Introduction. It is known that bacterial infections represent a common complication during viral respiratory tract infections such as influenza, with a concomitant increase in morbidity and mortality. Nevertheless, the prevalence of bacterial co-infections and secondary infections in critically ill patients affected by coronavirus disease 2019 (COVID-19) is not well understood yet. We performed a review of the literature currently available to examine the incidence of bacterial secondary infections acquired during hospital stay and the risk factors associated with multidrug resistance. Most of the studies, mainly retrospective and single-centered, highlighted that the incidence of co-infections is low, affecting about 3.5% of hospitalized patients, while the majority are hospital acquired infections, developed later, generally 10–15 days after ICU admission. The prolonged ICU hospitalization and the extensive use of broad-spectrum antimicrobial drugs during the COVID-19 outbreak might have contributed to the selection of pathogens with different profiles of resistance. Consequently, the reported incidence of MDR bacterial infections in critically ill COVID-19 patients is high, ranging between 32% to 50%. MDR infections are linked to a higher length of stay in ICU but not to a higher risk of death. The only risk factor independently associated with MDR secondary infections reported was invasive mechanical ventilation , but also steroid therapy and prolonged length of ICU stay may play a pivotal role. The empiric antimicrobial therapy for a ventilated patient with suspected or proven bacterial co-infection at ICU admission should be prescribed judiciously and managed according to a stewardship program in order to interrupt or adjust it on the basis of culture results. Streptococcus pneumoniae and Haemophilus influenzae, as it had already been described during the previous viral epidemic infections [Whether critically ill patients affected by coronavirus disease 2019 (COVID-19) are at higher risk of bacterial infections has already been a matter under discussion. The main concern is related to the time of infection development, whether early at admission with SARS-CoV-2 infection or later during the intensive care unit (ICU) stay, which is indeed per se a risk factor of developing hospital acquired infections. Bacterial infections are commonly reported at admission in cases of viral respiratory tract infection such as influenza and its incidence is highly variable: around 20–30% of patients are diagnosed with severe influenza . Therefofections ,6,7. MosIt is not well understood which is the main underlying mechanism of SARS-CoV-2 infection in determining the pathophysiology that drives to over-infections. Regarding bacterial infections, one of the hypotheses is that the viral infection causes a direct damage of the lower airway epithelial cells associated with an impaired mucociliary clearance that facilitates bacterial binding to cell surfaces . A similIt is also reported that SARS-CoV-2 directly infects human gut enterocytes with resulting disruption of the gut barrier. This might increase the intestinal permeability and facilitate bacterial translocation, one of the most important risk factors for the development of blood stream infections (BSI) . The hypMoreover, there is an increasing number of studies reporting a probable or possible correlation between fungal and SARS-CoV-2 infections. Patients with acute respiratory distress syndrome have an increased risk of developing invasive aspergillosis, even in the absence of underlying immunocompromised conditions ,18. GalaThe simultaneous spread of two viruses clearly emphasizes the importance of screening for other clinically important co-circulating respiratory pathogens. Influenza AH1N1 co-infection was associated with increased severity of clinical manifestations and ICU admission.In the present review, we tried to understand more clearly the role of secondary bacterial infections in critically ill patients affected by COVID-19 and describe the possible risk factors and mechanisms that contributed to increase the incidence of multidrug resistant (MDR) pathogens, during SARS-CoV-2 pandemic.Enterococcus faecium, Staphylococcus aureus, Acinetobacter baumannii, carbapenem resistant Enterobacterales. Additional information on potential risk factors for secondary infections and multi-drug resistance, such as steroids administration, and broad spectrum antibiotic empirical therapy at admission, were extracted.We performed a literature search for the present study on the lines of search for a narrative review, but with some structural features of systematic review methodology as reported in Carbapenem resistant enterobacterales (CPE) and Pseudomonas aeruginosa  and of the Center for Disease Control and Prevention (CDC), MDR infection is described as an acquired infection non-susceptible to at least one agent in three or more antimicrobial categories [Klebsiella pneumoniae (CR-Kp) [The extensive use of broad-spectrum antimicrobial drugs during the COVID-19 outbreak might have contributed to the selection of pathogens with different profiles of resistance. All microorganisms such as tegories . An incitegories ,36,38,45 (CR-Kp) . They re (CR-Kp) . Further (CR-Kp) ,49,50. AGram negative bacteria; the whole sample of patients described had an MDR infection and the majority of infections reported were caused by CPE (46%). Moreover, nearly the whole studied population was on an empiric antibiotic therapy upon admission (97%) compared to the study by Karulli, and 35% of them received steroids [Klebsiella pneumoniae and Pseudomonas aeruginosa. In the latter, differently from the two previous studies, patients were younger and few of them received steroids (4.7%). The only risk factor independently associated with MDR secondary infections was the invasive mechanical ventilation  [Staphylococcus Aureus (MRSA), vancomycin-resistant Enterococcus faecium and carbapenem resistant Acinetobacter baumannii [Enterococcus spp., with a higher rate of mortality (49% vs. 26%). Moreover, they observed that patients who developed BSI during their ICU stay had a higher sequential organ failure assessment (SOFA) score , but they did not report whether their patients were on an empirical antimicrobial therapy at ICU admission and if they received steroids [2/FiO2 ratio < 100 . The results confirmed the median time for the first secondary BSI at 13 days from hospital admission with a prevalence of Gram-positive among the isolates (Staphylococcus epidermidis and Enterococcus faecium), while at 16 days for the first pLRTI, mainly caused by Gram-negative MDRO . The authors observed an increase of antimicrobial resistance compared with an historical cohort of patients before the SARS-CoV-2 outbreak, with a higher incidence rate of Gram negative MDRO among BSIs, mainly due to Acinetobacter baumannii, while among Gram-positive isolates there was an increase of vancomycin-resistant Enterococcus faecium.Most of the published studies on COVID-19 patients with complications of secondary infections were retrospective and reported single-centered data on a small number of patients, but the incidence and the time of development of the secondary infections were almost homogeneous . Karullisteroids . Baiou A2–1.114) . A reduc2–1.114) . Cultreraumannii . Nasir Naumannii . Giacobbaumannii ,39. The steroids . Interessteroids . A recensteroids . Ripa etsteroids . CriticaEnterobacterales with resistance to carbapenems and MRSA, especially in VAP. The number of patients that were on empirical antibiotic therapy was similar to the other single-center studies (69%), but nearly half of these patients received broad spectrum drugs. Notably, at the multivariable analysis they showed that treatment with broad spectrum antibiotics was independently associated with HAI , which paradoxically described a protective role of antimicrobial treatment against the development of secondary infections; however, they reported that the policy of all participating ICUs was to interrupt empiric antimicrobial therapy early if culture results were negative for bacterial infections at admission. In addition, the authors observed that patients complicated with septic shock resulted in a doubled mortality rate (52%). The other multicenter study, published by Giacobbe D.R. et al. involved nine Italian hub hospitals that admitted 586 patients with severe COVID-19 during the first wave of the outbreak between February and May 2020. Among them, 171 (29%) patients developed an infection with diagnosis of VAP. The incidence rate of VAP was slightly lower when compared to the study by Grasselli et al. (18 vs. 26 events per 1000 ventilator days). However, the incidence of MDR infections was similar and very close to all the other studies that reported this data. The most frequently isolated germs were Pseudomonas aeruginosa (35%), Staphylococcus aureus (23%) and Klebsiella pneumoniae (19%). Among the MDR microorganisms, the most frequently isolated species were methicillin-resistant S. aureus (10%) and carbapenem-resistant Gram-negative bacteria (32%). They expressed the outcome of their population at 30 days as a 30-day case-fatality and it was quite high , but it was evaluated only on patients who developed a secondary infection. Other studies reported the mortality rate over the whole COVID-19 population; therefore, no conclusion could be drawn comparing the outcome of the results among the selected studies. Interestingly, the authors found that septic shock and ARDS at VAP onset were strongly associated with an increase of 30-day case-fatality . This association was confirmed when they analyzed the subgroup of patients who developed VAP with the BALF culture positive. The incidence rate of VAP reported in patients for both studies was surprisingly high, especially when compared to that related to non-COVID-19 critically ill patients (one out of 19 episodes per 1000 ventilator days) [Only two multicenter retrospective studies reported the