The Omicron variant of SARS-CoV-2 led to a steep rise in transmissions. Recently, as public tolerance for isolation abated, CDC guidance on duration of at-home isolation of COVID-19 cases was shortened to five days if no symptoms, with no lab test requirement, despite more cautious approaches advocated by other federal experts.We conducted a decision tree analysis of alternative protocols for ending COVID-19 isolation, estimating net costs (direct and productivity), secondary infections, and incremental cost-effectiveness ratios. Sensitivity analyses assessed the impact of input uncertainty.Per 100 individuals, five-day isolation had 23 predicted secondary infections and a net cost of $33,000. Symptom check on day five (CDC guidance) yielded a 23% decrease in secondary infections (to 17.8), with a net cost of $45,000. Antigen testing on day six yielded 2.9 secondary infections and $63,000 in net costs. This protocol, compared to the next best protocol of antigen testing on day five of a maximum eight-day isolation, cost an additional $1,300 per secondary infection averted. Antigen or polymerase chain reaction testing on day five were dominated (more expensive and less effective) versus antigen testing on day six. Results were qualitatively robust to uncertainty in key inputs.A six-day isolation with antigen testing to confirm the absence of contagious virus appears the most effective and cost-effective de-isolation protocol to shorten at-home isolation of individuals with COVID-19. The SARS-CoV-2 B.1.1.529 (Omicron) variant was designated a variant of concern by the World Health Organization in November 2021, as it had several mutations that are suspected to impact its transmissibility and disease severity. public hWhile quantitative studies on the viral kinetics and pathophysiology of the Omicron variant are underway, decision makers must offer timely guidance that balances public health and economic considerations in their COVID-19 isolation recommendations. Antigen testing could offer benefits over using symptom status as a marker of infectivity given the high rate of asymptomatic COVID-19 infections. We aimedWe modeled six different protocols for ending COVID-19 isolation. Using a customized decision tree template, we compared the number of secondary COVID-19 infections that occurred when individuals followed each of these different protocols. We adopted a societal perspective and a two-week time horizon to capture all costs and secondary infections. The cohort consisted of 100 individuals in the US who had COVID-19 (confirmed by PCR and/or antigen test) and were on the fifth day of isolation. We modeled only individuals with asymptomatic or mild COVID-19; those with more severe disease would be hospitalized rather than isolating at home and therefore were not included.Interventions were selected to demonstrate current policy options as well as alternatives that might reduce transmissions while also shortening isolation duration. While not exhaustive, these protocols represent a variety of options that might warrant further evaluation. In all strategies, individuals leaving isolation were assumed to follow best practices for infection prevention, which at the time of the analysis included mask wearing. Individuals could leave their home the day after their isolation ended .Person with confirmed COVID-19 stays at home for five days, then can leave without any further consideration.Person with confirmed COVID-19 stays at home for five days. On day five, they review their symptoms. Those who were asymptomatic or fever free for 24 hours can end isolation, while those with persisting symptoms continue to isolate until day ten.Person with confirmed COVID-19 stays at home for five days. On day five, they perform a rapid antigen test. Those who test negative can end isolation while those who test positive continue to isolate until day ten.Person with confirmed COVID-19 stays at home for five days. On day five, they conduct a PCR test. Those who test negative can end isolation while those who test positive continue to isolate until day ten. We assumed results are obtained within 24 hours.Person with confirmed COVID-19 stays at home for six days. On day six, they perform a rapid antigen test. Those who test negative can end isolation while those who test positive continue to isolate until day ten.Person with confirmed COVID-19 stays at home for five days. On day five, they perform a rapid antigen test. Those who test negative can end isolation while those who test positive continue to isolate until day eight instead of day ten (no re-test is done).We assumed that no one in the cohort was SARS-CoV-2-naïve . As such, individuals were either still carrying contagious virus or had cleared all viable virus. We defined a frontloaded distribution for infectivity over ten days following symptom onset , based on empirical data on culture-positivity of patient samples.–17 For tWe used data specific to the Omicron variant when available to parameterize the model. Otherwise, we used data generated during the wildtype (Alpha) and B.1.617.2 (Delta) variant waves. Key model inputs are presented in eff), which implicitly accounts for infection prevention measures that were in place such as mask wearing, was used to calculate the number of secondary infections that occur per index case. As of January 16th, 2022, Reff was estimated as 1.2 in the US.[eff for de-isolation protocols requiring longer isolation periods.The probability of carrying viable SARS-CoV-2 was 90% on day five and 70% on day six, quickly dropping to zero by day ten from the start of isolation, based on studies of previous variants.–17 The en the US. We adjusForty percent of relevant COVID-19 cases were asymptomatic, yieldingCosts were calculated from a societal perspective and in 2022 US dollars. A rapid antigen test cost $10, while a PCR test cost $150., 24, 26 We compared the number of secondary infections, societal net costs, and, when appropriate, incremental cost-effectiveness ratios (ICERs) given different de-isolation protocols. De-isolation protocols that led to fewer net costs and fewer secondary infections than their comparator were dominant; no ICERs were calculated.We conducted deterministic and probabilistic sensitivity analyses to assess uncertainty in key inputs in eff (see S1 Appendix). A low-risk scenario was defined representing the infected individual re-joining the household where everyone was fully vaccinated and continued to wear masks for the next five days . A medium-risk scenario reflected individuals starting to see few non-household members, all of whom were fully vaccinated, but mask-wearing was inconsistent . Finally, a high-risk scenario was defined in which both vaccination and mask-wearing was inconsistent, and a greater number of social contacts were occurring . We did not conduct multivariate sensitivity analyses on the scenarios.Additionally, we simulated three separate risk scenarios to evaluate how the environment individuals are re-entering upon ending isolation would affect outcomes. The change in the risk of infection due to varying vaccination rate, mask-wearing, 28–30, ®  and sensitivity analyses were conducted using @RISK® . The decision tree and all data are available upon request.The model was built in ExcelBase-case results from a societal perspective  are presented in Antigen testing on day five of an eight-day isolation period cost an additional $1,150 per secondary infection averted compared with symptom check. This drop in the ICER represents extended dominance over theThe most cost-effective de-isolation protocol was performing an antigen test on day six of a ten-day isolation period. This protocol led to $63,200 in net costs and 2.9 secondary infections, yielding an ICER of $1,293 per secondary infection averted versus an antigen test on day five of an eight-day isolation, again representing extended dominance. Both antigen and PCR testing on day five were dominated by antigen testing on day six; they led to greater net costs and more secondary infections.If productivity losses were omitted, leaving just direct costs, antigen testing on day six was strictly dominant  over all other de-isolation protocols .In one-way sensitivity analyses where key inputs were varied one at a time, antigen testing on day five prevented between 51–85% secondary infections over symptom check, depending primarily on antigen test sensitivity for transmissible viral loads. Secondary infections prevented with an antigen test on day six versus day five was mostly related to the relative reduction in viable viral load from day five to six and varied between 35–67%. Antigen test on day six, compared to the next most cost-effective option (antigen test on day five of eight-day isolation) prevented between 3.6 and 8.0 secondary infections. This value was most sensitive to the probability of having transmissible virus on day five .Probabilistic Monte Carlo analyses showed that antigen testing on day six was either dominant or cost-effective with ICERs up to $3,816 per secondary infection averted, given varying inputs. This outcome was most sensitive to uncertainty in the probability of a persistent high viral load, followed by the community transmission rate and the direct medical cost per COVID-19 infection. Antigen test on day six always prevented more secondary infections than antigen test on day five of eight-day isolation but had a nearly 90% probability of having greater net costs .In all three of the risk scenarios considered, antigen testing on day six remained the optimal de-isolation protocol . Both thUnder the base-case assumption of 100% adherence to a symptom check protocol, at least 30% antigen test availability  was necessary for antigen testing to prevent more secondary infections than the symptom check if done on day five. Similarly, PCR tests had greater benefit than symptom check when test availability was greater than approximately 27%. In a two-way sensitivity analysis, if symptom check adherence was below 70%, then <20% antigen test or <19% PCR test availability was sufficient for these tests to prevent a greater number of transmissions than the symptom check protocol on day five. Notably, antigen testing on day six prevented more secondary infections than symptom check on day five even when test availability was as low as 1%, due to the added day of isolation.We compared health and cost outcomes associated with different de-isolation protocols to end COVID-19 isolation for those with confirmed asymptomatic or mild COVID-19. All ICERs we calculated had favorable cost-effectiveness ratios. We found that while symptom check without testing on day five of isolation did reduce secondary transmissions after de-isolation by 23% compared to no testing, it still led to nearly 18 secondary infections per 100 individuals and had the least favorable cost-effectiveness ratio due to high medical costs for secondary infections. The most cost-effective protocol was to remain in isolation through day six and then perform an antigen test, which dominated both antigen testing and PCR testing on day five. Antigen testing on day six led to an overall 87% decrease in secondary infections compared to no testing and cost an additional $1,300 per secondary infection averted compared to the next best option.Notably, threshold analysis on antigen test availability suggested that the benefit of antigen testing on day six might be primarily due to the extra day of isolation (during which the probability of still carrying infectious virus quickly begins to drop), rather than the ability of the test to identify those who still might have a transmissible viral load. This insight warrants further evaluation using emerging data on the viral dynamics of Omicron. Moreover, antigen testing on day six was associated with lower productivity loss than antigen testing on day five; even though everyone remained in isolation for one more day, more individuals were cleared for de-isolation on day six than would have been on day five. The four days gained by this portion of index cases offset the extra day lost by everyone. Workforce shortages have been an important adverse effect of COVID-19 isolation.