High vaccination coverage is considered to be key in dealing with the coronavirus disease (COVID-19) pandemic. However, vaccine hesitancy can limit uptake. We examined the specific coronavirus beliefs that persons have regarding COVID-19 and COVID-19 vaccines and to what extent these beliefs explain COVID-19 vaccination intentions. We conducted a survey among 4,033 residents of the Netherlands that examined COVID-19 vaccination intentions and various beliefs. Random forest regression analysis explained 76% of the variance in vaccination intentions. The strongest determinant in the model was the belief the COVID-19 crisis will only end if many persons get vaccinated. Other strong determinants were beliefs about safety of vaccines, specifically in relation to vaccine development and approval process;  benefits of vaccination; social norms regarding vaccination behavior; and effectiveness of vaccines. We propose to address these specific beliefs in communications about COVID-19 vaccinations to stimulate vaccine uptake. The COVID-19 pandemic has profoundly affected global health and well-being. Since 2020, countries worldwide have experienced high rates of illness and death caused by COVID-19, and many societies have dealt with often stringent outbreak control measures. The successful development of effective vaccines has provided a much-wanted major step toward controlling the pandemic. However, for the vaccines to be successful during outbreak control, a high and equally distributed vaccine uptake is essential. Next to possible barriers of limited COVID-19 vaccine availability and accessibility, vaccine hesitancy can also form a considerable barrier to reaching a high vaccine uptake.–––The public acceptance of vaccines has been a global concern for decades. Before the COVID-19 crisis, in 2019, the World Health Organization declared vaccine hesitancy as one of the top 10 global public health threats  by using an online survey panel . Members from this survey panel were recruited by using random samples of name and address data. The sample invited for participation to this survey was selected to be representative of the general population of the Netherlands (>18 years of age) on the basis of demographic characteristics.We conducted an online survey during March 12–22, 2021. At that time, 1.5 million of the 17.5 million residents of the Netherlands were partly or fully vaccinated against COVID-19  .We measured vaccination intention as follows. All respondents who indicated to have received an invitation for a COVID-19 vaccination but who had not yet been vaccinated were asked, “Do you want to get vaccinated against the corona virus?” Respondents who indicated not yet to be invited for a COVID-19 vaccination were asked, “If you are invited for a COVID-19 vaccination, do you then want to get vaccinated?” Both questions could be answered on a 5-point Likert scale: 1. Certainly not; 2. Probably not; 3. Don’t know; 4 Probably yes; 5. Certainly yes. The answers to both questions were taken together in 1 variable that was called COVID-19 vaccination intention.To identify the beliefs about COVID-19 and the COVID-19 vaccines that should be assessed in this study, we studied literature on sociopsychological determinants of vaccination intentions to identify main elements in mental models of persons underlying COVID-19 vaccination intentions  . The 25 We analyzed on responses of (at that time) unvaccinated respondents. Descriptive analyses were performed for vaccination intention and the 25 beliefs about COVID-19 and the vaccines. In addition, we calculated Pearson correlations (2-tailed) between vaccination intention and all 25 beliefs and between all beliefs separately.We subsequently performed a regression analysis by using Random forest (RF) . RF is aWe considered 4 types of output from the RF regression analyses. The first output was the variable importance ranking (VIR), which ranks independent and control variables in terms of how much these contribute to explaining the dependent variable. The second output was the partial dependence  that indicates the direction and strength of the relationship between the independent and dependent variable. The third output was the cumulative variance explained, which is the variance explained after adding an independent variable to the model in the sequence of the VIR. The fourth output was the total variance explained.>18 years of age) for demographic characteristics (The survey response rate was 59% (n = 4.033). The survey population was reasonably comparable to the general population in the Netherlands (eristics .+SD) response of vaccination intention was 4.2 (+1.2).Most  unvaccinated respondents indicated that they would certainly want to get vaccinated against COVID-19, 645 (17.8%) would probably want to get vaccinated, 257 (7.1%) did not know (yet), 213 (5.9%) would probably not want to get vaccinated, and 247 (6.8%) indicated certainly not. The mean (Descriptive statistics and Pearson correlations (2-tailed) with vaccination intention showed that all 25 beliefs were significantly (p<0.001) correlated with vaccination intention . Correla+1.0) for the belief about high likelihood of infection and 3.0 (+1.3) for the belief about possibility of severe illness. For loved ones, these COVID-19 risk perceptions were scored relatively higher: 3.5 (+1.1) for high likelihood of infection and 3.9 (+1.1) for possibility of severe illness. Respondents valued the likelihood to infect others if infected themselves with a value of 3.3 (+1.2).Personal risk perceptions of respondents for COVID-19 were moderate, with values of 2.9 (+1.3), and 28.3% of respondents believed that the vaccines were developed too quickly . With regard to the effectiveness of vaccinations, respondents believed that vaccines would protect them well against COVID-19 . Respondents seemed somewhat unsure about whether vaccines only protect for a short while  and whether one can still infect others after vaccination . At the time of data collection, scientific knowledge about those last 2 vaccine aspects was also limited.Safety of vaccinations was generally trusted by respondents , but som+1.2) and as a means to return to a life without COVID-19 restrictions sooner . With regard to alternatives to vaccination, respondents generally did not believe that there were (sufficient) drugs that could cure COVID-19 , and few respondents believed that they were immune to COVID-19 . Some support was found for the belief that one’s good health would protect against COVID-19 .In terms of  benefits of vaccinations, vaccinations were commonly seen as the only way out of the COVID-19 crisis , and the beliefs that most of the respondent’s family and friends , as well as most residents in the Netherlands , would get vaccinated were largely supported. Accessibility of vaccination did not seem a large obstacle because the belief that getting vaccinated would take a lot of time or effort was not strongly supported among the respondents .We found that perceived social norms were generally in favor of vaccination. Beliefs that friends and family expected that the respondent would get vaccinated . We provide the VIR with the 10 best explaining beliefs . We alsoThere was no clear selection of the 25 beliefs that explained variance in vaccination intention considerably more strongly than all other beliefs. Instead we observe a gradual progression in explanatory value of various beliefs . Because there are many intercorrelations between the beliefs , and man>65 years). We repeated the main analysis with a binary dependent variable, explaining differences between those with low vaccination intentions  and those who were unsure . Results were consistent with those of the main analyses and did not affect our conclusions.We conducted sensitivity analyses in which we repeated the main RF analyses for 3 age groups  benefits of vaccination, social norms regarding vaccination behavior and the effectiveness of vaccines were, relative to other beliefs, strong determinants of vaccination intention for persons. The strongest determinant in the model was the belief “the corona crisis will only end if many people get vaccinated.”Our study results showed strong beliefs, and the explanatory value of these beliefs, about  benefits after being vaccinated or reaching a high vaccination coverage. The belief that the COVID-19 crisis will only end if many persons get vaccinated could  explain more than half of the variance in COVID-19 vaccination intentions. It is striking that this belief seemed to be, at least somewhat, a better determinant of vaccination intentions than beliefs about personal protection against the vaccine-preventable disease or beliefs about safety of vaccines, which have often been identified as the most essential psychosocial determinants of vaccination intentions  after reports of possible severe adverse events; during the second half of our data collection, the Netherlands also temporarily suspended these vaccinations  benefits of COVID-19 vaccination, related to the relaxation of COVID-19 control measures, are likely to play a major role in vaccination decisions of persons. Providing clear perspectives with regard to these benefits might increase vaccination uptake. At the same time, it is highly essential to address the uncertainties with regard to those social benefits and to prevent future disappointments and decreases in trust and support. Second, social norms regarding peers have been shown to be an essential factor in COVID-19 vaccination intentions, which suggests the potential for norms to induce interventions to increase vaccination uptake. Future research should focus on the characterization and identification of like-minded social networks who are hesitant to vaccinate against COVID-19 to provide well-tailored interventions. Finally, it is highly essential to provide transparent and accessible information about vaccine development and approval process and the probability of potential adverse events caused by vaccination to address concerns about safety of COVID-19 vaccines.Additional information on COVID-19 vaccination intent and belief that COVID-19 vaccination will end the pandemic among persons in the Netherlands.