incidence and risk factors for secondary infections in critically ill patients ,41. The or days) ,55. The or days) ,12. FurtAcinetobacter baumannii and Klebsiella pneumoniae as the most frequently isolated infection found mainly in critically ill patients. Almost half of the patients who acquired secondary infections died and the critically ill patients showed an increased mortality rate 45/69 (65%) compared to less severe patients, 5/33 (15.2%) [Lastly, Li J. et al. reported 102 (6.8%) who developed secondary bacterial infections among COVID-19 patients. Similarly to most of the selected studies, they observed that critically ill patients were more prone to develop secondary bacterial infections because they received invasive mechanical ventilation and had a higher rate of central catheter placement. Among patients who developed secondary infections, 69 cases (4.6%) were caused by MDRO, with carbapenem resistant  (15.2%) .The present review had some limitations. First, it is not a systematic review, therefore we did not provide a statistical analysis that gives more strength to selected studies. Moreover, we did not provide a detailed incidence and description of the mechanisms of resistance, because most of the studies did not report them, due to the retrospective collection of the data. Furthermore, we might underestimate the real incidence of MDRO, because of the scarcity of microbiological samples, especially BALF for LRTI, the execution of which might increase the risk of viral transmission among the operators.Critically ill patients admitted with COVID-19 are at high risk of developing healthcare associated secondary infections, especially of bacterial origin, with a variable rate of resistance profiles. The main sites of healthcare infections are the lower respiratory tract, associated with invasive mechanical ventilation, and the blood stream, mainly catheter-related infections. The incidence rate of lower respiratory tract infections was interestingly higher when compared to those related to non-COVID-19 critically ill patients, mainly due to a pathophysiological mechanism mediated by the viral infection. The strongest risk factors associated with secondary infections were mainly a critically ill condition and the need of invasive treatment together with the extensive use of corticosteroids. Main risk factors for ventilator associated pneumonia were the use of PPI and the gastroesophageal reflux disease, and in BSI a strong association with the corticosteroids use alone in association with tocilizumab, as reported by a single center study . The onl


There were 2 phase 2 trials,Most recently, avacopan was approved for the treatment of AAV by the US Food and Drug Administration and approval is recommended by a committee of the European Medicines Agency to the European Commission.A total of 8 adult patients with granulomatosis with polyangiitis and microscopic polyangiitis were treated within the avacopan compassionate use program at our institute. Patient and AAV-relevant characteristics are summarized in Disease courses of individual patients before and after the initiation of avacopan are depicted in As found in Even though, during compassionate use, adverse events are not structurally registered, no adverse events, side effects, or infections related to avacopan were reported. There were 6 patients who are currently satisfactorily continuing avacopan. Case 1 participated in the initial stages of the compassionate use program in which avacopan use was allowed for 1 year only. Case 3 stopped avacopan because of a pregnancy wish.This study describes real-life practice data on the compassionate use of avacopan in difficult-to-treat patients with AAV. Avacopan contributed to achieving and maintaining remission in these patients with AAV while breaking through steroid dependency and allowing steroid reductions. Obviously, it is impossible to determine the clinical efficacy of avacopan owing to previous and concomitant intensive remission-induction immunosuppression and concomitant rituximab maintenance treatment in 4 patients. Nevertheless, avacopan had beneficial and added value in the treatment of our difficult-to-treat AAV cases with respect to improved disease control and reduced steroid-related toxicity.During the avacopan compassionate use program at our center, we applied for avacopan in cases 1 to 4 because of insufficient response to previous intensive remission-induction therapy, including high-dose steroids. All 4 patients achieved clinical and persistent remission on avacopan initiation. Furthermore, cases 5 and 6 achieved full remission for the first time since their diagnoses during avacopan treatment, in contrast to previous continuous active disease with steroid dependence. Noteworthy, in 1 patient (case 5), we experienced that reinstitution of avacopan successfully diverted a major disease flare. The latter is corroborated by early phase 1 study data revealing a dose-related, pharmacologic C5a-R inhibition.With respect to steroid use, only 3 of 8 patients required prednisone in a low dosage after 1 year of avacopan treatment. In addition, in 2 cases of steroid dependency, steroids could not be tapered <15 mg/d. With avacopan treatment, steroids were fully tapered to 0 in 1 patient (case 5) and a clinically relevant reduction to 7.5 mg/d in the second patient (case 6). Taken together, compassionate-use avacopan allowed to ameliorate disease and to significantly reduce and stop steroid use in difficult-to-treat patients with AAV while improvement of steroid-related toxicity was observed.Last, this study on a case series of compassionate-use avacopan provides guidance to future observational studies with avacopan. The challenge to identify beneficial effects of avacopan in clinical data of patients with AAV is defined by the absence of disease activity, disease flares, steroids, and steroid-related toxicity. It requires careful considerations to firmly determine benefit by proving the absence of clinically relevant events that physicians automatically strive for in routine clinical practice. Furthermore, it will remain a significant challenge to prove the clinical efficacy of avacopan on the background of highly intensive immunosuppression necessary for remission induction in AAV. Thus, to further investigate the potential benefits of avacopan in patients with AAV in observational studies, we emphasize assessing accurate medical histories with emphasis on disease courses, steroid dosing, steroid-related toxicity using validated GTI scores, and disease-relevant patient-reported outcomes.In conclusion, we here provide the first real-life practice observations on the compassionate use of avacopan in difficult-to-treat patients with AAV. Our study describes the clinical added value of avacopan in AAV treatment and that beneficial effects of avacopan are predominantly determined by the absence of adverse events, such as persistent disease activity, steroid dependence, and steroid-related toxicity.The work of YKOT was supported by the Dutch Kidney Foundation (17OKG04) and by the Arthritis Research and Collaboration Hub Foundation. Arthritis Research and Collaboration Hub is funded by Dutch Arthritis Foundation (ReumaNederland). YKOT received an unrestricted research grant and consultancy fees from Vifor Pharma. All the other authors declared no competing interests.


How can machine learning be used to estimate per-protocol effects in randomized clinical trials?In a cohort of 1227 women derived from secondary analysis of a randomized clinical trial, ensemble machine learning with augmented inverse probability weighting was used to estimate the per-protocol effect of daily low-dose aspirin on pregnancy detected using human chorionic gonadotropin (hCG) levels. Relative to placebo, adherence to the assigned treatment protocol was associated with an increase of 8.0 hCG-detected pregnancies per 100 women, approximately double the intention-to-treat estimates.These findings suggest that in per-protocol analysis, machine learning techniques may allow for confounder adjustment while reducing the occurrence of model misspecification. This secondary analysis of a randomized clinical trial uses machine learning methods to estimate the per-protocol effects of a low-dose aspirin regimen on pregnancy in the Effects of Aspirin in Gestation and Reproduction trial. In randomized clinical trials (RCTs), per-protocol effects may be of interest in the presence of nonadherence with the randomized treatment protocol. Using machine learning in per-protocol effect estimation can help avoid model misspecification owing to strong parametric assumptions, as is common with standard methods .To demonstrate the use of ensemble machine learning with augmented inverse probability weighting (AIPW) for per-protocol effect estimation in RCTs and to evaluate the per-protocol effect size of aspirin on pregnancy.This secondary analysis used data from 1227 women in the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial, a multicenter, block-randomized, double-blind, placebo-controlled clinical trial of the effect of daily low-dose aspirin on pregnancy outcomes in women at high risk of pregnancy loss. Participants were recruited at 4 university medical centers in the US from June 15, 2007, to July 15, 2012. Women were followed up for 6 menstrual cycles for attempted pregnancy and 36 weeks of gestation if pregnancy occurred. Follow-up was completed on August 17, 2012. Data analyses were performed on July 9, 2021.Daily low-dose (81 mg) aspirin taken at least 5 of 7 days per week for at least 80% of follow-up time relative to placebo.Pregnancy detected using human chorionic gonadotropin (hCG) levels.Among the 1227 women included in the analysis , 1161 (94.6%) were non-Hispanic White and 858 (69.9%) adhered to the protocol. Five machine learning models were combined into 1 meta-algorithm, which was used to construct an AIPW estimator for the per-protocol effect. Compared with adhering to placebo, adherence to the daily