Climate, technologies, and socio-economic changes will influence future building energy use in cities. However, current low-resolution regional and state-level analyses are insufficient to reliably assist city-level decision-making. Here we estimate mid-century hourly building energy consumption in 277 U.S. urban areas using a bottom-up approach. The projected future climate change results in heterogeneous changes in energy use intensity (EUI) among urban areas, particularly under higher warming scenarios, with on average 10.1–37.7% increases in the frequency of peak building electricity EUI but over 110% increases in some cities. For each 1 °C of warming, the mean city-scale space-conditioning EUI experiences an average increase/decrease of ~14%/ ~ 10% for space cooling/heating. Heterogeneous city-scale building source energy use changes are primarily driven by population and power sector changes, on average ranging from –9% to 40% with consistent south–north gradients under different scenarios. Across the scenarios considered here, the changes in city-scale building source energy use, when averaged over all urban areas, are as follows: –2.5% to –2.0% due to climate change, 7.3% to 52.2% due to population growth, and –17.1% to –8.9% due to power sector decarbonization. Our findings underscore the necessity of considering intercity heterogeneity when developing sustainable and resilient urban energy systems. This study quantifies mid-21st century hourly building energy use in 277 urban areas in the USA, revealing spatially and temporally heterogeneous changes influenced by future climate, population dynamics, and electric power sector decarbonization. In densely populated urban areas, the share of energy use and emissions attributable to buildings can be even higher2. With ongoing climate change, technological innovations, and socio-economic developments, future urban building energy use in the U.S. is expected to change as well4. However, the current methods and data used for predicting future building energy consumption in the U.S. suffer from limited resolutions7, leading to substantial uncertainties in existing projections. Consequently, these projections often lack the necessary detail to effectively inform decision-making in individual cities.Residential and commercial buildings together are responsible for 39% of the U.S. energy consumption and 28% of the U.S. greenhouse gas emissions8. Compared with top-down approaches, bottom-up methods can have finer spatial resolutions that are more suitable for subnational analysis9. Statistical bottom-up models project future building energy demand based on mathematical relationships between historical energy use and weather data 6. Nevertheless, considering the lack of high-resolution energy consumption data and/or meteorological records, statistics-based city-scale studies have been rare11. In comparison, engineering-based bottom-up approaches can achieve a higher spatial resolution and more explicitly account for the impacts of future changes on specific end-use types12. Leveraging hourly or even sub-hourly physics-based building energy models driven by meteorological data, engineering-based approaches also enable detailed peak demand analysis, providing a more comprehensive understanding of energy demand patterns.Current understanding of future building energy use mainly draws upon top-down and bottom-up studies13. However, bottom-up methods, especially engineering-based ones, often oversimplify the impacts of future climate changes on meteorological conditions. Due to the high computational cost and lack of reliable, long-term meteorological data, building energy models have relied primarily on typical meteorological year (TMY) data that piece together “typical” historical months of weather observations from different years. Future weather patterns are then approximated with a “morphing method”, which imposes monthly or daily changes in meteorological variables from climate projections onto historical TMY data14. These TMY-based methods cannot capture the interannual variability of meteorological conditions, particularly during extreme events, nor can they adequately reflect the interdependence among changes in meteorological variables under future climate conditions15.Meteorological data are one of the essential factors that determine building energy consumption and its evolution over time16, urban population growth directly impacts total floor area and consequently drives changes in city-scale building energy use4. Household income and electricity price are additional socio-economic factors that can influence urban building energy use, especially cooling demand in low- and middle-income countries with low air conditioning (AC) adoption18. Building energy use can be further affected by shifts in building technologies, including electrification and energy-efficiency measures, although their impacts are contingent on occupant behavior such as thermostat setpoints20. Moreover, the primary energy consumption of buildings (source energy), including electricity and fossil fuels consumed in buildings  and energy losses during generation, transmission, and distribution, is influenced by electric power sector evolutions. In particular, the transition from fossil fuels to renewables in the power sector can substantially reduce energy losses during electricity generation21. Despite their importance, these various possible futures and their spatial variability have rarely been considered in existing bottom-up studies for cities, and their impacts on urban building energy use have not been well characterized.In addition to meteorological conditions, urban building energy use is influenced by a range of factors, including socio-economic developments, building technology advancements, possible changes in individual behavior, and evolutions in the electric power sector. As a key indicator of socio-economic developments22. Decadal-scale hourly meteorological data derived from multiple sources for historical and future periods (the 2010s and 2050s) enable a multiscale, detailed assessment of climate change impacts on urban building energy use. Specifically, we downscale and bias-correct climate projections from a set of Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models under four warming scenarios. We maintain building technologies, occupant behavior, and other socio-economic factors at their current levels, except for the AC penetration rate, and establish baseline projections for different future warming scenarios.In this study, we quantify the city-scale building energy use for 277 urban areas across the contiguous U.S. (CONUS) in the mid-21st century, with a focus on assessing the impacts of climate change, population growth, and power sector decarbonization. We develop a hybrid bottom-up modeling approach that integrates extensive hourly physics-based building energy simulations with statistical models constructed using a high-quality and high-resolution end-use load databaseFor site energy use of buildings , we focus on energy use intensity (EUI), an indicator calculated as the final energy consumed in buildings divided by the total floor area of all buildings in an urban area. The use of building EUI can effectively isolate the impact of climate change from that of population change. Based on the types of final energy consumed in buildings, building EUI can be classified as electricity EUI (E-EUI) and non-electricity EUI . For source energy use of buildings in each urban area, we incorporate high-resolution population projections consistent with different warming scenarios, and further consider the potential impact of two possible electric power sector trajectories with different levels of decarbonization.Four representative warming scenarios  are considered in this study: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. These scenarios encompass a diverse set of socio-economic storylines and global radiative forcing levels, representing a wide range of possible future climates. Under different warming scenarios, the mid-century annual building E-EUI for cooling in U.S. urban areas, on average, is projected to increase by 11.9–25.6% when compared with the conditions during the period 2010–2019 (hereafter reference decade)  as well as the growing demand for cooling in response to future warming. For example, the projected increase in the future AC saturation rate for urban areas in the Northwest nearly doubles the increase in building E-EUI for space cooling under all four warming scenarios  allows for the examination of mid-century city-scale building EUI and its frequency across different temporal scales. Notably, large seasonal variations are observed in the projected building E-EUI changes. Compared with the historical reference period, the decreases and increases in future E-EUI primarily occur in November–March and May–September, respectively , mainly driven by reduced space heating demand  is projected to drop in most U.S. urban areas in the 2050s, driven by the reduced space heating demand . As revealed by the decomposition, under all SSPX-Y scenarios except SSP3-7.0, population growth serves as the primary driver of future source energy use changes for most urban areas, followed by power sector change and climate change  with different assumptions regarding occupant behavioral changes43. Despite the important role that income plays in determining building energy use in low- and middle-income countries44, several studies have suggested very low household income elasticity of residential building energy use in the U.S. when other building characteristics are controlled46.Changes in building construction and operation, building codes, appliance efficiency, and other economic factors such as income and price can also influence building EUI47 at the reference-decade levels. This is mainly because the current building stock and its occupant behavior are well understood from extensive survey data. By focusing on the impacts of climate change, population growth, and power sector decarbonization, our findings establish a baseline for future research to delve into the complex socio-economic impacts