Identifying Patterns of Clinical Interest in Clinicians' Treatment Preferences: Hypothesis-free Data Science Approach to Prioritizing Prescribing Outliers for Clinical Review.

BACKGROUND: Data analysis is used to identify signals suggestive of variation in treatment choice or clinical outcome. Analyses to date have generally focused on a hypothesis-driven approach. OBJECTIVE: This study aimed to develop a hypothesis-free approach to identify unusual prescribing behavior in primary care data. We aimed to apply this methodology to a national data set in a cross-sectional study to identify chemicals with significant variation in use across Clinical Commissioning Groups (CCGs) for further clinical review, thereby demonstrating proof of concept for prioritization approaches. METHODS: Here we report a new data-driven approach to identify unusual prescribing behaviour in primary care data. This approach first applies a set of filtering steps to identify chemicals with prescribing rate distributions likely to contain outliers, then applies two ranking approaches to identify the most extreme outliers amongst those candidates. This methodology has been applied to three months of national prescribing data (June-August 2017). RESULTS: Our methodology provides rankings for all chemicals by administrative region. We provide illustrative results for 2 antipsychotic drugs of particular clinical interest: promazine hydrochloride and pericyazine, which rank highly by outlier metrics. Specifically, our method identifies that, while promazine hydrochloride and pericyazine are barely used by most clinicians (with national prescribing rates of 11.1 and 6.2 per 1000 antipsychotic prescriptions, respectively), they make up a substantial proportion of antipsychotic prescribing in 2 small geographic regions in England during the study period (with maximum regional prescribing rates of 298.7 and 241.1 per 1000 antipsychotic prescriptions, respectively). CONCLUSIONS: Our hypothesis-free approach is able to identify candidates for audit and review in clinical practice. To illustrate this, we provide 2 examples of 2 very unusual antipsychotics used disproportionately in 2 small geographic areas of England

Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England.

BACKGROUND: Approaches to addressing unwarranted variation in health care service delivery have traditionally relied on the prospective identification of activities and outcomes, based on a hypothesis, with subsequent reporting against defined measures. Practice-level prescribing data in England are made publicly available by the National Health Service (NHS) Business Services Authority for all general practices. There is an opportunity to adopt a more data-driven approach to capture variability and identify outliers by applying hypothesis-free, data-driven algorithms to national data sets. OBJECTIVE: This study aimed to develop and apply a hypothesis-free algorithm to identify unusual prescribing behavior in primary care data at multiple administrative levels in the NHS in England and to visualize these results using organization-specific interactive dashboards, thereby demonstrating proof of concept for prioritization approaches. METHODS: Here we report a new data-driven approach to quantify how "unusual" the prescribing rates of a particular chemical within an organization are as compared to peer organizations, over a period of 6 months (June-December 2021). This is followed by a ranking to identify which chemicals are the most notable outliers in each organization. These outlying chemicals are calculated for all practices, primary care networks, clinical commissioning groups, and sustainability and transformation partnerships in England. Our results are presented via organization-specific interactive dashboards, the iterative development of which has been informed by user feedback. RESULTS: We developed interactive dashboards for every practice (n=6476) in England, highlighting the unusual prescribing of 2369 chemicals (dashboards are also provided for 42 sustainability and transformation partnerships, 106 clinical commissioning groups, and 1257 primary care networks). User feedback and internal review of case studies demonstrate that our methodology identifies prescribing behavior that sometimes warrants further investigation or is a known issue. CONCLUSIONS: Data-driven approaches have the potential to overcome existing biases with regard to the planning and execution of audits, interventions, and policy making within NHS organizations, potentially revealing new targets for improved health care service delivery. We present our dashboards as a proof of concept for generating candidate lists to aid expert users in their interpretation of prescribing data and prioritize further investigations and qualitative research in terms of potential targets for improved performance

Epigenetic Reprogramming Sensitizes CML Stem Cells to Combined EZH2 and Tyrosine Kinase Inhibition.

UNLABELLED: A major obstacle to curing chronic myeloid leukemia (CML) is residual disease maintained by tyrosine kinase inhibitor (TKI)-persistent leukemic stem cells (LSC). These are BCR-ABL1 kinase independent, refractory to apoptosis, and serve as a reservoir to drive relapse or TKI resistance. We demonstrate that Polycomb Repressive Complex 2 is misregulated in chronic phase CML LSCs. This is associated with extensive reprogramming of H3K27me3 targets in LSCs, thus sensitizing them to apoptosis upon treatment with an EZH2-specific inhibitor (EZH2i). EZH2i does not impair normal hematopoietic stem cell survival. Strikingly, treatment of primary CML cells with either EZH2i or TKI alone caused significant upregulation of H3K27me3 targets, and combined treatment further potentiated these effects and resulted in significant loss of LSCs compared to TKI alone, in vitro, and in long-term bone marrow murine xenografts. Our findings point to a promising epigenetic-based therapeutic strategy to more effectively target LSCs in patients with CML receiving TKIs. SIGNIFICANCE: In CML, TKI-persistent LSCs remain an obstacle to cure, and approaches to eradicate them remain a significant unmet clinical need. We demonstrate that EZH2 and H3K27me3 reprogramming is important for LSC survival, but renders LSCs sensitive to the combined effects of EZH2i and TKI. This represents a novel approach to more effectively target LSCs in patients receiving TKI treatment. Cancer Discov; 6(11); 1248-57. ©2016 AACR.See related article by Xie et al., p. 1237This article is highlighted in the In This Issue feature, p. 1197

Characterization of pathogenic germline mutations in human Protein Kinases

Background Protein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites.<p></p> Results Pathogenic Protein Kinase mutations perturb essential aspects of protein function, including disruption of substrate binding and/or effector recognition at family-specific positions. Interestingly these mutations in Protein Kinases display a tendency to avoid structurally relevant positions, what represents a significant difference with respect to the average distribution of pathogenic mutations in other protein families.<p></p> Conclusions Disease-associated mutations display sound differences with respect to neutral mutations: several amino acids are specific of each mutation type, different structural properties characterize each class and the distribution of pathogenic mutations within the consensus structure of the Protein Kinase domain is substantially different to that for non-pathogenic mutations. This preferential distribution confirms previous observations about the functional and structural distribution of the controversial cancer driver and passenger somatic mutations and their use as a proxy for the study of the involvement of somatic mutations in cancer development.<p></p&gt

A population-based matched cohort study of major congenital anomalies following COVID-19 vaccination and SARS-CoV-2 infection.

Evidence on associations between COVID-19 vaccination or SARS-CoV-2 infection and the risk of congenital anomalies is limited. Here we report a national, population-based, matched cohort study using linked electronic health records from Scotland (May 2020-April 2022) to estimate the association between COVID-19 vaccination and, separately, SARS-CoV-2 infection between six weeks pre-conception and 19 weeks and six days gestation and the risk of [1] any major congenital anomaly and [2] any non-genetic major congenital anomaly. Mothers vaccinated in this pregnancy exposure period mostly received an mRNA vaccine (73.7% Pfizer-BioNTech BNT162b2 and 7.9% Moderna mRNA-1273). Of the 6731 babies whose mothers were vaccinated in the pregnancy exposure period, 153 had any anomaly and 120 had a non-genetic anomaly. Primary analyses find no association between any vaccination and any anomaly (adjusted Odds Ratio [aOR] = 1.01, 95% Confidence Interval [CI] = 0.83-1.24) or non-genetic anomalies (aOR = 1.00, 95% CI = 0.81-1.22). Primary analyses also find no association between SARS-CoV-2 infection and any anomaly (aOR = 1.02, 95% CI = 0.66-1.60) or non-genetic anomalies (aOR = 0.94, 95% CI = 0.57-1.54). Findings are robust to sensitivity analyses. These data provide reassurance on the safety of vaccination, in particular mRNA vaccines, just before or in early pregnancy

Pregnancy outcomes after SARS-CoV-2 infection in periods dominated by delta and omicron variants in Scotland: a population-based cohort study

