observation period (CRMS-P), 16 (40%) had an initial SC value <30 mmol/L and 24 (60%) had an initial SC value in the intermediate range. Eleven (28%) of these patients were either lost to follow-up or had their care transferred to another CF Center after moving to another state. The remainder 29 (72.5%) patients are followed at our CF center for continued evaluation as per the CRMS guidelines.Characteristics of the 63 infants who were classified as CRMS on their initial encounter are shown in Staphylococcus aureus (MSSA) and Pseudomonas aeruginosa requiring eradication. Three patients had Pseudomonas aeruginosa growth on oropharyngeal cultures and 4 patients had MRSA growth on oropharyngeal cultures. None of the children with initial diagnosis of CRMS had evidence of hospitalizations for severe respiratory concerns prior to their transition to a CF diagnosis.Diagnostic data for the 11 total infants initially designated with CRMS who had a final diagnosis of CF (CRMS-CF) are shown in P  or CRMS-Carrier  groups  or CRMS-Carrier  groups. Because IRT was elevated in the CRMS-CF group , as was first SCT, we assessed inclusion of both IRT and SCT in the model to assess prediction of CF transition. First successful SCT was found to predict CRMS-P vs. CRMS-CF (p = 0.001) and CRMS-CF vs. CRMS-Carrier (p = 0.0149). Including IRT value does not further increase ability to predict CF transition. When we evaluated the trajectory of SCT repeated measures over time between the CRMS-CF and CRMS-P groups , we found that SCT concentrations increase with age  over both groups. The mean first SCT concentration in the CRMS-CF group is higher than for CRMS-P and the CRMS-CF group increases more with age (1.23 mmol/L/year) compared to those in the CRMS-P group . Stool elastase was collected from 38 children in the CRMS subgroups. Only one child had elastase <200 µg/g (transitioned to CRMS-CF) and otherwise no differences were appreciated between any of the subgroups.In contrast to infants who were diagnosed with CF after completing the initial NBS process, infants initially diagnosed as CRMS and then transitioned later to a diagnosis of CF (CRMS-CF) had a higher, non-significant difference in the IRT value (97.8 ng/ml) on NBS compared to CRMS-) groups . The CRMP group (range 1–22), and 42% of children in the CRMS-Carrier group (range 1–2). There was a non-significant difference in the total number of cultures per patient collected between the CRMS-CF (mean = 5.5) vs. CRMS-P (mean = 3.4) groups. Results showing oropharyngeal flora and probable environmental contaminants were excluded, and number of unique pathogenic species ever cultured were normalized to number of cultures per child. Those in the CRMS-CF group had a slightly higher, non-significant number of unique species (0.75 species/culture) identified on microbiology compared to CRMS-Carrier (0.41 species/culture) and CRMS-P (0.61 species/culture) groups. Certain known CF-related pathogens  were isolated at least once in a greater proportion of children in the CRMS-CF group  compared to CRMS-P  or CRMS-Carrier  groups (A majority (76%) of children in the CRMS subgroups had a result for at least one sputum swab culture. Culture results were collected from all children with an initial designation of CRMS, with any cultures obtained after transition to a diagnosis of CF excluded. We had at least one culture in all of the children in the CRMS-CF group (range 1–16), 80% of children in the CRMS-) groups .P, CRMS-CF, and CRMS-Carrier on weight-for-age-percentile or height-for-age-percentile, and no trend for CRMS-CF to be smaller than the other groups for these measures. When examining change over time in the anthropometric data between CRMS-CF and CRMS-P , we found that children in the CRMS-P group have higher weight-for-height percentiles at first observation  compared to children in the CRMS-CF group. Children in the CRMS-CF group appear to have a faster rate in growth than children in the CRMS-P group, but remain below the weight-for-height of those in CRMS-P by the time of the last observation . Among children in the CRMS-CF group, each additional month is associated with an increase in weight-for-height percentile of 0.21 (p = 0.01), while children in the CRMS-P group have a non-significant change in weight-for-height percentile . We do not see differences in their weight-for-age percentile or height-for-age percentile trajectories.We examined weight-for-height, weight-for-age, and height-for-age using percentiles created by the CDC 2000 growth charts for children age 0 to 20 years old . These pOur single-center analysis identified that 18%  of all children who met CRMS criteria on initial encounter transitioned to a conclusive diagnosis with CF, with 4 of these 11 children (36%) diagnosed after the age of 6 years of age. One-third of the children who transitioned were in the low intermediate range (30–39 mmol/L) on initial SCT, whereas the trajectory of SCT progression per year on repeated measurements was significantly greater in those who transitioned than those who did not. No child who transitioned to a diagnosis of CF had an initial SCT value in the normal range. No child who transitioned to CF demonstrated initial symptoms, and most were diagnosed while still asymptomatic on the basis of sweat test in the diagnostic range on repeat testing or genetics. While there were some modest differences in growth, there were no clear thresholds for predicting transition in diagnosis on the basis of growth parameters.Biomarkers, such as IRT or SCT, may be helpful in predicting those who will transition to a CF diagnosis. Several studies have looked into the role initial and repeat SCT values may serve in predicting which infants with CRMS may potentially transition to a CF diagnosis , 20, 23.While IRT is useful for screening for CF, its utility alone to predict a transition to CF among those who initially meet CRMS criteria is low , 28. DesChildren with CRMS designation who transition to a CF diagnosis are not easily distinguishable from those who do not on the basis of clinical symptoms. Nine out of 11 of our CRMS-CF population remained pancreatic sufficient according to elastase results (and one unknown as the patient was followed elsewhere for care after diagnosis). There appears to be a modest