To weight or not to weight? Studying the effect of selection bias in three large EHR-linked biobanks

Objective

To explore the role of selection bias adjustment by weighting electronic health record (EHR)-linked biobank data for commonly performed analyses.

Materials and methods

We mapped diagnosis (ICD code) data to standardized phecodes from three EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n=244,071), Michigan Genomics Initiative (MGI; n=81,243), and UK Biobank (UKB; n=401,167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to be more representative of the US adult population. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted four common descriptive and analytic tasks comparing unweighted and weighted results.

Results

For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB's estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted PheWAS for colorectal cancer, the strongest associations remained unaltered and there was large overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates.

Discussion

Weighting had limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation more. Results from untargeted association analyses should be followed by weighted analysis when effect size estimation is of interest for specific signals.

Conclusion

EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.

Peripheral blood BDNF and soluble CAM proteins as possible markers of prolonged disorders of consciousness: a pilot study

Although clinical examination still represents the gold standard for the differential diagnosis of prolonged disorders of consciousness (pDoC), the introduction of innovative markers is essential for diagnosis and prognosis, due to the problem of covert cognition. We evaluated the brain-derived neurotrophic factor protein (BDNF) and the soluble cell adhesion molecules proteins (CAMs) in a cohort of prolonged disorders of consciousness patients to identify a possible application in the clinical context. Furthermore, peripheral blood determinations were correlated with imaging parameters such as white matter hyperintensities (WMH), cranial standardized uptake value (cSUV), electroencephalography (EEG) data and clinical setting. Our results, although preliminary, identify BDNF as a possible blood marker for the diagnosis of pDoC (p value 0.001), the soluble CAMs proteins CD44, Vcam-1, E-selectin (p value < 0.01) and Icam-3 (p value < 0.05) showed a higher peripheral blood value in pDoC compared with control. Finally, soluble Ncam protein could find useful applications in the clinical evolution of the pDoC, showing high levels in the MCS and EMCS subgroups (p value < 0. 001) compared to VS/UWS.

Design and analysis heterogeneity in observational studies of COVID-19 booster effectiveness: A review and case study

We investigated the design and analysis of observational booster vaccine effectiveness (VE) studies by performing a scoping review of booster VE literature with a focus on study design and analytic choices. We then applied 20 different approaches, including those found in the literature, to a single dataset from Michigan Medicine. We identified 80 studies in our review, including over 150 million observations in total. We found that while protection against infection is variable and dependent on several factors including the study population and time period, both monovalent boosters and particularly the bivalent booster offer strong protection against severe COVID-19. In addition, VE analyses with a severe disease outcome (hospitalization, intensive care unit admission, or death) appear to be more robust to design and analytic choices than an infection endpoint. In terms of design choices, we found that test-negative designs and their variants may offer advantages in statistical efficiency compared to cohort designs.

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

OBJECTIVE

To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity.

METHODS

Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis.

RESULTS

The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection.

CONCLUSION

By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.

Using Multi-Modal Electronic Health Record Data for the Development and Validation of Risk Prediction Models for Long COVID Using the Super Learner Algorithm

BACKGROUND

Post-Acute Sequelae of COVID-19 (PASC) have emerged as a global public health and healthcare challenge. This study aimed to uncover predictive factors for PASC from multi-modal data to develop a predictive model for PASC diagnoses.

METHODS

We analyzed electronic health records from 92,301 COVID-19 patients, covering medical phenotypes, medications, and lab results. We used a Super Learner-based prediction approach to identify predictive factors. We integrated the model outputs into individual and composite risk scores and evaluated their predictive performance.

RESULTS

Our analysis identified several factors predictive of diagnoses of PASC, including being overweight/obese and the use of HMG CoA reductase inhibitors prior to COVID-19 infection, and respiratory system symptoms during COVID-19 infection. We developed a composite risk score with a moderate discriminatory ability for PASC (covariate-adjusted AUC (95% confidence interval): 0.66 (0.63, 0.69)) by combining the risk scores based on phenotype and medication records. The combined risk score could identify 10% of individuals with a 2.2-fold increased risk for PASC.