BACKGROUND: Evidence suggests that the SARS-CoV-2 omicron (B.1·1.529) is associated with lower risks of adverse outcomes than the delta (B.1.617.2) variant among the general population. However, little is known about outcomes after omicron infection in pregnancy. We aimed to assess and compare short-term pregnancy outcomes after SARS-CoV-2 delta and omicron infection in pregnancy. METHODS: We did a national population-based cohort study of women who had SARS-CoV-2 infection in pregnancy between May 17, 2021, and Jan 31, 2022. The primary maternal outcome was admission to critical care within 21 days of infection or death within 28 days of date of infection. Pregnancy outcomes were preterm birth and stillbirth within 28 days of infection. Neonatal outcomes were death within 28 days of birth, and low Apgar score (<7 of 10, for babies born at term) or neonatal SARS-CoV-2 infection in births occurring within 28 days of maternal infection. We used periods when variants were dominant in the general Scottish population, based on 50% or more of cases being S-gene positive (delta variant, from May 17 to Dec 14, 2021) or S-gene negative (omicron variant, from Dec 15, 2021, to Jan 31, 2022) as surrogates for variant infections. Analyses used logistic regression, adjusting for maternal age, deprivation quintile, ethnicity, weeks of gestation, and vaccination status. Sensitivity analyses included restricting the analysis to those with first confirmed SARS-CoV-2 infection and using periods when delta or omicron had 90% or more predominance. FINDINGS: Between May 17, 2021, and Jan 31, 2022, there were 9923 SARS-CoV-2 infections in 9823 pregnancies, in 9817 women in Scotland. Compared with infections in the delta-dominant period, SARS-CoV-2 infections in pregnancy in the omicron-dominant period were associated with lower maternal critical care admission risk (0·3% [13 of 4968] vs 1·8% [89 of 4955]; adjusted odds ratio 0·25, 95% CI 0·14-0·44) and lower preterm birth within 28 days of infection (1·8% [37 of 2048] vs 4·2% [98 of 2338]; 0·57, 95% CI 0·38-0·87). There were no maternal deaths within 28 days of infection. Estimates of low Apgar scores were imprecise due to low numbers (5 [1·2%] of 423 with omicron vs 11 [2·1%] of 528 with delta, adjusted odds ratio 0·72, 0·23-2·32). There were fewer stillbirths in the omicron-dominant period than in the delta-dominant period (4·3 [2 of 462] per 1000 births vs 20·3 [13 of 639] per 1000) and no neonatal deaths during the omicron-dominant period (0 [0 of 460] per 1000 births vs 6·3 [4 of 626] per 1000 births), thus numbers were too small to support adjusted analyses. Rates of neonatal infection were low in births within 28 days of maternal SARS-CoV-2 infection, with 11 cases of neonatal SARS-CoV-2 in the delta-dominant period, and 1 case in the omicron-dominant period. Of the 15 stillbirths, 12 occurred in women who had not received two or more doses of COVID-19 vaccination at the time of SARS-CoV-2 infection in pregnancy. All 12 cases of neonatal SARS-CoV-2 infection occurred in women who had not received two or more doses of vaccine at the time of maternal infection. Findings in sensitivity analyses were similar to those in the main analyses. INTERPRETATION: Pregnant women infected with SARS-CoV-2 were substantially less likely to have a preterm birth or maternal critical care admission during the omicron-dominant period than during the delta-dominant period. FUNDING: Wellcome Trust, Tommy's charity, Medical Research Council, UK Research and Innovation, Health Data Research UK, National Core Studies-Data and Connectivity, Public Health Scotland, Scottish Government Health and Social Care, Scottish Government Chief Scientist Office, National Research Scotland

Targeting BCR-ABL-Independent TKI Resistance in Chronic Myeloid Leukemia by mTOR and Autophagy Inhibition