difference in the isolation of pathogens, including CF-related pathogens before the CF diagnosis was confirmed in the CRMS-CF group. However, for most of these children, there were no concerns regarding chronic symptoms suggestive of CF prior to diagnosis. With a modestly higher isolation of CF-related pathogens identified in those who ultimately transitioned to a CF diagnosis, we speculate that appropriately powered, systematic studies of the colonization status in this population may provide greater insight into those who may need longer follow-up or more aggressive evaluation. There are also some modest differences in growth parameters. None of these present clear-cut thresholds to help with prognostic counseling.There has been progress over the past ten years with respect to the evaluation, designation and early management of infants with CRMS , 23, 29.Despite the published and updated guidance, there is variation in practice and management for children with CRMS designation, possibly due to the lack of clear evidence supporting risk assessment and counseling. One of the unfortunate consequences of this inconsistent management is the potential for patients to be lost to follow-up. At our center, we identified 12 CRMS children that had been lost to follow-up between the years 2008 to 2019. In 2019, our institution implemented a center wide protocol based on updated guidelines, with our center re-engaging nearly 50% of that patient population for continued care. Given the data found in our analysis, we would emphasize the importance of consistency amongst CF providers at a center in ensuring this patient population is managed appropriately. Additionally, increased anxiety is exhibited by families for conditions associated with an inconclusive diagnosis, which can be ameliorated by providing clear information to families . While tThere are several limitations to our study. This study was conducted using retrospective chart review over a more than a decade, during which our institution underwent several changes in medical record keeping. Our analysis was conducted for a single state with one CF Center and therefore is associated with a small sample size. Over the 12 years of study evaluation, there were many different providers providing care, as well as changes in evidence-based guidelines for management. In our study, we retrospectively categorized each participant according to the updated 2017 diagnostic criteria to improve consistency across all years of evaluation. For many outcomes of interest, the practice variation resulted in missing data. For example, we were unable to identify one or more repeat SCT results for 8 (12.7%) of the patients, and other data such as culture results, fecal elastase, and growth parameters. Additionally, other states may use a different CF NBS algorithm or a different mutation screening panel, which may yield different results.In conclusion, we describe a long-term, retrospective study of children designated CRMS and followed thereafter at an accredited CF Center. Our data complements previous studies that have noted a transition from CRMS designation to a conclusive CF diagnosis, while providing some important caveats to prior publications. Our results suggest that all infants with IRT+/DNA+ and a SCT within the intermediate range between 30 and 59 mmol/L need to be monitored closely for CF with strong consideration for repeat SCT, expanded genotyping, and microbiologic surveillance. Furthermore, children with elevated initial SCT, as well as those whose SCT is increasing in childhood, are likely higher risk for transition to a CF diagnosis. A multi-center effort to systematically study this population over childhood with harmonization of diagnostic criteria, evaluation, and follow-up should be initiated. This study should include assessment of current and novel biomarkers to better predict CFTR dysfunction as early as possible, especially with the advent of highly effective modulator therapy which may delay or prevent manifestations of CF disease.


The potential for transmission of donor-derived infections (DDIs) is impossible to eliminate, but a thoughtful and systematic approach to donor evaluation can mitigate the risk. Prevention is a key issue and clinicians must maintain a high index of suspicion and remain vigilant in staying up to date on emerging infections. COVID-19 and Monkeypox have represented a new challenge for infectious disease screening and recommendations have been evolving, as knowledge in the field has grown. Additional considerations for pretransplant deceased donor screening include testing for neglected and endemic infectious diseases such as strongyloidiasis and HTLV 1/2. Molecular diagnostic tests have improved awareness on pathogenicity of mollicutes and fungi in the setting of DDIs. The aim of this review is to provide an update on the most recent literature on DDI with a special focus on these emerging hot topics.Donor screening for uncommon pathogens must be guided by knowledge of changing epidemiology of infectious disease and availability of new diagnostic methods.Appropriate screening, early recognition, timely reporting, close monitoring, and appropriate management are essential to help reducing the risk of emerging DDIs. Expected and unexpected donor-derived infections (DDIs) remain an intrinsic risk of solid organ transplant (SOT) and, although rare, have been associated with significant morbidity and mortality. According to the most recent report of the Disease Transmission Advisory Committee unexpected DDIs were rare, occurring in 0.18% of all SOT recipients. However, graft loss or death occurred in about one-third of recipients with DDIs, with higher rates associated with parasitic and fungal diseases ..1].At the beginning of the Coronavirus disease 2019 (COVID-19) pandemic, the speed of the disease, the overwhelm of healthcare system and uncertainties on the risk of COVID-19 transmissibility from donors to recipients significantly affected the landscape of organ donation and transplantation worldwide [▪▪].Early in the outbreak period, international Transplant Societies recommended screening of donors for severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) and recommended against the use of organs from donors with active SARS-CoV-2 infection . However7The possibility of SARS-CoV-2 transmission through organ donation has only been reported for lung transplantation and seems it may occur possibly with intestinal donation . On the ▪▪]. Recent small series have shown that lungs from donors with a positive SARS-CoV-2 PCR may be successfully utilized for lung transplantation with careful donor selection, including asymptomatic status, positive nasopharyngeal (NPS) swab, negative bronchoalveolar lavage, cycle threshold values more than 34, first positive NPS COVID-19 test older than 20 days . Further studies with more granular clinical data are needed to assess the long-term outcomes of recipients of organs from donors with active SARS-CoV-2 infection.There is a lack of robust investigations evaluating the longer-term evolution of these recipients. Outcomes of kidney recipients of donors with active or resolved COVID-19 have been observed to be comparable to recipients of COVID-negative donors  over more than 2 years of follow-up ,15. Simi7https://worldhealthorg.shinyapps.io/mpx_global/). On July 23, 2022, the WHO declared this outbreak a public health emergency of international concern. Since then, the frequency of reported cases has decreased substantially .Since May 2022, a Mpox (Monkeypox) outbreak has been reported from countries where the disease is not endemic with the highest number of cases in the USA, South America, and Europe  .Uncertainties of clinical outcomes in immunosuppressed people has raised concerns about the risk of Mpox for SOT recipients . MoreoveThere are two available vaccines that can reduce the risk of developing Mpox and may be used for pre and postexposure prophylaxis . The modAs things stand now, no DDI Mpox infection has been described, but in case of transmission treatment with antiviral in monotherapy (tecovirimat) or combination treatment (tecovirimat and cidofovir/brincidofovir) may be considered. When initiating treatment, clinicians should assess for potential drug interactions between tecovirimat and immunosuppressive agents. In addition, the use of intravenous vaccinia immune globulin and vaccine as postexposure prophylaxis may be considered .Human T-cell leukemia virus type-1 and type-2 (HTLV-1 and HTLV-2) infection is a neglected disease, despite infecting 10 million people worldwide and severe illnesses developing in 5–10% of carriers . The speThe diagnosis of HTLV-1/2 infection is generally based upon serologic testing. The most frequently used screening tests are enzyme immunoassay (EIA), ELISA, and chemiluminescence (CLIA), which may have variable predictive value depending on the population and the employed test. Western blot is usually used for confirmatory testing and distinguish between infection with HTLV-1 and the less pathogenic HTLV-2. Of interest that it has recently been suggested that a signal/cut-off (S/CO) ratio higher than the manufacturer's recommendation of 1.0 in the Abbott Architect antibody assay is a reliable measure of HTLV-1/2 infection. Interpretation of these ratios can assist clinicians in the assessment of low reactive samples and reiterates the need for faster access to confirmatory testing ▪.https://freepub.edqm.eu/publications/PUBSD-88/detail)  in liver transplant recipients.Acute graft versus host disease (aGVHD) is a rare, but severe complication of liver transplantation and maybe caused by the activation of donor immune cells in the graft against the host shortly after transplantation. Of interest that a recent study showed that HTLV-1/2 infection of donor-derived T cells might promote aGVHD ▪▪ in livThe debate is still ongoing; however, we believe that with the improvement of the currently available HTLV-1/2 diagnostic assays, universal screening might be introduced to minimize the risk of transplanting organs from HTLV-1-infected donors, avoiding organ wastage due to false-positive results.Ureaplasma species  and Mycoplasma hominis, herein referred to as mollicutes, are bacteria that colonize the genitourinary system and the upper respiratory tract in humans.Difficulty identifying these organisms that lack a cell wall is a significant barrier to diagnosis and special cultures, PCR screening, and further tests are required for the diagnosis.▪▪,In the postlung transplant setting, these organisms are known to cause mediastinitis, surgical site infection pericarditis, empyema, bronchial anastomosis complications, and have been associated with fatal cases of hyperammonemia syndrome, which is far more frequent in lung recipients ,33▪▪,34.33M. hominis and Ureaplasma species infections is heavily debated. Pretransplant recipient urine screening had shown to have a low yield and has not been correlated with posttransplant mollicute infection. In contrast, most M. hominis and Ureaplasma species infections in lung transplant recipients have been observed to be donor-derived .The usual mechanism of infection in SOT recipients is reactivation of chronic  therapy . S. steroutcomes ,40▪▪.S. stercoralis to be strongly considered also in donors with epidemiologic risk factors (based on country of origin and travel history) or unexplained eosinophilia . Other specific clinical circumstances associated with transmission of filamentous fungi through organ donation include being a transplant recipient, brain hemorrhage as the cause of death, prolonged ICU stay, and mechanical ventilation [▪▪].Aspergillosis and other molds may also be transmitted from infected donors and can cause invasive fungal infections in transplant recipients with very high mortality rates (up to 50%). Of interest that aspiration of water during drowning events can expose victims to environmental molds  ▪▪. Othertilation . Donor-dtilation . Therefotilation ▪▪.A key issue with respect to diagnosis is the need to develop sensitive and reliable assays . Of noteDonor-derived infections continue to be a challenge. The discovery of and the spread of emerging or reemerging pathogens continue. Due to the shortage of organs for transplantation, innovative screening approaches must be constantly applied to improve safety and quality of organs allocation, along with awareness of epidemiological changes. It is of paramount importance that the transplant community continues to engage in high-quality research and innovation to develop new diagnostic tools, therapies, and a more tailored approach for SOT recipients who remain at risk of old and new emerging DDIs.P.G. and M.P. performed the literature research and wrote the manuscript. P.G. and M.P. are the infectious diseases second opinion of the Italian National Center for Transplantation.The authors received no financial support to produce this manuscript.P.A.G. has the following conflict of interest: Consulting fees from Merck, Sharp & Dohme, Gilead Sciences, Takeda, Allovir; member of speakers’ bureau for Merck, Sharp & Dohme, Gilead Sciences, Takeda, Astra-Zeneca; M.P. has no conflicts of interest to declare. Please refer to the accompanying ICMJE disclosure forms for further details.


