ClinicalTrials.gov Identifier: NCT04475081). Included was an outcome to evaluate the myeloid-derived suppressor cell populations in blood between those administered the MMR vaccine vs placebo. The unanticipated emergency approval of several COVID-19 vaccines in the midst of the MMR clinical trials eliminated the ability to examine effects of the MMR vaccine on COVID-19-related health status. Unfortunately, we were also unable to show any impact of the MMR vaccine on peripheral blood myeloid-derived suppressor cells due to several inherent limitations , that also included a collaboration with a similar trial  in St. Louis, MO. In contrast, monitoring the COVID-19 vaccine response in trial participants revealed that high COVID-19 antibody titers occurred more often in those who received the MMR vaccine vs placebo. While the trial was largely inconclusive, lessons learned from addressing several trial-associated challenges may aid future studies that test the non-specific beneficial immune effects of live attenuated vaccines.Mortality in COVID-19 cases was strongly associated with progressive lung inflammation and eventual sepsis. There is mounting evidence that live attenuated vaccines commonly administered during childhood, also provide beneficial non-specific immune effects, including reduced mortality and hospitalization due to unrelated infections. It has been proposed that live attenuated vaccine-associated non-specific effects are a result of inducing trained innate immunity to function more effectively against broader infections. In support of this, our laboratory has reported that immunization with a live attenuated fungal strain induces a novel form of trained innate immunity which provides protection against various inducers of sepsis in mice via myeloid-derived suppressor cells. Accordingly, we initiated a randomized control clinical trial with the live attenuated Measles, Mumps, Rubella (MMR) vaccine in healthcare workers in the greater New Orleans area aimed at preventing/reducing severe lung inflammation/sepsis associated with COVID-19 ( Mechanistically, live vaccines have been shown to traffic to the bone marrow where they can train leukocyte precursors to function more effectively against broader infectious insults.6 Work from our laboratory demonstrated that immunization with a live attenuated fungal strain induces trained innate protection against various inducers of sepsis. Unlike other forms of TII, we discovered that protection against sepsis is mediated by long-lived Gr-1+ myeloid-derived suppressor cells (MDSCs).9 In other experimental sepsis models, MDSCs have been shown to inhibit pathological inflammation and prevent mortality (reviewed in 10). Therefore, we have proposed the term trained tolerogenic immunity 10 to discriminate this form of TII from the previously identified trained immunity involving enhanced inflammatory responses. 13Epidemiological studies on the use of live attenuated vaccines (LAVs) have reported beneficial non-specific effects, including reduced mortality and hospitalization due to unrelated infections.14 Prior to the 3rd and 4th wave of the COVID-19 pandemic (October 2020), the overall death rate among all COVID-19 patients in the US was 3%. This rate increased to 14–24% if hospitalized and >40% if additionally admitted to the ICU. In response to the COVID pandemic, we postulated that the live attenuated MMR vaccine administered to adults could induce trained MDSCs as a stop-gap measure to inhibit the pathological inflammation/sepsis associated with COVID-19. We outlined this strategy in a well-received and widely discussed commentary15 and subsequently initiated a randomized control clinical trial (MMR vs placebo) in eligible healthcare workers (HCWs) in the greater New Orleans area  through the Louisiana State University Health Sciences Center. The trial was ultimately terminated early because the COVID vaccines became widely available a few months after enrollment began (Dec. 2020). However, we completed the trial in all those enrolled (n=34) and entered into a collaboration with colleagues at the Washington University School of Medicine in St. Louis who were conducting a similar, but more global trial evaluating the effects of the MMR vaccine in HCWs in nine countries . A sub study within the CROWN CORONATION trial collected blood samples from participants in St. Louis, MO post-MMR/placebo and COVID-19 vaccination. Cytokine and chemokine responses to SARS-CoV-2 and other stimuli were measured by the study team in St. Louis , and a portion of the samples were shipped for MDSC analysis as per the protocol of the LSU study team. 16Mortality due to COVID-19 is associated with severe acute respiratory distress syndrome (ARDS) and sepsis, characterized by high SOFA  score, an indicator of sepsis.17, including against SARS-CoV-2.20 However, our overarching hypothesis is that administration of the live attenuated MMR vaccine would induce the trained innate tolerogenic MDSCs that would mitigate pathology-inducing inflammation/sepsis. Here we summarize the results of the clinical trial relative to the MDSCs and serology for both the MMR and COVID vaccine-related antibodies. We also outline a number of challenges faced during the trial and lessons learned that may apply to similar trials in the future.It is important to point out that other investigative groups have initiated similar clinical trials using LAV for the purpose of inducing cells trained for non-specific immune enhancement ® II vaccine or placebo  via subcutaneous injection in the arm at a baseline visit following informed consent. Subjects were recruited from local hospitals throughout the greater New Orleans area. Subject consenting, interviewing, vaccine administration and biospecimen collection was performed by the CTRC staff, under full Personal Protective Equipment (PPE) protection. Following informed consent, subjects were asked to complete the Baseline Demographic & History Questionnaire disclosing their demographic information, employment, medication, vaccination, and medical history. Specifically, the medical history placed emphasis on the presence of diabetes, hypertension, heart disease, and their treatments/medications. Subjects then had their height, weight, body mass index (BMI), vital signs, and pulse oximetry measured. Female subjects of childbearing potential were given a urine-based pregnancy test . A total of 20cc of blood was collected along with a nasopharyngeal swab for baseline laboratory analyses . In total, 34 participants were enrolled; 15 in the placebo group and 19 in the MMR group. The demographics were as follows: The median age was 52 yrs (range 25–81 yrs) with 70% female. Ethnicity consisted of 73% white, 9% black, 9% Latino and 9% other with excellent matching between the respective arms.Eligible healthcare workers (HCWs) in the greater New Orleans area (n=34) meeting eligibility criteria were enrolled into a 12-month study by the Louisiana State University Health Sciences Center (LSUHSC) Clinical & Translational Research Center (CTRC) affiliated with the larger statewide Louisiana Clinical and Translational Research Science (LA CaTS) Center. The study was approved by the Institutional Review Board (IRB) associated with Louisiana State University Health – New Orleans. The subjects were blindly randomized to receive the live attenuated M-M-RRepeat biosampling occurred on days 14, 30, and 60 post-injection. A subset of subjects also had blood sampling at 6–8 months post-vaccination to match the time point of a collaboration with Washington University School of Medicine in St. Louis, MO (see details below). A portion of the whole blood samples (10cc) was shipped overnight on the day of collection to a flow cytometry testing facility . The sera from the remaining blood sample was aliquoted and stored at −80°C. At each follow-up visit, anthropometric measurements, vital signs measurement, and symptom assessment for the presence of symptoms related to SARS-CoV-2 infection , general well-being , and any changes in medications, medical status, and employment were also collected utilizing the Follow-up Symptom & History Questionnaire. Telephone follow-up calls utilizing the Follow-up questionnaire were made on a monthly basis between the 60-day follow-up visit and the 12-month endpoint visit. If a subject developed symptoms potentially associated with SARS-CoV-2 infection at any point during the 12-month study period, he/she was seen in the clinic by the Infectious Disease (ID) Co-investigator (MEH), and a repeat collection of blood and nasopharyngeal biospecimens were performed for analysis, and the subject was asked to complete the Follow-up questionnaire. Subjects were asked to report the development of any potential COVID-19-related symptoms or positive SARS-CoV-2 infection testing outside of the study, as well as any symptoms potentially related to MMR vaccination. The COVID-19 vaccines (when available) were not denied to any enrollee and COVID-19-vaccinated individuals were accepted into the trial based on outcome measures that could still theoretically be met.Primary outcome measures were peripheral blood monocytic MDSCs (M-MDSC) and/or granulocytic MDSCs (G-MDSC) determined by flow cytometry from whole blood samples at baseline and again at 14, 30, and 60 days post-injection of the MMR vaccine or placebo. Other outcome measures included antibodies to measles and/or mumps post-MMR vaccination as a confirmation for a positive response to the vaccine, and SARS-CoV-2 RNA testing at baseline, 14, 30, and 60 days post-vaccination, and at any point over the 12-month period that symptoms arose. Secondary outcome measures were detection of SARS-CoV-2 antibodies (seropositive) and any evidence of infection, sepsis/lung inflammation, ICU/ventilator usage, in-patient health related co-morbidities and self-reporting mental status  over the 12-month period post-vaccination, predominantly via self-reporting utilizing the Follow-up Symptom & History Questionnaire.The study in HCW was initiated in Sept 2020 with an expected sample size of 50 (n=25/arm). The trial began with steady but slower than expected enrollment. However, in December 2020 with ~30 subjects enrolled, novel COVID-19 mRNA vaccines were given emergency FDA approval (Pfizer or Moderna) for HCW and first responders. While those with detectable antibodies to SARS-CoV-2 (evidence of previous exposure/infection) were initially excluded from our trial, we allowed COVID-19 vaccinated individuals to enroll despite the fact that the secondary outcome of COVID-19 related health status would be compromised. We also retained individuals already enrolled that subsequently chose to receive a COVID-19 vaccine. To improve the potential to monitor secondary