The heterogeneity of the SARS-CoV-2 immune responses has become considerably more complex over time and diverse immune imprinting is observed in vaccinated individuals. Despite vaccination, following the emergence of the Omicron variant, some individuals appear more susceptible to primary infections and reinfections than others, underscoring the need to elucidate how immune responses are influenced by previous infections and vaccination. IgG, IgA, neutralizing antibodies and T-cell immune responses in 1,325 individuals (955 of which were infection-naive) were investigated before and after three doses of the BNT162b2 vaccine, examining their relation to breakthrough infections and immune imprinting in the context of Omicron. Our study shows that both humoral and cellular responses following vaccination were generally higher after SARS-CoV-2 infection compared to infection-naive. Notably, viral exposure before vaccination was crucial to achieving a robust IgA response. Individuals with lower IgG, IgA, and neutralizing antibody responses postvaccination had a significantly higher risk of reinfection and future Omicron infections. This was not observed for T-cell responses. A primary infection before Omicron and subsequent reinfection with Omicron dampened the humoral and cellular responses compared to a primary Omicron infection, consistent with immune imprinting. These results underscore the significant impact of hybrid immunity for immune responses in general, particularly for IgA responses even after revaccination, and the importance of robust humoral responses in preventing future infections. In this study, the authors investigate immune responses following a third (booster) SARS-CoV-2 vaccination dose in a cohort of healthcare professionals in Denmark. They find stronger immune responses among those with a prior infection, and correlation between lower antibody responses and higher risk of subsequent breakthrough infection. The high vaccination rates, the higher prevalence of SARS-CoV-2 infections, and the appearance of novel variants have caused a change in the person-to-person transmission dynamics2. Because of the novelty of SARS-CoV-2, no or limited immunity existed in the early days of the pandemic. However, this picture has changed due to the continuous SARS-CoV-2 exposure and extensive vaccination campaigns. Despite the efforts, the frequency of breakthrough infections in vaccinated individuals, including reinfections in individuals previously infected with SARS-CoV-2 is progressively increasing2. This has become particularly relevant after the emergence of the highly contagious Omicron variant and its sublineages. SARS-CoV-2 is continuously evolving and mutations on the spike (S) protein confer profound immune evasion potentials posing a significant threat to antibody therapies and currently authorized Coronavirus Disease 2019 (COVID-19) vaccines4.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination has primarily been shown to protect against severe disease but not viral transmission10. However, there is limited knowledge about the interindividual susceptibility to breakthrough SARS-CoV-2 infections in vaccinated individuals and reinfections, particularly concerning the Omicron variants12. Despite vaccinations, some individuals seem more prone to primary infections and reinfections than others.It is now well established that both the humoral and cellular immune responses after SARS-CoV-2 vaccination differ highly between individuals. Several contributing factors affecting immunity have been identified, including age, sex, comorbidities, medication, and previous SARS-CoV-2 infections14. Concerning systemic IgA immunity, vaccination in infection-naive individuals mounts a weak response, while hybrid immunity mounts a stronger and more sustained IgA response10. Whether the systemic IgA response is associated with protection against breakthrough infections is still not resolved.A positive effect of hybrid immunity (a combination of SARS-CoV-2 infection and vaccination) on protecting against SARS-CoV-2 is expected since the commonly used vaccines mount little mucosal IgA responses15. The consequence of this phenomenon is that the immune system cannot mount more effective responses following new variant infections or vaccines resembling the original immunogen. The phenomenon was first described for the influenza virus and later for human immunodeficiency virus (HIV), dengue fever, and lately, SARS-CoV-217 and may be relevant for both B- and T-cell immunity.The concept of immune imprinting, also known as the original antigenic sin, is the immune system’s propensity to limit its response to new variant antigens after responding to the original antigenIn this study, we examined the influence of previous infection and vaccination on the degree of reinfection and future infection with Omicron, as well as immune imprinting at the level of antibody isotypes, antibody neutralizing capacity, and T-cell responses. We also explored the potential benefits of hybrid immunity.10. A total of 1325 healthcare professionals were included in the present study, of which 1145 (86.4%) were female, with a median age of 52 (IQR: 41–59) years. All individuals had received a third dose of the BNT162b2 vaccine (boost) at a median of 295 days after administering the first vaccine dose  with five natural cubic splines (NCS) on the 12-month period to study the IgG dynamics over time Fig. . Since tDue to the large number of predictive values (model outcomes) provided by the different GLMMs, predictive values are reported only on females due to simplicity and power rationale. All predicted values for all age groups, infection status, and sex can be found in the respective tables , followed by a rapid waning. Administration of a boost resulted in the IgG levels being restored to similar levels as observed following the prime in all three groups defined as (i) infection-naive individuals, (ii) individuals infected before Omicron and iii) individuals infected with Omicron Fig. . IndividA significant interaction between days from the first vaccine dose and sex was observed (p = 0.011). IgG levels after the boost in infection-naive males were higher than in infection-naive females, contrary to the IgG peak levels observed after the prime. This interaction was only observed in infection-naive individuals but not in individuals with hybrid immunity, where the IgG levels were higher in females than males both after the prime and boost  over time. Significant differences in the nAbs dynamics were observed in all three groups  compared to levels before boosting (6-month sampling)  and those who did (reinfected) were studied Fig. . Of noteTo evaluate the association between immune responses and the risk of future infections, IgG, IgA, nAb, and IFN-γ levels were studied in a subcohort of infection-naive individuals who remained uninfected (not future infected) after the 12-month sampling and infection-naive individuals who became infected after sample collection assessed by a positive RT-PCR test (future infected) Fig. . No signTo assess the impact of possible imprinting from previous SARS-CoV-2 variants on the vaccine immune response, we studied IgG, IgA, nAb, and IFN-γ levels in a subcohort of individuals exposed to an earlier SARS-CoV-2 variant who experienced reinfection with Omicron (reinfected) and infection-naive individuals who experienced a primary infection with Omicron (infected with Omicron). In both groups, infections occurred before the sample collection following the boost  was detected, the infection status, as well as the age group and sex, had an impact on the magnitude of the response. It has been reported that neither age nor sex is an influencing factor on IgG levels after the booster24, leading to breakthrough infections and reinfections. In the present study, we observed a higher reinfection rate among individuals infected before Omicron (37.5%) compared to other studies30. We also observed that nearly half of the infection-naive individuals (48.4%) became infected with SARS-CoV-2 (Omicron) for the first time following the booster dose. The high infection rates are probably related to the dominance of the Omicron variant in Denmark during the sampling period and because of the discontinuation of COVID−19 restrictions by the end of January 2022, leading to a rise in viral transmission rates.Despite the humoral and cellular increase after the booster dose, it has been reported that vaccine effectiveness wanes rapidly compared to the prime doses, probably due to the high incidence of the Omicron variant35 and back-boosting might occur, suggesting that previous immune imprinting in vaccine-naive individuals might modulate the immune system at the time of vaccine priming. Nevertheless, this notion should be considered hypothetical, requiring in-depth B memory cell studies and granular data to elucidate further.Breakthrough infections are a challenge of the currently approved vaccines by the EMA (European Medicine Agency), which requires a detailed evaluation of the mechanism underneath. Therefore, we assessed whether certain immune responses to the BNT162b2 vaccine could be associated with breakthrough infections or reinfections. Individuals infected with the Omicron variant by the 12-month sampling had significantly lower levels of IgG, IgA and nAbs following the prime compared to infection-naive individuals, the major difference being the IgA levels. Whether these individuals infected with Omicron by the 12-month sampling had exposure to seasonal human coronaviruses prior to the first vaccination is unknown. However, cross-reactive responses between different coronaviruses have been reportedSimilar trends were observed in individuals who would become infected with Omicron after the sample collection as they had lower levels of IgG, IgA and nAbs after the boost compared to those who remained infection-naive in the same period. No significant differences between vaccination intervals were observed between future infected individuals and those who remained infection-naive, neither in relation to sex nor age. The findings suggest that interindividual differences in the speed of waning immunity increases the risk of breakthrough infection with the Omicron variant.38 here, we can substantiate these findings. Hybrid immunity has been shown to confer more robust protection against future SARS-CoV-2 reinfections, probably due to the broader recognition of different antigens not included in the vaccine design and boosting mucosal immunity39. Nevertheless, reinfections in previously SARS-CoV-2 infected individuals are still common due to the highly mutated Omicron variant and its sublineages. A high proportion of individuals infected with variants before Omicron were reinfected after boost administration. Here, we report a weaker humoral response in individuals who experience reinfections in the future, characterized by a lower peak generated and/or a more marked waning after the prime compared to individuals who do not experience reinfections.Several studies have reported greater immune responses in individuals with hybrid immunity compared to infection-naive individuals;41. This observation, however modest, was particularly evident in relation to circulating IgA levels, which were negative or close to the positivity threshold in individuals who experienced a breakthrough infection or reinfection. The extend of circulating IgA contribution to protection against infection is not clear and discrepancies regarding its association with mucosal IgA have been reported46. Circulating IgA function has been described in relation to induction of proinflammatory cellular functions, such as phagocytosis, antibody-dependent cellular cytotoxicity (ADCC), degranulation, antigen presentation, and release of inflammatory mediators48. A substantial serum IgA-related SARS-CoV-2 neutralizing activity has been shown, being more evident in previously infected individuals and enhanced upon vaccination43. Furthermore, a protective role of serum IgA against SARS-CoV-2 infection has been described50, although the protection appears short-term and modest compared to IgG50. The role of circulating IgA in the protection against SARS-CoV-2 remains elusive and further investigations are needed.Taken together, lower humoral responses following vaccination are associated with increased risk of breakthrough infections and reinfections, as reported previously51. However, cellular immunity appears to remain unaltered after vaccination, suggesting it is the main mechanism preventing COVID-19 severity outcomes but not so efficiently protecting against SARS-CoV-2 transmission and thus breakthrough infections51. This supports the notion that humoral responses are more important for viral transmission and thus breakthrough infections than cellular responses, as IgG antibodies have been correlated with protection against