In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not openly available due to their confidential nature. This has hampered the development of reproducible and generalisable machine learning applications in health care. Here we introduce the Health Gym - a growing collection of highly realistic synthetic medical datasets that can be freely accessed to prototype, evaluate, and compare machine learning algorithms, with a specific focus on reinforcement learning. The three synthetic datasets described in this paper present patient cohorts with acute hypotension and sepsis in the intensive care unit, and people with human immunodeficiency virus (HIV) receiving antiretroviral therapy. The datasets were created using a novel generative adversarial network (GAN). The distributions of variables, and correlations between variables and trends in variables over time in the synthetic datasets mirror those in the real datasets. Furthermore, the risk of sensitive information disclosure associated with the public distribution of the synthetic datasets is estimated to be very low.  Reinforcement learning1 (RL) is an area of artificial intelligence (AI) which learns a behavioural policy–a mapping from states to actions–which maximises a cumulative reward in an evolving environment. Recent studies that combine RL with neural networks have achieved super-human performances in tasks from video games2 to complex board games3. The success of RL was greatly facilitated by the availability of standard benchmark problems: tasks with publicly available datasets which allowed the research community to develop, test, and compare RL algorithms . Health-related data is, however, not as easily accessible due to privacy concerns around the disclosure of private information. To address this challenge, this paper introduces the Health Gym project–a collection of highly realistic synthetic medical datasets that can be freely accessed to facilitate the development of machine learning (ML) algorithms, with a specific focus on RL.e.g., human immunodeficiency virus (HIV) infection) or with potentially life-threatening conditions  often prescribe a series of treatments to maximise the chances of favourable outcomes. This generally requires modifying the duration, dosage, or type of treatment over time; and is challenging due to patient heterogeneity in responses to treatments, potential relapses, and side-effects. Clinicians often rely, at least in part, on clinical judgement to prescribe sequences of treatments, because the clinical evidence base is incomplete and available evidence may not represent the diversity of real-life clinical states. There is thus vast potential for RL algorithms to optimise personalised treatment regimens, as shown by early research on antiretroviral therapy in HIV8, radiotherapy planning in lung cancer9, and management of sepsis10. Nonetheless, some authors have highlighted the lack of reproducibility and potential for patient harm inherent in these methods11. In particular, recommendations made by RL algorithms may not be safe if the training data omit variables that influence clinical decision making, or if the effective sample size is small12.Clinicians treating individuals with chronic disorders  have also been used to generate realistic medical time series18.One of the main difficulties in developing robust RL algorithms for healthcare is the highly confidential nature of clinical data. Researchers are often required to establish formal collaborations and execute extensive data use agreements before sharing data. One approach to overcome these barriers is to generate synthetic data that closely resembles the original dataset but does not allow re-identification of individual patients and can therefore be freely distributed. Synthetic data generation has previously been applied to computed tomography imagesacute hypotension19, sepsis10, and HIV20. All datasets were generated using GANs and the MIMIC-III22 and EuResist23 databases. MIMIC-III comprises health-related data for patients who stayed in intensive care units (ICUs) of the Beth Israel Deaconess Medical Centre  between 2001 and 2012. Within MIMIC-III, we identified two cohorts of patients: 3,910 patients with acute hypotension and 2,164 patients with sepsis. Similarly, the EuResist Integrated Database was used to extract longitudinal information related to 8,916 people with HIV. For acute hypotension and sepsis, we extracted the related timeseries of vital signs, laboratory test results, medications , and demographics. For people with HIV, we included demographics and time series of antiretroviral medications, cluster of differentiation 4 + T-lymphocytes (CD4) count, and viral load measurements.The Health Gym project is a growing collection of synthetic but realistic datasets for developing RL algorithms. Here we introduce the first three datasets related to the management of i.e., personal identifiers have been removed and other pre-processing steps such as date shifting were applied to minimise disclosure risk); however, there is a small remaining risk that personal information may be disclosed if an “attacker” or “adversary”  is able to link our published data back to personal identifiers. To minimise this risk, we evaluated the synthetic data using current best practices24 in terms of both membership disclosure  and attribute disclosure . In the Usage Notes section, we provide a broader impact statement to discuss the implementations and applications of our work.Both MIMIC-III and EuResist contain only de-identified data . For patients in MIMIC-III requirement for individual consent was waived because the project did not impact clinical care and all protected health information was deidentified21 and EuResist23 databases to generate three synthetic datasets. The inclusion and exclusion criteria used to define the patient cohorts were adapted from previous studies: Gottesman et al.19 for defining the patient cohort with acute hypotension, Komorowski et al.10 to define the sepsis cohort, and Parbhoo et al.20 to define the HIV cohort. Further details are provided below with technical details documented in Section 1 of the Supplementary Materials. Our synthetic datasets thus include variables that can be used to define the observations, actions, and rewards associated with RL problems for the management of these clinical conditions.In this work, we applied GANs to longitudinal data extracted from the MIMIC-IIIIn order to describe our data generation procedure, the rest of this section starts by describing the real datasets and then provides details on our neural network design for generating the synthetic datasets. Our synthetic datasets include all variables in their real counterparts, in the identical formats, and are described in the Data Records section.i.e., quantiles and mean values) of the real datasets in previous studies: see Feng et al.26 for the details of MIMIC-III, and see Oette et al.27 for the details of EuResist.The set of variables contained in each dataset is reported below. Interested readers can find the descriptive statistics , systolic and diastolic blood pressures (SBP and DBPs);laboratory results of Alanine and Aspartate Aminotransferase (ALT and AST), lactate, and serum creatinine;2) and fraction of inspired oxygen (FiO2);mechanical ventilation parameters such as partial pressure of oxygen  scoreadministered fluid boluses and vasopressors; andurine outputs.The real dataset for the management of acute hypotension was originally proposed in the work of Gottesman e.g., the need for specific laboratory tests. Hence the real dataset includes variables with suffix (M) to indicate whether a variable was measured at a specific point in time.Further details are reported in Table et al. used this dataset to develop an RL agent which suggested the optimal amounts of fluid boluses and vasopressors for the management of acute hypotension. Notably, they binned both fluid boluses and vasopressors into multiple categories for the RL agent to make decisions in a discrete action space. Section 1.1 of the Supplementary Materials contains the technical details for deriving this real dataset.In their work, Gottesman et al.10 was also derived from MIMIC-III. It is more complex than the real hypotension dataset and comprises 44 variables, including vital signs, laboratory results, mechanical ventilation information, and various patient measurements. The complete list of variables is reported in Tables The real sepsis dataset constructed by Komorowski 29; hence, the shortest patient record contains 2 data points, whereas the longest contains 20 data points. Section 1.2 of the Supplementary Materials describes the technical details for deriving the real sepsis dataset.The real sepsis dataset contains time series data for 2,164 patients. However, the duration of hospital stay varies for each patient. The shortest record is 8 hours long and the longest record lasts 80 hours. Furthermore, the data are reported in 4-hour windowset al. employed an RL agent to prescribe different doses of intravenous fluids and vasopressors based on a patient’s clinical variables. Their RL agent was trained by assigning rewards depending on whether the patients transitioned to a more favourable health state following the actions taken.In their paper, Komorowski et al. in our real sepsis dataset. Namely, we did not include the four items of PaO2/FiO2 ratio (P/F ratio), shock index, sequential organ failure assessment (SOFA) score, and systemic inflammatory response syndrome (SIRS) criteria. These items were excluded because they can easily be derived from the other variables that are included. We provide further information on deriving these auxiliary variables in Section 1.2.2 of the Supplementary Materials.We purposely left out some variables from the work of Komorowski et al.20. In their paper, Parbhoo et al. extracted a cohort of people with HIV from the EuResist23 database; and proposed a mixture-of-experts approach for the therapy selection for people with HIV. They first used kernel-based methods to identify clusters of similar people, and then they employed an RL agent to optimise the treatment strategy.Our real HIV dataset is based on the study of Parbhoo World Health Organisation (WHO)30 on the standardisation of antiretroviral therapy for HIV. We included 8,916 people from the EuResist database who started therapy after 2015 and were treated with the 50 most common medication combinations, including 21 different types of medications. Refer to Section 1.3.1 of the Supplementary Materials for a discussion on the WHO guideline.Although our real HIV dataset was based on their work, we made additional changes to the real HIV dataset in order to reflect a recent guideline published by the The variables in our real HIV dataset are reported in Table Similar to the real sepsis dataset, the length of therapy in the real HIV dataset varies across people. Thus, we truncated the records and modified their lengths to the closest multiples of 10-month periods. Hence, the real HIV dataset consists of people with 10, 20, 30, etc month-long data. The shortest patient record is 10 months long whereas the longest patient record is 100 months long. Since each data entry summarises patient observations over a 1 month time period, the shortest record is of length 10, and the longest record is of length 100. Similar to the hypotension dataset  is shown in Fig. generator and the discriminator (or critic)–to create highly realistic synthetic data.The overarching pipeline of the The process of training a GAN model can be thought as a two-player-game with two complementary training dynamics. At first, the generator produces synthetic data samples. Then, these synthetic data are compared with samples of the real clinical data by the discriminator. The job of the discriminator is to distinguish between real and synthetic data. A mathematical description of the training procedure for the GAN is reported below. Training is concluded when the discriminator can no longer tell the real and synthetic data apart. That is, a generator is considered to be able to create highly realistic synthetic data when the discriminator