outcomes of COVID-19-related health status, we opened enrollment to the public. Although we enrolled several more subjects, interest waned as COVID vaccines became more widely approved and available to the general public. Thus, we were unable to thoroughly evaluate the outcome of the MMR vaccine on COVID-related health issues.ClinicalTrials.gov Identifier: NCT04333732). While blood samples were not collected at baseline, a sub study was initiated to collect 20cc of blood from those administered the placebo (n=42) or MMR vaccine (n=46) in the St. Louis cohort at 6–8 months after injection of the MMR or placebo injection (NCT04646239). A portion of the blood sample (10cc) was shipped overnight to the Flow Contract Site Laboratory for MDSC analysis as per LSU protocol. All the enrolled subjects were also COVID-19 vaccinated during the period of the sub study. The demographics of the St. Louis cohort were as follows: The median age of those enrolled was 41 yrs (range 25–71 yrs) with 66% female. The ethnicity included 84% white, 3% Latino, 6% Asian, and 7% other with excellent matching between the arms.In addition to the LSUHSC cohort, our clinical trial group initiated a collaboration with investigators at Washington University School of Medicine, St. Louis, MO who were conducting a similar clinical trial in HCWs in several countries, entitled CROWN CORONATION  and G-MDSC (CD33highCD66bhighHLA-DRdim) populations were analyzed in the whole blood sample. For the MMR group, samples were evaluated at baseline, 14, 30, and 60 days post-vaccination. For the placebo group, although blood was drawn at the same time points, only the baseline and 30-day samples were evaluated for MDSC populations. In some participants (n=16) an additional sample was collected at 6–8 months to match the sampling protocol of the colleagues in St. Louis for the purpose of MDSC analyses.M-MDSCs  at baseline and several follow-up visits as a measure of vaccine efficacy.Approximately 10 ml of whole blood was collected into a Vacutainer tube containing clot activator (BD Biosciences). Blood sera were obtained by centrifugation at 1000x Nasal swabs were tested for SARS-CoV-2 RNA using RT-qPCR Rapid Detection Kit according to manufacturer’s instructions .Serum samples were tested for the presence of antibodies against SARS-CoV-2 nucleocapsid and Spike protein  by in-house ELISA assays for evidence of clinical infection or COVID-19 vaccination, respectively. RBD (Arg319-Phe541) or nucleocapsid, (0.05 micrograms per well) of wild type SARS-COV-2  were placed on Immunolon 2 plates  in 0.9 M Sodium carbonate buffer (pH 9.5), blocked  and sera added at 2-fold dilutions starting at 1/10, detected with goat-anti-human IgG (H and L) antibody (Invitrogen). Negative controls were derived from archived serum samples collected prior to 2015. End-point dilution titers were defined as the lowest dilution of sera that produced a signal greater than 3-standard deviations over the average of the negative controls.Subject data for MDSC populations, measles and mumps virus antibody titers, and SARS-CoV-2 spike protein antibody titers were recorded using means and 95% confidence intervals (CIs) for the two study groups (placebo and MMR) at each time point. in M-MDSC and G-MDSC populations between the placebo and the MMR groups at each time-point were analyzed using the Student’s t-test. The differences in the percentages of MDSC populations between baseline and the subsequent time points post-vaccination within a study group were analyzed using the paired Student’s t-test. Changes in measles and mumps virus antibody titers between a specific time-point and baseline for a study group were calculated using log2 fold changes and data analyzed using the paired Student’s t-test. In addition, comparisons in measles and mumps virus antibody titers between the two study groups at any one time point were analyzed using the Student’s t-test. To follow the normality assumption, the log2-transformed SARS-CoV-2 spike protein titers were used for further analyses. Comparisons in SARS-CoV-2 spike protein antibody titers between the MMR and placebo groups were analyzed using the Student’s t-test. In addition, the high titer status of the SARS-CoV-2 spike protein  between the two study groups was evaluated using Fisher’s exact test.The study enrolled 34 participants although only 33 were included in the analysis (15 in the placebo group and 18 in the MMR group) for the analysis (one subject voluntarily withdrew). Differences in percentages of MDSC populations in blood leukocytes between baseline and the subsequent time points post-MMR vaccination for the MMR group are shown in Baseline percentages of M-MDSCs and G-MDSCs in blood leukocytes were similar for the placebo and MMR groups  . In addiThere was one case of COVID-19 in the New Orleans cohort. The case was reported 10 months post-baseline sampling and was in the MMR group. The subject had also received a COVID-19 vaccine 11 months previously with subsequent boosters. The infection occurred during the Omicron variant outbreak (winter 2021) and symptoms reported were very mild, with only some fatigue and lethargy. Samples were taken during the infection period despite the mild symptoms. The percentage of MDSCs remained unchanged . The positive case was confirmed by SARS-CoV-2 PCR and serum antibodies for the SARS-CoV-2 nucleocapsid. No other subjects were positive for SARS-CoV-2 via PCR or serum nucleocapsid antibodies.MMR-associated serology was also evaluated at day 60 post-MMR vaccination or placebo injection compared to baseline for the New Orleans cohort. Results show that antibodies to both measles and mumps virus were increased in those administered the MMR vaccine, whereas no increases were seen in those that received the placebo injection. Antibodies to mumps virus were significantly increased at 60-days post-MMR vaccination compared to baseline (p=0.003). There was a trend for increased antibody titers to measles at 60-days post-MMR vaccination with a marginal significance (p=0.068). Group comparisons in mumps and measles virus antibody titers were also evaluated at 60-days post-injection . For mumWe also evaluated whether individuals administered the MMR vaccine developed a stronger antibody response to the subsequent administration of one of the mRNA COVID-19 vaccines (Pfizer or Moderna). For this, sixteen participants from the New Orleans cohort who were COVID-19 vaccinated after enrollment into the trial  were evaluated for differences in SARS-CoV-2 spike protein antibody titers using log2-transformed titer levels in serum. Serum samples included any/all study-associated sample collections >2 months post-COVID-19 vaccination. While there was a trend toward increased titers of SARS-CoV-2 spike protein antibody in the MMR group compared with the placebo group, this difference was not statistically significant (p=0.171) . This tr10). Under this predication large numbers of MMR-induced MDSCs would not be detected in blood until they were signaled to migrate out of the bone marrow by an infection such as SARS-CoV-2-related septic event. As no COVID-19-related sepsis cases occurred during the trial, likely mitigated in part by emergency authorization of novel COVID-19 vaccines for HCWs, there was no instance where MDSCs would have migrated from the bone marrow to be detectable in blood. The only breakthrough case of COVID-19 occurred late in the study, was likely caused by the omicron variant, and only resulted in mild symptoms.The lack of demonstrable changes in MDSC levels post-MMR vaccination were not totally unexpected, including early or later time points. As noted previously the MDSC populations comprise <1% of leukocytes in the blood. While changes in either M-MDSC or G-MDSC subsets were hypothesized for those administered the MMR vaccine, showing detectable differences would have required considerable expansion in study enrollment and/or minimal variability between individuals. Unfortunately, changes post-vaccination were relatively small with significant variability. Hence, the study lacked the statistical power to identify clear differences. Secondly, although blood is the only compartment that can be evaluated in a clinical setting for such cells, the current hypothesis is that LAV-induced MDSCs reside primarily in the bone marrow until recruited in response to an infectious insult (reviewed in article link). In addition, epidemiological data demonstrated a correlation between recent administration of the live attenuated measles-rubella vaccine  and low COVID-19 death rates compared to reported rates elsewhere within the same time frame.21 Interestingly, a recent paper detailed findings from an MMR vaccination campaign following 255 subjects in March 2020 , many whom were family members of COVID-19+ subjects. Results showed a 9–14% SARS-CoV-2 infection rate and very mild COVID-19-related symptoms in the subjects that had received the MMR vaccine.22 While these data are individually circumstantial, collectively, they strongly support potential beneficial effects of the MMR vaccine against severe symptoms associated with the unrelated SARS-CoV-2 infection.Independent of these clinical data, there remains strong corroborating circumstantial clinical evidence that recipients of the MMR vaccine just prior to or during the pandemic were largely spared from severe lung inflammation and sepsis associated with COVID-19 infection. Anecdotal evidence includes a report from the U.S.S Roosevelt which documented an outbreak of COVID-19 in 955 sailors early in the pandemic, with most reporting minor symptoms and only one hospitalization. The lack of severe COVID associated outcomes may be due to the fact that MMR vaccinations are administered to all U.S. Navy recruits , more of the individuals previously vaccinated with MMR had a stronger antibody response (high titers) to the COVID-19 vaccine-induced spike protein compared to those given the placebo . While the non-specific beneficial effects of LAVs have been related primarily to the prevalence or susceptibility to infection, the concept of LAVs enhancing responses to other vaccines is novel. Although we were not able to show statistical significance for an MMR-associated enhanced response to the COVID-19 vaccines, the trend toward a stronger response would support this novel concept.10 The original impetus for this concept came from our recently published studies using an animal model of polymicrobial sepsis.9 We discovered that systemic immunization with a live attenuated fungal strain (Candida dubliniensis) or abiotic cell wall products could protect mice against lethal sepsis .9 In these models of infection, unimmunized animals succumb to sepsis in 24–48 h while immunized animals exhibit very low sepsis scoring and eventually clear the infection with little to no mortality. Accordingly, these studies demonstrated that protection was associated with reduction in systemic proinflammatory cytokines. 