Background Imatinib and second-generation tyrosine kinase inhibitors (TKIs) nilotinib and dasatinib have statistically significantly improved the life expectancy of chronic myeloid leukemia (CML) patients; however, resistance to TKIs remains a major clinical challenge. Although ponatinib, a third-generation TKI, improves outcomes for patients with BCR-ABL-dependent mechanisms of resistance, including the T315I mutation, a proportion of patients may have or develop BCR-ABL-independent resistance and fail ponatinib treatment. By modeling ponatinib resistance and testing samples from these CML patients, it is hoped that an alternative drug target can be identified and inhibited with a novel compound. Methods Two CML cell lines with acquired BCR-ABL-independent resistance were generated following culture in ponatinib. RNA sequencing and gene ontology (GO) enrichment were used to detect aberrant transcriptional response in ponatinib-resistant cells. A validated oncogene drug library was used to identify US Food and Drug Administration–approved drugs with activity against TKI-resistant cells. Validation was performed using bone marrow (BM)–derived cells from TKI-resistant patients (n = 4) and a human xenograft mouse model (n = 4–6 mice per group). All statistical tests were two-sided. Results We show that ponatinib-resistant CML cells can acquire BCR-ABL-independent resistance mediated through alternative activation of mTOR. Following transcriptomic analysis and drug screening, we highlight mTOR inhibition as an alternative therapeutic approach in TKI-resistant CML cells. Additionally, we show that catalytic mTOR inhibitors induce autophagy and demonstrate that genetic or pharmacological inhibition of autophagy sensitizes ponatinib-resistant CML cells to death induced by mTOR inhibition in vitro (% number of colonies of control[SD], NVP-BEZ235 vs NVP-BEZ235+HCQ: 45.0[17.9]% vs 24.0[8.4]%, P = .002) and in vivo (median survival of NVP-BEZ235- vs NVP-BEZ235+HCQ-treated mice: 38.5 days vs 47.0 days, P = .04). Conclusion Combined mTOR and autophagy inhibition may provide an attractive approach to target BCR-ABL-independent mechanism of resistance

A Phase II study of neoadjuvant axitinib for reducing the extent of venous tumour thrombus in clear cell renal cell cancer with venous invasion (NAXIVA).

BACKGROUND: Surgery for renal cell carcinoma (RCC) with venous tumour thrombus (VTT) extension into the renal vein (RV) and/or inferior vena cava (IVC) has high peri-surgical morbidity/mortality. NAXIVA assessed the response of VTT to axitinib, a potent tyrosine kinase inhibitor. METHODS: NAXIVA was a single-arm, multi-centre, Phase 2 study. In total, 20 patients with resectable clear cell RCC and VTT received upto 8 weeks of pre-surgical axitinib. The primary endpoint was percentage of evaluable patients with VTT improvement by Mayo level on MRI. Secondary endpoints were percentage change in surgical approach and VTT length, response rate (RECISTv1.1) and surgical morbidity. RESULTS: In all, 35% (7/20) patients with VTT had a reduction in Mayo level with axitinib: 37.5% (6/16) with IVC VTT and 25% (1/4) with RV-only VTT. No patients had an increase in Mayo level. In total, 75% (15/20) of patients had a reduction in VTT length. Overall, 41.2% (7/17) of patients who underwent surgery had less invasive surgery than originally planned. Non-responders exhibited lower baseline microvessel density (CD31), higher Ki67 and exhausted or regulatory T-cell phenotype. CONCLUSIONS: NAXIVA provides the first Level II evidence that axitinib downstages VTT in a significant proportion of patients leading to reduction in the extent of surgery. CLINICAL TRIAL REGISTRATION: NCT03494816

OpenSAFELY: The impact of COVID-19 on azathioprine, leflunomide and methotrexate monitoring, and factors associated with change in monitoring rate.

AIMS: The COVID-19 pandemic created unprecedented pressure on healthcare services. This study investigates whether disease-modifying antirheumatic drug (DMARD) safety monitoring was affected during the COVID-19 pandemic. METHODS: A population-based cohort study was conducted using the OpenSAFELY platform to access electronic health record data from 24.2 million patients registered at general practices using TPP's SystmOne software. Patients were included for further analysis if prescribed azathioprine, leflunomide or methotrexate between November 2019 and July 2022. Outcomes were assessed as monthly trends and variation between various sociodemographic and clinical groups for adherence with standard safety monitoring recommendations. RESULTS: An acute increase in the rate of missed monitoring occurred across the study population (+12.4 percentage points) when lockdown measures were implemented in March 2020. This increase was more pronounced for some patient groups (70-79 year-olds: +13.7 percentage points; females: +12.8 percentage points), regions (North West: +17.0 percentage points), medications (leflunomide: +20.7 percentage points) and monitoring tests (blood pressure: +24.5 percentage points). Missed monitoring rates decreased substantially for all groups by July 2022. Consistent differences were observed in overall missed monitoring rates between several groups throughout the study. CONCLUSION: DMARD monitoring rates temporarily deteriorated during the COVID-19 pandemic. Deterioration coincided with the onset of lockdown measures, with monitoring rates recovering rapidly as lockdown measures were eased. Differences observed in monitoring rates between medications, tests, regions and patient groups highlight opportunities to tackle potential inequalities in the provision or uptake of monitoring services. Further research should evaluate the causes of the differences identified between groups