is guessing randomly. When the training ends, we use the generator to create our Health Gym synthetic datasets.G and the discriminator as D. Furthermore, we use Xreal and Xsyn to represent the real and synthetic datasets; and likewise, we designate xreal and xsyn as real and synthetic data batches respectively.In the descriptions below, we denote the generator as G creates the synthetic data based on pseudo-random inputs z. The elements of z are sampled from a multivariate Gaussian distribution, and they can be considered latent variables that describe intrinsic aspects of the clinical dataset. The task of network G is to transform a time series of latent descriptions into a set of synthetic but realistic time series of clinical variables As shown in Fig. Long Short-Term Memory32 (biLSTM) recurrent neural network (RNN) module to interpolate the relations among the latent features along the time dimension. The RNN is then followed by three fully connected dense layers33–high dimensional non-linear transformations responsible for feature extraction and synthetic data construction.The intermediate steps of the generator transformation are illustrated in Fig. xsyn, we forward the synthetic data along with a batch of real data xreal to the discriminator (or critic) D. As shown in Fig. D  contains binary and categorical variables, we use soft embeddings35 to represent them as numeric vectors in a machine-readable format. The discriminator D employs two fully connected dense layers to interconnect all features of the data. Then, it employs a biLSTM RNN to interpolate the extrated features along the time dimension; before using a third fully connected dense layer to combine all features to output a realisticness score. Section 2.1 of the Supplementary Materials reports the technical details on network dimensionalities and variable embeddings.In order to evaluate the realisticness of the synthetic data Wasserstein GAN with Gradient Penalty37 (WGAN-GP) to train our GAN model. The networks were updated usingWe adopted the training objective of LD; and likewise the generator network was trained by minimising LG. Note, a critic serves a very similar role to a discriminator and hence we used the two terms interchangeably. Specifically, a discriminator is trained to correctly identify xsyn from xreal, while a critic estimates the distance between xsyn and xreal.The critic network was trained by minimising 39 which was constructed through the Kantorovich-Rubinstein duality theorem40. This required the theoretical guarantees on the smoothness of network D; in practical terms, this was enforced by the gradient penalty loss term to satisfy the Lipschitz continuity with the gradient normality of 1. Furthermore, the constant λGP served as a regularisation term that controlled the strength of the gradient penalty loss.The first two terms of Eq.  form theD to compare the realisticness of the synthetic data G is trained to fool the critic D by maximising the realisticness D is fine-tuned to maximise the difference in the realisticness between the real data and the synthetic data G.An intuitive interpretation of Eqs.  and 2)  2) can bmentclass2pt{minimPearson’s r correlation41ri,j)( for every unique pair of variables Xi)( and Xj) files and are accessible through the Health Gym website . The synthetic hypotension and sepsis datasets are currently hosted on PhysioNet43–a research resource for complex physiologic signals which also hosts the official MIMIC-III22 database. The synthetic HIV dataset is on FigShare44.All of our synthetic datasets are stored as All synthetic datasets follow the formats of their real counterparts which we described in The Real Datasets in Methods. This section describes specific properties of the synthetic datasets. Quality assurance tests are reported later in the Technical Validation section.et al.19 containing 3,910 synthetic patients. Like its real counterpart, there are 48 data points per patient representing time series of 48 hours. There are hence 187,680  records (rows) in total.The synthetic hypotension dataset is 21.7 MB and follows the format of the real hypotension dataset of Gottesman st variable contains the synthetic patient IDs and the 22nd variable indicates the hour in the time series. The units and descriptive statistics of the clinical variables are shown in Table i.e., the 25th percentile, median, and 75th percentile) for the numeric variables; and the share, in percentage, of each unique class for the binary and categorical variables.The synthetic hypotension data comprises 22 variables (columns). The first 20 variables are listed in Table e.g., urine, serum creatinine) are right-skewed, whereas binary and categorical variables are heavily class imbalanced. This will likely require variable transformation for downstream machine learning applications. Interested readers may consider our proposed pre-processing scheme in Section 2.1.2 of the Supplementary Materials.The information presented in this table corresponds to the distributions of the synthetic variables in Fig. et al.10 containing 2,164 synthetic patients. The synthetic dataset is designed with 20 data points per patient representing times series of 80 hours of data reported in 4-hour windows (80 = 20 × 4). There are hence 43,280  records in total.The synthetic sepsis dataset is 16 MB and follows the format of the real sepsis dataset of Komorowski th variable contains the synthetic patient IDs and the 46th variable indicates the time steps in the time series. Table i.e., the first, second, and third quartiles). Table quasi-identifiers45, age and gender, that may be used to disclose personal information. A disclosure risk assessment is reported in the Technical Validation section.The synthetic sepsis dataset contains 46 variables–the first 44 variables are listed in Tables The distributions of the variables are shown in Figs. 2, Temp, PTT, PT, and INR were instead originally stored as numeric variables in the MIMIC-III database21. These 5 variables were converted into categorical variables because their original distributions were extremely skewed and it was difficult to apply appropriate power-transformations. We decided to categorise these 5 numeric variables into deciles as reported in Table Cs–e.g., category C1 corresponds to values that lie within the 0th and the 10th percentile; and category C5 corresponds to values that lie within the 40th and 50th percentile.There are two types of categorical variables in the sepsis dataset. GCS, for example, is a categorical variable by design–it is a clinical point-based system to measure a person’s level of consciousness. The 5 variables of SpOet al.20. It contains 8,916 synthetic patients associated with time series of 60 months. The HIV data are reported in 1-month intervals; and hence there are 60 data points per patient and 534,960  records in total.The synthetic HIV dataset is 42.6 MB and is similar to the real HIV dataset employed by Parbhoo The synthetic HIV dataset contains 15 variables–the first 13 variables are listed in Table Not Applied. This was because the medications in these categories can be substituted by medications from the other classes. A general discussion on medications for ART can be found in Section 1.3.3 of the Supplementary Materials.The distributions of the variables in the dataset are shown in Fig. This section includes a Realisticness Validation Procedure, a Disclosure Risk Assessment, and a Utility Verification. The first part demonstrates the quality of the generated synthetic datasets; the second part discusses the potential risk of an adversary learning sensitive information about a real person from the synthetic records; and the third part compares the suggested actions of RL agents trained on our Health Gym datasets against RL agents trained on the real datasets.24, the Realisticness Validation Procedure serves to confirm that our synthetic datasets fulfil the fidelity of individual data points and the fidelity of the population. That is, we first ensure that the distributions of individual variables are sufficiently similar between the real and the synthetic datasets. We then check that all correlations between variables and trends over time in the real datasets are mirrored in the synthetic datasets.Based on previous work on the validation of synthetic medical datadisclosure control literature45, we will show that our synthetic datasets have a low membership disclosure risk and a low attribute disclosure risk. Membership disclosure refers to the scenario where an adversary is able to match a synthetic record to a real record; and attribute disclosure occurs when an adversary with partial information about a real individual is able to learn new information about that individual from a synthetic record.In the Disclosure Risk Assessment, we show that while our synthetic datasets are realistic, it remains very unlikely for an adversary to learn any sensitive information about a real person using our synthetic datasets. Based on risk metrics from the 47. To this end, we will train RL agents using the synthetic and real datasets and compare their suggested actions to manage patients’ clinical conditions.While realisticness and security are crucial, it is also important to perform a Utility Verification to inspect whether machine learning algorithms trained with our synthetic datasets result in similar outcomes as those trained with the real datasets51 that leveraged GANs to create synthetic data and evaluated the generated data only qualitatively. We summarised the elements of our three-stage validation procedure in Fig. static properties of the synthetic data and assess whether the distributions and statistical moments  of the real and synthetic variables are sufficiently similar. Since our generated data are time series, the third stage conducts an additional set of visual comparisons to test the properties of the synthetic variables over time.Our validation procedure goes beyond prior workXsyn on top of the probability density function of its corresponding real variable Xreal. These plots were generated using kernel density estimations (KDE)52. Binary and categorical variables were compared by means of side-by-side histogram plots.In the first stage, we superimposed the probability density function of a synthetic numeric variable two-sample Kolmogorov-Smirnov test55 (KS test), the two independent Student’s t-test57 (t-test), the Snedecor’s F-test59 (F-test), and the three sigma rule test60. The KS test compares the overall similarity between the distributions of real and synthetic variables. The t-test determines whether there are significant differences between the mean values of the real and synthetic variables; and the F-test compares their variances. Furthermore, the three sigma rule test uses the standard deviations of the real data to check whether the majority of the synthetic data was comprised within a probable range of the real variable values. Definitions and implementations of each test are reported in Sections 3.1–3.4 of the Supplementary Materials.The statistical tests in stage two include the analysis of variance (ANOVA) F-test and the three sigma rule test. The categorical ANOVA F-test checks the similarity in variances but over different classes. An overview of this procedure is presented in Algorithm 1 in Section 3.5 of the Supplementary Materials. No multiple testing corrections61 were applied, see Section 3.5 of the Supplementary Materials for more details.We organised the statistical tests in a hierarchical manner. Each synthetic variable  was first assessed using the KS test. The KS test is the most difficult test; and when it was passed, we concluded that a synthetic variable faithfully represents its real counterpart. If a synthetic numeric variable failed the KS test, we applied the t-test, the F-test, and the three sigma rule test. If a synthetic categorical variable failed the KS test, we assessed it further using the Kendall’s rank correlation coefficients62. A brief description is provided below, technical details and a discussion on alternative correlation measures67 can be found in Section 4 of the Supplementary Materials.Our third stage