CONCLUSIONS

We identified several factors predictive of diagnoses of PASC and integrated the information into a composite risk score for PASC prediction, which could contribute to the identification of individuals at higher risk for PASC and inform preventive efforts.

Epidemiologic Questionnaire (EPI-Q) - a scalable, app-based health survey linked to electronic health record and genotype data

The Epidemiologic Questionnaire (EPI-Q) was established to collect broad, uniform, self-reported health data to supplement electronic health record (EHR) and genotype information from participants in the University of Michigan (UM) Precision Health cohorts. Recruitment of EPI-Q participants, who were already enrolled in 1 of 3 ongoing UM Precision Health cohorts-the Michigan Genomics Initiative, Mental Health Biobank, and Metabolism, Endocrinology, and Diabetes cohorts-began in March 2020. Of 54,043 retrospective invitations, 5,577 individuals enrolled, representing a 10.3% response rate. Of these, 3,502 (63.7%) were female, and the average age was 56.1 years (standard deviation, 15.4). The baseline survey comprises 11 modules on topics including personal and family health history, lifestyle, and cancer screening and history. Additionally, 11 optional modules cover topics including financial toxicity, occupational exposure, and life meaning. The questions are based on standardized and validated instruments used in other cohorts, and we share resources to expedite development of similar surveys. Data are collected via the MyDataHelps platform, which enables current and future participants to share non-Michigan Medicine EHR data. Recruitment is ongoing. Cohort data are available to those with institutional review board approval; for details, contact the Data Office for Clinical and Translational Research (DataOffice@umich.edu).

Design and Analysis Heterogeneity in Observational Studies of COVID-19 Booster Effectiveness: A Review and Case Study

Background

Observational vaccine effectiveness (VE) studies based on real-world data are a crucial supplement to initial randomized clinical trials of Coronavirus Disease 2019 (COVID-19) vaccines. However, there exists substantial heterogeneity in study designs and statistical methods for estimating VE. The impact of such heterogeneity on VE estimates is not clear.

Methods

We conducted a two-step literature review of booster VE: a literature search for first or second monovalent boosters on January 1, 2023, and a rapid search for bivalent boosters on March 28, 2023. For each study identified, study design, methods, and VE estimates for infection, hospitalization, and/or death were extracted and summarized via forest plots. We then applied methods identified in the literature to a single dataset from Michigan Medicine (MM), providing a comparison of the impact of different statistical methodologies on the same dataset.

Results

We identified 53 studies estimating VE of the first booster, 16 for the second booster. Of these studies, 2 were case-control, 17 were test-negative, and 50 were cohort studies. Together, they included nearly 130 million people worldwide. VE for all outcomes was very high (around 90%) in earlier studies (i.e., in 2021), but became attenuated and more heterogeneous over time (around 40%-50% for infection, 60%-90% for hospitalization, and 50%-90% for death). VE compared to the previous dose was lower for the second booster (10-30% for infection, 30-60% against hospitalization, and 50-90% against death). We also identified 11 bivalent booster studies including over 20 million people. Early studies of the bivalent booster showed increased effectiveness compared to the monovalent booster (VE around 50-80% for hospitalization and death).Our primary analysis with MM data using a cohort design included 186,495 individuals overall (including 153,811 boosted and 32,684 with only a primary series vaccination), and a secondary test-negative design included 65,992 individuals tested for SARS-CoV-2. When different statistical designs and methods were applied to MM data, VE estimates for hospitalization and death were robust to analytic choices, with test-negative designs leading to narrower confidence intervals. Adjusting either for the propensity of getting boosted or directly adjusting for covariates reduced the heterogeneity across VE estimates for the infection outcome.