–37 AntigBy modeling different risk scenarios, we demonstrated that the de-isolation environment had a considerable impact on the cost-effectiveness of testing strategies. Regardless of the risk scenario, the optimal protocol remained antigen testing on day six, which became dominant over other protocols in high-risk situations and remained cost-effective, although cost-effectiveness was less favorable in low-risk situations. Nevertheless, these findings suggest that much like other public health policy decisions throughout the pandemic, de-isolation guidelines must evolve as the context of the pandemic shifts. For example, potential new surges may call for more stringent policies with longer minimum isolation and more sensitive tests, while declining transmissions may allow more lenient approaches. It is plausible that the CDC has reached this same conclusion and proposed a symptom check rather than an antigen test because of an expectation that transmissions would subside in the weeks following the announcement of the new guidance. While reasonable at first glance, this could be a risky approach; loosening infection prevention measures may have prevented the expected drop in transmissions, leading instead to a quick rise in cases that would have prohibited the loosened guidance being put in place to begin with. Indeed, our modeling of the CDC guidance resulted in a substantial number of secondary COVID-19 cases given the transmission rate at the time the guidance was issued, some of which were avoided with a different de-isolation approach.Evidence from earlier SARS-CoV-2 variants suggest that transmissibility of the virus peaks by approximately the fifth day from symptom onset, and swiftly drops afterward.–17, 38 Heff over the 10 days following COVID-19 confirmation, but a portion of transmissions occur prior to the index case learning their COVID-19 status and entering isolation. As such, we have overestimated the number of secondary infections in our model and underestimated ICER values. Decreasing the residual R would increase ICERs but given favorable ICERs even in the low-risk scenario, we believe the implications of our findings would not be affected. More importantly, given the novelty of the Omicron variant, we had to rely on studies of prior variants for these two important factors. While variance in neither of these inputs changed cost-effectiveness results qualitatively, they did have an impact on the number of further transmissions after isolation and thus the level of cost-effectiveness. Additional studies on the viral kinetics of the Omicron variant are necessary to refine these estimates.This study had important limitations, especially regarding uncertainty in key inputs such as the viral kinetics of SARS-CoV-2 and sensitivity of antigen tests. First, we distributed RThe Omicron variant of SARS-CoV-2 presents a new threat to public health due to its high transmissibility and potential ability to evade vaccine-induced immunity. Cost-effectiveness analyses can help decision makers assess the trade-offs between the economic disadvantages and health risks of adopting different COVID-19 de-isolation guidance. Using a decision tree model and Omicron-specific data when available, we found that ending isolation in five days given a negative symptom check left substantial risk of transmission and was not the most cost-effective strategy even when high productivity losses of longer isolation were accounted for. Instead, our findings suggest a baseline isolation duration of six days, at which time an antigen test, if available, can be conducted to confirm that the individual no longer carries transmissible SARS-CoV-2.


This cross-sectional study investigates the association of branded prescription drug rebate size and patient out-of-pocket costs. Are prescription drug rebates associated with increased patient out-of-pocket costs?In this cross-sectional study of estimated rebates for 444 unique branded drugs with prescriptions filled by 38 131 unique individuals, increased rebate sizes were associated with increased out-of-pocket costs for those with Medicare, commercial insurance, or no insurance. Associations between rebates and out-of-pocket costs were associated with simultaneous increases in list prices.These findings suggest that while drug manufacturers may increase list prices in order to offer larger rebates to insurers, such increases were associated with increased out-of-pocket costs, especially among individuals without insurance. Over the past decade, branded prescription drug manufacturers have substantially increased list prices while offering larger rebate payments to health care insurers. Whereas larger rebates can partially offset increases in list prices for insurers, patient out-of-pocket costs may be directly associated with list prices for individuals without insurance and indirectly associated with list prices for individuals with insurance through deductibles or coinsurance.To investigate the association between rebates and patient out-of-pocket costs and whether this association differs by coverage type  and before and after 2014.This cross-sectional study was conducted using data from the Medical Expenditure Panel Survey (MEPS) combined with pricing data for single-source branded drugs from SSR Health from 2007 through 2018. The study was conducted among a nationally representative sample of the noninstitutionalized civilian US population. Included individuals were respondents to MEPS with at least 1 prescription for a single-source branded drug who were covered by Medicare or commercial insurance or were uninsured during an entire year. Data analyses were conducted from August 2019 through March 2021.Estimated rebate size.Out-of-pocket costs per prescription were calculated, adjusting for year and drug.Among 38 131 individuals with at least 1 prescription, the mean age was 54 years , with 22 044 women (57.8%) and 29 086 White individuals (76.3%). The sample included 444 unique drugs with a survey-weighted total of 4.7 billion prescriptions. Estimated mean (SE) rebates increased from $34 ($1) per prescription in 2007 to $374 ($9) per prescription in 2018. The rebate sizes were associated with statistically significant mean out-of-pocket increases per branded prescription of $4  from 2007 to 2013 and $11  from 2014 to 2018. From 2014 to 2018, rebate sizes were associated with statistically significant mean increases in out-of-pocket costs per prescription of $13  for individuals with Medicare, $6  for individuals with commercial insurance, and $39  for individuals without insurance. After adjusting for list prices, there was no association between rebates and out-of-pocket costs, with a change from 2014 to 2018 of −$0.01 .These findings suggest that drug manufacturers may have provided larger rebates to insurers primarily by increasing list prices and that individuals without insurance had greater cost increases. The results emphasize the need for policy solutions that decouple list prices and out-of-pocket costs. These companies negotiate discounts on list prices from drug manufacturers in the form of rebates in exchange for providing favorable insurance coverage. Such rebates can contribute substantially to insurer savings and, therefore, future enrollee premium savings. For instance, Medicare Part D collected $24 billion in rebates in 2018.4 However, PBMs can keep a portion of the rebates, so stakeholders, including the US Department of Health and Human Services, have raised concerns that manufacturers may increase list prices to offer larger rebates to PBMs.6 Indeed, list prices for branded drugs increased by 159% from 2007 to 2018.7Controlling prescription drug spending is a national priority for the public,9 and commercial plans.11 Alternatively, rebates may be negatively associated with out-of-pocket costs if PBMs provide more favorable insurance coverage  in exchange for larger rebates from manufacturers.Larger rebates may be associated with out-of-pocket costs via 2 opposing mechanisms . Through13 medication adherence,14 and health in some populations.17 Our primary objective was to investigate the association between rebate size and patient out-of-pocket costs and whether this association differed by coverage type  and by period  in a nationally representative sample. Secondarily, we evaluated whether list prices were associated with the increase in out-of-pocket costs found with increasing rebate size.Measuring the direction and magnitude of association between rebates and out-of-pocket costs is important because increased out-of-pocket costs are associated with negative outcomes in patient financial burden,STROBE) reporting guideline for cross-sectional studies.The Kaiser Permanente Washington Health Research Institute Institutional Review Board approved this study and determined that it was not human participants research and so informed consent was not necessary. The study is reported using the Strengthening the Reporting of Observational Studies in Epidemiology  to obtain nsurance coverage and demographic information  on surveyed individuals and the 2007 to 2018 MEPS Prescribed Medicines Files to obtain information on each prescription drug purchase made by these individuals. Advantages of the MEPS include its nationally representative sample of the US civilian noninstitutionalized population, including individuals without insurance. This survey also accurately measures out-of-pocket costs because it directly collects payment information from pharmacies, therefore excluding patient assistance programs  from out-of-pocket cost calculations .18 The data set contained drugs from more than 100 companies, accounting for more than 90% of US branded drug sales and has been used in multiple studies to document list and net price trends.20 For each branded drug, we calculated yearly list and net prices using the mean of quarterly prices weighted by sales volume.7Next, we obtained a 2007 to 2018 data set from SSR Health that contains quarterly list prices  and estimated net prices. These drug-specific net prices represent the revenue received per unit of drug by a manufacturer that reflects all price reductions, including rebates, discounts provided to 340B health care institutions, copayment coupons to