9 Subsequent mechanistic studies identified Gr-1+ MDSCs as critical for protection and suppression of systemic cytokines9. In addition, the ability of the live attenuated fungal strain to induce protection correlated with the infiltration of the fungi into the bone marrow.6 Therefore, we hypothesized that interaction of the attenuated fungal strain with MDSC precursors in the bone marrow leads to training via epigenetic reprogramming. These trained MDSCs can be then activated and released upon exposure to a lethal challenge and exert suppressive effects to ablate sepsis as a novel form of trained innate immunity called ‘trained tolerogenic immunity’.10 Recognizing that severe COVID-19 cases often result in sepsis, together with circumstantial data supporting the positive effects of a recent vaccination with a live attenuated vaccine against severe COVID-19, we proposed this concept for mitigation of COVID-19 sequelae. This ultimately led to the subsequent initiation of the MMR clinical trial.15We contend that the positive effects that have been reported for the MMR vaccine preventing severe COVID-19 disease is due in part to induction of a novel form of trained innate immunity. In this context, live attenuated vaccine microbes train MDSC precursors that function to suppress the cytokine storm that is associated with severe lung inflammation and sepsis.26 However, the proposed mechanisms for protection induced by the LAVs included enhanced trained innate immunity via cytokine production such as type I interferon, or alternatively, cross-reactivity of SARS-CoV-2 virus to antibodies generated in response to the respective vaccine. This also prompted several retrospective studies evaluating correlations of prior MMR vaccinations with COVID-19-related symptoms or molecular analyses of antibodies specific to measles, mumps, or rubella for cross-reactivity to SARS-CoV-2 30. As for prospective randomized controlled trials (RCT), in Brazil, a RCT comparing MMR vs. placebo was largely completed prior to distribution of COVID-19 vaccine. Results showed that those receiving the MMR vaccine exhibited ~50% risk reduction in COVID symptoms following infection and >75% reduction in COVID-related treatment regimens.20 In Europe, a double-blinded RCT comparing BCG vaccination vs. placebo in individuals at risk for COVID-19 was initiated prior to distribution of COVID vaccines.19 The trial included volunteers >50 yrs of age and results showed that BCG-vaccinated individuals had a 68% reduction of risk to develop COVID-19 in a 6-month follow-up. Of the eight participants hospitalized for SARS-CoV-2 infection, two were in the BCG group and 6 in the placebo group. While the sample size was small in many of the studies, together they continue to support the beneficial non-specific immune effects of LAVs for unrelated infections, either by tolerogenic TII, enhanced TII, or some level of cross-reactivity. The CROWN CORONATION study is still under analysis with results pending.Several other groups also proposed or initiated clinical trials with MMR or other LAVs recognizing the clinical observations being reported.There were several challenges during the trial that were mostly related to enrollment. First, COVID vaccines became available shortly after the trial began and was distributed first to HCW. This affected both the LSUHSC cohort and the St. Louis cohort and effectively compromised the ability to use COVID health status as an outcome. In the case of the LSUHSC cohort, which had slower recruitment than the St. Louis cohort, interest in the trial waned as the COVID-19 vaccines became available. In response to this, enrollment was quickly extended to the general public who had not yet received one of the COVID-19 vaccines. However, attempts to enroll large numbers were relatively unsuccessful prior to the expanded emergency authorization of COVID-19 vaccines for the general public. We also noted that up to 50% of HCW staff in area nursing homes were reluctant to accept COVID-19 vaccines. However, they were also reluctant to participate in the MMR clinical trial, likely due to widespread misinformation and vaccine hesitancy. Other incentives were offered including off-site enrollment and compensation for time/transportation but these also failed to stimulate additional interest. Finally, prior to the funding of the trial a grass roots campaign for adult MMR vaccination had been established based on all the circumstantial evidence surrounding the positive effects of LAVs. Hence, many potential trial participants opted to get the MMR vaccine at their local pharmacy rather than enroll in the trial and risk a placebo randomization.Despite the inconclusive results for this MMR-COVID trial, the evidence remains strong for LAVs such as MMR to play a role in mitigating severe inflammation and sepsis. As additional clinical trials are initiated potentially to continue to test the hypothesis, we provide several important lessons learned from this trial that can be applied to future trials. First, since blood is the lone minimally-invasive compartment to monitor changes in MDSCs, and with percentages of MDSCs in the blood extremely low, the primary outcome for future trials should be health status that includes monitoring sepsis scoring and powered adequately for prevalence of sepsis in any given population. The MDSC evaluation should be a secondary outcome with expected increases in blood during recovery from a sepsis diagnosis. Therefore, there is no need to conduct multiple blood samplings other than baseline and potentially one interim sample for LAV (MMR) response verification. Relative to the MMR vaccine responses, our results showed a ~70% response rate in a population with a median age of ~50 yrs, Moreover, we were only able to show a clear significant increase in antibody titers to the mumps virus post-MMR vaccination compared to baseline with a trend toward an increase in antibody titers to measles virus. Hence, we recommend that a booster be given at ~30 days post-initial MMR vaccination for a stronger response. Finally, for optimal participant enrollment it is recommended to incorporate the simplest design possible without compromising the results. Because the participants for this type of trial are all healthy volunteers rather than patients with a disease/syndrome-associated , making it as easy as possible to participate will increase probability of strong enrollment. While randomized control trials are preferred, use of an experimental design that does not include a formal placebo group  may still provide informative data. An example would be testing in a geriatric population that may not consent to several blood draws, especially if randomized to the control group with little to no benefit. This does limit the blinding of the trial but likely would not compromise the results of gathering data on sepsis diagnoses in the entire population.In conclusion, while these current trials were largely inconclusive for the outcomes expected, this should not deter future trials to test the hypothesis that LAVs induce/active MDSCs that can mitigate severe inflammation/sepsis. To this end, future trials incorporating suggestions from this trial may include nursing home residents where sepsis can be common and lead to significant mortality. Currently there are no vaccines or preventive therapies for sepsis of broad microbial origin. Testing the effects of the MMR to reduce the prevalence of lethal sepsis, along with evidence for increases in peripheral MDSCs following recovery from confirmed cases, may have a significant impact on the incidence of sepsis nationwide or worldwide.1


This study identifies that almost two-thirds of patients are not compliant with CRS medications after ESS. NINA participants reported lower post-operative SNOT-22 scores compared to INA and adherent participants. Future studies should focus on educating patients to continue with medications post-operatively despite an initial improvement in symptoms.This study aimed to evaluate factors that may predispose patients to not adhere to prescribed medication after endoscopic sinus surgery (ESS) and to compare SNOT-22 scores at 0–12 months post-operatively between adherent and non-adherent patients. CRS patients who underwent ESS between 2012 and 2016 were recruited to this retrospective cohort study. Adherence was assessed through a questionnaire and review of medical notes. Ninety-four participants were included . Of those, 66% did not adhere to their prescribed post-operative CRS medication timing or dosage. The most common reason for non-adherence was improvement of symptoms (17%), followed by deterioration of symptoms (11%) and side effects (10%). Post-operative SNOT-22 scores were lowest for non-intentionally non-adherent (NINA) participants with a mean of 10.5 [95% CI: 7.47–13.5], compared to 25.0 for intentionally non-adherent (INA) [95% CI: 17.6–32.4] and 17.7 for adherent patients [95% CI: 13.7–21.7],  Medication compliance is defined as adherence to a prescribed treatment or following the instructions given by a health care practitioner, in accordance with the prescribed interval, dosage and frequency ,2,3. FaiOne in three patients with CRS are reported to have poorly controlled symptoms in primary care , and theUnfortunately, adherence to treatment regimens in CRS is persistently impaired. Usage of intranasal corticosteroids in CRS is reported to be as low as 18%, and as low as 1% for nasal douching . In deveThe objectives of this study were to evaluate factors that may predispose patients to non-adherence to prescribed medications after endoscopic sinus surgery (ESS) and to compare SNOT-22 scores 0–12 months post-operatively between adherent and non-adherent patients. A cohort study was performed to capture retrospective data on included participants. All subjects provided written informed consent. There was no reimbursement or incentive for participation in this study.Adult patients over 18 years of age who underwent ESS for CRS at James Paget University Hospital between 2012 and 2016 were considered for inclusion if they met the CRS diagnosis criteria outlined in the European Position Paper on Rhinosinusitis and Nasal Polyps (EPOS 2012). Participants all had a >12-week history of nasal congestion and/or nasal discharge along with hyposmia and/or facial pressure/pain and confirmation of disease on endoscopy and/or CT scan. All participants consented to undergo ESS as part of their treatment pathway.Rare or complex sinus conditionsCRS secondary to systemic disease such