Oncolytic viruses have emerged as a promising modality in cancer treatment given their high synergy with highly efficient immune checkpoint inhibitors. However, their potency is limited by their rapid in vivo clearance. To overcome this, we coated oncolytic vaccinia viruses (oVV) with erythrocyte-derived membranes (EDMs), hypothesizing that they would not only remain in systemic circulation for longer as erythrocytes would when administered intravenously, but also respond to environmental pH cues due to their membrane surface sialic acid residues. For this, we developed a model based on DLVO theory to show that the acidic moieties on the surface of EDM confers it the ability to respond to pH-based stimuli. We corroborate our modeling results through in vitro cell culture models and show that EDM-coated oVV infects cancer cells faster under acidic conditions akin to the tumor microenvironment. When EDM-coated oVVs were intravenously injected into wild-type mice, they exhibited prolonged circulation at higher concentrations when compared to the unprocessed oVV. Furthermore, when EDM-coated oVV was directly injected into xenografted tumors, we observed that they were suppressed earlier than the tumors that received regular oVV, suggesting that the EDM coating does not hinder oVV infectivity. Overall, we found that EDM was able to serve as a multi-functional encapsulant that allowed the payload to remain in circulation at higher concentrations when administered intravenously while simultaneously exhibiting pH-responsive properties. In the past two decades of clinical testing, multiple attempts to develop viruses as oncolytic agents have yielded mixed results; while some oncolytic viruses (OVs) have made it to the market, there have also been notable failures in other developments ,2. TodayHowever, some obstacles remain; oVVs, being obligate intracellular parasites, are readily and rapidly cleared by a variety of host defenses . This phTo address this issue, multiple OV coating/stealthing strategies have been tested including cellular, polymer, and nanoparticle modifications. Cellular stealthing has included variously sourced stem cells to deliver OVs systemically to tumors, often taking advantage of the inflammation-homing activity of mesenchymal stem cells and has been tested with adenovirus ,11,12, hMore recently, studies have shown that cell membrane-based encapsulations of adenoviruses can confer them a pseudo-envelope, even though adenoviruses are not naturally enveloped viruses. This pseudo-enveloped virus has exhibited meaningful benefits, such as prolonged circulating half-life, immune evasion, and tumor specificity. Here, we report that this can also be applied onto the conventionally enveloped oVV by coating the oVV with erythrocyte-derived membrane (EDM). By using the nonimmunogenic erythrocyte membrane as the oVV encapsulant, we can take advantage of the privileges conferred by its receptor expression, many of which allow the erythrocyte to remain in circulation for up to 3–4 months ,25. Addi2. oVV derived from the Western Reserve strain with preferential tumor replication by the disruption of the thymidine kinase and vaccinia growth factor genes, with an inserted gene encoding an enhanced green fluorescent protein (eGFP) was kindly provided by Dr. Richard Koya (University of Chicago) and its construction is described in detail elsewhere [The human small cell adenocarcinoma line A549, human breast adenocarcinoma line MCF-7, and African green monkey kidney cell line VERO E6 were obtained from the American Type Culture Collection . We maintained these cell lines in Dulbecco modified Eagle’s media (DMEM) containing L-glutamine  amended with 10% fetal bovine serum  and 1X antibiotic–antimycotic (Gibco) in a humidified cell culture incubator at 37 °C and 5% COlsewhere . In brieStudies conducted in vitro and in the xenograft model were performed with erythrocyte-derived membrane (EDM) derived from human donor erythrocytes, while studies conducted in vivo with wild-type mice were performed with EDM derived from mouse erythrocytes. Both EDM materials were generated as previously described for the enrichment of cellular membrane by others . BrieflyEncapsulation of oVV was achieved by fine membrane extrusion as has been previously described by others ,29. Brie6 pfu/mL. From this, we further diluted the sample to yield concentrations of: 1.50 × 106 pfu/mL, 3.00 × 105 pfu/mL, and 3.00 × 104 pfu/mL. A negative control comprising only EDM without any oVV was used and labelled as 0 pfu/mL. These samples are then used for coating an ELISA plate (n = 3). After incubation, we decanted all samples from the plate and washed 4 times with PBS. We blocked the plate with a solution of PBS plus 5% BSA (0.05 g/mL) and allowed the plate to incubate for 1 hour at room temperature. The block was decanted, and the plates were washed 4 times with PBS. We added polyclonal anti-Vaccinia HRP-conjugated antibody diluted to 4 µg/mL in blocking buffer to the plate and again incubated the plate for 1 hour at room temperature. After, the antibody was decanted. The plate was again washed 4 times with PBS. Finally, 100 µL of TMB solution  was added to each well. We monitored each assay well for development and stopped the TMB reaction with 1.0 M hydrochloric acid. Absorbances of each well were read with a BioTek plate reader at 450 nm.Enzyme-linked immunosorbent assay (ELISA) detecting oVV was performed using the protocol that follows. We procured Rabbit anti-vaccinia HRP-conjugated polyclonal antibody as a detector (Abcam#87387), which was generated in rabbits against whole vaccinia virus. Serving both as a positive control and as a standard curve, freshly thawed oVV is first diluted to the same effective concentration as the extruded sample used for infecting cells; which is 3.00 × 10This ELISA is also used to determine the coating efficiency using the following steps that are performed alongside the concentration curve protocol. The extrusion product, which we conservatively assumed contains a mix of EDM-coated oVV and untouched oVV, is directly used to coat the bottom of other wells on the same ELISA plate, and then subjected to the same protocol. We hypothesize that of the mixture, only oVV will contain any reactivity against the antibody, and produce a positive HRP signal, while the EDM-coated oVV will not.Dynamic light scattering (DLS) particle sizing and zeta potential measurements were both measured using a Malvern Panalytical Zetasizer Nano ZS in accordance with the manufacturer’s user manual. For particle size measurements, we gently pipette-mixed the sample of interest and aliquoted 300 µL of the sample into a disposable semi-micro-Vis cuvette (EP0030079353). We measured the particle size using the optimized particle size protocol from the Zetasizer program. To measure the zeta potential, we gently pipette-mixed the sample of interest, diluted the sample 1:10 in DI water, aliquoted 800 uL of the diluted sample into a folded capillary cuvette (DTS1070), and finally measured the zeta potential using the optimized particle size protocol from the Zetasizer program. This was repeated across samples at different pHs. We adjusted the pH of the sample using hydrochloric acid. We again repeated the measurements at different concentrations of EDM and allowed the sample to incubate at room temperature for different amounts of time after dilution to ensure that the samples reached equilibrium.avg) [z-avg is the average diameter of the particles. We obtained dz-avg using the DLS measurements, and that diameter scales cubically with volume. Then, we obtained the number concentration of particles in the system (n) by dividing the mass concentration of particles (C) by the relative average particle volume (Vavg):In order to determine the relative interparticle forces at different pHs, we first obtained a relative interparticle distance. Others previously devised a method to approximate the interparticle distance in colloidal mixtures utilizing particle concentration (n), which can be derived from the average relative particle volume (Vavg) . We detei), given by:We assume that the zeta-potential of the suspension (ζ) is proportional to the relative magnitude of charge of the particles’ surface  the viability at 24 hours via a neutral red reuptake viability assay; and (2) total virus titer at 24 hours using a qPCR against viral DNA.For in vitro studies, A549, Vero E6, and MCF-7 cells were plated into 24-well plates at a density of 100,000 cells per well and incubated overnight for adhesion. The day following plating, we used DMEM (sans serum) “as is” for the neutral pH condition. For the acidified condition, to best mimic the pH conditions of a tumor microenvironment, we acidified DMEM with hydrochloric acid to a pH of ~5.2. Subsequently, we reconstituted the bare and coated viruses into their respective conditions, neutral or acidified, to an equivalent MOI of 0.5 and added to the cells for our infection phase, comprising 1 hour of incubation in a humidified incubator at 37 °C, 5% CO2 for one hour and a half, during which living cells will take in neutral red. After incubation, we removed the neutral red stain from the cells and then washed cells with PBS. To obtain a representative viability, we released the neutral red from live cells by first adding 50 µL of Sorenson’s citrate buffer and then adding 50 µL of 200 proof ethanol. The amount of neutral red dye present is directly correlated with the number of live cells in culture. We measured the amount of neutral red in each well using a BioTek plate reader by measuring the absorbance of the solution in the 540 nm wavelength.The neutral red reuptake assay quantitatively measures the number of viable cells. This assay was performed on infected A549, Vero E6, and MCF-7 cells 24 hours post-infection. First, we aspirated the virus-containing media from the wells and washed the cells once with PBS. Subsequently, we diluted 0.3% Neutral Red dye  100-fold in DMEM, added the diluted dye into the wells, and allowed it to incubate in a humidified incubator at 37 °C, 5% COQuantitative polymerase chain reaction (qPCR) was used for the quantification of the number of oVVs or EDM-coated oVVs present. Here, in this study, it is used in several instances. (1) To measure the concentration of EDM-coated oVVs in the extrusion product to ensure that the encapsulation did not exclude any oVVs; (2) To measure the total number of copies of oVVs that would be present in A549, Vero E6, and MCF-7 cells 24 hours post infection with their respective experimental sample; (3) Concentration of oVVs or EDM-coated oVVs present in mouse blood, organs, or tumor in their respective in vivo experiments.We extracted oVV DNA following the manufacturers protocol . Briefly, we added a volume of vDNA extraction buffer equivalent to 4 times the plated volume to each well of a 24-well plate and incubated for 10 minutes. The samples were transferred to IC column tubes, centrifuged, and washed two times with DNA wash buffer. Then, we eluted the extracted oVV DNA with 20 µL of elution buffer. Once the oVV DNA was extracted from the column tubes, we added 2 uL of the oVV DNA to 18 µL of a SYBR green containing “one step” Mastermix  with 1 µmol forward and reverse primer against the Vaccinia E3L gene. The sequences used were: forward-primer 5′-TCCGTCGATGTCTACACAGG-3′ and reverse-primer 5′-ATGTATCCCGCGAAAAATCA-3′ . A standoVV and EDM-coated oVV inoculums were prepared as described using mouse EDM and injected intravenously into 24 healthy 6-week-old female C57/BL6 mice per experimental group. Six mice from each experimental group were then sacrificed in groups at the following timepoints: 15 minutes, 1 hour, 2 hours, and 4 hours, and the maximum amount of blood was withdrawn via aortic puncture. Whole blood was then used for qPCR in accordance with the protocol outlined in 2.8. All animal experiments were conducted at Anticancer Inc. . The sacrificed mice were then dissected, and its various organs  extracted. The organs were then frozen at −80 °C until they were ready for tissue digestion, upon which viral DNA was then quantified.To prepare the mouse tissue for DNA extraction and subsequently qPCR, we first cut up to 100 mg of organ tissue into small pieces, placed organ pieces into a 1.5 mL microfuge tubes, and recorded the weight of each sample. To begin the digestion, the sliced tissue is then submerged in 400 µL a proteinase-k buffer containing 190 µL DNase free water, 190 µL 2X proteinase K buffer, and 20 µL of proteinase K. We then incubated the tissue in a water bath set at 55 °C for 2 hours. Then, we centrifuged the 1.5 microfuge tubes to pellet the tissue debris and extracted the supernatant to collect vDNA for qPCR. We ran qPCR using the same primers, program, and procedure as previously described.6 MDA-MB-231 human breast cancer cells that have been genetically engineered to stably express red fluorescent protein (RFP) into their second left mammary fat pad and allowed to grow. Once the tumor reached ~50 mm3, they were ready for dosing. Mice were sorted into 3 equal groups to receive EDM, oVV, or EDM-coated oVV; the concentration of viral inoculums was equivalent to 1 × 107 pfu in 100 uL PBS of oVV-GFP. Human EDM was used for the EDM-only control, and EDM-coated oVV. Tumors were monitored for size by 3 direction caliper measurements to compute total volume. Fluorescent imaging for green fluorescent protein (GFP) and RFP was captured, and each mouse was monitored for weight for a total of 2 weeks. All animal experiments were conducted at Anticancer Inc. . The fluorescent imaging was digitally processed to remove autofluorescent signal from the skin and fur of the mice. A digital binary mask is then generated using signal in the red fluorescent channel gating for presence of cancer cells, and then used to look for co-localizations of GFP.Tumor xenografts were generated orthotopically using surgical protocols as described elsewhere. In brief, 6 female NOG mice (5–6 weeks old) were injected with a single dose of 5 × 10Since we know that the original oVV contains an uncharacterized mix of enveloped and unenveloped viruses, we can expect that the extruded product will contain oVV, EDM-coated oVV, and empty vesicles. Using a rabbit polyclonal antibody that binds to oVV (both enveloped and unenveloped forms), and the pre-processed oVV/EDM mixture, we were able to generate an ELISA standard curve that shows a linear relationship between the amount of detectable oVV and its resulting colormetric signal A. We theTo measure the consistency of the extrusion process, we characterized the product size of the extruded product in contrast with the encapsulating membrane material and the starting virus across a triplicate of runs E, and 2FThe presence of sialic acid residues that can exist in an equilibrium comprising protonated and deprotonated residues presents an opportunity to study the attractive and repulsive forces governing EDM stability at different pHs A,B.As we incrementally change the pH from 7.5  to ~4.5 (acidic), we observed that the zeta potential of EDM increase in a nonlinear fashion, which can only be described as gradual between the range of 7.5–6, going from −34 mV to −20 mV, and increasingly steep between 6 and 4.5, going from −20 mV to +5 mV A. CorrelTo better understand the relationship between zeta-potential and particle size, we set up a framework of analysis to quantify a relative repulsive force between the particles. This shows the general trend of diminishing repulsive force with increasing acidity. Our results here very coarsely show an exponential relationship between the relative repulsive force and decreasing pH from 6 to 4. Specifically, within the range of pH 6 to 4, for every decrease in pH of 1, which corresponds to an increase in H+ concentration of one order of magnitude, we observe a decrease in the relative repulsive force of two orders of magnitude. It is also at this pH range that the repulsive forces have undergone a decrease in an order of magnitude relative to the physiological pH, suggesting that the original repulsive force is an order of magnitude higher than the attractive Van der Waals force at physiological pH C and 4D.Findings of aggregation and fusion of EDM in acidic conditions point to a possibility for a lysosomal-like infection mechanism of the coated virus into cancer cells. To probe this hypothesis, we assayed for cell viability and total virus titer. Cell viability, as assayed using the neutral red reuptake assay, trended downwards across the experimental samples ranging from (1) neutral pH + oVV; (2) acidic pH + oVV; (3) neutral pH + EDM-coated oVV; and (4) acidic pH + EDM-coated oVV. For comparison, we provided a negative control for the cells that were unperturbed by any environmental cues, as well as a vehicle + environmental control where we perturbed the system with EDM and acid without introducing any oVV (3). Our results here show much higher viabilities for our controls than for our experimental samples. For the oVV, our results only show a significant increase in cancer cell killing in the MCF-7 cell line with increasing acidity. However, the most substantial viability decrease emerges from the comparison between the cell killing of the bare virus and coated virus, at both the neutral pH and the acidic pH. This shows that the coating of the virus improves on its ability to kill cancer cells at all pHs. Lastly, by comparing the cell viabilities between neutral and acidic samples of the coated virus, we see a slight but not