patients, other purchase discounts, distribution fees, and product returns. SSR Health estimates these net prices quarterly for each drug by dividing drug-specific US revenues reported by the manufacturer by drug-specific US sales unit volume for all branded drugs sold to non-Medicaid health care payers by publicly listed companies.We included individuals who filled a branded drug of interest during a given year and who, for the entire year, were enrolled in Medicare or commercial insurance or were uninsured. Individuals simultaneously enrolled in Medicare and commercial plans  were categorized as having Medicare plans. Individuals covered under Medicaid  were excluded because out-of-pocket costs under Medicaid are typically small.21 For drugs with generic equivalents approved during the study period, we excluded the quarter before market exclusivity was lost and all subsequent quarters. This was done because prescription volume, and therefore rebate estimates, are less reliable during the transition from single-source branded drug to multisource branded drug.Drugs of interest were defined as single-source branded prescription drugs  observed in both the SSR Health and MEPS data sets in any observed quarter. We excluded branded drugs with generic equivalents because we expected rebates to be small for these drugs.20 but this difference also includes other discounts, as noted in a previous section. An industry report22 indicated that rebates represented two-thirds of the difference between list and net price in 2018.Our primary variable of interest was list price minus net price. For simplicity, we call this quantity “estimated rebates,” as in other studies,Our outcome was out-of-pocket costs per branded prescription from MEPS. This represents the pharmacy-reported amount that an individual or family member paid for the medication and excludes payments made on their behalf, such as by manufacturers through copayment coupons.To account for secular changes that may affect associations between rebates and out-of-pocket costs , we included categorical variables for each year . To draw inferences on the association between rebate size and out-of-pocket cost within drugs, we included drug-specific indicator variables . We interacted coverage type  indicator variables with estimated rebates to allow for differential associations by coverage type in our overall analyses , and the requirement that plans have annual drug out-of-pocket maximum limits.We used the MEPS survey weights in all analyses to generate nationally representative estimates. Our unit of analysis was at the prescription level. All spending variables were adjusted to 2018 dollars using the US Consumer Price Index. All models adjusted for covariates described in the previous section. Robust standard errors were used to calculate 95% CIs, which were used to evaluate significance; results were considered significant if CIs did not cross 0. Analyses were conducted from August 2019 through March 2021 using Stata/MP statistical software version 15.1 (StataCorp).25 We used recycled predictions to compare adjusted estimates of out-of-pocket costs based on observed rebate levels and no rebates   to calculate out-of-pocket costs for prescription fills that are subject to deductibles or coinsurance.27 As a second sensitivity analysis, we calculated rebates as gross prices from MEPS minus net price.28 Third, because MEPS improved the quality of out-of-pocket cost data starting in 2009,29 a sensitivity analysis excluded all data before 2009. As a data check, we replicated list price and net price trends from a 2020 descriptive study7 that used SSR Health data  and 1 for our dependent variable . Because net prices from SSR Health include nonrebate discounts, we also obtained net price estimates from the Federal Supply Schedule (FSS) from the Department of Veterans Affairs National Acquisition Center.Additionally, rebates are expected to affect only individuals subject to cost sharing . Therefore, as a further sensitivity analysis, we estimated the association between rebates and out-of-pocket costs by calculating the probability of incurring any out-of-pocket costs for a branded prescription fill using a binomial distribution and logit link. We then estimated the increase in out-of-pocket costs associated with rebates among prescriptions with nonzero out-of-pocket costs by log-transforming rebates and out-of-pocket costs and then evaluating the association using linear regression.31 Individuals with Medicare insurance made the most branded prescription purchases, while individuals who were uninsured made the fewest such purchases. Individuals who were uninsured were younger, in poorer health, and more likely to be members of a racial minority group and had lower personal income compared with our overall sample , with 22 044 women (57.8%) and 29 086 White individuals (76.3%). Reflecting the inclusion criteria of individuals using single-source branded drugs, our sample included a larger proportion of individuals with Medicare insurance and fewer individuals without insurance. For example, among 13 024 individuals in our sample from 2014 to 2018, compared with the 2018 general US population, 5773 individuals (44.3%) vs 13.8% of individuals had Medicare insurance and 672 individuals (5.2%) vs 8.9% of individuals had no insurance .30,31 In4 Mean (SE) estimated rebates ranged from $17 ($0)for hypothyroid treatments to $11 567 ($1869) for hepatitis C treatments  per branded prescription in 2007 to 2013 and $11 per branded prescription  in 2014 to 2018 . This asIn our secondary analyses, we found that a $1 increase in rebate size was associated with an increase in list price per branded prescription of $2.75  from 2007 to 2013 and $2.09  from 2014 to 2018  that ties out-of-pocket costs to list prices. Furthermore, our secondary analyses indicated that the positive association of rebates with out-of-pocket costs was associated with simultaneous increases in list prices. Larger rebates without list price increases should benefit enrollees through lower insurer spending and potentially lower premiums without increasing out-of-pocket costs for individuals actually filling prescriptions. Our results indicated that when list prices were constant, increased rebates were not associated with increased patient out-of-pocket costs.36 Our findings, combined with the probability that individuals with worse health status require more intensive pharmacologic treatment, suggest a potential disproportional clinical outcome associated with increasing rebates among individuals without insurance. Furthermore, individuals without insurance were more likely to be in racial minority groups, which may amplify preexisting disparities in health care access.38 Additionally, whereas individuals with insurance benefit from increasing rebates (through decreased future premiums),39 individuals without insurance do not.Our findings have health equity implications. Rebates were associated with increased out-of-pocket costs that were more than 3-fold greater among individuals without insurance than among individuals in the overall sample in 2014 to 2018 . This ad40 Indeed, findings from Trish et al40 suggest that increases in list prices may have contributed to the increased numbers of individuals in Medicare who reached catastrophic out-of-pocket spending levels. Nevertheless, the increase in costs associated with rebates also grew among individuals covered by commercial insurance, potentially due to increased uptake of high-deductible health plans among this population.41 This outcome suggests that rebates and increases in list prices may be associated with negative outcomes among individuals with commercial insurance in coming years.We also found that the increases in out-of-pocket costs associated with larger rebates were greater for individuals covered by Medicare than for those covered by commercial insurance. This could be because coinsurance is more commonly used in Medicare plans and because Medicare Part D plans typically do not have annual out-of-pocket maximums.42 Furthermore, this population has been growing in recent years, and the COVID-19 pandemic may add to the number of individuals who lose employer-based insurance coverage and become newly uninsured and exposed to list prices.43 This situation highlights the need for affordable insurance options, such as through Medicaid expansion, which decreases health care-related financial distress.44 Federal proposals to share rebates at the point of sale with patients enrolled in Medicare Part D raise concerns about increasing premiums, in part because changing rebate strategies does not guarantee equivalent list price reductions from manufacturers.46 Another alternative is encouraging plans to use copayments  rather than coinsurance, thereby decoupling out-of-pocket costs from list prices.Overall, these results suggest that manufacturers may have perverse incentives to increase list prices in order to offer larger rebates. While this may partly dampen the growth in insurer costs and overall premiums, it is associated with increased out-of-pocket costs for patients filling prescriptions, especially for individuals without insurance. The uninsured population, while relatively small, is of high concern for the US public.20 and in determining value-based drug prices,47 it is not known how well the data truly represent net prices paid by PBMs. These rebate estimates may also be less precise for specific drug classes.48 We therefore focused our analyses on overall associations across classes and are further reassured that sensitivity analyses estimating rebates from alternative data sources yielded similar conclusions. Fourth, our rebate estimates were means across coverage types, but rebates may vary across coverage types. However, commercial rebates are expected to be within 10% of Medicare rebates, according to an industry report.49 Fifth, our analyses focused on filled prescriptions and did not address that patients who could not afford out-of-pocket costs may not have filled their prescriptions.Our study has several limitations. First, our results do not necessarily generalize to drugs used to treat rare conditions because MEPS masks the identity of drugs used by fewer than 200 000 people per year. These drugs typically have smaller rebates. Second, our results do not generalize to the fewer than 10% of branded drugs marketed by nonpublicly traded companies, which are not in the SSR Health data set. Third, while SSR Health net price estimates are increasingly applied in researchThis study found that rebates for single-source branded drugs increased substantially over the past 12 years. We found that rebates, likely through simultaneous increases in list prices, were associated with out-of-pocket cost increases and that these impacts appear to have grown. This system amplifies health equity concerns, as individuals without insurance are disproportionately disadvantaged. These findings suggest that with the number of individuals without insurance expected to increase, future research and policy considerations should focus on potential solutions to decouple list prices and out-of-pocket costs, especially for individuals without insurance.


