To estimate the dose–response associations between non-occupational physical activity and several chronic disease and mortality outcomes in the general adult population.Systematic review and cohort-level dose-response meta-analysis.PubMed, Scopus, Web of Science and reference lists of published studies.Prospective cohort studies with (1) general population samples >10 000 adults, (2) ≥3 physical activity categories, and (3) risk measures and CIs for all-cause mortality or incident total cardiovascular disease, coronary heart disease, stroke, heart failure, total cancer and site-specific cancers .196 articles were included, covering 94 cohorts with >30 million participants. The evidence base was largest for all-cause mortality , and incidence of cardiovascular disease  and cancer . In general, higher activity levels were associated with lower risk of all outcomes. Differences in risk were greater between 0 and 8.75 marginal metabolic equivalent of task-hours per week (mMET-hours/week) , with smaller marginal differences in risk above this level to 17.5 mMET-hours/week, beyond which additional differences were small and uncertain. Associations were stronger for all-cause (relative risk (RR) at 8.75 mMET-hours/week: 0.69, 95% CI 0.65 to 0.73) and cardiovascular disease  mortality than for cancer mortality . If all insufficiently active individuals had achieved 8.75 mMET-hours/week, 15.7% (95% CI 13.1 to 18.2) of all premature deaths would have been averted.Inverse non-linear dose–response associations suggest substantial protection against a range of chronic disease outcomes from small increases in non-occupational physical activity in inactive adults.PROSPERO registration number CRD42018095481. Cardiovascular disease (CVD) is the leading cause of death globally, responsible for 17.9 million annual deaths in 2019,Higher levels of physical activity (PA) are associated with lower risk of all-cause mortality, CVD and some site-specific cancers.Accurate estimation of the dose–response association between PA and disease outcomes, combined with prevalence estimates, is a prerequisite to assessing the population disease burden of insufficient PA and the potential impact of changes to population levels of PA. Modelling studies that evaluate public health strategies aimed at increasing population activity levels have found that the shape of the dose–response association makes a significant difference when quantifying population health impacts.Our objective was to quantify dose–response associations between non-occupational PA and several CVD, cancer and mortality outcomes using a novel harmonisation framework to overcome the challenges posed by different PA measurement methods. This framework allowed us to compare studies measuring and reporting PA in a wide variety of ways on the same activity exposure scale of marginal MET-hours per week (mMET-hours/week). This meant we could be more inclusive of the available evidence than previous meta-analyses and we could explore the dose–response relationships between non-occupational PA and nine site-specific cancers for the first time. To contextualise our results for public health promotion, we generated potential population impact fractions (PIFs) to estimate the proportion of preventable deaths and disease outcomes at different non-occupational PA levels.The study protocol is available at PROSPERO (registration number CRD42018095481).et al.10.1136/bjsports-2022-105669.supp1Supplementary dataOnly articles that examined leisure-time PA, alone or in combination with other domains or specific types of activity, were included. We excluded articles whose measures of PA contained occupational activity that could not be factored out and that investigated individual domains of PA that did not include leisure-time activity .The outcomes of interest were the following:All-cause, total CVD and total cancer mortality.Total CVD, coronary heart disease, stroke and heart failure incidence .Total and site-specific  cancer incidence . These site-specific cancers were selected based on previously reported associations with PA.We searched PubMed, Scopus, Web of Science and the reference lists of reviews retrieved from our systematic review, known to the authors or cited in the 2018 US Physical Activity Guidelines Advisory Committee Scientific Report.Titles and abstracts, and subsequently full texts, were screened independently twice for eligibility . DisagreIf multiple articles reported results on the same cohort and outcome, we followed specific criteria  to selecData from each paper were extracted by one reviewer and double-checked independently by a second reviewer. Disagreements were checked against the full text and resolved by discussion.We extracted data on publication , study characteristics , PA exposure assessment  and outcome assessment  codes as reported in the PROSPERO registration and For each exposure category, we extracted information to quantify PA volume, number of cases, number of participants and/or person-years of follow-up, and risk estimates with 95% CIs. Risk estimates from the most adjusted model were used. When available, risk estimates of the most adjusted model without adiposity-related covariates , waist circumference) were retrieved and used in sensitivity analyses. Exposure data were extracted to the level of precision reported in the published study before being harmonised to PA volume in mMET-hours/week. Risk estimates were extracted exactly as reported. Results reported separately by sex or other attributes  or for multiple cohorts within an article were treated as separate associations. If the original publication did not fully report information required for the meta-analysis, data were obtained from other publications using the same cohort, imputation procedures  or by coOSF repository. The last columns of the table on ‘Details of included articles and original and harmonized exposures’ in the same OSF repository contain the details of how the original PA exposure categories of each study were harmonised to mMET-hours/week values.A comprehensive data harmonisation was conducted to combine PA data from different sources. We harmonised reported PA exposure levels from all included studies into a common metric of non-occupational PA volume in mMET-hours/week, reflecting the rate of energy expenditure outside of work, above the resting metabolic rate (1 MET). This allows correct equating of PA volume of time spent at different PA intensity levels.PA exposures described in studies as frequency and duration measures were converted to weekly duration. If session duration was not provided, we assumed a duration of 0.75 hours/session . Categorical frequency data  were converted to weekly duration using assumptions for both frequency  and duration of sessions. When PA intensity was not explicitly described, we considered the reported activities as light, moderate or vigorous based on the description provided and using the Compendium of Physical Activities.For studies reporting energy expenditure without adjustment for body weight , energy expenditure was divided by reported weights to derive MET equivalents . If unavailable, body weight was calculated from reported BMI and height. If BMI was reported without height, a mean value from national survey data was used for height.To marginalise studies reporting volume of PA in gross units, 1 MET-hour was subtracted for each hour of activity. If the mean duration was not available, we used a conversion equation derived from all remaining studies where both volume and duration were available .Some articles provided aggregated exposures of non-occupational and occupational PA alongside other behaviours, such as sleep or sedentary time. When quantified information about these behaviours was available, this was subtracted from the point estimate of each exposure category. If no quantifiable data were available, we assumed occupational activity to be 40 hours/week at 1.25 MET (or 0.25 mMET) or the value of the lowest exposure category and subtracted this from all exposure categories.We conducted a meta-analysis for any outcome with results from at least four cohorts. Where necessary, risk estimates were recalculated to set the least active category as the referent.We performed a two-stage random-effects meta-analysis. In the first stage, we estimated study-specific dose–response coefficients using generalised least squares to incorporate the correlation within each set of log-relative risks.To investigate the potential effect of study-level confounders on the pooled results across outcomes, we conducted subgroup analyses with the 11 studies that reported results for all all-cause, CVD and cancer mortality outcomes and contrasted them with results from the corresponding main analysis. We also conducted subgroup analysis by sex using studies that reported separate results for men and women.PIFs were calculated for all outcomes based on PA exposure levels in the population of all included cohorts for a given outcome , using a fixed PA level of 8.75 mMET-hours/week, relevant to the six sources of bias.dosresmeta package (V.2.0.1).https://github.com/meta-analyses/.Analyses were performed using R V.4.0.5 and the Patients and the public were not involved in the development of this work.OSF repository, and data underlying each of the dose–response estimations can be downloaded at https://shiny.mrc-epid.cam.ac.uk/meta-analyses-physical-activity/.We screened titles and abstracts of 48 525 articles, of which 1280 were selected for full-text screening. A total of 196 articlesMost study participants reported non-occupational PA levels below 17.5 mMET-hours/week (76% of person-years), with almost all data below 35 mMET-hours/week (94% of person-years). https://shiny.mrc-epid.cam.ac.uk/meta-analyses-physical-activity/.Inverse, curvilinear dose–response associations between PA and most outcomes were observed, with stronger associations at lower volumes of PA. In most cases, diminishing marginal differences in risk and increasing uncertainty were observed at high PA volumes, particularly beyond 17.5 mMET-hours/week. Interactive dose–response curves and the exposure distributions are available at The association was stronger and more curvilinear for all-cause and CVD mortality than for cancer mortality , table 1A strong curvilinear association was observed for total CVD incidence (27% lower risk (95% CI 21 to 31) at 8.75 mMET-hours/week). However, the associations were weaker and more linear for the incidence of specific CVD outcomes , with the strongest association observed for coronary heart disease (21% lower risk (95% CI 16 to 26) at 8.75 mMET-hours/week) , table 1The association was weaker and more linear for total cancer incidence (12% lower risk (95% CI 8 to 15) at 8.75 mMET-hours/week). For the incidence of site-specific