as cystic fibrosis and granulomatous disease Suspected malignancyPregnant or lactating womenImmunodeficiency states including HIV and selective or multiple antibody deficiency statesInability to give consent or to understand and comply with study instructionsExclusion criteria were as follows:Data extracted included age, gender, ethnicity, aspirin and non-steroidal anti-inflammatory drug (NSAID) sensitivity status, pre-operative Lund-Mackay CT scores and pre- and post-operative SNOT-22 scores . A questionnaire for participants was created to assess adherence to prescribed treatment and to assess whether patients noted a positive or negative impact of these treatments on their health. All patients were asked the same 25 questions regarding their demographic characteristics, smoking and allergy status, family and CRS history, comorbidities and type of treatment prescribed, including their perceptions of treatment and adherence . QuestioData were extracted from the hospital electronic medical record system and entered into a standardised collection form. All clinical notes for relevant visits were reviewed. Any missing data from the questionnaire were completed in accordance with participants’ medical notes. If treatment information was not available it was defined as not taken or not useful. All eligible patients that attended our institution for ESS between 2012 and 2016 were approached twice for inclusion, independent of age, sex, ethnicity or post-surgical results. After this, they were considered non-responders. All responses were verified objectively against the patient’s medical records, to improve the accuracy of data on medication usage and adherence as far as possible. Cochran’s equation was used to estimate the sample size . For thiThe study population was divided into three different groups according to their reported adherence with prescribed medication: adherent, non-intentionally non-adherent (NINA) and intentionally non-adherent (INA). INAs were defined as participants who reported stopping their CRS medication due to side effects, worsening symptoms or improvement of symptoms, and who were also identified as non-adherent from their medical records. This was confirmed as participants who answered ‘yes’ to questionnaire items 21, 22 or 23. NINAs were defined as participants who reported forgetting to take their CRS medication or who reported not always taking their medication at the correct time, and who were also identified as non-adherent from their medical records. This was confirmed as participants who answered ‘yes’ to questionnaire item 19 or ‘no’ to item 20. Adherence was at 12 months following surgery or at the time of last follow-up, whichever was later.t-test where the distribution was normal; otherwise, we used a Mann–Whitney test. In univariate analyses, categorical variables were compared between adherent and non-adherent using chi-squared tests. Adherence was estimated as the percentage of patients who were fully adherent to the drug regime prescribed. The 95% CIs were estimated to present uncertainty in the estimate. The distribution of demographic and clinical variables was examined among the different adherent groups. A p-value less than 0.05 was considered statistically significant.Statistical analysis was performed using RStudio . For continuous variables, results were expressed as means and standard deviations or in box plots. Data distribution was tested for normality using the Shapiro–Wilk test. For dichotomous variables, frequencies and percentages were calculated. For continuous variables, we compared the means between adherent and non-adherent groups using a Student’s A total of 158 patients who underwent ESS during the study period were approached. Of these, 94 participants (59.5%) chose to participate in the study. The characteristics of our study population are shown in Full adherence to their drug regimen was reported by 32 patients (34%). 31 participants (33%) unintentionally did not adhere to their prescribed treatment time or dosage  and 31 participants (33%) stopped medical therapy deliberately . The most common reason for INA was improvement of symptoms, followed by deterioration of symptoms despite medication usage and then side effects from medication . Figure The association of demographic and clinical characteristics of participants to adherence status is shown in Post-operative SNOT-22 scores were lowest for NINAs, with a mean difference of −14.52 (95% CI: −23.48–−5.56) compared to INA, and −7.17 (95% CI: −15.96–1.63) compared to adherent patients. Overall, post-operative SNOT-22 scores improved at 12 months in all groups but this difference was not statistically significant . NINAs hAdherence to medical treatment post-operatively is an important component of CRS patient management following ESS. Almost two-thirds of patients in this study were not adherent to medications, suggesting that post-surgical adherence in patients with CRS is a target for improvement in clinical practice. In order to do so, we must understand and address any factors that drive this poor adherence.In our study, better quality of life outcomes after ESS were achieved by patients with non-intentionally non-adherent behaviour. This may be attributable to patients forgetting to take their treatment due to a low symptom burden, resulting in fewer prompts and reduced motivation to adhere to treatment. Our study assessed adherence at a minimum of 12 months post-operatively. A longer follow-up duration may have revealed a subsequent deterioration in SNOT-22 scores for non-adherent patients, so future studies should explore the difference in outcomes between adherent and non-adherent patients beyond 12 months post-operatively. Wahid et al. quantified medication usage by measuring direct medication costs in a case-control study of 139 CRS patients. They found an inverse relationship between medication usage and health-related quality of life, supporting the paradoxical relationship between adherence and SNOT-22 scores seen in our study .The most common reason in this study for intentional non-adherence was symptom improvement. This highlights the need to counsel CRS patients about their disease course and emphasise that non-adherence to treatment may lead to recurrence of symptoms later on, despite initial post-operative improvement in quality of life. This was a single-centre study, which may not provide a representative sample of the wider CRS population. For example, the average age for participants in this study was 60 years, which was higher than in other studies of CRS patients ,17,18,19Although multiple studies have explored adherence in asthma, COPD and other chronic respiratory illnesses ,21,22,23Previous studies have found lower age to be associated with non-adherence to post-ESS medication ,36,37,38Similarly, Nabi et al. found that pre-operative SNOT-22 scores predicted adherence to nasal sprays 12 months after ESS in a study of 60 patients . They foStudies of patients suffering from other chronic diseases such as hypertension have identified that intentional non-adherence is driven by patient beliefs about their illness or treatment, whilst unintentional non-adherence is more closely related to demographic factors such as age . UnderstFuture prospective studies are required to further investigate predictors of medication adherence post-ESS. Multi-centre studies with follow-up beyond 12 months, using standardised measures such as the SNOT-22 score, are essential to explore the relationship between adherence and post-operative quality of life. The ongoing MACRO randomised controlled trial aims to follow participants for up to five years after interventions that include endoscopic sinus surgery . This wiThe advent of biologic drugs in the United Kingdom for CRS patients with severe comorbid asthma warrants an improved understanding of the factors which impair medication adherence. Although the use of biologic drugs for CRS is currently limited to a small number of patients, candidates for biologic therapy may need to demonstrate adherence to courses of oral corticosteroids . Some paThis study identifies that almost two-thirds of patients are not compliant with CRS medications after endoscopic sinus surgery, with symptom improvement being the most common reason for INA. NINA participants reported lower post-operative SNOT-22 scores compared to INA and adherent participants. Future studies should focus on exploring quality of life outcomes beyond 12 months post-surgery and on educating patients to continue with medications post-operatively despite an initial improvement in symptoms.


Men who have sex with men who ever injected drugs (ever MSM-IDU) carry a high hepatitis C virus (HCV) burden. We estimated whether current HCV testing and treatment in San Francisco can achieve the 2030 World Health Organization (WHO) HCV elimination target on HCV incidence among ever MSM-IDU.A dynamic HCV/HIV transmission model among MSM was calibrated to San Francisco data, including HCV antibody  and HIV prevalence  among ever MSM-IDU. MSM had high HCV testing  and diagnosed MSM had high HCV treatment . Following coronavirus disease 2019 (COVID-19)–related lockdowns, HCV testing and treatment decreased by 59%.Among all MSM, 43% of incident HCV infections in 2022 were IDU-related. Among ever MSM-IDU in 2015, HCV incidence was 1.2/100 person-years . Assuming COVID-19–related declines in HCV testing/treatment persist until 2030, HCV incidence among ever MSM-IDU will decrease by 84.9%  over 2015–2030. This decline is largely attributed to HCV testing and treatment . Slightly greater decreases in HCV incidence (94%–95%) are projected if COVID-19 disruptions recover by 2025 or 2022.We estimate that HCV incidence will decline by >80% over 2015–2030 among ever MSM-IDU in San Francisco, achieving the WHO target. Modelling projects that HCV incidence will decrease by ≥80% over 2015–2030  among men who have sex with men and who inject drugs in San Francisco, largely due to high HCV testing and treatment. The development of highly curative direct-acting antiviral (DAA) therapy for hepatitis C virus (HCV) infection prompted the World Health Organization (WHO) to call for global elimination of HCV as a public health problem by 2030 , includiTo achieve the WHO elimination goal, it is essential to scale-up HCV testing and treatment . With 3 Building upon prior multisector collaborative efforts to reduce HIV, San Francisco introduced the first city-focused strategic plan to eliminate HCV in the United States. Started in 2016, End Hep C SF is an initiative of the public health department, research university, and community partners, which collaborates around prevention, testing, and care activities, prioritizing communities hardest hit by