The present work was aimed to perform a computational study on FDA-approved drugs, similar to piperine scaffold, to find possible Mpro inhibitors. Firstly, virtual screening studies were performed on a library of FDA-approved drugs . Among imported 43 drugs to virtual screening, 34 compounds were extracted. Four top-ranked drugs in terms of the highest interactions and the lowest binding energy were selected for the IFD study. Among these selections, lasofoxifene showed the lowest IFD score . The stability of lasofoxifene in the COVID-19 Mpro protein active site was confirmed with 100 ns MD simulation. Lasofoxifene binding free energy was obtained -107.09 and -173.97 kcal mol-1, using Prime MM-GBSA and g_mmpbsa methods, respectively. The identified lasofoxifene by the presented computational approaches could be a suitable lead for inhibiting Mpro protein and COVID-19 treatment.The coronavirus disease-2019 (COVID-19) was first recognized in Wuhan, China, and quickly spread worldwide. Between all proposed research guidelines, inhibition of the main protease (M Coronavirus disease 2019 (COVID-19) is an emerging developed human infectious from coronavirus family, outbreak in Wuhan, China, and has been outspreading quickly in China and other countries since December 2019 , plays a prominent role in viral gene expression and replication . The first and largest ORF contains genetic codes for two polyproteins termed ppla and pplab, which are autoproteolytically cleaved into 15 or 16 nonstructural proteins (nsp) . Among tpro protein is exceptionally conserved among the coronavirus family. Mpro dimer consists of 3 structural domains; domain I (residues 8-101), domain II (residues 102-184) have an antiparallel β-barrels, and domain III (residues 201-303) that is linked to domain II using a long loop region (residues 185–200). COVID-19 Mpro has a cysteine-histidine catalytic dyad, and its binding site is placed in a gap between domains I and II. Unlike other viral proteases, Mpro utilizes cysteine amino acid instead of serine for nucleophilic attacks in the active site , myricetin , and quercetin  compounds. Curcumin was another compound that showed low binding energy in COVID-19 Mpro active site . Piperine was another compound with a -5.16 kcal/mol binding energy in the Mpro protein active site. The virtual studies based on quercetin and curcumin scaffolds have been performed with COVID-19 Mpro protein  with at least 40% similarity with piperine scaffold were extracted from Drug Bank (www. drug bank. ac). The 3D structure of all compounds was drawn in Marvin Sketch v5.7, ChemAxon (10), and kept in a PDB format. Docking simulation studypro protein was obtained from the Protein Data Bank (PDB ID 6lu7) (Forty-three drugs were subjected to molecular docking studies. The molecular docking process was steered using the AutoDock4.2 software package . The PDBk PDB ID lu7 (6). ID 6lu7) . The pdbID 6lu7) . A grid 5. Finally, the docking process was performed by AutoDock 4.2. Docking results were analyzed in terms of conformation and binding energy in the active site of the Mpro protein. The best drugs with the lowest binding energy and the highest interaction in the binding pocket were chosen. The drug-protein interactions were imagined using AutoDockTools 1.5.6 and PyMOL Tcl  and were prepared using Schrodinger’s protein preparation wizard module  force field to optimize the geometry and minimize the energy of the Mpro protein  is a powerful and precise method to compute ligand and receptor flexibility . To undeMM-GBSA calculationpro complex from IFD was further investigated with MM-GBSA procedure in the prime segment of Schrödinger suite 2015 (bind) between ligand and receptor with more precision. In this method, molecular mechanical (MM) energies are considered with a continuum solvent generalized Born (GB) model for polar solvation and a solvent-accessible surface area (SASA) for non-polar solvation  simulation studies. The molecular dynamic simulation was carried out by the GROMACS-2019.3 package  500 ps simulation in the NVT ensemble ; 2) 1 ns simulation in the NPT ensemble . In the end, the MD simulation was run at 300 K temperature for 100 ns. The Particle Mesh Ewald (PME) method and the linear constraint (LINCS) algorithm were employed to calculate long-range electrostatic interactions and covalent bond constraints, respectively. Moreover, structure imagining was done by VMD 1.8.6 carbonyl]alanyl-L-valyl-N-1--4-benzyloxy)-4-oxo-1-{[(3R)-2-oxopyrrolidin-3-yl]methyl}but-2-enyl)-L-leucinamide, crystallography ligand) and Mpro protein was compared with the crystallographic poses in PDB: 6lu7. The binding pocket of N3 was investigated as the following: the catalytic His163 and Cys145 residues in the bottom of the packet formed two hydrogen bonds, and hydrogen bonds were seen between Gly143 and Glu166 amino acids in the edges pocket with N3. The main hydrophobic amino acids that sat around N3 were His41, Phe140, Lue141, Asn142, Ser144, His164, Met165, His172, and Gln189, as seen in The validation docking was carried out and interactions between pro inhibitors, a structure-based virtual screening (VS) study was carried out over created library from the Drug Bank web server. Docking runs were done for all 43 drugs similar to the piperine scaffold, and its results were organized in terms of binding energy and fundamental catalytic interactions. Thirty-four of them were sat in the Mpro active site, but between them, four drugs  with the lowest binding energy were selected for further investigation. Among these four drugs, curcumin structurally showed the most similarity to the piperine scaffold. In lasofoxifene, alvimopan, and donepezil structures, saturated rings are piperidine or pyrrolidine rings, the same as piperidine ring in piperine structure.To predict new MCurcumin, extracted from a plant called “turmeric,” with the lowest binding energy, acts as a lipoxygenase inhibitor and prevents tumor invasion by irreversibly binding CD13/aminopeptidase. Turmeric is commonly consumed as a color food, and its root is also used in a few medicinal products to treat pain and inflammation, such as osteoarthritis. The medicinal properties of turmeric have been known for thousands of years, but curcumin from natural dietary or synthetic sources gained first approval by the Food and Drug Administration (FDA) in 2013. Lasofoxifene is a selective estrogen receptor modulator (SERM) with a non-steroidal structure that selectively binds to estrogen receptors α and β. The European Commission approved it in March 2009. Lasofoxifene could be used for postmenopausal osteoporosis treatment to decrease the risk of both vertebral and nonvertebral fractures. Alvimopan competitively binds to the μ-opioid receptor as a selective antagonist. FDA licensed alvimopan in 2008 to speed up the time to upper and lower gastrointestinal recovery following surgeries. Finally, donepezil increases the accessibility of acetylcholine at the synapses by binding reversibly to acetylcholinesterase and inhibiting the hydrolysis of acetylcholine. Donepezil gained approval by FDA in 2004 and could be used to treat confusion (dementia) related to Alzheimer’s disease. Induced fit docking analysis Although molecular docking simulation using Autodock software is highly favored in presenting the ligand poses within the protein active site, the protein is considered rigid in docking calculations. In this study, the IFD method was carried out to account for both ligand and receptor flexibility. N3 model  were superimposed, and RMSD of 2.34 Å was obtained for entire heavy atoms (excepting the hydrogen atoms). Therefore, the IFD module could be predicting the binding interactions between inhibitors and Mpro protein. The validation of the IFD model was carried out before the docking simulation. The IFD-generated el cyan,  and the  yellow,  in N3/Mppro protein and the four obtained drugs with the lowest binding energy by Autodock. The IFD results are reported in pro and lasofoxifene-Mpro complexes were obtained lower than alvimopan and donepezil complexes. It suggests that the binding affinity of curcumin and lasofoxifene complexes is also lower than alvimopan and donepezil complexes, the same as the calculated binding affinity by Autodock. The IFD generated interactions between curcumin and lasofoxifene with Mpro protein are shown in pro protein via hydrogen bonds lasofoxifene formed hydrogen bonds only with Cys145, Gly143, and Glu166 amino acids. As seen in IFD was performed between MPrioritization of IFD-studied compounds based on MM-GBSA methodbind of the compounds and the contribution of main energy components  were reported in bind. This suggests that lasofoxifene is the most stable ligand in the Mpro protein binding pocket. In Table 2, the free energy components revealed that the lipophilic and van der Waals interaction energies (ΔGLipo and ΔGvdW) have the most significant contribution in the ligands binding energy. The binding energy contribution of the main amino acids in the active site is shown in pro protein active site, the contribution of lipophilic interactions is more than hydrogen bond interactions. The contribution of His163 and Glu166 was more than other amino acid residues in the binding pocket. Combining more energy terms such as surface accessibility area and solvation energy with a suitable force field can make more satisfactory accuracy for the ligand binding energy computing . Thus foMolecular dynamic simulation analysispro protein. Recently curcumin was introduced as Mpro inhibitor . The contribution of each main amino acid in the binding energy has shown in The binding free energy has also been recalculated for Mpro protein has been known as a critical strategy for COVID-19 treatment, highly effective Mpro inhibitors have been investigated with various in silico and traditional methods. In this study, virtual screening with different computational methods has been introduced to predict new Mpro inhibitors. According to this method, virtual screening was first performed on 43 drugs (similar to piperine scaffold). Finally, 34 medicinal compounds were extracted with therapeutic effects against osteoporosis, Alzheimer’s, cancer, and reduced heart rate. Among extracted drugs, curcumin, lasofoxifene, alvimopan, and donepezil demonstrated the lowest binding energy and the most interactions with the COVID-19 Mpro protein active site. Among extracted drugs, lasofoxifene, as a third-generation SERM with high selective affinity for both ERa and ERb subtypes, was the best in terms of binding energy and the critical interactions with amino acids. An induced-fit docking method was performed to consider the flexibility of both ligand and receptor. The obtained score from IFD confirmed molecular docking results, and the IFD score of lasofoxifene was observed lower than others . Lasofoxifene binding free energy, calculated by two programs including Prime MM-GBSA and g_mmpbsa, showed the lowest value; -107.09 and -173.97 kcal mol-1, respectively. And finally, MD simulation investigations on the finest docked pose of lasofoxifene-Mpro and curcumin-Mpro complexes were employed to reveal the overall stability of the two complexes. RMSD, RMSF, and Rg results obtained from MD simulation revealed that lasofoxifene and curcumin exhibited the best stability in the Mpro active site. As a result, lasofoxifene or its close derivatives can be considered promising drugs for the treatment of COVID-19.Since inhibition of MWe would like to express our special thanks to the Hormozgan University of Medical Sciences for their financial support (Project No. 990193).The authors declare no conflict of interest.Maryam Abbasi: Participated in the study’s design, carried out IFD study, performed molecular dynamic simulation, and drafted the manuscript. Hojjat Sadeghi-aliabadi: Participated in the study’s design, was responsible for the study registration and gave final approval of the version to be published.


