Coronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics.In this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred , implying that 17%  of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics.In this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic. Andrew Stokes and co-workers report a county-level analysis of excess deaths owing to COVID-19 in the United States. The Coronavirus Disease 2019 (COVID-19) pandemic has resulted in excess mortality that would not have occurred in the absence of the pandemic.Excess deaths include deaths assigned to COVID-19 in official statistics as well as deaths that are not assigned to COVID-19 but are attributable directly or indirectly to COVID-19.While prior studies have identified significant racial and socioeconomic inequities in directly assigned COVID-19 deaths, few studies have documented how excess mortality in 2020 has differed across sociodemographic or health factors in the United States.Leveraging data from 2,096 counties on COVID-19 and all-cause mortality, we assessed what percentage of excess deaths were not assigned to COVID-19 and examined variation in excess deaths by county characteristics.In these counties, we found that for every 100 deaths directly assigned to COVID-19 in official statistics, an additional 20 deaths occurred that were not counted as direct COVID-19 deaths.The proportion of excess deaths not counted as direct COVID-19 deaths was even higher in counties with lower average socioeconomic status, counties with more comorbidities, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported a higher proportion of excess deaths not assigned to COVID-19.Direct COVID-19 death counts significantly underestimate excess mortality in 2020.Monitoring excess mortality will be critical to gain a full picture of socioeconomic and racial inequities in mortality attributable to the COVID-19 pandemic.To prevent inequities in mortality from growing even larger, health equity must be prioritized in the policy response to the COVID-19 pandemic. The novel Coronavirus Disease 2019 (COVID-19) is an international public health emergency caused by the respiratory droplet transmission of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) .Vital registration data sourced from death certificates on cause of death are likely to underestimate the mortality burden associated with the COVID-19 pandemic for several reasons ,5. FirstThe term “excess deaths” refers to differences in mortality relative to what would have been expected in the absence of the COVID-19 pandemic. Typically, excess deaths are computed relative to a recent historical benchmark for the same population. Excess deaths include deaths directly assigned to COVID-19 on death certificates and excess deaths not assigned to COVID-19, which were either misclassified to other causes of deaths or were indirectly related to the COVID-19 pandemic. Using deaths from all causes to measure the excess mortality impact of the COVID-19 pandemic can help circumvent biases in vital statistics, such as low COVID-19 testing rates, reporting lags, and differences in death certification coding practices and capture excess deaths indirectly related to the COVID-19 pandemic. As such, estimates of excess deaths from all causes associated with the pandemic provide a useful measure of the total mortality burden associated with COVID-19 .Previous reports have estimated excess deaths in several different ways. The National Center for Health Statistics (NCHS) estimates excess mortality through comparison of mortality levels in 2020 to historical mortality data by week and geographic location ,22. WeinIn this paper, we take advantage of county-level variation in COVID-19 mortality to estimate its relationship with all-cause mortality across US counties. We anticipate that counties with higher mortality from COVID-19 will also have experienced greater increases in mortality from other causes of death because the impact of the pandemic is not registered in COVID-19 deaths alone. We use the relationship between COVID-19 mortality and changing mortality from all causes of death to estimate excess mortality that was not directly assigned to COVID-19 as a cause of death. While prior studies generated a prediction of expected mortality in 2020 based on historical trends and then compared expected to observed mortality to estimate excess deaths, our model takes advantage of county variation to estimate simultaneously the mortality trend and the imprint of COVID-19. We then examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by area-level sociodemographic and health characteristics, allowing us to identify population subgroups with a disproportionate number of excess deaths that were not directly assigned to COVID-19. Our estimates provide an alternative approach to calculating excess deaths that can complement existing approaches.Our analytic plan, designed prior to reviewing our data or beginning analysis, was to estimate excess deaths in 2020 using the relationship between 2020 all-cause mortality, directly assigned COVID-19 mortality, and historical all-cause mortality at the county-level. Although we did not have a formal, prespecified plan, our a priori hypothesis was that excess deaths would exceed directly assigned COVID-19 deaths and that the percent of excess deaths not assigned to COVID-19 would vary by county-level factors. During the revision process, we introduced sensitivity analyses to assess the robustness of our results to alternative inclusion criteria, modified our approach to examining variation in excess deaths not assigned to COVID-19 to allow for full model stratification, and updated the analyses to reflect the most recent data and allow for a time lag that would better account for possible bias caused by incomplete data. The results of this study are reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines .We used NCHS provisional data on all-cause mortality and directly assigned COVID-19 mortality by county of residence from January 1 to December 31, 2020. The data were considered provisional due to a time lag between the occurrence of deaths and the completion, submission, and processing of death certificates. To account for possible lags in mortality reporting, we used data generated on March 12, 2021 (10 weeks after the end date), meaning that all deaths occurring before December 31 that were reported before March 12 were captured. Prior analyses of NCHS provisional data have found that provisional mortality data can have low completeness in the first month after a death occurs but are more than 75% complete within 8 weeks after a death . The oriTo construct a historical comparison period, we utilized data from CDC WONDER reporting all-cause mortality for 2013 through 2018 by county of residence. We also used US Census data on county population and age distribution from 2013 to 2020 and data on sociodemographic and health factors from a variety of consolidated sources including the 2020 RWJ Foundation County Health Rankings. A list of these data sources is provided in We produced crude death rates for all-cause and directly coded COVID-19 mortality in 2020 using the reported death counts and the estimated county-level population on July 1, 2020. To compute an average historical death rate for 2013 to 2018, we divided the sum of deaths from 2013 to 2018 by the total population from 2013 to 2018.Prior literature has documented differences in COVID-19 mortality by sociodemographic and health factors ,34. In tWe first modeled all-cause mortality in 2020 as a function of historical all-cause mortality, directly assigned COVID-19 mortality in 2020, and an error term:M(i) = Death rate from all causes in county i in 2020M*(i) = Average death rate from all causes, county i in 2013–2018C(i) = COVID-19 death rate in county i in 2020ε = Error termα represents changes in mortality that are independent of COVID-19 mortality and that are common across regions. The value of β1 represents the extent to which past levels of all-cause mortality in a county are replicated in 2020. If β1 = 1.0, for example, then this would indicate that all-cause mortality in 2020 was on average equal to that in 2013 to 2018, plus or minus the value of α. Together, combinations of α and β1 indicate how mortality changes that are not associated with COVID-19 vary with the level of all-cause mortality in 2013 to 2018. The value of β2 indicates the extent to which mortality from COVID-19 affects all-cause mortality in 2020 after adjusting for historical mortality patterns. If β2 = 1.0, this would imply that each death coded to COVID-19 would be associated with one additional death from all causes combined. Values of β2 greater than 1.0 would suggest that the effect of the COVID-19 pandemic in a county is not fully reflected in deaths assigned to COVID-19 and that excess deaths are being attributed to some other causes of death. Values of β2 less than 1.0 would suggest that COVID-19 is over-recorded as a cause of death, or reductions in mortality are occurring for other causes.The parameters of Eq  were estIn secondary analyses, we limited our sample to counties with 50 or more COVID-19 deaths to assess the sensitivity of our results to counties reporting relatively small numbers of COVID-19 deaths. We also limited our sample to counties with populations greater than or equal to 50,000 to assess the robustness to excluding counties with relatively small populations. In further sensitivity analyses, we performed the regression among all counties in the original NCHS dataset, including counties that were eliminated by our study’s exclusion criteria. To assess the robustness of our results to alternative modeling approaches, we