statistically significant improvement in the killing rate of the cancer cell B, and 5CTo better understand the role of environmental cues in viral-mediated oncolysis, we quantified the viral titer in the cell lysate using qPCR to obtain an intracellular viral concentration. The exact concentrations were obtained by fitting the CT values onto a standard curve with known viral concentrations. This viral concentration is then normalized to the neutral red assay readout to normalize our results to the number of remaining cells left in the culture. Here, we see the intracellular virus titers increase across the experimental samples ranging from (1) neutral pH + bare virus; (2) acidic pH + bare virus; (3) neutral pH + coated virus; and (4) acidic pH + coated virus E, and 5FPast studies have shown that cell membrane-coated nanoparticles resist clearance and were able to persist in circulation for longer without sequestration into off-target organs . Here, wTo allay concerns that the coating of the virus alters its physicochemical properties, and thus diverts the viral payload to off-target organs where it becomes ineffective, we characterize the biodistribution of the virus across various organs of the mouse at the terminal timepoint of our PK study. Our results here show that both oVV and EDM-coated oVV share similar biodistributions, except for the liver, where EDM-coated oVV is present in lower concentrations B. To comGiven the results from our in vitro studies, we then sought to understand its physiological correlation as a treatment in an in vivo cancer model. Here, we chose the MDA-MB-231 breast cancer tumor as it has been shown to establish acidic tumors, and its stable expression of RFP will allow us to monitor the progression of the cancer in real time without harming the mouse. The viruses are injected intratumorally in accordance with our timeline A. This pIn tracking the size and intensity RFP signal, imaging results at day 7 qualitatively show that while the virus by itself can suppress the growth of the tumor; however, the coated virus is able to exert a greater influence on the tumor and can almost eliminate it B, left. In our external tumor size measurements, we also observed at 7 days post-injection that the tumors that received the uncoated virus continue to measure similarly to the negative control in size, while the tumors that were injected with the coated viruses had begun to diverge and shrink in size. This increment in tumor shrinking rate indicates that the coated viruses were able to infect the cancer cells much faster and initiate their replication before the uncoated viruses were able to do so. The uncoated viruses were also eventually able to suppress the growth of the tumor to equal the oncolysis of the coated virus but taking seven more days to do so. This suggests that it took a longer time for the number of uncoated viruses to build up to a threshold concentration for the oncolysis to take place C.Our work here builds on previous studies showing that the encapsulation of oncolytic viruses with cell membranes improves the infectivity of oncolytic viruses both in vitro and in vivo ,27. PastTo generate these non-viral enveloped OVs, we used erythrocyte membrane as the non-virus envelope. Erythrocyte membrane derivation has been fine-tuned over decades since it was discovered and is well characterized. Erythrocyte membranes also bear many hallmarks of its original cell, including the expression of cell surface markers that allow it to remain in systemic circulation for longer periods of time. This could be important for the systemic administration of OVs.Our studies show that, after encapsulation, the EDM undergoes consistent reduction in size from 1000 nm to a very uniform distribution of a new EDM-coated oVV with an even distribution peaking at a size of 450 nm, with a low PDI of 0.274 F. It is It is important to note here that at the last step of oVV production, lysis of the cells used to grow the oVVs, releases both IMVs and EEVs and the all the oVVs are collected after purification. Studies have shown that these IMVs often comprise the majority of the entire viral population, with EEVs constituting the remainder . The comBy coating the oVV with EDM, we recapitulate the functionality granted to the virus by the envelope. The envelope is a key component for many enveloped viruses as viral surface proteins directs infection. While EDM-coating does not bear any viral proteins, it can disguise the virus as an erythrocyte, which is nonimmunogenic and, through the expression of CD47 expressed on its surface , prevents the virus from phagocytosis ,25. We fTo test our hypothesis in a controlled environment, we treated several cell lines in vitro following the previously defined experimental groups of: (1) neutral pH + bare virus; (2) acidic pH + bare virus; (3) neutral pH + coated virus; and (4) acidic pH + coated virus. We also prepared negative controls with no treatment and environmental/vehicle controls where the cells are subject to pH perturbations in the presence of EDM but without any viruses. No significant differences were observed between the negative control and the environmental/vehicle control, which shows that neither the pH nor the presence of EDM were cytotoxic to the cells. Minimal differences were observed between experimental groups 1 and 2, with group 2 presenting as slightly more cytotoxic B, and 5CWhile our results also yielded no significant differences in viability between the coated virus groups, we detected significant differences in the intracellular viral concentration whether the infection was achieved under acidic or neutral conditions A–F. To qIn the other arm of our study, we continue to explore the retention of circulating viruses that can be conveyed onto the oVV through encapsulation. Manufactured oVV can be cleared or deactivated very quickly by many different mechanisms, such as sequestration into the liver or neutralization by complement proteins . Our expGiven that the pharmacokinetics of the virus was altered by the coating A, it is To measure the translation of this increase in infectivity into a physiologically relevant in vivo model, we chose (1) the MDA-MB-231 breast cancer xenograft model; and (2) to administer the virus intratumorally. Breast cancer was chosen as studies have shown that it can develop acidic tumors , and theOur fluorescent imaging showed a decrease in RFP signal as we compare between the mice that only received EDM, the mice that received the oVV, and mice that received the EDM-coated oVV. This proves that the EDM-coated virus can trigger cancer cell death faster than the oVV itself. Both groups did not show much GFP, which is a direct result of the rapid cancer cell death upon oVV infection and its subsequent apoptosis. This would cause the transcribed GFP to leak out of the tumor, and it would then cease to be detected B.The tumors that received EDM-coated oVV demonstrated a much faster tumor shrinking response when compared to the tumors that received the oVV C. This eIt is also important here to note that, at the end of the study, both the coated and uncoated viruses managed to achieve the same efficacy, noting that the virus is efficacious, albeit slower to act. Ultimately, our goal here was to demonstrate two things: (1) that biological membrane coatings that are not viral envelope can still assist oVV therapy overcome certain barriers, in this case achieving prolonged circulatory retention without sacrificing its infectivity, and (2) that these coating may present stimuli responsive properties that scientists and clinicians can take advantage of when considering the development of a therapeutic. We also would like to add that, since we utilized a xenograft mouse model, there would be a lack of clearance and neutralization of the native oVV. However, had this study been performed in an immune competent mouse, the differences could have been much bigger because the slower acting therapeutic would be more liable to immune clearance, even when injected intratumorally.In conclusion, our results show that coating oVVs with EDM is a promising strategy for addressing oVVs’ clinical limitations. Coated oVVs demonstrated not only the ability to remain in circulation for longer, but also showed enhanced infectivity through either better or faster uptake of the virus into the cells. This improved infectivity has also demonstrated the potential to be enhanced within an acidic environment, which may be a way to target the infection to within the tumor as it can enter cancer cells faster. Our model built on DLVO theory not only suggests that this rapid infection can be further improved through a reduction in external repulsive forces, but also by increasing the attractive forces that the payload has for its target.