infection52. However, the importance of IgA antibodies, regarded as a primary defense mediator on mucosal surfaces, remains unclear in this context, even within SARS-CoV-2 infected individuals. We cannot confidently provide the origin of the IgA measured in circulation or whether infected and naive individuals present similar specific IgA portfolios.Neutralizing activity against Omicron is reduced compared to previous variants53. To test this hypothesis, we compared the immune responses between individuals previously exposed to SARS-CoV-2 with an earlier variant  and subsequently reinfected with Omicron and infection-naive individuals who experienced a primary infection with Omicron. It has been reported that Omicron reinfections limit antibody boosting in individuals previously infected with a distant strain54. We also observed that humoral responses in this population were less pronounced compared to previously infection-naive individuals with a primary Omicron infection, following the hybrid immune-damping phenomenon described by Reynolds et al.54. In contrast, infection-naive individuals exposed to Omicron presented a very different dynamic response, which was significantly boosted54. These higher levels against ancestral RBD might be related to the recognition of conserved epitopes, driving the proliferation of memory B-cells boosting the original antibody response58. However, focus on antibody maturation and breadth recognition of other variants are necessary. Moreover, further investigations are required to evaluate whether this phenomenon, traditionally observed in the influenza vaccination strategy, will negatively hamper the immune response against future infections with novel SARS-CoV-2 variants54.Depending on the previous exposure to SARS-CoV-2, immune responses to vaccination differ between individuals, which could be attributed to immune imprinting59. The changes in IFN-γ level dynamics after the booster dose were observed to be less pronounced than the humoral dynamics. Nevertheless, a significant increase in IFN-γ levels was observed after the boost in infection-naive individuals and individuals infected with Omicron, but not in individuals infected before Omicron. Thus, immune imprinting may also play a role in T-cell responses.Cellular immunity, specifically T-cell immunity, is vital to limiting infection by viral clearance and provides clinical protection in COVID-1961. This shows that vaccination generates a robust cellular response, which can be boosted after the first antigen exposure. We could not distinguish between IFN-γ released from CD4+ and CD8+ T-cells but other studies have reported predominant and stronger responses from CD4+ T-cells upon peptide stimulation64. Consequently, we hypothesize that the observed correlation between IFN-γ and IgG levels might be linked to the interplay between CD4+ T-cells eliciting effective B-cell responses, as reported elsewhere62.No difference in IFN-γ levels was observed between vaccinated individuals experiencing reinfection with Omicron compared to vaccinated individuals experiencing a primary Omicron infection, as observed elsewhere23; although variables such as time from infection should be considered carefully. Nevertheless, it should be mentioned that although these models can show predicted values at any given time point, the time range with limited observed data available (days 250–350 after first dose) illustrated predicted data that appear biologically unrealistic. Thus we avoided assessing predicted values in this time range and only report when observed data demonstrated greater consistency. The two-part independent model allowed us to project the immunological event of the boost.In this study, we have investigated the humoral and cellular response dynamics in a comprehensive large-scale fashion. However, some limitations of the study are pertinent to acknowledge. Due to study design and logistic limitations, we did not collect pre-boost samples, which could influence the model fit. Quantification of the humoral and cellular responses was assessed using the ancestral strain RBD and S1 peptides, respectively. Therefore, we cannot exclude an underestimation of responses following infection due to the Omicron variant in individuals infected or reinfected, as variant-specific antigens were not included in the study. Despite this, using the original strain antigens allowed us to directly evaluate the influence of the BNT162b2 vaccine, designed from the ancestral S protein. It should be emphasized that IgA responses could be underestimated due to the sensitivity of the assay. The study cohort was represented mostly by females, the predominant sex in the healthcare sector in Denmark, which might skew the study. However, no significant differences in sex distribution were observed in the study subgroups. We could have underestimated the exact number of future infected individuals due to changes in the testing strategy in Denmark in the spring 2022. Moreover, viral exposure frequencies between infected/reinfected or not infected/reinfected individuals are inherently uncertain and could introduce bias. Additionally, we did not have information regarding chronic diseases or medication use among participants. However, there are several strengths worth mentioning; from a more epidemiological point of view, the large number of subjects with many repeated samples provided great power when analyzing the data. In addition, the large cohort allowed us to evaluate diverse infection status subgroups. Still, one of the greatest strengths is the use of GLMMs, a useful tool for modeling dynamics when repeated samples are not available at all time points and the possibility to adjust results for different covariates such as age groups and sexThe heterogeneity of the SARS-CoV-2 immune responses has become considerably more complex over time due to the different vaccine boosters, the number of antigen exposures and the frequent mutations of the virus. This complicates the vaccine strategy in future vaccination campaigns. Evaluation of both humoral and cellular responses, including the establishment of consensus about correlates of protection, is crucial to identify who may be eligible for additional vaccine boosters. Moreover, novel vaccine designs are required to improve IgA responses, which are primarily enhanced upon SARS-CoV-2 infection, suggesting it is a central component in immune protection.In conclusion, we demonstrate a modulation of the humoral and cellular responses after the booster dose, primarily influenced by the previous immunity of the individual. SARS-CoV-2 infection has an impact on inducing a robust IgA response after vaccination. Low IgG, IgA, and nAbs responses, but not T-cell responses, are associated with an increased risk of future SARS-CoV-2 infections. Finally, primary infection before Omicron and subsequent reinfection with Omicron significantly dampened the humoral and cellular response, consistent with immune imprinting.65. Sample collection did not interfere with the Danish COVID-19 vaccination program65. Participants included in the 12-month cohort were fully vaccinated with BNT162b2 (Comirnaty) COVID-19 vaccine (Pfizer-BioNTech) and received a booster dose from the same vaccine. Sample collection times spanned from baseline, 21 days, 2 months, 6 months, and 12 months approximately after the first dose. Participants filled out questionnaires with information regarding sex, age, height, and weight. Research Electronic Data Capture (REDCap) was employed to collect and manage the data66. Individuals included in the study received oral and written information before providing informed consent. Venous blood samples for humoral analyses were collected from baseline up to 443 days after administering the first vaccine dose at a different sampling collection round . The total number of repeated measurements per participant was between 3 and 5. Venous blood sampling for measurement of T-cell responses was performed only at the 6- and 12-month collection rounds and the number of repeated measurements per participant was from 1 to 2. Venous blood sample collection fulfilled the principles described in the Declaration of Helsinki. The protocol was approved by the Regional Scientific Ethics Committee of the Capital Region of Denmark (H-20079890).The study cohort was composed of healthcare professionals from Rigshospitalet and Herlev-Gentofte University Hospital  who participated at different sample collection time points in the 12-month prospective longitudinal observational period. Results of immune responses from previous rounds have been reported elsewhere10. A detailed description of the assay can be found in the Supplementary Information. Detection of total antibodies against the N protein was performed using the Elecsys® Anti-SARS-CoV-2 assay (Roche Diagnostics) on the COBAS 8000 platform (e801 module) following the manufacturer’s instructions. N protein antibody detection was used as a proxy to determine previous SARS-CoV-2 infections.Quantitative determination of antibody levels (IgG and IgA) in plasma against the SARS-CoV-2 ancestral spike S protein RBD were measured using a national validated in-house ELISA-based assay, as described before67. The pseudo-neutralizing assay used in this study has a high correlation (r = 0.9231) with the gold standard plaque reduction neutralization test67. A detailed description of the assay can be found in the Supplementary Information.Quantitative estimation of the inhibition degree of virus-neutralizing antibodies against RBD to bind the ACE-2 host receptor was performed using a surrogate in-house ELISA-based pseudo-neutralizing as previously described10. Quantitative IFN-γ released from stimulated T-cells was determined using an IFN-γ ELISA kit  following the manufacturer’s instructions. A detailed description of the assay can be found in the Supplementary Information.Stimulation of T-cells in fresh whole blood samples with peptides derived from the S1 subunit of S protein was performed using the SARS-CoV-2 IGRA stimulation tube set  following manufacturer’s instructions; specific incubation time is described previouslyInterpolation of circulating IgA and IgG levels, neutralizing antibody levels and IFN-γ levels was executed utilizing GraphPad Prism version 9.3.1 (GraphPad Software) using non-linear regression with four-parameter curve fitting. Interpolated levels of IgA and IgG antibodies were given in AU/ml, being the highest concentration of the calibrator at 200 AU/ml. The threshold for assay positivity was set to 100 and 225 AU/ml for IgA and IgG, respectively. Interpolated neutralizing antibody levels were given in IU/ml, the highest concentration of the calibrator 520 IU/ml . The threshold for assay positivity was set to 420 IU/ml. Interpolated IFN-γ levels were given in mIU/ml. The threshold for assay positivity was set to 200 mIU/ml.68. SARS-CoV-2 reinfection was defined as an individual with two consecutive positive RT-PCR results separated by 60 or more days69. Individuals lacking a first RT-PCR confirmation in SARS-CoV-2 infections detected in 2020 (protein N positive) were defined as reinfected when a positive RT-PCR result was performed 60 days or more after the first protein N seropositive sample. Hybrid immunity was defined as an individual being vaccinated with a COVID-19 vaccine and being infected with the SARS-CoV-2 virus. SARS-CoV-2 infections with the Omicron variant were defined by an individual with a positive RT-PCR result obtained from a test after the 20th of December 2021, when the Omicron variant was dominant in Denmark, specifically the BA.1 subvariant (69%), followed by the BA.2 subvariant (16%)70. Individuals with a positive RT-PCR result or seropositive sample before the 20th of December 2021 were considered to be infected with a previous variant of the virus present in Denmark before the outbreak of Omicron70 Statistical analyses were performed using R . Statistical differences between non-normally distributed data were assessed using the Mann-Whitney U test or Kruskal-Wallis test as appropriate. The projection of the antibody waning until boosting and the projection of the peak reached after boosting were modeled using a two-part independent model. This model was composed of one GLMM with two NCS from baseline to 6 months and a linear-mixed model on the 12-month round only. IgG and neutralizing antibody levels were modeled using a GLMMs with Gaussian distribution and five NCS to account for non-linear trends over time. The response was modeled from the time of first vaccine administration up to 443 days. Representation of T-cell dynamics was assessed using linear-mixed models from the time of the first vaccine administration, as these samples were only collected at two-time points. Due to the non-Gaussian distribution of the data, IgA levels were transformed into a binary variable defined as positive and non-positive responses. Positive responses were defined as IgA levels >100 AU/ml. Interactions were analyzed between days and infection status , days and reinfection , days and infection status after the 12-month sampling collection round , days and immune imprinting evaluation , days and age groups , and between infection status and age groups. Sex was included in the analysis as a covariate. Correlation between IFN-γ with IgG and IgA levels was evaluated using the Spearman Rank test. IgG, nAb and IFN-γ levels were log10 transformed and back-transformed when reported for all analyses. Associations between IgG, IgA, nAbs, or IFN-γ levels and age, sex, infection status and time from second vaccine dose, time from the third vaccine dose and time from the last infection in the reinfected, future infected, immune infected cohorts (respectively) were assessed using multiple linear regression. P-values reported from GLMMs were calculated using Type II Wald chi-square tests. P-values < 0.05 were considered significant. Smallest p-values reported are p < 2e−16 or p < 2.2e−16 as R software does not compute p-values with accuracy below 2e-16–2.2e-16 using the statistical analyses employed. A more detailed description of the models and R packages used can be found in the Supplementary Information.Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