HCV, including MSM and people who use drugs . LocallyWe developed a dynamic deterministic HCV and HIV transmission model among MSM in San Francisco, including transmission through IDU and sexual risk behaviors. MSM are stratified by HCV and HIV infection status and history of IDU . SpecifiIndividuals enter the model at age 15 years susceptible to HCV and HIV with no IDU history and not on HIV preexposure prophylaxis (PrEP). Entry into the model is set to balance (1) injection-related mortality (recent MSM-IDU only), (2) other non-HIV/HCV related death, and (3) aging out at age 65 years. Ages 15 and 65 years were set to reflect the onset and end of sexual activity . MSM alsHIV and HCV transmission through IDU only occurs between recent MSM-IDU contacts, while sexual HIV and HCV transmission can occur between any MSM contacts. Mixing to form these contacts is assumed to occur randomly. Based on data indicating similar numbers of sexual partners and condom use by injecting status , page 3,The risk of sexual HCV transmission depends on the chronic HCV prevalence among all MSM, while injecting HCV transmission risk depends on the chronic HCV prevalence among MSM-IDU. HCV infectivity is elevated among MSM who are HIV-HCV coinfected. Once infected with HCV, a proportion of MSM spontaneously clear infection, while the remainder develop undiagnosed chronic infection. Chronically infected MSM can undergo HCV testing, and once diagnosed, can initiate treatment, which can result in a sustained virological response (SVR). Treatment efficacy and duration depend on the type of treatment (classified as pegylated interferon or DAAs). Those who do not achieve SVR remain chronically infected. Cured individuals can become reinfected, diagnosed, and treated again at the same rates related to their risk groups, regardless of prior infection and treatment history (no US restrictions exist on retreatment). In the overall MSM population, HCV reinfection is higher than primary HCV due to heterogeneity in HCV risk by IDU status.The risk of injecting and sexual HIV transmission depends on the HIV prevalence among MSM-IDU and all MSM, respectively. From 2010, MSM can increasingly initiate PrEP and can cease PrEP at a fixed rate. Once infected with HIV, MSM not on PrEP are assumed to have undiagnosed infection. These MSM can undergo HIV testing, becoming diagnosed, with a time-varying proportion of diagnosed MSM receiving antiretroviral therapy (ART). MSM on ART have reduced HIV infectivity. Susceptible MSM on PrEP have a lower risk of HIV acquisition , 26 compThe model was parameterized and calibrated to San Francisco data, based on published literature and data analyses done on NHBS-MSM, SFAF-S, and medical record data at the community health center Strut . Key parThe model was also calibrated to several other data, including HCV antibody and HIV prevalence, proportions of MSM that recently/ever injected, and PrEP use . CalibraWe used the best available evidence locally , 43, 44 We evaluated the impact of ongoing and future HCV testing and treatment uptake on HCV transmission by estimating the relative reduction in HCV incidence over 2015–2022 or 2015–2030, and estimated the year when the relative reduction was 80% since 2015 (WHO target). Due to limited data on HCV and HIV service recovery following the COVID-19 pandemic, we modelled 3 possible recovery scenarios, and an additional scenario with increased high-coverage needle and syringe program (HCNSP) use:Status quo (SQ): No recovery. HCV and HIV testing and treatment and PrEP use following COVID-19 pandemic remain at pandemic levels.Scenario 1: Slow recovery. HCV and HIV testing and treatment and PrEP use return to prepandemic levels by end of 2025.Scenario 2: Rapid recovery. As scenario 1 but with recovery by end of 2022.Scenario 3: Rapid plus. As scenario 2 and with HCNSP scaled-up from 74%  to 100% coverage of recent MSM-IDU over 2022–2026, and sustained thereafter, with HCNSP reducing HCV and HIV acquisition risks by 56%  and 42% The relative reduction in HCV incidence for these scenarios were compared to 2 counterfactual scenarios, one with no HCV testing and treatment over 2015–2030 and another with no scale-up in HCV testing and treatment in 2015. These were used to determine the percentage of the relative reduction in HCV incidence attributed to all HCV testing and treatment, or to the scale-up in HCV testing and treatment since 2015, respectively. We also evaluated the proportion of new HCV infections averted over 2023–2030 for scenarios SQ and 1–3, compared to an additional counterfactual where no testing or treatment occurred over 2023–2030. Lastly, we also projected what decrease in HCV incidence would have occurred if the COVID-19 pandemic had not occurred.In sensitivity analyses, we explore the relative reduction in chronic HCV prevalence, as it could be a reliable alternative to validating HCV elimination .A linear regression analysis of covariance (ANCOVA) was done to determine which parameter uncertainties contribute most to variability in the relative reduction in HCV incidence over 2015–2030 for scenario SQ. The proportion of each model outcome's sum-of-squares contributed by each parameter was calculated to estimate the importance of individual parameters to overall uncertainty. All simulations were performed using Matlab R2020a.The model calibrated and validated well to data . It projIn 2015, we estimated HCV incidence to be 1.2 per 100 person-years  among ever MSM-IDU  and consFollowing DAA scale-up in 2015, the estimated rates of HCV diagnosis and HCV treatment if diagnosed among ever MSM-IDU were 39.3/100 person-years (30.0–47.7) and 29.4/100 person-years , we project that HCV incidence among ever MSM-IDU will decrease by 84.9%  over 2015–2030 . Most ofIf COVID-19–related service disruptions recovered by the end of 2025 (scenario 1/slow recovery) or 2022 (scenario 2/rapid recovery), then HCV incidence is projected to decrease by 93.9%  or 95.0% , respectively, over 2015–2030 among ever MSM-IDU . In thesIn contrast, if HCV testing and treatment ceased over 2023–2030, HCV incidence starts to increase and only a 75.5% decrease  occurs over 2015–2030 among ever MSM-IDU, with only 20% of model runs projecting an 80% decrease in incidence by 2030 . Lastly,For all scenarios, results are similar for all MSM groups . ChronicCompared to a counterfactual in which no HCV testing and treatment occurs over 2023–2030, continuing these services while assuming no recovery in services following the COVID-19 pandemic (SQ) would avert 20%  of incident HCV cases over 2023–2030 among ever MSM-IDU . ConversOur ANCOVA analyses indicate that variability in the relative reduction in HCV incidence achieved over 2015–2030 is mostly (74.1%) due to uncertainty in the scale-up of HCV treatment in 2015 .Our model suggests that, although IDU is only reported by a minority of MSM (13%), it accounts for nearly half (43%) of all incident HCV cases in this population and only 2.8% of HIV infections. We also found that HCV incidence has already decreased considerably (approximately 70%) over 2015–2022 among ever MSM-IDU in San Francisco, with this decline being largely (approximately 86%) attributed to the high levels of testing and treatment over this period. Trajectories of progress towards the WHO HCV incidence target seem modestly influenced by how fast disruptions in services recover following the COVID-19 pandemic, with most scenarios and projections suggesting an 80% reduction in HCV incidence over 2015–2030. Despite considerably higher HCV burden among ever MSM-IDU compared to MSM non-IDU, we project similar trajectories of change in HCV incidence for both. Going forward, it is essential that existing HCV interventions are sustained, as removing them would cause an increase in HCV incidence and prevent us from achieving the WHO target.To our knowledge, this study is the first to consider IDU as a driver of HCV transmission among MSM in HCV transmission models, and to evaluate whether HCV testing and treatment services are reaching this high-risk group. It is also the first MSM-focused model of the HCV epidemic in the United States. We note several limitations. First, the independent contribution of IDU, relative to sexual risk, in driving HCV transmission among MSM is difficult to quantify and could have been misestimated in our study, even though it was informed by detailed local epidemiological data. IDU and sexual risk practices are self-reported, thus prone to misclassification due to recall errors and because they represent sensitive information. Furthermore, while we did not find evidence of a difference in sexual risk practices among MSM in our study by IDU status , page 3,Fifth, our model does not capture new HCV infections among MSM in San Francisco acquired through contacts with other populations. We did not model partnerships with people who inject drugs (PWID) due to evidence of limited interaction with this population . Recent Our findings indicating a pronounced decrease in HCV incidence among ever MSM-IDU and MSM non-IDU likely reflect San Francisco's multilayered efforts to increase HCV service access for high-risk populations through integration of services. San Francisco has long been a leader in the prevention and treatment of HIV for MSM, and several HIV-focused programs have added HCV testing and treatment into routine sexual health care. It developed the first city-focused strategic plan to eliminate HCV  and an HWe note that results are unlikely to be representative of the wider MSM-IDU and MSM communities in the United States. Few US states have implemented HCV elimination plans and MSM elsewhere often have lower access to services compared to those based in San Francisco. Furthermore, additional research is needed to assess whether other populations with overlapping risk behaviors are adequately served by HCV prevention and treatment services. We recently reported that, compared to MSM-IDU, PWID-MSM in San Francisco present greater socioeconomic disadvantage, have higher HCV prevalence, and lower engagement in services , suggestIn conclusion, despite a considerably higher HCV burden among ever MSM-IDU relative to MSM non-IDU in San Francisco and recent reductions in HCV testing and treatment due to the COVID-19 pandemic, our model suggests that the WHO HCV incidence target will be achieved in both populations. This finding can be attributed to the high intensity of HCV testing and treatment among MSM in this city, prior to COVID-19 disruptions, and illustrates what can be achieved by creating a robust HCV elimination program that prioritizes vulnerable populations.The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.jiad169_Supplementary_DataClick here for additional data file.