on city-scale building energy use under each future scenario. The explicit representation of these impacts is possible considering the physics-based nature of the proposed bottom-up approach, which can incorporate projected changes in building shells, equipment efficiency, and occupant behavior. Furthermore, our high-granularity approach opens up new possibilities for high temporal resolution analyses on the intricate interactions between building energy consumption, building heat emission, and electricity generation that are sensitive to weather and climate changes.Nevertheless, here we hold the characteristics of the building stock in each urban area fixed22 to scale up the simulated site energy use  from individual building level to building stock level in each urban area. We then trained location-specific statistical models based on historical data from the EULP dataset to calibrate our city-scale results. These statistics-based calibration models aim to minimize discrepancies between the scaled results and the EULP dataset while capturing usage patterns that are potentially missing in the prototype models.To evaluate the behaviors and responses of city-scale building energy consumption to climate change in the U.S., we developed a bottom-up approach that integrates physics-based building energy models and statistical methods  Public Use Microdata Sample data. Each PUMA contains at least 100,000 people  and is much smaller in urban areas than in rural areas. To reconcile the mismatch between urban areas and PUMAs, we identified one or more PUMAs to represent each urban area. More specifically, a PUMA is selected if at least 10% of its spatial extent is within the boundaries of an urban area (hereafter representative PUMAs). On the one hand, this threshold ensures that we cover a sufficiently large number of CONUS urban areas. On the other hand, the selection also covers part of the urban periphery which may undergo future urban development and potential population growth. In cases where a PUMA spans multiple urban areas, we assigned it to the urban area that encompasses the largest proportion of the PUMA’s spatial extent to avoid double counting. Following these procedures, we excluded urban areas without any representative PUMAs and retained 277 out of 481 CONUS urban areas in this study. These 277 urban areas cover 1,540 PUMAs across the country  level48. In addition, a previous study49 has demonstrated that using a subset of 216 TMY3 stations is sufficient to capture the granularity of different climate conditions in the CONUS. Considering the data quality, distance to urban areas, elevation, and climate types, we further improved the granularity by selecting 252 urban weather stations to represent 277 CONUS urban areas  identified 936 weather stations over the CONUS in the Typical Meteorological Year (TMY3) dataset and classified these stations based on their data uncertainty and completeness of the long-term historical observations; Class I and Class II stations have lower uncertainty data and more complete period of record than Class III stations50. Post-processing was performed to correct mismatches in weather station identifiers and geographical locations. Solar radiation-related variables, including global horizontal radiation, direct normal radiation, diffuse horizontal radiation, and sky cover, were retrieved from the half-hourly, 4-km National Solar Radiation Database (NSRDB)51. These variables were computed using the Physical Solar Model (PSM)52, which is a two-step physical model driven by cloud properties, atmospheric profiles, aerosol properties, and albedo from multiple datasets, and are available since 1998. All raw data were converted from Coordinated Universal Time (UTC) to local time. Sea level pressure was converted to surface pressure at each station using the hypsometric equation. A multi-step gap-filling approach was applied to fill gaps in hourly observations based on data from nearby stations and reanalysis data from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2)53 54 and are also part of the core set of illustrative scenarios in the Intergovernmental Panel on Climate Change (IPCC)’s Sixth Assessment Report. Compared with the commonly used combinations of SSP and Representative Concentration Pathways (RCPs) in the literature, these SSPX-Y scenarios enhance the alignment between the socio-economic backgrounds and the resulting emissions futures  approximated using kernel density estimates were further adopted to improve the performance of the original QDM method57, and a 3-month moving window was used to fit all CDFs. Following previous studies58, the additive form of QDM was used for temperatures and pressure, whereas the multiplicative form was used for wind, humidity, and radiation variables. Similar to the treatment of historical weather data, quality control was carried out to smooth out outliers and ensure that variables satisfy psychrometric relationships.We then applied the quantile delta mapping (QDM) method61). Specifically, we used 40 residential prototype buildings (2 building types × 4 foundation types × 5 heating system types) and 28 commercial prototype buildings (14 building types × 2 heating system types) at each station  systems, but also more complex behaviors such as interzone air mixing and fenestration systems. For each representative urban weather station, the response of building energy use to climate change was evaluated using a set of residential and commercial prototype building models developed by the Pacific Northwest National Laboratory  to avoid the potential impact of the COVID-19 pandemic on energy use. We performed a total of 206,040 simulations  for this decade using EnergyPlus. Future simulations also last for a decade (2050–2059), but a decadal run was carried out for each climate model under each SSPX-Y scenario, resulting in a total of ~8.24 million simulations . All EnergyPlus runs were performed in parallel mode on the Sherlock cluster at Stanford University. Each simulation was conducted using 15-min timesteps for zone heat balance model calculation, and electricity, natural gas, fuel oil, and propane consumption data for different end-use categories were output at an hourly resolution.22, primarily developed by the NREL. Harnessing numerous data sources and the two best-in-class building stock energy models of the U.S. residential and commercial sectors (ResStock and ComStock), the EULP dataset provides 15-min resolution load profiles for all major residential and commercial building types and end uses as well as building stock characteristics for all locations in the CONUS, and is one of the most comprehensive building load databases in the U.S. In particular, the two building stock models are notable for their ability to achieve high temporal and spatial resolutions. Using statistical distributions of building characteristics, they simulate ~900,000 individual building models to represent the energy consumption patterns of the entire U.S. building stock.Another cornerstone of the bottom-up approach for urban building energy use is a fine-granularity end-use (site) energy consumption dataset, the EULP databaseSome major data sources used for developing building stock models include building codes, the U.S. Energy Information Administration’s Residential Energy Consumption Survey (RECS) and Commercial Buildings Energy Consumption Survey (CBECS), and the U.S. Census Bureau’s American Housing Survey (AHS) and ACS. For both residential and commercial building stocks, the sensitivity of model parameters was first quantified with trained random forest models. These input parameters were then adjusted following a region-by-region calibration approach to improve the consistency between the results and additional empirical data . Extensive and thorough validation across different spatial and temporal scales has been carried out using public and private datasets, demonstrating the accuracy of this dataset in characterizing the U.S. building stock. Further details of the two building stock models and the calibration and validation of the EULP database are provided in Supplementary Method 61.It is noteworthy that the physics-based simulations in the EULP database also rely on EnergyPlus driven by hourly weather data. Given this inherent similarity, we anticipated that the response of individual loads  to historical weather simulated in our study would align with the EULP data. In addition, to use the EULP database in our city-scale calibration models, we aggregated the 15-min, PUMA-level EULP outputs of residential and commercial building energy consumption to the hourly scale for each of the 277 U.S. urban areas  are generally consistent with those in the EULP data , we used the 2nd, 5th, 95th, and 98th percentiles as well as nine deciles of the distribution as the bins’ cutoffs to ensure that each bin has sufficient data samples.To reduce these discrepancies and better capture the response of diverse city-scale building stock to weather conditions, we implemented a post hoc calibration approach using a set of location-specific statistical models. For each end-use load category 22. Then for each urban area, the hourly time series of residential and commercial building energy consumption, Et, can be computed asEk,t denotes other less- or non-weather-dependent loads from the validated EULP database, which are nearly identical in different years. Equations  and future  building energy uses. Note that the future site energy consumption in Eq.  with the trained calibration models separately for residential and commercial buildings in each urban area. Additionally, we further trained calibration models for water heating energy consumption in residential buildings due to the stochastic occupant behavior model used in ResStockmentclass2pt{minimmentclass2pt{minimr values for the comparisons between the calibrated total building energy use and the EULP data are well above 0.99  at time t roughly equals 1.0,λ is a normalization constant. Similar relationships were observed using the EULP database for the selected 277 urban areas in the CONUS: δ = 1.013 (R2 = 0.926) for P = population, and δ = 1.012 (R2 = 0.948) if the base in Eq.  nearly linearly scale with urban populatione in Eq. . Therefoentclass1pt{minimaEt,hisSI, = Et,his, as the historical floor area is constant. Equation (The above relationship has important implications for changes in energy use intensity (EUI), an indicator used in this study to isolate the impact of climate change from that of population change. The relative change in EUI under future conditions, when compared with historical conditions, can be calculated as ting Eq.  into thi67 used an empirical model to determine future saturation based on the current rate and cooling degree days. However, the empirical model was trained using data from only 39 U.S. urban areas. Here we used historical residential building data from 277 CONUS urban areas and developed an exponential saturation function,S0 is the AC saturation rate in the 2010s, and CDD0 is the mean annual cooling degree days during the same period. We then used the slope of this saturation curve to project the future saturation rate in the 2050s for each urban area,St is the future AC saturation rate during 2050–2059, and CDDt is the mean annual cooling degree days in the 2050s. We derived St separately for each climate model projection under each SSPX-Y scenario. This ensures the diverse response of market penetration in future CONUS urban areas under different climate projections.Future warming will also increase the market penetration of air conditioners, especially in regions where the current penetration rate is relatively low, such as the Northwest . Previous studiesR2 (0.112), and the change of AC saturation rate induced by warming estimated using the curve is one order of magnitude smaller than its residential counterpart. In addition, the current AC penetration rate of commercial buildings in the selected 277 urban areas is in general much higher than that in residential buildings. For commercial buildings, on average 98.9% of 277 urban areas have a saturation rate higher than 80% . Therefore, we only considered saturation rate change for the residential sector.For commercial buildings, a linear saturation curve was fitted based on historical data from the EULP. However, the regression function has a very low 25. However, we did not observe such relationships in this study, which mainly results from the spatially varied compensation between increasing cooling demand and decreasing space heating demand under mid-century warming conditions. Nevertheless, we observed significant linear dependence of both building cooling demand and space heating demand on warming. For each urban area, the projected changes in annual cooling demand, space heating demand, and air temperature from a wide spectrum of 40 potential trajectories (10 CMIP6 climate models × 4 SSPX-Y scenarios) provide a sufficiently large sample size to build linear regression models . The minimum R2 values for the responses of E-EUI for cooling, E-EUI for space heating, natural gas EUI for space heating, fuel oil EUI for space heating, and propane EUI for space heating are 0.757, 0.666, 0.673, 0.716, and 0.596, respectively. In addition, the warming-response of E-EUI for space cooling with AC saturation rate fixed at the reference-decade level exhibits a spatial pattern similar to the case with changing AC saturation rate . However, the use of uniform conversion factors across the entire country can largely overlook the substantial regional variability of fuels and resources used in electricity generation21.It is noteworthy that the end-use energy consumption of buildings (site energy consumption) does not account for energy losses during generation, transmission, and distribution. To assess the actual primary energy used by buildings (source energy consumption), national-level site-to-source conversion factors have been commonly used in existing impact assessments  by 2050. This zero-carbon scenario is close to the carbon-pollution-free electricity goal set in the U.S. long-term strategy. The evolution of the generation mix under these two scenarios is shown in Supplementary Fig. 68.For the business-as-usual scenario, we used outputs from the Mid-case projection, which prescribes mid-level demand growth, system cost, resources, prices, and technology inputs. This business-as-usual scenario represents the current views of energy technologies. It only reflects the impacts of existing state, regional, and federal carbon policies enacted as of June 2021, with no new carbon policies applied in the future. The zero-carbon scenario is a variant of the Mid-case projection, which assumes a drastic decarbonization of the U.S. electric power sector. Under this scenario, COThe finest geographic unit of the generation mix data from the Cambium dataset is the balancing area (BA) , and EUISI, F, and CF are site EUI, total floor area, and the site-to-source conversion factor, respectively. Here site EUI for energy type i equals total site energy use for this energy type, EiSI,, divided by the floor area of the entire building stock. Then the above equation can be rewritten asSource energy consumption of urban buildings under different future scenarios is collectively influenced by climate change, population change, and changes in energy sources and technologies used for electricity generation (generation mix). The total source energy consumption of buildings in an urban area, PR yieldsAfter substituting Eq.  into Eq.PRhis = 1, as the population is constant for the historical period. Generalizing Eqs. 12\documewith Eq. .28 to decompose the contributions of these three drivers to total source energy consumption changes. Using an additive decomposition form, the change in total source energy consumption of buildings in an urban area can be attributed asWe then applied the logarithmic mean Divisia index (LMDI) method69 (see Supplementary Method We further conducted an uncertainty analysis using a modified elasticity-based decomposition methodFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting SummarySource Data


PCOS is a syndrome of ovarian dysfunction associated with recurrent pregnancy loss. Several correlating factors have been investigated that influence the risk of pregnancy loss in PCOS. However, uncertainty remains about their contribution to pregnancy loss and prognosis. This review of literature aims to identify what is known and what requires further investigation on the relationship between PCOS and recurrent pregnancy loss, to guide future research and optimize medical guidance throughout pregnancy.a review of literature was performed on several search engines using the following terms; polycystic ovarian syndrome, PCOS, recurrent pregnancy loss, recurrent miscarriage, RPL, aborted fetus, abortus provocatus, miscarriage and habitual abortion.37 articles were included; 3 systematic reviews, 1 meta-analysis, 2 randomized controlled trials, 6 prospective cohort studies, 22 case-control studies and 3 case series. The main objectives investigated by studies were pregnancy complications, pregnancy loss and live birth in the PCOS population.Studies that investigated the relationship between PCOS and recurrent pregnancy loss are few and inconsistent and warrant further research. Factors apt for further investigation include the extent to which PCOS phenotypes, BMI, obesity, insulin resistance, hyperandrogenemia, SHBG, hs-CRP, CTRP6, adiponectin, plasma leptin, homocysteine, AMH and thrombophilia contribute to further risk of miscarriage. Other factors requiring further exploration in relation to risk for miscarriage in PCOS patient with RPL include sOB-R, PAI-Fx and the Factor-V-Leiden mutations. These criteria include 1. Oligo- or anovulation, 2. Clinical and/or biochemical signs of hyperandrogenism, 3. Polycystic ovaries on imaging (after exclusion of other etiologies). PCOS is diagnosed if two or more of these criteria are present  articles were selected with consideration of the inclusion and exclusion criteria Table 1.Three systematic reviews, one meta-analysis, two randomized controlled trials, six prospective cohort studies, twenty-two case-control studies and three case series were included. Main objectives of the included studies were miscarriage rate and pregnancy complications which included gestational diabetes, pregnancy-induced hypertension, preeclampsia and coagulation disorders in the PCOS population. Additionally, studies that investigated live birth as an outcome mainly studied it in relation to metformin use in the PCOS population. Only two amongst these studies identified a PCOS-RPL population, highlighting an express need for the investigation of PCOS-associated RPL.3.13.1.1A case-control study investigated endothelial dysfunction in non-pregnant PCOS women without identification of RPL  Table . CRP lev3.1.2A meta-analysis was performed on pregnancy outcomes in patients with different PCOS phenotypes as these phenotypes could represent different pathophysiological entities Table 3 3.1.3Plasma leptin and soluble leptin receptor (sOB-R) levels have been studied in PCOS patients in the context of RPL. A total of 324 PCOS patients were studied, which consisted of a 199 PCOS-infertile cohort and a 125 PCOS-RPL cohort. In addition, 144 healthy non-PCOS controls were included as comparator (Another investigated protein was C1q/TNF- α-related protein 6 (CTRP6). CTRP6 is a newly identified adiponectin paralog. CTRP6 is expressed in multiple tissues including adipose tissue, heart, placenta and brain. It is proposed to be a regulator of multiple metabolic effects – mainly the regulation of complement activation . Moreove3.1.4Metformin had an effect of decreasing fasting glucose levels, fasting insulin and serum total testosterone levels in the PCOS population. The effect on testosterone was stronger in the non-obese subgroup who received metformin when compared to those who had placebo . In this3.23.2.1Being overweight is a risk factor for miscarriage , 17. One2 – highlighting the importance of obesity over PCOS  was studied in the context of PCOS and RPL along with components of metabolic syndrome and cardiovascular biomarkers. It is involved in energy homeostasis and tissue inflammation and is suggested to increase insulin sensitivity –33. The 3.2.4Thrombophilic disorders were more frequently associated with PCOS than non-PCOS patients in a RPL population; a case-control study found that 70,7% of the PCOS-RPL versus 47.8% of the non-PCOS RPL population had associated thrombophilic disorders . Protein-1  and 11,5 ± 2,24 vs. 7,55 ± 2,45 μmol L-1 ] ] ] , 23 and p<0,05)] , 8. Furtp<0,05)]  24)..