also estimated the relationship between COVID-19 and all-cause mortality using a negative binomial regression model.We did not control for age in our primary analysis because the effect of age should be partially captured in the historical mortality term. In a sensitivity analysis, we reestimated the primary OLS model using indirectly age-standardized death rates to adjust for differences in the counties’ age distributions. Deaths by age were not available at the county level so direct standardization of death rates was not possible. Indirect standardization adopts the age-specific death rate schedule for the whole US and applies it to the age distribution of a county to predict the number of deaths in that county . It thenβ2 coefficient), we fully stratified our primary regression model into population-weighted quartiles of various county-level sociodemographic and health factors. We then produced a forest plot displaying the β2 coefficients for the upper and lower 25% of each county-level factor. Counties with high β2 coefficients represent counties that have a high proportion of excess deaths not assigned to COVID-19 compared to their direct COVID-19 deaths. To understand how this relationship translated to absolute numbers of deaths, we also calculated predicted excess death rates in each of the strata using the fully stratified model coefficients. We determined the average observed directly assigned COVID-19 death rate for each stratum by calculating the weighted mean and then found the predicted excess death rate not assigned to COVID-19 by multiplying the average observed direct COVID-19 death rate for the stratum by the β2 coefficient minus 1. In a sensitivity analysis, we repeated both of these analyses using indirectly age-standardized death rates.To identify county-level characteristics that modified the relationship between direct COVID-19 mortality and excess mortality . This combination suggests that crude all-cause mortality rose on average across counties between 2013 and 18 and 2020. The coefficient of β2 is estimated to be 1.20 . This value suggests that, for every 100 deaths assigned to COVID-19, the number of all-cause deaths rose by 120. This result implies that (120–100)/120 or 17%  of all excess deaths were not directly assigned to COVID-19 on death certificates. In absolute terms, 367,820 directly assigned COVID-19 deaths occurred in the 2,096 counties in the study between January 1 and December 31, 2020, meaning there were 74,172  excess deaths not directly assigned to COVID-19 for a total of 441,992  excess deaths.β2 was 1.21 . β2 was 1.21  when we limited to counties with 50,000 or more residents, and β2 was 1.20  when we included counties with 10 to 19 COVID-19 deaths. Our results were relatively consistent with indirect age standardization, with the percent of excess deaths not attributed to COVID-19 equaling 13% . When we used an alternative modeling approach , we found that 22% of excess deaths were not assigned to COVID-19, suggesting that our OLS results may be conservative . The figure displays estimates of the β2 coefficient across models that were fully stratified into population-weighted quartiles for various sociodemographic and health factors. For sociodemographic factors, the β2 coefficient was higher in counties with the lowest median household incomes  and the least formal education  than in counties with the highest median incomes  and most education . The β2 coefficient was also elevated in counties with more non-Hispanic Black residents  and fewer non-Hispanic White residents  compared to counties with fewer non-Hispanic Black residents  and more non-Hispanic White residents . Regarding health factors, the β2 coefficient was higher in counties with the most poor or fair health  and the most diabetes  compared to counties with the least poor or fair health  or the least diabetes . Regionally, the β2 coefficient was elevated in the South  and West  compared to the Northeast  and Midwest . In this study, we estimated that 17% of excess deaths attributable to the COVID-19 pandemic were not assigned to COVID-19 on death certificates. The proportion of excess deaths not counted as direct COVID-19 deaths was even higher in counties with lower average socioeconomic status, counties with more comorbidities, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported a higher percentage of excess deaths not assigned to COVID-19, indicating a pattern related to structural racism.Prior estimates of excess mortality not assigned to COVID-19 have been relatively similar despite being based on different methodological approaches. Weinberger and colleagues identified 95,235 directly coded COVID-19 deaths from March 1 to May 30, 2020 and 122,300 total excess deaths attributable to the pandemic . This inThe coefficients from our primary model indicate that mortality would have risen between 2013 and 2018 and 2020 even in the absence of the COVID-19 pandemic. This prediction is consistent with a rising trend in national crude death rates between 2013 and 2018. The crude death rate rose from 821.5 deaths per 100,000 people in the year 2013 to 867.8 deaths per 100,000 people in 2018 . EstimatAs noted earlier, excess deaths not assigned to COVID-19 could include deaths involving COVID-19 that were misclassified to other causes of death and deaths indirectly related to the COVID-19 pandemic. The NCHS has examined excess deaths not assigned to COVID-19 by cause of death nationally. As of December 22, 2020, NCHS has attributed 38,115 excess deaths to Alzheimer disease and related dementias, 22,661 excess deaths to hypertensive diseases, 14,684 excess deaths to ischemic heart disease, 14,194 excess deaths to diabetes, and 3,060 excess deaths to influenza and pneumonia . It is pIn this study, we found that counties with low median incomes and less formal education and counties in the South and West reported high numbers of excess deaths not assigned to COVID-19 compared to direct COVID-19 deaths, suggesting that COVID-19 mortality may be especially undercounted in these areas. Determining the cause of potential undercounting is an important area for future research. Possible factors could include lower rates of COVID-19 testing in these populations , reducedIndirect deaths, such as suicide, homicide, or drug overdose and deaths related to reductions in healthcare use, may also be higher in these areas. Faust and colleagues found that only 38% of excess deaths in 2020 among adults aged 25 to 44 years were attributed to COVID-19, suggesting substantial mortality among younger adults during the pandemic that is not accounted for in COVID-19 death tallies . AnalysePrevious research has shown that counties with a higher percentage of Black residents have reported more mortality attributable to COVID-19 ,49. Accoβ2 is biased downwards, then we will have underestimated the magnitude of excess deaths. Another limitation of this study is that we lacked disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics. As a result, age-compositional effects were addressed using an indirect age standardization procedure, and predictors were assessed based on their distributions at the county level. Future research based on county-level data with further disaggregation is needed to confirm our findings. Lastly, the sociodemographic and health factors examined in this analysis were based on data from 2010 through 2018. County-level distribution of these factors may have changed between that time and 2020 when the mortality data were analyzed.Our analysis had several limitations. The 2020 all-cause mortality and COVID-19 mortality data used were provisional. Counties may have differential delays in reporting death certificate data that vary by county, state, rurality, or other area-level factors. In particular, counties that are currently reporting lower all-cause mortality in 2020 than in the historical period are notable. While it is possible that these counties represent random annual variation in death rates or incomplete data, these counties could also have experienced reductions in mortality as a result of strong public health measures that reduced other causes of death such as influenza . If our In line with previously published studies, our findings suggest that the overall mortality burden of COVID-19 considerably exceeds reported COVID-19 deaths. Using provisional vital statistics county-level data on COVID-19 and all-cause mortality in 2020 from the NCHS, we estimated that 17% of all excess deaths in the US from January 1 to December 31, 2020 were excess deaths not assigned to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our findings emphasize the importance of considering health equity in the policy response to the pandemic, such as in access to COVID-19 vaccines. As the impact of COVID-19 continues to spread in the US, analysis of excess deaths will continue to be a valuable proxy measure for assessing the overall mortality burden of COVID-19. This study highlights the importance of considering excess deaths beyond those directly assigned to COVID-19 in the overall assessment of the mortality impact of the COVID-19 pandemic and provides a new method for doing so.The interpretations, conclusions, and recommendations in this work are those of the authors and do not necessarily represent the views of the Robert Wood Johnson Foundation.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigCOVID-19, Coronavirus Disease 2019.(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S1 TableSTROBE, Strengthening the Reporting of Observational Studies in Epidemiology.(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableOLS, ordinary least squares.(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.