Diarrhea continues to be a leading cause of death for children under-five. Amongst children treated for acute diarrhea, mortality risk remains elevated during and after acute medical management. Identification of those at highest risk would enable better targeting of interventions, but available prognostic tools lack validation. We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build clinical prognostic models (CPMs) to predict death  in children aged ≤59 months presenting with moderate-to-severe diarrhea (MSD), in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using repeated cross-validation. We used data from the Kilifi Health and Demographic Surveillance System (KHDSS) and Kilifi County Hospital (KCH) in Kenya to externally validate our GEMS-derived CPM. Of 8060 MSD cases, 43 (0.5%) children died in treatment and 122 (1.5% of remaining) died after discharge. MUAC at presentation, respiratory rate, age, temperature, number of days with diarrhea at presentation, number of people living in household, number of children <60 months old living in household, and how much the child had been offered to drink since diarrhea started were predictive of death both in treatment and after discharge. Using a parsimonious 2-variable prediction model, we achieved an area under the ROC curve (AUC) of 0.84  in the derivation dataset, and an AUC = 0.74  in the external dataset. Our findings suggest it is possible to identify children most likely to die after presenting to care for acute diarrhea. This could represent a novel and cost-effective way to target resources for the prevention of childhood mortality. Close to 500,000 children under 5 years of age die from diarrhea every year, mostly in low- and middle-income countries (LMICs) . While cIn this study, we aimed to develop clinical prognostic models (CPMs) to identify those most likely to die among community-dwelling children under 5 years presenting to care for acute diarrhea. CPMs are algorithms that aid clinicians in interpreting clinical findings and making clinical decisions . BuildinThis study used existing datasets and involved no additional data collection or interaction with study participants. Participants’ caregivers provided informed consent, in writing or witnessed if caregivers were illiterate. The Global Enteric Multicenter Study (GEMS) study protocol was approved by ethical review boards at each field site and the University of Maryland, Baltimore, USA. The study from the Kilifi Health and Demographic Surveillance System (KHDSS) and Kilifi County Hospital (KCH) in Kenya was approved by the Kenya Medical Research Institute (KEMRI) National Ethical Review Committee. All possible diarrhea cases were included in the analysis.We derived CPMs for death using data from cases from The Global Enteric Multicenter Study (GEMS), which has previously been described in-depth , 10. GEMWe externally validated our CPMs using data from the Kilifi Health and Demographic Surveillance System (KHDSS) and Kilifi County Hospital (KCH) in Kenya . ChildreWe examined three similar but distinct outcomes: death anywhere after enrollment at the health facility  within 91 days post-enrollment, death at the enrolment health center , and post-discharge death (reported by caregiver (GEMS) or census (KHDSS) reported death after being discharged from medical care within 91 days post-enrollment). If a child died at the enrolment health center, they were not included in the analysis of post-discharge death. Children were also excluded from the post-discharge death analysis if post-discharge follow-up data were missing.Over 130 potential GEMS predictors were considered, including descriptors of the child, household, and community .Second, we used repeated cross-validation to assess generalizable model discrimination. For each of 100 iterations, separate logistic regression and random forest regression models were fit to a random 80% sample of the full analytic dataset (training set) using a subset of the top-ranked predictive variables. We examined the top 1–10, 15, 20, 30, 40, and 50 of predictors. Each of these models were then used to predict the outcome on the remaining 20% of the analytic data (testing set). We used the receiver operating characteristic (ROC) curves and the cross-validated C-statistic (area under the ROC curve (AUC)) to assess model discrimination. Discrimination is defined as how well a model can separate individuals who will or will not experience the outcome, in this case death.https://github.com/LeungLab/MortalityCPR).Third, we assessed model calibration, or agreement between the predicted and observed risk of the outcome. We estimated the true mean using logistic regression while subtracting out the estimate  to assess the calibration intercept, or calibration-in-the-large. We assessed the spread of the estimated probabilities using calibration slope. To do this, we fit a logistic regression model CPM-predicted log-odds as the independent variable and log-odds of the true status as the dependent variable. We also graphically assessed “moderate calibration.” In each iteration of each n-variable model fit, we calculated the predicted probability of death for each child. We then binned these predicted probabilities into deciles, and calculated the proportion of each decile who truly experienced the outcome for each iteration of each n-variable model. We then calculated the mean predicted probability and mean observed proportion for each decile across iterations, and then plotted these averages for each n-variable model   of different predicted probability cutoffs. Previous studies have identified age and MUAC as key risk factors for death following diarrhea . Given tThere were 9439 children with MSD enrolled in GEMS. Of these, 840 children were excluded for having missing follow-up data, and 79 were excluded for having follow-up data outside of the 91 day study follow-up period, leaving an analytic sample of 8520. An additional 460 observations were dropped for missing predictor data, leaving 8060. Of these, 165 (2.0%) of children died by 91 days after enrollment at the health facility, including 43 (0.5%) during treatment, and 122 (1.5% of remaining) after discharge Fig A in .In GEMS data, the top predictors of death after enrollment are listed in The maximum AUC attained with the model predicting any death after enrollment was 0.88  with a model of 30 variables, while an AUC of 0.84 , 0.86  and 0.86  was obtained with a CPM of 2, 5, and 10 variables, respectively Fig B in . At a seGiven the discriminative performance observed in The results of the sensitivity analyses are presented in Table D in Using the 2-variable CPM derived in GEMS described above, we explored how accurately our CPM identified children who went on to die during our study period. Using a CPM-derived predicted probability of ≥0.10 as a positive screen for risk of death, we observed a sensitivity (Se) of 0.28 and a negative predictive value (NPV) of 0.98 in GEMS. In contrast, using an observed MUAC of <12.5 as a positive screen for death resulted in a Se of 0.66 and a NPV of 0.99. However, almost 6 times as many children screened positive for risk of death using the MUAC-based approach compared to our CPM-based approach (17.5% vs 3.1% of patients screen positive). Increasing the predicted probability threshold of our CPM screen led to decreasing sensitivity and fewer children screening positive  as well as at external validation . There have previously been limited efforts to identify which children are more likely to die after presenting to care for acute diarrhea. While a number of studies have explored risk factors of post-discharge mortality after seeking care for diarrhea in LMICs , 14, preIn our model derivation, we found a similar set of top predictors for the categories of: any death after enrollment, death during treatment, and death after discharge, as well as for different age subgroups and GEMS study sites. Mid upper arm circumference (MUAC) was the top predictor for all CPMs. Low MUAC has previously been recognized as a good predictor of mortality , 16. WhiWe found similar discriminative ability for predicting death at different time points , in different age subsets, and at different GEMS study sites. As predictors in our derivation dataset were collected only at enrollment, we were unable to examine if updated values at discharge could improve our post-discharge mortality prediction. However, others have found no differences in cross-validated discriminative ability even with updated predictors at discharge .Our CPM is promising as a screening tool to identify children likely to die after presentation, and therefore who may benefit from more intensive care and follow-up. While young age and poor nutritional status are known risk factors of poor diarrhea outcomes, our CPM performed better than simple age and MUAC based cutoff screening criteria. The highest screening sensitivity was achieved by using MUAC<12.5 as a screening cutoff for all children age 0-59mo (Se = 0.66), meaning this screening protocol correctly captured two-thirds of children who died in the study period. Such screening at presentation would allow for early intervention and intensive follow-up to potentially avoid these deaths. However, using these criteria, 17.5% of presenting children would have screened positive. This may be an unrealistically large portion of children for whom to provide intensive treatment and follow-up care. In contrast, if our CPM-calculated predicted probability of death was used as a screening tool in GEMS, we would have correctly identified 28% of children who went on to die within the study period, while only having 3.1% of those presenting to care screen positive. Future research should explore the effectiveness, viability, and ethics of using such predictive screening tools to allocate limited resource-intensive acute and follow-up care.Shigella spp was common among children without dysentery (and therefore did not meet antibiotic recommendations) who died in GEMS [Utilization of a CPM at time of clinical presentation could offer a timely and efficient way to identify children most likely to benefit from targeted, resource-intensive interventions such as additional staffing during treatment, extended treatment at care facility, and at-home follow-up care post-discharge. However, this assumes the children predicted to die would avoid death with adequate intervention. A recent systematic review of interventions for post-acute diarrhea sequelae suggests that the majority of existing intervention strategies are not effective at reducing mortality . While n in GEMS . AdditioOur study has a number of strengths and limitations. We derived CPMs for death from a multi-site, prospective study that included longitudinal follow-up with extensive etiologic testing. While our data are older, GEMS remains one of the largest and most comprehensive studies of pediatric diarrhea, which is necessary for conducting extensive predictor variable exploration. We used random forests for variable selection, which do not require assumptions to be made about the underlying variables. They also tend to outperform  conventiIn conclusion, we used data from a large multi-country study to derive and an external dataset to validate clinical prognostic models for death. Our findings suggest it is possible to identify children most likely to die after presenting to care for acute diarrhea. This could represent a novel and cost-effective way to target resources for the prevention of childhood mortality.S1 Text(DOCX)Click here for additional data file.


Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block  and three-block approaches . The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage. Globally, over 250 million people are affected by osteoarthritis (OA) . The worOA is often attributable to ageing (idiopathic) or trauma (post-traumatic) and is associated with severe pain, restricted joint mobility, and articular cartilage matrix erosion. At present, there is no cure for OA; however, several repair strategies are available during arthroscopy to treat joint injuries and prevent OA development and progression. The efficacy of a repair technique fundamentally depends on an accurate diagnosis of the severity of the lesion . UsuallyVibrational spectroscopic methods such as near-infrared (NIR), mid-infrared (MIR), and Raman spectroscopies are emerging techniques in biomedical applications and have demonstrated significant diagnostic potential for evaluating cartilage properties ,10,12. HIn comparison, a NIR spectrum captures the features from absorption arising from the interactions between light and chemical bonds such as OH, CH, NH, and CO in cartilage. Thus, they may provide complementary information regarding tissue integrity . It is pData fusion or data blocking refers to combining two or more datasets from different modalities to obtain greater insights than one would if one used the datasets individually . Data fuThis study investigates the potential of data fusion/data blocking of MIR, NIR, and Raman spectroscopic data for characterising degenerative changes in articular cartilage. We utilise previously reported datasets ,21,22 onSample extraction: Fresh bovine knees  were obtained from a local abattoir; no ethical permission was needed. A total of 12 patellae were collected. Among them, two patellae showed signs of natural damage; hence, they were excluded from the study. Samples were extracted from the patellae and subjected to different damage treatments within one hour of patellae extraction from the joints. The cartilage surfaces of the patellae were kept hydrated during this process by placing PBS-soaked gauze on the cartilage surfaces. Specimens were kept hydrated between the measurements by immersing them in PBS. All the measurements were carried out from the centre of the same sample.Only ten patellae with no visible signs of damage were extracted from the knee joints and utilised for experiments. Cylindrical osteochondral specimens  were removed from different anatomical locations of the medial and lateral sides of the patellae I and prem = 200 g, diameter = 1 cm) from a height of 7.5 cm. The drop heights of the impactor and the energy delivered to the cartilage surface were determined based on preliminary testing to produce chondral cracks. The impactor was removed from the specimens immediately after the impact damage to avoid creep deformation. Samples were placed in phosphate-buffered saline (PBS) and allowed to recover for one hour. Specimens in group M2 were subjected to mechanical abrasion to fibrillate the surface of cartilage via a custom-made grinding device  and abrasion . Specimens in group M1 were subjected to mechanical damage using a custom-made drop tower IIa. In bg device IIb. The Enzymatic damage: The enzymatic damage group was divided into three sub-groups based on the enzyme type and incubation time IIc. Two 2 in PBS containing the respective enzymes and complementary antibiotics . The incubation time of 24 h for collagenase induced severe cartilage damage, thus simulating advanced tissue degeneration. In contrast, incubation times of 30 min for trypsin and 90 min for collagenase were applied to induce mild damage, simulating early-stage cartilage degeneration. The enzymatic treatments were conducted on larger rectangular tissues samples (10 × 9 × 15 mm) to minimise the impact of lateral penetration of enzymes into the cartilage from the cut edges, followed by extraction of cylindrical plugs (d = 7 mm) from the centre of the samples. All specimens were used in the experiments within 10 h of extraction from the knee joints. Samples were kept in PBS before and after measurements to keep them hydrated.The three enzymatic treatment subcategories were collagenase 24 h (E1 n = 12), collagenase 90 min , and trypsin 30 min . Before degradation, the samples were pre-warmed to 37 °C. They were then incubated at 37 °C and 5% COFor further analysis, the different damaged cartilage sub-groups were pooled into the “damaged cartilage” group, and the corresponding control samples were pooled into the “normal cartilage” group. Thus, the classification problem in this study was to differentiate between damaged and healthy samples.NIR Spectroscopy: NIR spectra were acquired using a spectrometer , a light source , and a custom-made diffuse reflectance arthroscopic fibre-optic probe. The stainless-steel fibre-optic probe had a tip (d = 3.25 mm) shaped like a conventional arthroscopic hook. The probe consisted of 114 optical fibres, with 100 fibres illuminating the sample from the light source and 14 fibres collecting the diffuse reflected light from the sample to the spectrometer.NIR spectra were obtained from the exact location before (pre) and after (post) degradation, and the samples were immersed in PBS during measurement to imitate in vivo arthroscopy. Three spectra were acquired per sample from the centre of the specimen using Avasoft software . Each spectral acquisition consisted of 50 co-added scans, with an integration time of 16 ms.−1 full-range grating, 10X objective, and 50 μm confocal pinhole. The spectral integration time for each Raman spectrum was 0.5 s, and 120 scans per spectrum were averaged. The measurements were carried out using the OMNICxi software. Experimental parameters were selected based on preliminary experiments to maximise the signal-to-noise ratio and minimise sample auto-fluorescence. We adopted the 785 nm laser as it is optimal for this application. It has been observed that 785 nm laser excitation can largely avoid fluorescence compared to 532 nm, which can swamp the Raman signals with sample background fluorescence.Raman Spectroscopy: Raman spectra were acquired using a DXR2xi Raman microscope . The configuration and parameters of our experiments were as follows: laser wavelength 785 nm, laser power of 30 mW, 200–3400 cmSpecimens were placed on the sample stage, and Raman spectra (3 spectra per sample) were acquired from the centre of each sample. To keep the samples hydrated, they were stored in PBS before and after taking measurements. The total measurement time for all three spectra per specimen was under 5 min.Mid-infrared Spectroscopy: MIR spectra were recorded using a Thermo Nicolet iS50 FTIR spectrometer . The system consisted of a global MIR source and a liquid nitrogen-cooled mercury cadmium telluride (MCT) detector and was coupled with a custom-made ATR probe .−1, digital spacing 0.2411 cm−1, averaging 64 scans, and spectral range 4000 cm−1–400 cm−1. Prior to sample measurement, a background spectrum was recorded from the air. All measurements were carried out using the OMNIC software .Samples were kept moist and placed on the stage, and three MIR spectra were recorded per sample from the centre of each sample. MIR spectra were acquired by first establishing contact between the sample and the ATR crystal of the probe. Spectral data were then recorded using the following acquisition parameters: spectral resolution 2 cmSpectral data from the three modalities  were analysed using the following data analysis workflow: (i) data pre-processing, (ii) building one-block classification models, (iii) building multi-block classification models. The models differentiating between damaged and healthy samples were developed using the PLS-DA algorithm, and classification accuracy was evaluated using a leave-one joint-out cross-validation approach, i.e., in each step of cross-validation, samples from one bovine knee were taken out entirely from the training set and utilised as an independent test set to assess the model’s performance.https://github.com/uef-bbc/nippy (accessed on 23 June 2023)), an open-source pre-processing toolbox [Data pre-processing: Before data analysis, all raw spectral data  were pre-processed to reduce spectral data variance originating from sources such as light scattering, background signals, or instrumental artefacts. Spectral pre-processing was conducted using nippy , a highlighted in One-block models: The pre-processed data were subjected to PLS-DA analysis, and classification models were built using one data block at a time. PLS-DA is a multivariate classification method that regresses matrix Y (response variables) onto matrix X (predictor variables), where Y is a matrix of class belongingness, i.e., 0  or 1 (damaged cartilage) values, while X is a matrix of spectral data. The models were optimised using leave-one joint-out cross-validation, and the number of latent variables (LVs) were selected to minimise the prediction error of cross-validation. Details of LVs are provided in their respective tables.Multi-block models: Multi-block analysis was carried out in a two-block fashion by combining two spectroscopic data blocks at a time  and in a three-block style by combining all three data blocks . The data corresponding to different techniques  were used in the multi-block PLS-DA analysis . Multi-bMIR, NIR, and Raman spectroscopy are non-destructive, rapid, label-free, and complementary techniques that provide information on articular cartilage structure and molecular composition ,21,22,24The classification accuracy of the one-block PLS-DA models was highest for NIR data (with a sensitivity of 90% and a specificity of 97%), followed by MIR data (sensitivity and specificity of 92%) and then Raman (sensitivity = 83% and specificity = 68%) data a. The thThe scatter plot and block weights for a three-block model combining MIR, NIR, and Raman to differentiate between the healthy and damaged groups are presented in The influence of the weights for each data block  on the three-block model are shown in The third way to interpret the three-block model is by using Regression coefficients—the actual regression model—which allows for interpretations at the lowest level. By inspecting the regression coefficients, one can observe the differences between samples in different groups and identify influential spectral bands that differentiate samples. The information obtained from the regression coefficients of each block was used to create the correlation loading plot presented in The correlation loading plot  shows thThe peaks between the blue and red circles are interconnected by a correlation coefficient between 0.5 (at the dotted red circle line) and 1 (at the blue circle line) . The cor−1 is correlated to some peaks of the amide bands  provide the most important variables: strong positive and negative correlation among the peaks of amides I, II, and III bands, and the ester peak at 1740 cmde bands . The MIRWe also observed that the model somewhat better explains the variation due to E1 (collagenase 24 h treated) group. Therefore, this group correlates with some of the peaks in the 1st LV more than the other damage groups .Although the results show a relatively low classification