Chronic cough affects approximately 10% of adults. The lack of ICD codes for chronic cough makes it challenging to apply supervised learning methods to predict the characteristics of chronic cough patients, thereby requiring the identification of chronic cough patients by other mechanisms. We developed a deep clustering algorithm with auto-encoder embedding (DCAE) to identify clusters of chronic cough patients based on data from a large cohort of 264,146 patients from the Electronic Medical Records (EMR) system. We constructed features using the diagnosis within the EMR, then built a clustering-oriented loss function directly on embedded features of the deep autoencoder to jointly perform feature refinement and cluster assignment. Lastly, we performed statistical analysis on the identified clusters to characterize the chronic cough patients compared to the non-chronic cough patients.The experimental results show that the DCAE model generated three chronic cough clusters and one non-chronic cough patient cluster. We found various diagnoses, medications, and lab tests highly associated with chronic cough patients by comparing the chronic cough cluster with the non-chronic cough cluster. Comparison of chronic cough clusters demonstrated that certain combinations of medications and diagnoses characterize some chronic cough clusters.To the best of our knowledge, this study is the first to test the potential of unsupervised deep learning methods for chronic cough investigation, which also shows a great advantage over existing algorithms for patient data clustering. Chronic cough (CC), or cough lasting more than 8 weeks, affects approximately 10% of adults and is a common outpatient complaint. Affected individuals can cough hundreds or even thousands of times per day . Common Machine learning methods have been extensively used to analyze EMR data to predict and classify disease states , 7, modeOne potential solution is to employ unsupervised clustering analysis to uncover subgroups within clinical data . UnsuperUsing EMRs of a large statewide academic health system and a public county hospital, we constructed two cohorts: (1) patients with CC and (2) patients with a cough who did not meet the CC criteria. We extracted patients with cough aged 18–85 years with at least one outpatient visit between 10-01-2005 and 09-30-2015.The CC  cases arWe first applied the deep clustering algorithm with auto-encoder embedding (DCAE) algorithm to generate clusters by using both Non-CC and CC data. The compared methods include: (1) K-means Clustering: In order to fairly compare with other methods, we firstly apply the PCA algorithm to reduce the dimensionality of the original data to 256, then use the K-means algorithm for clustering. (2) Hierarchical agglomerative clustering (HC): The hierarchical agglomerative clustering algorithm introduced in . (3) ConFigure Finally, we also found that the number of CC dominated clusters (which means over 50% of the instances in a cluster are CC) are more than Non-CC dominated clusters (shown in Table Our DCAE model (shown in Eq. ) uses thBy applying the DCAE algorithm, we identified four clusters with high purity values from the results when the number of clusters is set to 21. Three of them are CC clusters , CC-1 and CC-2 have more than 50% of the patients having symptom diagnoses. In addition, over or close to 50% of the patients in CC-1 and CC-2 have mental disorders. The smallest cluster (CC-3) also has 36.36% of patients having mental disorders as a diagnosis. Only 11.88% of patients in the Non-CC have mental disorder diagnoses.From the medication aspect, more than 50% of the patients in the CC clusters were prescribed antiasthmatic and bronchodilator medications, whereas only 12.8% of Non-CC patients in the analysis cohort were prescribed these medications in this category. About 40% of the patients in the CC clusters take corticosteroids, whereas only 12.05% of the Non-CC patients take this type of medication. These three types of drugs are often used to treat patients with CC –21, chrop value < 0.0001) between the Non-CC and CC clusters. We only listed the diagnoses that have over 10% of CC patients in any CC cluster to demonstrate the significance of those diagnoses on CC patients. Over 50% of patients in the CC cluster have a diagnosis of ‘Cough’, whereas only 3.76% of the patients in the Non-CC cluster have a diagnosis of ‘Cough’. The diagnoses of ‘Shortness of Breath’ and ‘Other Dyspnea’ are more than 15% in any CC cluster, but only less than 2% in the Non-CC cluster.Given that CC is mainly a type of respiratory disease and the percentage of respiratory diagnoses of each CC cluster is over 90%, and there is no specific ICD code for CC, we further analyzed the significance of each diagnosis under this respiratory category to investigate whether some of the diagnoses are more used for or related to CC. Table We further analyzed the significance of diagnoses under categories circulatory system diagnosis or endocrine disorders to investigate whether some of the diagnoses are more related to CC, with the results are shown in Tables In this study, CC patients were extracted from the EMR using the rule-based algorithms developed by Weiner et al. . Given tComparison across different CC clusters reveals significant differences in comorbidities and use of medications. These significant differences across different CC clusters might be due to the underlying heterogeneity in CC development in different patients. A better understanding of the causes of underlying heterogeneity in CC patients and disease etiology can be helpful in the development of effective treatment strategies and individualized patient care. In this research, due to a large number of features, the unsupervised learning methods are based on the clinical features of diagnosis only. Other structured data, such as medication and lab tests, were not explicitly utilized for the unsupervised learning even though we showed that there are significant differences in medications among these clusters. These observations suggest that there is potential to improve the clustering performance if medication and lab test results are utilized for cluster generation. On the other hand, the high dimensionality of the features will require more innovations in integrative clustering algorithms that are robust to noise. Furthermore, since CC is a syndrome that is not coded, the unstructured clinical notes may contain more information that can be used. This is the first study to apply deep learning based unsupervised learning approach to characterize CC using EMR data. This study suggests that deep learning-based unsupervised learning can be used in payer databases to estimate the burden of CC and the allocation of healthcare resources. Currently, we focus on structured data. However, we are carrying out a parallel study on extracting features from the clinical notes of this patient cohort which will allow further comparison of the contribution of unstructured data in patient clustering and stratification.In this paper, we developed and evaluated deep learning-based unsupervised learning algorithms to identify clusters of CC patients. The proposed DCAE deep learning method could better aggregate subjects into different groups than other methods while preserving the intrinsic structure of data and minimizing the clustering loss to manipulate the embedded feature space appropriately. We applied statistical analyses on the diagnoses data within the EMR to characterize and compare the CC and non-CC patient clusters identified using the DCAE. The results indicate that we can identify characteristics of patients within different CC clusters and between CC and non-CC clusters. This is the first study to apply deep learning based unsupervised learning approach to characterize CC using EMR data. Because CC is associated with other chronic conditions, for future work, further analysis will be carried out to extract the sub-clusters within the large CC cluster by considering the combination of diagnosis, medication, and lab tests, as well as potential information from unstructured clinical notes.For the current study, we utilized the structured data within the EMR to explore the clusters of CC and Non-CC cases, defined as positive instances of cough encounters within each patient's study period. Patient features were constructed from the medical history of the study period. In this study, we used the diagnoses as clinical features to generate clusters of patients for unsupervised learning algorithms. After the clusters are identified, diagnoses, medication, and lab tests are used to evaluate the differences among the patients. The patient diagnosis in our data is encoded with the International Classification of Diseases (ICD-10) codes. Considering a large number of distinct ICD codes, we used the first three digits of ICD-10. There is a total of 1228 unique three-digit ICD-10 codes in the study cohort. To construct the data representations, we applied Bag-of-Word , a naturf (.) and decoder network g(.) where the encoder network aims at compressing the data into the hidden space with lower dimensionality and the output network is exploited to reconstruct the original input from the hidden layer data. More specifically, given the input data X = TRN×d, the objective function of DAE can be formulated as follows:Our clustering method is based on Deep AutoEncoders (DAE) framework . GeneralAs shown in Eq. , DAE aimAs discussed in the previous section, the DAE framework can learn compressed but meaningful encoding of the input data. Based on the DAE, we proposed a novel end-to-end deep clustering algorithm with auto-encoder embedding (DCAE) to aggregate the cough patients into different clusters. As shown in Fig. entclass1pt{minimaFor parameter settings of our DCAE model, we tune the parameter α in Eq.  from 0.1Descriptive statistics were performed to determine patient characteristics associated with different cohorts or clusters. Characteristics of individuals presenting to different cohorts or clusters were calculated using mean and standard deviation for continuous variables (age), and frequency or proportion for categorical variables. Comparisons between the cohorts or clusters were performed using the Chi-square test for categorical variables. All statistical analyses were performed using SAS version 9.4 .