of validation considers correlations between variables and between trends over time, computed using static correlation coefficients for each pair of variables in the synthetic dataset Xsyn and the real dataset Xreal . Next, the correlation coefficients for the two datasets were displayed side-by-side for visual comparison. Ideally, the synthetic dataset should mirror both the directions (positive or negative) and magnitudes of correlations between variables in the real dataset.First, we calculated the 68. The trend indicates the general upward or downward slope of variable over time, and the cycle refers to local periodic patterns. Then, we computed and compared the average correlation in trends and average correlation in cycles .Though informative, the correlation between variables does not provide any information about whether temporal behaviours over time are captured by the synthetic dataset. Hence, we linearly decomposed each variable as a trend with cycleThe plots for the first stage of the validation procedure for the hypotension dataset are shown in Fig. 2 passed both the t-test and the F-test. This means that these synthetic variables do not perfectly capture the real variable distributions; however, their means and variances are still representative of their real counterparts. These observations are supported by the subplots in Fig. The results of stage two are shown in the hierarchically structured Table After confirming the realisticness of the individual synthetic variables, we assessed the relations between variables and their longitudinal properties in the third validation stage. We illustrate the static correlations in Fig. For the synthetic sepsis dataset, we observe in Figs. The correlations computed in stage three of the validation procedure are visualised in Figs. Qualitative comparisons between the distributions of the real and synthetic HIV datasets are shown in Fig. For stage three, we present the correlations in Figs. We performed two tests to evaluate the likelihood of an attacker learning sensitive information about an individual from the generated synthetic datasets.L2 norms) between records in the real dataset Xreal and records in the synthetic dataset Xsyn. We verified that all distances were greater than zero, i.e., that no records in the synthetic datasets perfectly matched any records in the real datasets.The first test was to ensure that no records in the real datasets were simply copied by the GAN to the synthetic datasets. We computed the Euclidean distances  which, in combination, may be used by an adversary to uniquely identify a person . Variables which in combination constitute personally identifying information are known as quasi-identifiers. Individuals with the same combination of quasi-identifiers  form an equivalent class.The second test concerned the et al.70 introduced two types of disclosure risks based on the concepts of quasi-identifiers and equivalent classes. Depending on the direction of attack71, an adversary may attempt to learn new information about a person either by finding out whether an individual in the population (or database) is also included in the real or synthetic dataset (population-to-sample attack) or by linking an individual in the real or synthetic dataset back to the original database (sample-to-population attack).El Emam et al. assumed that the real dataset was sampled randomly from the database, in this study the real datasets were constructed using publicly accessible inclusion and exclusion criteria . Therefore, we assumed that the adversary had access not only to the database  but also to the real dataset. One of the likely reasons to conduct a population-to-sample attack is to determine whether an individual has a specific condition or illness that led to their inclusion in the dataset. However, when the inclusion criteria are known, population-to-sample attacks become less relevant than sample-to-population attacks, which may be used to learn additional sensitive information about an individual in the synthetic dataset.Whereas El Emam i.e., the chance of matching a random individual in the synthetic dataset to an individual in the real dataset) can be computed asS is the number of records in the synthetic dataset, Fs is the size of the equivalent class in the real dataset that shares the same combination of quasi-identifiers as a specific record s in the synthetic sample, and Is is a binary indicator variable equal to one if at least one real record matches the synthetic records s. Interested readers can find more details on El Emam et al. ‘s metric in Section 6.1 of the Supplementary Materials.The risk of a successful synthetic-to-real attack . Therefore no record was leaked into the synthetic dataset.Through the Euclidean distance test, we found that no record in the synthetic sepsis dataset was identical to any record in the real sepsis dataset. The smallest distance between real and synthetic records was 328.78 ( > 0), which was considerably larger than the smallest distance for the hypotension data (49.06). This is likely due to the larger number of variables in the sepsis dataset  and gender were combined to create different equivalence classes The minimal Euclidean distance between any pair of real and synthetic HIV records was 0.11 (>0); and hence no data leaked from the real dataset into the synthetic dataset. This value is relatively low for two reasons: 1) there are very few variables in the HIV dataset; 2) most variables are either binary or categorical. The reasons that inflate the Euclidean distance for sepsis are thus the same reasons that deflate the Euclidean distance for HIV.e.g., male Asian and female Caucasian). The risk of a successful synthetic-to-real attack was estimated to be 0.041%. This risk is again much lower than the typical 9% threshold, indicating that also the synthetic HIV dataset can be released with minimal risk of sensitive information disclosure.The HIV variables include the quasi-identifiers gender and ethnicity. These two variables were combined to create different equivalence classes  unique actions; whereas DO comprised the remaining 18 variables  unique actions; DO comprised the remaining 42 variables  and the maximum dose of vasopressors in the same time frame (Max Vaso) were used to define the action space The heatmap in Fig. A, resulting in 24 ( = 6 × 4) unique actions; DO comprised the remaining 11 variables  and complementary NNRTI (Comp. NNRTI) were used to define the action space The heatmaps in Fig.  = ,suggested 0.00% of times by an agent trained on the real data and 23.73% of times by an agent trained on the synthetic data; and = ,suggested 16.90% of times by an agent trained on the real data and 0.02% of times by an agent trained on the synthetic data.While the direct cause of the mis-alignments remains uncertain, these differences are likely caused by the highly sparse feature representation space of the HIV dataset. As mentioned in the Methods section, we selected the top 50 medication combinations spanning 21 medications from the EuResist database. However, the medication combinations were then deconstructed into five categorical variables–Base Drug Combo (with 6 classes), Comp. INI (with 4 classes), Comp. NNRTI (with 4 classes), Extra PI (with 6 classes), and Extra pk-En (with 2 classes)–hence resulting in a total of 1,152 ( = 6 × 4 × 4 × 6 × 2) potential synthetic medication combinations. It is thus difficult to capture the 50 medication combinations that were observed in the real dataset out of the 1,152 candidates and hence introducing extremely high sparsity.20 and recent offline RL methods that encourage the selection of actions observed in the data may be useful for this task83. It is also possible that the dataset does not include important confounders  which may be necessary to map a patient’s clinical history to an optimal combination of medications.A previous study also observed the difficulty of training RL agents for optimising ART in HIV patientsAlthough the utility of the synthetic HIV dataset appears to be lower than the synthetic acute hypotention and synthetic sepsis datasets, its variables are highly realistic  and we believe that it is still a useful dataset to develop and prototype RL algorithms. As shown in Fig. et al.84 proposed a twin-encoder approach to separately embed numeric and non-numeric data, which required adding a matching loss for training the generative model. In contrast, the GAN model proposed in this study does not require the extra architectural constraint; instead, our binary and categorical variables are mapped to continuous vectors through the use of soft-embeddings.This paper introduces the Health Gym project with three highly realistic synthetic datasets generated with GANs. We are aware of only one other GAN-based model which attempted to create both numeric and non-numeric variables at the same time. Li 20 and thus our Health Gym datasets include the variables that can be used to define the observations, actions, and rewards for training RL agents for the management of clinical conditions. While our Health Gym datasets were primarily prepared for RL, our datasets also contain sufficient information for developing supervised or unsupervised machine learning models85.We based our work on published inclusion and exclusion criteriaControlled Data Processing Workflow86 for external researchers to train models, develop scripts, and then to compare and test them with real data.The authors of this manuscript would like to emphasise that, while our synthetic datasets are realistic, the generated synthetic datasets should not be regarded as replacements for the real datasets. Furthermore, we will continue to incorporate synthetic data into a In this paper, we used batch-constrained Q-learning to evaluate whether our synthetic datasets could be used to train RL agents that behave similarly to those trained using the real datasets. We intend to leverage recent advancements in offline RL algorithms as part of future work to further evaluate the utility of the Health Gym datasets.87.Whereas optimal policies determined using the synthetic hypotension and sepsis datasets were very similar to optimal policies determined using the real datasets, considerable differences were observed for the HIV data. This may indicate that the current Health Gym GAN requires further fine-tuning to fully capture the complexity of a dataset consisting of multiple inter-connected categorical variables. For instance, the recurrent components of the Health Gym GAN could potentially benefit from existing work on network simplification88. In future work, we plan to explore the use of diffusion models for improving the sample diversity and robustness of the training process of our generative models. Furthermore, the generated data could be made more realistic, and the generative process more explainable, by incorporating causal layers89.Diffusion models are an alternative approach for generating synthetic data, and they have recently achieved results comparable to state-of-the-art GANsSupplementary Figure 1Supplementary Figure 2Supplementary Figure 3Supplementary Materials for the Health Gym Paper