low-dose aspirin protocol for at least 5 of 7 days per week was associated with an increase in the probability of hCG-detected pregnancy of 8.0  more hCG-detected pregnancies per 100 women in the sample, which is substantially larger than the estimated intention-to-treat estimate of 4.3  more hCG-detected pregnancies per 100 women in the sample.These findings suggest that a low-dose aspirin protocol is associated with increased hCG-detected pregnancy in women who adhere to treatment for at least 5 days per week. With the presence of nonadherence, per-protocol treatment effect estimates differ from intention-to-treat estimates in the EAGeR trial. The results of this secondary analysis of clinical trial data suggest that machine learning could be used to estimate per-protocol effects by adjusting for confounders related to nonadherence in a more flexible way than traditional regressions.NCT00467363ClinicalTrials.gov Identifier:  Importantly, ITT effects capture the impact of assigning treatments to individuals. The ITT approach does not provide estimates of the effects that would be observed if all individuals adhered with a desired treatment protocol5 have called for a more formal approach to per-protocol effect estimation in RCTs, and several per-protocol analyses12 have demonstrated important deviations from ITT estimates when nonadherence is accounted for. Unfortunately, when per-protocol effects are targeted in RCTs, all limitations associated with observational studies must be considered, such as confounding bias.4 Machine learning methods can be used with augmented inverse probability weighting (AIPW) and stacked regression models to overcome some of these limitations14 and to estimate per-protocol effects when adjusting for confounding variables. However, compared with traditional regression models, machine learning methods may be better suited to avoiding problems with model misspecification.16 For example, a model would be misspecified if a linear regression were used to fit 2 variables with nonlinear relations .17 Many machine learning algorithms can avoid these problems,16 but they have not yet been applied to scenarios in which per-protocol effects are of primary interest.Several investigatorsIn this report, we illustrate the use of machine learning methods to estimate the per-protocol effects of low-dose aspirin on pregnancy in the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial. We evaluate how machine learning methods can be used to estimate per-protocol effects and discuss the feasibility and trade-offs of using machine learning methods for adherence-adjusted analyses.20 and a complete copy of the trial protocol is available in CONSORT) reporting guideline.In this secondary analysis of an RCT, we used the data from the EAGeR trial—a multicenter, block randomized, double-blind, placebo-controlled clinical trial. The EAGeR trial recruited women aged 18 to 40 years who were actively trying to become pregnant and who had 1 or 2 prior pregnancy losses and no history of infertility from 4 university medical centers in the US from June 15, 2007, to July 15, 2012. Follow-up was completed on August 17, 2012. A total of 1228 women were recruited and randomized. Most of the participants (1161 [94.5%]) self-identified as non-Hispanic White race and ethnicity. For as many as 6 menstrual cycles, participants were followed up biweekly in their first 2 cycles and monthly afterward while attempting pregnancy. If a pregnancy was observed, follow-up continued throughout pregnancy for the live birth outcome . Institutional review board approvals at each clinical site and data coordinating center were obtained. A data safety and monitoring board was also formed to ensure participants’ safety and monitor the efficacy of the trial. Missing data were addressed via single imputation. Details about study design, eligibility criteria, baseline characteristics, and other relevant information has been published elsewhere,With 1:1 randomized treatment allocation, the treatment group (n = 615) received preconception-initiated daily low-dose aspirin (81 mg) plus folic acid (400 μg) and the control group (n = 613) received placebo plus folic acid (400 μg). For women who became pregnant, study treatment was to continue until week 36 of gestation. A total of 1227 women were included in the analysis of the present study (1 participant had missing follow-up data).4Adherence was assessed via bottle weight measurements in both groups during regular follow-up visits. Weekly adherence status was determined by evaluating whether a participant took their assigned pills for at least 5 of 7 days  during a given week. A woman was deemed adherent with the study protocol if, in any given week during follow-up, she took a pill on at least 5 of 7 days. For each woman, this time-varying measure was categorized as adherent if the mean adherence during follow-up was at least 80% of their follow-up time before becoming pregnant or the entire follow-up time for those without pregnancy. Notably, this adherence status is a dichotomized, time-fixed variable, which is commonly used in a typical per-protocol analysis but differs from the previous analyses of this trial.Pregnancy detected with human chorionic gonadotropin (hCG) levels during the defined treatment period was the primary outcome for this analysis. Pregnancies were determined by a positive result on a real-time hCG pregnancy test , which was sensitive to 25 mIU/mL of hCG. The test was conducted at each study visit when expected menses were absent or by batched urine testing using daily first morning urine collected at home, stored on the last 10 days of each participant’s first cycle after randomization, and analyzed in the laboratory.Baseline data on demographic, behavioral, and pregnancy history information were obtained via questionnaires, including age, race and ethnicity, educational level, marital status, income, frequency of exercise, alcohol and cigarette use in the past year, number of prior pregnancy losses, and number of months attempting pregnancy before randomization. Physical measurements of height and weight were used to calculate body mass index at baseline. Blood samples were also collected to measure serum high-sensitivity C-reactive protein levels using an immunoturbidimetric assay  with a detection limit of 0.0015 mg/dL .Postrandomization confounders, including unusual (or excessive) bleeding and nausea and/or vomiting, were collected via questionnaire at regular intervals during follow-up. Similar to overall adherence status, we dichotomized these 2 postrandomization confounders by setting the values to 1 if a woman experienced unusual bleeding for at least 50% or nausea and/or vomiting for at least 20% of their follow-up time at least 1 of 7 days (20%) per week.2 test for categorical variables and by Kruskal-Wallis test for continuous variables. To examine the impact of different adherence thresholds on the overall findings as well as the number of individuals who were adherent and nonadherent with treatment in the samples, we explored protocols under assigned treatment for at least 4 of 7 days, 5 of 7 days, and 6 of 7 days of a given week for 60%, 70%, and 80% of person-weeks of follow-up.In this study, we selected a protocol in which women would adhere to their assigned treatment for at least 5 of 7 days of a given week and for more than 80% of their follow-up time before pregnancy. This protocol allows us to evaluate whether consistently taking aspirin (vs placebo) is associated with an increased probability of experiencing an hCG-detected pregnancy. Differences in treatment, outcome, baseline characteristics, and postrandomization confounders between adherence status were tested by χ21 To estimate this per-protocol effect of interest with machine learning methods, we used an AIPW estimator with an ensemble machine learner known as the Super Learner .24 Per-protocol effects were quantified on both the risk difference and the risk ratio scales for the pregnancy outcome.Our target per-protocol effect is defined as the average per-protocol treatment effect among women who adhered to the aspirin protocol.23Stacking is a machine learning technique that combines several different algorithms into a single meta-algorithm. The benefit of using stacking as opposed to a single regression model or machine learning algorithm  is flexibility; stacking algorithms can combine the strengths of each individual algorithm based on how they fit the data, thus avoiding the need of the potentially strong assumptions on which single algorithms rely for validity. The stacking technique first trains several machine learning models individually as the first layer. Estimates (or predictions) of the individual models from the first layer are then used as the input for the second layer, which is the meta-algorithm. Cross-validation is used to determine the importance of each first-layer algorithm in the overall meta-algorithm and to avoid potential overfitting.25 random forests,26 and extreme gradient boosting.27 For multivariate adaptive regression splines, random forests, and extreme gradient boosting, a grid of tuning parameters was included in the stacking algorithm. All algorithms were combined into the meta-algorithm via nonnegative least squares. The predictions from these stacked models were then used to construct the AIPW estimator.In this study, we stacked 5 regression models : a standard generalized linear model with main effects only, a standard generalized linear model with main effects and all 2-way interactions, multivariate adaptive regression splines,22 Augmented inverse probability weighting is consistent as long as at least the exposure model or the outcome model is correctly specified. Further, AIPW performs well, even when using flexible machine learning methods.24 Using the aforementioned stacked machine learning algorithm, we estimated propensity scores by modeling the exposure with the aforementioned baseline covariates (exposure model) and constructed the