Several Silicon on Insulator (SOI) wafer manufacturers are now offering products with customer-defined cavities etched in the handle wafer, which significantly simplifies the fabrication of MEMS devices such as pressure sensors. This paper presents a novel cavity buried oxide (BOX) SOI substrate (cavity-BOX) that contains a patterned BOX layer. The patterned BOX can form a buried microchannels network, or serve as a stop layer and a buried hard-etch mask, to accurately pattern the device layer while etching it from the backside of the wafer using the cleanroom microfabrication compatible tools and methods. The use of the cavity-BOX as a buried hard-etch mask is demonstrated by applying it for the fabrication of a deep brain stimulation (DBS) demonstrator. The demonstrator consists of a large flexible area and precisely defined 80 µm-thick silicon islands wrapped into a 1.4 mm diameter cylinder. With cavity-BOX, the process of thinning and separating the silicon islands was largely simplified and became more robust. This test case illustrates how cavity-BOX wafers can advance the fabrication of various MEMS devices, especially those with complex geometry and added functionality, by enabling more design freedom and easing the optimization of the fabrication process. Standard SOI substrates were originally developed to enable perfect dielectric isolation in electronic devices. Nowadays, SOI wafers have also become an important substrate material for the fabrication of MEMS devices. SOI wafers consist of a handle wafer that provides mechanical strength during the fabrication process, a device layer in/on which the devices are fabricated, and a buried oxide (BOX) layer that separates the device layer from the handle wafer see a. Apart Cavity-SOI substrate is a substrate that has been derived from the silicon-on-insulator-family . It is aCavity-BOX is an advanced substrate with custom-defined cavities etched in the buried oxide see c. It is The advanced SOI substrate with cavities in the BOX can significantly simplify processes, such as microfabrication of highly integrated foldable devices ,5 or 3-d2 membrane can severely hamper the follow-up processes. Employing the cavity-BOX substrate with patterned buried oxide  probe see . The higThe DBS demonstrator was fabricated to illustrate the advances resulting from applying the cavity-BOX to the process. The DBS demonstrator presented here is a semiflexible mechanical structure, composed of silicon island and polymer-based flexible film, without interconnects or integrated electronic components.The fabrication of the DBS demonstrator can be separated into two parts: the cavity-BOX SOI substrate preparation and the DBS demonstrator fabrication. The main technical challenge arises from the fact that the submicron (less than 1 µm) alignment accuracy between the buried cavity-BOX mask and the structures on top of the device layer must be guaranteed. To overcome that problem, the alignment marker transferring strategy, proposed and developed by C. Mountain et al. , was app2 layer for wafer bonding was grown on both sides of the wafer. The customized cavity-BOX pattern was aligned with the markers, and dry etched into the SiO2 layer, landing on the silicon , an 80 µm-thick device layer, and 1 µm thermal oxide on the backside of the wafer.2 layer was then deposited on top of the original 1 µm-thick thermal oxide layer on the backside of the wafer and patterned into the silicon DRIE etching mask. Next, a 500 nm-thick PECVD SiO2 layer was deposited on the front side of the wafer as an adhesion layer. Subsequently, a 3 µm-thick polyimide layer (PI2610 Microsystems) was coated on top of the SiO2 adhesion layer  sunken surface of the device layer due to the cavities in the BOX.The cavity-BOX is formed by the bonding process of two oxide layers: 1 µm of pre-patterned SiOo 500 nm b. The poAfter the cavity-BOX substrate preparation, no visible sunken surfaces were observed in the device layer. A scanning acoustics microscopy (SAM) was applied to inspect the bonding quality, as shown in 2 etch. As a result of reducing the helium flow in the etching recipes, the wafer temperature could not be well maintained during the process, hence, leading to an increased nonuniform etch rate across the wafer. The etching slop was also enlarged due to the loss of temperature control.During the backside processing, it appeared that the 4 mm terrace width on the front side of the SOI wafer caused compatibility issues with the PAS5500 ASML wafer stepper and the SPTS Pegasus DRIE etching tool. During the backside lithography process, the wafer stepper needed to handle the wafer from the front side. The positionings of a few vacuum pads on the wafer stepper robot arm were in the 4 mm terrace region, which led to a loss of vacuum. The wide terrace additionally caused helium leakage errors on the chuck of the dry etching tools. To continue processing the 4 mm terrace cavity-BOX SOI wafers, the robot arm of the wafer stepper was explicitly tuned to accept the wafers, and special dry etching recipes with low helium flow were developed for the silicon and SiOTherefore, the terrace width is preferred to be as small as possible for cleanroom compatibility. In contrast, the terrace edge should be as far as possible from the markers to avoid its interference with the alignment process, requesting a large terrace width. A trade-off test was performed to define the optimal terrace width. Wafers with different terrace widths were tested in the standard 6-inch cleanroom processing line in the PAS5500 ASML wafer stepper, an SPTS Pegasus, ICP, and APS etching tools. The test results indicate that the terrace width of 1.8 to 2.5 mm should provide sufficient compatibility.To release the demonstrator and form the semiflexible device structure, several etching steps were performed from the backside of the wafer. These etching steps include the following: (1) The DRIE etching of the handle wafer substrate landing on the cavity-BOX; (2) thinning down the cavity-BOX to form the pre-patterned oxide mask; (3) etching of the device layer and the polyimide adhesion oxide layer using the oxide mask f,h.After fabrication, the demonstrator was taken out of the silicon frame see . The demAdvanced cavity-BOX SOI substrates with a buried oxide mask were developed, prepared, and applied in the process of a semiflexible microfabricated device. The oxide mask was first fabricated on the handle wafer and then bonded with the oxide on the device layer to form the cavity-BOX. The alignment between the structures on the device layer and the buried oxide mask was successfully realized by employing the front-to-front markers transferring strategy. The advanced SOI substrate was implemented for a DBS demonstrator fabrication. The cavity-BOX layer was used as an etch-stop layer and a pre-patterned two-step DRIE mask to etch through the device layer from the backside of the wafer. This two-step oxide layer was successfully used to compensate for etch rate differences during the bulk of the silicon removal. The application of the cavity-BOX substrate to the DBS demonstrator fabrication proved a more robust and significantly simplified process and large design freedom, including the coexistence of high-precision micron-sized features and a large millimeter-sized opening. The semiflexible DBS demonstrator with a length of 18 mm and a diameter of 1.39 mm was successfully fabricated and assembled.The cavity-BOX SOI has a great potential to simplify the fabrication of various MEMS devices, where device thinning and precise silicon structure separation/definition is needed. The cavity-BOX SOI substrate with a terrace width from 1.8 mm to 2.5 mm is compatible with standard cleanroom equipment for both the front and backside processes. This terrace width also permits applying the high precision (less than 500 nm) alignment strategy of the BOX pattern and the device layer structures. Depending on design requests, the customized cavity-BOX SOI can be provided by the commercial SOI suppliers in large volume, hence, extensively enabling the scaling up of the production.


The limitations and false-negative results of Real-time Polymerase chain reaction (RT PCR) in diagnosing COVID-19 infection demand the need for imaging modalities such as chest HRCT to improve the diagnostic accuracy and assess the severity of the infection.The study aimed to compare the chest HRCT severity scores in RT-PCR positive and negative cases of COVID-19.This cross-sectional study included 50 clinically suspected COVID-19 patients. Chest HRCT and PCR testing of all 50 patients were done and the chest HRCT severity scores for each lung and bronchopulmonary segments were compared in patients with positive and negative PCR results. Chi-square and Mann Whitney U test were used to assess differences among study variablesChest HRCT severity score was more in PCR negative patients than in those with PCR positive results. However, the difference was not significant (p=0.11). There was a significant association in severity scores of the anterior basal segment of the left lung (p=0.022) and posterior segment upper lobe of right lung (p=0.035) with PCR results. This association was insignificant for other bronchopulmonary segments (p>0.05).CR negativity does not rule out infection in clinically suspected COVID-19 patients. The use of chest HRCT helps to determine the extent of lung damage in clinically suspected patients irrespective of PCR results. Guidelines that consider clinical symptoms, chest HRCT severity score and PCR results for a confirmed diagnosis of COVID-19 in suspected patients are needed. The novel Coronavirus disease 2019 (COVID-19), an emergent outbreak is a distinctive form of progressing viral pneumonia caused by SARS-CoV-2 If a patient is a clinical suspect of COVID-19 infection, , a negative PCR does not necessarily rule out active infection as PCR can have a false negative result The High-Resolution Computed tomography (HRCT) scan of the chest is an important imaging technique that aids in the screening, diagnosis, and management of COVID-19. The most common characteristic findings on chest HRCT scan of a COVID-19 pneumonia patient are air space consolidation, traction bronchiectasis, broncho-vascular thickening in the lesion, fibrosis, bilateral, subpleural, or peripheral ground-glass opacities (GGO), and crazy paving appearance  scan and the chest HRCT severity scores for each bronchopulmonary segment were calculated separately by two consultant Radiologists.All scans were performed with Multidetector (16) Toshiba Aquilion CT scanner  with tube voltage of 120 kvp and current of 350 mA. All patients were scanned in supine position. During the image acquisition, all lung fields from the apices to the bases were covered in a single breath hold and 1mm slice thickness. All images were analyzed for GGO, crazy paving appearance, consolidation, mixed patterns, nodules, and reverse halos sign Strict infection control measures were adopted during the CT scan of the patients which included strict adherence to PPE guidelines, disinfection of radiology equipment with 70% iso propylalchol and terminal cleaning of the room with diluted bleach, physical and temporal segregation of radiology teams. As per local Hospital policy allotted time was given for suspected or confirmed covid patients in the afternoon after which the room was cleaned and disinfected. After at least 30 minutes the machine was then made ready for re use.Each bronchopulmonary segment was given a score of 0, 1, or 2 depending upon the above-mentioned parenchymal involvement. The score of 0 indicated negligible parenchymal involvement, the score of 1 showed up to 50% while a score of 2 suggested the involvement of more than 50% of lung parenchyma. The chest HRCT severity score was thus calculated by adding the individual score of each segment Patients with known history of Chronic obstructive pulmonary disease (COPD), recent thoracic trauma/surgery, asthma, interstitial lung disease, pulmonary fibrosis, Cor pulmonale, lung malignancies, sarcoidosis, priorly diagnosed with viral pneumonia other than COVID-19 , and other conditions that may cause opacities in the lung on chest HRCT were excluded from the study. Patients were clinically assessed for the above mentioned co-morbid conditions. Moreover, we