The purpose of the present study was to investigate to what extent motions influence the quantitative accuracy of cardiac 15O-water PET/CT scans. Simulations of 4 types of motions with amplitude of 5 to 20 mm were performed based on data from 10 scans. An automated motion detection algorithm was evaluated on clinical and simulated motion data. MBF and PTF of all simulated scans were compared to the original scan used as reference.Frequency and magnitude of motion was assessed visually using data from 50 clinical Patient motion was detected in 68% of clinical cases by visual inspection. All observed motions were small with amplitudes less than half the LV wall thickness. A clear pattern of motion influence was seen in the simulations with a decrease of myocardial blood flow (MBF) in the region of myocardium to where the motion was directed. The perfusable tissue fraction (PTF) trended in the opposite direction. Global absolute average deviation of MBF was 3.1% ± 1.8% and 7.3% ± 6.3% for motions with maximum amplitudes of 5 and 20 mm, respectively. Automated motion detection showed a sensitivity of 90% for simulated motions ≥ 10 mm but struggled with the smaller (≤ 5 mm) simulated (sensitivity 45%) and clinical motions (accuracy 48%).Patient motion can impair the quantitative accuracy of MBF. However, at typically occurring levels of patient motion, effects are similar to or only slightly larger than inter-observer variability, and downstream clinical effects are likely negligible.The online version of this article (10.1007/s12350-021-02550-9) contains supplementary material, which is available to authorized users. First, misalignment between the PET and CT scan leads to erroneous attenuation correction.15O-water PET/CT and to develop a method for automated motion detection.The purpose of the present study was to investigate to what extent motion affects the quantitative accuracy of cardiac 15O-water PET/CT stress scans. In addition, to assess the frequency and magnitude of real occurring motions, a set of clinical scans were visually inspected for motion. A number of different hallmarks of motion detectable in the PET data were identified and the accuracy of automated motion detection based on these hallmarks was evaluated.For this purpose, motion simulations were performed based on clinical 15O-water PET/CT were included in the study. Of these, data from 10 randomly selected patients was used for motion simulations, and motion in the original data was ruled out via visual inspection. Data from 50 different consecutive patients was used for visual assessment of the frequency and magnitude of real occurring clinical patient motion. Finally, data was included from the only four patients (out of circa 500) in our clinic during the last 2 years where severe motion artefacts were noted by the nuclear medicine physician reading the scan. Since only anonymized images were used and the present work was purely an image processing study, this study did not require ethics permission according to the Swedish Law on Medical Research in Humans.A total of 64 clinical stress scans from patients referred for assessment of myocardial ischemia with 15O-water according to our clinical standard protocol. Hyperemic MBF was induced using a continuous infusion of adenosine (140 μg·(kg × min)−1) starting 2 min prior to the start of aquisition and continuing throughout the whole scan.Patients were scanned using a Discovery MI PET/CT . The protocol started with a low dose CT scan during normal breathing for attenuation correction. Then 4 min rest and stress scans were perfomed after automated bolus injection of 400 MBq Evaluation of clinical patient motion was performed by an experienced nuclear medicine physician by visual inspection of 50 consecutive clinical stress scans in aQuant software .Simulations of patient motion were performed using an in-house developed tool in Matlab, based on reconstructed image data from 10 clinical stress scans. In addition, a synthetic patient was created to study the effect of motion completely without the influence of existing pathology of the clinical patients. This was done by taking the coordinates from an analyzed original scan from one patient and adding homogenous kinetics to all voxels of RV and LV cavity and myocardial regions as well as extra-cardiac regions.When a stress agent like adenosine is used, discomfort may be experienced leading to a stiffening reaction followed by a gradual relaxation back to original position as the patient gets accustomed. This was simulated as an initial anterior and cranial displacement at the start of the scan with an exponential slide down towards the original position (type 1).Type 2 motion corresponds to a linear slide in caudal direction during the whole scan.A cough was simulated as an anterior motion up and down during a full single frame at the peak of the myocardial TAC (during the first pass through the LV) and after 1 min (type 3).Cardiac creep was simulated as a linear slide in cranial direction starting after 1 min, mimicking the reduced effect of a regadenoson bolus or the effect of terminating an adenosine infusion (type 4).Simulations of induced motion were performed by moving the PET matrix frame by frame simulating 17 motions (M1-M17) divided into 4 types at different amplitudes, see Figure Effect of misalignment between the CT and PET was included by un-applying the attenuation correction prior to the motion simulations, and then re-applying it after motion was added. This resulted in a simulated movement of the dynamic PET scan with attenuation correction based on the original position. All motion-simulated scans (N = 170) and corresponding 10 original scans were fully analyzed as a complete clinical case in the aQuant software. In short, the software uses a basis function implementation of the single-tissue compartment model with corrections for partial volume and spill-over which was used to compute MBF and perfusable tissue fraction (PTF) images.A single point of the myocardial TAC after the first pass in LAD, RCA or LCX with a standardized residual from the modelled fit larger than 3.A significant linear trend in fit residuals in any of the LAD, RCA or LCX regions.A right and left ventricular first pass peak area under the curve (AUC) difference larger than 10%.15O-water is freely diffusible and hence, the arterial and venous TAC should convergence once equilibrium is reached during the last minute of the scan.A difference of more than 10% between arterial or venous TAC and average myocardial TAC in the last two frames (3 to 4 min). This is based on the fact that A left ventricular spill-over fraction larger than 30% in any myocardial segment according to the 17-segment model. Generally, spill-over fractions are below 20%, and a larger spill-over fraction could be a sign of motion.An algorithm was developed for automatic motion detection that consisted of five different tests (see appendix for a more detailed description):The algorithm was applied to all simulated scans (N = 170), the 50 clinical scans that had been visually inspected for existence of motion, and the scans of the 4 patients with known large motion artefacts. Automated motion detection was considered positive if one of the five tests were fulfilled.P-value < .05 was considered significant. Statistical analysis was performed in Matlab.Data are presented as mean ± standard deviation (SD). To analyze differences between simulated and original scans, the non-parametric paired Wilcoxon sign-rank test was used. A two-sided Patient motion was detected in 34 of 50 patients (68%) by visual inspection. All observed motions were small with a score of 1. The 4 patients with known motion artefacts were evaluated at a score of 2.−1 for all patients. For one patient, the result from 20 mm cough at peak myocardial TAC (M12) was excluded due to large motion artifacts that made delineation of the myocardial wall impossible.In the original scans, small inter-frame motion was visually observed in 1 of 10 patients, but was judged to not bias the results as it occurred in the last 2 min of the scan and the results of the simulations based on the data of this patient did not stand out in any way. Global stress MBF ranged between 0.83 and 4.3 mL·(g·min)In Tables Figures s4 and s5 shows polar plots of the synthetic patient and Figures s6 and s7 polar plots of a typical patient for MBF and PTF with the different types of motion. For motion in the inferior direction  a typical pattern of a decreased MBF in RCA and increased MBF in LAD was seen. In these cases, PTF showed the opposite effect with an increase in RCA and a decrease in LAD. For cardiac creep  MBF increased in RCA and decreased in LAD, whereas there were only minor effects on PTF. Motion 12, simulating a cough with a large displacement in anterior direction during one frame at peak activity in the myocardial wall, had a large impact on MBF. Later, at 1 min after administration, though, the effect of the same motion was minimal, demonstrating the vulnerability of MBF to sudden motion during the first pass of the tracer.The automatic motion detection algorithm identified motion in 74% of all simulated scans, with a detection rate ranging from 20% to 100% per simulated motion, see Table 15O-water PET. The frequency and magnitude of patient motion in clinical scans was found to be frequent (68%) but generally minor. Simulated motion showed potentially severe consequences on MBF values in motions larger than 10 mm. Using these data, an algorithm for motion detection was developed which detected simulated motions of 10 mm or above with 90% sensitivity, but motion of 5 mm with only 45% sensitivity. As shown by the simulations, MBF was generally decreased in the region of myocardium in which direction the motion occurred. For motions in the inferior direction  MBF in RCA was decreased, whereas for motions in the cranial direction  MBF in LAD was decreased. PTF trended in the opposite direction, with an increase in the region of myocardium in which direction the motion occurred. This pattern can clearly be seen in the polar plots of the synthetic patient in Figures s4 and s5 for motion types 1, 2 and 4 (M1–9 and M16–17). Type 3 motion with a simulated large cough at the peak of the myocardial TAC (M12) resulted in large artifacts in the polar plot of both MBF and PTF. The vulnerability of MBF to sudden coughing motion during the first pass is clearly demonstrated when almost the entire artifact is gone when the same motion is simulated just after the first pass, at 1 min. However, it should be emphasized that peak first pass occurs during a narrow time window lasting about 15 s, and thus motion artefacts of this type (M12) should be highly unfrequent. In Figures s6 and s7, the same patterns can be seen for a clinical patient, although not as clear as for the synthetic patient.Patient motion during cardiac PET studies is a common problem, emphasizing the need for motion correction. In this study, we investigated the influence of patient motion on the quantitative accuracy of Even if a clear pattern for each simulated motion type could be seen, the effect of motion is not completely predictable. A high inter-patient variation was seen in the deviations of both MBF and PTF from the original scan, even when the exact same motions were simulated for each patient. For example, the 10 mm linear slide in inferior direction (M8) showed on an overall decrease in MBF for RCA, but for one patient MBF was actually increased. In another patient, MBF in LAD was increased for the 10 mm cardiac creep (M17), whereas this motion overall resulted in a decrease in MBF.15O-water PET/CT rest scans and observed a deviation for patients with most severe motions of 34% ± 62%, substantially worse than the simulations in the present study.K1) divided by the clearance rate (k2), which are both to a large extent determined from the earlier part of the scan. For wall delineation in the study by Koshino et al., images consisting of a subtraction of the early time frames from the later frames were used, thus being sensitive to motion during both early and late part of the scan. Koshino et al. also used a second scan with 15O-CO as the reference for extracting the LV TAC which might induce additional uncertainties compared to the cluster analysis used in the present study. For other perfusion tracers with a high retention in the myocardium, a sum of the later frames is typically used for wall delineation. Hence, any motion occurring between the early part of the scan, important for perfusion quantification, and the late part of the scan where the wall is delineated, will result in motion-induced errors. Since there is a longer period of time for motion to take place, the likelihood of motion to affect wall delineation increases. On the other hand, for motions extending over a short period of time, for example during a cough, the effect will be more evened out in the longer time frames. Furthermore, for all perfusion tracers except for 15O-water, MBF is calculated from the uptake rate K1, which is highly dependent on an accurate attenuation correction and errors as high as 500% have been