outcome model using the exposure and those covariates. Cross-fitting, an additional layer of the fitting process on top of the stacking machine learning, is applied in the AIPW estimator to obtain valid inference  and to further avoid overfitting.24Augmented inverse probability weighting is a “doubly robust” estimator that relies on estimating the exposure model  and the outcome model separately  and then combining the predictions from these models into a single estimator that quantifies the average treatment effect.28 inverse probability weighting,29 targeted maximum likelihood estimation,30 and AIPW and unadjusted per-protocol effects (with different thresholds) estimated by g-computation, inverse probability weighting, targeted maximum likelihood estimation, and AIPW. We constructed g-computation and inverse probability weighting with a standard generalized linear model with main effects only. Targeted maximum likelihood estimation is also a doubly robust estimator, which performs well when machine learning methods are used. We constructed the targeted maximum likelihood estimator using the same stacking machine learning algorithms for the AIPW. Further, we repeated all analyses after adjusting for postrandomization confounders .Sensitivity analyses were conducted by using other thresholds of time-fixed adherence status, which is a combination of adherence to at least 4, 5, and 6 days in a given week during at least 60%, 70%, and 80% of person-weeks of follow-up. In addition, we also provided the ITT estimates obtained via g-computation,31 A prior study using the data resampled from the EAGeR trial24 has shown excellent statistical performance for the AIPW package. Targeted maximum likelihood estimation was conducted with the tmle package.32 The code needed to reproduce our analyses is available in the eMethods in P < .05 indicated statistical significance.All analyses were performed in R, version 3.6.2 . We conducted the implemented AIPW estimation using the AIPW package. The AIPW package supports the Super Learner package for stacking machine learning with cross-validation and provides a user-friendly interface for cross-fitting.21 = 278.6; P < .001) as well as non-Hispanic White race and ethnicity , high school education , marital status , annual income , and history of smoking in the past year  but not with the randomized treatment assignment. A total of 1227 women were included in the analysis . In the EAGeR trial, most of the participants were non-Hispanic White (1161 [94.6%]), had at least a high-school education (1057 [86.1%]), and were married (1123 [91.5%]) and employed (919 [74.9%]). The estimated per-protocol effect of low-dose aspirin on hCG-detected pregnancy is shown in Using other estimation methods, the per-protocol estimates remained similar, including the unadjusted estimates . Similar4 However, unlike previous research, we used nonparametric machine learning methods to estimate these effects. Our analyses demonstrate a novel approach for per-protocol effect estimation using advanced statistical methods. In addition, our results suggest that a preconception low-dose aspirin regimen increases hCG-detected pregnancies for women with 1 or 2 prior pregnancy losses who adhered to at least 5 of 7 days of low-dose aspirin therapy for at least 80% of the follow-up.We demonstrate the use of stacked machine learning with AIPW in estimating the per-protocol effects in the EAGeR trial. Our time-fixed, per-protocol analysis results were consistent with previous findings of the per-protocol effect estimate of aspirin that accounted for the time-varying nature of adherence and select time-varying confounders.35 Although they can also be used for effect estimation, additional steps are needed.14 Importantly, these steps nclude the need to adjust for relevant confounders and to use doubly robust methods such as AIPW.Supervised machine learning algorithms have been widely adopted to predict various health outcomes.16 For example, the inclusion of an interaction term in a regression model is determined by the investigators’ domain-specific knowledge, whereas tree-based models  adopt a more data-adaptive approach to interaction inclusion.37 Failure to include an interaction term may result in model misspecification and lead to biased effect estimation. However, as a result of this increased data adaptiveness and extra modeling flexibility, tree-based models—and flexible machine learning in general—are more likely to overfit the data and have larger mean squared error.36 To mitigate these issues, combining tree-based methods  and regression-based methods  is advisable.16 In our study, we stacked 5 different machine learning models to create added flexibility and used cross-validation to mitigate overfitting.The benefits of using machine learning with doubly robust methods lie primarily in the ability to avoid strong parametric modeling assumptions. Machine learning models can be more flexible and data adaptive than traditional regression models.13The purpose of this study was to illustrate the use of machine learning as an alternative approach for per-protocol effect estimation rather than convincing the readers to use machine learning only. However, it should be recognized that if the model is correctly specified (even this is a rare scenario), parametric regression is more statistically efficient than flexible machine learning methods.38 However, in the EAGeR trial, the ITT effects of low-dose aspirin on hCG-detected pregnancy differed substantially from the estimated per-protocol effect owing to nonadherence with the specified protocol during follow-up. In many settings captured by clinical trials with repeated opportunities to take the assigned treatment , perfect adherence is unlikely, and a practical adherence level has to be chosen based on either clinical knowledge or the data at hand. In the EAGeR trial, the adherence rate declined over time and dropped quickly after the start of pregnancy.4 We defined adherence based on a protocol of taking 5 of 7 pills in a given week for at least 80% of person-weeks because some literature suggests that a biological effect of low-dose aspirin could be achieved at this adherence level4 and because of the relatively short half-life of aspirin.39We used supervised machine learning methods with doubly robust estimators to quantify the per-protocol effect of aspirin on hCG-detected pregnancy. In an RCT where all participants are fully adherent with the treatment protocol, per-protocol effect will be identical to ITT effects.4 that properly adjusted for postrandomization confounding, albeit with methods that were much less flexible . This finding lends additional empirical support to the use of daily low-dose aspirin in increasing hCG-detected pregnancies.We found that our unadjusted estimates were similar to estimates we obtained by improperly adjusting for postrandomization confounders  but properly adjusting for baseline confounders . In addition, these results aligned closely with those from a prior study41 can be incorporated with our approach as well because the modification of ITT may not be free of confounding . Further, adjusting for covariates with machine learning in (modified) ITT analysis can improve statistical efficiency for higher precision of treatment effect estimates.42Our analytic approach using time-fixed adherence can be more broadly applied in other analysis principles of RCTs as well as in observational studies, particularly those with only 1 time point. For example, our approach can be directly applied to the as-treated analysis in RCTs, such as a trial for evaluating the efficacy of emergency contraception. Modified ITT (despite not being consistently defined)1 In contrast, an appropriately adjusted per-protocol analysis can be used to estimate the effect of taking the active treatment according to the specifications of the protocol, allowing estimation of the treatment efficacy. Similar to most per-protocol analyses, our study relied on time-fixed adherence status, which is an important limitation. Although our effect estimates of low-dose aspirin on hCG-detected pregnancy are similar to those of the prior study that accounted for time-varying adherence,4 limitations should be considered when conducting a time-fixed, per-protocol analysis. First, in conducting a time-fixed analysis, we had to collapse time-varying adherence status into a single time point, losing detailed information of how adherence changed during follow-up. Second, time-fixed analyses are generally unable to appropriately adjust for time-varying confounders, such as unusual bleeding and nausea. For example, at a given time point, adherence to treatment is associated with an increased likelihood of adverse effects , which is further associated with a decreased likelihood of adherence at the next time point. Therefore, postrandomization confounders  could simultaneously mediate and confound the effect of adherence status, requiring an analytic approach that we did not use.28 In addition, other common limitations of observational studies should also be considered in the per-protocol analysis, such as unmeasured confounders. Last, we had limited information on important variables such as race and ethnicity, which limits the generalizability of our findings.This study has some limitations. In a well-conducted trial, an ITT approach provides unbiased estimates of the assignment effect. The ITT estimates capture the impact of the treatment assignment strategy and generally can be interpreted as the effectiveness of recommending or prescribing one treatment compared with another.This secondary analysis of a randomized clinical trial suggests that machine learning methods with doubly robust estimators, such as AIPW, can be used to estimate per-protocol treatment effects. Furthermore, our empirical findings align with prior results supporting the prophylactic use of daily low-dose aspirin to improve the chances of hCG-detected pregnancy in women at high risk of pregnancy loss.