Acute myocardial infarction (AMI) was of interest due to its potential association with select biologics and the lack of an externally validated International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) algorithm.Healthcare administrative claims data hold value for monitoring drug safety and assessing drug effectiveness. The U.S. Food and Drug Administration Biologics Effectiveness and Safety Initiative (BEST) is expanding its analytical capacity by developing claims-based definitions—referred to as Develop and apply an ICD-10-CM-based algorithm in a U.S. administrative claims database to identify and characterize AMI populations.® MarketScan® Research Databases and characterized using descriptive statistics.A comprehensive literature review was conducted to identify validated AMI algorithms. Building on prior published methodology and consistent application of ICD-9-CM codes, an ICD-10-CM algorithm was developed via forward-backward mapping using General Equivalence Mappings and refined with clinical input. An AMI population was then identified in the IBMBetween 2014–2017, 2.83–3.16 individuals/1,000 enrollees/year received ≥1 AMI diagnosis in any healthcare setting. The 2015 transition to ICD-10-CM did not result in a substantial change in the proportion of patients identified. Average patient age at first AMI diagnosis was 64.9 years, and 61.4% of individuals were male. Unspecified chest pain, hypertension, and coronary atherosclerosis of native coronary vessel/artery were most commonly reported within one day of AMI diagnosis. Electrocardiograms were the most common medical procedure and beta-blockers were the most commonly ordered cardiac medication in the one day before to 14 days following AMI diagnosis. The mean length of inpatient stay was 5.6 days . Findings from this ICD-10-CM-based AMI study were internally consistent with ICD-9-CM-based findings and externally consistent with ICD-9-CM-based studies, suggesting that this algorithm is ready for validation in future studies. Acute myocardial infarction (AMI) is a common form of coronary artery disease caused by a sudden interruption of blood flow to the heart that results in decreased oxygen supply to the myocardium and clinical or laboratory evidence of myocardial injury. While declines in AMI hospitalization and mortality rates have been observed in recent years , it remath (ICD-9-CM) to the ~70,000-code 10th (ICD-10-CM) Revision in October 2015, requiring definitions using this coding system standard to be updated. It should be noted that the World Health Organization maintains ICD-10, that other variations of the ICD-10 exist globally and that other countries have adopted ICD-10 at different times.Administrative healthcare claims data hold broad value in enhancing and optimizing clinical health service delivery. In addition to this, public and private health plan administrative claims databases are useful tools for epidemiologic and drug safety studies, giving researchers access to large patient cohorts ,5. HowevThe U.S. Food and Drug Administration (FDA) Center for Biologics Evaluation and Research (CBER) BEST Initiative is seeking to expand its capacity to assess the safety and effectiveness of biologic products in the U.S. Biologics are preventive and therapeutic products manufactured in living systems, and are distinct from pharmaceuticals ; biologics include vaccines, blood, allergenics, tissues, and cellular and gene therapies . In suppRecognizing that the absence of validated ICD-10-CM algorithms can hinder more current administrative claims research in the U.S., we aimed to develop a new healthcare claims-based definition for AMI using ICD-10-CM and to assess the impact of migration from ICD-9-CM to ICD-10-CM diagnosis codes on the identification of AMI in a U.S. administrative claims database. The objectives of this study are therefore 1) to build on prior published AMI algorithms to develop an ICD-10-CM algorithm that identifies AMI in the U.S., 2) to evaluate the feasibility of algorithm use by applying it to a large U.S. administrative healthcare claims database, 3) to characterize the AMI population cohorts to assess clinical plausibility, and to 4) to assess the impact of the U.S. transition to ICD-10-CM on the identification of AMI in a U.S. administrative claims database.algorithms. Findings informed the development of a draft AMI algorithm that was subsequently refined by consultation with a diverse range of clinical subject matter experts.Authors conducted a literature search in April, 2019  to identify relevant claims-based published definitions of AMI, referred to as https://www.fda.gov/BiologicsBloodVaccines/ScienceResearch/default.htm; https://www.sentinelinitiative.org/communications/publications). This was followed by a structured review to identify relevant articles in PubMed, Medline and Google Scholar. Pertinent grey literature was identified via searches of publication repositories of relevant organizations, including but not limited to the U.S. Armed Forces Health Surveillance Branch, Agency for Healthcare Research and Quality and Health Canada Canada Vigilance Program.The aim of this literature search was not to conduct a full systematic review, but to identify the most pertinent literature and best practices to inform algorithm development. Authors first reviewed relevant FDA Sentinel publications available through the public websites (Articles were retained if they reported an administrative claims-based approach for defining AMI (ICD-9 and onwards) with articles from the U.S. and those that reported measures of diagnostic accuracy of most interest. The focus of this coding effort was to identify episodes of AMI so that future studies could employ this algorithm to identify and validate AMI, irrespective of potential underlying cardiovascular risk factors. Therefore, although angina pectoris and chronic ischemic heart disease are closely related conditions and risk factors for AMI, we excluded diagnostic codes related to subsequent episodes of AMI care, intermediate coronary syndrome, old myocardial infarction, angina, and chronic ischemic heart disease.Section 3.1) informed the development of a draft algorithm. An ICD-10-CM-based algorithm was developed from published applications of ICD-9-CM-based algorithms associated with strong measures of validation performance via forward-backward mapping using General Equivalence Mappings. This process involves translating ICD-9-CM codes to ICD-10-CM (forward-mapping), then translating the identified ICD-10-CM codes back to ICD-9-CM (backward-mapping) to capture codes that may not have one-to-one mapping between versions and to identify additional related codes that may be of relevance. Recent publications have reported that this approach is appropriate for efforts to define AMI using ICD codes ) and medians (and interquartile ranges [IQR]), for continuous variables. Because one of the goals of the study was to provide descriptive statistics of AMI in a U.S. healthcare database, testing for statistical significance was not conducted. Qualitative comparisons were undertaken through visual inspection of quantitative results to assess epidemiologic trends and the impact of the transition to ICD-10-CM.Concurrent medical conditions were analyzed to assess whether findings were consistent with clinical expectations and between ICD-9-CM and ICD-10-CM cohorts. These conditions were identified based on diagnostic (ICD-9-CM or ICD-10-CM) codes reported within one day of the AMI event. Conditions were categorized by an experienced physician (TAB) into the following categories: cardiac, endocrine, gastrointestinal, hematologic, infectious disease, metabolic, musculoskeletal, neuropsychiatric, pulmonary, renal, respiratory and vascular diseases and factors influencing health status. The same physician also categorized prescription codes into mutually exclusive treatment categories  according to target organ system. Procedure codes were grouped into evaluation and management (E/M), emergency medical services (EMS), imaging, laboratory, medication/medication management, pathology, procedure, and ultrasound/doppler categories. Concurrent conditions were queried from one day before to one day following AMI diagnosis, while treatments and procedures were queried from one day before to 14 days following AMI diagnosis. These time periods were based on the judgement of clinical subject matter experts , and were chosen to limit the inclusion of unrelated diagnoses while capturing potential lags in processing of claims.Cardiac conditions and drugs identified in the MarketScan Research Databases were of greatest interest. Agents within the cardiac medication category were further classified according to drug class. The frequency of each cardiac drug class was then assessed for individuals receiving an AMI diagnosis in any healthcare setting and individuals receiving an AMI diagnosis in an inpatient setting.The MarketScan Research Databases meet the Health Insurance Portability and Accountability Act requirements for fully de-identified data sets, protecting the privacy of patients and providers. As this study involved the retrospective analysis of de-identified and anonymized data, no ethics approval was required.S1 Appendix). The use of ICD-9-CM codes 410.x0  and 410.x1  to identify potential AMI cases was consistent across studies, and no studies reported improved diagnostic accuracy associated with including procedural or prescription claims. This included a prior U.S. validation study, which was based on a comprehensive review of U.S. and international studies that analyzed administrative claims data used to identify AMI [A total of 16 articles were retained for extraction  codes were identified ,20. In gTable 1, while the detailed algorithm code list is included in S2 Appendix. Any (≥1) of the codes included in Table 1 were sufficient to warrant a positive case identification, irrespective of healthcare setting or coding position. The algorithm was constructed using only diagnostic coding classification systems (ICD-9-CM and ICD-10-CM). Other classification systems, including procedural , laboratory  and prescription  classification systems, were not included in the algorithm, in keeping with the approaches described in prior publications [The overarching AMI categories selected are summarized in ications ,23 whichSections 3.3.1–3.3.4) and, of these individuals, the majority  received an AMI diagnosis in an inpatient setting. The “inpatient setting” group was examined for concurrent conditions (Section 3.3.3), drug orders and procedures (Section 3.3.4), and average length of hospital stay (Section 3.3.5), with additional analyses for this subpopulation included in S3 Appendix.Between 2014–2017 (inclusive), there were 41,172,696 unique individuals in the MarketScan Research Databases  continuously enrolled in a health care plan for at least one calendar year, out of 57,305,367 individuals  who were enrolled for at least one day in this period. Overall, 268,424 (0.65%) received at least one AMI diagnosis (ICD-9-CM or ICD-10-CM) in any healthcare setting  accounted for 44.9% and 42.7% of patients receiving an AMI diagnosis (ICD-9-CM or ICD-10-CM) in any healthcare and inpatient settings, respectively, despite accounting for only 6.7% of all enrolled individuals. The mean age at first AMI diagnosis (ICD-9-CM or ICD-10-CM) was 64.9 years for individuals receiving an AMI diagnosis in any healthcare setting and 65.1 years in the inpatient setting. In addition, there were more AMI cases among males relative to females in both any healthcare setting  and the inpatient setting . Compared to those receiving an ICD-9-CM diagnosis, patients receiving an ICD-10-CM diagnosis in any healthcare setting were slightly older, and a lower proportion were male.Demographic characteristics of the enrolled and AMI populations, overall and according to ICD-CM version, are summarized in Table 3. From 2014–2017, 2.83–3.16 individuals/1,000 enrollees/year received ≥1 AMI diagnosis. The annual proportion of enrolled individuals receiving ≥1 AMI diagnosis did not change substantially as a result of the 2015 transition to ICD-10-CM . It should be noted that enrollment in the MarketScan Research Databases decreased over time, although, as shown in Fig 1, this did not appear to meaningfully impact the proportion of enrolled individuals receiving an AMI diagnosis.The annual counts of patients with ≥1 diagnosis code for AMI in any healthcare