excluded that patients admitted in ICU setting or suffering from severe infection. Furthermore, patients who did not consent to participate in the study were also excluded. The study was ethically approved by the institutional research forum.The study included all patients fulfilling the inclusion criteria that were admitted in isolation with suspected COVID-19 infection. Due to this our sample size was limited to only 50 patients. Convenience sampling technique was used for patient selection.The normality of the data was checked using the Shapiro Wilk test. The Median age of the patients was calculated. The overall chest HRCT severity score for each lung and as well as for both lungs were calculated. The difference in median CT severity scores across PCR positive and negative patients was assessed using the Mann Whitney U test. Association between categorical variables was tested using the Chi-square and Fischer exact test. A p-value of less than 0.05 was considered statistically significant. The data analysis was performed on SPSS version 25.Out of 50 clinically suspected patients, 32 had positive on PCR while 18 had negative PCR results of SARS-CoV-2. The median age of the patients was 52.5 years. Detailed analysis of the chest HRCT severity score of each bronchopulmonary segment revealed that the lower lobe and lingula of the left lung and lower lobe of the right lung had more severity scores than other lobes. The prompt diagnosis of SARS-CoV-2 is important in hospitalized clinically suspected patients to develop appropriate preventive and curative strategies and to also avoid grave complications. In our study, we used a high-resolution CT scan because HRCT helps the radiologists to better visualize lung pathologies than the conventional chest CT scan or X-ray In our study, the bronchopulmonary segment of the lower lobes showed higher severity scores than the segments of other regions of the lung. Specifically, the posterior regions of the lower lobes were more involved than other regions. This finding is consistent with a study by Song et al which concluded that regions of lower lobes were predominantly involved in more than 80% of the patientsIn our study, the median HRCT severity score of patients with negative PCR results was more (15.5/40) than the patients with positive PCR (11/40). However, this difference was not statistically significant (p=0.11). This finding suggests that PCR negativity does not rule out COVID-19 infection in clinically suspected patients. As a strict and rigorous exclusion criterion was applied in our study, the likelihood of abnormalities in the lungs due to some other pathology was scarce. Moreover, the patients were clinically suspected for SARS-CoV-2 infection which indicates a high probability of false-negative PCR results in such patients as such patients are clinically symptomatic. This finding is consistent with a large-scale study on 1014 patients in China showed high sensitivity of chest CT scan (97%) in diagnosing COVID-19 in highly suspected cases. In this large-scale study, 308 patients with initial negative RT PCR had positive findings consistnt with COVID-19 on CT chest that led to their revaluationA study showed that the sensitivity of RT PCR increases from 79% to 88% if it is combined with CT scan findingsSmall sample size and having a cross-sectional study design account for a few limitations of our study. Using a control group with proper matching would have made the results of our study more comprehensive. Nonetheless, the results of our study should be given serious consideration as it may help in the early diagnosis of false-negative cases. More studies with large sample sizes are needed to further evaluate the role of chest HRCT scan in diagnosing COVID-19The chest HRCT scan is an important and supporting investigation to confirm and rule out COVID-19 infection in clinically suspected patients. HRCT severity score is a reliable index to assess the severity of SARS-CoV-2 infection. The bronchopulmonary segments of posterior regions of lower lobes were more affected than other regions of the lungs. We recommend using the chest HRCT in combination with severity score to improve diagnostic accuracy in clinically suspected or RT-PCR positive COVID-19 patients. There is a need for curation of specific guidelines that consider chest HRCT severity score, clinical manifestations, and PCR results to confirm COVID-9 in clinically suspected individuals.


Introduction: Circulatory assistance from a SynCardia Total Artificial Heart (SynCardia-TAH) is a reliable bridge-to-transplant solution for patients with end-stage biventricular heart failure. Ischemic strokes affect about 10% of patients with a SynCardia-TAH. We report for the first time in the literature two successful thrombectomies to treat the acute phase of ischemic stroke in two patients treated with a SynCardia-TAH in the bridge-to-transplant (BTT). Case report: We follow two patients with circulatory support from a SynCardia-TAH in the bridge-to-transplant for terminal biventricular cardiac failure with ischemic stroke during the support period. An early in-hospital diagnosis enables the completion of a mechanical thrombectomy within the first 6 h of the onset of symptoms. There was no intracranial hemorrhagic complication during or after the procedure and the patients fully recovered from neurological deficits, allowing a successful heart transplant. Conclusion: This case report describes the possibility of treating ischemic strokes under a SynCardia-TAH by mechanical thrombectomy following the same recommendations as for the general population with excellent results and without any hemorrhagic complication during or after the procedure. Mechanical circulatory support (MCS), a left ventricular assistance device (LVAD) or a Total Artificial Heart as a bridge to a heart transplant enables patients with mono- or biventricular failure to successfully reach heart transplantation in 50 to 80% of cases ,2. AmongNeurological complications represent one of the main complications under assistance. To avoid compromising the bridge to transplantation, both the prevention and management of strokes are critical. According to the literature, ischemic strokes occur in approximately 13% ,5 of patThe anticoagulation and platelet anti-aggregation protocol for SynCardia-assisted patients consists—according to the manufacturer and studies cited by the manufacturer—of therapeutic anticoagulation with unfractionated heparin combined with platelet mono-anti-aggregation introduced when the risk of bleeding is ruled out postoperatively after SynCardia implantation. In some cases, depending on the patient and the benefit-risk balance, dual platelet aggregation may be used in addition to therapeutic anticoagulation.At a distance from the surgery and once the clinical situation has stabilized and the risk of bleeding has been ruled out, a relay of unfractionated heparin by an anti-vitamin K treatment can be carried out.There are few data on the management of ischemic strokes in patients under circulatory assistance with a SynCardia-TAH .The aim of this case report is to share a monocentric experience of the management of two patients with ischemic stroke during circulatory assistance by a SynCardia-TAH in the bridge-to-transplant. For these two patients, assistance with a SynCardia-TAH was motivated by non-reversible biventricular heart failure suitable for heart transplant with identified temporary contraindications that prohibited an immediate heart transplant.1Patient 1 2), with end-stage ischemic heart failure, complicated by renal failure requiring extra-renal dialysis treatment. The patient was implanted with a 70cc SynCardia-TAH (body surface area: 2.22 m2). The anticoagulation protocol consisted of treatment with unfractionated heparin with an anti-Xa target activity of 0.20–0.40 IU/mL, associated with acetylsalicylic acid at 160 mg per day . He did not have any hemorrhagic complication but presented numerous thrombotic complications unrelated to heparin-induced thrombocytopenia or heparin resistance: deep vein thrombosis of the right internal jugular vein and vena cava, several small pulmonary embolisms responsible for a pulmonary infarction, right and left kidneys infarction, splenic infarction and several small ischemic strokes with no significant clinical translation . The main thrombotic complication was the occurrence of an embolic ischemic stroke on the 57th day of assistance. This occurred due to an occlusion of the left middle cerebral artery responsible for right hemiplegia. In the 12 days prior to the stroke, his anti-Xa activity ranged from 0.3 to 0.7 IU/mL. At this time, relay by anti-vitamin K treatment was not initiated because unfractionated heparin was easier to manage in this patient’s context. The early in-hospital diagnosis of the neurological deficit allowed for rapid management by thrombo-aspiration with an aspiration catheter alone, performed 100 min after the onset of symptoms. This thrombectomy enabled immediate revascularization of the territory of the left middle cerebral artery. No hemorrhagic complications occurred following the thrombectomy despite the maintenance of anticoagulant and platelet anti-aggregation treatments without any modification in the immediate pre- and post-interventional period. Subsequently, the neurological deficit fully regressed and the patient had no adverse event. After this complication, therapeutic anticoagulation and platelet anti-aggregation with acetylsalicylic acid were continued according to the same protocol.The first patient was a 44-year-old man, 1.90 m tall and weighing 94 kg , with end-stage heart failure related to dilated cardiomyopathy, secondary to anthracycline treatment for nephroblastoma in childhood, associated with non-compaction of the left ventricle. This heart failure was complicated by renal failure requiring extra-renal dialysis treatment. The patient was implanted with a 50cc SynCardia-TAH (body surface area: 1.64 m2). The anticoagulation protocol consisted of treatment with unfractionated heparin with a target anti-Xa activity of 0.20–0.40 IU/mL associated with acetylsalicylic acid of 75 mg per day . The post-operative follow-up was marked by one repeat operation for hemostasis in the first 24 h, followed by several thrombotic complications such as a pulmonary embolism, pulmonary infarction, and deep venous thrombosis of the inferior vena cava and the left iliac vein. These thrombotic complications were unrelated to heparin-induced thrombocytopenia and were attributed to both a biological inflammatory syndrome and circulatory assistance by the SynCardia. The complications led us to upgrade the anti-Xa activity target to 0.35–0.50 IU/mL after 39 days of assistance .The second patient was a 35-year-old man, 1.75 m tall and weighing 53 kg , thrombectomy appears to be a potentially risky option due to the higher risk of intra-or post-procedure intra-cranial hemorrhage. However, for both patients, thanks to an early clinical and scanner diagnosis, mechanical thrombectomy could be performed within 6 h of the stroke. This allowed for an ad integrum recovery of neurological deficits and neither of the patients presented intracranial hemorrhagic complications during or after the procedure. Despite the need for therapeutic anticoagulation and platelet anti-aggregation treatment to prevent thromboembolic risk under a SynCardia-TAH, the mechanical thrombectomy could be performed with efficiency and without any adverse event. Subsequently, both patients benefited from a heart transplant with good results.Stroke remains a dreaded complication of all types of short- to long-term MCS, with a higher incidence in LVAD therapy than with a Total Artificial Heart.Although patients treated with a SynCardia-TAH are not comparable to ischemic stroke patients in the general population, particularly in terms of the thrombotic and hemorrhagic risk balance, these patients’ experience show that the use of mechanical thrombectomy, when indicated, is possible with excellent results and without any hemorrhagic complication.Thus, guidelines for the acute management of ischemic stroke appear to be applicable to patients with a SynCardia-TAH. Mechanical thrombectomy should be considered in the case of ischemic stroke under a SynCardia-TAH.