-1  and insulin resistance was found in PCOS-RPL populations when compared to non-PCOS RPL populations . After cIn a prospective cohort study, 19 PCOS women were compared before and after treatment with metformin-enoxaparin in terms of fetal loss rate. These women had 40 pregnancies without treatment and when they fell pregnant for a second time, they had 24 pregnancies with metformin-enoxaparin treatment . The fet3.2.5The contribution of the M2 haplotype of the Annexin 5 gene (ANXA5) has been studied in PCOS related recurrent pregnancy loss . ANXA5 iFrequencies of the methylenetetrahydrofolate reductase (MTHFR) A1298C polymorphism were significantly higher in the PCOS-RPL and PCO-RPL populations compared to healthy non-RPL controls . No signSignificant differences were observed in the IL-6-174G/C GG genotype frequency between PCOS-RPL and the control group (non-RPL non-PCOS population) . Signifi3.2.6A prospective cohort study observed significantly higher levels of serum testosterone, dehydroepiandrosterone sulfate (DHEAS), fasting blood glucose, fasting insulin and lower insulin sensitivity in the PCOS-RPL population compared to the PCOS population without RPL .Metformin is a relatively well-studied intervention in the context of PCOS and pregnancy loss Table 5.A number of studies were performed on the effect of metformin in the general non-RPL PCOS population. Five studies showed a reduction in miscarriage rate when metformin was administered –48. One Two studies were inconclusive on the effect of metformin on pregnancy loss. One study investigated the effect of metformin (500mg twice daily) on late miscarriage (week 13 until week 22 + 6 days) in 470 women with PCOS . The stu3.2.7Anti-Mullerian hormone (AMH) has been studied as a predictive factor of pregnancy outcome in the PCOS population . A trend3.3Four studies , 50, 51 Two studies report a significantly higher chance of delivering prematurely in patients with PCOS in comparison to those without PCOS , 51.3.3.1Khomami et al. reported a higher prevalence of GDM, gestational hypertension and PE for ovulatory, anovulatory and hyperandrogenic phenotypes of PCOS which was independent of obesity. The prevalence of these outcomes was similar in women with and without PCOS with BMI >30 kg/m2, which indicates a key role for obesity in the pathogenesis of these pregnancy disorders. However, no RPL population was identified within the studied PCOS population3.3.2Aside from its effects on pregnancy loss, metformin was studied in the context of pregnancy complications. It significantly decreased the rates of gestational diabetes, pregnancy induced hypertension, preeclampsia and intrauterine growth restriction in the cohort that continued metformin throughout pregnancy . Moreove3.43.4.1The meta-analysis by Ma et al. not only studied miscarriage rate but also live birth rate (LBR) within different PCOS phenotypes. There was no statistically significant difference in LBR between the PCOS-hyperandrogenism and PCOS-normal androgen group , and there was no difference in LBR with further subgroup analysis of individual PCOS-phenotypes Table 7.3.4.2Boomsma et al. describes a significantly lower neonatal birthweight of babies born from PCOS mothers with no macrosomia compared to non-PCOS controls. This finding is unexpected as PCOS is usually associated with metabolic dysfunction, GDM and macrosomia. The finding of low neonatal weight may be a reflection of underlying placental dysfunction as the association between PCOS and hypertensive disorders of pregnancy is well described. Furthermore, babies from PCOS mothers were more frequently admitted to the neonatal intensive care unit (NICU) and had significantly increased perinatal mortality when compared to non-PCOS pregnancies. This may additionally be due to an increased premature delivery rate in the PCOS population since the reasons for NICU admission included hypoglycemia, jaundice and respiratory distress syndrome. Causes for perinatal death included lethal malformations, cervical insufficiency, sepsis and placental abruption. However, this study was conducted in a PCOS population with no identification of a RPL population. In contrast, Rees et al. did not report a difference in stillbirth nor Apgar scores in PCOS-complicated pregnancies compared to non-PCOS populations, but found a significantly higher prevalence of neonatal jaundice and respiratory distress syndrome and a trend toward lower birth weight and increased hypoglycemia in their PCOS population. Again, no RPL population was identified.3.4.3LBR was also studied in relation to metformin use in the PCOS population. In the study by Nawaz et. al., LBR was significantly improved in PCOS women treated with metformin (500mg tds) (n=119) when compared to the PCOS population without metformin (n=78) - 92% and 70% respectively. The PCOS groups did not all manifest RPL, and no subgroup analysis was performed on the RPL populations. Likewise, the study by Morin-Papunen et al. showed a significantly higher LBR in the PCOS-metformin population when compared to the PCOS-placebo group. The most beneficial effects of metformin were found in obese women. The systematic review by Morin-Papunen and colleagues reported a modest effect of metformin on LBR in the PCOS population with a number needed to treat of 13 for one livebirth. A subgroup analysis examining BMI found no effect of BMI on LBR.In the study by Glueck et al. favorable effects of metformin were found in the PCOS population . In thisThe previously described metformin+diet study which investigated 76 PCOS women showed an increase in live-births from 40 (53%) to 62 (82%) when metformin+diet was the intervention .Lastly, Ramidi et al. studied enoxaparin-metformin combination in a PCOS population with ≥1 SAB . Dosage 4The intention of this review of literature was to present a clear overview of what is known about the relationship between PCOS and RPL, and which PCOS phenotypes might pose increased risk for further miscarriage or improved the chances of livebirth. Regrettably, studies that provided information on this relationship are scarce and results were frequently inconsistent. Given paucity of data, the review additionally included studies that explored potential mechanistic aspects behind PCOS associated pregnancy loss. The paucity of studies on data association between PCOS phenotypes and further risk of miscarriage and/or livebirth rates, particularly in PCOS patients with RPL, imposed a great a limitation of our study. Because results and studied groups were frequently inconsistent, this review portrays the need for clarification about the relationship and pathophysiology between PCOS and RPL.The risk of GDM, PIH and PE was higher in the PCOS population without identification of a RPL population when compared to the non-PCOS population. A clear increase in spontaneous miscarriage risk was found in overweight PCOS patients. Being overweight and obese is a recognized risk factor for sporadic miscarriage. Also, miscarriage rate was frequently higher among PCOS patients with hyperandrogenism , however subgroup analyses of other PCOS phenotypes revealed no differences in miscarriage rate. Of importance, the definition of PCOS itself as well as that of its phenotypes varied widely between studies, making it difficult to compare study findings.Multiple studies included PCOS patients undergoing ART, but no PCOS phenotypes or characteristics were identified and thus sub-analyses on the various techniques and the rate of miscarriage were not performed. This further raises the question of the potential influence of ART interventions on outcomes in PCOS populations.Multiple studies reported elevated hs-CRP, triglycerides, insulin resistance and homocysteine and lower levels of adiponectin in the non-pregnant PCOS-RPL population. Women with PCOS and hyperinsulinemia have low-grade chronic inflammation which reflects the CRP levels and possibly causes endothelial dysfunction . Also, eElevated levels of leptin and decreased levels of sOB-R were found in PCOS patients. Moreover, elevated leptin levels were shown to increase the risk of RPL in PCOS. However, no correlation between leptin polymorphisms and the serum levels of leptin were found. It therefore remains inconclusive whether elevated leptin levels drive the pathophysiology of RPL. Also, the level of free testosterone was associated with an increased risk of RPL in PCOS.Information on metformin was occasionally contradictory and inconclusive. The most beneficial effects of metformin were found in obese women but, in some studies, the dose of metformin in obese women was higher than that used in non-obese women while in other studies identical doses were maintained. Additionally, some studies only included women with PCOS and IR in the PCOS population and compared this cohort to healthy controls with no PCOS or IR, raising the question of whether the effect of metformin was secondary to PCOS, IR or both. One study reported on effects of a combination of metformin and diet, but it became impossible to draw conclusions on the individual contributions of metformin or diet since both likely act synergistically.Hypofibrinolysis was associated with non-pregnant PCOS-RPL women. Evidence on elevated levels of PAI-Fx in the non-pregnant PCOS population was coherent and associated with worse pregnancy outcomes in PCOS-RPL women. Also, in PCOS women with a history of ≥1 SAB, PAI-Fx levels remained unchanged or rose during the first trimester.Higher levels of homocysteine and higher prevalence of hyperhomocysteinemia were found in the PCOS-RPL population. After controlling for IR, the correlation between the rate of miscarriage and HHcy became even greater. Pregnancy salvage was lower in HHcy irrespective of PCOS, IR