setting during the study period ranged from 61,165 to 80,411, as summarized in Fig 2) suggest that the proportion of those experiencing AMI is highest among males, with the number of events increasing beginning at approximately age 40 years among males and females.The gender-specific proportions of individuals receiving a first AMI diagnosis according to age , essential hypertension , and atherosclerosis  were the most commonly reported diagnosis codes, in addition to an AMI diagnosis beyond the diagnosis used to identify the individual  reported within one day of the first).The two cohorts of patients receiving ≥1 AMI (either ICD-9-CM or ICD-10-CM) diagnosis codes in any healthcare setting between 2014–2017 were assessed to determine the most frequent diagnosis codes reported in the day immediately before or after receipt of an AMI diagnosis. The ten most common cardiac diagnoses are presented in  Concurrent cardiac conditions were more common in the inpatient setting, though unspecified chest pain , atherosclerosis , essential hypertension  and additional AMI diagnoses remained most common.Results of the same analyses limited to the inpatient setting  are detailed in  The top 100 codes for all treatment categories and the assigned drug class are included in  for patients receiving an AMI diagnosis in any healthcare setting. Results of the analysis limited to those receiving the diagnosis in the inpatient setting  are provided in  Receipt of medications from each drug class was more common in the inpatient setting, although the most common drug classes were consistent between individuals receiving an AMI diagnosis in any healthcare setting and in the inpatient setting, with beta-blocker , anti-lipids  and anti-platelet  agents being the most frequently prescribed.The cohorts of patients receiving ≥1 AMI diagnosis codes in any healthcare setting  and in the inpatient setting  between 2014–2017 were assessed to examine the most frequent medications and procedures coded one day before through two weeks following an AMI diagnosis. The most commonly reported cardiac medications during this 15-day time period are grouped by drug class and summarized in The most common medication prescribed in any healthcare setting  and the inpatient setting  was atorvastatin calcium. Other drug products of interest included aspirin and nitrates, based on the common clinical practice of these agents being administered for AMI. However, aspirin was only reported in 5.5% of patients in any healthcare setting (7.6% in inpatient cohort), while one nitroglycerin formulation was reported in 15.0% of those in any healthcare setting (20.2% in inpatient cohort).Table 6, while the top 100 codes for all procedure categories are included in  Results of the analysis limited to patients receiving an AMI diagnosis in an inpatient setting  are provided in  Hospital care and emergency department visits were noted as the most common E/M procedure codes observed .The most common evaluation and management (E/M) procedures (coded among at least 10% of patients in the 15-day period defined surrounding an AMI diagnosis) are listed in Across all procedure categories, three procedure codes for routine electrocardiogram (EKG) were the most common codes observed in any health care setting: interpretation and report only , tracing only , and tracing with interpretation and report . Similar to diagnosis codes, procedure codes are not mutually exclusive and the structure of procedural coding for an EKG in the U.S. allows for various services: one code associated with tracing, interpretation, and reporting (reported for 16.4% of patients), one code associated with the tracing only (reported for 20.0% of patients), and one code associated with interpretation and reporting only (reported for 71.1% of patients). A separate query found that 79.0%  of patients in any healthcare setting and 89.5%  of patients in the inpatient setting received at least one of these three codes in the 15-day period of interest.Of the 170,147 individuals receiving a diagnosis of AMI in the inpatient setting, the mean length of hospital stay was 5.6 days (SD 7.9 days), while the median was 3 days . Overall, 69.9% of patients had a length of stay between 1–5 days, 17.8% had a length of stay between 6–10 days and 6.9% had a length of stay over two weeks.This study aimed to build upon prior AMI studies to develop an algorithm that identifies AMI events using U.S. administrative healthcare claims data. The literature review conducted to inform development of the new AMI algorithm reported strong diagnostic accuracy associated with ICD-9-CM codes 410.x0  and 410.x1  but did not identify validation studies involving ICD-10-CM algorithms for AMI. Findings from this literature review were used to inform the development of an ICD-10-CM algorithm which was reviewed by clinical subject matter experts and applied in the MarketScan Research Databases to describe the population receiving AMI diagnosis codes in any healthcare setting and the inpatient setting between 2014 and 2017. Findings related to population demographics, common concurrent diagnoses, treatments, and procedures were generally consistent between ICD versions, with existing literature and with clinical expectations.While ICD-10-CM AMI algorithms were not identified, a recent systematic review of claims-based definitions of AMI reported that ICD-based algorithms performed well across measures of validation performance . AnotherBetween 2014 and 2017, the proportion of individuals receiving an AMI diagnosis did not vary substantially from year to year. Patients receiving an ICD-10-CM diagnosis were slightly older and slightly more likely to be female than those receiving an ICD-9-CM diagnosis, in any healthcare setting and in the inpatient setting. Diagnoses were more common among males and among those over 40 years of age, with an average age at first AMI diagnosis (as reported in the MarketScan Research Databases between 2014 and 2017) of 64.9 years in the any healthcare setting population and 65.1 years in the inpatient setting population. This finding is similar to the overall mean age of 65.2 years reported among patients hospitalized for ST-elevation AMI in California , but mayWe found that the non-AMI diagnosis codes most frequently reported within one day of an AMI diagnosis were chest pain, atherosclerosis and essential hypertension, for both ICD-9-CM and ICD-10-CM analyses and in both any healthcare and inpatient settings. This is consistent with symptoms and known risk factors for AMI and prior studies that report an association between hypertension and an increased risk or attack rate in AMI patients ,29–32.Atorvastatin calcium was the most commonly reported drug in the one day before through 14 days following first AMI diagnosis  in our study. While aspirin and nitrates are among the most important medications administered on initial presentation of AMI and other acute coronary syndromes, if not contraindicated, these were not among the most commonly coded medications in our database. In the any healthcare setting, we found one formulation of nitroglycerin and aspirin were reported for 15.0% and 5.5% of AMI patients, respectively. It is possible that the lower than expected reporting of these agents is due to administration prior to arrival in the emergency department, either at home or in an ambulance, or due to these agents not being submitted for reimbursement in administrative claims.Beta-blockers  represented the most commonly prescribed drug class for AMI ordered in the 15-day period surrounding AMI diagnosis . All drugs among the classes assessed were more frequently ordered among the inpatient setting population than those among the any healthcare setting population. Notably, an antiplatelet agent–clopidogrel–was the second most commonly ordered drug in both the any healthcare setting and inpatient setting populations, with other antiplatelet agents also noted . Other medication classes commonly indicated in the treatment of AMI included statin therapy and renin-angiotensin-aldosterone system inhibitors, which comprised the majority of remaining medications identified. These findings are consistent with clinical recommendations for treatment of AMI during the period of study . Other sWe also examined common medical procedures coded the day before through 14 days following AMI diagnosis. An EKG is required to diagnose and classify AMI and would be expected in all cases of AMI . An EKG The median length of stay for inpatient admissions was 3 days. This is consistent with data reported by others and in alignment with clinical guidelines indicating that low-risk AMI patients may be safely discharged within 72 hours of admission ,37,38. TStrengths of this study are the development of an AMI algorithm for ICD-10-CM based on a comprehensive review of AMI coding definitions available in the literature and active engagement with clinical subject matter experts. This effort builds on a prior study by Panozzo and colleagues  is inforThe study also includes important limitations. First, the AMI algorithm has not been subjected to a medical record-based validation study, and diagnostic accuracy may vary based on healthcare setting and other criteria (such as diagnosis coding position). Although prior AMI studies reported strong validation performance measures for ICD-9-CM algorithms, these may not be directly transferable to the application of this algorithm in the MarketScan Research Databases. In addition, differences in coding standards and practices between ICD-9-CM and ICD-10-CM and across databases, as well as variance in population characteristics and prevalence of AMI, can impact measures of validation performance. As such, the performance characteristics of the algorithm defined herein remain undefined. For the purpose of this study, the algorithm was applied in the most inclusive manner possible, and positive AMI case identification was independent of coding position or healthcare setting . Thus, more non-AMI cases may have been included than in prior studies that further restricted their study population criteria. We recognize that this more inclusive approach may result in the identification of events associated with particular procedures or conditions, and that future applications of the algorithm may restrict codes based on position or healthcare setting, though this will be dependent on the specific research question. Also, more specific analyses based on care type  or urgency were considered outside the scope of this initial application. Findings in our study may not be representative of the U.S. population, as our analyses were restricted to insured individuals within specified health care plans who may differ in their access to care and health status from the general population. We also were unable to account for the effects of race and ethnicity, as this information was missing in a substantial proportion of claims. Analyses were also based on administrative healthcare claims data, which provide information on medications ordered but do not capture medication indication, dispensation, or consumption, nor do they reflect patient adherence. Lastly, we could not calculate AMI incidence rates because our analyses only considered the first AMI within a given time period, and therefore we cannot exclude the possibility of an individual having an MI prior to entry into the study. The algorithm proposed herein can be applied in other databases to assess incidence rates. However, the characteristics of the AMI population in our study suggest that AMI cases were appropriately identified and that that this algorithm is a candidate for future validation studies utilizing a clinical database.In conclusion, this study aimed to apply a rigorous approach to develop an algorithm to identify AMI events in administrative claims data. The algorithm developed is based on a comprehensive review of the literature, informed by prior validation studies and refined through a forward-backward crosswalk from ICD-9-CM to ICD-10-CM and substantial clinical consultation. Application of the algorithm in a large administrative claims database supports analyses of population groups that received AMI diagnoses and common concurrent diagnoses, treatments and procedures. Findings are consistent between ICD versions , with existing literature and with clinician expectations.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.S3 Appendix(DOCX)Click here for additional data file.