Is an initial treatment of physical therapy cost-effective compared with an initial treatment of glucocorticoid injection for patients with knee osteoarthritis?In this economic evaluation that included 156 adults participating in a randomized clinical trial, participants receiving physical therapy gained more quality-adjusted life-years compared with those receiving glucocorticoid injection. Costs related to knee care were similar between groups, although total medical costs for any reason were higher in the physical therapy group.The results of this study suggest that although the mean cost of providing an initial course of physical therapy may be higher than an initial course of glucocorticoid injections, the greater improvement in quality-adjusted life-years may be worth the additional cost. Physical therapy and glucocorticoid injections are initial treatment options for knee osteoarthritis, but available data indicate that most patients receive one or the other, suggesting they may be competing interventions. The initial cost difference for treatment can be substantial, with physical therapy often being more expensive at the outset, and cost-effectiveness analysis can aid patients and clinicians in making decisions.To investigate the incremental cost-effectiveness between physical therapy and intra-articular glucocorticoid injection as initial treatment strategies for knee osteoarthritis.This economic evaluation is a secondary analysis of a randomized clinical trial performed from October 1, 2012, to May 4, 2017. Health economists were blinded to study outcomes and treatment allocation. A randomized sample of patients seen in primary care and physical therapy clinics with a radiographically confirmed diagnosis of knee osteoarthritis were evaluated from the clinical trial with 96.2% follow-up at 1 year.Physical therapy or glucocorticoid injectionThe main outcome was incremental cost-effectiveness between 2 alternative treatments. Acceptability curves of bootstrapped incremental cost-effectiveness ratios (ICERs) were used to identify the proportion of ICERs under the specific willingness-to-pay level ($50 000-$100 000). Health care system costs  and health-related quality-of-life based on quality-adjusted life-years (QALYs) were obtained.P = .003). Physical therapy was the more cost-effective intervention, with an ICER of $8103 for knee-related medical costs, with a 99.2% probability that results fall below the willingness-to-pay threshold of $100 000.A total of 156 participants  were randomized 1:1 and followed up for 1 year. Mean (SD) 1-year knee-related medical costs were $2113 ($4224) in the glucocorticoid injection group and $2131 ($1015) in the physical therapy group. The mean difference in QALY significantly favored physical therapy at 1 year (0.076; 95% CI, 0.02-0.126; A course of physical therapy was cost-effective compared with a course of glucocorticoid injections for patients with knee osteoarthritis. These results suggest that, although the initial cost of delivering physical therapy may be higher than an initial course of glucocorticoid injections, 1-year total knee-related costs are equivalent, and greater improvement in QALYs may justify the initial higher costs.NCT01427153ClinicalTrials.gov Identifier:  This economic evaluation compares the cost-effectiveness of physical therapy vs intra-articular glucocorticoid injection as initial treatment strategies for knee osteoarthritis. It is the eighth most expensive of 154 health conditions to manage in the US, costing more than $80 billion in 2016.4 With so many choices, patients and clinicians often find it challenging to establish optimal treatment plans. One critical component for decision-making is the cost and accessibility of the intervention.Effective nonsurgical treatment options are available for knee OA that should be considered before surgery. The American College of Rheumatology provides guidelines that include 6 strongly and 8 conditionally recommended nonpharmacological interventions, as well as 3 strongly and 4 conditionally recommended pharmacological treatments.6 indicating that patients or their practitioners are more likely to choose one or the other. Exercise therapy, a core nonpharmacological intervention for knee osteoarthritis, is a staple of physical therapy programs. Manual therapy also has proven to be an effective treatment in several clinical trials.9 A single glucocorticoid injection typically delivered during the same consultation with the diagnosing clinician may be initially less expensive and less time intensive than a course of physical therapy. However, there is concern that glucocorticoid injections may accelerate degenerative changes in the longer term and may not add to the benefit of physical therapy in the short term.13 Consideration for both short- and longer-term outcomes, treatment risks and adverse effects, and the downstream effect of each treatment choice should influence and inform these decisions instead of initial cost alone. Value is defined by the cost incurred to generate a change in outcome.14 Some interventions with lower initial costs may not carry that cost-effectiveness into the long term. Although physical therapy was clinically more effective than glucocorticoid injection in a recent trial,15 a typical course is usually more costly than a single injection .16 Given the high prevalence of knee osteoarthritis and the frequency of practical decisions made about best treatment options, we aimed to assess the cost-effectiveness of physical therapy compared with glucocorticoid injection from the health care perspective.Physical therapy and intra-articular glucocorticoid injections are common treatments for knee osteoarthritis. Data from large health care systems suggest that they are infrequently delivered together,15 that captured health care system costs, including knee-specific care costs, and quality-adjusted life-years (QALYs), which is a 2-dimensional metric that incorporates both health-related quality of life and survival. We used the Panel on Cost-Effectiveness in Health and Medicine guidelines for conducting the cost-effectiveness studies18 as well as the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) reporting guideline19 to guide reporting of these results. Although we noted a priori that we would capture health care use and compare it between groups in our published protocol,20 we did not formally establish a statistical plan for the cost-effectiveness analysis at that time. Therefore, this evaluation should be considered an unplanned secondary analysis. The health economist and team of statisticians were blinded to the results of the primary study, and the original publication was not published before they planned and performed these analyses. The statistical analysis plan was developed and finalized before receipt of any data. This case analysis comes from the health care perspective rather than payer or societal perspective. The health care perspective includes both current and future costs of care, both related and unrelated to the condition.21 The exception is that out-of-pocket costs were not assessed; however, there is no copayment for care in military treatment facilities, making this potentially less relevant. All participants provided written informed consent. All data were deidentified. Ethics approval was granted for the trial by the Army Pacific Health Command Institutional Review Board.We conducted an economic evaluation using patient-level data from a randomized clinical trial15 Briefly, we recruited patients who met clinical and radiographic criteria for the presence of knee osteoarthritis established by the American College of Rheumatology21 from October 1, 2012, to May 4, 2017, at 2 large military hospitals in the US. Patients were followed up until May 5, 2018. Subsequently, we randomly assigned participants to receive a management strategy that consisted of intra-articular glucocorticoid injections (n = 78) or physical therapy (n = 78), and the primary outcome was the difference in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total score at 1 year. We also collected WOMAC scores at 4, 6, and 26 weeks. Specific details of the treatment in each arm are reported in the published protocol and trial manuscript.20 Baseline characteristics of the cohort are described in The full results of the 1-year randomized clinical trial were previously reported.22 Patients in this study had no copayments, which allowed capture of all medical care that was sought by each participant. Exceptions could include care not covered and paid fully out-of-pocket, such as massage therapy or chiropractic services. Although there is no actual exchange of money for care in military facilities, each element of care is assigned a predetermined value and price by the US Defense Health Agency, which is reported in the MDR, to facilitate budgeting and business case analyses. We used these cost values for care in military facilities and costs actually paid by TRICARE for all care in-network facilities. We identified knee-related care through the use of the International Classification of Diseases, Ninth Revision (ICD-9) and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis codes, as well as relevant procedures based on Current Procedural Terminology (CPT) codes. Both diagnoses and procedure codes used are listed in the eAppendix in the We used the Military Health System Data Repository (MDR) to source all costs of care. The MDR captures care for all outpatient and inpatient encounters, ancillary services (radiology and laboratory tests), and pharmacy transactions from military and civilian clinics where TRICARE  is the payer. The Military Health System is a closed, single-payer government health system, where typically less than 3% of patients younger than 65 years carry other health insurance.23 Estimated marginal means with 95% CIs were reported.The adjusted generalized linear model with γ distribution