reported in simulations of 82Rb scans, which was mainly caused by misalignment with the CT.15O-water, with MBF calculated from the washout rate k2, it has been shown that PET-CT misalignments have very limited effect on MBF quantification.13 Obviously, attenuation correction misalignment errors will instead have significant effects on PTF.Deviation of both MBF and PTF increased with increasing amplitude of the simulated motion. Global absolute average deviation of MBF was 3.1% ± 1.8% and 7.3% ± 6.3% for motions with maximum amplitudes of 5 and 20 mm, respectively. Koshino et al studied real occurring patient motions in Real occurring clinical interframe patient motion was evaluated by visual inspection, and shown to occur frequently (68%). Importantly, all visually detected motions were small at less than half the LV wall thickness. In line with the presented simulations, this suggests that even if patient motion frequently occurs, the impact on MBF can be expected to be small in the majority of cases. However, the relatively high inter-patient variation in the simulation results implies that, occasionally, even smaller motions at 5 mm amplitude could substantially impact MBF quantification.15O-water PET scans it is important to be able to detect the occurrence of motion, in order to identify when a false positive perfusion defect has been induced or when a true one has been masked. Automated motion detection could be used as a beneficial tool to alert the observer to check the scan for motion during analysis. The algorithm used in this study is based on residual analysis to detect inconsistency in TACs, on non-convergence of arterial, venous and late myocardial TACs, or on a large difference in right and left ventricular AUC. Automated motion detection was highly successful at detecting the more severe types of simulated motions, but for motion up to 5 mm the detection rate was low. As all clinical real occurring motions corresponded to maximum one half myocardial wall thickness or roughly 5 mm, automatic motion detection also struggled with the 50 clinical cases. It should be noted, however, that the simulations show that a motion of 5 mm in most cases has an effect that is similar to or only slightly larger than inter-observer variability.15O-water PET.In the clinical evaluation of A.A gradient from top left to bottom right in the whole PTF polar plot combined with an unexpected anterior-septal apex PTF defect and an inferior apex defect in MBF. The short axis PTF inferior wall and MBF anterior wall looks smeared and thickened. The PTF defect was not expected for this patient that was evaluated for stable angina without history of infarction. This corresponds to type 1 motion (M6), or relaxation after initial stress agent effects.B.Substantial smearing of the inferior wall with no distinct separation from the abdominal signal. This is a typical sign of motion in the inferior direction .C.Substantial smearing of the anterior wall. This it is a clear sign of an anterior motion, which could arise during cardiac creep .In addition to the motion detection algorithm, we could also identify three typical signs of motion that could be used to visually assess motion when viewing parametric images, as shown in Figure s8A–C:15O-water, motion correction is further challenged since 15O-water is freely diffusible and has no retention in the myocardium that otherwise can be used as a reference for image co-registration during later parts of the scan. Promising results of motion correction for dynamic 82Rb studies have been published but to our knowledge no motion correction method for 15O-water has yet been presented.Motion detection is an important step towards solving the problem of patient motion but motion correction is the endpoint of the solution. Motion correction can be done either on a frame-by-frame-basis, using image co-registration, or using data-driven methods on raw PET data. The rapid tracer kinetics in cardiac PET, especially during the earlier parts of the scan, results in images with substantially different activity distributions between frames, which highly challenges image co-registration. When using 15O-water cardiac PET and may induce false positive or false negative results in the most extreme cases. In clinical practice, patient motion is frequently observed but at a magnitude where it has limited effects, similar to or only slightly larger than inter-observer variability. Severe motion with significant impact on MBF could reliably be detected.Patient motion can impair the quantitative accuracy of 15O-water PET.Patient motion is frequently observed but at a magnitude that has limited impact on the quantitative accuracy of MBF calculated from Supplementary material 1 (PPTX 406 kb)Supplementary material 2 (M4A 6698 kb)Supplementary material 3 (DOCX 1070 kb)Below is the link to the electronic supplementary material.


Cardiovascular risk factors have a recently established association with cognitive decline and dementia, yet most studies examine this association through cross-sectional data, precluding an understanding of the longitudinal dynamics of such risk. The current study aims to explore how the ongoing trajectory of cardiovascular risk affects subsequent dementia and memory decline risk. We hypothesize that an accelerated, long-term accumulation of cardiovascular risk, as determined by the Framingham Risk Score (FRS), will be more detrimental to cognitive and dementia state outcomes than a stable cardiovascular risk.We assessed an initially healthy, community-dwelling sample recruited from the prospective cohort Betula study. Cardiovascular disease risk, as assessed by the FRS, episodic memory performance, and dementia status were measured at each 5-year time point (T) across 20 to 25 years. Analysis was performed with bayesian additive regression tree, a semiparametric machine-learning method, applied herein as a multistate survival analysis method.APOE ε4 carriers.Of the 1,244 participants, cardiovascular risk increased moderately over time in 60% of sample, with observations of an accelerated increase in 18% of individuals and minimal change in 22% of individuals. An accelerated, as opposed to a stable, cardiovascular risk trajectory predicted an increased risk of developing Alzheimer disease dementia  or vascular dementia  and was associated with an increased risk of memory decline . A stable cardiovascular risk trajectory appeared to partially mitigate Alzheimer disease dementia risk for The findings of the current study show that the longitudinal, cumulative trajectory of cardiovascular risk is predictive of dementia risk and associated with the emergence of memory decline. As a result, clinical practice may benefit from directing interventions at individuals with accelerating cardiovascular risk. Notably, ≈80% of patients diagnosed with AD exhibit vascular pathology when inspected at autopsy.5 Other studies have shown altered blood-brain barrier integrity,6 alterations in cerebral blood flow,7 increased blood pressure,8 and increased cerebrovascular resistance.9 It is also of note that in preclinical AD, detectable changes in vasculature occur before the detection of current standard AD biomarkers, β-amyloid and tau.10 In line with these observations, the use of blood pressure medication by hypertensive patients has been associated with a reduction of dementia risk.11 While there are likely multiple routes by which the onset of dementia occurs, interventions targeting a reduction of cardiovascular risk represent thus far one of the most viable and promising strategies for preventing dementia onset or progression.12 This is exemplified by the declining incidence rate of dementia within industrialized nations over the last few decades, suggested to be a result at least partially of the successful long-term treatment of cardiovascular disease (CVD).13 Despite the strong links between CVD risk and dementia, few studies have examined longitudinal CVD risk in relation to cognitive decline and dementia.While vascular dementia (VaD) de facto results from vascular injury,14 This score has previously been found to be a significant predictor of late-life cognitive decline15-20 and dementia.21 Previous investigations have typically assessed dementia incidence or cognitive decline as a function of cross-sectional FRS value.21-23 Such an approach may underestimate the role of CVD risk for cognitive aging: CVD risk increases with age, and there may be large individual differences in the accumulation of risk factors over time.12 Following the accumulated change of vascular risk is of particular importance because it more closely captures the ongoing, summative nature of vascular risk development. Understanding the impact of an evolving temporal CVD risk could help guide clinical practice by elaborating the potential for mitigation of dementia risk via interventions directed at CVD risk. Recent research has shown that a longitudinal worsening of CVD risk is associated with midlife cognitive decline,24 and the worsening of single CVD risk factors has been shown to be associated with an increased dementia risk.26 However, few studies have examined, or are equipped to examine, the relationship of longitudinal changes in multiple CVD risk factors with regard to cognitive variation and dementia. Consequently, the current study aims to provide an understanding of the impact of longitudinal, dynamic CVD risk for both dementia incidence and episodic memory (EM) decline. This is achieved by using data from the Betula study, a longitudinal, multicohort study of memory, aging, and dementia. CVD risk, dementia status, and memory performance were measured at 5-year time points across 20 to 25 years in a large sample of healthy adults . We hypothesized that the longitudinal, accumulated trajectory of CVD risk, as determined by the FRS, can be divisible across the sample and that such divisions will show differences in cognitive decline and dementia risk. Specifically, we hypothesized that individuals with an accelerated CVD risk will have a higher incidence of dementia at older ages and a higher incidence of EM decline in midlife, before the onset of dementia.The Framingham Risk Score (FRS) is a well-validated, multivariable risk function used to quantify the risk of a CVD event occurring within a subsequent 10-year period.27 Two samples, randomly sampled from the population registry and recruited at 2 different time points (T), were included . Data were collected from participants in both samples at 5-year intervals across 25 and 20 years, respectively (27). A total of 1,244 participants who had sufficient data and attended at least 2 time points were included in the current work.The data reported in the present study were obtained from the longitudinal, population-based Betula study, based in Umeå, northern Sweden.ectively . At recrThe Regional Ethical Vetting Board at Umeå University approved this study, and written informed consent was obtained for all participants.APOE ε4 carrier status was determined via PCR and dichotomized into carriers/noncarriers of the ε4 allele.27At each 5-year test session, participants underwent cognitive testing, health examinations , interviews, and completed lifestyle questionnaires. All tests were carried out or guided by a licensed nurse. 