or obesity which raises the contribution of HHcy in the PCOS-RPL population. Elevated AMH is a proposed biomarker for the risk of miscarriage in pregnant women with PCOS. Also, progressive correlation of M2 haplotype carriage with the number of miscarriages was found in the PCOS-RPL population but characteristics distinguishing the PCOS population from the PCOS-RPL population were not found. It therefore remains unclear whether M2 carriage is an independent risk factor of RPL irrespective of PCOS.Babies from PCOS mothers were more frequently administered to the NICU and perinatal death was significantly higher in the PCOS population compared to the non-PCOS population which could be due to more frequent premature delivery or contribution from placenta mediated disease. Unfortunately, no RPL population was identified. Livebirth was mainly an outcome of studies that examined metformin therapy in the PCOS population. Overall, the studies show an increase in live birth and lower premature delivery rates in pregnant PCOS patients treated with metformin when compared to placebo or no treatment. This is important, as livebirth is the outcome of ultimate importance when PCOS affected couples are trying to conceive. Unfortunately, of the studies that reported on livebirth after metformin treatment, four studies partly included either RPL populations or populations that had ≥1 previous SAB’s but no sub-analyses on these sub-populations were performed and two studies did not identify the number of previous miscarriages.Unfortunately, the underlying pathophysiological mechanisms behind PCOS associated RPL , remains unclear.Our study has some limitations. Firstly, the definition of RPL varied, with some studies defining recurrent pregnancy loss as ≥2 consecutive miscarriages whereas others used the definition of ≥3 consecutive pregnancy losses. Secondly, RPL populations were frequently included in the PCOS populations but were not specified for subgroup analysis. Often no data was available on the number of miscarriages per women and sub-analyses on this data were not performed. It therefore remains challenging to draw conclusions on outcomes when minimal information is available on the number of previous miscarriages in the studies, particularly when the RPL population with PCOS is not specifically identified. This is crucial as the risk of spontaneous abortion increases as the number of previous miscarriages is higher . SimilarWe propose studies that identify different PCOS phenotypes based on the previously described biochemical and clinical characteristics to differentiate PCOS patients at high risk for further pregnancy loss from those who will achieve successful pregnancies. This is necessary to optimize our understanding of PCOS-associated RPL, and in the future adapt medical guidance throughout pregnancy, as well as unravel novel therapeutic interventions aimed at optimizing livebirths and reducing the risk of pregnancy loss in pregnant women with PCOS.5We aimed to identify and characterize phenotypes that associate with increased risk of miscarriage in PCOS -associated RPL. Published studies on this subject are scarce and results are often inconsistent and confounded.The extent to which BMI, obesity and PCOS phenotypes contribute to the risk of miscarriage in the PCOS populations with RPL as well as putative contributors to underlying pathophysiology, including, inter alia, insulin resistance, serum androgen levels, SHBG, hs-CRP, CTRP6, adiponectin, plasma leptin, homocysteine, AMH and thrombophilia warrant urgent exploration. Other elements include sOB-R, PAI-Fx and the Factor-V-Leiden mutations. Studies investigating this, along with those examining the relationship between PCOS-related RPL and other pregnancy morbidities such as gestational diabetes, preterm birth and hypertensive disorders of pregnancy will be welcome contributions to the field.The original contributions presented in the study are included in the article/The authors confirm contributions to the paper as follows: study conception and design: RW, MM; Data collection: RW. Analysis and interpretation of results: RW. Draft manuscript preparation: RW. All authors contributed to the article and approved the submitted version.


This secondary analysis of the INVESTED randomized clinical trial assesses whether high-dose compared with standard-dose influenza vaccine was temporally associated with cardiovascular (CV) events among patients with high-risk CV disease during periods of high influenza-like illness activity. Does high-dose compared with standard-dose influenza vaccine reduce cardiopulmonary events during periods of high, local influenza activity?In this secondary analysis of the INVESTED randomized clinical trial of 3094 participants from US sites with available weekly Centers for Disease Control and Prevention–reported influenza-like illness (ILI) activity, ILI was temporally associated with cardiopulmonary and cardiovascular (CV) hospitalizations. However, high-dose compared with standard-dose influenza vaccine did not significantly reduce occurrence of the primary outcome.The findings indicate that influenza activity was temporally associated with CV events in patients with high-risk CV disease, and temporal CV risk was not significantly reduced by a higher influenza vaccine dose. Influenza-like illness (ILI) activity has been associated with increased risk of cardiopulmonary (CP) events during the influenza season. High-dose trivalent influenza vaccine was not superior to standard-dose quadrivalent vaccine for reducing these events in patients with high-risk cardiovascular (CV) disease in the Influenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated Heart Failure (INVESTED) trial.To evaluate whether high-dose trivalent influenza vaccination is associated with benefit over standard-dose quadrivalent vaccination in reducing CP events during periods of high, local influenza activity.This study was a prespecified secondary analysis of INVESTED, a multicenter, double-blind, active comparator randomized clinical trial conducted over 3 consecutive influenza seasons from September 2016 to July 2019. Follow-up was completed in July 2019, and data were analyzed from September 21, 2016, to July 31, 2019. Weekly Centers for Disease Control and Prevention (CDC)–reported, state-level ILI activity was ascertained to assess the weekly odds of the primary outcome. The study population included 3094 patients with high-risk CV disease from participating centers in the US.Participants were randomized to high-dose trivalent or standard-dose quadrivalent influenza vaccine and revaccinated for up to 3 seasons.The primary outcome was the time to composite of all-cause death or CP hospitalization within each season. Additional measures included weekly CDC-reported ILI activity data by state.P < .001), CP hospitalization , and CV hospitalization , after adjusting for state, demographic characteristics, enrollment strata, and CV risk factors. Increased ILI activity was not associated with all-cause death . High-dose compared with standard-dose vaccine did not significantly reduce the primary outcome, even when the analysis was restricted to weeks of high ILI activity . Traditionally warmer months in the US were associated with lower CV risk independent of local ILI activity.Among 3094 participants , we analyzed 129 285 person-weeks of enrollment, including 1396 composite primary outcome events . A 1% ILI increase in the prior week was associated with an increased risk in the primary outcome (odds ratio [OR], 1.14; 95% CI, 1.07-1.21; In this secondary analysis of a randomized clinical trial, ILI activity was temporally associated with increased CP events in patients with high-risk CV disease, and a higher influenza vaccine dose did not significantly reduce temporal CV risk. Other seasonal factors may play a role in the coincident high rates of ILI and CV events.NCT02787044ClinicalTrials.gov Identifier:  High-dose vaccine was not superior to standard-dose vaccine for the primary end point, which assessed benefit from 2 weeks following vaccination through the end of July of each influenza season.21 Whether treatment efficacy may be different during periods of high influenza-like illness (ILI) activity is not known. In this analysis, we compared locally defined ILI activity, as provided by the Centers for Disease Control and Prevention (CDC), with outcomes in INVESTED and assessed whether high-dose compared with standard-dose influenza vaccine modified the events in association with locally determined ILI activity.The Influenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated Heart Failure (INVESTED) trial assessed the efficacy of high-dose trivalent or standard-dose quadrivalent influenza vaccine in North American participants with high-risk CV disease during the 2016 to 2019 influenza seasons.21 This study was a preplanned analysis of the parent trial and follows the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline for randomized studies.22 Ethics committees at each enrolling site approved the protocol or the review was ceded to a central institutional review board. All participants provided written informed consent in accordance with established guidelines.This secondary analysis of a randomized clinical trial was a prespecified analysis of the INVESTED trial, a pragmatic, randomized, double-blind, active comparator trial conducted at 157 participating centers in the US and Canada from September 2016 to July 2019. Follow-up was completed in July 2019. Patients with high-risk CV disease previously hospitalized for either MI in the past 12 months or HF in the past 24 months were randomized to high-dose or standard-dose influenza vaccine. The protocol and primary results have been previously published .21 This Influenza-like illness was defined based on the percentage of visits to sentinel clinicians for fever (temperature ≥37.8 °C) and a cough and/or a sore throat without a known cause other than influenza as reported to the CDC US Outpatient Influenza-Like Illness Surveillance Network. Therefore, ILI could have included any respiratory pathogen presenting with these symptoms. We included