cancers, curvilinear and stronger associations were observed for head and neck, myeloid leukaemia, myeloma, and gastric cardia (from 35% to 22% lower risk at 8.75 mMET-hours/week). Weaker and more linear associations were observed for lung, liver, endometrial, colon and breast (from 16% to 5% lower risk at 8.75 mMET-hours/week). Non-significant associations were observed for bladder, oesophageal, prostate and rectal cancer , table 1Assuming a causal relationship between non-occupational PA and the examined health outcomes, shifting the distribution of non-occupational PA in the cohorts towards the equivalent recommended level of moderate-to-vigorous aerobic activity (8.75 mMET-hours/week) would have averted a substantial fraction of the population disease burden. For example, if all individuals accumulated at least 8.75 mMET-hours/week, then 15.7% (95% CI 13.2 to 18.2), 12.3% (95% CI 9.4 to 15.2) and 7.1% (95% CI 5.1 to 9.0) of all-cause, CVD-related and cancer-related deaths, respectively, potentially would have been averted. Additionally, 10.9% (95% CI 8.4 to 13.3) and 5.2% (95% CI 3.6 to 6.9) of all incident cases of CVD and cancer would have been prevented. Notably, 10.1% (95% CI 8.3 to 11.9) of all deaths would have been prevented if all adults achieved at least half this PA level .The pooled analysis from the 11 studies that reported results for all-cause, CVD and cancer mortality showed weaker associations for all-cause mortality and stronger associations for CVD and cancer mortality .Sex-stratified results were available for all-cause, CVD and cancer mortality and incidence of total CVD, coronary heart disease, stroke, total cancer, and cancers of the colon and rectum. There was some evidence of stronger associations in women than in men for all-cause and CVD mortality and CVD incidence, but the reverse for cancer mortality and incidence .Across the five outcomes with the largest number of separate results , 83%–93% of the studies used statistical adjustments to control for other morbidities at baseline, often combined with exclusion of adults with more serious pre-existing conditions. Studies that predominantly used statistical adjustments tended to report stronger associations for CVD mortality compared with studies that excluded adults with pre-existing conditions at baseline. Between 54% and 59% of the studies did not exclude mortality and disease events occurring within the first few years of follow-up, and those that did tended to report weaker associations for all-cause mortality and CVD mortality and incidence. The median follow-up time was 10 years for each outcome, with no evidence of bias between studies with longer and shorter follow-up time. Data imputation was required for 58%–64% of the studies, but no evidence of bias was found. The most frequently used procedures for exposure harmonisation were measurement unit conversions (42%–49% of studies) and the assumption of standard values for the intensity and duration of PA reported (39%–50%), but no evidence of bias was found. In 75% of the studies, the original exposure assessment did not include occupational PA and no evidence of bias was observed for studies requiring domain separation .In studies that did not report the duration or intensity of PA, assuming (where appropriate) shorter session duration (0.5 hours instead of 0.75 hours) and lower intensity (1 mMET lower for moderate and vigorous PA) did not materially alter the dose–response associations for most outcomes, especially analyses which included the largest number of separate results . When shSensitivity analyses using analytical models that did not include adjustment for adiposity showed stronger associations for all-cause and CVD mortality, and the incidence of total CVD, heart failure, and site-specific cancers of the endometrium, kidney, lung and rectum . NotablyWe present comprehensive dose–response meta-analyses of the associations between non-occupational PA and a wide range of mortality and disease outcomes. Our extensive exposure data harmonisation allowed inclusion of a larger evidence base than previous meta-analyses for 17 of 22 health outcomes investigated. For instance, our meta-analysis for all-cause mortality includes over 160 million person-years and 810 000 deaths. These are 17 and 7 times larger, respectively, than what were included in the previously largest dose–response analysis to date (9.39 million person-years and 117 000 deaths).We showed inverse non-linear dose–response associations for all-cause mortality and a range of CVD and cancer outcomes. This suggests that the greatest potential benefits can be achieved through small increases in non-occupational PA for those with an inactive lifestyle, with incrementally smaller additional benefits up to approximately 17.5 mMET-hours/week. Above this level, the evidence base was weaker. Shifting population levels of activity towards achieving the equivalent to 150 min/week of moderate-to-vigorous aerobic PA (8.75 mMET-hours/week) potentially would have prevented 16% of all premature deaths recorded.et al’set alet alCompared with previous studies, we found similar associations for all-causeCompared with the results from the GBD,It is surprising that the risk difference for all-cause mortality  is similar to that for total CVD (29%) and much larger than for total cancer mortality (15%). Equally, it is surprising that the association for total CVD incidence is stronger than that for coronary heart disease or stroke. Weaker associations would be expected for more composite outcomes  than for the outcomes within them for which a significant association has been established. Potential explanations include detection bias for some disease events, misclassification of causes of death , differential residual confounding, reverse causality by outcome, and different inclusion criteria by disease outcome, such as the inclusion of different disease groups which may have stronger associations with PA.Our risk of bias and sensitivity analyses showed that the methodological assumptions and decisions made for the pooled analyses did not significantly impact the dose–response associations observed. However, some methodological differences between the original studies seem to alter the magnitude of the pooled associations. Studies that did not exclude people with pre-existing conditions at baseline or mortality or disease events occurring within the first years of follow-up tended to observe stronger associations, as did studies with shorter follow-up, which may in part be indicative of reverse causality.A strength of our study was the use of comprehensive exposure data harmonisation methods that enabled the inclusion of a larger evidence base. This also allowed us to study health associations over a wider range of the PA exposure and increased the density of data in the area of the dose–response curves with high non-linearity. Another strength is the use of more sophisticated meta-analytical methods that allow the shape of the dose–response association to vary across the exposure range, rather than linear models of transformed exposure or grouping doses within high versus low PA or multiple exposure intervals. We improved the placement of spline knots compared with our previous workOur study also has limitations. There are levels of the PA exposure for which health associations are not reported in many of the original studies and hence there is still substantial uncertainty at those levels in the meta-analyses for some outcomes. Many of the included studies relied on self-reported questionnaires without validation or calibration data that could be used in the exposure harmonisation. In the exposure data harmonisation procedures, it was also sometimes necessary to make assumptions about parameters of PA, such as intensity and duration, where these were not explicitly reported. Nonetheless, our sensitivity analyses demonstrated the robustness of our results to the assumptions made. Measurement error is likely to lead to an underestimation of the true association between PA and the various outcomes, as demonstrated by the stronger associations observed in studies using device-based measures of PA.Our findings support the current PA recommendations of 150–300 min/week of moderate aerobic PA ,It should be noted that our exposure estimates are derived from a variety of self-reported questionnaires that capture mostly moderate and vigorous activities. These exposure estimates differ from those derived using device-based measures, which also record light-intensity and intermittent activities that are more difficult to recall.Future research should investigate the reasons for the apparent inconsistencies in dose–response associations between composite and individual disease endpoints. Although our risk of bias and sensitivity analyses showed robustness to the approaches we took during data handling and dose harmonisation, future studies could quantify methodological uncertainties  and propagate them in the aetiological analyses to provide more realistic uncertainty ranges for the dose–response associations. The evidence base was weaker for very high volumes of activity and further research is required to ascertain the shape of associations more reliably at the higher end of the PA continuum.We found evidence of dose-dependent associations between increasing non-occupational PA and a wide range of mortality, CVD and cancer outcomes. The strongest associations were observed for all-cause and CVD mortality, with weaker associations for the incidence of cancer, including variation by site. Appreciable population health benefits might be gained from increasing PA levels of people who are inactive to just half the current health recommendations, with further benefits for all reaching at least the recommended level and smaller additional benefits beyond that.Higher levels of physical activity are associated with lower rates of premature death and chronic disease outcomes.The shape of the dose–response association has been more difficult to determine and has not been established for a range of chronic diseases.Accurate estimation of the dose–response association between physical activity and disease outcomes is needed to assess the disease burden of physical inactivity and the potential impact of changes to population levels of physical activity.Our findings suggest an appreciably lower risk of mortality, cardiovascular diseases and cancers from the equivalent of 75 min/week or less of moderate-intensity aerobic physical activity .Our results include the first dose–response meta-analysis of nine site-specific cancers: bladder, esophageal, gastric cardia, head and neck, kidney, liver, lung, myeloid leukaemia and myeloma.One in 10 premature deaths could have been prevented if everyone achieved even half the recommended level of physical activity.