Digital health interventions have been shown to be clinically-effective for type 2 diabetes mellitus (T2DM) and hypertension prevention and treatment. This study synthesizes and compares the cost-effectiveness of text-messaging, smartphone application, and websites by searching CINAHL, Cochrane Central, Embase, Medline and PsycInfo for full economic or cost-minimisation studies of digital health interventions in adults with or at risk of T2DM and/or hypertension. Costs and health effects are synthesised narratively. Study quality appraisal using the Consensus on Health Economic Criteria (CHEC) list results in recommendations for future health economic evaluations of digital health interventions. Of 3056 records identified, 14 studies are included . Ten studies are cost-utility analyses: incremental cost-utility ratios (ICUR) vary from dominant to €75,233/quality-adjusted life year (QALY), with a median of €3840/QALY . One study finds no QALY difference. None of the three digital health intervention modes is associated with substantially better cost-effectiveness. Interventions are consistently cost-effective in populations with (pre)T2DM but not in populations with hypertension. Mean quality score is 63.0% (standard deviation 13.7%). Substandard application of time horizon, sensitivity analysis, and subgroup analysis next to transparency concerns  downgrades quality of evidence. In conclusion, smartphone application, text-messaging, and website-based interventions are cost-effective without substantial differences between the different delivery modes. Future health economic studies should increase transparency, conduct sufficient sensitivity analyses, and appraise the ICUR more critically in light of a reasoned willingness-to-pay threshold.Registration: PROSPERO (CRD42021247845). Additional estimates indicate 541 million people having impaired glucose tolerance and 319 million having impaired fasting glucose levels, otherwise defined as prediabetes1, and are as such at increased risk to progress to T2DM2.An estimated 537 million people, of which 44.7% undiagnosed, are nowadays living with diabetes mellitus worldwide. Type 2 Diabetes Mellitus (T2DM), formerly known as non-insulin-dependent or adult-onset diabetes, accounts for 90% of the population with diabetes3, another major risk factor for cardiovascular disease. Regarding its prevalence, hypertension affects around 1.28 billion or one-third of all adults between 30 and 79 globally, with 46% of them being unaware of the condition (https://www.who.int/news-room/fact-sheets/detail/hypertension). Prevalence is slightly decreasing in high-income countries but it is still on the rise in low- and middle-income countries4. An additional quarter to half of the global adult population is presumed to have pre-hypertension, defined as high normal office systolic and diastolic BP5.T2DM often occur together with hypertension1 and high BP4, respectively. It is encouraging that preventive measures targeting modifiable lifestyle risk factors could result in substantial health and economic gains6. Lifestyle interventions focusing on diet modifications and increased physical activity have been proven effective in reducing HbA1c-levels and BP values7, and despite some discrepant results and varying study quality, these lifestyle interventions were found to be cost-effective as well10. Lifestyle interventions are thus valid strategies but the cost-effectiveness of various programmes and their drivers still need to be better documented. Staff labour cost is such an important driver, accounting for the larger part of lifestyle intervention costs and could thus be a potential target for improving cost-effectiveness11, for instance with the use of digital health interventions.Both diabetes and hypertension impose a substantial burden on healthcare budgets with 11.5 and 10% of global health expenditures spent on diabetes12 that can foster clinical-effectiveness and cost-effectiveness of such lifestyle measures. The potential is great since 92% of the global population uses a mobile phone13. The clinical effectiveness of digital health in diabetes and hypertension management has been confirmed20 but limited cost-effectiveness data have been referred to as one of the major barriers for widespread implementation21. Yet, Iribarren et al.22 reported that 75% of full economic studies  in a broad range of conditions found cost-effective or cost-saving results for mobile health solutions. Specifically, for T2DM, previous systematic reviews focused mainly on partial economic evaluations 24. Nevertheless, the few full economic evaluations showed favourable results for digital health intervention modes such as phone/video calls, Short Message Service (SMS), and telemonitoring23.There is increasing evidence of digital health interventions as a practical, low labour and low cost delivery mode20. This systematic review extends previously published systematic reviews that were mainly based on partial economic evaluations. Although such evaluations can provide some trends in this field, it is critically important in rapidly evolving research fields, such as digital health, to synthesize and report on more comprehensive economic evaluations25. The current systematic review focuses on three specific digital technology modes and discusses the health economic evidence for each mode separately. Finally, this review puts emphasis on the methodological quality appraisal, with a focus on making informed recommendations to improve methodological quality.The current study aims to systematically review full health economic evaluations of