The distinction between coronavirus disease 2019 (COVID-19) and thyroid storm can be extremely difficult to determine on clinical grounds alone as there is significant overlap between the signs and symptoms of each.We present a case of a patient with thyroid storm triggered by underlying COVID-19 infection.Infection with severe acute respiratory syndrome coronavirus 2 is linked to dysregulation of the thyroid gland through numerous mechanisms, although thyroid storm triggered by COVID-19 appears rare, with only a single case previously identified in the literature. Infection with coronavirus disease 2019 (COVID-19) can lead to dysfunction of numerous organ systems, and associated thyroid disease is no exception.A 24-year-old female with a past medical history of Graves’ disease presented to the emergency department with one week of fever, cough, myalgias, and congestion. The patient’s vital signs included a heart rate of 133 beats per minute, blood pressure of 134/82 millimeters of mercury, respiratory rate of 24 breaths per minute, oxygen saturation of 98% on room air, and an oral temperature of 102.3° Fahrenheit. Her physical exam was notable for a thin, diaphoretic, and anxious-appearing female who was tremulous and had obvious exophthalmos.Initial resuscitation included a 2-liter bolus of normal saline, 975 milligrams (mg) of oral acetaminophen, and 400 mg of oral ibuprofen. Antibiotics were considered but not administered given high initial suspicion for thyroid storm with viral trigger. A full complement of laboratory studies, including a complete blood count, basic metabolic panel, venous blood gas, blood cultures, thyroid function tests, urinalysis, and urine pregnancy test, were ordered. Diagnostic imaging included a portable chest radiograph (CXR). Computed tomography pulmonary angiography (CTPA) was considered, but the risks of worsening her potential thyrotoxicosis with an iodine-contrast load was thought to outweigh the benefits. Despite these interventions, the patient’s vital signs and clinical condition did not improve.The patient’s labs showed an undetectable thyroid stimulating hormone and a free thyroxine of >7.770 nanograms per deciliter (ng/dL) (reference range 0.89–1.76 ng/dL) along with a positive SARS-CoV-2 polymerase chain reaction. The remainder of her laboratory results were unremarkable, and her CXR was without infiltrates. At this point, the patient was suspected to be in thyroid storm and had a Burch-Wartofsky score of 60. In consultation with endocrinology, the patient was started on 1 gram of propylthiouracil, 60 mg of propranolol, 100 mg of hydrocortisone, and 4 grams of cholestyramine. She had notable improvement in her symptoms and vital signs and was admitted to the intensive care unit (ICU) for further care. After 36 hours in the ICU, the patient was transferred to a medical-surgical floor and subsequently discharged home with endocrinology follow-up.There are multiple mechanisms by which thyroid dysfunction may occur in the setting of SARS-CoV-2 infection including lymphocytic proliferation of the thyroid and via a “cytokine storm” induced by the proinflammatory state of COVID-19.4In the COVID-19 era, it is especially important for clinicians to maintain a high index of suspicion for thyroid storm in toxic-appearing patients. Many of the clinical features of COVID-19 infection including fever, fatigue, tachycardia, and diaphoresis overlap with those of thyroid storm making them difficult to distinguish.11These patients may not manifest symptoms of hyperthyroidism until several weeks after the contrast load. This becomes especially problematic when attempting to exclude a diagnosis of pulmonary embolism (PE) in these patients. While infection with SARS-CoV-2 appears to be a risk factor for developing a PE, diagnosing this condition requires the use of a CTPA and thus a contrast load.The treatment of thyrotoxicosis should not vary significantly in those with underlying COVID-19 compared to those with another trigger. Treatment of mild thyrotoxicosis in COVID-19 patients without underlying thyroid disease does not necessitate thionamides, and most of these patients will recover spontaneously.14What do we already know about this clinical entity?Infection with severe acute respiratory syndrome coronavirus 2 can trigger thyroid dysfunction leading to both hypothyroidism and hyperthyroidismWhat makes this presentation of disease reportable?The incidence of overt thyroid storm triggered by coronavirus disease 2019 (COVID-19) is rare, with only 2 cases reported in the literature.What is the major learning point?Clinicians must maintain a high index of suspicion for thyroid storm in patients presenting with COVID-19 as clinical features overlap.How might this improve emergency medicine practice?Consider ordering thyroid studies in all patients with severe COVID-19 infection.Infection with SARS-CoV-2 has been linked with thyroid disease, but this is just the second case report of a patient with overt thyroid storm in the setting of COVID-19 infection. Clinicians should consider performing thyroid function testing in critically ill patients admitted with COVID-19, and they should be cautious of using iodine contrast in the diagnostic approach to these patients.


Difficulties in Emotional Regulation Scale (DERS-18) data were collected at baseline, prior to each session, and at one week, one month, and three months postintervention and analyzed. Qualitative data collected from sessions, debriefs, and follow-up interviews were transcribed and descriptively analyzed. Results demonstrated statistically significant decreases in DERS-18 scores from preintervention to postintervention at each timepoint. Qualitatively, participants perceived improvements in ER within specified DERS-18 domains. We describe how 3MDR’s unique and novel approach addresses ED through cognitive–motor stimulation, narration, divergent thinking, reappraisal of aversive stimuli, dual-task processing, and reconsolidation of traumatic memories. More studies are needed to better understand the underlying neurobiological mechanisms by which 3MDR addresses ER and PTSD.Multi-modal motion-assisted memory desensitization and reconsolidation therapy (3MDR), an interactive, virtual reality-assisted, exposure-based intervention for PTSD, has shown promising results for treatment-resistant posttraumatic stress disorder (TR-PTSD) among military members (MMs) and veterans in randomized controlled trials (RCT). Previous research has suggested that emotional regulation (ER) and emotional dysregulation (ED) may be factors which are correlated with symptom severity and maintenance of TR-PTSD. This embedded mixed-methods pilot study ( Posttraumatic stress disorder (PTSD), one of the leading mental health disorders reported by military organizations worldwide, has detrimental effects, such as attrition and overburdening of healthcare resources ,2. MilitTreatment approaches for PTSD among MMs and veterans have variable effectiveness. Veterans see significantly worse treatment outcomes when compared with other populations that experience PTSD . FurtherMulti-modal motion-assisted memory desensitization and reprocessing therapy (3MDR) is a novel interactive psychotherapeutic approach for TR-PTSD ,9,10 thaThe 3MDR intervention used in this study consisted of eight sessions over an eight-week period, beginning with one to two pre-platform preparatory session(s), during which the patient selects and orders images and music. Symbolic representations in the form of images  related to the patient’s traumatic experiences are selected and ordered from least to most distressing. Music is also identified that reminds the patient of the past time of trauma, facilitates the emotional memory network , and supports a return to the present . Participants were also familiarized with the virtual environment and 3MDR protocol in a preparatory session. The six platform sessions were 90 min therapy sessions in the virtual reality environment (VRE), including a 30 min debrief. Each platform session involves three phases . In the pre-platform phase, the therapist and patient confirm the order of images and music for the session. During the platform phase, the patient dons a safety harness and is accompanied by a 3MDR therapist while walking continuously on a treadmill at a self-selected pace. The patient first warms up by walking on the treadmill while listening to self-selected music connecting them to traumatic experiences and then, during each of seven 3–5 min cycles, walks down a virtual “hallway” toward a self-selected trauma-related image. The patient describes the image, physical sensations, and feelings to the therapist. The therapist assists the patient with generating descriptive words/phrases that are then projected in front of the image on the screen and which the patient is instructed to read aloud. For a duration of 30 seconds thereafter, the patient views a ball oscillating horizontally in the foreground of the image and reads out numbers as they appear on the ball. The patient cools down after the seventh cycle by walking while listening to self-selected music that facilitates reconnection to the present. Each session is concluded with a post-platform phase which includes debriefing, discussion, reconsolidation, and a mental wellness check/self-care plan. A post-intervention session occurred where an in-depth semi-structured qualitative interview and quantitative questionnaires were conducted. In-depth descriptions of 3MDR have been published elsewhere . This prFurther investigation is required regarding the mechanisms by which 3MDR addresses PTSD and components of the intervention that differentiate its efficacy from other therapeutic interventions. One proposed mechanism relates to the manner in which 3MDR addresses emotional regulation (ER) and dysregulation (ED).ER is the ability to adaptively respond to negative emotions that arise from threatening or aversive stimuli ,16. As pAssociated with reduced ER ,19,20, sThere is a crucial need for novel and improved treatment approaches that address all aspects of TR-PTSD among MMs and veterans, including those at adequately address ED as part of PTSD pathology. Currently, 3MDR as a novel therapy is