28; diagnosis of AD and/or VaD was therefore determined via evaluation of what was considered the primary pathology underlying the cognitive impairment. Medical, psychiatric, and pharmacologic data from inpatient and outpatient care were integrated, when available, in the dementia diagnosis process. Brain imaging may have been available as part of a participant's external clinical assessments but was not implemented within the study as a direct source of information from which to guide the diagnosis procedure. Participants who were found to have dementia at either the time of recruitment or the first diagnostic follow-up were excluded from the study. To increase diagnostic precision, follow-up assessments with the computerized medical record system were performed without the study physician having access to previously determined status with regard to dementia status, subtype, and disease onset. In addition to the medical records, the results from health and memory assessments were considered with regard to the diagnosis. Participants fulfilling 1 or several of the following criteria were considered high risk and underwent more extensive evaluation: (1) low score on a composite cognition and memory tests (≥1.8 SDs below age-based norms), (2) suspected dementia signs observed by the staff conducting the health assessments and cognitive testing, (3) Mini-Mental State Examination score <24 or a decline of at least 3 points in the Mini-Mental State Examination score relative to the prior testing occasion, or (4) a subjective sense of memory impairment reported by the participant. Disease onset was defined as the time at which the clinical symptoms became sufficiently severe to interfere with social functioning and activities of daily living. Individuals with cardiovascular burden accompanied by neurologic signs and a fluctuating course were diagnosed as having VaD. In some instances, a mixed condition was evident; these cases were denoted dementia not otherwise specified and were excluded from the analysis (n = 11 at T6). The diagnosed AD dementia and VaD cases all showed a progressive decline as evident by symptoms attributable to AD dementia or VaD, respectively. Furthermore, patients exhibiting an unspecified condition or exhibiting long-term low cognitive capacity after trauma, stroke, tumor, or subarachnoid hemorrhage were excluded from the analysis.Dementia diagnoses were determined through evaluation of written and computerized medical records covering the time span from recruitment to the end of the study period. Diagnoses were clinical and based on the DSM-IV criteria29 Composite scores were constructed by summation of performance on each task. The resulting scores ranged from 0 to 76 , with a higher score indicating better EM performance.An EM composite score was constructed from performance on 5 tasks: (1) immediate free recall of visually and orally presented short sentences, (2) delayed cued recall of nouns from the previously presented sentences, (3) immediate free recall of enacted sentences, (4) delayed cued recall of nouns from the enacted sentences, and (5) immediate free recall of a list of orally presented nouns. Each test has been described in detail previously.14 which is a tool widely used in clinical settings to predict the likelihood of adverse CVD events. We used the office-based version of the score, which is computed from age, sex, systolic blood pressure, blood pressure medication usage, body mass index, smoking status, and diabetes diagnosis. These factors were aggregated into a multivariable CVD risk score per individual according to earlier descriptions14 that represents a probability of having any form of CVD event within a 10-year period. Risk scores were calculated at every time point. The average rate of CVD change over time was analyzed with a linear model based on the FRS values of up to 5 repeated time points over 20 to 25 years while accounting for nonignorable dropout with a pattern mixture modeling approach.30 Participants with ≥2 FRS values were classified as having average, accelerated, or stable CVD risk trajectories in relation to their age and individual baseline risk. Accelerated and stable CVD risks were defined as rates of change >1 or <1 SD from the mean, respectively. The average group showed a steady increase in FRS values, reflecting the typical progression of CVD risk. For individuals diagnosed with dementia, only FRS scores before diagnosis were considered. On a group level, each FRS group had similar average starting values  is a bayesian machine learning method that combines ensemble learning and semiparametric regression. BART can be implemented for a variety of outcomes, including continuous, binary, and time to event with right censoring. In this work, BART is used to estimate multistate models, to impute missing covariates in the FRS, and to estimate EM decline.Bayesian additive regression tree (BART32 with the approach described previously.33 Details of the procedure can be found in the eMethods (links.lww.com/WNL/B894).BART was first used to impute the missing covariates in the FRS score, assumed to be missing at random,34 The first model is the illness-death model in which each individual starts in the healthy state and, depending on event, transitions to either a state of dementia (AD dementia or VaD) or the final state, death. An individual first diagnosed with dementia can also transition to the final state of death. In the second model, AD dementia and VaD were treated as 2 separate states. The multistate model can be decoupled into a set of survival models, fitting separate intensities to all permitted transitions with the use of BART while making appropriate adjustments to the risk set.35 BART was chosen because of its performance as a flexible framework capable of modeling complex, nonlinear, and interaction relationships of covariates when predicting or explaining survival time .To characterize the association between dementia, mortality, and CVD risk, we consider 2 multistate models.38 to study the association between CVD risk and dementia risk. This involves estimating the BART survival models while adjusting for covariates  and the FRS groups. When the model has been fitted, we use Monte Carlo sampling and regression standardization to estimate marginal measures of association, i.e., sample pseudodata  for the baseline confounders for each posterior sample of the parameters . Similarly, because we aimed to capture cognitive changes that occur before the onset of dementia, the participants <70 years of age at study inclusion were analyzed for EM changes. Samples from 1,000 of the standardized survival functions were used to obtain the target posterior distribution and to ensure negligible Monte Carlo error. The chains were carefully monitored for convergence and mixing via trace plots.BART is used mainly for making predictions and does not provide coefficient estimates. To overcome this, we implement a bayesian version of regression standardizationlinks.lww.com/WNL/B894). Analyses were conducted with R version 3.6.3 .Implementation of BART for the analysis of cross-sectional FRS values and for the prediction of EM decline is described in the eMethods .Seventy-eight individuals (6.3% of the sample) developed AD dementia, and 39 (3.1%) developed VaD within the 20- to 25-year study time span. In older groups , 32% developed AD dementia and 16% developed VaD. Only participants >65 years of age developed dementia within this cohort. For AD dementia, the absolute risk reduction (ARR) across the whole sample was 2.7% for those in the average group relative to those in the accelerated group, with a number needed to treat (NNT) of 37. Furthermore, the ARR was 2.5% for those in the stable group relative to those in the accelerated group, with an NNT of 40. For VaD, also across the whole sample, both the average and stable groups had an ARR of 3.4% compared to the accelerated group and an NNT of 30 also for both comparisons. At T3 , 1.5% of the sample dropped out. This was followed by 6% of the remaining sample at T4, 30% at T5, and 39% at T6.links.lww.com/WNL/B894) and from 4.1 to 3.3 across the study time span for VaD . Furthermore, those with an average trajectory compared to those with a stable trajectory were more likely to develop AD dementia, with an RR ranging from 3.1 to 2.2 across the study time span . For VaD, the RR for participants with an average trajectory relative to a stable trajectory ranged from 2.1 to 2.0 across the study time span .Findings from the multistate model show that older adults who had a subsequently stable CVD risk trajectory from study inclusion had a reduced likelihood of developing AD dementia or VaD during the study period relative to participants with an accelerated CVD risk trajectory . Detailelinks.lww.com/WNL/B894). The same split using the last available FRS values  was not found to be a significant predictor of AD dementia or VaD. No other significant differences were found in comparisons of FRS groups split by cutoffs at either baseline or with data from the last available time point. Similarly, comparisons of groups split by tertile or quartile risk level did not reveal any significant differences.Groupings of participants were also made and compared according to FRS cutoffs of <6%, 6% to 20%, and >20%. No associations were found in relation to AD dementia with this approach. Predictions of VaD outcome using baseline cutoffs were revealed to be significant, with the high-risk group (>20%) showing an increased incidence relative to the medium-risk group (6%–20%) from T3 onward, with an average RR of 2.4 at T3 and 2.2 at T5 . Similarly, the RR ranged from 2.6 to 1.8 for the average group relative to the stable group . The RRs for the average and accelerated APOE ε4+ participants were significantly elevated relative to all APOE ε4− participants, presumably as a result of elevated genetic and vascular risk. Average APOE ε4+ participants compared to accelerated APOE ε4− participants had an RR of 2.3 to 2.5 , while accelerated APOE ε4+ compared to accelerated APOE ε4− participants showed an RR of 5.0 to 4.7 . However, the stable APOE ε4+ participants did not show a significantly increased risk relative to the accelerated APOE ε4− participants , suggesting that a degree of mitigation occurred for those APOE ε4+ participants. For APOE ε4− participants, the RR for those in the accelerated group relative to the stable group ranged on average from 7.0 to 4.1 across the study time span , while the RR for the average group ranged from 3.0 to 2.3 relative to the stable group across the study time span .Due to the increased risk of AD dementia for ajectory . Both APlinks.lww.com/WNL/B894). In total, 142 (11.2%) participants died within the study time span (40% of adults >70 years of age at study inclusion). CVD risk trajectory group was not found to be predictive of survival either in the absence of dementia or after AD dementia. For VaD, however, an accelerated CVD risk trajectory, relative to those with an average trajectory, was found to predict a decreased rate of survival after diagnosis, with an RR of 5.1 to 2.7 across the study time span .Because death is a competing risk for outcome in the elderly, we also set out to investigate whether FRS trajectory is associated with survival either after or in the absence of dementia .In total, 379 individuals  exhibited relative EM decline during the observation period. Analysis was carried out on those ≤65 years of age  at study inclusion. The risk for EM decline was increased in individuals with an accelerated CVD risk trajectory relative to those with a stable risk trajectory , with an21-23 The study classifies temporal CVD risk using an analysis of longitudinal data, and links it to both cognitive decline and dementia incidence within the same cohort. These results point to the significance of accumulated vascular risk in relation to healthy brain aging and promote the importance of continual CVD risk observation to allow mitigation via medical treatment or interventions focused on increasing cardiovascular health.40The findings of the current study suggest that the relative trajectory of CVD risk is a significant component in determining the risk of developing dementia in late life and is associated with midlife EM decline in a healthy sample at inclusion. Our findings are complementary to previous research showing an association between longitudinal CVD risk and pathologic cognitive decline yet provide a more dynamic view of this process via a contrasting methodologic approach.14 and has for this reason been widely used in research and in clinical practice to predict future CVD risk. Several risk parameters were elevated for the group with an accelerating CVD risk, indicating that such acceleration may arise from an accumulation of damage from a combination of risk factors over time.41 While the degree of influence of single risk factors on dementia risk may vary across individuals, addressing a combination of modifiable factors is currently suggested