Background: COVID-19 pandemic had resulted in nationwide lockdown as a disease control measure. Potential harm to self and baby due to COVID-19 infection as well as uncertainties about delivery are among contributors to maternal anxiety. We aimed to assess the prevalence of psychological distress among pregnant women during the Malaysian Movement Control Order (MCO).Methods: A cross-sectional study was conducted between May and June 2020 in a teaching hospital in Kuala Lumpur, Malaysia. A self-administered electronic questionnaire was distributed which included the following; (1) Depression, Anxiety and Stress Scale-21 (DASS 21), (2) Short Warwick Edinburgh Mental Wellbeing Scale (SWEMWBS), (3) MCO effect questionnaire, and (4) newly designed COVID-19 pregnancy-related anxiety. Chi-square test and logistic regression were performed to determine significant associations whilst mean scores comparison were conducted through Mann-Whitney-U-test.Results: Four hundred and fifteen women were included in the final analysis. The prevalence of psychological distress among our cohort was 14.7%; with a two-fold increase of risk among the non-Malays  whilst a greater number of social support showed a protective effect . Malay ethnicity (p < 0.001) alongside greater household income (p = 0.014) were positive predictors of a higher sense of maternal wellbeing. Multiparous women and those of higher economic status experienced the more negative effect of the MCO. Around 88% of our women reported a higher level of COVID-19 pregnancy-related anxiety. Younger (p = 0.017) and first-time mothers (p = 0.039) were more likely to be anxious. Although adequate maternal knowledge on COVID-19 was associated with a greater sense of maternal wellbeing (p = 0.028), it was also linked to a higher level of COVID-19 related anxiety .Conclusion: There was a relatively low prevalence of psychological distress among expectant mothers in Malaysia during the first wave of the COVID-19 pandemic. Expectant mothers should receive accurate and reliable information on the effect of COVID-19 on pregnancy to relieve some maternal anxiety. Maternal health screening is important to identify individuals who would benefit from extra support and mental health intervention, especially in prolonged lockdown. Corona Virus Disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which was first identified in Wuhan, China. The rapid spread of the virus since its first detection in December 2019, had resulted in a global outbreak and the declaration of a pandemic by the World Health Organization (WHO) on 11 March 2020. To date, around 188 million people were infected worldwide with four million recorded mortalities  among pregnant women may be higher than in the general population. Physiological and immunity changes during pregnancy increase the women's susceptibility to severe disease . Data frThe combination of disease pandemic and national lockdown would inevitably lead to psychological distress and a low state of wellbeing among pregnant women . PsycholRisk of COVID-19 transmission to self and fetus alongside uncertainties about deliveries, loss of household income, and domestic conflict was among the stressors which contributed toward pregnancy-related anxiety . StudiesEvidence shows that prenatal psychological distress affects both maternal and fetal wellbeing, with a potentially long-term effect on child development , 17. MatWe conducted a cross-sectional study during the Malaysian MCO; from May 2020 till June 2020 among women who received obstetric care in a teaching hospital in Kuala Lumpur. Prior study approval was obtained from the Ethics Committee of Universiti Kebangsaan Malaysia (FF-2020-211). The inclusion criteria were pregnant Malaysian women aged above 18 years old and able to understand the Malay language, whilst exclusion criteria were women with abnormal fetuses or stillbirth. Participants recruitment was conducted among women who: (1) attended the outpatient clinic for an antenatal appointment, and (2) admitted to the obstetric ward for delivery or other medical complications. Eligible women were invited to complete the electronic version of the questionnaire through Google form, which contained a consent section. Socio-demographics and clinical data were included in the data collection. We also evaluated the maternal knowledge, perception, and practice during the COVID-19 pandemic as part of this research and the relevant results had already been published .Our survey was conducted in Malay, the country's national language. The following instruments were used for data collection.DASS 21 is a self-reporting tool measuring characteristic attitudes and symptoms of depression, anxiety, and stress . There aThe Short Warwick–Edinburgh Mental Wellbeing Scale (SWEMWBS) is a shorter version of the 14-items Warwick–Edinburgh Mental Wellbeing Scale (WEMWBS), which was originally developed to monitor wellbeing in the general population , and to We assessed the maternal perception of the effect of MCO on their wellbeing using a three-item questionnaire which was developed by Kalok et al. The questionnaire previously demonstrated a good internal consistency  . The parMCO has disrupted your daily life.MCO has affected your physical wellbeing.MCO has affected your mental/psychological wellbeing.The total score for all three responses was calculated, and a cut-off level based on the mean score was determined , 32. A tA five-item questionnaire was designed to assess COVID-19 pregnancy-related anxiety. Each woman was asked to respond to each item on COVID-19 using a scale; 1  to 7 (very worried):Infection to selfInfection to babyCOVID-19 causing miscarriageCOVID-19 causing preterm birthCOVID-19 causing abnormality to babyThe total score ranged from 7 to 35. We used a 50% cut-off level (score ≥ 18) to indicate greater maternal anxiety.We had asked the participants to indicate their perceived source of social support. Social support may be in various forms including emotional, physical, or even financial. The participants were given a list that includes family, friends, employer, and government; and they were free to choose as many as they deemed relevant.We developed a nine-item questionnaire that covered the sign and symptoms of COVID-19, methods of transmission, and disease prevention; based on the available literature. Total knowledge score ranged from 0 to 9. Participants' overall knowledge was categorized as adequate if the score was more than 50% . The assThis study was part of research that included the evaluation of maternal knowledge on COVID-19. Our sample size was determined based on the assumption that the probability of having adequate maternal knowledge was 50.0% . Taking The study data were analyzed using the Statistical Package of Social Sciences (SPSS) Version 24.0 . Data were presented as mean  or number, n  for continuous and categorical data, respectively. The scores for DASS 21, SWEMWBS, MCO effect, and COVID-19 pregnancy-related anxiety were inspected for normality using the Kolmogorov–Smirnov test. The internal consistency of all the questionnaires was assessed using Cronbach's alpha. Cronbach's alpha > 0.7 was regarded as satisfactory. All of the items in the newly designed COVID-19 related anxiety questionnaire underwent exploratory factor analysis to confirm the number of factors. The Kaiser rule  was applied to determine the number of dimensions to extract, whilst the sampling adequacy was assessed through Bartlett's test of sphericity and Kaiser–Meyer–Olkin (KMO). The correlations between the COVID-19 pregnancy-related anxiety and psychological distress, maternal wellbeing, and MCO effect were assessed using Pearson's correlation.p-value <0.05 was considered statistically significant.Chi-square test and univariate analysis were performed to determine the significant factors associated with (1) maternal psychological distress, (2) higher sense of maternal wellbeing, (3) negative effect of MCO, and (4) greater COVID-19 pregnancy-related anxiety. The statistically significant variables were analyzed in the multiple variable logistic regression, which included the adjustment for age, ethnicity, and parity, to produce the adjusted odd ratios (AORs) and the corresponding 95% confidence interval. The mean scores for SWEMWBS, MCO effect, and COVID-19 related anxiety were also compared using different maternal characteristics. A We approached four-hundred and fifty women for this study and 93% completed the questionnaire. Five women were excluded due to incomplete data; resulting in a total number for analysis of 415. The demographic and clinical characteristics of our cohort are demonstrated in p = 0.049) whilst those with a greater number of social support were almost 50% less likely to suffer from psychological distress (p = 0.026); as demonstrated in The Cronbach alpha for the Malay version of DASS-21 was 0.960. The proportion of our women who reported symptoms of depression, anxiety, and stress were 4.3, 14.0, and 5.8%, respectively; as demonstrated in p < 0.001) as depicted in U-test as the SWEMBBS scores were not normally distributed. We found that the Malay women (p = 0.001) and those with greater income (p = 0.033) displayed significantly higher wellbeing scores. We used the total score of 26 and above as an indicator of a higher sense of maternal wellbeing. Chi-square and logistic regression analysis confirmed that Malay ethnicity along with greater household income (>RM5000) as an independent predictor of a higher sense of maternal wellbeing as shown in The mean (SD) SWEMWBS score among our cohort was 26.90 (6.40). The seven-item questionnaire demonstrated good internal consistency with Cronbach's alpha of 0.961. SWEMWBS was positively correlated with depression, stress, and anxiety (p = 0.045); as shown in p = 0.001).We found that women who were non-Malay, tertiary educated, those with greater household income, and multiparous displayed significantly higher scores ; suggestp < 0.001, supporting the sample factorability. Exploratory factor analysis on the five items confirmed single factor loading. Mann-Whitney-U-test revealed that Malay ethnicity, non-tertiary education, and adequate knowledge on COVID-19 were associated with higher anxiety scores  was 0.776, and Bartlett's Test of Sphericity reached statistical significance with y scores .By using a fifty-percent threshold; we found that 88.2% of our women demonstrated a greater level of maternal anxiety. Age below 35, Malay ethnicity, and adequate knowledge of COVID-19 were associated with a greater level of COVID-19 pregnancy-related anxiety .The COVID-19 outbreak and nationwide lockdown had a significant psychological impact on vulnerable groups such as pregnant women. We presented the first study which evaluated the effect of the global pandemic on maternal mental health among Malaysian women. Our results demonstrated that Malay women had a lesser odd of experiencing psychological distress and were more likely to report a greater sense of maternal wellbeing. Women of higher socioeconomic status reported a higher sense of maternal wellbeing; however, they were also more likely to experience the negative effect of the lockdown. We also found that there was a high level of COVID-19 pregnancy-related anxiety among our women especially among the young and Malay mothers; as well as those who had sufficient knowledge on COVID-19.Various studies on maternal mental health during the global pandemic showed an uptrend in the symptoms of psychological distress. A recent systematic review by Suwalska et al. found that the prevalence of depression and anxiety among perinatal women were as high as 56.3 and 77%, respectively , 34, 35.The COVID-19 outbreak had inevitably changed the delivery of health services including obstetric care. Hospitals had to ensure adequate resources to fight the pandemic by canceling elective procedures and outpatient clinics. Alteration to antenatal appointments  as well In keeping with the previous study, we found a significant negative correlation between the SWEMWBS score and all the components of DASS 21 . Malay wNationwide lockdown had a significant socio-economic impact on the population. Closure of offices and business premises resulted in job losses and financial insecurity, whilst reduced family contact and