and log link was used, controlling for age, sex, body mass index, smoking status, and Kellgren-Lawrence radiographic severity score.24 The EQ-5D is used to assess the perceived limitations of an individual related to mobility, self-care, regular daily activities, discomfort or pain, and anxiety or depression.25 The utility score was calculated for each individual anchored from 0 (death) to 1 . The QALY was calculated from estimated EQ-5D at the 1-year interval as follows: QALY = baseline EQ-5D + [(1-year EQ-5D − baseline EQ-5D)/2]. The effectiveness analysis was conducted using an adjusted generalized linear model, reporting estimated marginal means with 95% CIs.Effectiveness was measured using QALYs. We used an established mapping algorithm to generate EuroQol 5-Dimension (EQ-5D) health utilities from the WOMAC scores at all time points used in the trial .15 a typical course of physical therapy may initially be more expensive and usually requires more resources . Therefore, we assessed whether an initial treatment choice of physical therapy could be cost-effective compared with an initial choice of glucocorticoid injection. The goal was to determine the incremental cost per effectiveness obtained. This goal was accomplished by calculating the incremental cost divided by the incremental effectiveness to derive the ICER.By comparing between treatment arms, the costs and QALYs were assessed at 1-year follow-up, using conventional decision rules and estimating incremental cost-effectiveness ratios (ICERs) as appropriate. The ICERs were calculated by dividing the difference in costs between both interventions by differences in effectiveness between both interventions. Although the effectiveness of physical therapy was superior in the original trial,28 with compelling arguments to justify it as the minimum threshold.30 To express the most cost-effective strategy of the base case in US dollars, we used the net monetary benefit framework to demonstrate the net benefit of each strategy . A positive value indicates the intervention is cost-effective at the given WTP level.The willingness-to-pay (WTP) estimates usually vary by country, with more developed countries often willing to pay more for improvements in QALYs. Although there is no universal consensus for what ICER threshold should be used in the US, we used the threshold of $100 000 per QALY because it is commonly used in the US, particularly with osteoarthritis populations,Because this is a closed, single-payer government system, we assumed that lack of medical encounters within MDR indicated that the care did not happen, and therefore there were no missing health care utilization data. The MDR has a robust data validation system in which data are imported daily from more than 260 sources worldwide and missing elements are continuously validated across other data sources for 90 days before the validated variable is turned from raw to final+. We pulled all data 90 days after the last participant reached the 1-year mark to ensure final validated data for every individual. We assumed that missing data for the outcomes (WOMAC and subsequent EQ-5D estimation) were missing completely at random (MCAR) and tested this assumption using the Little MCAR test.To manage the uncertainty associated with this calculation, we used a bootstrapping method composed of 1000 replications to obtain a 95% CI of the ICER. The resulting acceptability curve of these bootstrapped ICERs show the proportions of ICERs under the WTP level.32 For the third analysis, we considered only individuals for whom there were complete follow-up data at 1 year . The fourth and fifth analyses excluded patients who received treatment from the other arm .We planned a priori a series of sensitivity analyses. First, we considered 2 alternate mapping algorithms to establish health utilities from the WOMAC.P < .05. The statistical analyses were conducted using Stata software, version 12 (StataCorp LLC).The statistical significance level was set at 2-sided 33 using other follow-up time points as predictors in the imputation model. Every participant had at least 3 of the 5 total data time points. The Little MCAR test suggested that data were indeed missing at random . Baseline variables and demographic characteristics of each group are given in A total of 156 participants  were randomized 1:1 and followed up for 1 year. Six participants did not provide outcomes data at 1 year . For those individuals, we used multiple imputation ,P = .03)  . Unadjus15 These values included the trial intervention costs. When the costs were adjusted for age, body mass index, female sex, smoking status, and radiographic severity, however, participants in the physical therapy group spent an adjusted mean of $615 more than those in the glucocorticoid injection group, but the mean difference was not significant  (P = .049) (Mean (SD) knee-related medical costs for the 1-year follow-up were similar between groups  for physical therapy) when unadjusted for any covariates  . In our trial, 14 individuals in the injection group eventually went to physical therapy, and 4 individuals underwent surgery. None in the physical therapy group had surgery. This finding could explain some of the higher-than-expected costs in the injection group. Patients should be made aware of these outcomes when considering the 2 treatment options, as well as the short-term efficacy of a single glucocorticoid injection, typically 1 to 6 weeks,38 have assessed the cost-effectiveness of physical therapy for knee osteoarthritis. Bove and colleagues26 compared 4 physical therapy regimens: exercise and manual therapy or exercise alone, each with and without booster treatment sessions. Booster sessions typically consist of 1 to 3 visits that occur several months after the initial physical therapy episode of care to provide additional treatment, check exercises, and provide further reassurance and encouragement. Either treatment group was more cost-effective if it included the booster sessions, despite the additional cost incurred with the extra visits. In similar fashion, our trial also included an option for a booster session for all patients in both the physical therapy and corticosteroid injection arms. Silva et al27 found a 3-year physical activity program for nonactive patients with knee osteoarthritis to be cost-effective when considering long-term improvements in QALYs and reductions in diabetes and cardiovascular disease severity. The ICER in the study by Bove et al26 was $12 900 per QALY gained and was $16 100 per QALY gained for Silva et al.27 In a study in New Zealand,37 exercise therapy and manual physical therapy were both cost-effective compared with usual care, but the combination of both was not. For the group that received a combination of both, investigators noted time restraints that likely reduced the dosing and potentially the effect size. Those findings are noteworthy, although that study had a different comparator than was used in our study, given that the physical therapy program in our study integrated manual techniques with exercise therapy, and patients received an adequate dose of both. Another study38 in the UK found 12 sessions of physical therapy to be cost-effective during a 30-month period when compared with usual care.Other studiesAfter a series of robust sensitivity analyses based on various assumptions, our results remained unchanged. Physical therapy remained cost-effective compared with glucocorticoid injection in every sensitivity model associated with knee-related medical costs. When considering total medical costs for any reason, all models except for one found physical therapy to be cost-effective. Although this was only 1 of 6 models, total medical costs for any reason is likely not ideal for measuring cost-effectiveness because there is the potential for many additional confounders when considering total health costs for any reason (compared with only knee-related costs). Many of these factors are outside the ability of the research team to control in the analysis .We used a single-payer government health system, which can limit the generalizability of these findings to other US health systems. For example, individuals seeking care in military hospitals and clinics do not usually have copayments, and direct cost would not be a barrier to seeking care. Costs of physical therapy can vary widely; therefore, different health plans could affect the cost-effectiveness of this intervention. Our focus was on the health care perspective, and as such, we did not capture data from the societal perspective; however, it is likely that the generalizability of results that focus on the societal perspective would also have been limited to this particular setting. Although we had outcomes from the WOMAC available at 5 time points, the costs represented a single value of costs expensed during the entire year and therefore could not be attributed to each of the time points individually. Finally, we used a mapping algorithm to generate EQ-5D values from the WOMAC, and although we conducted multiple sensitivity analyses using several other prominent mapping algorithms, it is possible that results would be different if QALYs were available directly from the EQ-5D. These results need validation in other settings.A course of physical therapy was a cost-effective intervention compared with a course of glucocorticoid injections for patients enrolled in this trial. Clinicians should consider that although the initial cost of delivering physical therapy may be slightly higher than an initial glucocorticoid injection, the mean improvement in QALYs at 1 year may also be greater with physical therapy. The combination of superior long-term outcomes, cost-effectiveness, and low risk with physical therapy as provided in this clinical trial provides an additional perspective to inform shared decision-making about value-based case between health care practitioners and patients.