What is the association of an ischemic event vs a bleeding event with mortality among patients with myocardial infarction (MI)?In this cohort study of 86 736 patients in Sweden with a recent MI. Recurrent ischemic events were more common and associated with higher risk of 1-year mortality compared with bleeding events.The findings of this study suggest that, although strategies to individualize intensity and duration of antithrombotic treatment after MI typically assign similar weight to bleeding and ischemic events, the associated mortality risk may be higher for ischemic events. Antithrombotic treatment after myocardial infarction (MI) should be individualized based on the patient’s risk of ischemic and bleeding events. Uncertainty remains regarding the relative prognostic importance of the 2 types of events, and further study would be useful.To compare the association of ischemic vs bleeding events with mortality in patients with a recent MI and assess whether the relative mortality risk for the 2 types of events has changed over the past 2 decades.A cohort study based on nationwide registers in Sweden, 2012-2017, was conducted. Data were analyzed between July 2021 and May 2022. Patients with MI who were discharged alive with antithrombotic therapy  were included in the analysis.The incidence of a first ischemic event  or bleeding event  up to 1 year after discharge and the mortality risk up to 1 year after each type of event were assessed. Cox proportional hazards regression models were used to estimate adjusted hazard ratios (aHRs) for 1-year mortality after an ischemic and bleeding event vs no event, and after an ischemic vs bleeding event. Adjusted HRs for mortality after ischemic vs bleeding events were compared among patients discharged in 1997-2000, 2001-2011, and 2012-2017.Of 86 736 patients discharged after MI in 2012-2017 , 4039 individuals experienced a first ischemic event (5.7 per 100 person-years) and 3399 experienced a first bleeding event (4.8 per 100 person-years). The mortality rate was 46.2 per 100 person-years after an ischemic event and 27.1 per 100 person-years after a bleeding event. The aHR for 1-year mortality vs no event was 4.16  after an ischemic event and 3.43  after a bleeding event. When the 2 types of events were compared, the aHR was 1.27  for an ischemic vs bleeding event. There was no statistically significant difference in the aHR of an ischemic vs bleeding event in 1997-2000, 2001-2011, and 2012-2017.In this nationwide cohort study of patients with a recent MI, postdischarge ischemic events were more common and associated with higher mortality risk compared with bleeding events. This cohort study compares mortality associated with recurrent ischemic vs bleeding events during antithrombotic therapy among patients in Sweden with myocardial infarction. European2 and US3 guidelines therefore recommend individualized antithrombotic therapy based on the patient’s ischemic and bleeding risk.6 For example, the duration of DAPT can be prolonged8 for patients with high ischemic risk and without high bleeding risk, and DAPT can be shortened or deescalated10 for those with a high bleeding risk and lower ischemic risk.13Antithrombotic treatment is used to reduce the risk of recurrent ischemic events in patients with acute myocardial infarction (MI). Although most patients receive dual antiplatelet therapy (DAPT) with aspirin and a P2Y12 inhibitor,16 uncertainty remains regarding the relative importance of ischemic vs bleeding events among patients with a recent MI. Several studies have assessed the mortality associated with recurrent MI and bleeding in patients with acute coronary syndrome or undergoing percutaneous coronary intervention (PCI).17 However, most of these studies were based on randomized clinical trials21 with uncertain generalizability to patients seen in routine clinical practice, assessed events occurring beyond 1 year after PCI when DAPT has been terminated for most patients,13 or compared the mortality risk associated with recurrent MI vs bleeding, although ischemic stroke is an important ischemic event whose risk can be modified using antithrombotic treatment.13 In addition, during the past decades, reperfusion therapy, improved secondary prevention,23 and high-sensitivity cardiac troponins have been introduced; these advances may limit the applicability of previous studies to contemporary clinical practice. Data on the occurrence of and the mortality associated with ischemic and bleeding events may inform the bleeding vs ischemic risk trade-off for treatments of patients with MI.Although the strategies to individualize antithrombotic treatment are based on calculations that give equal weight to ischemic and bleeding events,In this study, we used nationwide registers in Sweden to compare the association of ischemic vs bleeding events with 1-year mortality in patients with a recent MI who received antithrombotic therapy. We also examined how the association of an ischemic vs bleeding event with mortality had changed over the past 2 decades.24 a national register including patients with acute MI admitted to a coronary care unit or other specialized inpatient facilities in Sweden. We linked these data to the National Patient Register (NPR)25 (data on comorbidities and study outcomes) and the Swedish Population Registers26 ; details regarding the data sources are provided in the eMethods and eTable 1 in the STROBE) reporting guideline.In this cohort study, we used data from the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART),International Statistical Classification of Diseases, 10th Revision [ICD-10] code I21) who were enrolled in the SWEDEHEART registry from January 1, 1997, to December 31, 2017, and were discharged alive. For patients with multiple admissions during the study period, 1 admission was randomly selected. We excluded patients discharged without any antithrombotic treatment  or with missing data on any of the covariates used in the primary analysis  or in the NPR (days 31-365 after discharge) with ICD-10 code I21 as primary diagnosis. Events registered in SWEDEHEART during the first 2 days after discharge and in the NPR during the first 30 days after discharge were not considered to avoid including multiple entries of the index MI event. Ischemic stroke (days 1-365 after discharge) was defined as hospitalization registered in the NPR with ICD-10 code I63 as the primary or secondary diagnosis  and as we considered it more likely that a bleeding event occurred immediately after an ischemic event  than an ischemic event occurring immediately after a bleeding event.23 In the primary analysis, only data from 2012-2017 were used. In the secondary analysis assessing potential changes in the association of mortality with ischemic vs bleeding events, data from all 3 time periods were used.Patients were categorized based on their date of discharge into 3 time-periods that were selected to broadly reflect different paradigms for treatment of patients with MI: 1997-2000 (before routine PCI and DAPT were introduced), 2001-2011 (when routine PCI and DAPT were implemented) and 2012-2017 (contemporary practice with wide use of more potent P2Y12 inhibitors as part of DAPT).We described the cumulative incidence of ischemic and bleeding events in the first 365 days after discharge from the index MI using Kaplan-Meier methods. Patients were followed up from discharge until an ischemic or bleeding event, death, end of the study , or 365 days after discharge.28 We used Cox proportional hazards regression models with all-cause death as the outcome to estimate crude hazards ratios (HRs) and adjusted HRs (aHRs) for 1-year mortality associated with an ischemic and bleeding event compared with no event. In this analysis patients were followed up from discharge after their index MI to death, 730 days after discharge, 365 days after an ischemic or bleeding event, or end of the study . Patients who experienced their first bleeding or ischemic event after 365 days from discharge were censored at the time of this event. The adjusted Cox proportional hazards regression model was adjusted for sex, age, year of discharge, comorbidities (as measured at the index MI), and discharge medications, including antithrombotic treatment ; this method allows for patients to change exposure category during follow-up to estimate risk of the outcome associated with each exposure category.Next, we restricted the analyses to patients experiencing an ischemic or bleeding event within 365 days after discharge from the index MI. Starting from the day of the ischemic or bleeding event, patients were followed up until death, end of the study , or 365 days after the event. We used Cox proportional hazards regression models to estimate crude HRs and aHRs for 1-year mortality associated with an ischemic event compared with a bleeding event. In addition to the variables listed in eTable 1 in the To assess whether the risk of mortality after an ischemic vs bleeding event had changed across time periods, data from all 3 periods  were used. In each time period, we calculated the crude HRs and aHRs of mortality for an ischemic vs bleeding event among patients who experienced either type of event. We tested for differences in the HR by using an interaction term between time period and ischemic vs bleeding event.One sensitivity analysis was performed to assess the potential association between additional variables, including smoking status, hemoglobin level, and estimated glomerular filtration rate, and the results. In this analysis, we included only patients with data available on the additional variables and performed the primary analyses with and without adjustment for the additional variables. In a post hoc sensitivity analysis, we categorized patients experiencing an ischemic event and a bleeding event on the same day they were exposed to a bleeding event.Three post hoc analyses were performed: (1) the ischemic event definition was restricted to include only recurrent MI, (2) separate analyses were performed for those discharged with OAC and without OAC, and (3) potential differences in short-term mortality were assessed for the 2 types of events, HRs were estimated for mortality within 30 days after an ischemic vs bleeding event.P < .05 was used and all tests were 2-sided. All statistical analyses were performed using Stata, version 15 (StataCorp LLC).Hazard ratios are presented with 95% CIs. A significance level Characteristics at the index MI of patients discharged in 2012-2017 (n = 86 736) are reported in When assessing the characteristics of patients by their postdischarge event status , we noteDuring 365 days after discharge from the index MI, 4039 patients experienced a first ischemic event , and 3399 patients experienced a first bleeding event  A. A totaIn the analyses comparing the risk of 1-year mortality after an ischemic event and bleeding event vs no event, 9671 patients died during follow-up. Of these, 1292 patients died after a first ischemic event , 715 died after a first bleeding event , and 7664 died after no event . Compared with no event, both ischemic events  and bleeding events  were associated with an increased risk of death . In the P values for interaction vs 1997-2000: P = .91 for 2001-2011 and P = .65 for 2012-2017).Characteristics of patients discharged in 1997-2000, 2001-2011, and 2012-2017 are reported in eTable 5 in the Adjustment for smoking status, hemoglobin, and estimated glomerular filtration rate did not significantly change the estimated mortality risks. Among the 77 293 patients  with data on these variables available, the aHR adjusted for variables in eTable 1 in the In analyses restricting the ischemic event definition to recurrent MI, the difference in the mortality risk associated with