Throughout the COVID-19 pandemic, many hospitals conducted routine testing of hospitalized patients for SARS-CoV-2 infection upon admission. Some of these patients are admitted for reasons unrelated to COVID-19 and incidentally test positive for the virus. Because COVID-19–related hospitalizations have become a critical public health indicator, it is important to identify patients who are hospitalized because of COVID-19 as opposed to those who are admitted for other indications.We compared the performance of different computable phenotype definitions for COVID-19 hospitalizations that use different types of data from electronic health records (EHRs), including structured EHR data elements, clinical notes, or a combination of both data types.We conducted a retrospective data analysis, using clinician chart review–based validation at a large academic medical center. We reviewed and analyzed the charts of 586 hospitalized individuals who tested positive for SARS-CoV-2 in January 2022. We used LASSO (least absolute shrinkage and selection operator) regression and random forests to fit classification algorithms that incorporated structured EHR data elements, clinical notes, or a combination of structured data and clinical notes. We used natural language processing to incorporate data from clinical notes. The performance of each model was evaluated based on the area under the receiver operator characteristic curve (AUROC) and an associated decision rule based on sensitivity and positive predictive value. We also identified top words and clinical indicators of COVID-19–specific hospitalization and assessed the impact of different phenotyping strategies on estimated hospital outcome metrics.P<.001) and performed similarly to a model that combined clinical notes with structured data elements . Assessments of hospital outcome metrics significantly differed based on whether the population included all hospitalized patients who tested positive for SARS-CoV-2 or those who were determined to have been hospitalized due to COVID-19.Based on a chart review, 38.2% (224/586) of patients were determined to have been hospitalized for reasons other than COVID-19, despite having tested positive for SARS-CoV-2. A computable phenotype that used clinical notes had significantly better discrimination than one that used structured EHR data elements (AUROC: 0.894 vs 0.841; These findings highlight the importance of cause-specific phenotyping for COVID-19 hospitalizations. More generally, this work demonstrates the utility of natural language processing approaches for deriving information related to patient hospitalizations in cases where there may be multiple conditions that could serve as the primary indication for hospitalization. Hospitalization due to COVID-19 has become a key public health indicator. One of the primary goals of vaccination against SARS-CoV-2, the etiological agent of COVID-19, is to reduce the incidence of severe disease and death, with hospitalization serving as a primary end point in vaccine efficacy trials -6. SimilInternational Classification of Diseases, 10th Revision (ICD-10) code for COVID-19. Previous reports have noted that incidental positives may account for around 26% of all COVID-19–positive patients [Due to concerns related to the hospital-based spread of SARS-CoV-2, many institutions routinely perform SARS-CoV-2 testing in patients who are admitted to the hospital, regardless of the primary reason for admission ,13. Althpatients .Given the public health importance of identifying hospitalizations due to COVID-19 rather than hospitalizations in which SARS-CoV-2 infection was identified incidentally, methods  are needed to distinguish the two conditions in retrospective data sources. Such phenotypes would be instrumental in retrospective studies of patients with COVID-19 and in public health surveillance. In this study, we seek to (1) motivate the need to identify patients who were admitted because of COVID-19 versus patients who incidentally tested positive for SARS-CoV-2 during admission, (2) explore the potential of using both structured data  and unstructured data  to construct computable phenotypes, and (3) illustrate the inferential biases that may arise if phenotyping methods cannot distinguish the reason for hospitalization.We performed a retrospective study of patients aged >18 years who were hospitalized with a documented positive SARS-CoV-2 test result during January 2022. We conducted our study at Duke University Health System (DUHS), which consists of 1 quaternary academic medical center and 2 associated community-based hospitals.This study was designated as exempt human subjects research by the DUHS Institutional Review Board (IRB number: Pro00109397).Using DUHS EHR data, we identified all patients who were admitted during the week of January 16 to 22, 2022, with documentation of a positive SARS-CoV-2 test result in the prior 20 days. Charts from this week were specifically reviewed in part due to a data request from the North Carolina Division of Public Health to understand the epidemiology of COVID-19–related hospitalizations. We excluded individuals with a resolved COVID-19 isolation status, as well as those who were admitted prior to January 1, 2022, to create a cohort of patients who were likely infected with the Omicron variant of SARS-CoV-2. During this period, the Omicron variant was the predominant SARS-CoV-2 variant in circulation within the United States and was associated with the largest wave  of SARS-To generate a criterion standard for classification, 6 trained health care professionals manually reviewed patient records for the index admission to adjudicate whether SARS-CoV-2 infection was the primary reason for admission or an incidental finding. Health care professionals attributed hospitalizations as those due to COVID-19 if admissions were due to primary manifestations of SARS-CoV-2 infection, such as hypoxia or the need for supplemental oxygen, or due to COVID-19–associated complications, such as dehydration or weakness.For each admission reviewed, we extracted structured EHR data elements recorded during hospitalization and captured within the Duke Clinical Research Datamart—an EHR database that is based on an extension of the PCORnet Common Data Model  . Clinicanltk package in Python (Python Software Foundation) [To analyze the clinical notes, we used the term frequency–inverse document frequency (TF-IDF) approach. The TF-IDF approach  generatendation)  to tokenWe first described the clinical characteristics of patients hospitalized due to COVID-19 versus those with incidental COVID-19 by using standardized mean differences (SMDs), with an SMD of 0.10 indicating a clinically meaningful difference. Next, we developed 3 classification models for COVID-19–specific hospitalization; one was based entirely on structured EHR data elements, a second was based on clinical notes alone, and a third used both structured data elements and clinical notes. We used LASSO (least absolute shrinkage and selection operator)  logisticWe evaluated the six classification models by calculating the area under the receiver operator characteristic curve (AUROC), along with associated 95% CIs. We identified the top clinical features and words that appeared in clinical notes based on the LASSO and random forest models. We plotted the precision-recall curve to better understand the performance of a classification model and assessed the impact of different rule-based phenotypes.As a way to understand the importance and potential impact of accurate phenotyping, we performed an illustrative association analysis, evaluating the relationship between vaccination status and the following hospital outcome metrics: length of stay, intensive care unit (ICU) utilization, and in-hospital mortality. These were chosen, since they are standard quality metrics for operational purposes. We regressed each outcome onto vaccination status. We used a log-linear model for length of stay and used logistic regression for ICU utilization and in-hospital