restricted recreation activities would increase emotional stress.Our study demonstrated that the lockdown has a negative effect on maternal wellbeing. We found that women with higher education and income level experienced a greater negative effect of MCO. These expectant mothers with higher economic status were more likely to have to work from home and found their social activities restricted during the lockdown. Multiparous mothers also demonstrated higher MCO effect scores compared to first-time mothers. Lack of childcare and online classes for school-going children might pose extra pressure on these mothers during the pandemic.Over four-fifths of our women expressed worry about the risk of COVID-19 infection to themselves and their babies. Lebel et al. found that elevated symptoms of depression symptoms were associated with an increment in maternal concern on the COVID-19 threat to own life and potential harm to the baby . A studyCurrently, observational data have not suggested any link between COVID-19 and miscarriage, fetal anomaly, or spontaneous preterm births. Vertical transmission is plausible; however, the mechanisms are unclear and severe neonatal COVID-19 disease is fortunately rare . It is tp = 0.034), however, the relationship was not significant following adjustment for age, ethnicity, and parity. These women also reported a higher level of COVID-19 pregnancy-related anxiety; which can be explained by the lack of available data on the effect of COVID-19 on pregnancy during the early wave of the pandemic.Our study evaluated the effect of adequate COVID-19 knowledge on maternal psychology during the pandemic. Liu et al. demonstrated that pregnant women with relatively more knowledge on COVID-19 and rational risk perception (not too nervous about epidemic control or going out) were less likely to be anxious . We founWomen's cultural background, which is influenced by race and religion; often plays a role in maternal perception, practice, and attitude toward the COVID-19 pandemic. Our previous published findings demonstrated that Malay women demonstrated a more positive attitude toward the MCO . A studyWe presented the first Malaysian study that evaluated the psychological impact of COVID-19 on pregnant women during the first wave of the pandemic. The sample size was adequate to meet the statistical requirement and our newly designed COVID-19 pregnancy-related anxiety demonstrated good internal consistency and construct validity. Our study was among the few which assessed the influence of adequate knowledge of COVID-19 on maternal mental health. The validated Malay version of SWEMWBS is a valuable tool to evaluate the perception of wellbeing not only among pregnant women but also applicable to the general Malaysian population.Our study is limited by its cross-sectional design. The data collection was conducted in the early part of the COVID-19 pandemic when there was little knowledge on the novel virus. This could explain the high level of COVID-19 pregnancy-related anxiety among our cohort. Unfortunately, the country is still under lockdown with an increased number of positive cases and deaths. Malaysia is suffering from the third wave of the pandemic and the health system is currently under strain from the exponential rise of severe cases . The proThe COVID-19 pandemic and national lockdown had a negative impact on maternal mental health wellbeing. Social support offers protection against psychological distress, as previously demonstrated by other studies. Dissemination of accurate information on COVID-19 effect on pregnancy is essential to address the knowledge gap among expectant mothers and reduce pregnancy-related anxiety. Mental health screening is essential in prolonged pandemics and lockdown to identify vulnerable individuals, in the effort to safeguard maternal psychological wellbeing and promote healthy prenatal attachment during this testing time.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The studies involving human participants were reviewed and approved by Universiti Kebangsaan Malaysia Ethics Committee. The patients/participants provided their written informed consent to participate in this study.AK, RA, and ZM: conceptualization. AK, SAS, and SS: methodology. AK and SS: formal analysis. AK and SAS: investigation. AK: writing—original draft preparation and project administration. AK, RA, ZM, and SS: writing—review and editing. RA: supervision. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


There is a lack of knowledge how patients with COVID-19 disease differ from patients with similar signs or symptoms (but who will have a diagnosis other than COVID-19) in the prehospital setting. The aim of this study was to compare the characteristics of these two patient groups met by the emergency medical services.All prehospital patients after the World Health Organisation (WHO) pandemic declaration 11.3.2020 until 30.6.2020 were recruited for the study. The patients were screened using modified WHO criteria for suspected COVID-19. Data from the electronic prehospital patient reporting system were linked with hospital laboratory results to check the laboratory confirmation for COVID-19. For comparison, we divided the patients into two groups: screening- and laboratory-positive patients with a hospital diagnosis of COVID-19 and screening-positive but laboratory-negative patients who eventually received a different diagnosis in hospital.p < 0,001). The number of cases in which no signs or symptoms of COVID-19 disease were reported, but patients turned out to have a positive test result was 125 .A total of 4157 prehospital patients fulfilled the criteria for suspected COVID-19 infection during the study period. Five-hundred-thirty-six (12.9%) of the suspected cases received a laboratory confirmation for COVID-19. The proportion of positive cases in relation to suspected ones peaked during the first 2 weeks after the declaration of the pandemic. In the comparison of laboratory-positive and laboratory-negative cases, there were clinically insignificant differences between the groups in age, tympanic temperature, systolic blood pressure, heart rate, on-scene time, urgency category of the call and mode of transportation. Foreign-language-speakers were overrepresented amongst the positive cases over native language speakers (26,6% vs. 7,4%, In a sample of suspected COVID-19 patients, the laboratory-positive and laboratory-negative patients were clinically indistinguishable from each other during the prehospital assessment. Foreign-language-speakers had a high likelihood of having Covid-19. The modified WHO criteria still form the basis of screening of suspected COVID-19 patients in the prehospital setting. The coronavirus disease 2019 (COVID-19) pandemic has had a tremendous impact on health care systems worldwide. In Finland various recommendations and restrictions for the population and a partial lockdown of the society have helped the health care system to run effectively during the pandemic. The emergency medical service (EMS) is often the first link in the chain of care especially when the symptoms are severe . HoweverPrehospital data and hospital records are not usually linked and thus the final proportion of COVID-19 positive patients in the prehospital setting remains unknown. There has also been a lack of knowledge whether the confirmed COVID-19 patients differ clinically from other prehospital patients with similar signs or symptoms. The EMS have been forced to raise the suspicion of COVID-19 with a low threshold if the patient has met the criteria as defined by the World Health Organisation (WHO) . ConsequStudies of the effects of previous pandemics on EMS are limited. H1N1-pandemic highlighted the need for continued education and communication regarding infection control and the appropriate use of personal protective equipment (PPE) . RefusalThe aim of this EMS based study was to compare the characteristics of suspected COVID-19 patients who received laboratory confirmation to suspected COVID-19 patients who were laboratory-negative and eventually received a different diagnosis. In addition, we were interested in the clinical features of screening negative (asymptomatic) patients who later received COVID-19 diagnosis.We conducted a retrospective cohort study of all patients encountered by the EMS using a uniform electronic patient report (EPR) system.The capital city of Helsinki and the surrounding area have a population of 1,260,000 inhabitants. The natively spoken languages are Finnish and Swedish. The EMS is organized by Helsinki University Hospital (HUS) and the ambulance services are provided by three Rescue (Fire) Departments and two private ambulance companies. Interhospital patient transportation is not part of EMS. Also, nursing home calls are mainly handled by other agencies than EMS. All emergency phone calls (112) are received and processed by the emergency medical communication centre. The four-step triage classification (A-D) of calls is described in Table The signs and symptoms referring to COVID-19 disease were defined in EMS as: fever ≥38 °C, cough, shortness of breath, sore throat, diarrhoea and/or loss of taste or smell. Any one of these criteria caused patients to be treated as a suspected COVID-19 case. The criteria were slightly more narrow compared to WHO criteria  and theyThe test used for laboratory confirmation of COVID-19 infection from nasopharyngeal swabs was a real-time reverse transcription-polymerase chain reaction test by the laboratory at the Helsinki University Hospital . Test results were screened up to 14 days before and 10 day after the EMS contact. Test sampling  was done only either in hospitals or in outpatient test sites.The study period begun from the beginning of WHO pandemic declaration March 11th, 2020) and continued until the number of new confirmed cases was minimal . This time-period correlates with the first wave of pandemic in Finland. We included all EMS encountered patients in all age groups with a Finnish social security number or a temporary social security number for non-Finnish residents. The exclusion criteria were a diagnosis of COVID-19 at the time of the EMS call . The study plan was approved by the appropriate research body of HUS. Due to the nature of the study neither ethical committee approval nor consent from the patients was required. Finnish research legislation does not require ethical committee approval of retrospective studies based solely on patient records.During the study period EMS encountered 40,777 patients of which 661 tested positive for SARS-CoV-2 regardless of whether Covid-19 infection was suspected by the EMS staff or not.  speakers. All foreign language speakers had Finnish social security number and they were living in Finland. The group comparison is presented in Table The number of cases in which no signs or symptoms of COVID-19 disease were reported in prehospital phase, but patients turned out to have a positive SARS-CoV-2 test result was 125 . Characteristics of these patients are described in Table This is one of the first studies combining comprehensive prehospital EPR data with hospital laboratory-confirmed COVID-19 diagnosis in patients managed by the EMS. We found that the proportion of laboratory confirmed COVID-19 cases in relation to suspected ones was high during the first weeks after the declaration of the pandemic. At that phase patients tended to stay at home too long before seeking diagnostics and treatment. Although several prehospital parameters were associated with the final diagnosis of COVID-19, their clinical significance was low and did not give added value to the modified WHO criteria for a suspect case, except for the native language of the patient.The modified WHO criteria were negative in 125 patients who later received a laboratory-confirmed COVID-19 diagnosis. Although these cases represented only 0,3% of the whole EMS patient population they cause a potential occupational health risk for EMS personnel. In a study from Seattle, USA, almost one third of COVID-19 patients encountered by the EMS did not present with fever, respiratory difficulty or cough [Most of the previous studies of COVID-19 in the prehospital setting have not focused on patient assessment or diagnostics but rather on demand and performance