the 2 types of events was smaller than in the primary analysis .13 Third, we included only ischemic and bleeding events occurring after discharge from the MI because the risk of these events guides antithrombotic therapy; some previous studies35 have also included events during in-hospital stay for the initial MI or PCI. Taken together, by using nationwide registers to include more than 85 000 patients with MI seen in routine clinical practice with almost complete follow-up regarding hospitalizations and death, our study adds to the knowledge regarding the relative incidence of and mortality risk associated with ischemic vs bleeding events.In contrast to our findings, previous studies of patients with a recent MI or PCI have found similar mortality risks associated with recurrent MI vs major bleeding event.36 The bleeding definition used in our study included any bleeding that led to or occurred during rehospitalization; as such, the definition was broad and included both minor and major bleedings. Although it is likely that the mortality risk associated with a bleeding event would be higher in analyses restricted to severe bleedings, such an outcome definition would also lead to a lower incidence rate of bleeding. Despite the use of a broad bleeding outcome, we found that the incidence of recurrent ischemic events was approximately 20% higher than that of bleeding events.The mortality risk associated with a bleeding event varies substantially depending on the definition of such events.37 Using the aHR and incidence rates in our study population, the population-attributable fraction was 10.1% for ischemic events and 5.2% for bleeding events, indicating that ischemic events might have a larger total influence on mortality in the population. In addition, we defined ischemic and bleeding events based on diagnoses registered during hospitalizations. Given that deaths due to recurrent ischemic events that occur without earlier hospitalization  are likely to be more common than such deaths from bleeding events, it is possible that our study has underestimated the relative risk of death associated with an ischemic vs bleeding event.When using risk-based strategies for guiding treatment decisions, it is necessary to not only consider the relative mortality risks but also account for the incidence of the events of interest. It may be best to guide clinical decisions by the absolute risk increase or reduction conferred by the treatment, and this estimate depends on the incidence of the event, the relative risk of the event associated with the treatment and, potentially, the association of the event with death. One way of approximating the importance of an exposure while accounting for both how common it is and its association with mortality is to calculate the population-attributable fraction.38 and PCI31 may not be generalizable to contemporary practice, we assessed whether the association of ischemic vs bleeding events with mortality had changed during the past 2 decades. In accordance with a recent meta-analysis of 16 trials from 2008 to 2020,17 we found no significant interaction between time period and type of event with respect to the association with mortality risk. However, as has been described in previous studies,39 the rates of ischemic events in 2012-2017 were less than half of those in 1997-2000 and the rates of bleeding events had nearly doubled. Based on the data in our study, the population-attributable fraction for ischemic events had decreased from 16.5% to 10.1% and increased for bleeding events from 2.8% to 5.2% during the study period.Because data from more than a decade ago, before broad use of potent P2Y12 inhibitors40; as in previous studies, the estimates from our analyses should be considered as theoretical estimations of the relative importance of ischemic vs bleeding events with respect to mortality risk. Second, although we adjusted our analyses for many covariates, the possibility of confounding due to unmeasured patient characteristics affecting both the risk of ischemic vs bleeding events and mortality cannot be ruled out. Third, although validation studies in Swedish registers have shown high positive predictive values for cardiovascular and bleeding events,27 misclassification and underreporting of events might have introduced bias in our analyses. Fourth, we only assessed and compared the risk of mortality associated with ischemic and bleeding events although these events may also be differentially associated with reductions in quality of life and long-term comorbidity.This study has limitations. First, because ischemic and bleeding events are not interventions, it is not possible to define a causal effect of these exposures on mortalityIn this nationwide study of patients with a recent MI, we assessed the association of postdischarge ischemic events and bleeding events with mortality. We found that ischemic events were more common and associated with higher mortality risk as compared with bleeding events.


Is a 30-day, automated text message–based transitional care management program associated with a reduction in readmissions or emergency department visits after hospital discharge?In this cohort study, an automated text message–based program was associated with a statistically significant 41% lower odds of 30-day use of acute care resources.These findings suggest that primary care–based transitional care management programs can consider automated texting strategies to augment support for patients after hospital discharge with limited added burden to staff. This cohort study assesses whether a 30-day intervention using automated texting is associated with reductions in use of acute health care resources among patients discharged from the hospital. Posthospital contact with a primary care team is an established pillar of safe transitions. The prevailing model of telephone outreach is usually limited in scope and operationally burdensome.To determine whether a 30-day automated texting program to support primary care patients after hospital discharge is associated with reductions in the use of acute care resources.This cohort study used a difference-in-differences approach at 2 academic primary care practices in Philadelphia from January 27 through August 27, 2021. Established patients of the study practices who were 18 years or older, were discharged from an acute care hospitalization, and received the usual transitional care management telephone call were eligible for the study. At the intervention practice, 604 discharges were eligible and 430  were enrolled in the intervention. At the control practice, 953 patients met eligibility criteria. The study period, including before and after the intervention, ran from August 27, 2020, through August 27, 2021.Patients received automated check-in text messages from their primary care practice on a tapering schedule during the 30 days after discharge. Any needs identified by the automated messaging platform were escalated to practice staff for follow-up via an electronic medical record inbox.The primary study outcome was any emergency department (ED) visit or readmission within 30 days of discharge. Secondary outcomes included any ED visit or any readmission within 30 days, analyzed separately, and 30- and 60-day mortality. Analyses were based on intention to treat.A total of 1885 patients  representing 2617 discharges  were included in the analysis. The adjusted odds ratio (aOR) for any use of acute care resources after implementation of the intervention was 0.59 . The aOR for an ED visit was 0.77  and for a readmission was 0.45 . The aORs for death within 30 and 60 days of discharge at the intervention practice were 0.92  and 0.63 , respectively.The findings of this cohort study suggest that an automated texting program to support primary care patients after hospital discharge was associated with significant reductions in use of acute care resources. This patient-centered approach may serve as a model for improving postdischarge care. A variety of postdischarge care management strategies aim to bridge gaps in care and identify needs early, often with the goal of reducing subsequent use of acute care resources.5The period after discharge from an acute care hospitalization is a vulnerable time for patients9 which aligns with Medicare’s billing requirements for transitional care management (TCM) services.10 This approach has proven effective in some settings in reducing unplanned readmissions; however, the calls are limited in scope and present a significant operational burden.12 In our experience, the calls can be time intensive, often go unanswered, and generally connect with patients only once, early in the course of their recovery.A common approach is to use a single primary care–based, nurse-led telephone call to identify needs shortly after discharge,14 have demonstrated reductions in readmissions with limited added burden to staff. Text messaging has also been associated with higher rates of engagement compared with calls.15To overcome these limitations, we sought to test a model of postdischarge care management outreach using automated text messaging with 2-way capability. This approach has the potential to overcome several limitations of call-based programs: (1) automation allows scaled-up outreach, conserving staff time for patients with identified needs; (2) 2-way texting offers a low-friction medium for patients to initiate interactions; and (3) texting allows for asynchronous communication. Prior studies using automated calls as a tool to support postdischarge care managementWe developed a 30-day postdischarge intervention using automated texting, implemented it in a single primary care practice, and compared the intervention practice with a control practice. We tested the hypothesis that the intervention would be associated with reductions in postdischarge use of acute care resources. In addition, we evaluated patient engagement and experience.STROBE) reporting guideline.We designed and implemented a new 30-day postdischarge intervention using automated texting to supplement the standard of care. This was implemented in a single primary care practice within Penn Medicine from January 27 through August 27, 2021. We compared changes in use of acute care resources among hospital discharges of our intervention practice and a control practice during the 30 days after discharge using a cohort study design with a difference-in-differences approach. Both practices are housed in the same building 1 floor apart, are served by Penn Medicine primary care clinicians and staff, and have similar patient populations. This study was determined by the University of Pennsylvania Institutional Review Board to meet criteria for quality improvement and therefore did not require review or informed consent. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (Eligible patients in both the control and intervention practices were adults (18 years and older) who were discharged from an acute care hospitalization and who were eligible for and received a TCM telephone call. Exclusion criteria for the standard-of-care TCM telephone calls are (1) planned chemotherapy admissions; (2) certain scheduled surgical procedures, including spinal surgery, joint replacements, gastric bypass, transurethral resection of the prostate, gynecological procedures, and transplants; and (3) obstetrics admissions.16 Patients were then screened for exclusion criteria based on data available in these reports and, where available, electronic medical record (EMR) data. Patients could be enrolled for each discharge during the study period.Patients were identified for inclusion through the usual workflow in these practices, which use daily HealthShare Exchange reports, a health information exchange for the greater Philadelphia region that provides practices with information on discharges from all regional hospitals.Patients from the control practice received the standard-of-care TCM telephone call from their practice within 2 business days of discharge. This call is meant to identify any needs soon after discharge and consists of a set of questions related to follow-up appointments, medications, symptoms, and home care needs. Patients were also scheduled for a postdischarge office visit during the call. If they did not answer the first call, one more attempt was made; further outreach was left to the nurses’ discretion.Patients from the intervention practice received the same TCM telephone call with the same process and were additionally told about the texting program. They had the opportunity to verbally decline enrollment at this time. Patients identified as not having a texting-capable phone or the ability to text in English were not enrolled in the program. Patients who did not answer the TCM call were by default enrolled in the texting program at the time of the unanswered call . On enrollment (day 0), patients received an introductory message describing the program and advising them how to reach out or opt out at any time. They were asked if they had an appointment with their primary care clinician or a specialist within the next 2 weeks. If they answered no, their response was escalated back to the practice via the EMR for help in coordinating an appointment as necessary.The day after enrollment (day 1), patients began receiving check-in messages on a tapering schedule , patients received a closing message and a 1-item survey (the Net Promoter Score [NPS] question) gauging their satisfaction with the program.The texting program was built and managed by Way to Health, a National Institutes of Health–funded platform designed to provide automated technology infrastructure in support of clinical care and care delivery innovation research. Escalations were routed to a regularly monitored inbox in the EMR.Demographic and clinical information was collected from Penn Medicine’s EMR (Epic Systems Corporation). These data were collected for all eligible discharges in both the intervention and control practices between August 27, 2020, and August 27, 2021 (patients could be represented more than once if they had multiple discharges during this period). The intervention was launched on January 27, 2021; the preintervention and postintervention periods were defined as discharges either before or after and including this date. User engagement and satisfaction data for those enrolled in the intervention were collected from the Way to Health platform.19)  risk score  at any Penn Medicine or non–Penn Medicine facility in the HealthShare Exchange database within 30 days of discharge. Secondary outcomes included 1 or more ED visits  within 30 days and 1 or more readmissions within 30 days. As a safety outcome, we also examined death within 30 and 60 days of discharge.20 The NPS is reported as an integer value from −100 to 100, with higher values indicating greater likelihood to recommend the program  to 10 (extremely likely) on the NPS question: “How likely are you to recommend Penn Medicine’s discharge follow-up program to a friend or colleague?”We compared changes in 30-day use of acute care resources (both the composite measure and ED visits and readmissions separately) using a difference-in-differences approach, excluding patients who died within 30 days of discharge. To avoid selection bias , we used an intention-to-treat approach in the analyses: all patients to whom a TCM telephone call was placed (whether or not it was answered) were included in the analysis of the effectiveness outcomes at both the intervention and the control practices regardless of whether they were successfully enrolled in the texting program.To visually inspect the data for preintervention parallel trends while limiting the large variation in monthly rates of 30-day use of acute care resources, we calculated a 3-month rolling mean. In addition, we conducted formal statistical testing for parallel trends by using a logistic regression model with an interaction between practice and time before the start of the intervention. We used a logistic generalized estimating equation model for each outcome . The difference-in-differences was estimated by the coefficient for the interaction term between the period (preintervention and postintervention start date) and the practice (intervention and control). We adjusted for patient demographic characteristics , risk estimators (UPHS risk score and Charlson Comorbidity Index), and length of hospital stay. We used similar models to assess differences in death outcomes within 30 and 60 days of discharge.P < .05 indicated statistical significance.All statistical analyses used Stata software, version 16.1 (StataCorp LLC). Two-sided To ensure that our findings were not due to specific trends at the preselected control practice, we extracted data for 5 additional practices in Philadelphia. Using these data, we conducted a secondary analysis with the same model but with a larger control group consisting of 6 total practices (eTable 5 in the The study sample included 1885 unique patients (771 from the intervention practice and 1114 from the control practice), with a total of 2617 discharges . Among the 1885 patients, the mean (SD) age was 63.2 (17.3) years; 1101 (58.4%) were women and 784 (41.6%) were men; 1034 (54.9%) were White; and 976 (51.8%) were insured by Medicare. The sample at the intervention practice had a higher proportion of women (501 [65.0%] vs 600 [53.9%]) and Black patients (364 [47.2%] vs 390 [35.0%]) and had slightly higher UPHS risk scores . Demographically, patients in the intervention and control practices were otherwise broadly similar .Of the 604 eligible discharges during the intervention period, 430 were from patients enrolled in the intervention  . Of the In the months before the intervention, the trends in 30-day use of acute care resources were similar between the 2 practices . A regreP = .02) at the intervention practice compared with the control practice (P = .33). For a readmission within 30 days, the odds were 55% lower .The 30-day rate of use of acute care resources changed from 15.2% before the intervention to 19.8% after the intervention for the control practice and from 20.3% to 16.5%, respectively, for the intervention practice. In the adjusted model, the odds of 30-day use of acute care resources after implementation of the intervention were 41% lower  and 0.63 , respectively  for a mean (SD) of 1.4 (1.5) per day. Of these, 260 (86.4%) were in response to an automated check-in and 41 (13.6%) were initiated by the patient outside a regular check-in window. A total of 338 participants (82.8%) responded to either the introductory message or the first check-in message. Of those who received messages for the full 30 days (n = 373), 165 (44.2%) responded to the closing NPS question. The NPS was +67.21 The mechanism through which this compound program prevents use of acute care is likely complex, but we theorize that more frequent check-ins and a lower friction medium for patient-initiated outreach lead to earlier identification of needs and a greater likelihood that issues will be escalated to and handled by the primary care practice than another setting.22In this cohort study, a text message–based automated system of monitoring patients after hospitalization was associated with a 41% reduction in the odds of 30-day use of acute care resources, suggesting that a 30-day automated texting program can improve postdischarge outcomes among primary care patients. This outcome was driven largely by a 55% decrease in the odds of a 30-day readmission. The program’s high degree of automation required minimal effort beyond usual care. To the best of our knowledge, this is the first study to experimentally test the benefit of an automated texting program on postdischarge outcomes among primary care patients.28 Prior studies have evaluated these approaches in a variety of clinical contexts, including after surgery and in specific disease states, such as heart failure. Of those studies that have evaluated the association with the use of acute care resources, the findings have been mixed, with some finding no significant difference,24 and others similarly finding an associated reduction in readmissions.25 Our study is, to our knowledge, the first to evaluate a text message–based, automated hovering approach in a broad primary care population.This study builds on a growing body of literature around the utility of automated hovering and text message–based strategies to support patients in the transition from hospital to home.29 Only 8.6% of those enrolled opted out of our program. The NPS of those who remained was +67, which is considered excellent.30 High rates of satisfaction has been a consistent finding in other similar programs.27In addition to the associated reduction in the use of acute care resources, this program was highly acceptable to patients: 82.8% of patients responded to at least 1 of our introductory messages—far higher than rates of response to our traditional transitions calls. This outcome is consistent with other work that has compared text messaging with traditional call-based approaches.Last, our intervention was integrated into the preexisting workflow of the practice care management team, for whom responding to patient calls from recently discharged patients is part of their daily standard work. The intervention was integrated into these existing structures and generated a mean of 1.4 escalations per day, which the team considered a manageable workload. Although the staff continued to place the standard telephone calls during this pilot, future work can test replacement of these calls with messaging entirely.31 may pose barriers for some patients, particularly in terms of cost for those who do not have unlimited texting plans. However, past work32 has indicated that technology can also reduce disparities, especially for postdischarge care. Additionally, despite adjustment for several covariates, this nonrandomized assessment is at risk of residual confounding. These findings are encouraging and warrant confirmation in a randomized study.This study has some limitations. It was deployed at a single site, and patients were not randomized. Patients may have been excluded from the intervention based on considerations not readily identifiable in the EMR. We accounted for this factor by including all patients who received a TCM telephone call in our analysis (intention-to-treat approach), which should eliminate potential selection bias between the intervention and control groups and, if anything, bias our results toward the null. We included mortality as a secondary, safety outcome, although we were limited to information available in our EMR, which is not always complete. This study occurred during the COVID-19 pandemic, which may have enhanced uptake of the intervention, as it was a time when practices and patients became more accustomed to remote outreach and virtual care. This factor is not likely to limit generalizability going forward, because these features have now become more standard in the primary care toolbox. Net promoter scores may have been influenced by selection of those who completed the 30 days of the study and chose to respond. Texting, although generally widespread and accessible,The findings of this cohort study suggest that an automated texting program to support care management for primary care patients after hospital discharge is highly feasible and acceptable. The program was associated with significant reductions in the use of acute care resources during the 30 days after discharge.