The Advent of Artificial Intelligence (AI) has led to the use of auditory data for detecting various diseases, including COVID-19. SARS-CoV-2 infection has claimed more than six million lives to date and therefore, needs a robust screening technique to control the disease spread. In the present study we created and validated the Swaasa AI platform, which uses the signature cough sound and symptoms presented by patients to screen and prioritize COVID-19 patients. We collected cough data from 234 COVID-19 suspects to validate our Convolutional Neural Network (CNN) architecture and Feedforward Artificial Neural Network (FFANN) (tabular features) based algorithm. The final output from both models was combined to predict the likelihood of having the disease. During the clinical validation phase, our model showed a 75.54% accuracy rate in detecting the likely presence of COVID-19, with 95.45% sensitivity and 73.46% specificity. We conducted pilot testing on 183 presumptive COVID subjects, of which 58 were truly COVID-19 positive, resulting in a Positive Predictive Value of 70.73%. Due to the high cost and technical expertise required for currently available rapid screening methods, there is a need for a cost-effective and remote monitoring tool that can serve as a preliminary screening method for potential COVID-19 subjects. Therefore, Swaasa would be highly beneficial in detecting the disease and could have a significant impact in reducing its spread. The virus transmission begins once a healthy individual is exposed to the respiratory droplets originating from an infected person. The average incubation period for the disease symptoms to manifest varies from 2–14 days2. The early symptoms comprise dry cough, fever, fatigue, and loss of smell and taste. Some patients may experience shortness of breath, cardiac issues, and pneumonia-like symptoms, which can ultimately result in death. Many people are also experiencing post-covid acute symptoms which affect their overall health status4. The containment of COVID-19 outbreaks became very difficult because of the unavailability of quick and effective pre-screening techniques. Most of the viral and serological testing methods available are very expensive, time-consuming, require technical expertise and are not always reliable, especially in detecting the new SARC-CoV-2 variants6.The SARS-CoV-2 infection first surfaced at the end of December 2019, affecting nearly 769 million people across the globe7. Physiologically, coughing is responsible for removing any obstruction in the airways via explosive expulsion of the air8. While this serves some benefit to the patient, it often results in the dissemination of airborne infectious aerosols10. That being said, cough is very useful in diagnosing and detecting various disease processes. Literature suggests that glottis movement during a cough can be used to differentiate between various respiratory conditions including pertussis, bronchitis and asthma12. With cough being such a prominent and unique aspect of multiple disease processes, more reports suggest its possible use in detecting Covid-1914. Cough sound analysis has proven effective for diagnosing diseases like tuberculosis (TB). However, the application of AI cough analysis for COVID-19 detection has not been fully explored. Further research and validation are necessary to ascertain its accuracy and potential usefulness in the screening of COVID-19.Cough is a common symptom of COVID-1916. In fact, various groups have highlighted the benefits of these machine-learning techniques in detecting COVID-19 over pre-screening methods such as, RT-PCR21. While there is significant interest and investigation in developing an AI-based model for Covid-19 detection, no current tools are available in the market to our knowledge. This is largely due to the difficulty in the acquisition of cough sound data from crowdsource open access datasets and lack of proper technical/clinical validations to scale up these tools for mass screening of COVID subjects23.In the past few years, substantial strides have been made exploring the utility of AI in medicine—particularly in using machine learning techniques to analyse cough sounds for multiple respiratory pathologiesOur research provides a comprehensive methodology for developing, validating, and testing the “Swaasa AI platform” as a rapid Point of Care tool to screen and prioritize potential COVID-19 cases. This Software as a Medical Device (SaMD) evaluates SARS-CoV-2 infection through analysis of a 10-s cough sound recording. Our study is distinct in that it includes cough recordings from COVID-19 positive patients, healthy subjects, and patients with various respiratory conditions. We conducted a feature analysis of known COVID-19 and non-COVID-19 coughs and discovered a distinct signature in COVID-19 coughs that a machine learning model can identify. Unlike prior research that relied on crowdsource cough databases for data collection, this study uses data acquired in a clinical setting from a diverse and comprehensive background bolstering its utility as a robust model. We trained two parallel models, a CNN model along with a FFANN model and merged their final layers to increase accuracy. Our model achieved 96% accuracy on the test dataset during derivation and 76% during validation phase, with a positive prediction value of 70.73% in real-time scenarios. This cost-effective, non-invasive screening tool is valuable for detecting COVID-19 cases. However, further validation studies involving larger and more diverse populations are necessary to improve accuracy and applicability globally.2*P(1 − P)/d2, involved selecting values for Z (level of confidence), P , and d (precision corresponding to the effect size)24. According to the sample size calculation, a total of 1152 subjects was appropriate for validating if the device could detect COVID-19 with a 90% sensitivity on considering a 2.5% error for a 95% confidence interval (CI) and a prevalence of 0.75%. Considering all the conditions, we pooled the data collected from two individual clinical trial studies for developing and evaluating our model. We considered a total of 1052 participants in the present study, out of which 62% were controls. Control subjects comprise healthy individuals as well as subjects who were displaying various respiratory disease symptoms but were negative for COVID-19 via RT-PCR. To prevent any potential bias in the model, we ensured that an equal number of COVID-19 and non-COVID-19 subjects were used during the training process.To determine the sample size for our study, we employed a formula that incorporates various assumptions about key variables. The formula, n = Z25. Next, the cough sound was collected by a trained health care personnel using a smartphone (Android or iPhone). The participants were instructed to take a deep breath and cough 2–3 times until the recording stopped i.e., 10 s. We also provided specific instructions to each participant, including sitting comfortably in a quiet place, holding the recording device 4–8 inches away from their mouth at 90-degree angle with their face. We also applied a noise reduction algorithm to filter out background noise in the audio recordings. Valid coughs were detected using a cough/non-cough classifier, which screened the dataset for coughs with high background noise. We also implemented several safety measures to prevent the transmission of disease, including requiring participants to wear a surgical mask during the audio recording process and cleaning the phone used for recording after each recording.The cough data was collected at Andhra Medical College, Visakhapatnam, India as a part of four individual studies entitled “Development, Validation, Pilot Deployment of an ultra-scalable technology—Swaasa AI, as an auxiliary to COVID-19 Rapid test” and “COVID-19 Cough Sound Analysis Using Swaasa Artificial Intelligence Platform”. The studies were registered under Clinical Trials Registry- India CTRI/2021/09/036,489, CTRI/2021/07/035,096, and were begun after getting the approval from the Andhra Medical College—Institutional Ethics Committee (IEC). The methodologies performed throughout the study were in accordance with the set guidelines. A duly signed written informed consent was also collected from all the enrolled subjects before starting the trial, which was followed by collection of the demographic details and the vital signs. Patients were then interviewed for the Part I of the St. George's Respiratory Questionnaire (SGRQ) and COVID-19 symptoms in order to gather their symptomsFollowing the cough sample collection, the patients were subjected to a reference standard test (RT-PCR). The inclusion criteria for the enrolment are that the Patients must be (a) male and female patients age ≥ 18 years, who were (b) recently diagnosed with COVID-19  and were (c) able to read, understand and sign the informed consent form. Whereas male and female patients age < 18 years, who were (b) on ventilators support, (c) asymptomatic patients attending isolation ward for COVID-19 testing and (d) Pregnant females were completely excluded from the current study. COVID-19 precautionary and infection control measures were followed strictly.29. Correlation-based feature selection was used to reduce the feature size from 209 to 17026. When the model is unsure if COVID-19 will be detected as yes/no, it provides an inconclusive output as shown in the block diagram Fig. In the derivation phase of the study, a machine learning-based model was developed and trained for the detection of COVID-19 using cough sounds. A total of 803 cough events were extracted from cough data collected form 252 subjects which were tested positive for COVID-19 by RT-PCR. Event extraction was carried out using the moving window signal standard deviation technique, and a cough/non-cough classifier was used to segregate the events into actual coughs and non-coughs. A total of 1946 cough events were extracted from both COVID-19 likely-Yes and No subjects. The features were extracted from both the time and frequency domain of each cough event, resulting in 209 features that includes information about age, gender, and various audio features such as Mel Frequency Cepstral Coefficients (MFCC), spectral features , chroma features, contrast features, tonnentz features, zero-crossing rate (ZCR), energy, skewness, and kurtosisIn the Clinical validation phase, the trained model was tested on 233 subjects recruited from different locations, including isolation wards and COVID testing centers. The subjects underwent both the screening test using the model and the standard RT-PCR test for COVID-19 diagnosis. The test results from both methods were compared to each other by a statistician. A consolidated test summary sheet was generated containing the results of both the standard diagnostic testing and the model's output.During the Pilot Phase, the trained model was externally validated to determine its effectiveness as a screening tool for detecting COVID-19 prior to clinical diagnosis. The sample size for this phase consisted of 177 individuals who were identified as presumptive COVID-19 cases and recruited from a peripheral healthcare center. To measure the model's effectiveness, the ratio of patients truly diagnosed as COVID-19 positive via standard diagnostic techniques to all those who were predicted to be COVID-19 positive via the model was calculated. The diagnostic performance of the model was evaluated using various metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Again, the effectiveness of the model was assessed by comparing it with standard diagnostic methods like RT-PCR, and the results were analysed using statistical methods.27 illustrates instances in which the model responded positively to a given class, whereas the red portion highlights instances in which it responded negatively. To “explain” a prediction, we refer to the display of textual or visual artefacts that give qualitative understanding of the link between the instance's components  and the prediction made by the model.The green portion of the Local interpretable model-agnostic explanations (LIME) representation28 with 95% confidence intervals. To better understand the model's performance in screening COVID-19 subjects, we also calculated the confusion matrix across the test set.A comprehensive assessment of model performance on the test set includes accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and ROC. To measure the variability of these parameters, we used the Clopper-Pearson methodCough sound data was collected from 252 COVID-19 positive subjects in the derivation phase. Data collected from 390 COVID-19 negative subjects in one of our earlier studies was also considered in this phase. Among 252 subjects, 60% were male and 40% were female, with age ranging from 18 years to 64 & above  in various propositions. A total of 1213 records data was added to various classifiers. The final confusion matrix for derivation phase is represented in Table The 252 subject data was divided into training 173) and test (79). The training data was internally divided into training and validation as required to build as well as optimize the model performance based on K-fold cross validation technique. All the 252 subject data was annotated with disease condition as COVID-19 i.e., COVID-19 likely as “yes”. For COVID unlikely, data representing other disease conditions was added from pre-existing datasets73 and teThe model was also evaluated on crowdsourced data, which includes COVID-19 Likely “yes” data from EPFL and Cambridge datasets, whereas COVID-19 Likely “no” is the in-house data. The final confusion matrix for this dataset is presented in Table The features listed in Table Extremely low spectral frequencies have been observed in conditions such as COPD and ILD as compared to asthma, which has an intermediate spectral frequency. On the other hand, we found that the spectral components are very high in diseases in which mucus accumulates in the airways and fluid accumulates in the parenchyma region. High spectral content is the distinguishing feature of COVID-19 cough from other respiratory diseases.31.Feature analysis studies of cough sounds have revealed that it could be utilized for distinguishing diseases. The cough duration and frequency distribution has been found to be unique in a specific respiratory disease, including COVID-19We compared the LIME maps of various respiratory diseases with COVID-19 which are enlisted in Table From the LIME map analysis, we can conclude that COVID-19 related cough has a unique signature. These key signatures are detected by features extracted from coughs that can be further identified and characterized by machine learning models.Out of 234 subjects participated in the validation phase, 22 were found to be COVID-19 positive and 211 COVID-19 negative by standard diagnostic methods such as RT-PCR. Results of 1 subject remained inconclusive, hence didn’t consider that datapoint. Confusion matrix for validation phase of the model is illustrated in Table A total of 183 patients were recruited for the pilot testing phase. Out of these 183 subjects, model was able to identify 82 subjects as having a likely presence of SARS-CoV-2. Out of these 82 subjects, 58 truly turned out to be COVID-19 positive with a Positive predictive value (PPV) of 70.73%. The confusion matrix for this phase is enlisted in Table The screening of COVID-19 patients by the model is time saving as compared to the currently used traditional procedures. Additionally, our model does not need any trained professional; a community healthcare worker can also perform the screening. The technician did not need any specialised equipment or supplies. The only prerequisites to complete the exam are a smartphone and a stable internet connection.32. However, the excessive cost of rapid screening diagnostic kits as well as multiple genetic variants of the virus poses a major hindrance in conducting large-scale screening operations33. Various researchers across the globe are working to find a cost-effective solution to keep a check on the rapidly mutating virus35.The sense of urgency in comprehending, detecting, and finding a cure for SARS-CoV-2 has resulted in expedited scientific progress. This progress has been instrumental in reducing disease transmission through the acquisition of crucial knowledge, the implementation of preventive measures, and the advancement of effective treatments36, including COVID-19, using computed tomography (CT), chest radiography (CXR), and even coughing pattern38. Specifically the information in the cough has been previously applied in the diagnosis and prediction of various diseases, including lung cancer, bronchitis, pneumonia, COPD, and asthma40.Machine learning (ML) has immense potential for accurate and rapid detection of various medical conditions44. We conducted the derivation phase, validation phase and pilot screening on a comparatively large cohort, whereas previous studies were performed on either smaller scale or crowdsource datasets which are highly unreliable38. In a prior study the authors extracted the MFCCs from cough recordings and fed them into a pretrained CNN model, which resulted in an AUC (Area Under the ROC Curve) of 97% with a sensitivity and a specificity of 94.2%17. Another AI-based COVID-19 cough classifier study includes the analysis of cough recorded over a smartphone, which was able to distinguish COVID-19 positive cases from both COVID-19 negative and healthy coughs. An AUC (Area Under the ROC Curve)of 98% was achieved using the Resnet50 classifier to discriminate between COVID-19 positive and healthy coughs, while to differentiate between COVID-19 positive and negative coughs an LSTM classifier was used with an AUC of 94%19. In one of the studies both coughs and breathing sounds were used to identify how distinct COVID-19 sounds were as compared to asthma patients or healthy individuals. They highlighted that how a simple binary machine-learning classifier can distinguish COVID-19 cough sounds from healthy subjects by achieving an overall AUC of above 80%41. A recent study made use of an ensemble-based multi-criteria decision-making (MCDM) method for detecting COVID-19 from cough sound data and achieved an AUC of 95%38.In this study, we developed the model by merging the final output layers of the two separate models i.e., the tabular model (training input: primary and secondary features) and CNN model (training input: MFCC spectrograms). Hence, our model provides better prediction outcome as compared to the either logical repression or CNN model used alone by other researchersOur model achieved an accuracy of 75.54% with 95.45% sensitivity and 73.46% specificity in the clinical validation phase. The pilot testing was undertaken in a real primary care setting to test the accuracy of the tool. Upon deployment as a screening and triaging tool prior to molecular testing, this model was proven statistically effective in identifying high-risk patients for confirmatory testing. In the pilot phase also, the model achieved a positive prediction value of 70.73% in a clinical setup at a tertiary care hospital.Swaasa has been specifically developed as a screening tool with the primary objective of assisting healthcare professionals in identifying individuals who may potentially have COVID-19. By harnessing the capabilities of our advanced model, our aim is to significantly improve the efficiency and precision of the screening process. This, in turn, facilitates the prioritization of individuals for further evaluation, leading to more targeted and timely interventions for those suspected of being infected with COVID-19. Our model serves as a valuable resource in enhancing the accuracy and effectiveness of COVID-19 screening efforts.


What is the most cost-effective method for measuring blood pressure among patients with hypertension: at-home self-monitoring or in-clinic monitoring?In this systematic literature review of 16 studies, at-home self-monitoring was the most cost-effective strategy long-term compared with traditional blood pressure monitoring in clinics, with 24-hour ambulatory blood pressure monitoring and at-home blood pressure monitoring combined with additional support or team-based care being more cost-effective compared with at-home blood pressure monitoring alone.These findings suggest that clinicians, hospitals, health care systems, third-party payers, and other stakeholders should prioritize at-home self-monitoring of blood pressure as the main strategy for blood pressure measurement among patients with hypertension. This systematic review examines evidence for the cost-effectiveness of at-home blood pressure (BP) self-monitoring vs monitoring in a clinical setting among patients with hypertension. Blood pressure monitoring is critical to the timely diagnosis and treatment of hypertension. At-home self-monitoring techniques are highly effective in managing high blood pressure; however, evidence regarding the cost-effectiveness of at-home self-monitoring compared with traditional monitoring in clinical settings remains unclear.To identify and synthesize published research examining the cost-effectiveness of at-home blood pressure self-monitoring relative to monitoring in a clinical setting among patients with hypertension.A systematic literature search of 5 databases  followed by a backward citation search was conducted in September 2022. Full-text, peer-reviewed articles in English including patients with high blood pressure (systolic blood pressure ≥130 mm Hg and diastolic blood pressure ≥80 mm Hg) at baseline were included. Data from studies comparing at-home self-monitoring with clinical-setting monitoring alternatives were extracted, and the outcomes of interest included incremental cost-effectiveness and cost-utility ratios. Non–peer-reviewed studies or studies with pregnant women and children were excluded. To ensure accuracy and reliability, 2 authors independently evaluated all articles for eligibility and extracted relevant data from the selected articles.Of 1607 articles identified from 5 databases, 16 studies met the inclusion criteria. Most studies were conducted in the US (6 [40%]) and in the UK (6 [40%]), and almost all studies (14 [90%]) used a health care insurance system perspective to determine costs. Nearly half the studies used quality-adjusted life-years gained and cost per 1–mm Hg reduction in blood pressure as outcomes. Overall, at-home blood pressure monitoring (HBPM) was found to be more cost-effective than monitoring in a clinical setting, particularly over a minimum 10-year time horizon. Among studies comparing HBPM alone vs 24-hour ambulatory blood pressure monitoring (ABPM) or HBPM combined with additional support or team-based care, the latter were found to be more cost-effective.In this systematic review, at-home blood pressure self-monitoring, particularly using automatic 24-hour continuous blood pressure measurements or combined with additional support or team-based care, demonstrated the potential to be cost-effective long-term compared with care in the physical clinical setting and could thus be prioritized for patients with hypertension from a cost-effectiveness standpoint. Self-monitoring at home has emerged as an effective and more convenient approach in monitoring BP levels in comparison with blood pressure measurements taken at a clinic.8 However, to our knowledge, there is no systematic review of the available literature documenting the cost-effectiveness of at-home self-monitoring BP techniques for patients with hypertension over the past decade. In this study, we aimed to systematically gather research findings in the existing literature and to identify whether self-monitoring blood pressure at home is more cost-effective than measuring blood pressure at the clinic. By presenting evidence on the comparative effectiveness of self-monitoring programs, clinicians, stakeholders, and patients can make more informed decisions about the added value of transitioning to self-monitoring BP programs.Multiple studies have explored the costs, benefits, and effects of at-home self-monitoring BP techniques compared with traditional BP measurement in clinical settings.PRISMA) and reporting guidelines for economic evaluations