digital health interventions targeting the prevention and treatment of T2DM and/or hypertension in adults with (pre)diabetes and/or (pre)hypertension: smartphone applications, text-messaging, and websites are the subject of investigation30, text-messaging36, or smartphone application interventions39. An overview of study characteristics, intervention details, and health economic outcomes can be found in Table 31 and Zhang et al.36 reported on the same trial but the former was a trial-based analysis while the latter was model-based to extrapolate costs and effects on the long-term.The process of study identification, screening, and inclusion is displayed in the PRISMA flow diagram in Fig. 26, one in Central America32, two in South America36, four in East Asia39, one in South Asia33, two in the Middle East35, and three in Europe29. Eight studies included people with T2DM39, two included people with prediabetes34, and four studies focused on people with hypertension38.Included studies reflected a broad geographic distribution with one study conducted in North America39, while one was a retrospective matched cohort study applying similar analytics35. The within-trial analysis of Derakshandeh-Rishehri et al.30 applied a patient perspective but this is disputable. One study used a decision tree-based model with a time horizon of 6 months and a patient perspective38. Five studies used a Markov model to estimate long-term  costs and effects based on clinical trial inputs, and whereby three applied a public healthcare system perspective36 and one a healthcare payer perspective26. Finally, there was one Markov-model study which did not directly stem from one particular implementation study  and which applied a 20-year horizon37. All studies with a time horizon of more than 1 year applied discount rates for both future costs and health outcomes between 3 and 5%37.Five studies were within-trial analyses with a time horizon between 6 and 18 months and a public healthcare system perspective38 and three in people with T2DM39. Smartphone applications were used for monitoring, treatment adaptation, and communication between patients and healthcare professionals . The smartphone applications in Li et al.39 and Cunningham et al.29 were also used for patient education. The smartphone application in Zhang et al.38 included a health agenda . None of the four studies on smartphone applications included non-digital intervention features.Four studies evaluated the use of smartphone applications, one in people with hypertension36 and five website-based studies30 were included. One intervention combined text-messaging and websites27, while five other interventions also comprised (non-)digital health modalities such as the implementation of a case manager or teleconsultation36. Text-messaging was used to encourage the adoption of healthier lifestyle behaviours by participants. The length of the intervention ranged from 16 weeks to 2 years, and the frequency of text messages could be as high as daily but it was not always reported. The website-based intervention component consisted of educational web pages and social network support groups, often in addition to teleconsultation, face-to-face follow-up, and/or telemonitoring.Seven text-messaging38 or an enhanced version of care as usual 39.Interventions were compared to care as usual38. Some studies reported clinical outcomes such as systolic blood pressure reduction31, HbAc1 reduction35, proportion of population reaching hypertension31 or glycemic control39, life years gained34, and points gained on the problem areas in diabetes control (PAID) scale27. The cost-minimisation study of Chen et al.26 reported on the return on investment.Ten studies reported on the cost per quality adjusted life year (QALY) as the primary health economic outcome39. A rather short time horizon was applied in more than half of the studies39 despite a long-time horizon being recommended in evaluating cost-effectiveness of chronic diseases to capture all relevant costs and effects. Moreover, all but one study27 did not provide sufficient argumentation for choosing another perspective to the societal one. Finally, only six studies reported both probabilistic sensitivity results plus another kind of sensitivity analysis such as threshold analysis or one-way sensitivity analysis on top of the point estimate results36.Table 39.Among the studies expressing results in QALYs, the ICURs varied between dominant  and €75,233/QALY, with a median of €3840/QALY . One study did not find a QALY difference , the ICUR appeared to be robust in probabilistic sensitivity analysis36. This can be related to the low intervention costs since Islam et al.33 demonstrated that programme costs could at least be doubled while remaining cost-effective. Wong et al.34 even calculated that programme costs could be 50 times greater before the break-even point would be reached. Moreover, Li et al.27 argued that the health economic results of text-messaging can be even further improved by upscaling so that the cost per person decreases. Importantly, the ICUR in Gilmer et al.32 turned cost-effective only after 10–20 years, which was inconsistent with other studies that demonstrated cost-effectiveness in the short term33.Text-messaging alone, or in combination with other intervention aspects , was found to be cost-effective30, dominant29, or cost-saving26, even though only a natural effect  was found in the study by McManus et al.28. Yet scenario analysis, in which the intervention effect partly faded away, and probabilistic sensitivity analyses showed the results to be robust at given thresholds29.Website-based interventions appeared to be cost-effective31 reported on patient baseline characteristics: the intervention appeared to have greater value for money in populations of younger age, subjects with higher cardiovascular risk, higher body mass index, and women. The gender difference has been reported by Cunningham et al.29 as well.Sensitivity and subgroup analyses were limited in most studies, which restricts the identification of cost-effectiveness drivers. First, Augustovski et al.31, thus being indicative of better cost-effectiveness although the observed differences were not statistically significant. Meanwhile drop-out rates did not impact the ICER/ICUR in Wong et