mortality. Each regression was performed by using the full cohort and compared to a model that only included patients who were determined to have been hospitalized due to COVID-19. We also tested for an interaction between vaccination status and the cause of hospitalization. We emphasize that these were illustrative analyses, and they were not meant to infer any causal effects of vaccination but rather to illustrate the importance of using cause-specific phenotyping for relevant COVID-19 outcomes.All work was performed in R version 4.1.2   and PythIn total, we reviewed the charts of 630 patients who were admitted and tested positive for SARS-CoV-2. After excluding patients younger than 18 years and patients with privacy restrictions, our data set included 586 unique patients who were hospitalized and had tested positive for SARS-CoV-2. Of these, 224 (38.2%) were determined, through clinician review, to have been hospitalized for reasons other than COVID-19. During their assessments, our chart reviewers noted that it was often readily apparent which hospitalizations were attributable to COVID-19 and which were not.Characteristics, by admission cause, are shown in P<.001). The LASSO model using both clinical notes and structured data elements  had similar discrimination to that of the LASSO model based solely on clinical notes (P=.91).After tokenizing words and removing terms with fewer than 50 occurrences, our models included 7953 unique terms. There was minimal difference between the LASSO and random forest models. The random forest model based solely on clinical notes, the one based solely on structured data elements, and the one that used both clinical notes and structured data elements had AUROCs of 0.882 (95% CI 0.85-0.909), 0.829 (95% CI 0.794-0.864), and 0.890 (95% CI 0.864-0.916), respectively. The LASSO model based solely on clinical notes  had better discrimination than the LASSO model based solely on structured data elements . We found similar patterns in analyses of other in-hospital outcomes; vaccination was associated with reduced risks of ICU utilization and in-hospital mortality among patients hospitalized for reasons other than COVID-19 when compared to those among patients hospitalized due to COVID-19. Effects were robust to adjustment for age  had a relatively low positive predictive value across settings and time periods. These findings highlight the need for additional contextual data to identify acute cases of COVID-19. The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) conducted a similar study of EHR data from 12 clinical sites to identify combinations of structured data elements to generate a reliable computable phenotype for hospitalization due to COVID-19, with a reported AUROC of 0.903 [P<.001). This result is not surprising, as the clinical narrative often includes important nuance, and as our chart reviewers noted, it was often readily apparent which hospitalizations were attributable to COVID-19 and which were not. Of note, chart reviewers in our study classified hospitalizations that were indirectly due to SARS-CoV-2 infection, such as those due to COVID-19–related weakness or delirium, as hospitalizations due to COVID-19, which could partly explain the observed difference in discriminatory ability between our study and the study conducted by the 4CE.Prior work by Lynch and colleagues  evaluateof 0.903 . SimilarBy using the TF-IDF approach in conjunction with LASSO regression, we identified both individual terms and the direction of the association between each term and the hospitalization indication. Although the TF-IDF approach is a simple natural language processing (NLP) approach, it is also very scalable, interpretable, and implementable. Our results highlight the power of even simple natural language models. The terms that best predicted hospitalizations due to COVID-19 included common descriptors that were used in the clinical care of patients with COVID-19, such as “hypox” (likely shortened from “hypoxia” or “hypoxic”), or COVID-19 therapies like remdesivir. Conversely, the terms that were not associated with hospitalizations due to COVID-19 included words related to surgery—a common indication for hospital admission that is generally unrelated to SARS-CoV-2 infection.To help contextualize our results, we also assessed the real-world impact of using an accurate phenotype for COVID-19–specific hospitalization. In studying hospitalized patients with COVID-19, the simplest analysis would be to include all patients with a COVID-19–positive test result. As our illustrative analysis showed, when using this full but heterogeneous cohort, the results suggested that vaccination status is associated with a shorter length of stay. However, when we limited the analysis to only include patients who were identified as having been hospitalized due to COVID-19 , the analysis indicated that vaccines are not associated with a shorter length of stay. We interpreted these data as indicating that, conditional on someone being sick enough to be hospitalized due to COVID-19, vaccines provide no additional benefit in terms of the length of hospitalization. Similar patterns were found for other hospital outcome metrics. Although this analysis was not intended to be a causal analysis, it did illustrate how the use of accurately classified cohorts is important for the calculation of standard outcome metrics and likely impacts other related association analyses.More broadly, this work highlights the importance and challenge of phenotyping cause-specific events. Although there is rich literature on computable phenotypes, most of this literature is geared toward the identification of chronic diseases . However, few computable phenotypes have focused on cause-specific events . Such cause-specific phenotypes often exhibit poor specificity and can require algorithms that are more complex than those required for chronic conditions. As this work shows, and as suggested by others, NLP-based phenotyping approaches are becoming more common, and further comparisons between NLP approaches and other methods will be needed to determine whether using text data can improve cause-specific phenotypes.Although our study used rigorous methods, there are some key limitations. First and most notably, our findings are primarily illustrative and may not represent a generalizable algorithm for phenotyping COVID-19–specific hospitalizations. This study was conducted across a single hospital system, and it may not be reflective of practices at other institutions. Importantly, we would not expect our specific phenotype algorithm to be generalizable to other institutions. Second, we only looked at 1 period of time, namely the January 2022 Omicron wave; however, there are documented differences in the rate of hospitalization and positive test results over the course of the pandemic, and our models may not accurately reflect distinguishing factors in other waves. Third, another limitation is that, given the time constraints of chart reviews, we were only able to analyze a relatively small sample. In particular, the small sample size limited our ability to apply more sophisticated NLP-based approaches, such as the use of n-grams.Overall, our results show that a sizable number of people who were hospitalized and tested positive for SARS-CoV-2 were hospitalized for reasons other than COVID-19. The conflation of these individuals can impact our understanding of hospital outcome metrics. We constructed a strong classification model that can be used as a computable phenotype to distinguish patients who were hospitalized due to COVID-19 from those who incidentally tested positive for SARS-CoV-2 but were hospitalized for other reasons. Moreover, we found that while structured data elements are useful in constructing such a phenotype, clinical notes had a higher positive predictive value than that of structured data elements alone. Future work should seek to explore the generalizability of such phenotypes across institutions and different waves of the COVID-19 pandemic.