of the EMS, and occupational safety , 5, 9–11A recent study from Sweden found that up to 54% of COVID-19 patients presented with primary symptoms not typical of COVID-19 . This prBlood oxygen saturation did not become significant in differentiating the patient groups in our study. However, worsening rates of hypoxemia have been reported in patients with respiratory symptoms treated by EMS after the beginning of pandemic in Tijuana, Mexico . A similThe study sample included all patients who needed the EMS in the capital area and therefore the results can be considered epidemiologically comprehensive.In the prehospital setting, initial screening for COVID-19 is mandatory but the key issue is what kind of false negative and false positive screening rates can be accepted. The rates may differ regarding on the point of view – patient assessment or occupational safety of the staff. Patients who do not have, or for some reason do not report, any symptoms in COVID-19 screening are a challenge for EMS. Unexpectedly, we found falls to be a common cause for calling help in this patient group. In elderly people, falls may be caused by medical conditions where the general condition of the patient begins to deteriorate.If the patient’s native language was other than Finnish or Swedish, we found the likelihood of COVID-19 to be significantly higher. This is supported by the epidemiological data that COVID-19 was more common in certain immigrant groups in Finland. These immigrant families may have several members living in small apartments, which may have important implications for patient assessment and the occupational safety of EMS staff. Our findings are emphasized by the fact that foreign language speakers were not tourists. They were living in Finland permanently or on a long-time basis and they had Finnish social security number.The ambulance transport rate in the group with a final diagnosis of COVID-19 was lower than in the group with other diagnosis. This finding is not explained by the studied parameters. Some patients were instructed to arrange their own transport to an emergency department. The national recommendation to the public in March “to stay home if you have COVID-19 like symptoms” may have also affected the decision making of EMS personnel.The study was limited by the fact that in the early phase of the pandemic, SARS-CoV-2 testing capacity was limited and patients with modest or atypical symptoms may not have been tested as actively as patients presenting during the later phase of the pandemics first wave. In one febrile patient, who later tested positive for SARS-CoV-2, the EMS personnel failed to suspect a COVID-19 infection.Considering the continuum of this pandemic, future pandemics, and other infectious disease threats, there is a need for studies that can provide tools (e.g. clinical characteristics, triage models , point-oSuspected cases of COVID-19 who eventually received a different diagnosis, and laboratory confirmed COVID-19 cases had minor differences in their prehospital clinical parameters which were not clinically significant. Foreign-language-speakers had a high likelihood of having Covid-19. We found no clinical signs that could improve modified WHO criteria in screening for suspected COVID-19 patients in EMS.


To estimate changes in public mask-wearing behavior in response to public health policies during COVID-19.Panel of observed public mask-wearing.Counts of adult behavior in Marion County, Indiana, between November 15, 2020, and May 31, 2021.(1) Removal of state masking requirement; (2) introduction of the National Strategy for the COVID-19 Response and Pandemic Preparedness; (3) the Centers for Disease Control and Prevention (CDC) recommendation that vaccinated individuals did not need to wear masks in public; and (4) COVID-19 vaccine availability.Percent observed with correct mask-wearing.Fixed-effects models estimated the association between policies and mask-wearing.P = .032).Ending Indiana's mask requirement was not associated with changes in correct mask-wearing. The CDC's recommendation was associated with a decrease of 12.3 percentage points in correct mask-wearing (95% CI, −23.47 to −1.05; Behavior encouraged by local mask requirements appeared to be resilient to changes in state policy. CDC recommendations appeared influential. Policies requiring face masks in public have been one strategy to address the COVID-19 pandemic.5When the next pandemic arises, public health policies will again be necessary to reduce transmission. Furthermore, public health agencies will be tasked with setting and enforcing policies within this complex dynamic.The association between COVID-19—related public health policies and mask-wearing behavior was assessed in a weekly, unbalanced panel of public behavior in a suburban/urban metropolitan area in Indiana over the course of 6 months. Trained observers collected counts of mask-wearing behavior. This study was approved by the Indiana University Institutional Review Board (2009738644A002).www.maskcount.com). Data were recorded independently for presumed males and for presumed females. The app automatically geocoded and time-stamped each observation session. In a training data set of images of mask-wearing behavior, the 17 trained observers exhibited very high agreement (κ = 0.92). This work is a longitudinal and policy-focused expansion of our prior cross-sectional study of mask-wearing behavior earlier in the pandemic.7At each site, trained observers counted correct mask-wearing behavior of all observable individuals who (1) appeared to be older than 2 yearsCorrect mask-wearing was defined as any cloth face covering, N95, or surgical mask that covered the mouth and nose, including the nostrils, and extended below the chin.6The determinants of interests were the presence of those public health policies that could influence mask-wearing. Marion County and the state of Indiana required masks for 4 months prior to the start of the study. Indiana removed its requirement on April 6, 2021, but the county requirement remained. At the federal level, the National Strategy for the COVID-19 Response and Pandemic Preparedness  requested that Americans wear masks in public for 100 days.Observation timing was described as morning, afternoon, or evening and weekday or weekend. We estimated crowd density by dividing the total number of individuals observed by the observation duration and into low, medium, and high tertiles. Weekly COVID-19 case counts and cumulative vaccination coverage were standardized by the resident population.The unit of analyses was the data collection session . Frequencies and percentages of the time-invariant and time-varying measures described the sessions. We organized the 907 sessions into a weekly longitudinal panel. The panel was unbalanced as each observation site was not observed each of the 29 weeks. Fixed-effects GLM fractional logistic regression models described the association between policy changes and the percentage of observed correct mask-wearing. Fractional logistic models are appropriate for continuous dependent variables, with values bounded between 0 and 1, which means the models are useful for proportion outcomes. Models were implemented in Stata 16, with location dummies accounting for the repeated measures, time dummies accounting for linear trends, the number of observations at each site as weights, and cluster robust standard errors. We created a fully adjusted model using all time-varying measures and excluding collinear measures and expressed coefficients as marginal effects. To illustrate correct mask-wearing over time, we graphed the weekly conditional means from the fully adjusted fractional logistic model.2 tests.In the final 10 weeks of the study, we recorded whether the observed sites had entrance signage about mask requirements, masks available for customers/patrons, hand sanitizer available, or a masked employee  stationed at the entrance. We compared percentages between location types and changes in percentages after policy changes using χThe study period included a total of 907 observation sessions with 76 100 counts of mask-wearing behavior. Overall, the mean percentage of individuals correctly wearing masks indoors was 86% (Table P < .001). Correct mask-wearing was 2 percentage points lower in the evenings . Correct mask-wearing was nearly 3 percentage points higher at locations with highest crowd density .In the fully adjusted model Table , presumeP = .032) after controlling for other factors.Neither the release of the National Strategy for the COVID-19 Response and Pandemic Preparedness in January nor the ending of Indiana's public mask requirement in April was associated with subsequent changes in correct indoor masking-wearing in the county Table . As noteIn unadjusted models, a 1-point increase in the prior week COVID-19 case rate (per 1000) was associated with a 1.3 percentage point increase in correct mask-wearing and a 1-point increase in the prior week's cumulative vaccination rate (per 100 000) was associated with 1.6 percentage point decrease in correct mask-wearing. Neither association persisted in the adjusted model.In the last 10 weeks of the study, the majority of observed indoor locations had signs requiring masks (81%). Across sites, efforts to encourage mask-wearing were least common at fitness locations and smaller retail stores. Overall, the individual policies and procedures of observation sites did not change dramatically pursuant to changes in state and national policies see the .In this longitudinal panel of direct observation of public behavior, a majority of observed individuals correctly wore a face mask during the period when a county mask requirement was in place. While concerns exist about differing state and local public health policies,11encouraged for all and only required in federal buildings and during interstate and international travel.Federal policy, in the form of a strategic plan, was not associated with behavior. January 2021's National Strategy for the COVID-19 Response and Pandemic Preparedness represented a stronger endorsement of mask-wearing in public than the prior administration.recommended masking in public.However, the CDC recommendation that fully vaccinated individuals did not need to be masked in public was associated with a significant reduction in indoor mask-wearing behavior. This finding is consistent with the 2020 increase in mask-wearing when the CDC While our fixed-effects approach has strengths, this study faces limitations in internal and external validity. Our study period did not include the introduction and ending of the county-level mandate, so we are unable to assess the impact of changes in local policy. Findings may not be generalizable to other geographies or other types of settings . We do not have a comparison group to account for other external events that may have occurred at the same time as key policy changes. For example, the CDC recommendation occurred the same week the state of Indiana announced availability of vaccinations to those 12 years and older. However, the increased availability of vaccine for more members of the public is likely not an alternative explanation as this age group did not begin to be fully vaccinated until nearly 3 weeks later .Over the course of the COVID-19 pandemic, health policies at all levels of government evolved and changed. However, mask policies, and in particular mask requirements in public, did not achieve universal mask adherence and variation existed across settings and by gender. Nevertheless, the majority behavior encouraged by local mask requirements appeared to be resilient to changes in state-level policy, illustrating the importance of local government and local public health agency actions in communicable disease prevention. CDC recommendations appeared to be influential.Public health policies, as in the case of face-masking, were not always consistent between levels of government, varied over time, and were only one concurrent intervention to address the pandemic.Local policies may still be influential even when not aligned with the state, particularly when the local policy is enforced more stringently.Federal strategic plans may not be very influential on individual behavior; however, CDC recommendations may still be important.