Impact of vaccination on the association of COVID-19 with cardiovascular diseases: An OpenSAFELY cohort study

Acknowledgements: This study was supported by the COVID-19 Longitudinal Health and Wellbeing National Core Study, funded by the UKRI Medical Research Council (MC_PC_20059); the COVID-19 Data and Connectivity National Core Study, funded by the UKRI Medical Research Council; and by the CONVALESCENCE long COVID study, funded by the UK National Institute for Health and Care Research (COVID-LT-009). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. GC, SI and AWo are supported by the British Heart Foundation (RG/13/13/30194; RG/18/13/33946), BHF Cambridge Centre of Research Excellence (RE/18/1/34212) and NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). RK, GDS, and VW are supported by the Medical Research Council Integrative Epidemiology Unit at the University of Bristol [MC_UU_00011/1; MC_UU_00011/4]. RK and JACS are supported by the NIHR Bristol Biomedical Research Centre and by Health Data Research UK South-West. YW was supported by a UKRI MRC Fellowship (MC/W021358/1) and received funding from the UKRI EPSRC Impact Acceleration Account (EP/X525789/1). SI and AWo were funded by a British Heart Foundation–Turing Cardiovascular Data Science Award (BCDSA/100005). SI is funded by the International Alliance for Cancer Early Detection, a partnership among Cancer Research UK C18081/A31373, Canary Center at Stanford University, the University of Cambridge, OHSU Knight Cancer Institute, University College London, and the University of Manchester. RK and JM were supported by NIHR ARC West. RD and JACS were supported by Health Data Research UK. WW is supported by the Chief Scientist’s Office (CAF/01/17). AWo and WW are supported by the Stroke Association (SA CV 20/100018). AWo is part of the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No 116074. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. We are very grateful for all the support received from the OpenSAFELY team and the TPP Technical Operations team throughout this work, and for generous assistance from the information governance and database teams at NHS England and the NHS England Transformation Directorate. We thank the CONVALESCENCE Study Long Covid PPIE group for their input and for sharing their experiences and expertise throughout the duration of the project.Funder: UKRI Medical Research Council (MC_PC_20059) UK National Institute for Health and Care Research (COVID-LT-009)AbstractInfection with SARS-CoV-2 is associated with an increased risk of arterial and venous thrombotic events, but the implications of vaccination for this increased risk are uncertain. With the approval of NHS England, we quantified associations between COVID-19 diagnosis and cardiovascular diseases in different vaccination and variant eras using linked electronic health records for ~40% of the English population. We defined a ‘pre-vaccination’ cohort (18,210,937 people) in the wild-type/Alpha variant eras (January 2020-June 2021), and ‘vaccinated’ and ‘unvaccinated’ cohorts (13,572,399 and 3,161,485 people respectively) in the Delta variant era (June-December 2021). We showed that the incidence of each arterial thrombotic, venous thrombotic and other cardiovascular outcomes was substantially elevated during weeks 1-4 after COVID-19, compared with before or without COVID-19, but less markedly elevated in time periods beyond week 4. Hazard ratios were higher after hospitalised than non-hospitalised COVID-19 and higher in the pre-vaccination and unvaccinated cohorts than the vaccinated cohort. COVID-19 vaccination reduces the risk of cardiovascular events after COVID-19 infection. People who had COVID-19 before or without being vaccinated are at higher risk of cardiovascular events for at least two years.</jats:p