Sunitinib and pazopanib are among the most prescribed targeted therapies for the systemic management of advanced renal cell carcinoma (RCC), but published cost comparisons between the 2 agents are few and limited by methodological and population differences. Also, sunitinib is administered on a 4-week on/2-week off cycle, and pazopanib is taken continuously. Thus, appropriate use and cost comparisons between the 2 drugs require methodological approaches to account for these differences. One way to accomplish this is to substitute expected for observed days supply. Recognizing the effects of nonrepresentative days supply values is important for assessing real-world treatment patterns and costs.To (a) characterize demographic and clinical characteristics among patients with RCC newly initiating sunitinib or pazopanib, using a large administrative claims dataset; (b) characterize treatment patterns, persistence, and costs for each treatment group; and (c) assess the effect on treatment patterns and costs for sunitinib by substituting 42 days for prescriptions with 28- or 30-day supplies to account for sunitinib’s 4-week on/2-week off dosing schedule.P < 0.05 for all comparisons except for interactions with significance set at P < 0.10. The effects of substituting 42 days supply for sunitinib prescription records with 28 or 30 days supply were determined.This was a retrospective cohort study using health care claims data from the Truven MarketScan Research Databases, which include enrollment information and medical and pharmacy claims. Baseline patient demographic and clinical characteristics and treatment patterns  were compared. Health care costs were calculated as mean daily index medication costs and as total, medical, and medication  costs over the 12 months post-index period. Inclusion criteria were continuous health plan enrollment between 6 months pre-index and 12 months post-index; no RCC medications 6 months pre-index; ≥ 2 RCC diagnoses within ±180 days of index; and age ≥ 20 years. For demographic and clinical characteristics, treatment patterns, and costs, means (± standard deviations) for continuous data and relative frequencies for categorical data were reported. Chi-square tests or Student t-tests were used to evaluate differences other than costs. A generalized linear model with gamma distribution and log link was used for evaluating costs, controlling for patient demographic and pre-index clinical characteristics, persistence days, and index medication. All statistical tests were 2-tailed with significance set at P = 0.0409), with fewer patients taking sunitinib continuing treatment than patients taking pazopanib. However, with substitution, treatment patterns differed significantly , but with more sunitinib patients than pazopanib patients continuing treatment. Without substitution, unadjusted daily mean index medication costs were significantly different for sunitinib ($216) versus pazopanib . Substitution of sunitinib days supply eliminated the significant differences in daily index medication costs between treatment groups. The 1-year RCC-related and all-cause medication, medical, and total unadjusted costs were not significantly different between treatment groups, and substitution had no effect on these costs. After adjustment for possible confounding factors, these cost results were similar to those found with unadjusted analyses.In total, 609  sunitinib patients and 183  pazopanib patients were included. Demographic and clinical characteristics were similar for each treatment cohort. The persistence periods and number of prescriptions filled were also similar. Without substitution, significant differences were observed between treatment groups in patterns of index medication use (overall In this study, patients with RCC who were initiating sunitinib and pazopanib had similar demographic and clinical characteristics and drug persistence patterns. The effect of substituting days supply values was demonstrated as an approach to considering differences in dosing cycles. Substitution significantly reduced sunitinib mean daily index medication costs and eliminated or reversed the direction of significant differences in costs between drugs during the persistence period. No significant differences were observed in unadjusted or adjusted 1-year costs. What is already known about this subjectSunitinib and pazopanib are commonly used as first-line treatment for advanced renal cell carcinoma (RCC).Persistence with self-administered oral oncology medications is vital to the success of treatment.Assessment of persistence (duration of therapy) and related costs of sunitinib requires consideration of the 4-week on/2-week off dosing schedule.What this study addsThe majority of prescription records contains days supply values that do not accurately reflect the recommended dosing schedule for sunitinib.Substitution of days supply to account for the recommended sunitinib dosing schedule eliminated the difference between groups in the proportion of patients continuing index treatment and in the mean daily index medication cost that resulted from using observed days supply.Total all-cause and RCC-related costs did not differ between index drug treatment groups over 1 year of follow-up.1 Approximately 62,700 new cases and 14,240 deaths due to kidney cancer are estimated to occur in 2016.1 A substantial proportion (15%-40%) of cases are diagnosed in late stages or will develop distant metastases,4 and estimates of 5-year relative survival are only 8%-12% for late-stage metastatic cases.5 Sunitinib and pazopanib are among the most prescribed targeted treatments for RCC,7 and pazopanib was found to be noninferior to sunitinib in a randomized clinical trial.8 However, compliance with therapy is critical to treatment success, and safety, adverse events, quality of life, and costs are factors that may affect compliance with treatment in real-world practice. Recent studies employing health care claims data reported similar persistence and compliance patterns between these agents.10Renal cell carcinoma (RCC) accounts for 2%-3% of all malignancies diagnosed in adults and approximately 85% of kidney cancers.11 treatment costs are known to affect patient adherence to self-administered oncologic medication and are of increasing importance to U.S. payers and oncology professional organizations.12 In choosing between efficacious drugs, costs may influence treatment preference for payers and patients. Yet, cost comparisons between the commonly used RCC first-line drugs sunitinib and pazopanib in real-world populations are few, and methodological differences limit interpretation across studies. Hansen et al. (2015) suggested that costs were lower for pazopanib-treated patients than for patients treated with sunitinib; however, the cohorts were not compared within the context of real-world treatment, but rather in the context of patients participating in a clinical trial, which does not provide an accurate profile of costs incurred in routine clinical practice.13 Racsa et al. (2015) presented differences between patients newly treated with sunitinib and pazopanib, suggesting higher annual costs for sunitinib, yet differences did not persist with adjusted models and were only statistically significant for nonadherent patients, perhaps because of the inclusion of patients previously treated for RCC.10In addition to adverse events,A preliminary analysis of sunitinib prescription records revealed that the majority (75%) of records had 28 or 30 days supplies indicated, although 42 days is the accurate supply for 1 prescription, given the recommended dosing schedule of 4 weeks on therapy followed by 2 weeks off. Objectives of the current study were to (a) characterize demographic and clinical characteristics among treatment-naïve patients with RCC newly initiating sunitinib or pazopanib from a large administrative claims dataset; (b) characterize treatment patterns, persistence, and costs associated with each treatment group; and (c) assess the effect on treatment patterns and costs for sunitinib by substituting 42 days for prescriptions that have 28 or 30 days supplies to account for sunitinib’s dosing schedule. Also examined were differences in costs by patient age (< 65 vs. ≥ 65 years) to serve as a possible proxy for commercial versus Medicare-insured status.This study used data from the Truven Health MarketScan Research Database, which provides enrollment data, paid medical claims for inpatient and outpatient utilization, and pharmacy claims for over 170 million U.S. patients. Truven Health databases comprise the health care characteristics of covered populations and providers, including employer-sponsored commercial plans as well as Medicare-supplemental employer-sponsored plans (both Medicare- and employer-paid portions). This study used HIPAA-compliant, de-identified data, as specified in US 45 CFR 46 and was thus exempt from review and approval by an institutional review board.International Classifications of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes (189.0) for RCC and at least 1 prescription fill for sunitinib or pazopanib ; and prescription records with nonpositive days supply.Pre-index (baseline) demographic characteristics included age, gender, geographic region of residence  and continuous health plan enrollment (days) pre-index and post-index. Pre-index (baseline) clinical characteristics included the following:14Deyo Charlson Comorbidity Index (CCI) score and number and percentage (n %) of patients with each CCI comorbidity in 6 months pre-index period (Days from the earliest RCC diagnosis to the index date (back to January 2008).Six-month pre-index RCC-related (records with ICD-9-CM 189.0) and all-cause inpatient admissions, number and percentage of patients with an admission and per-patient mean (± standard deviation [SD]) number of admissions.Metastases (ICD-9-CM codes 196.xx-199.xx): Percentage of patients during 6 months pre-index and January 1, 2008-September 30, 2013.The persistence period consisted of the number of days from the start of the index medication to the end date of the last prescription’s days supply or the day before the start of a non-index RCC medication prescription within the allowed gap 30 days; . The folContinued: Patients did not switch or discontinue index medication .Early Switch: Patients switched to a nonindex medication while in possession of index medication (before run-out of last prescription’s days supply). There was at least 1 > 30-day gap for index medication during follow-up and at least 1 nonindex RCC medication within the 30-day gap.Delayed Switch: Patients switched to a nonindex medication after completion of the last index medication prescription’s days supply. There was at least 1 > 30-day gap for index medication and at least 1 nonindex RCC medication > 30 days after the last index prescription’s days supply before reinitiation of index medication or end of follow-up.Interrupted: Patients’ treatments were halted but reinitiated with index medication. That is, there was reinitiation of the index medication after the last index prescription’s days supply plus at least 30 days, with no nonindex RCC medications before reinitiation.Discontinued: Patients ceased treatment with index medication, with no switch to nonindex RCC medications from last index prescription through follow-up and no reinitiation of index medication from last index prescription’s days supply plus at least 30 days through the end of follow-up.The RCC medications included sunitinib, pazopanib, temsirolimus, axitinib, everolimus, interferon alfa-2B recombinant, bevacizumab, sorafenib, aldesleukin, and interferon alfa-2a .Costs were categorized into the following 3 groups:Index medication costs: Mean daily index medication cost during the persistence period was determined using persistence days as the denominator.1-year RCC-related costs: Payer-paid costs were determined over the 12-month follow-up for RCC medical (diagnosis-related), RCC medication, and total RCC-related  costs. RCC-related medical costs were based on inpatient and outpatient visits with an ICD-9-CM code of 189.0. Costs related to RCC medication included administrations and prescriptions for sunitinib, pazopanib, aldesleukin, axitinib, bevacizumab, everolimus, interferon α-2a, sorafenib, interferon α-2B recombinant, and temsirolimus.1-year all-cause costs: All-cause medical (inpatient + outpatient), all-cause medication, and all-cause total  costs were determined over the 12 months post-index. Any health care cost, regardless of diagnosis or type of medication, was included.Demographic and pre-index clinical characteristics, treatment patterns, and costs between treatment groups were compared using means [± SD] for continuous variables and relative frequencies  for categorical data.The current starting dose schedule recommendation for sunitinib is 50 mg orally every day for 4 weeks followed by 2 weeks off therapy . The days supply value in the prescription record is integral to calculations of persistence, yet the majority of records in a preliminary analysis had 28 or 30 days supply, which does not account for the 2 weeks off therapy. Thus, we evaluated the effect of substituting 42 days for sunitinib prescription records with 28 or 30 days supply on calculations of mean days supply, treatment patterns including persistence, and costs. To serve as a possible proxy for commercial versus Medicare-insured status, differences in costs were evaluated for patients aged < 65 years versus patients aged ≥ 65 years.P < 0.05, except for treatment group by age interactions where significance was set at P < 0.10.Chi-square tests were used to test for differences in categorical variables, and Student t-tests were used for continuous variables other than costs. A multivariable regression model was used to examine differences in costs between sunitinib and pazopanib. Since health care costs are typically non-normally distributed, a generalized linear model with a gamma distribution and log link was used for evaluating costs (dependent variable), controlling for patient demographics and pre-index characteristics, persistence days, and index medication (as predictor variables). All statistical tests were 2-tailed with significance set at Following application of inclusion/exclusion criteria, 609 patients initiating treatment with sunitinib and 183 patients initiating treatment with pazopanib were identified as the study sample . Of thesP = 0.002). Of note, the length of time between the earliest pre-index RCC diagnosis  and the index date was also shorter for sunitinib (373 days) than pazopanib .No significant differences in mean age, gender, geographic region, pre-index inpatient admissions, or metastatic diagnosis were observed between sunitinib or pazopanib treatment groups . Mean  daily costs were similar between comparators: $178.49 [51.89] for sunitinib versus 176.65 [48.93] for pazopanib (P = 0.669). The adjusted 1-year RCC-related and all-cause costs were not significantly different between index medication treatment groups, and substitution had no effect on these costs.After adjusting for possible confounding factors , we foun13 Using health care claims, we compared patient characteristics and medication use patterns between treatment-naïve RCC patients initiating sunitinib and pazopanib as first-line therapy. This study contributes unique health care cost information and demonstrates the effect of using substitution of days supply to address the difference in dosing schedules of these 2 drugs. There are few studies comparing first-line RCC targeted treatment groups, but we highlight key similarities and differences with this study.Accurate understanding of treatment persistence and costs in a real-world setting is an important contributor to decision making regarding the first-line treatment of RCC. Existing literature describing cost differences between sunitinib and pazopanib include a study applying costs to a clinical trial population and a real-world study of pazopanib and sunitinib in first or later lines of therapy for RCC.15 Similar to our results, the results of the only other real-world study comparing sunitinib and pazopanib treatment patterns using CCI scores found a similar extent of comorbidity between treatment groups.9 In the current study, a significantly longer time from RCC diagnosis to index date and longer pre-index enrollment period for pazopanib patients than sunitinib patients was observed, although reasons for these differences are not clear. It is possible that the baseline health of patients or physician experience influenced the choice of index treatment. Because pazopanib was approved for advanced RCC late in 2009, physicians may have been more inclined to prescribe sunitinib as first-line treatment in the early years of the study period. This might also explain the larger proportion of sunitinib patients in the study overall.Mean age, the predominance of males, and geographic distribution of patients were similar between treatment groups and comparable to national statistics and previous studies of various targeted RCC treatments using health care claims.16 A few retrospective studies have assessed persistence to tyrosine kinase inhibitors in RCC, but comparisons are hampered by dissimilar measures and methodology. Important factors that must be considered when comparing treatment patterns and costs over time are similarities or differences in dosing schedules. The problem of drug exposure misclassification with inaccurate pharmacist-recorded days supply has been described, and correction of days supply by substitution has been used in disease states where noncontinuous daily drug dosing occurs.19 Packaging of sunitinib as 28 count and U.S. health plan dispensing requirements likely contribute to pharmacist entry of 28 count or 30 count in the days supply field of the prescription record.Increasing use of oral oncology agents provides convenience advantages over clinic-delivered treatments, but it has been reported that persistence to self-administered drugs may be problematic.10 Despite different methods and patient selection criteria, both studies yielded similar conclusions as the current study, that is, no significant differences in persistence between treatment groups. Taken together, all studies further emphasize that consideration of dosing schedule is needed when comparing sunitinib to other first-line RCC treatments.For the current study, the need to accommodate for non-continuous daily dosing through correction of days supply by substitution was informed through preliminary analysis. This analysis revealed that most sunitinib prescriptions had days supply values of 28 or 30, both less than the dosing cycle of 42 days, but prescription date intervals were consistent with a dosing cycle of 42 days. The effect of substitution was evident through changes in persistence results and consequent conclusions regarding differences between treatment groups that involve this measure. In their studies comparing treatment patterns between sunitinib and pazopanib, DaCosta Byfield et al. (2015) adjusted for sunitinib’s dosing schedule using different methods, and Racsa et al. used substitution methods similar to the current study.The final objective of this study was to compare mean daily index medication costs and all-cause and RCC-related health care costs during the 12-month follow-up period. It was hypothesized that substitution would affect cost calculations because days supply affects calculations of the persistence period and, as a result, costs calculated over the persistence period. Without