Personal neoantigen vaccines are considered to be effective methods for inducing, amplifying and diversifying antitumor T cell responses. We recently conducted a clinical study that combined neoantigen nanovaccine with anti-PD-1 antibody. Here, we reported a case with a clear beneficial outcome from this treatment. We established a process that includes comprehensive identification of individual mutations, computational prediction of new epitopes, and design and manufacture of unique nanovaccines for this patient. Nanovaccine started after a relapse in third-line treatment. We assessed the patient’s clinical outcome and circulating immune response. In this advanced pancreatic cancer patient, the OS associated with the vaccine treatment was 10.5 months. A peptide-specific T-cell response against 9 of the 12 vaccine peptides could be detected sequentially. Robust neoantigen-specific T cell responses were also detected by IFN-γ ELISPOT and intracellular cytokine staining. In conclusion, sustained functional neoantigen-specific T cell therapy combined with immune checkpoint targeting may be well suited to help control progressive metastatic pancreatic cancer. Pancreatic cancer is one of the most common cancers in the world. It is a devastating malignant disease with a median survival of 3-6 months and a 5-year survival rate of less than 5% , 2. As iIn recent years, immunotherapy plays an increasingly important role in the treatment of solid tumors –11. ImmuCancer vaccines have shown an advantage in treating solid tumors. Individual cancer neoantigen vaccines are capable of eliciting a powerful T-cell response and have been shown to achieve significant clinical efficacy , 14. SevAdvances in the definition of neoantigens provide a wealth of options for cancer treatment. Another advance in vaccine development is engineered nanoparticles (NPs), which serve as a vaccine delivery platform to protect the antigenic components of the vaccine while delivering innovative adjuvants that fine-tune the immune response –23. NocaIn a Phase Ib clinical trial, Ott et al. demonstrated the feasibility, safety, and immunogenicity of a combination of personalized neoantigen vaccine and PD-1 inhibition in the treatment of advanced solid tumors. Vaccine-induced T cells persist for a period of time, exhibit cytotoxicity, and can metastasize to tumors. Epitope spread and major pathological tumor reactions were detected after vaccination . Based oThe patient was female, 67-year-old. Radical resection of body and tail of pancreas was performed after the diagnosis of PDAC in Dec-2018. According to UICC TNM nomenclature the tumor was classified as a completely resected, poorly differentiated ductal adenocarcinoma stage IIA, pT3pN0M0. After surgery, the patient received 6 cycles of gemcitabine plus oxaliplatin. Unfortunately, the enhanced CT scan in Sep-2019 revealed retroperitoneal lymph node metastases. The patient received second line treatment: tomotherapy plus albumin paclitaxel. The cumulative dose for retroperitoneal metastatic lymph nodes was: Planning Target Volume (PTV): 50Gy/10f, as shown in Because of the continued rise of CA-199 and the emergence of new lesions, the patient herself strongly requested to be administrated to our clinical trial of personalized neoantigen nanovaccine combined with anti-PD-1 antibody based immunotherapy. According to the patient gene expression profile, substrings within the 15 mers that had a binding affinity of less than 500 nM for this patient’s HLA allele were considered as candidates and synthesized Methods for predicting and identifying antigenic peptides were provided in To preparation of neoantigen nanovaccine, we introduced a cysteine residue to the C-terminal of the neoantigen peptide. The free sulfhydryl group provided the potential to connect peptide to the DSPE-PEG-Mal through Michael addition reaction , and again at Weeks 3, 6, 11, 14 and 25 post-vaccination  with irradiated autogenous PBMC loaded with mixed peptide were added to duplicate wells for 18–20 hours. For example, antigen peptide 01 and antigen peptide 02 were combined to form a peptide pool, antigen peptide 03 and antigen peptide 04 were combined to form a peptide pool, and so on. Because the number of cells tested was not enough, we only did duplicate wells. AS shown in The results of in vitro IFN-γ Elispot showed that the immune response of the patient’s antigen-specific T lymphocytes to these peptides exhibiting dynamic changes over time, AS shown in This is a case of immune integration therapy with obvious benefits. The patient progressed after multi-line therapy and then received individualized nanovaccine-based immune integration therapy. Fortunately, the patient benefited from nanovaccine-based immunotherapy. The OS associated with the nanovaccine treatment was 10.5 months. The treatment was effective and had few side effects, so the patient was satisfied with the treatment.Despite the therapeutic effect of immune checkpoint blockade, most solid tumor patients treated with anti-PD-1/PD-L1 monotherapy did not achieve objective response, and most tumor regression was partial rather than complete in pancreatic cancer –29. It iConventional tumor therapeutic vaccines usually select tumor-associated antigen that are highly expressed by tumor cells, but showed limited therapeutic efficacy. Neoepitopes derived from somatic mutation appear to represent ideal targets for T vaccine–based cancer immunotherapy. Our preclinical animal studies demonstrated the number of tumor-infiltrating T cells nanovaccine treatment group was significantly higher than free vaccine treatment group, and the article is in the submission.This is a case of comprehensive treatment with obvious benefits. In the first stage, the patient benefited from postoperative chemotherapy and second-line treatment, and the disease stabilized for some time. But serum CA19-9 started to increase and CT scans showed new metastatic lesions in the liver, so the patient herself requested to be administrated to our clinical trial of personalized neoantigen nanovaccine combined with anti-PD-1 antibody based immunotherapy. After 9 cycles of vaccine treatment, CA19-9 declined rapidly and the Magnetic Resonance Imaging (MRI) scan performed 3.5 months after immunotherapy showed a remarkable regression of liver lesion and this patient had significantly beneficial results from neoantigen nanovaccine combined with immune checkpoint inhibitors.in vitro IFN-γ Elispot showed that the immune response of the patient’s antigen-specific T lymphocytes to these peptides exhibiting dynamic changes over time. In view of the importance of CD8+ and CD4+ T cells in mediating tumor cell killing, the nanovaccine, by utilizing MHC-I peptides and MHC-II peptides, was designed to activate both types of T cells. A substantial proportion of antigen-peptide-specific CD4+ and CD8+ T cells were detected. Our detection methods are limited, and we will use more methods to prove the effectiveness of nanovaccines in future studies. For example, tetramer detection, single cell transcriptome analysis, neoantigen-specific TCR clonotypes diversify, epitope spreading of T cell responses and gene expression profiles in individual neoantigen reactive T cell.The immunogenicity of each of the 12 mutation peptides administered in this study was analyzed by IFN-γ release, 9 out of 12 peptides after each cycle of injection showed a dynamic change. The results of In conclusion, we demonstrate that the T-cell response could be produced by the personal neoantigen nanovaccine in patients with advanced pancreatic cancer. The combination of functional neoantigen-specific T cells with immune checkpoint targeting therapy may help control progressive advanced pancreatic cancer. Larger randomized clinical trials are needed to identify and validate the possibility of designing a nano-vaccine for pancreatic cancer in future.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Medical Ethics Committee of Drum Tower Hospital Affiliated to Nanjing University Medical School. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.QL and BL designed the clinical trial. JSha and JD composed the manuscript and provided figures. YZ, XQ, MT, XQ, LC and CL did the work of the acquisition, analysis and interpretation of the data. JShe and JY revised the manuscript critically for important intellectual content, and agreement to be accountable for all aspects of the work, in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors contributed to the article and approved the submitted version.This work was supported by grants from the National Natural Science Foundation of China (NO. 81902914).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.