al.34. Costs were important drivers of cost-effectiveness in other studies as well38.Secondly, intervention aspects influenced the ICER/ICUR as well. A less intensive so less costly intervention following lower treatment adherence was reported by Augustovski et al.32, and the impact of transition probabilities, utility values, and discount rate on the ICUR were mixed38.Third, modelling assumptions was the third and most investigated pillar of what drives cost-effectiveness results. The value for money improved with longer time horizons39, while cost-effectiveness results in hypertension populations were more mixed38.Whether digital health interventions targeting (pre)T2DM versus hypertension populations resulted in different cost-effectiveness outcomes, is difficult to assess because only three studies targeted populations with hypertension. However, it seems that digital health interventions targeting (pre)T2DM populations showed consistently positive cost-effective results29, two studies where the digital health mode was part of a broader digital intervention including telemonitoring35, and four studies (three interventions) where the digital health mode was part of a broader health system intervention including digital and non-digital components36. Website-interventions, text messaging, and smartphone applications were complemented by, or were seen as a complement of, other intervention components in four out of five, four out of six, and one out of four times, respectively. Gilmer et al.32 and Zhang et al.36 evaluated two of the broader health system interventions and found relatively higher health effects  compared to stand-alone interventions. Note that these two studies applied a long-term perspective, contrary to McManus et al.28 who evaluated a broad health system intervention and who found only a systolic blood pressure reduction on the short-term but no QALY improvement.Whereas six studies evaluated one particular digital health mode, there were two studies that combined two of the digital health modes under investigationThis review aimed to synthesize the available health economic evidence of digital health interventions in populations with or at risk of T2DM and/or hypertension. Digital health interventions were restricted to smartphone applications, text-messaging, and website-based interventions. The three digital health intervention modes were found to be cost-effective or cost-saving compared to care as usual and, most of the time, to enhanced care as usual too. Median ICUR of cost-utility studies was low with €3840/QALY.20. However, increased effects did not always offset additional costs: when comparing the three digital intervention modes with (enhanced) care as usual, our analysis did not show a strong preference in terms of cost-effectiveness for one particular mode.Recent meta-analyses from our team have shown the three digital health interventions to be equally effective in reducing BP in adults with hypertension, while text-messaging and smartphone application interventions were associated with increased improvements in glycaemic control compared to website-based interventions in adults with T2DM40, so patients and professionals could be less motivated to do something about it. For example, New Zealand does not have hypertension guidelines but bases its care recommendations on a cardiovascular risk score41. Moreover, a global consensus definition of hypertension is lacking . Smartphone applications, websites and text-messaging may have a significant clinical impact on BP, but there are possibly other approaches or other health objectives that better justify the money invested. This remains to be tested as the health economic evidence of smartphone apps, text-messaging, and website-based interventions in populations with hypertension remains very limited.Digital health interventions seem to be consistently cost-effective in populations with (pre)T2DM but not in populations with hypertension. One possible explanation could be that the cost-effectiveness of implementing a digital health mode depends on the perceived severity of a condition and hence the urge to act upon. Hypertension is so widespread that some might perceive it merely as a risk factor instead of a disease42. Patients who adhere with a smartphone application showed, for instance, better medication adherence43. However, high drop-out rates of 40% (95% CI 16–63%) in RCT’s testing smartphone applications have been demonstrated as well44. It has been suggested that attrition could, for instance, be reduced by using user feedback to enhance user experience, by enabling the possibility for users to contact health professionals , by focusing on self-management skills, by increasing health literacy, and by combining smartphone applications with internet or telehealth solutions44.Among other process evaluation constructs, adherence and reach are two important ones with a major impact on digital health interventions’ cost-effectiveness31 suggested better cost-effectiveness when the intervention was less intense. Although their statement should be interpreted cautiously because of overlapping confidence intervals between the different intervention intensities, these observations might be in line with the results of a meta-analysis on drop-out rates of exercise interventions that demonstrated a higher likelihood of drop-out in more intensive interventions45. Therefore, future interventions should carefully consider which features needs to be combined, knowing that more intervention features could improve effectiveness but a too intense intervention may also increase complexity of use thus having a detrimental impact on both drop-out and cost-effectiveness. Participant input via co-design may be of a help from the evidence gathering to the real-world testing stage46.Differentiating between primarily digital health interventions and primarily health system interventions with a digital component is warranted. Our results suggest that health system interventions might have the potential to gain more health effects on the long-term compared to a stand-alone digital health mode intervention, although current evidence is limited and mixed. However, Augustovski et al.42. Whereas clinical effectiveness on the individual level can be optimised by adding possible