accuracy for the one-block model based on the Raman data compared to the NIR and MIR data, we believe this could be due to different sampling volumes; for example, MIR and NIR spectra were acquired using fibre-optic systems, whereas Raman spectra were obtained using a microscopic system. The spot sizes of the MIR, NIR, and Raman systems were 2.5 mm, 2 mm, and 3.8 μm, respectively, resulting in different sampling volumes. Moreover, as multiple chondral (cartilage) cracks were observed in the impact damage group, sampling with a smaller spot size might result in higher variation.The multi-block (two-block and three-block) models showed similar or improved performance compared to the one-block models. The improvement between the two-block (MIR and NIR) model (94% accuracy) and one-block NIR model (93% accuracy) can be considered relatively marginal, and the same can be said for the three-block  and one-block NIR models. However, in terms of sensitivity and specificity, the models built on multi-block data, such as the two-block or three-block models, appeared to be more stable than the one-block models.The most significant advantage of multi-block models lies in their interpretability rather than increased classification accuracy. Therefore, a vital advantage of the multi-block models was to provide an overview of the combined datasets and knowledge of how each block, represented by the different spectroscopic techniques (separately and combined), contributes to the discrimination between normal and damaged samples.Although advanced methods, such as neural networks and deep learning, have advantages over classical machine learning methods, they require a large amount of data—much larger than what was used in this study—to develop robust models. The deep learning methods applied for spectroscopic data did not perform much better than the classical machine learning methods in classification models ,26. AugmThe NIR and Raman datasets provide additional discriminatory information since the model based on MIR data only is worse in differentiating the groups compared to the three-block model a. The coSeveral studies have explored the implementation of multi-modal data fusion to predict blood pressure, stroke or to identify abdominal cancer ,28,29. DWe implemented multi-modal data fusion methods, going beyond the black-box techniques and providing further insight into the spectral feature-based models. The results show that analysing MIR, NIR, and Raman spectra through the use of multi-block models accurately identifies experimental cartilage damage. Multi-block analysis has the added value of enabling more detailed interpretations compared to the one-block models, exhibiting the improved multi-modal models attributed to the biochemical changes in cartilage tissue. The information reported in this study could help develop multi-modal (two or three blocks) spectroscopic techniques to determine the optimal surgical treatment during cartilage repair via arthroscopy.


Intensive care unit (ICU) survivors often suffer from long-term mental problems and a reduced health-related quality of life (HRQoL). Symptoms of depression, anxiety, and post-traumatic stress disorder may render patients mentally frail post-ICU, resulting in impaired recovery and an increased informal caregiver burden. The aim of this study was to investigate the prevalence of mental frailty up to 12 months after ICU admission and pinpoint markers for early risk-assessment in clinical practice.A retrospective cohort study (2012–2018) in which clinical and post-ICU data of long-stay (⩾48 h) ICU-patients was used. Mental frailty was identified as clinically relevant symptoms of depression, anxiety, or post-traumatic distress disorder at 12 months after discharge.The prevalence of mental frailty at 12 months post-ICU among the total group of 239 patients was 38%. Mental frailty was defined as clinically relevant symptoms of depression, anxiety, and/or trauma. To achieve this, previously validated cut off values were used for the HADS  and TSQ (⩾6), and CSI (⩾7).A significant proportion of ICU-survivors can be identified as mentally frail, which is associated with impaired HRQoL at baseline and post-ICU, and high caregiver strain. These findings emphasize the need for integrative aftercare programs for both the patient and their informal caregivers. Over the past decade, the number of patients surviving critical illness has increased due to advances in intensive care medicine. Critical illness and admission to the ICU exposes patients to a number of stressors, including but not limited to: invasive medical procedures, delirium, loss of control, unfamiliar surroundings, and the inability to communicate. A combination of these factors results in a substantial number of ICU-survivors experiencing long-term symptoms of anxiety, depression, and post-traumatic stress disorder.5 These mental health-related problems can lead to poor health-related quality of life (HRQoL), the hallmark of the Post-Intensive Care Syndrome (PICS).Mental health-related problems are common in Intensive Care Unit (ICU) survivors and are often associated with limitations in recovery. and results in higher healthcare utilization and the inability to participate in society.8 In addition, long-term impairments pose a high burden on the patient’s informal caregivers, as they often provide informal care. Although the impact of critical illness on relatives and informal caregivers is yet to be fully understood, a large proportion of caregivers experience depressive symptoms, trauma-related problems, and restrictions in daily living over a longer period of time.10Impaired mental health and other PICS-related health complaints curtail a patient’s chances of meaningful recovery. Long-term non-recovery in ICU survivors is common12 Using threshold values for symptoms of depression, anxiety, and trauma may provide an indication of long-term mental frailty. Over the last decade, frailty has been introduced as a predominantly physical phenotype associated with aging and reflecting in higher risk of health problems, care needs, and mortality. Several frailty indices have been developed to predict morbidity and mortality across the public and clinical health domain. As assessing the frailty phenotype may be troublesome, shorter indices like the clinical frailty scale (CFS) are often used in acute care. However, these practical tools do not include a comprehensive assessment of mental health and may therefore be less suitable to assess mental frailty and rehabilitation needs in a long-term care setting. Thus, identifying patients with mental frailty can support the search for useful markers of long-term impairments in mental health.Although knowledge on mental health-related recovery is expanding, it is still unclear which patients are in need of additional support. Previous studies on recovery in mental health have predominantly studied the presence of isolated symptoms  rather than relevant composite measures such as frailty, and did not use clinically relevant threshold values.Furthermore, pre-admission mental frailty may affect ICU-survivors in their capability to achieve a meaningful recovery after critical illness. Adding a detailed assessment of psychiatric history, including substance abuse, psychotic disorder, and personality disorders, can help identify specific patients at risk of long-term impairments in mental health. Lastly, including the possibility of an aggravating effect of ICU-related delirium may give essential information regarding the impact of critical illness on long-term mental wellbeing.To this end, this study aims to investigate the prevalence of mental frailty up to 12 months after ICU admission and pinpoint markers for early risk-assessment in clinical practice. In addition, the presence of psychiatric history or delirium were evaluated as possible predictors for mental frailty. Finally, the impact of mental frailty on patient health-related quality of life and informal caregiver strain was evaluated.In this retrospective single-center cohort study, all long-stay (⩾48 h) ICU patients visiting the specialized outpatient post-ICU clinic between 2012 and 2018 were included. The study was performed in a tertiary teaching hospital in Leeuwarden, The Netherlands. Following local protocol, long-stay ICU-patients visited the outpatient clinic at 3 months after ICU-discharge. At that time point and 12 months after discharge patients and their informal caregivers completed a predefined standard care set of questionnaires. Data from all patients who completed mental wellbeing assessment were retrieved from electronic patient records. As patient-record data were analyzed anonymously, the ethical committee waived the need for informed consent . As part of standard care protocol, several post-ICU outcomes were collected by outpatient clinic professionals at 3 and 12 months after admission.Information regarding demographics, comorbidities, disease etiology, ICU-morbidity, and post-ICU outcomes were collected from electronic patient records. Psychiatric history was documented as the presence of depression, bipolar disorder, PTSD, anxiety, psychotic illness, personality disorder, or a history of substance abuse based on hospital electronic patient records. Statistics regarding the presence of delirium were obtained from patient records of daily clinical assessment using the Confusion Assessment Method for the ICU (CAM-ICU) and day-to-day status reports. Mental wellbeing was further assessed using the Hospital Anxiety and Depression Scale (HADS)  and the Trauma Screening Questionnaire (TSQ) .18 Coping strategies were assessed using the Coping Inventory for Stressful Situations (CISS-21) . The survey set for caregivers consisted of the Caregiver Strain Index (CSI)  and a modified caregiver TSQ . Patients were retrospectively allocated to the group with mental frailty or the group with no mental frailty at 12 months. Mental frailty was defined as clinically relevant symptoms of depression, anxiety, and/or trauma. To achieve this, previously validated cut off values were used for the HADS  and TSQ (⩾6), and CSI (⩾7).In patients, HRQoL was assessed using the Dutch version of the 36-Item Short Form Health Survey  .Patient and caregiver data were stored in a coded data file in a dedicated research directory. Demographics, comorbidities, disease etiology, ICU-morbidity, and post-ICU outcomes are presented for cases with mental frailty and patients without. All data were visually inspected for normality and underwent normality testing using a Shapiro-Wilk approach. As the majority of data were found to be non-normally distributed, results are displayed as median and  or percentages in case of categorical data. Within-group analyses were conducted using Wilcoxon signed rank tests for continuous data and Related-Samples McNemar Change tests for dichotomous data. Between-group assessment was performed using Mann-Whitney U tests for continuous data or a Chi-square tests when comparing group proportions.p-value of 0.05 was considered statistically significant for all results. Analyses were performed in IBM SPSS Statistics for Windows, version 27.0 . Figures were designed using GraphPad Prism, version 9 . A size-proportional Venn diagram was made using the online BioVenn tool.Odds ratios (OR) for mental frailty at 12 months post-ICU were calculated using binary logistic regression analysis after assessing for collinearity. With this analysis, the impact of clinically relevant characteristics at baseline (delirium in ICU and psychiatric history) and 3 months mental wellbeing scores  on the odds for mental frailty was assessed. For trauma, anxiety, and depression scores, binary variables were used in the model to indicate if patients reached cut-off values at 3 months. A second model was analyzed using absolute trauma, anxiety, and depression scores at 3 months. The analyses were adjusted for sex, age, and severity of illness (APACHE III). A 2-sided Of the 250 patients that were eligible for the study, a total of 239 patients completed mental health assessment at 12 months after ICU discharge . Based op = 0.010, p = 0.017, 17%–27%, p = 0.045). If patients experienced a combination of psychiatric symptoms, it was most often depression and anxiety at both time points . A marginal proportion of patients with mental frailty experienced a combination of depression, anxiety, and trauma-related issues.The percentage of patients experiencing mental frailty, previously described as at least one clinically relevant psychiatric symptom, increased over time . This difference in caregiver strain was similar at 12 months post-ICU . Moreover, absolute CSI scores decreased over time in the non-frail group. In addition, caregivers of patients with worse mental health outcomes more often experienced above threshold trauma symptoms at 12 months  .Univariate analyses of patient mental health scores and caregiver strain indices singled out several differences between groups . At bothPatients with mental frailty more often reported the use of avoidance (at 12 months) and emotional coping strategies (at 3 and 12 months) . The useR2 = 0.320, R2 = 0.386).Using multivariate analysis, depression score of ⩾8 at 3 months was identified as a statistically significant marker for an almost 10-fold increase in odds of mental frailty at 12 months . Similar patterns were observed at 12 months where frail patients scored significantly lower with regards to mental HRQoL , impaired role functioning due to emotional problems , energy/fatigue , and social functioning   at 3 months resulted in a tenfold increase in odds and may be an appropriate marker for early detection of patients at risk.23 In this study, using a mental frailty definition based on threshold levels of depression, anxiety, and trauma, 38% of patients were identified as “frail” after 12 months. A previous study in the post-acute setting showed that anxiety, depression, and trauma-related symptoms were common at 1 year after discharge. Similar to findings in the current study, there was a large overlap between mental health problems , supporting the use of a mental frailty framework. A second study among 629 patients found that close to a third was suffering from symptoms of anxiety, followed by PTSD and depression at 3 months after discharge. Despite a rather large variation in the percentage of patients with mental health problems and heterogeneity in distribution of symptoms, the current study confirms that mental frailty is common in ICU survivors.Mental frailty is a new and relatively unexplored aspect of long-term vulnerability in patients and healthy individuals. Despite the generally physical focus within the concept of frailty, an increasing level of attention has been attributed to the impact of mental and cognitive wellbeing on health and susceptibility to disease. However, current data suggest that these factors have a limited impact on the odds for mental frailty when including mental health parameters to the prediction model. Previous studies confirmed the lack of association between psychiatric history, delirium, and long term mental health.25 Our model identified higher depression scores at 3 months as a strong predictor for long-term mental frailty. These findings suggest that using psychological screening tools for depression, among others, may be an effective screening tool for mental frailty in the post-critical illness rehabilitation setting. In addition, including sub-clinical depression scores in post-ICU assessments of recovery may be necessary to properly identify patients in need of after care as symptoms in patients with frailty may worsen over the year.The general assumption is that a psychiatric history before admission and presence of delirium during admission make patients more vulnerable for long-term mental morbidity. In addition, patients that were experiencing mental frailty at 12 months scored higher on emotional coping at both time points, which may be an interesting finding to explore further in future research. The difference in coping may also be a contributing factor of our finding that mental frailty was correlated with higher caregiver burden and caregiver trauma symptoms. At both 3 and 12 months, a fifth of caregivers indicated unhealthy levels of strain. Although the subject of caregiver burden and PICS-Family (PICS-F) are still underrepresented in medical outcome research, a study of 94 relatives of ICU survivors found similar results. Additional studies found a correlation between impaired HRQoL of the patient and caregiver burden.27 These findings may be self-evident, aside from the fact that caregiver strain remained high even after patient health status improved. Such discrepancy indicates that it’s essential to include caregivers of patients in ICU aftercare practices.Long-term mental frailty has far-reaching consequences for the patient and their informal caregivers. In this study, patients with mental frailty scored significantly lower on several domains of HRQoL, namely mental health, problems in role functioning due to emotional problems, energy/fatigue, and social functioning. Specifically in the domains of role functioning and energy/fatigue patients with mental frailty did not match age-adjusted reference values.28 For research purposes this construct provides valuable information of a patient’s mental vulnerability during ICU-recovery. However, in clinical practice frailty encompasses not only mental health, and both physical and cognitive health should be taken into account when assembling a risk profile. In addition, depression scores at 3 months were included in our prediction model while depression scores at 12 months were used in the outcome measure. Although both variables passed the collinearity test, this could have an impact on the prediction model. Furthermore, despite including extensive information on psychiatric history, this study did not use data on medication use during ICU-stay, sedation techniques, and other factors that may have affected mental recovery.Although this study comprises valuable data regarding mental frailty post-ICU, there are some limitations to consider. To our knowledge, this is the first time a mental frailty construct is built from the presence of above threshold symptoms of depression, anxiety, and trauma. Nevertheless, the scales used for identification of mental frailty have not been validated for this purpose. To illustrate, even though the TSQ has been validated and used frequently to evaluate post-traumatic stress disorder-related symptoms after critical illness in patients and informal caregivers, it may be less suitable to assess complex trauma or the long-term impact of the often traumatic ICU admission.In conclusion, there is a large proportion of ICU-survivors with increasing mental frailty in the first 12 months after critical illness. Psychiatric history at admission and delirium in ICU did not increase the odds for unfavorable outcome. However, depression scores at 3 months may be used as a marker for long-term mental frailty in clinical practice. Future research is needed to investigate the individual trajectories of mental health-related symptoms to uncover the cause of increased depression and anxiety-related symptoms throughout the first year of ICU recovery. Mixed-method studies, combining qualitative and quantitative outcomes, could provide valuable insights on the nature and extent of long-term mental health-related problems in ICU survivors. Mental frailty results in HRQoL impairments and higher caregiver burden, which emphasizes the need for integrative aftercare programs for both the patient and their informal caregivers.Click here for additional data file.Supplemental material, sj-docx-1-inc-10.1177_17511437221139547 for The prevalence of mental frailty in ICU survivors and informal caregiver strain: A 1-year retrospective study of the Frisian aftercare cohort by Lise F E Beumeler, Carina Bethlehem, Thialda T Hoogstins-Vlagsma, Tim van Zutphen, Hanneke Buter, Gerjan J Navis and E Christiaan Boerma in Journal of the Intensive Care Society


Local recurrences, isolated regional recurrences, and loco-regional recurrences are frequent after treatment for advanced stage head and neck squamous cell carcinoma. Salvage surgery may be possible in selected patients, taking into account the location and stage of the recurrent disease, patient age and comorbidities, tumor HPV status, and functional sequelae in relation to initial therapy. Results vary according to these parameters. Adjuvant treatments after salvage surgery are under investigation. The aim of this review is to present current knowledge concerning the incidence and management of recurrent head and neck squamous cell carcinoma and current data concerning survival and morbidity after salvage surgery.Surgery with adjuvant chemoradiotherapy or chemoradiotherapy is the mainstay in treatment for advanced stage head and neck squamous cell carcinoma; however, locoregional recurrences are frequent. Salvage surgery could be proposed in selected patients to improve local control, disease-free, and overall survival. Factors for improved disease-free and overall survival in patients treated with salvage surgery include age, tumor location, the initial T stage, HPV status, resection margins, and the time elapsing from the initial treatment. Clinical trials with adjuvant therapies have shown promise after salvage surgery in terms of tolerance and response, but clinical guidelines for using these adjuvant treatments are currently lacking. The aim of this review is to present current knowledge concerning the incidence and management of recurrent head and neck squamous cell carcinoma and current data concerning survival and morbidity after salvage surgery. Surgery with postoperative chemoradiation or definitive chemoradiation is the main first-line treatment modality for advanced-stage head and neck squamous cell carcinoma (HNSCC) ,2. LocorSalvage surgery includes open, transoral, and robot-assisted techniques. When feasible, salvage surgery has been shown in retrospective studies to improve local control, disease-free, and overall survival (DFS and OS) in patients treated with salvage surgery compared to patients either not eligible for salvage surgery or treated non-surgically. Five-year overall survival ranges from 21% to 61% ,6,7,8. SRecurrent disease is generally defined as the re-appearance of cancer after a lapse