to conduct this study.12 Five databases were searched  in September 2022  or utilizing 24-hour continuous BP monitoring (referred to as ambulatory BP monitoring [ABPM]), which uses a portable device to automatically measure a patient’s BP at regular intervals, such as every 20 to 30 minutes over a continuous period.15Inclusion criteria were established a priori as full-text, peer-reviewed articles in English language including patients with high BP  at baseline without any restrictions on comorbidities. To ensure consistency in the study population, we excluded studies with pregnant women and children. All studies needed to include a comparative economic analysis between at-home BP self-monitoring (intervention) and traditional BP measurements in physical clinical settings . The intervention included patients self-monitoring their BP at home and transmitting measurements to a health care professional via any means . At-home self-monitoring could involve either periodically taking readings, such as twice each morning and evening during the first week of every month16 or additional pharmacist care.19 No restrictions were placed on the type of clinician, health care setting, or additional interventions studied. The outcome measures included incremental cost-effectiveness ratios (ICERs) and incremental cost-utility ratios (ICURs).20 These were obtained from studies conducting cost-effectiveness analysis (CEA) or cost-utility analysis (CUA). CEA is a method of comparing costs with respect to a health outcome measured in nonmonetary values, such as costs added per 1–mm Hg reduction in SBP.21 CUA is a method often used interchangeably with CEA and measures the health outcomes using quality-adjusted life-years (QALYs).The self-monitoring intervention could also be combined with additional behavioral interventions, such as nurse-tailored behavioral interventions22 to determine the inclusion of each study .Two researchers (M.A.H. and T.G.) independently conducted a quality assessment using the Joanna Briggs Institute Critical Appraisal Checklist for Economic Evaluations36 representing 16 distinct studies  reported all relevant costs and outcomes for each alternative identified,30 and all studies established clinical effectiveness.31 Most studies (13 [81%]) adequately described costs and outcomes, acknowledged limitations, and addressed concerns regarding the accuracy of the measurements.30 Similarly, 12 studies (75%) provided sufficient explanations about how costs and outcomes were measured.30 Differential timing was taken into account in all of the studies.31 An incremental analysis of costs and outcomes was conducted in 15 studies (94%).31 A sensitivity analysis was conducted in 15 studies (94%).30 The majority of the studies provided a comprehensive assessment of the cost-effectiveness of the intervention over the control.30 All of the studies adequately described the study setting and discussed the issue of generalizing the results to a different setting with similar characteristics.31All of the included studies had a well-defined question and a comprehensive description of alternatives.29 1 was a quasi-experimental study,31 and 8 were simulation models, of which 7 were Markov models30 and 1 was a microsimulation model.27 All interventions were compared with usual care (UC). The definition of usual care had minor differences but was generally considered as patients monitored per usual clinical care from their clinician without self–BP monitoring.31 All articles included an intervention in which patients self-measured their BP at home using an electronic device and transferred their BP results to clinicians at a different location . HBPM was classified into 3 categories37: HBPM alone, HBPM with additional support , and HBPM within team-based care (TBC), which involved more labor-intensive efforts, often combined with other behavioral interventions, such as pharmacist care19 or nurse behavioral interventions.16 Blood pressure measured over a continuous 24-hour period was referred to as ABPM28  in 1 study.28 In 9 studies , a willingness to pay threshold (WTP) to determine whether an intervention was cost-effective was mentioned. A sensitivity analysis was conducted in 15 studies (94%)30 30 .31 Of the remaining 7, all were studies with time periods ranging from 6 to 24 months.29 Of the 12 identified studies that compared the cost-effectiveness of HBPM vs UC, 5 (42%) reported the intervention as being cost-effective.31 Notably, all 5 of these HBPM interventions included either additional support30 or TBC31 alongside HBPM alone. In the single study that compared HBPM alone vs HBPM with additional support vs UC, HBPM with additional support was the most cost-effective option, with the reported ICER of HBPM alone vs usual care being £3035 and the reported ICER of HBPM alone vs HBPM with additional support being £17 424 at a WTP threshold of £20 000 per QALY gained.13Of the 16 identified studies, 9 reported that an at-home BP monitoring intervention (HBPM or ABPM) had an ICER within the WTP thresholds, indicating the intervention as cost-effective compared with clinical UC.28 Of 8 simulation models using a time period of 10 years or more, all 8 reported that either HBPM or ABPM were more cost-effective than UC30  to be cost-effective over usual care, particularly long term. HBPM was also found to be cost-effective when combined with additional support or within TBC compared with HBPM alone. For studies comparing HBPM alone with UC and with ABPM, ABPM was found to be the most cost-effective method.40 However, our findings suggest that at-home BP self-monitoring interventions, particularly ABPM and HBPM with additional support or TBC, exhibit superior incremental gains in the long run compared with UC but may not demonstrate well-justified costs given short-term differential outcomes over periods of 6 to 24 months. This is not surprising given the high upfront fixed costs  to implement an at-home self-monitoring intervention and the clinical course and time required to achieve substantially improved health outcomes and to avoid adverse events related to uncontrolled BP. Combined, our findings suggest that clinicians, hospitals, health care systems, third-party payers, and other stakeholders should consider the long-term incremental benefits and improvements in patients’ BP, quality of life, and reductions in adverse outcomes, which might well justify both the upfront and longer-term costs of more resource-intensive at-home self-monitoring methods over traditional usual care and at-home self-monitoring only. The documented cost-effectiveness of BP self-monitoring at-home with additional support or TBC aligns with previous studies that conducted reviews and synthesized costs and benefits of self-monitoring methods across various diseases and settings.37The clinical effectiveness of at-home self-monitoring is well-documented in most of the retrieved studies, even among those that used trial time periods of 6 to 24 months and showed improvements in either BP control or reductions in mean BP levels.41 It is currently estimated that 17.1 million adults in the United States have masked hypertension.27 This is crucial because one-time or limited office measurements, despite being the traditional method, may vary considerably due to different techniques and methods used, particularly in the presence of a physician or nurse.42 Hence, at-home self-monitoring might be more accurate and allow tailored interventions early on, which are critical to control BP and mitigate the associated risks of masked hypertension or prevent unnecessary treatment uptake due to white-coat hypertension. At-home self-monitoring also has the potential to improve patient and clinician satisfaction, particularly in terms of the convenience of recording and sharing BP measurements from home vs going to the clinic. Moreover, self-monitoring at home facilitates easier communication with the health care professional when needed, particularly if coupled with additional behavioral interventions or additional support and resources, which allow patients to receive timely advice during consultations and enable the health care team to gain a better understanding of the patient’s condition.31 This can thereby increase patient motivation to monitor BP, which would consequently improve the care provided by enabling the health care professionals to better tailor their treatment approach and ultimately enhance patient outcomes.43At-home self-monitoring, particularly ABPM, might further enable the detection of masked and white-coat hypertension. These terms refer to the detection of elevated BP in a clinical setting (known as white-coat hypertension) and low BP using HBPM or ABPM, or vice versa (known as masked hypertension).Although this study was based on a systematic literature search and the findings are reported and synthesized in a robust manner, we note that the detailed comparison of many study results and their generalizability across different perspectives and settings remains challenging, particularly due to high variability and inconsistency in reported costs. Almost all the studies included in our review did not account for indirect and intangible costs, such as productivity losses or patient out-of-pocket costs. In addition, given the limited number of studies found in this systematic review that used a societal perspective, future work is needed to conduct cost-effectiveness analyses and evaluations across the various types of BP measurement methods from the societal perspective and incorporate indirect and intangible patient-level costs. Including such costs might significantly affect the incremental cost differences across competing alternatives and thus findings and future recommendations. Along these lines, we did not find any studies comparing ABPM with HBPM with additional support or TBC—all 3 of which were found to be more cost-effective than UC and HBPM alone in separate analyses. Future studies might consider conducting cost-effectiveness analyses with such comparisons. Finally, we note that patient experiences and preferences related to the differences between various home-based care and monitoring systems vs traditional clinical based care can critically affect patients’ adherence and, in turn, prevention, and timely treatment initiation and should thus also be considered when determining the WTP pay for such interventions.22 and the representation of the results in tabular summaries. In addition, this is the most up-to-date study that systematically gathers and synthesizes the available literature on the cost-effectiveness of at-home self-monitoring for patients with hypertension, to our knowledge.This study has several strengths, including a thorough systematic review, data collection, dual independent review of the processes at all stages, utilizing recommended guidelines for conducting the systematic review,Nevertheless, this study also has some limitations. First, most studies included a single stakeholder perspective and thus might not be generalizable. Second, the reported costs across the studies varied due to the types of cost data being included. The details of types of costs included were not consistently provided, making it challenging to compare findings among different studies. In addition, most studies utilized different definitions of cost inputs, with medical and program costs defined differently. More research is needed to further evaluate the cost-effectiveness of self-monitoring BP at home in comparison with usual care using longer time periods with costs reported in consistent and clear manner and across multiple perspectives.In this systematic review, at-home blood pressure monitoring demonstrated the potential to be cost-effective over the long-term in comparison with the more usual clinical patient care. Compared with HBPM alone, ABPM and HBPM with additional support or TBC were also found to be more cost-effective. Future work is needed to compare these alternatives directly from a cost-effectiveness standpoint and to provide clinicians, stakeholders, and patients with more evidence to prioritize specific home-based BP programs.