to be the best approach for mitigating or preventing the onset of dementia.42 Therefore, assessment of CVD profiles, rather than single risk factors, is encouraged to capture a comprehensive state of CVD-related dementia risk.24The FRS is well validated and easily accessedAPOE ε4 in AD risk and the relatively increased proportion of APOE ε4+ participants in the stable CVD risk group compared to the accelerated risk group, the proportion of participants developing AD was still significantly lower in the stable CVD risk group. This points toward the importance of maintaining a stable CVD risk even in the presence of increased genetic risk.FRS values are elevated by aging alone; hence, active suppression of the modifiable variables of the FRS  would have to take place to maintain a stable CVD risk trajectory. Furthermore, because the cross-sectional FRS values offered less predictive utility for survival or dementia outcomes, it is suggested that the relative accrual of multiple risk factors plays a larger part in determining outcome than standalone measurements. An interesting finding is that, despite the strong role of 43 Of the multitude of factors likely modulating this process, vascular changes are among the first10 and have consistently been linked to cognitive decline across the lifespan. EM decline, while showing marked interindividual variation in the rate and pattern of change, is strongly associated with later AD incidence.44 In particular, early and rapidly declining EM performance is associated with the transition to an AD state.30 Similar EM deficits are found in individuals with VaD; however, the degree of impairment is generally lower than in AD.45 The FRS trajectories within the current study are shown to predict the transition from healthy to a dementia state and are associated with EM decline events at earlier ages. This suggests a chain of events whereby increased CVD risk leads to EM decline and ultimately dementia.The pathologic processes culminating in dementia take place across several years before symptomatic manifestation (such as EM decline).46 Because the current study was not able to incorporate data regarding protein deposition (such as β-amyloid or tau), we are not able to entirely exclude the possibility of such a mediating factor. However, previous research has thus far not been able to establish a causal direction of such mediating factors. Furthermore, a large data-driven analysis has suggested that vascular factors influencing AD risk arise before the emergence of abnormal protein accumulation, raising the possibility that the apparent mediation arises as a result of early vascular risk.10 While the exact mechanisms underlying a potential association between early vascular risk and tau or β-amyloid deposition remain to be elucidated, both cerebral hypoperfusion and blood-brain barrier degradation have been found to be associated with vascular risk factors  and to emerge independently of tau biomarker abnormalities in patients with AD.48Limitations of the current research include the potential for misclassification of AD and VaD cases. While the diagnosis procedure used all resources available within the study framework (detailed above), this did not include neuroimaging or biomarker assessment as part of the standard diagnostic procedure. As a result, diagnoses of AD and VaD reflect the primary pathology as defined per the DSM-IV criteria and are not necessarily made with absolute certainty. In light of this, the RRs for both investigated forms of dementia, while significantly increased in a similar manner, should be treated with caution. However, given the similarity in results for AD and VaD, our findings likely present a generalized increased risk for all-cause dementia. Limitations also include the inability to firmly determine whether the sequence of EM decline leading to dementia is initiated by an accelerated FRS trajectory. Furthermore, while the current study focuses on the involvement of cardiovascular risk in the emergence of cognitive decline and dementia, we cannot rule out that other factors can influence the outcome. For example, tau deposition in the brain has recently been highlighted as a potential mediator of brain atrophy and cognitive decline related to vascular risk.6-49 Hippocampal integrity (which is degraded within AD) has been shown to be particularly sensitive to vascular stress and critical for EM function.49 Such a mechanism may thus serve as a potential mediator of CVD risk and cognitive decline, with vascular stress negatively affecting brain reserve, leading to variable cognitive outcomes downstream of that. The link between low CVD risk and preserved EM may reflect increased brain maintenance within that subgroup, manifested as reduced vascular neuropathology such as fewer lesions and lower perfusion.50Previous research implicates the importance of maintaining vascular health throughout life as a protective measure against both emergent dementia and cognitive decline, particularly with regard to mechanistic damage , which can be irreversible and can elicit further damage.Our results indicate that cardiovascular-associated dementia risk likely results from the dynamic progression of combinatorial effects rather than static risk factors. Future research may benefit from further exploring how longitudinal CVD risk affects the accumulation of neuropathology.


In England, routine invitations for cervical screening were reduced between April 2020 and June 2020 due to the COVID-19 pandemic. We quantify the impact of COVID-19 disruptions on attendance and excess diagnoses of cervical cancer (CC).Using Public Health England CC screening data on laboratory samples received in 2018 as a baseline we quantify the reduction in screening attendances due to the COVID-19 pandemic between April 2020 and March 2021 for women aged 25–64. We model the impact on excess CC diagnoses assuming once invitations resume 87.5% of women attend within 12 months and 12.5% delay screening for 3 or 5 years (depending on age).The number of samples received at laboratories was 91% lower than expected during April, 85% during May and 43% during June 2020 compared to the same period in 2018. Although on average laboratories received 12.6% more samples between August 2020 and April 2021 than over the same months in 2018, by April 2021 there was a short fall of 200,949 samples (6.4% fewer than in 2018). An excess of 41 CC  are predicted to occur among the estimated 1,024,794 women attending this screening round with a delay. An excess of 60 CC  are predicted to occur among the estimated 146,391 women who do not attend this screening round.Prompt restoration of cervical screening services limited the impact on excess CC diagnoses. However, in 2020 a 6.4% shortfall of screening samples was observed. Every effort should be made to reassure these women that services are open and safe to attend. Whilst, in England, cervical screening did not officially stop due to the pandemic, the volume of screening invitation letters was reduced from early April to June 2020. During June 2020, invitations were restricted to women on early recall so that they could be caught up first, as they have a higher risk of underlying lesions since they have previously tested positive for high-risk human papillomavirus (hr-HPV). England experienced a second wave of the pandemic (estimated to have started in September 2020 and ended in April 2021). During that wave, screening services remained open but women's reluctance to attend screening may still have affected attendance rates.Disruptions to screening due to the COVID-19 pandemic have been observed the world over. In the coming decades, these numbers are likely going to diminish also due to vaccination against most high-risk genotypes. Vaccination against HPV  was introduced in England in 2008 for girls aged 12–13 years (born from 1st of September 1995 onward) and, in 2008–2010, to a catch-up cohort aged 14–18 years (born from 1st of September 1990 to 31st of August 1995). Coverage among girls aged 12–13 years has remained around 86%.Cervical screening in England is offered to women aged 25–49 at three-yearly intervals and to those aged 50–64 at five-yearly intervals. All women receive personal invitation letters once they are due for screening or early recall testing, with reminders sent 3 months later. Screening is mostly carried out in general practise by specially trained nurses and was done using liquid-based cytology until December 2019, when the national roll out of hr-HPV primary testing was completed. The aim is to identify women who are at increased risk due to having a hr-HPV and abnormal cells in the cervix, so that their abnormalities can be treated before cancer develops. The aim of screening, therefore, is to reduce the numbers of women diagnosed with cervical cancer (CC) and those who die from it, for which hr-HPV testing is more effective than cytology. We now have better information on the effect of the disruption on the numbers of cervical samples processed in screening laboratories, and how those numbers changed on a monthly basis. Hence, we aimed to quantify the impact of the pandemic disruption on the incidence of CC among routine attenders to screening in England with greater precision than has so far been possible.We have previously estimated the effect of the COVID-19 disruption on CC incidence in England under a range of assumptions about the length of the disruption and the screening programme's approach to compensate for the lost opportunity to be screened.We used data from Public Health England (PHE) routine statistics reporting the numbers of laboratory samples received and booked onto the laboratory data system . This so The change from cytology to hr-HPV-based primary screening would not have changed the number of women invited to screening in 2020 and 2021 as the next invitation date is set based on the previous test result which would have been carried out using liquid-based cytology.Year 2018 was used as the baseline representing a “normal” screening year during which women could attend screening without delays. Year 2019 was not considered for a baseline. In that year, PHE ran a successful public campaign promoting screening uptake.We estimated the impact of COVID-19 disruptions on screening participation among women aged 25–64 who were due to be sent an invitation between April 2020 and December 2021, and on the excess CC incidence as a consequence of missed screening appointments. For April to December 2021, laboratory sample data have not yet been made available. As the data indicated that the monthly volumes of samples returned close to the normal levels, we assumed that the numbers of women attending screening each month during this period was equal to the numbers reported during 2018.To model the impact of an increase in samples received from April to December 2021, observed laboratory samples in 2018 were increased by 5% each month and this figure was taken as the total samples processed each month between April and December 2021. In this analysis the additional ‘capacity’ allows more women to be screened on time reducing the total number with delays to screening of up to 12 months.The difference between the numbers of women expected to attend for screening each month (i.e. baseline estimates from January to December 2018) and the numbers attending each month during April 2020 to December 2021 was assumed to be the number of women who had had their screening delayed (if it was a positive number) or the additional number of women that had been tested in that month i.e., women who had been caught up with screening after the delay (if it was a negative number). Note that women could have their screening delayed both between April and June 2020 and thereafter either due to lack of appointments or because they continued to be hesitant to attend. We were agnostic to the reasons for the delay. Hence, we assumed that 12.5% of women will delay screening until their next invitation (3 or 5 years later depending on age).A survey of women's attitudes to screening carried out by Jo's CC Trust during the first months of the pandemic found that