in time during which there is no detectable disease; however, there is no uniform definition of locally recurrent HNSCC. This general definition distinguishes recurrence from persistent disease in which there is no point at which the tumor is no longer detectable or a short period where the tumor is not detectable or evaluable. The time period without a detectable tumor used to define recurrence varies among authors and publications, and there is no consensus today as to the disease-free period needed to define recurrence as opposed to persistent disease or second primary tumors. In many publications, the cutoff used to distinguish recurrence from persistent disease is 6 months ,6,8. OthRecently, a definition has been proposed by a team of specialists based on a systematic review of the literature . This deRecurrence can occur in the anatomical site of the initial disease, in neck lymph nodes, and/or in distant metastatic sites. Metachronous primary disease occurs after a lapse of time during which there is no detectable disease but occurs in an anatomical site that is distinct from that of the initial primary tumor. This distinction might be impossible to determine when recurrence occurs in a site close to or adjacent to the initial primary tumor site. 18FDG-PET-CT imaging. Clinical symptoms are non-specific and may be misleading. Patients may experience chronic pain after definitive radiation therapy or chemoradiation, which might be at the forefront of the symptomatology and mask pain related to a recurrent tumor. Radiation-induced trismus may also mask trismus due to the tumor and make the physical examination of the oral cavity and oropharynx almost impossible. The re-appearance of referred otalgia on the side of the initial tumor may be relevant for the suspicion of recurrence. Dysphagia and pharyngeal and/or laryngeal oedema may also be chronic and lead to the delayed diagnosis of recurrence. Laryngeal immobility may be observed after definitive radiation therapy or chemoradiation but may also be an indirect sign of tumor recurrence. Mucosal ulcerations or chronic infection with radionecrosis might present in a clinically similar manner to recurrent or persistent cancer, and differential diagnosis is often difficult as well.Recurrent disease may be suspected upon physical examination, through clinical signs and symptoms, a follow-up with cross-sectional imaging or Lee et al. investigated optimal surveillance visit schedules for patients who had survived advanced head and neck cancer . They re18FDG-PET-CT for nodal metastasis was recently reported by Rovira et al. in a group of 22 recurrent/persistent primary head and neck cancer patients after bioradiotherapy who underwent salvage surgery, suggesting the abstention of treatment of the neck in radiologically negative neck cases [In a case-matched study comparing the histopathology of upfront versus salvage total laryngectomies by Zbären et al.,  recurrenWhen performing biopsies, one must target the lesions visualized both clinically and on imaging, and often, deep biopsies must be performed to identify submucosal recurrence. Repeated biopsies are frequently necessary for negative initial biopsies due to edema and necrosis. Performing the directed biopsies of PET-CT-detected subsites might further reduce the chances of a false negative biopsy .Approximately 50% of patients with initially advanced stage III-IV disease treated with radiation therapy or chemoradiation present with a local and/or regional recurrence at some point during follow-up. In the prospective study by Beitler et al., the 2-year locoregional recurrence-free survival rates ranged from 45.7 to 53.8% for the four different radiation therapy fractionation arms delivered to a total of 1076 patients with stage III/IV tumors with all anatomic locations combined .Not all patients are eligible for salvage surgery. Thirty-seven percent of patients with a laryngeal or hypopharyngeal tumor were eligible for salvage surgery in the study by Van der Putten et al.,  which coIn patients eligible for and treated with salvage surgery, two-year overall survival rates were reported to range from 27 to 71% ,8,10,33 -Initial tumor stage: A lower initial tumor stage carries a more favorable outcome; the initial N3 stage has been shown to be a factor for lower overall survival after salvage surgery .-The stage of the recurrent tumor .-Patient age: patients aged < 58–65 years ,11,33 wh-HPV-positive disease: patients with HPV-positive disease have higher rates of disease-free survival after salvage surgery .-Resection margins after salvage surgery: high rates of R1 resection—14 to 40%—after salvage surgery have been reported by experienced teams despite wide, macroscopically complete resection ,34. An R-The time elapsed between initial therapy and salvage surgery: late recurrences, after 1 year ,10,11,15-Tumor location: disease-free survival after salvage surgery for laryngeal primary tumors has been shown to be significantly better than hypopharyngeal and oropharyngeal tumors ,9,34. Hi-Perineural invasion in the salvage resection specimen has also been implicated as a factor of less favorable prognosis in terms of survival for laryngeal carcinoma ,39.Several factors have, thus, been shown to be implicated in disease-free and overall survival after salvage surgery: An initial tumor designation of stage IV and the presence of a concomitant local and regional recurrence were found to be factors that decreased overall survival. For patients without either of these factors, 2-year overall survival was 83% versus 49% for patients with one of these prognostic factors and 0% in patients with both of these factors . Likewisp = 0.05) [For recurrent nasopharyngeal carcinoma, in a retrospective study of 272 patients, 30.9% received surgery, whereas 35.7% received re-irradiation only . The 5-y = 0.05) . Indepenp = 0.03), the absence of a disease-free interval (p < 0.01), and advanced recurrent tumor stage (p = 0.07) were associated with lower overall survival. Patients with recurrent neck disease (p = 0.01) and positive surgical margins (p = 0.04) experienced higher rates of recurrence after salvage surgery. For HPV-related oropharyngeal cancer, Taniguchi et al. found heterogeneity when reporting overall and disease-specific survival outcomes after salvage surgery [Reported 3-year survival rates following salvage surgery for oropharyngeal carcinoma ranged from 34 to 62% ,46. In t surgery . Two-yea surgery  and 91%  surgery , and 5-y surgery  and 43%  surgery . These fFor laryngeal carcinoma, Scharpf et al. reported a series of 147 patients who underwent salvage laryngectomy for recurrent or persistent disease after radiotherapy or chemoradiation . The 2-yUnlike the lymphatic spreading to the neck of treatment-naïve tumors of the head and neck that follow a predictable pattern, isolated local recurrences pose a dilemma as to whether to surgically treat the neck and to what extent due to distorted lymphatic drainage. An aberrant lymphatic spread in patients with a recurrence of oral cavity cancer has been demonstrated using a sentinel lymph node biopsy. den Toom et al. performed a sentinel lymph node biopsy in patients who had been treated previously for nodal disease. Unexpected drainage patterns were observed in 30% of patients, and in 12% of patients, no lymphatic drainage was seen . While aThree meta-analyses of elective neck dissection in rN0 necks failed to show a survival advantage for neck dissection over observations in recurrent laryngeal cancer after salvage total laryngectomy ,57,58. FPostoperative complications, reported in 22–92% of cases, are more frequent after salvage surgery and include early complications such as a fistula, bleeding, infection, flap necrosis, aspiration pneumonia, the decompensation of comorbidities, and postoperative death . Late coIn a systematic review, Van der Putten et al. reported fistula rates ranging from 4 to 73% after salvage laryngectomy . In a reIn terms of functional results, in the study by Tan et al., 53% of patients treated with salvage surgery did not require a feeding tube or gastrostomy, and 55% did not have a tracheotomy or tracheostoma . Functiop = 0.01) [For eligible patients, functional outcomes are improved by the use of transoral laser or robot-assisted techniques for salvage surgery, when possible, compared to open salvage surgery ,71. Howe = 0.01) . Again, Current guidelines recommend enrolling patients in a clinical trial when feasible .For patients treated with re-irradiation after salvage surgery, two-year overall survival rates range from 21% to 81%, and 2-year locoregional control rates from 4% to 100%. Grade 3 or 4 late toxicities are reported in 13–50% of patients, with the rate of treatment-related deaths ranging from 0% to 8% .p = 0.06). Disease-free survival was significantly improved in the re-irradiation arm, with a hazard ratio of 1.68 , but overall survival was not statistically different between these two patient groups. A second randomized study of post-salvage surgery reirradiation was compared with two modalities: 53 patients were randomized to receive either 60 Gy over 11 weeks (once-daily split course) with concomitant 5FU—hydroxyurea or 60 Gy (1.2 Gy twice daily)—over 5 weeks with cetuximab. The main endpoint was acute toxicity, which did not differ between the treatment arms, nor did the overall or disease-free survival rates [A phase III clinical trial including 130 patients evaluated adjuvant re-irradiation in terms of disease-free survival and overall survival. Patients treated with salvage surgery for recurrence or for a metachronous second primary head and neck cancer in an already-irradiated field were randomized between re-irradiation (with split course radiotherapy delivering 60 Gy over 11 weeks with concomitant 5-fluoro-uracil and hydroxyurea) versus the best supportive care [al rates .The risk factors for survival after reirradiation were evaluated in a retrospective database study on 103 patients, of which 46 also underwent salvage surgery . The autA recently published phase II trial evaluated weekly oral methotrexate and oral celecoxib twice daily for 6 months . The priFollowing the important breakthrough showing improved survival with nivolumab for recurrent and/or metastatic platinum-refractory disease ,78, a reOther treatments that might be associated with salvage surgery are theoretical or have only been reported in small cohorts of patients. One theoretical approach that needs to be validated in clinical trials is the administration of immunotherapy before salvage surgery to induce a response that could improve outcomes and/or optimize the selection of patients eligible for salvage surgery . The rolSalvage surgery is an important therapeutic option in selected patients, with better disease-free survival outcomes in patients < 65 years old with lower-stage recurrences occurring more than 6 months from the initial therapy, laryngeal recurrences, and isolated local or regional disease . Flap placement (free or pedicle) has been shown to improve the postoperative course. Prospective studies are currently aiming to improve survival with adjuvant reirradation, chemotherapy, and/or immunotherapy regimens after salvage surgery.