BackgroundThe Connecticut Orthopaedic Institute (COI) conceptualized a Pivot Plan during an elective surgery moratorium at the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic with the goal of planning and executing orthopedic procedures safely. With the resumption of elective surgeries and the continued planning of surgical recovery over the months (and possibly years) to follow, facilities must brace themselves for repeat waves of COVID-19. Thereby, herein we share the Pivot Plan, its implementation process, evaluation of patient safety, and program performance during a pandemic. This could inform the efforts of other institutions seeking to restart non-emergent surgeries during similarly trying times in the future. MethodsThe COI formed a multidisciplinary team of leaders that met weekly to design a Pivot Plan and a dashboard to guide the resumption of surgeries and assess the performance of the Pivot Plan. The plan revolved around four domains: safety, space, staff, and supplies. It was implemented in two COI-affiliated facilities: MidState Medical Center (MMC) and St. Vincent’s Medical Center (SVMC). Monthly metrics from May to November 2020 were compared to the six-month averages for the pre-pandemic baseline period from September 2019 to February 2020.ResultsThe total number (N) of elective orthopaedic cases prior to the pandemic pre-COVID averaged 372 cases per month for MMC and 197 cases for SVMC. During the pandemic post-COVID, N averaging at 361 for MMC and 243 for SVMC illustrates COI was able to perform elective surgeries amid a worsening pandemic. Same-day (SD) discharge rates for total joint arthroplasty (TJA) pre-COVID averaged 8% for MMC and 3% for SVMC. Post-COVID, the SD average was 16.7% for MMC and 11.4% for SVMC. This data indicates that orthopaedic providers were cognizant of length of stay in order to reduce the risk of in-hospital exposure to COVID-19. The 30-day readmission (30R) rate for TJA pre-COVID averaged 1.4% for MMC and 2.7% for SVMC. A high level of care and follow-up is reflected in a lower average 30R post-COVID, 1.1% for both MMC and SVMC. Transitions for TJA patients to their home settings after surgery also reflect the quality of care and the efficiency of the patient throughput process with necessary precautions in place. Post-COVID, the patient transition to home (T) averaged 98.1% for MMC and 97.5% for SVMC compared to T = 96.8% for MMC and 88% for SVMC pre-COVID. No patients experienced deep vein thrombosis or pulmonary embolism during the time period of the project. Positive COVID-19 diagnosis 23 days after discharge was 0% at MMC and 0.2% at SVMC.ConclusionThe COI Pivot Plan was successfully implemented at two different hospitals offering elective orthopaedic surgeries to a varied patient population. The precautions taken by COI were effective in controlling the spread of the SARS-CoV-2 virus while returning to elective orthopaedic surgery. Furthermore, data collected before and after the onset of the COVID-19 pandemic indicated that program performance and quality improved. In the year 2020, the medical field was struck by an unprecedented pandemic of catastrophic proportions. According to the CDC, 2,074,529 cases of coronavirus 2019 (COVID-19) had been confirmed globally as of April 17, 2020 . These nDue to the large number of unknowns surrounding the current pandemic, sharing strategies to safely resume surgery is crucial as healthcare systems seek to return to and maintain surgical volume during and after COVID-19. In this report, we describe the precautionary planning steps taken by the Connecticut Orthopaedic Institute (COI) and its parent corporation Hartford HealthCare (HHC) to resume elective orthopaedic surgeries after they were suspended due to COVID-19 and we share information regarding process changes and performance metrics. The COI holds The Joint Commission’s Advanced Certification for Total Hip and Knee Replacement surgery and performs over 2,000 total joint replacement surgeries annually. In response to operational changes that were required during the early stages of the pandemic, the COI formed a multidisciplinary team of leaders from programming, operations, marketing, and research that met weekly to design a Pivot Plan dashboard. The dashboard was based on countermeasures put into place as elective surgeries were resumed after the first COVID-19 peak in April 2020. Outcome metrics were tracked weekly on the dashboard. This “Pivot Plan” was implemented in two COI- affiliated facilities: MidState Medical Center (MMC), a 156-bed acute-care hospital located in Meriden, CT in the central region and St. Vincent’s Medical Center (SVMC), a 473-bed community teaching hospital located in Bridgeport, CT in the southern region.This article will be presented as a poster at the National Association of Orthopaedic Nurses (NAON) 41st Virtual Annual Congress on May 22, 2021.Connecticut Orthopaedic Institute Pivot PlanIn line with directives from The Centers for Disease Control and Prevention and the United States Surgeon General, the COI’s parent healthcare system, HHC, postponed all elective surgeries starting March 19, 2020. In the initial wave of the pandemic, the peak incidence of COVID-19 occurred in Connecticut around April 22, 2020 -6. The CSafetySafety was the preeminent consideration, encompassing all other Pivot Plan elements. COVID-19 is associated with a host of adverse postoperative outcomes in orthopaedic populations, as well as others. Thus, it is critical to ensure patient safety throughout the hospital stay -9. ChallSpaceChallenges that were associated with this domain included reestablishing specialty care units and/or spaces that had been used to care for COVID-19 cases during the peak of the pandemic and ensuring that all geographical spaces in the hospital were adjusted and/or modified to provide the maximum “COVID-safe” environment. Additional challenges included the need to direct and control traffic to minimize people's interactions and equipment within specific spaces, as well as the need to accommodate a backlog of postponed elective surgeries and anticipated essential surgeries that would place additional demands on limited space.StaffDuring the peak of the pandemic, many surgical staff members had been re-deployed to various departments with the COVID-19 pandemic effort. In order to support the resumption of surgical procedures, the COI needed to recall these staff members to their original roles. Management had to carefully consider whether restaffing levels could support the resumption of surgical cases. Additionally, they had to decide if the units caring for COVID-19 patients could maintain adequate staffing levels without the continued support of the perioperative staff.SuppliesAdministrators and leadership needed to closely monitor the use and availability of all supplies, including personal protective equipment (PPE), medical/surgical supplies, medications, anesthesia supplies, and sterilization supplies.In April 2020, the COI formed a multidisciplinary team of leaders that met weekly to design a Pivot Plan and an outcomes dashboard to guide the resumption of elective surgeries. Metrics were tracked weekly and were abstracted from Cipher Health, Premier, and Epic reports. Monthly metrics from May to November 2020 were compared to the six-month averages for the pre-pandemic baseline period September 2019 to February 2020. This project was reviewed by the Hartford Healthcare Institutional Review Board (Assurance #FWA00021932) and deemed to be exempt from IRB oversight as it did not meet the federal definition of research.The Pivot Plan elements implemented across the three phases are summarized below. Timelines for the implementation of these elements based on the localized effect of the pandemic at both sites are presented in Figure Phase I: preoperative careVirtual Preoperative EducationDesigned to minimize patient exposure to COVID-19, virtual preoperative education was initiated in March 2020 at SVMC and in April 2020 at MMC. A virtual online class was started at MMC, while SVMC began to offer a navigator-led class via telephone. As part of the virtual education, patients gained online access to educational materials available anytime from the convenience of their homes. MMC also began to offer live virtual webinars in addition to the existing online preoperative classes. Implementation of virtual preoperative education coincided with other system-wide efforts to reduce patient contact with COVID-19.Clinical Command CenterThe Clinical Command Center was implemented in April 2020 to facilitate the scheduling of preoperative COVID-19 testing as per HHC System Guidelines (Table Preadmission Testing Center (PAC)The PAC was established as the initial patient contact after the patient was scheduled for surgery. The primary goals were to 1) risk-stratify patients preoperatively and facilitate consults as indicated, 2) call patients one to two weeks before surgery to facilitate medical clearance by offering history and physical (H&P) and preadmission testing, 3) address any last-minute test-related questions or concerns and reinforce hospital safety measures, 4) offer COVID-19 polymerase chain reaction (PCR) testing within seven days of the date of surgery (Table Patient Frequently Asked Questions (FAQs)As scheduling resumed for limited orthopaedic procedures, the hospital and physician staff members received a list of FAQs in order to address their patient’s concerns regarding COVID-19 testing, safety, and preparation for surgery.Risk Assessment EvaluationsUpon resumption of elective orthopaedic surgeries, orthopaedic navigators at MMC and SVMC performed risk assessment evaluations according to system-wide criteria. The criteria and rollout dates are presented in Table Marketing and Patient Outreach Efforts to Increase Patient ComfortBeginning June 2020, the COI 1) mailed flyers containing hospital safety data to patients who had not rescheduled elective surgeries, 2) followed up with patients via patient navigators, 3) posted recent patient testimonials about their experiences during the pandemic on the COI website, 4) created a 30-second advertisement addressing COVID-19 safety and the COI’s commitment to delivering a high level of care to our patients, 5) went live with providers on social media platforms and gave interviews on a local news station to reiterate COVID-19 safety, and 6) developed a safety message video for social media highlighting some of the COI amenities, as well as COVID-19 precautions.Phase II: inpatient carePreoperative ElementsWith the return of elective orthopaedic surgeries, patients were required to wear masks to the hospital and began to be temperature and symptom screened at the front entrance . The preoperative team reviewed COVID-19 test results and documented the patient’s and/or healthcare proxy’s desire to proceed with surgery amidst the COVID-19 pandemic. Other system-wide COVID-19 practices included limiting elevator