CDC data from 108 US sites and territories; sites from Florida and Canada were not included due to unavailable weekly ILI data. Race categories included Asian, Black, First Nations or American Indian, White, and other , and ethnicity categories included Hispanic or Latino, non-Hispanic or non-Latino, and other . Race and ethnicity were ascertained by self-report.Data were analyzed from September 21, 2016, to July 31, 2019. We compared publicly available, weekly, CDC-reported, state-level ILI with the weekly occurrence of the primary outcome  and secondary outcomes  among US participants from September 2016 to July 2019 using logistic regression models. Weekly ILI activity from 0 to 9 weeks prior to (lag 0 to lag 9) the outcome of interest was considered to assess the temporal association between exposure and outcome. Model 1 was adjusted for state, demographics  and enrollment strata . Model 2 was additionally adjusted for CV risk factors .To assess the treatment outcome, we used logistic regression models to evaluate the weekly odds of the primary outcome for the high-dose compared with standard-dose vaccine treatment group adjusting for state to account for possible regional differences. In a sensitivity analysis, we analyzed the association between ILI activity and outcomes (1) only during months with typically higher influenza activity (September 15th to May 15th of each enrolling season) and (2) during weeks with higher ILI activity (>2 SDs above a state’s mean ILI activity). Last, we compared weekly ILI with outcomes adjusting for state, vaccine treatment assignment, and month, referenced to the month of July.P < .05. P values were not adjusted for multiple testing.Analyses were performed using Stata, version 14 (StataCorp LLC), and statistical significance was set at 2-sided In this sample of 3094 US participants . Of these, 30 (1.0%) were Asian, 690 (22.3%) Black, 12 (0.4%) First Nations or American Indian, 226 (7.3%) Hispanic or Latino, 2832 (91.5%) non-Hispanic or non-Latino, 2238 (72.3%) White, 124 (4.0%) other race, and 36 (1.2%) other ethnicity. Baseline characteristics and concomitant therapies were well balanced between treatment groups . The majP < .001), CP hospitalization , and CV hospitalization  after adjusting for state, demographic characteristics, enrollment strata, and CV risk factors (P = .06). Increased ILI activity was not associated with all-cause death .A 1% ILI increase in the prior week was associated with an increased risk in the primary outcome of composite of CP hospitalization or all-cause death  were associated with a lower risk of events even after adjusting for ILI . Figure P = .25), when restricting analysis to months with typically higher influenza activity , or during weeks of high ILI activity  in this temporal analysis.Evaluation of the weekly odds of the primary outcome by treatment yielded similar results to the primary results from the INVESTED trial. High-dose was not superior to standard-dose vaccine in its association with risk of the primary outcome  in the prespecified primary analysis after adjusting for state  and underscores the relatively higher occurrence of CV (n = 1141) compared with pulmonary events in this sample. Furthermore, individual-level ILI or infection was not assessed in this sample, so the true incidence of influenza is not known. Nevertheless, the temporal association with CV events supported prior findings of emergency department visits in New York City of the association of ILI with CV mortality and in observational data in an older cohort of the association of ILI with MI and HF events.24 Unmeasured environmental factors, such as temperature, absolute and relative humidity, and wind speed, have been associated with influenza outbreaks and CV events with some variation by influenza subtypes and may explain some of the seasonal variation.29 Registry data from Sweden have similarly demonstrated the impact of seasonal environmental factors, which attenuated the association between higher incidence of MI and higher incidence of influenza.30Although all participants received influenza vaccine, the temporal association between ILI activity and CV events remained. Additional analysis demonstrated that warmer months were associated with lower risk of CV events after accounting for ILI activity and vaccine dose, suggesting that other seasonal factors may also play a role in the seasonal variation in CV events. Vaccine effectiveness was not associated with a modification in the seasonality of ILI and CV events in another analysis of New York state data.Several limitations of this analysis should be noted. Inferences about the association between patient-level influenza illness or other respiratory viruses and subsequent events could not be made, as the respiratory influenza infection status of participants was not assessed in this study. State-level ILI activity may not have represented regional-level or patient-level ILI activity. Only states with available ILI activity were included in this analysis, which limited generalizability to participants, excluding those in Florida and Canada. That we did not randomize patients to placebo prevented us from drawing conclusions about the impact of any influenza vaccination on the temporal association between ILI activity and CV events. Also, the difference in valence between the high-dose trivalent influenza vaccine and the standard-dose quadrivalent vaccine may have contributed to the absence of outcome modification of the vaccine dose, with the standard-dose vaccine providing additional coverage for the influenza B/Yamagata strain. Changes in seasonal temperature, humidity, weather patterns, and other environmental factors were not accounted for in this analysis. Additionally, the trial had limited power for this prespecified secondary analysis.In this secondary analysis of the INVESTED randomized clinical trial evaluating the efficacy of high-dose trivalent vaccine compared with standard-dose quadrivalent vaccine in reducing CP events among patients with high-risk CV disease in the US, influenza activity was temporally associated with an increasing risk of CP events, yet a higher-dose influenza vaccine did not significantly reduce temporal CV risk. Other seasonal factors may also play a role in the higher rate of CV events associated with high rates of influenza. Further studies and additional measures are needed to understand additional factors influencing the temporal association between influenza and CV events and to assess new strategies to reduce events.


Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressive, life-threatening systemic disorder that is an underrecognized cause of heart failure (HF). When the diagnosis of wild-type ATTR-CM (ATTRwt-CM) is delayed, patients often undergo additional assessments, deferring appropriate management as symptoms potentially worsen. Prompt recognition of patients at risk for ATTRwt-CM is essential to facilitate earlier diagnosis and disease-modifying treatment. A previously developed machine learning model performed well in identifying ATTRwt-CM in patients with HF vs controls with nonamyloid HF using medical claims/electronic health records, providing a systematic framework to raise disease suspicion.To further evaluate this model’s performance in identifying ATTRwt-CM using a large claims database of older adults with HF and confirmed ATTRwt-CM or nonamyloid HF; and to explore the characteristics and health care resource utilization (HCRU) of patients with confirmed and suspected ATTRwt-CM.In this retrospective study, the prior model was applied using Humana administrative claims for patients diagnosed with ATTRwt-CM (cases) and nonamyloid HF (controls [1:1]). Patients were aged 65-89 years, had at least 2 claims for HF diagnosis (2015-2020), and were continuously enrolled in a Medicare Advantage prescription drug plan for at least 12 months before and at least 6 months after HF diagnosis. For the assessment of characteristics and HCRU, the suspected risk level was categorized based on the predicted probability (PP) from model output .Of 267,025 eligible patients, 119 (0.04%) had confirmed ATTRwt-CM; of 266,906 patients with nonamyloid HF, 10,997 (4.1%), 68,174 (25.5%), and 187,735 (70.3%) were categorized as high, moderate, and low risk for ATTRwt-CM, respectively. The model demonstrated sensitivity/specificity/accuracy/receiver operating characteristic area under the concentration-time curve of 88%/65%/77%/0.89, respectively, in differentiating ATTRwt-CM from nonamyloid HF. In patients with confirmed ATTRwt-CM, the mean (SD) time between HF and ATTRwt-CM diagnoses was 751 (528) days; 65% and 48% were hospitalized before and after ATTRwt-CM diagnosis, respectively. Atrial fibrillation was more common in patients with confirmed ATTRwt-CM and high risk (39% and 55%) vs low risk (27%). Hospitalization and emergency department visits after HF diagnosis were reported in 57% and 46% of patients with high ATTRwt-CM risk, respectively.The ATTRwt-CM predictive model performed well in identifying disease risk in the Humana Research Database. Patients at high risk for ATTRwt-CM had high HCRU and may benefit from the earlier suspicion of ATTRwt-CM. The model may be used as a tool to identify patients with a suspected high risk for the disease to facilitate earlier detection and treatment. Older adults with heart failure (HF) at risk for transthyretin amyloid cardiomyopathy (ATTR-CM) may need many visits to the hospital before they receive a correct diagnosis. Earlier suspicion of ATTR-CM may allow earlier diagnosis/treatment and avoid needless hospital visits. In this study, researchers used a new tool in medical claims data to look for people with HF and signs of ATTR-CM who may benefit from early testing for the disease. Implications for managed care pharmacyA machine learning predictive model performed well in identifying wild-type ATTR-CM (ATTRwt-CM) risk in older patients with HF in a large administrative claims database. In raising suspicion of ATTRwt-CM and highlighting the need for further confirmatory testing, model use may facilitate earlier detection and treatment. Given the high morbidity