intervention components to tailor care, less elaborated programmes may have a higher reach resulting in more population benefit within a closed budget. This could be of particular importance to digitally less-developed countries where digital interventions might be relatively more expensive. Scrutinizing the optimal intervention dose in different health systems including digitally less-developed countries is therefore paramount. Our results indicate for instance that text-messaging is appraised as cost-effective across studies, either in combination with other intervention features or not. Self-monitoring can also be a very powerful strategy to improve cost-effectiveness as well. It might therefore be an option to integrate such functionalities in smartphone applications.Given that there are hundreds of millions of people with or at risk of T2DM and hypertension, it is important to keep an eye on the scalability and budget impact of a new programme11. In this regard, it is important to stress the cost-effectiveness results of digital therapeutics despite the sometimes quite short time horizons applied.Health economic results can only be appraised correctly with an elaborate research question and sufficient context. The competing alternatives under investigation – care as usual in particular – should be detailed. The study’s perspective should be justified and the applied time horizon should capture relevant long-term costs and effects of preventive measuresTransparency is pivotal when reporting applied costs: which costs have been included exactly (which refers to the perspective) and how these were measured and valued should be stated.44.Health economic evaluations of digital health interventions often come with data uncertainty and assumptions. One-way and probabilistic sensitivity analyses are at least needed to address these uncertainties, preferably in different subgroups. Following key lesson 1 on applying an appropriate long time horizon, it is pivotal to scrutinize the impact of the intervention effect’s sustainability on the health economic outcome, especially given the high attrition and dropout rates in for instance app-based interventions48, but critics argued that a more conservative threshold of ±50% the GDP per capita would better capture opportunity costs49. Note that, given such a conservative threshold, most cost-effectiveness estimates of digital health interventions remain cost-effective. Furthermore, some studies applied natural units . For instance, Derakhshandeh-Rishehr et al.30, Faleh Al-Mutairi et al.35 and McManus et al.28 reported an increase in health effect at an increased cost and stated the result was cost-effective although no willingness-to-pay threshold or valid argumentation for the applied willingness-to-pay threshold was reported, respectively.An ICUR does not have an intrinsic value and should always be evaluated in light of a willingness-to-pay threshold. Most included studies applied a threshold value of one to three times the gross domestic product (GDP) per capita, as recommended by the World Health OrganisationOur quality appraisal demonstrated important methodological shortcomings. Based on these, our four key lessons for future health economic evaluations of digital health interventions are:These key lessons should be considered in future research. Such studies should also strive to address evidence gaps in the field. Head-to-head studies are definitely needed to determine the digital health mode with the best value for money in different subgroups operating within a particular health system. The uncertainty associated with long-term health economic evaluations can be reduced by designing trials with longer clinical follow-up periods so the sustainability of the intervention effect can be modelled more precisely. Moreover, budget impact estimates are truly relevant for policy makers given the high prevalence of T2DM and hypertension, while uptake and attrition rates should also be taken into consideration as they can also have a significant effect on the costs.20 scrutinizing the effectiveness of the three digital health intervention modes, and the thorough quality appraisal resulting in several key lessons for health economic research.The most important strengths of this review are the complementarity with previously published meta-analyses51. Second, studies on people with or at risk of T2DM or hypertension were included but the small number of studies impeded appropriate subgroup analyses. What may work in one population may not work in another. Third, only English articles were included and this may limit our conclusions, especially since T2DM and hypertension prevalence are high in large non-English-speaking countries such as India52 and China53. However, included studies from the Americas, Europe, Asia, and the Middle-East reflected a geographically and demographically diverse population. Fourth, digital health solutions in five website-based or text-messaging studies have been augmented with other intervention features such as healthcare professional education, telemonitoring and/or (tele)consultations. It is therefore not clear whether the intervention effect arises due to these additional intervention features or due to the digital intervention component. Fifth, the number of full health economic papers remain scarce, especially compared to the accumulating amount of clinical effectiveness evidence, and the results of Tsuji et al.37 are based on disputable assumptions. Because of the low number of included papers, additional analyses of the impact of study quality on results were not conducted. Sixth, no head-to-head health economic studies of the three digital intervention modes were found. Seventh, health economic studies might be subject to multiple sources of publication bias including a publication bias in first health outcome publications and next economic publications. Funnel plotting to investigate possible publication bias was not an option in this study but Moschonis et al.19 and Siopis et al.20 demonstrated respectively a small and non-existing publication bias in our health outcome reviews. It is of course still possible that a publication bias favoring cost-effective or cost-saving results remains in economic publications, especially given the suboptimal