substitution, sunitinib had a significantly higher average daily medication cost, but substitution eliminated the difference. No significant differences were observed between treatment groups stratified by the age 65 threshold in adjusted daily costs, RCC-related costs, or all-cause 12-month costs. Before adjustment for demographic and clinical characteristics, all-cause medical costs were significantly higher in the sunitinib group in patients aged ≥ 65 years but were not different after adjustment.7The authors reported an average across all drugs based on data from 2004-2010, but the results are comparable to the 1-year post-index RCC medication costs for sunitinib and pazopanib observed in the current study. Racsa et al. reported higher total health care costs than in the current study (with no significant differences between treatment groups) and also found that adjusted pharmacy costs for sunitinib and pazopanib patients were not significantly different, including index and non-index RCC medications.10 However, the magnitude of cost results are not comparable to the current study because of the much shorter treatment duration in the Racsa et al. study and because that study’s sample had more Medicare patients, whereas the current study had more commercial patients.10 Our examination by age group was intended to serve as a possible proxy for commercial versus Medicare insured status. Gross covered payments for both treatment groups were higher in patients aged < 65 years compared with those patients aged ≥ 65 years, but no significant differences were observed between treatment groups for either age group.Few studies have examined health care costs associated with RCC treatment. Geynisman et al. (2015) reported similar first-year, first-line oral drug costs for patients aged < 65 years.13 Hansen et al. used a clinical trial population and applied wholesale acquisition drug costs to treatment and assigned price weights from insurance data to select adverse events rather than study patients from routine clinical practice and the actual costs associated with their care.13 The authors’ conclusion that overall costs were greater for patients treated with sunitinib was based solely on differences observed in drug costs, and it is important to note that the reference dates for drug costs used were from different time points: preceding a price increase for pazopanib and following a price increase for sunitinib. Racsa et al. observed no difference in overall annual costs between the 2 treatment groups but did observe significantly higher annual costs in nonadherent sunitinib patients compared with nonadherent pazopanib patients.10 This difference may be explained by unreported factors and/or patient selection, since patients previously treated for RCC were included, and there was a significantly higher rate of previous treatment in the pazopanib group.10 Comparisons of cost in the published literature are few, and understanding the methodologies employed, results observed, and conclusions made is critical. Accurate data are vital in cost studies, as well as patient, physician, and payer considerations regarding treatment.Any economic comparison must have accurate cost data as its foundation, and so far, real-world studies using actual costs are lacking. To date, only 2 studies have directly compared health care costs between patients receiving sunitinib and pazopanib.Possible limitations to this study should be considered in the interpretation of the findings, in part because health care claims data were not created specifically for research purposes. Claims may contain errors in coding or may be incomplete. The presence of prescription fill claims cannot be considered to represent proper self-administration or adherence or include any drugs obtained outside of the pharmacies filing the claims, such as physician samples, or patients’ participation in a clinical trial. Nevertheless, there is no reason to believe these limitations would be present to differing degrees in patients taking sunitinib versus pazopanib. This study also did not assess survival or adverse events, which might influence persistence results. Also, this study’s results are not necessarily generalizable to patients who are not enrolled in similar health plans as those in this study, since the characteristics and gross covered payments may differ among different insured samples.This analysis of persistence and actual costs represents the experience of real-world clinical practice of first-line treatment of RCC with sunitinib and pazopanib. Accounting for sunitinib’s unique dosing cycle for persistence and cost calculations is critical when comparing first-line RCC targeted treatments. The substitution of sunitinib days supply corrected for the values not reflecting actual patient experience. Calculations affected by this measure include daily index medication costs. There were no significant differences in persistence and similar RCC-related and all-cause costs over the 12-month period following the initiation of sunitinib or pazopanib as first-line therapy for RCC. The results of this study support selection of initial systemic treatment for RCC based on patients’ individual clinical factors and physician experience in using these agents.


Although worsening liver-related symptoms during pregnancy can occur in primary sclerosing cholangitis (PSC), there are insufficient data to effectively counsel patients on their pre-conception risk and no clear recommendations on monitoring and management during pregnancy. We aimed to describe maternal liver-related symptoms in pregnancy, both before and after PSC diagnosis, and explore factors associated with worsening symptoms and liver-related outcomes.We conducted a multicentre retrospective observational study of females with PSC and known pregnancy with live birth, via the International PSC Study Group. We included 450 patients from 12 European centres. Data included clinical variables, liver-related symptoms (pruritus and/or cholangitis) during pregnancy, and liver biochemistry. A composite primary endpoint of transplant-free survival from time of PSC diagnosis was used.p = 0.03), which retained significance on multivariate analysis .There were 266 pregnancies in 178 patients following PSC diagnosis. Worsening liver-related symptoms were reported in 66/228 (28.9%) pregnancies; they had a reduced transplant-free survival (p = 0.01), which did not retain significance in a multivariable model .Abnormal biochemistry and/or liver-related symptoms (pruritus and/or cholangitis) were noted during pregnancy before PSC diagnosis in 21/167 (12.6%) patients. They had a reduced transplant-free survival from pregnancy (Liver-related symptoms are frequently encountered during pregnancies before the diagnosis of PSC, and pregnancy may expose the pre-clinical phase of PSC in some patients. Worsening liver-related symptoms were seen in a third of our cohort with known PSC during pregnancy; and this subgroup had a poorer prognosis, which may be related to more advanced liver disease at time of pregnancy and/or a more severe disease phenotype.Patients with PSC can develop worsening of their liver-related symptoms during pregnancy; however, risk factors for this and the long-term implications are not known. We identified that there is a significant risk of these symptoms in pregnancy, both before and after PSC has been diagnosed, particularly in patients with elevated alkaline phosphatase. Furthermore, our findings suggest that worsening symptoms during pregnancy may be associated with adverse long-term clinical outcomes of liver transplantation and death in patients with known PSC. This may be related to the presence of more advanced liver disease at time of pregnancy. This information can be used to counsel patients with PSC before conception and identify patients who need close follow-up after delivery.  •Liver symptoms are frequently reported in pregnancies prior to diagnosis of PSC.•Worsening liver symptoms are common during pregnancy in known PSC.•Pre-conception elevated ALP is a risk factor for worsening symptoms in pregnancy.•Patients with worsening symptoms in pregnancy have reduced transplant-free survival. Primary sclerosing cholangitis (PSC) is an immune-mediated chronic liver disease which is characterised by inflammation and fibrosis of the biliary tree, with development of multifocal biliary strictures and progressive hepatic fibrosis.,,,,PSC is commonly diagnosed at 25-40 years of age, and although there is male preponderance, a significant proportion of patients with PSC are females of child-bearing age during a period of peak fertility.We sought to address this through multicentre collaboration and description of maternal liver-related symptoms during pregnancy in patients with PSC. We hypothesised that patients with PSC are at increased risk of liver abnormalities during pregnancy, which can pre-date their diagnosis of PSC in the pre-clinical phase of PSC. Furthermore, we aimed to evaluate the effect of worsening symptoms during pregnancy on long-term maternal outcomes in patients with PSC.We conducted a multicentre retrospective observational study of female patients with PSC and known pregnancy. Members of the International PSC Study Group (IPSCSG) were invited to participate, and we received data from 12 centres in nine European countries , which includes transplant and non-transplant tertiary liver centres. Female patients with a known live birth pregnancy at any time point in relation to their PSC diagnosis were included. Patients were excluded  if they The diagnosis of PSC and subtypes of PSC was confirmed in each centre in accordance with international guidelines.Baseline clinical variables were collected from the pre-existing IPSCSG database.Symptoms during pregnancy, date of delivery, gestational