Considering the inconsistent results regarding the association between the severity and duration of olfactory dysfunction (OD), and the viral load in coronavirus disease 2019 (COVID-19) patients, we aimed to conduct this study.This is a prospective cohort study in which COVID-19 patients were evaluated for the initial cycle threshold value  measured by the nasopharyngeal samples along with olfactory function measured by the University of Pennsylvania Smell Identification Test (UPSIT) within 2 months of COVID-19 onset. Among 309 COVID-19 patients who were included in this study, 108 (34.9%), 112 (36.2%) and 89 (28.8%) were normosmic, hyposmic, and anosmic, respectively based on the UPSIT. The severity of COVID-19 and the rate of hospitalization were higher in anosmic patients . Moreover, significant associations between the initial Ct value and the severity of OD at admission and follow-ups were detected . Anosmic patients had higher Ct values in comparison with hyposmic (approx. 3-fold) and normosmic (approx. 12-fold) patients. The recovery rate after one- and two-month follow-ups was 47% and 84%, respectively. At the follow-ups, OD-recovered patients significantly had lower Ct values  in comparison with those who have not recovered yet  .Ct value seems to be a significant factor not only in predicting OD severity in COVID-19 patients but also in the OD recovery duration. This finding may be helpful to investigate the underlying mechanisms of OD in COVID-19 patients.  The 2019 outbreak of novel coronavirus disease (COVID-19) has posed a significant threat to global public health which was caused by the severe acute respiratory syndrome 2 virus (SARS-CoV-2 virus) , acute oThe diagnosis of COVID-19 is established when the viral nucleic acid is detected by real-time reverse transcriptase polymerase chain reaction (RT-PCR), in either the upper respiratory specimens via nasopharyngeal or oropharyngeal swabs or lower respiratory samples . RT- PCRThe relationship between Ct values and the severity of COVID-19 remains unclear. Based on a literature review, it has been reported that more severe patterns of COVID-19 infection are associated with a higher viral load of SARS-CoV-2 ,21. In contrast, Zou et al. found no significant difference in the viral load between asymptomatic and symptomatic individuals . These dThis observational cohort study was carried out at Ayatollah Taleghani Hospital between February and June 2021. We enrolled hospitalized adults with confirmed COVID-19 infection through PCR testing. Patients with a pre-existing history of OD before the pandemic, previous nasal surgery or radiotherapy, chronic rhinosinusitis, allergic rhinitis, head and neck trauma, a history of psychiatric or neurological disorders, and non-cooperative patients were excluded from the study.Ethical considerationsWritten informed consent was obtained from all the patients. No patients were obligated to participate in the study and patients were not precluded from treatments in case they did not participate in the study. The study was conducted in accordance with the ethical consideration of the ethics committee of the Shahid Beheshti University of Medical Sciences (IR. SBMU. MSP. REC. 1401.071) and the Helsinki Declaration of 1964 and its later amendments.Study designBased on clinical symptoms, an emergency physician evaluated suspected patients in terms of COVID-19 infection, and the RT-PCR test confirmed the COVID-19 disease. Nasopharyngeal samples were obtained by trained personnel from patients who presented symptoms suggestive of COVID-19 upon hospital admission.  The samples were transferred to the hospital's virology laboratory. RT-PCR was performed via Sansure Biotech's 2019nCoV 30Minute Nucleic Acid Reagent Kits , and the Roche light cycler. CT values were recorded for each patient.The participants information was collected using a pre-defined checklist. The information collected at admission included demographic characteristics such as age, gender, and BMI, clinical information including previous medical histories such as hypertension and diabetes, the onset of COVID-19 symptoms, the onset of OD, the severity of OD (subjective and objective), the viral load based on the cycle threshold of the RT-PCR test, and the severity of COVID-19 symptoms using the COVID-19 symptom index (CSI) . The CSIOlfactory evaluationTo accurately measure olfactory function, a modified Persian version of the UPSIT  was used ,26.  ThiStatistical analysis SPSS 26.0 was used to perform the statistical analysis . Categorical variables are reported as counts and percentages. The mean and standard deviation are provided as descriptive statistics for quantitative variables. The ANOVA test with Tukey post-hoc was conducted to evaluate the statistical significance of differences in continuous variables, such as cycle thresholds, across OD severity levels. Student T-test was used to compare Ct values between OD severity categories when OD was categorized as two separate groups of OD and normosmia. The Chi-square test was used to assess the significance of differences in categorical variable distribution across OD severity levels. Pearson's correlation coefficient was used to assess the relationship between subjective and objective OD responses. The statistical significance threshold was considered at P 0.05.2. Further clinical characteristics of the participants were shown in In this study, we enrolled 309 patients with confirmed COVID-19 including 229 (74.11%) females and 80 (25.89%) males. The mean age of the participants was 38.81 ± 11.16 years and their mean BMI was 26.49 ± 5.78 kg/mInformation on the clinical aspects of the course of COVID-19 in the participants is presented in Based on the UPSIT test, from the 309 patients diagnosed with COVID-19 at hospital admission, 108 of them were normosmic, 112 of them had some degrees of hyposmia and 89 of them were anosmic. Meanwhile, according to self-reported records, 78, 35, and 101 were normosmic, hyposmic, and anosmic, respectively. There was a significant direct correlation between objective and subjective (self-reported) tests of OD at the hospital admission  . Objective evaluation of OD after 30 and 60 days showed gradual recovery of OD. At admission, 201 (65.05%) of patients had OD, which declined to 108 (34.63%) and 32 (10.35%) of patients after 30 and 60 days, respectively. As demonstrated in Likewise, a comparison of Ct values according to objective OD severity at admission gave similar results, in which anosmic patients had higher Ct values and lower viral load in comparison with hyposmic (approx. 3-fold) and normosmic (approx. 12-fold) patients. Moreover, hyposmic patients also had higher Ct values and lower viral load (approx. 4-fold) in comparison with normosmic patients .As it is demonstrated in To assess the association of Ct values with OD recovery, we analyzed the patients with OD (including anosmia and hyposmia) respecting recovery (being normosmic) after one and two months. The recovery rate after one and two months was approximately 47% and 84%, respectively. Patients who recovered from OD after one month had significantly lower Ct values and higher viral load in comparison with patients who still had OD (p<0.0001). Similarly, patients who recovered from OD after two months had significantly lower Ct values and higher viral load in comparison with patients who still had OD after two months (p<0.0001) .In this cohort study, we aimed to investigate the association between OD and recovery from it with SARS-CoV-2 viral load and COVID-19 disease severity. OD typically appeared 2 days after the onset of other COVID symptoms with approximately 65 percent of confirmed COVID-19 patients experiencing OD ranging from mild hyposmia to anosmia. The objective evaluation of OD was closely correlated with self-reported OD. Patients who did not develop experience OD were referred and admitted significantly later than those who did, which may be attributed to the concerning effect of losing the smelling sense. Moreover, patients with anosmia experienced more severe disease, longer hospitalization, a lower rate of recovery from COVID-19, and higher risk of mortality. Patients with OD, either anosmia or hyposmia, had significantly higher Ct values and subsequently lower viral load when compared with normosmic patients. Due to the substantial evidence that suggests OD may be a significant manifestation of COVID-19 infection, researchers have focused more on the underlying pathophysiology and prognosis of this chemosensory dysfunction. The etiology of OD has not been completely known yet ,29. DuriPrimary investigations with long-term monitoring revealed a considerable frequency of OD among infected patients and also persistent severe OD, ranging from 5 to 11 percent of cases -45. ThisTo test this hypothesis, we aimed to conduct a cohort study to investigate whether viral load plays a role in the severity of OD in COVID-19 patients. We also monitored patients for two months to determine whether there is a correlation between the initial viral load and recovery from OD in anosmic patients. Overall, our findings revealed a significant relationship between Ct values and OD severity in COVID-19 patients, meaning that higher Ct values were associated with greater likelihood of OD. This means that viral load is inversely associated with the severity of OD. Moreover, we demonstrated that patients who recovered from OD after one or two months had significantly lower Ct values in comparison with patients with persistent OD after this time frame. To the best of our knowledge, there are not many research on this topic and just few studies currently have been published in this regard. These studies have the merit of being the first to explore the potential associations between viral load and the severity of OD or OD recovery duration in COVID-19 patients. In a retrospective cohort study on 599 outpatient cases, the association between the severity (anosmia and hyposmia), and recovery time of OD with Ct values of SARS-CoV-2 were evaluated. They found that the mean Ct values in patients with anosmia were considerably greater than those with hyposmia (p=0.02). However, there was no significant difference in mean Ct values between patients who fully recovered from OD and those who did not (p=0.62). Among the 83 COVID-19 patients who were recruited in a prospective cohort study in China, although the mean level of Ct values in anosmic patients was higher than those who have normosmia, the difference was not significant . On the In concordance with the result of this study, Vaira et al. showed that the correlation between viral load and olfactory scores at baseline (p = 0.844) and two-month follow-up p = 0.519) was weak and not significant among the 60 included COVID-19 patients. The study suggests that individual factors may contribute to olfactory dysfunction rather than viral load or activity . Additio9 was weaIn this prospective cohort study among 309 COVID-19 patients, we found a significant association between the Ct value and the severity of OD which was measured by a valid and reliable self-reported test. Moreover, in patients who developed OD, a significant correlation between the 1-month and 2-month OD recovery and the initial Ct value was detected. Ct value seems to be a crucial factor not only in predicting OD development in COVID-19 patients but also in the OD recovery duration. Further studies are needed to confirm our findings and explore other potential factors contributing to OD in COVID-19 patients. Given the potential long-term consequences of chronic OD, identifying risk factors related to persistent OD is crucial. Treatments with the mechanism of reducing viral load should be assessed for their efficacy in modulating COVID-19-associated OD in the future clinical trials.