under investigation for its efficacy in addressing combat-related TR-PTSD in military members and veterans. This study aims to examine whether 3MDR is effective at improving ED, and to identify potential neuropsychological mechanisms of 3MDR that may address ER and, consequently, TR-PTSD.This embedded mixed-methods study  is a sub-study of a larger mixed-methods waitlist control randomized controlled trial (RCT) exploring whether 3MDR is effective at addressing combat-related treatment-resistant PTSD among Canadian MMs and veterans. This sub-study analyzed ER questionnaire data collected using the Difficulties in Emotional Regulation Scale (DERS-18), which is one of several self-reported questionnaires used to assess and monitor symptom changes across the 3MDR intervention. Transcribed data collected during participant sessions, debriefing, and/or postintervention interviews was also used to analyze the functional impact of 3MDR on ED. The study has approval from the University Research Ethics Board (Pro00084466) and the Canadian Armed Forces (CAF) Surgeon General Research Program (E2019-02-250-003-0003).Two other papers from our group present analyses of certain aspects of the DERS-18 data used in the current study ,23. The 2 = 0.50) [A targeted study sample size of 40 MMs and/or veterans was set for the larger mixed-methods waitlist control RCT to account for a 20 percent dropout rate and allow for power at 32 participants. With four latent variables, for 80% significance at a 5% significance level, the sample size required for this study is 24  criteria for PTSD diagnosis , had PTSRecruitment was conducted by word of mouth as convenience and snowball sampling. Service providers supporting MMs and veterans, after being introduced to the study via word of mouth and institutional email, informed patients who met the study inclusion and exclusion criteria. Potential participants were provided with a “Permission to Share Contact Information with the Research Team” form by their service provider. Completed forms were forwarded to a researcher who contacted potential participants via phone or email, provided further information about the study, and determined eligibility to participate. Voluntary verbal and written informed consent were obtained from all CAF-MMs and veterans participating in the study. The study uses the Computer Assisted Rehabilitation ENvironment (CAREN) system, located in the Glenrose Rehabilitation Hospital in Edmonton, Alberta. Quantitative data, including self-report questionnaires, and qualitative data, including transcripts of participant interviews, were collected preintervention, at each session, and at one week, one month, three months, and six months postintervention. The DERS-18, a shortened yet comparative version of the DERS-36 found to effectively measure ER ,26, was Quantitative: Participants were asked to complete a DERS-18 questionnaire prior to starting the 3MDR intervention (T0 baseline) . FollowiQualitative: Audio and video recordings of data from 3MDR platform sessions, session debriefings, and T1 to T4 follow-up interviews were transcribed and deductively reviewed based on the domains of the DERS-18 for relevant changes in ER. Interview questions  probed changes in ER following 3MDR.p-value were collected to analyze for changes in DERS-18 scores. Changes in DERS-18 scores were analyzed in IBM SPSS using a Wilcoxon signed-ranks test to compare participant preintervention baseline scores and postintervention scores. Participants with preintervention baseline scores (T0) were compared with their respective postintervention scores . The Wilcoxon test is a nonparametric test designed to compare related samples . For eacQualitative data was deductively analyzed using descriptive analysis. The rationale behind selecting a deductive descriptive analysis approach was to compare and triangulate the quantitative results found with the DERS-18. Specifically, we sought to explore if participants noticed and/or experienced a tangible change in ER which they would attribute to 3MDR. Qualitative descriptive analysis is a widely cited research tradition and has been identified as important and appropriate for research questions focused on discovering the who, what, and where of events or experiences and gaining insights from informants regarding a less-understood phenomenon . It is aA concurrent parallel approach following a data transformation model was utilized in the data analysis process to converge the data for comparing and contrasting the quantitative statistical results with qualitative findings ,31. p-value of 0.021 .Mean participant total DERS-18 score one week following completion of 3MDR intervention (T1) was shown to decrease from baseline preintervention mean participant score . The Wilp-value of 0.028 . Mean participant DERS-18 score one month following completion of 3MDR intervention was also shown to decrease from the baseline preintervention mean participant score . The conp-value of 0.027 .Mean participant DERS-18 score three months postintervention also showed a decrease from the baseline preintervention mean participant score . The WilDecreases in DERS-18 score across time are summarized in Quotes collected from transcribed participant data are displayed in This study examined whether 3MDR is effective at improving ED among MMs and veterans with TR-PTSD. The results demonstrated a statistically significant decrease in DERS-18 scores  for all Several potential mechanisms of 3MDR may facilitate changes in ER. These include cognitive–motor stimulation, eye movement bilateral stimulation, and comprehensive treatment addressing ER. Cognitive-affective and motor systems are functionally interconnected, and subconsciously influence one another ,32,33. BThe EMDR working memory task during 3MDR aims to engage neural networks that may both enable the reduction of or disengagement from negative emotions and the fear response and set the stage for reconsolidation of traumatic memories. The working memory task, which has elements of EMDR given its use of eye movements in a sequential manner, is enhanced in 3MDR with the speaking aloud of numbers appearing on an oscillating ball. EMDR is hypothesized to promote neuroplasticity in regulatory regions related to emotional processing , such asUnderlying the efficacy of the 3MDR intervention in regulating ED may be the combination of treatment methods . Difficulties with ER are highly correlated with severity and maintenance of TR-PTSD ,20. 3MDR3MDR’s comprehensive and multi-modal approach may affect several neurobiological systems. An immersive, virtual reality-supported, exposure-based therapy, 3MDR may recruit and stimulate prefrontal regions, specifically the mPFC, that are dampened in the emotional undermodulation subtype. Through engaging these areas, 3MDR may facilitate proper inhibition of the amygdala and balance of the amygdalar–prefrontal feedback loop. Further, one of the most significant neural correlates of PTSD is hyperactivation of the amygdala ,39, whicED associated with both PTSD and MI may also be addressed through 3MDR. Exposure to morally injurious experiences, which frequently overlaps with exposure to traumatic experiences associated with combat-related TR-PTSD, is a high-risk factor for the development of MI ,7, and tAs a promising, innovative therapeutic intervention that has generated statistically significant outcomes, 3MDR warrants much further study and real-world application with trauma-affected populations. The effect of active participation and engagement of patients by way of walking while simultaneously talking and performing tasks is an area of ongoing investigation. Two RCTs have shown positive results, while a third is underway in the Netherlands and this fourth in Canada. Exploring underlying therapeutic principles and mechanisms involved in 3MDR that individually and collectively address various layers of ED is also an area requiring additional study. To further investigate hypothesized neurobiological mechanisms driving efficacy of 3MDR, and validate the findings from a biological perspective, the study would benefit from implementation of neurohormonal assessment, EEG or other neuroimaging techniques to analyze neuroactivity during, pre-, and postintervention. Continuation of the study and recruitment of further participants will allow more data collection and analysis, enabling greater sample sizes and improving validity of the results. In particular, generating more 6-month postintervention (T4) data would allow for deeper insight on long-term effects of 3MDR on ER. This study was positioned to assess data in a larger cohort. We experienced some barriers, however, on data collection due to COVID-19 restrictions. As analysis of the presented data sets the stage for new studies, we did not want to delay analysis of the results. We do need to report that, despite these data indicating statistically significant and promising changes in ER following 3MDR, the small sample size limits the validity and generalizability of the results. Ongoing data collection and analysis would allow for determination of stronger conclusions regarding the efficacy of 3MDR on addressing ED. n = 9) with combat-related TR-PTSD following 3MDR, with positive statistically significant preliminary outcomes lasting months following the intervention. It is hypothesized that 3MDR’s comprehensive, multi-modal treatment approach is responsible for the active engagement of suppressed stress regulatory circuits and stimulation of brain circuits  pertinent in emotional processing and fear inhibition. These may be underlying mechanisms by which 3MDR addresses cognitive avoidance and ED. Continuing research on this novel intervention’s ability to enhance ER in trauma-affected populations is warranted. The potential impact of 3MDR on ER is not only a promising development for TR-PTSD, but also for other mental health challenges, such as MI, that may contribute to ED. More studies are needed to assess if individuals from diverse other uniformed and civilian populations struggling with trauma-based mental health conditions may benefit equally from the 3MDR intervention.This study reports enhanced ER on a sample of military members and veterans (