age, mode of delivery, and need for urgent delivery as a result of liver disease were collected from review of case notes. In patients post-LT, additional information on pre-eclampsia, gestational diabetes and acute cellular rejection were collected. Details on ursodeoxycholic acid (UDCA) included use in pregnancies before PSC diagnosis, and whether it was continued, stopped, or started during pregnancies post-PSC diagnosis. Details on immunosuppression regimens were collected for pregnancies post-LT.Liver biochemistry  were collected where available at three time points: (i) peak value in the 6 months before conception; (ii) peak value during pregnancy; (iii) peak value in the 6 months after delivery. When performed, peak serum bile acids were also collected. The reference range of liver biochemistry for each laboratory value was obtained. For each individual the values are represented in reference to the upper limit of normal (ULN) and compared with their baseline pre-conception values. A threshold value of >25% rise from baseline pre-conception value was used, which was chosen as there is an intra-individual variability of ALP in PSC of 12-20% which is not associated with disease progression.In patients with pregnancies before their PSC diagnosis, the term ‘liver abnormalities’ was used for patients with: (i) a clinical diagnosis of ICP; (ii) abnormal liver biochemistry and/or liver-related symptoms during pregnancy without a diagnosis of ICP.worsening liver-related symptoms’ if they had a clinical diagnosis of ICP, new/worsening pruritus from pre-conception and/or an episode of cholangitis treated with antibiotics.Symptoms during pregnancies after PSC diagnosis were recorded as: (i) a clinical diagnosis of ICP; (ii) pruritus during pregnancy ; (iii) cholangitis during pregnancy . Patients with known PSC were defined with ‘Diagnostic criteria for ICP differs between major societies,2 or Fisher’s exact tests for categorical parameters, and Mann-Whitney U test or Kruskal Wallis test for continuous variables. Unadjusted odds ratios (ORs) for developing worsening liver-related symptoms during pregnancy was calculated and recorded as OR with 95% 95% CIs and p values. Transplant-free survival (LT or death) was examined with Kaplan-Meier curves and the log-rank test. To determine predictors of LT or death during follow up, multivariate logistic regression analysis was performed to assess which features were of independent significance. Variables with a value of p <0.2 and those of particular interest were included. Results were recorded as hazard ratios (HRs) with 95% CIs and p values.Continuous variables are expressed as median (IQR), categorical variables are represented as number (percentage), or where data are missing number/number available (percentage). Subgroup differences were analysed using the Χp values <0.05 were considered statistically significant.Statistical analysis of the data was performed using SPSS software  and visualisation was performed using GraphPad Prism . Two-sided The patient cohort consisted of 450 females with PSC and their characteristics are outlined in Maternal liver-related symptom data was available in 167 patients who were pregnant before their diagnosis of PSC, with a total of 299 pregnancies . The medvs. 40.7 [32.3-48.8] years; p = 0.06). There was a shorter interval between delivery and PSC diagnosis  in patients with liver abnormalities.In 21 (12.6%) patients there was evidence of liver abnormalities during pregnancy before their PSC diagnosis: 10 (6.0%) ICP and 11 (6.6%) other (biochemistry and/or symptoms). UDCA was used in at least one pregnancy in seven (4.2%) patients. On univariate analysis there were no patient factors associated with development of liver abnormalities during a pregnancy before PSC diagnosis ; althougp = 0.01; p = 0.84), where age at diagnosis  and development of large duct PSC  were associated with events of LT or death during follow up . There was a reduced 20-year transplant-free survival in those with liver abnormalities during pregnancy . There was no difference in 20-year transplant-free survival between those with and without liver abnormalities during pregnancy  pregnancies: 33 (14.5%) clinical diagnosis of ICP, 23 (10.1%) worsening pruritus, 34 (14.9%) new pruritus, and four (1.8%) episode of cholangitis . Serum bp = 0.03) and abnormal ALP before pregnancy  and worsening liver-related symptoms during pregnancy  were patient factors associated with a reduced transplant-free survival and retained significance on multivariate analysis . There was a reduced 20-year transplant-free survival in patients with pre-existing PSC who developed worsening liver-related symptoms during pregnancy  at the time of conception. The median age at delivery was 33.4 years , time from transplant was 5.0 years , and time from diagnosis of PSC 11.4 years . Delivery was at a median 37.5 weeks gestation . There were four spontaneous vaginal deliveries, two inductions, four elective caesarean sections, three emergency caesarean sections and were unknown in four. Pre-eclampsia was diagnosed in two patients and no patients developed gestational diabetes.The 12 pregnancies with complete data were maintained on calcineurin based immunosuppression during pregnancy  pregnancies a clinical diagnosis of ICP was made, the remainder did not report liver-related symptoms during pregnancy. The patient with known rPSC before pregnancy was diagnosed with ICP and also had oral antibiotics for cholangitis; they subsequently required re-do LT 3 years post-partum.,PSC is a rare disease with a paucity of data to inform women with PSC of the disease-specific risks associated with pregnancy, and to guide clinicians on their management during pregnancy.,,We identified liver abnormalities in 12.6% of patients during pregnancy before their PSC diagnosis. This subgroup with liver abnormalities had a shorter interval to subsequent PSC diagnosis than those without, and were frequently diagnosed in the early post-partum period. Although no control group is available for comparison, a clinical diagnosis of ICP was made in 6% of our patients, which is greater than the 1% described in European populations.,,Worsening of liver-related symptoms were commonly reported during pregnancy in patients with PSC and affected 28.9% of our population, which is similar to previously reported smaller cohorts.Liver biochemistry at the three time points  were only available for a limited number of patients in this study, however there were sufficient numbers to demonstrate a clinical signal. Before conception the majority of our cohort had elevations in liver enzymes with a normal bilirubin. Significant elevations in liver biochemistry from pre-conception baseline, defined as >25% rise, were identified in 18-28% during pregnancy, and 31-49% after delivery. Physiological elevations in ALP are commonly seen in pregnancy related to placental secretionWe demonstrated a prognostic impact of worsening liver-related symptoms during pregnancy, with a poorer transplant-free survival, which retained significance on multivariate analysis. Although the number of patients post LT in our cohort is small, the patient with known rPSC had a significant worsening of liver biochemistry during pregnancy, which progressed post-partum and they subsequently required re-transplantation. Unlike in patients pregnant before their PSC diagnosis, transplant-free survival should not be influenced by lead time bias, and this adds weight to the hypothesis that patients with a more advanced stage of liver disease and/or more severe phenotype of disease are at greater risk of developing worsening liver-related symptoms during pregnancy.,In addition to the challenges related to data collection and completeness of data, which is commonplace in large multicentre retrospective studies, there are limitations specific to our study which need to be addressed. We only report a population with a diagnosis of PSC and live birth pregnancy and do not have a matched-control PSC population without pregnancy or other aetiologies of liver disease with pregnancy. Although we have been able to explore the effect of some factors associated on long-term outcomes, we do not have data on liver fibrosis assessment, presence of portal hypertension, and extent of cholangiopathy; all of which contribute to prognosis in PSC.,Pre-conception counselling is important in patients with chronic liver diseaseIn conclusion, we have demonstrated that a significant proportion of patients with PSC experience new or worsening liver-related symptoms during pregnancy, both before and after their diagnosis of PSC. Furthermore, we have identified that those who develop worsening liver-related symptoms during pregnancy may have a poorer long-term prognosis. Although the aetiopathogenesis of worsening liver-related symptoms during pregnancy in PSC is not clear and requires further evaluation, it may be related to the stage of liver disease at conception, with a ‘stress test’ from the hormonal changes during pregnancy which results in a clinical deterioration. We recommend pre-conception counselling for women with PSC, with close surveillance of those who develop worsening symptoms during pregnancy as they may have a more severe phenotype and/or advanced stage of disease.The study was supported by the European Association for the Study of the Liver Registry Grant (Liver Disease in Pregnancy).Study concept and design: JSN, TJB, HUM, MAH, DJ. Acquisition of data: all authorsAnalysis of data: JSN, DJ. Drafting of manuscript: JSN, MAH, DJ. Critical review of the manuscript: all authors.Data from the current study are available from the corresponding author on reasonable request.The authors have no relevant disclosures.Please refer to the accompanying ICMJE disclosure forms for further details.