approximately 10–15% of women were unlikely to attend screening because of fears related to COVID-19.Supplementary Table 1 summarises assumed attendance distribution.We accounted for the usual delays in attending for screening after receiving an invitation. The distribution of attendance within the first 12 months after an invitation was estimated from the English HPV primary screening pilot.Table S1. This process was repeated until December 2021 which means that the last individual affected by a delay will either have attended by December 2022 or will have delayed screening for the full 3- (or 5)-year screening round, depending on age.In each month from July 2020 onwards, priority for attendance in the model was given to those women who were estimated to have experienced a delay due to COVID-19 disruptions, so that their delays would be minimised. If, in a given month, the number of such women was lower than the observed number of samples processed in English screening laboratories, we assumed that the remaining screening samples processed in that month were from women who were due to attend following a regular schedule, i.e. without a delay. Women on a regular screening schedule who could not be screened in that month were assumed to attend following the distribution described in  These statistics suggested that out of the total number of women tested in that year, 16.8% were aged 25–29, 15.4% aged 30–34, 15.2% aged 35–39, 13.4% aged 40–44, 13.9% aged 45–49, 10.8% aged 50–54, 8.0% aged 55–59 and 6.5% aged 60–64.The monthly numbers of laboratory samples reported to PHE were not broken down by women's age. Since the likelihood of developing high-grade cervical intraepithelial neoplasia (CIN) and CC is age dependent, we used proportions of women attending screening in five-year age groups from age 25 to 64 from the NHS Cervical Screening Programme statistics for financial year 2018/19.First we estimated the total women delayed by calendar month (women are delayed in the same way regardless of age) and then applied the age distribution. The pilot reported that 6.6% women aged 25–29, 1.6% aged 30–59 and 0.5% aged 60–64 had a CIN grade 2 or worse detected following hr-HPV primary screening, either at baseline or at one of two early recalls. By far most women in England who were due for screening in 2020 and 2021 would have attended hr-HPV-based screening for the first time.To estimate the number of screened women with a high-grade CIN detectable through hr-HPV screening, data from the first round within the English HPV primary screening pilot was used, when by far the majority of women were unvaccinated. modelling study. In that study, parameter sets were chosen to be consistent with the literature, otherwise they were rejected. Cumulative transition probabilities  from 1 month up to 12 months, and separately for 36 months (for women aged 25–49 years) or 60 months (for women aged 50–64 years) for those missing the whole screening round, are presented in Table S2.The numbers of women with a high-grade CIN lesion detected following a delay to screening were estimated by multiplying the above proportions and the estimated numbers of women with a screening delay. The proportion of high-grade CIN that would have progressed to CC, depending on the number of months by which screening was delayed, was based on progression estimates from the Landy et al. is presented in Table S3. Adjustments were made separately for those attending between June and August 2020 and for those attending from September 2021 because the proportions of vaccinated women were different.Estimates of the numbers of women in whom delays to screening led to progression of high-grade CIN to CC were adjusted for vaccination coverage in women aged 25–29 and 30–34. The proportion of the population by age at vaccination and calendar year who are assumed to have been protected by vaccination were used to adjust the proportion of high-grade CIN per 100,000 women screened to better reflect the risk in vaccinated women. Compared to unvaccinated women, odds of high-grade CIN were the lowest (OR = 0.14) among those vaccinated at ages 12 or 13 years. Thereafter, the odds ratio increased to 0.18 in those vaccinated age 14 years, 0.29 at age 15 years, 0.27 at age 16 years, 0.55 at age 17 years and 0.85 at age 18 years.The odds ratio of being diagnosed with a CIN grade 3 or worse by age at vaccination as reported for ScotlandThe number of samples received at laboratories in England was 91% lower than expected during April 2020, 85% during May 2020, and 43% during June 2020 . HencefoAlthough on average laboratories received 12.6% more samples between August 2020 and April 2021 than over the same months in 2018, by April 2021 there was still a shortfall of 200,949 samples (6.4% fewer than expected before the pandemic). This number represents on average about three weeks of screening under pre-pandemic circumstances.During the acute phase of the COVID-19 pandemic (April to June 2020), 467,687 women were estimated to have experienced a disruption to screening. Of these women, 58,461 (12.5%) would not attend screening during the current round and instead return 3 or 5 years from now (i.e. between 2023 and 2025). We estimated that all women who had their screening delayed during the acute phase and who would still attend, could do so by June 2021.An additional 703,440 women were estimated to have their screening delayed during the recovery phase (from July 2020 to December 2021). Of these, 87,930 would not attend until the next age-appropriate screening round.Of the estimated 1,024,736 women who experienced a screening delay of up to 12 months, the majority (52.3%) would be delayed up to 3 months, with an additional 18.8% having a delay of between 4 and 6 months . Among wA total of 60 excess cancers were estimated to occur among the 146,391 women who delay their screening until the next round on account of the COVID-19 pandemic. This is equivalent to a rate of 41.0 per 100,000 women delaying screening until the next round .Assuming that from April 2021 onwards there was no significant increase in the number of screening samples processed in English laboratories as compared with the numbers observed prior to COVID-19 disruption, women would likely continue to experience delays into 2023 (outcomes for these women have not been modelled). However, if the number of samples from April 2021 onwards could be increased by 5% above the levels observed in 2018, then the backlog accrued during the recovery period could be cleared by the end of June 2021 and 9 excess cancers prevented . Consistently with a previous analysis, women who delay their screening for an entire 3- or 5-year round (depending on age) would be affected the most However, that study assumed that all women would experience the same delays and estimated an excess of 315 cervical cancers following a three-month disruption with no increase in capacity. This is three times as many as estimated in the current study. It would appear that the English programme's ability to process an average of 20% more samples between August and December 2020 compared to pre-pandemic levels, combined with a rapid restoration of services, has contributed to reduce the excess numbers of CC diagnoses.Following a three-month disruption to cervical screening in England, 101 excess cases of CC were estimated to be diagnosed. For comparison, an average of 2626 cases were diagnosed per year in England in 2016–2018.We now know that English laboratories have been able to restore cervical screening services above pre-pandemic levels. Data on samples processed after April 2021 were not available. If the increase in screening attendance and laboratory testing volumes could be maintained at least 5% above the pre-pandemic levels for the rest of 2021, COVID-19-related backlogs may have been cleared as soon as June 2021, further limiting the impact on excess CC diagnoses. we assumed that 12.5% of previous regular attenders would not participate in this screening round. If fewer women delayed their screening for three or more years, fewer excess cancers would be observed. However, this would have increased the proportion of women delayed by up to 12 months during the recovery phase of the pandemic impacting backlogs to screening in 2021. It is worth noting that for the 12.5% of women who do not participate in this screening round we modelled the proportion who would develop cancer over a 3 or 5-year period but remain agnostic to whether these cancers were screen detected or not. Among women aged 60–64 one would expect some cancers to be diagnosed following presentation with symptoms, since they would not all be invited again for screening (depending on when they were last screened).This study has several limitations. The data on laboratory samples used for this study are not equivalent to the number of women screened, and will include a small proportion of samples from the same women, e.g. due to repeated sampling after an inadequate sample. Further, it included some women on early recall and those attending for an hr-HPV test following CIN treatment. Since no details on the screening history profile of those attending screening were available, we assumed that women who attend screening in the post-pandemic recovery period had similar attendance patterns as observed before the pandemic, and an average risk of high-grade CIN and CC. Consistent with survey data, in December 2020 urging providers to prioritise screening service maintenance without diverting screening staff towards other services. This guidance appears to have helped maintain screening services at pre-pandemic levels during January and February 2021, the second wave of the pandemic.NHS England and NHS Improvement (NHSEI) issued guidance The authors are aware of a small number of clinics with delays to colposcopy services for some groups of patients where the priority for appointments was given to those with a higher risk of cervical abnormality. However, this is not universal across England. In addition, 2020 saw the rollout of hr-HPV primary testing and a surge in colposcopy referrals was expected from 2021 onwards. If colposcopy capacity is in fact constrained, then this may have knock-on effects which may not be apparent until well into 2021 (data not available yet). The approach to prioritise patients with the highest likelihood of significant cervical abnormality and cancer will mitigate this potential impact.We have not modelled the impact of delays to those referred to colposcopy for further investigation. A joint communication from the Royal College of Obstetricians and Gynaecologists and the British Society for Colposcopy and Cervical Pathology in March 2021 indicated that colposcopy services were limited during the acute phase of the pandemic and that it had taken several months for them to recover, with many clinics, at that time, still unable to offer the same number of appointments as before the pandemic, due to local guidance on social distancing, increased cleaning, etc.22 that assessed the potential impact of COVID-19 disruptions to cervical screening using microsimulation models have also shown relatively small effects of the disruption on CC incidence and the clear advantage of a rapid catch-up on minimising excess cancers.Other studiesThe prompt restoration of cervical screening services appears to have limited the impact of the COVID-19 disruption on excess cancer diagnoses. However, a 6.4% shortfall in screening samples was observed between March 2020 and April 2021. Given the higher excess risk among women who delay their screening for 3 years or more, every effort should be made to promote the programme and reassure these women that services are open and safe to attend. This is particularly important while the COVID-19 pandemic continues, to avoid further increasing the number of women who delay their screening.Click here for additional data file.Supplemental material, sj-docx-1-msc-10.1177_09691413221090892 for COVID-19 disruption to cervical cancer screening in England by Alejandra Castanon, Matejka Rebolj, Francesca Pesola, Philippa Pearmain and Ruth Stubbs in Journal of Medical Screening