reporting of sensitivity analyses54.However, this systematic review also has limitations. First, the adult filter is not consistent between the five searched databases. In Medline, the adult filter is >19 years of age, whereas for EMBASE and PsycINFO it is >18. However, the proportion of the population with T2DM or hypertension at that age is smallIn conclusion, health economic evidence suggests that smartphone application, text-messaging, and website-based interventions are cost-effective and, in some cases, even cost-saving. It shows how challenging, but at the same time how possible, it can be to improve the health of the population while saving money.While previous research demonstrated that the three digital health intervention modes were equally clinically-effective in adults with hypertension, and that text-messaging and smartphone application interventions worked significantly better than website-based interventions in adults with T2DM, no cost-effectiveness evidence was found supporting one particular digital health intervention mode over another. Moreover, text-messaging, smartphone application, and website-based interventions appeared to be consistently cost-effective in populations with (pre)T2DM, but not in populations with hypertension.55.Based on the available evidence, policy makers and clinicians should make decisions on the most appropriate digital health interventions based on available budgets and well-defined health objectives. The high penetration rate of digital applications in diverse populations is a strength but it is pivotal to keep process evaluation constructs in mind. Key lessons for future health economic studies on how to design studies and report on the results are given. It is important to pay special focus on the context, report the costs included and how these were measured and valued, conduct sufficient sensitivity analyses, and appraise the cost-effectiveness result more critically in light of a reasoned willingness-to-pay threshold. Head-to-head studies are missing while this would enhance understanding and practice substantially. It is strongly recommended to consistently include a cost-effectiveness work package alongside clinical trials56. Five electronic databases  were systematically searched for scientific publications on September 2, 2022. The applied search strategy consisted of population-related and intervention-related keywords, developed by Moschonis et al.19 and Siopis et al.20, combined with a search string to detect economic evaluations, developed by Werbrouck et al.57. The latter was originally based on previously published search strings. References59, but was broadened to maximize sensitivity57. The search strategy is further completely consistent with Moschonis et al.19 and Siopis et al.20 the literature search was restricted by age (adults only), publication date (1 January 2009 onwards to include contemporary evidence only), and language (English), if the search engine allowed to do so. The search strategies for CENTRAL and CINAHL were further restricted to trials only and peer-reviewed manuscripts, respectively. The final search string can be found in Supplementary Methods The protocol (PROSPERO CRD42021247845) and reporting of this systematic review were consistent with the 2020 PRISMA guidelinesAs this study is a systematic review, ethical approval was not applicable.60 by two independent reviewers (RW and NV) based on a priori developed eligibility criteria , economic evaluation type, analytic approach, time horizon , discount rate , reference year of costs, willingness-to-pay threshold , intervention costs, health resource use and data sources, information regarding the base case and sensitivity analyses;Results and conclusion:  costs and effects, results from sensitivity analyses, author’s conclusions.Eligible full texts were screened by the first author (RW) and one-third of these full texts were screened by a second author (NV). Reference lists from articles that fitted the inclusion criteria were checked for missed articles. The following predetermined data were extracted from all included articles:61, study quality has been appraised with the Consensus on Health Economic Criteria (CHEC) list62, since this checklist enables the assessment of both trial- and model-based economic evaluations61. The two independent reviewers (RW and NV) followed Werbrouck et al.57, who suggested small adaptions to the checklist  was applied, was only considered applicable if a study’s time horizon was >1 year. Such adaptions resulted in a more valid appraisal of individual studies’ quality)57. Discrepancies were discussed by the two reviewers until consensus was reached by specifying assessment criteria.As recommended by van Mastrigt et al.The treatment in the comparator group has been dichotomised into care as usual (CAU) and enhanced care as usual (CAU + ). In the case of the latter, further description was provided.Perspective could either be (i) the public healthcare system perspective , (ii) the healthcare payer perspective , (iii) the societal perspective (i.e. including the payer perspective and costs from productivity losses), (iv) the patient perspective, or (v) the organisational perspective. In the case that the perspective was not explicitly stated, the authors made a judgement. A perspective could also be called ‘limited’: a limited societal perspective may for instance account for non-medical costs  but not for indirect non-medical costs .61, costs and incremental cost-effectiveness ratios (ICERs)/incremental cost-utility ratios (ICURs) were converted via an online calculator (https://eppi.ioe.ac.uk/costconversion/) to 2022 Euro currency values with Belgium as the reference country, to account for purchasing power parities.In order to improve the comparability between studies from different countries and different reference yearsThe evidence table summarises study characteristics, treatment alternatives, and results from the incremental base case analyses. Sensitivity analyses are addressed in text. The following methodology applies:Results were analysed together and per delivery mode, disease, and outcome measure. Moreover, possible cost-effectiveness drivers were explored.Further information on research design is available in the Supplemental MaterialReporting Summary