and mortality of ATTRwt-CM, and the current availability of disease-modifying therapy, model application in various potential clinical scenarios may play a valuable role in identifying undiagnosed patients in the future.1 More than 90% of all amyloid cardiomyopathy is attributed to acquired monoclonal immunoglobulin light chain–related amyloidosis and transthyretin protein–related amyloidosis.2 Transthyretin amyloid cardiomyopathy (ATTR-CM) may arise because of the presence of TTR gene variants but most often develops through the age-related accumulation of wild-type TTR protein.4 Traditionally, ATTR-CM has been considered a rare disease, but the frequency of its occurrence has not been well established. Over the past decade, the estimated prevalence of wild-type ATTR-CM (ATTRwt-CM) in patients with heart failure (HF) with preserved ejection fraction (HFpEF) has ranged from 7% to 17%.7 ATTR-CM is a life-threatening systemic disorder associated with cardiac and noncardiac manifestations; progressive physical disability; quality of life impairment; and high rates of morbidity, mortality, and health care resource utilization (HCRU).13 In untreated patients with wild-type and variant disease, postdiagnosis median survival is estimated to be 3.6 and 2.5 years, respectively.15Infiltration of the myocardium with deposits of extracellular amyloid fibrils formed by misfolded precursor proteins can result in amyloid cardiomyopathy.18 In a large, prospective, observational study of patients with ATTRwt-CM, 42% of patients experienced a delay in diagnosis of more than 4 years after the presentation of cardiac symptoms.10 Failure to promptly and accurately diagnose ATTRwt-CM can result in inappropriate consultations, assessments, and treatments prior to its diagnosis, as well as worse clinical outcomes after diagnosis and the initiation of appropriate disease-modifying therapy.20 Awareness has improved with greater recognition of the diagnostic “red flags” that can prompt suspicion of ATTRwt-CM and the availability of noninvasive imaging techniques that can be used to effectively assess at-risk patients.24 However, additional screening modalities are needed to enable early identification of patients with HF who may be at risk for this debilitating disease.In older individuals with HF, ATTR-CM is often overlooked as clinicians focus on more common causes of heart disease, such as hypertension or coronary artery disease.25 The model revealed cardiac and noncardiac clinical characteristics associated with ATTRwt-CM that could be used to differentiate the disease from other causes of HF. When validated in large, nationally representative databases, the predictive model identified ATTRwt-CM in patients with HF with high sensitivity, specificity, and accuracy, providing a systematic framework that could be used to raise suspicion of the disease in various settings. In this study, we sought to further evaluate the performance of the machine learning algorithm using claims data of older adults (≥65 years) with HF and a confirmed diagnosis of ATTRwt-CM who were enrolled in a Medicare Advantage prescription drug (MAPD) plan. In addition, we aimed to explore the characteristics and HCRU of patients with confirmed ATTRwt-CM and patients with nonamyloid HF who were at potential risk for ATTRwt-CM. In the latter analyses, we focused particular attention on patients identified as being at high risk for ATTRwt-CM, as this population may achieve the greatest benefit from earlier referral and disease management.To help enhance screening for ATTRwt-CM, researchers recently developed and validated a random forest–based machine learning risk prediction model using administrative medical claims data and electronic health records.In this retrospective, observational study, administrative claims data (Humana Research Database) were used to identify patients who had at least 2 claims for an HF diagnosis recorded at least 30 days apart, from October 1, 2015, through June 30, 2020. The most recent date in the date range was selected to minimize the potential impact of the novel coronavirus outbreak on HCRU findings. Eligible patients were aged between 65 and 89 years at the time of their first claim for HF (defined as the index date) and were continuously enrolled in an MAPD plan in the United States for at least 12 months before the index date and at least 6 months after the index date. Patients were followed until the end of enrollment or the end of the observation period , hospice admission, or death (whichever occurred first).This study included 2 main stages. In the first stage, among eligible patients with HF, we identified the ATTRwt-CM case cohort, which included patients who had at least 1 claim with a diagnosis code for ATTRwt-CM (E85.82) after the second claim for HF and had no claims with diagnosis codes for light chain amyloidosis, cerebral amyloid angiopathy, or end-stage renal disease during the study period. Among the remaining eligible patients with HF , controls were randomly selected using the machine learning algorithm to equal the number of ATTRwt-CM cases identified. The ATTRwt-CM case and nonamyloid HF control cohorts were selected in a 1:1 ratio, without matching, to evaluate the machine learning model’s performance in distinguishing ATTRwt-CM from nonamyloid HF.25 with the random forest–based machine learning risk prediction model.In the second stage, the machine learning model was used to assign a probability of suspected ATTRwt-CM risk in all patients with nonamyloid HF. The patients were categorized by suspected ATTRwt-CM risk level based on 3 thresholds of predicted probabilities (PPs): (1) those suspected to be at high risk for ATTRwt-CM ; (2) those suspected to be at risk for ATTRwt-CM ; and (3) those not suspected to be at risk for ATTRwt-CM . The PP thresholds were selected based on the original research conducted by Huda et alInternational Classification of Diseases, Ninth and Tenth Revisions  diagnosis codes were used to code diagnoses and procedures in the Humana claims database to identify patients and evaluate patient characteristics and HCRU in this study. All data used for this study were in an anonymized, structured format, excluding patient personal information. Because the data were not subject to privacy laws according to applicable legal requirements, patient informed consent was not required. The Humana Human Subject Protection Office deemed this study to be exempt from institutional review board approval because of its retrospective nature and use of a limited dataset of administrative claims.The predictive performance of the machine learning model in ATTRwt-CM cases and nonamyloid HF controls was evaluated based on the number of true positives, false positives, true negatives, and false negatives. The following parameters were calculated: sensitivity, specificity, precision , accuracy, and receiver operating characteristic area under the concentration-time curve (ROC AUC).Supplementary Table 1, available in online article).27 Clinical characteristics were evaluated based on pre-index medical and pharmacy claims using the Elixhauser comorbidity index,29 the RxRisk-V score,30 and frailty index31 (definitions in Supplementary Table 2). The prevalence of the individual Elixhauser comorbidities, quantity of unique medications, HF type, and health care provider specialties were also captured from medical and pharmacy claims.Patients’ pre-index demographic and clinical characteristics were assessed by ATTRwt-CM risk category. Demographic characteristics included age, sex, race, geographic location by region, population density, and low-income subsidy eligibility/dual eligibility at baseline  of patients with claims for all-cause physician encounters, outpatient visits, and specific imaging procedures . Acute care was calculated as the number (%) of patients with claims for all-cause hospitalizations, all-cause readmissions within 30 to 90 days of hospital discharge, and emergency department (ED) visits. The calculation of post–acute care included the number (%) of patients with claims for skilled nursing facilities, home health services, physical/occupational/speech therapy, and medical and social services. Pharmacy use was measured based on the number of unique prescription medications filled.Descriptive analyses were conducted to evaluate patient demographic and clinical characteristics at baseline  in patients with confirmed ATTRwt-CM (cases) and those with high, moderate, or low risk for the disease. Continuous variables were characterized by mean values with SD as well as median and interquartile range; categorical variables were characterized by counts and proportions.32After the index date, the time to the diagnosis of ATTRwt-CM and HCRU before and after ATTRwt-CM diagnosis were reported for patients with confirmed ATTRwt-CM. HCRU was reported from the second HF diagnosis until the date of ATTRwt-CM diagnosis and from the time of ATTRwt-CM diagnosis to the end of the study period. The count and proportion of patients with HCRU were reported. Counts less than 10 are suppressed to protect patient privacy and confidentiality.After the index date, HCRU was also assessed in patients at high risk for ATTRwt-CM. A sensitivity analysis of HCRU was conducted in patients in the high-risk cohort who had data available for at least 12 months after HF diagnosis. In the high-risk cohort, logistic regression analysis was performed on the demographic and clinical factors associated with all-cause hospitalizations, ED visits, and any post–acute care services to calculate odds ratios and 95% CIs.Data management and analyses for this study were performed using the Statistical Analysis Software Enterprise Guide Version 7.15.Supplementary Figure 1). In these patients, the mean (SD) time between the diagnosis of HF and the diagnosis of ATTRwt-CM was 751 (528) days; after the ATTRwt-CM diagnosis, the mean follow-up time was 327 (252) days to the end of the study. Of 266,906 patients with HF who did not have an ATTRwt-CM diagnosis, 119 were randomly selected as HF controls. In the latter patients with nonamyloid HF (including the nonamyloid HF controls), 10,997 (4.1%), 