capacity to a maximum of two persons at any one time, changing visitor restrictions , and changing the dress code for all staff . Intraoperative ElementsIn order to support orthopaedic surgeries that were deemed time-sensitive and essential for COVID-19-positive patients or a “person under investigation” (PUI), leadership at MMC and SVMC established a dedicated COVID-19 operating room (OR), which was decommissioned on June 29, 2020. With the return to elective surgeries, all staff was required to wear an N95 respirator in the operating room (OR). This requirement changed in July 2020 and was no longer required for patients with a documented negative COVID-19 test. All staff were required to wear and maintain their own surgical helmet and masks. As described under “Safety,” traffic through the OR was minimized under the surgeon’s direction.Postoperative Recovery ElementsThe Pivot Plan included the use of staggered patient bays in the post-anesthesia care unit (PACU) if sufficient room was available, and if bays held two patients, the curtain was utilized to separate the patients. Six-foot social distancing was implemented throughout the care process. Same-day (SD) surgical patients recovered and were transitioned to home from recovery spaces. Environmental Services increased the frequency of cleaning public spaces, lounges, and locker rooms.Inpatient ElementsInfection prevention was key to navigating the “new normal.” HHC rolled out a system-wide checklist for safety on April 28, 2020 to create an accountability and improvement model for implementing, scaling, and sustaining best practices for infection prevention. Checklist elements included PPE and infection prevention, facilities/infrastructure, team member management, patient monitoring and screening, visitor monitoring and screening, performance management, and quality control. Additionally, HHC implemented an infection prevention audit tool to document general observations on cleaning, hand hygiene, appropriate social distancing, universal face masking, and PPE compliance. On August 31, 2020, HHC expanded its PPE guidelines as shown in Table Phase III: postoperative discharge careIn the postoperative phase, discharge transportation was arranged to observe social distancing rules. Post-discharge instructions regarding COVID-19 began to populate the discharge summary automatically. Increased touch-points between the navigator and patients were used to mitigate any complications, questions, or concerns that may occur postoperatively. Discharge planning staff began to receive weekly updates on the operation and percentage of COVID-19-positive patients at all local skilled nursing facilities.Operational metrics are presented in Table The total caseload and the OR turn-around times were comparable pre and post-implementation of the Pivot Plan. The total number (N) of elective orthopaedic cases, including total joint arthroplasty (TJA), spine and podiatry cases in the six months prior to the coronavirus pandemic averaged at N = 372 cases per month for MMC and N = 197 for SVMC, respectively. Since the resumption of elective surgeries in May 2020, the average total elective orthopaedic caseload per month was N = 361  for MMC and N = 243  for SVMC. PACU holds  have been decreasing over time at MMC . While PACU holds were increasing at SVMC, they remained close to the pre-COVID baseline. Neither site experienced delays in surgeries due to missing COVID-19 test results. When elective surgeries resumed in May, more than 50% of COI patients used the PAC as a “one-stop-shop” for history and physical, pre-admission testing, as well as COVID-19 testing. Between March 2020 and May 2020, 489 cases were canceled at MMC, and 126 cases were canceled at SVMC due to the pandemic. By the end of October 2020, 83% and 90% of canceled cases at SVMC and MMC, respectively, had been rescheduled.Quality metrics are presented in Table SD discharge rates for total hips and knee replacements in the six months prior to the pandemic averaged 8% for MMC and 3% for SVMC. From May to November 2020, the average was 16.7%  for MMC and 11.4% for SVMC . Additionally, the 30-day readmission rate for hip and knee replacements was also recorded, with the six-month pre-COVID average being 1.4% for MMC and 2.7% for SVMC. After the seven-month data collection, the average for MMC and SVMC was 1.1%. The six-month baseline average for transitions to home (T) from the inpatient setting was 96.8% for MMC and 88% for SVMC. Data recorded during the pandemic suggested an average of 98%  for MMC and an average of 97.6%  for SVMC. No patients developed deep vein thrombosis (DVT) or pulmonary embolism (PE) postoperatively during the study period. Lastly, all TJA patients were contacted 23 days after discharge to assess for positive COVID-19 diagnosis. After seven months of data collection, the rate was at 0% at MMC and 0.2% at SVMC. Healthcare systems faced with reinstating elective arthroplasty in the midst of the ongoing pandemic must balance patients’ demand for these procedures with the concomitant need to mitigate the risk of exposure to COVID-19. As systems postponed elective surgeries, patients awaiting hip and knee arthroplasty were faced with waiting periods of unknown duration characterized by extreme discomfort and severely impaired quality of life -11. PatiData on SD discharges indicate that orthopaedic providers were cognizant of the length of stay and were diligent with efficient discharge after clinical criteria were met in order to reduce the risk of in-hospital exposure to COVID-19. Historically, approximately 8% of patients undergoing lower extremity TJA were discharged on the same day at MMC. With the implementation of additional precautions and return to elective surgery, this number climbed to 27% in May 2020, likely due to patient and provider fear of viral exposure during a prolonged hospital stay. It is worth noting that these initial patients were scheduled based on overall health conditions and limited comorbidities. The program’s nurse navigators risk-stratified these patients according to predetermined criteria that deemed them low-risk. However, in the seven months following the resumption of surgery, the SD discharge rates remained elevated. No month was recorded at or less than the baseline of 8% at MMC. In the month of August 2020, the program at MMC recorded the lowest rate of 11%, which was still higher than pre-COVID rates. In fact, the average rate of SD discharge throughout this time was 16.7%. A similar upward trend was observed at SVMC with an average rate of SD discharge of 11.4% compared to the pre-COVID six-month average baseline of 3%. This can perhaps be attributed to COVID-19 positivity rates at their lowest in the state, in addition to training the staff for SD discharge without any lapse in the care needed postoperatively as part of the Pivot Plan .Although SD discharge rates were more than double relative to baseline, the data illustrate that we maintained a high level of care, as reflected in the comparable rates of 30-day readmissions (30R) pre- and post-implementation of the Pivot Plan. Further, no patients experienced deep vein thrombosis (DVT) or pulmonary embolism (PE), despite the fact that previous groups have reported an increased incidence of venous thromboembolism (VTE) in patients undergoing elective arthroplasty during the pandemic, possibly due to the need to self-quarantine for a prolonged time before surgery . In six Transitions for TJA patients to their home settings after surgery also reflect the quality of care and the efficiency of the patient throughput process with necessary precautions in place. Prior to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the percentage of patients discharged home from MMC was 96.8%. During the pandemic, about 98% of patients were discharged home, indicating that the pandemic had no effect on discharge planning and placement at MMC. However, at SVMC, we observed a pronounced effect on patients transitioning to home with 97.6% compared to 88% pre-COVID, likely due to patient inclination for discharge to home, as well as the localized effect of the pandemic in the southern region that is in close proximity to New York City.Perhaps most illustrative of the successful implementation of precautions were the rates of postoperative COVID-19 diagnoses after hospital discharge. This information was reported by the patients during the postoperative telephone consult 23 days after patient discharge. With the resumption of elective orthopaedic surgeries in May, the COVID-19 diagnosis was 0% at MMC and 0.2% at SVMC, demonstrating the effectiveness of the precautions put in place at the system level. All patients were pre-screened with a COVID-19 PCR nasopharyngeal swab within four days of their procedure and then asked to quarantine up to the day of surgery. Additional precautions, such as a virtual preoperative class and a centralized testing center, helped patients reduce their exposure to the virus. In addition, an update to the history and physical note on the day of surgery needed a confirmed negative test result for COVID-19 in order to continue with the planned procedure. All surgical staff were also screened upon entry to the hospital with temperature checks and a COVID-19 questionnaire. Surgeries were rescheduled when patients experienced symptoms seven days prior to their procedure or had a positive PCR swab.Many elements of the COI Pivot Plan have also been incorporated by other institutions faced with restarting non-emergent arthroplasty during the ongoing pandemic. Screening and testing for COVID-19 prior to surgery, streamlining the presurgical process by limiting in-person contact, stringently utilizing PPE, practicing social distancing, prioritizing patients based on COVID-19 risk, and adapting the pre- and perioperative environment to limit the risk of infection are some of the elements shared in common with plans put forth by other institutions -15. The Through the collaboration of a multidisciplinary team, the COI Pivot Plan was successfully implemented at two different hospitals offering elective orthopaedic surgeries to a varied patient population. Data tracked through the dashboard support the effectiveness of the Pivot Plan in allowing the COI to safely resume elective orthopaedic surgeries. The precautions taken by COI in terms of safety, space, staff, and supplies across the preoperative, inpatient, and postoperative/discharge phases of care were effective in controlling the spread of the SARS-CoV-2 virus after the reinstatement of elective orthopaedic surgery. Furthermore, data collected before and after the onset of the COVID-19 pandemic indicated that the COI either maintained or improved upon key operational and quality measures. Moving forward, continued flexibility of staff, administration, and the healthcare system as a whole, as well as transparency between healthcare providers and patients, will be key to its continued success.