68,174 (25.5%), and 187,735 (70.3%) were categorized as high, moderate, and low risk for ATTRwt-CM, respectively.Among patients satisfying eligibility criteria , 119 patients (0.04%) had a confirmed diagnosis of ATTRwt-CM based on claims data , with positive predictive value, specificity, and accuracy of 71%, 65%, and 77%, respectively . AccordiNumerical similarities and differences were observed in the baseline demographic characteristics of patients with confirmed ATTRwt-CM and in those at high, moderate, and low risk for the disease . The meaNumerical disparities were also seen among patients in the ATTRwt-CM cohorts in certain baseline clinical characteristics . The meaLower proportions of patients with confirmed ATTRwt-CM and patients with a high risk of the disease  were diagnosed by primary care physicians than patients with low risk (35%). Higher proportions of patients in the confirmed and high-risk cohorts (28% and 17%) were diagnosed by cardiologists than those in the low-risk cohort (11%).Supplementary Table 3). In the moderate- and low-risk cohorts, 3 other comorbidities were reported in more than 25% of patients: chronic obstructive pulmonary disease, uncomplicated diabetes, and renal failure. More than 25% of patients in the low-risk cohort also had claims for peripheral vascular disease and complicated diabetes.Among the individual Elixhauser comorbidities, 3 were reported in more than 25% of patients across the ATTRwt-CM cohorts: congestive HF, cardiac arrhythmias, and uncomplicated hypertension . The proportion of patients with at least 1 hospitalization claim increased from 24% to 65% before and after HF diagnosis. Before and after the HF diagnosis, 29% and 48% of patients, respectively, had claims for hospital readmission, and 36% and 58% for an ED visit.In the sensitivity analysis, the proportion of patients at high risk for ATTRwt-CM who had claims for acute and post–acute care services consistently increased after their HF diagnosis was first recorded . The strongest risk factors for ED visits were orthostatic hypotension (1.79 [1.36-2.35]), percutaneous coronary intervention (1.59 [1.18-2.15]), and residence in the West region (1.42 [1.24-1.62]) . The strongest risk factors for any post–acute care services were patient age of 85 to 89 years (1.42 [1.22-1.65]), abnormal weight loss (1.35 [1.09-1.66]), and spinal stenosis (1.29 [1.07-1.56]) .Multiple factors were associated with HCRU among patients identified as being at high risk for ATTRwt-CM by the model. The strongest risk factors (odds ratio [95% CI]) for all-cause hospitalization were HFrEF at the time of HF diagnosis (2.54 [2.33-2.78]), HFpEF (1.76 [1.58-1.95]), and unstable angina (1.58 [1.24-2.02]) , but lower specificity (65%) and accuracy (77%). These disparities may be explained in part by differences in the diagnosis codes (ICD-9/10) and datasets  used in the prior validation work vs our research.The current study was conducted as part of a large-scale research initiative to identify patients with HF who were at risk for ATTRwt-CM using a machine learning predictive model. The original random forest–based model was developed to recognize phenotypic patterns suggestive of cardiac amyloidosis in patients with HF older than age 50 years based on ICD diagnosis codes in US medical claims data from IQVIA and electronic health record data from Optum.In the current study, we also evaluated the demographic/clinical characteristics and HCRU for patients with HF in the confirmed ATTRwt-CM cohort and high-, moderate-, and low-risk cohorts. We observed some differences in patient characteristics, including, most notably, that more patients in the confirmed ATTRwt-CM cohort were men and fewer were White than patients in the high-, moderate-, and low-risk cohorts. Patients in the confirmed ATTRwt-CM cohort had lower comorbidity scores and a higher proportion had HFpEF than patients in the high-, moderate-, and low-risk cohorts. These findings suggest that certain groups of patients with HF, eg, those who are female, non-White, and without an HFpEF diagnosis, those who are at high risk for ATTRwt-CM and not yet diagnosed, are potentially being overlooked. This possibility supports a need for additional research and efforts to educate health care providers and raise their awareness of populations at risk for ATTRwt-CM.On average, patients were diagnosed with ATTRwt-CM almost 2 years after their HF diagnosis. The proportion of patients with claims for hospitalization, hospital readmission, and ED visits decreased by at least 25% after their amyloidosis diagnosis compared with before their diagnosis, whereas rates of filled prescriptions decreased by more than 50%. These findings suggest that, although patients were found to have a serious cardiovascular condition, the intensity of care decreased once the etiology was accurately identified, as it allowed for focused management of their disease.In patients with confirmed ATTRwt-CM, the proportion with claims for most health care resources increased each year prior to the diagnosis of ATTRwt-CM. In patients suspected to be at high risk for ATTRwt-CM, the proportions of patients with claims for acute care services were lower than that in patients with confirmed ATTRwt-CM prediagnosis, but higher than that in patients with confirmed ATTRwt-CM postdiagnosis. In the sensitivity analysis evaluating HCRU the year before and after diagnosis of HF, the proportion of patients with claims for acute and post–acute care services increased during follow-up, with hospitalizations more than doubling.In our analysis of factors that influenced HCRU, patient age appeared to have an impact only on the frequency of claims for post–acute care services, whereas women had a higher risk of hospitalization, ED visits, and post–acute care services than men. Patients who were Black had lower odds for hospitalization (16%) but higher odds for ED visits (37%) than those who were White. Although patient history of some cardiac conditions, such as unstable angina, HFpEF/HFrEF, and syncope, was found to increase the risk for HCRU, others, such as atrial fibrillation, heart block, and cardiomyopathy, were shown to decrease the risk. Although further exploration of these findings is warranted, they may be attributable to differences in the management of these conditions.25 A strength of our analysis is the size of the database, as Humana is one of the largest MAPD health plan providers in the United States. Several limitations of the current study should also be considered, including shortcomings related to administrative claims data use, eg, potential errors in coding and missing data. In addition, because we used data from a US population of older adults (aged 65-89 years) who elected enrollment in a Humana MAPD plan, the results of our analysis may not be generalizable to patients outside of the United States, younger patients, or patients who have elected enrollment in other health plans. Moreover, the regional imbalance among individuals enrolled in a Humana MAPD plan is noteworthy, as approximately two-thirds and one-fifth of patients analyzed in this study were from the South and Midwest, respectively. The relatively small number of patients in the ATTRwt-CM case and nonamyloid HF control cohorts (n = 119 for each) should also be considered when interpreting the results of the current study. The mean duration of the prediagnosis and postdiagnosis periods differed between cohorts in the HCRU analyses, potentially affecting the results. Because of the retrospective nature of the data, relationships may be established based on temporality but not causality. Although our analyses were adjusted, we did not include certain confounding factors, which may have introduced bias.The strengths and limitations of the original random forest predictive model have been previously discussed.Like the original model, our algorithm predicted ATTRwt-CM risk based on ICD codes readily available in claims data. Without access to patient charts, we could not include clinical information not captured with an ICD code. The confirmed ATTRwt-CM cohort was identified based on an ICD-10 code first available in October 2017. We identified patients with claims for HF between October 2015 and June 2020 who were followed for at least 6 months. We were unable to flag some patients with HF in claims data and classify them as confirmed ATTRwt-CM cases, including patients who were identified between October 2015 and March 2017 with less than 6 months’ follow-up and an ATTRwt-CM diagnosis, and patients who received more general cardiac amyloidosis ICD codes.Moreover, patients were required to be enrolled 1 year prior to the index date and at least 6 months after the index date. ATTRwt-CM was confirmed and HCRU evaluated during the follow-up period, a potentially biased approach because of the lack of information after patients withdrew from the plan. The required 6-month enrollment after HF diagnosis was likely inadequate to verify a nonamyloid diagnosis. In our sensitivity analysis conducted in a subset of patients with 12 months’ follow-up, we found that more than 90% of the patients suspected to be at high risk for ATTRwt-CM had at least 12 months of follow-up enrollment. Given the expanded follow-up period from 6 to 12 months, the proportion of patients with claims for HCRU increased.25 These findings suggest that the algorithm may be used as a tool to identify patients with suspected ATTRwt-CM or, at a minimum, patients at high risk of the disease, to facilitate earlier referral, detection, and treatment. In general, HCRU proved to be highest in the year leading up to the diagnosis of ATTRwt-CM, as compared with 1-2 years before diagnosis or after diagnosis, although the use of post–acute care services  was substantially higher after diagnosis. Additional research is needed to better understand the impact of earlier ATTRwt-CM diagnosis on HCRU in patients with HF suspected to be at high risk for the disease.A risk prediction model for ATTRwt-CM in patients with HF performed well in a population of patients enrolled in a Humana MAPD, with performance levels approaching those reported elsewhere.