Conclusion

While the advantage of the second monovalent booster is not obvious from the literature review, the first monovalent booster and the bivalent booster appear to offer strong protection against severe COVID-19. Based on both the literature view and data analysis, VE analyses with a severe disease outcome (hospitalization, ICU admission, or death) appear to be more robust to design and analytic choices than an infection endpoint. Test-negative designs can extend to severe disease outcomes and may offer advantages in statistical efficiency when used properly.

Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience

India experienced a massive surge in SARS-CoV-2 infections and deaths during April to June 2021 despite having controlled the epidemic relatively well during 2020. Using counterfactual predictions from epidemiological disease transmission models, we produce evidence in support of how strengthening public health interventions early would have helped control transmission in the country and significantly reduced mortality during the second wave, even without harsh lockdowns. We argue that enhanced surveillance at district, state, and national levels and constant assessment of risk associated with increased transmission are critical for future pandemic responsiveness. Building on our retrospective analysis, we provide a tiered data-driven framework for timely escalation of future interventions as a tool for policy-makers.

COVID-19 outcomes by cancer status, site, treatment, and vaccination

BACKGROUND

Studies have shown an increased risk of severe SARS-CoV-2 related (COVID-19) disease outcome and mortality for cancer patients, but it is not well understood if associations vary by cancer site, cancer treatment, and vaccination status.

METHODS

Using electronic health record data from an academic medical center, we identified a retrospective cohort of 260,757 individuals tested for or diagnosed with COVID-19 from March 10, 2020, to August 1, 2022. Of these, 52,019 tested positive for COVID-19 of whom 13,752 had a cancer diagnosis. We conducted Firth-corrected logistic regression to assess the association between cancer status, site and treatment, vaccination, and four COVID-19 outcomes: hospitalization, intensive care unit admission, mortality, and a composite "severe COVID" outcome.

RESULTS

Cancer diagnosis was significantly associated with higher rates of severe COVID, hospitalization, and mortality. These associations were driven by patients whose most recent initial cancer diagnosis was within the past three years. Chemotherapy receipt, colorectal cancer, hematologic malignancies, kidney cancer and lung cancer were significantly associated with higher rates of worse COVID-19 outcomes. Vaccinations were significantly associated with lower rates of worse COVID-19 outcomes regardless of cancer status.

CONCLUSIONS

Patients with colorectal cancer, hematologic malignancies, kidney cancer or lung cancer or who receive chemotherapy for treatment should be cautious because of their increased risk of worse COVID-19 outcomes, even after vaccination.

IMPACT

Additional COVID-19 precautions are warranted for people with certain cancer types and treatments. Significant benefit from vaccination is noted for both cancer and cancer-free patients.

On cross-ancestry cancer polygenic risk scores

Polygenic risk scores (PRS) can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. However, their construction depends on the availability of summary statistics from genome-wide association studies (GWAS) independent from the target sample. For best compatibility, it was reported that GWAS and the target sample should match in terms of ancestries. Yet, GWAS, especially in the field of cancer, often lack diversity and are predominated by European ancestry. This bias is a limiting factor in PRS research. By using electronic health records and genetic data from the UK Biobank, we contrast the utility of breast and prostate cancer PRS derived from external European-ancestry-based GWAS across African, East Asian, European, and South Asian ancestry groups. We highlight differences in the PRS distributions of these groups that are amplified when PRS methods condense hundreds of thousands of variants into a single score. While European-GWAS-derived PRS were not directly transferrable across ancestries on an absolute scale, we establish their predictive potential when considering them separately within each group. For example, the top 10% of the breast cancer PRS distributions within each ancestry group each revealed significant enrichments of breast cancer cases compared to the bottom 90% (odds ratio of 2.81 [95%CI: 2.69,2.93] in European, 2.88 [1.85, 4.48] in African, 2.60 [1.25, 5.40] in East Asian, and 2.33 [1.55, 3.51] in South Asian individuals). Our findings highlight a compromise solution for PRS research to compensate for the lack of diversity in well-powered European GWAS efforts while recruitment of diverse participants in the field catches up.

The translation of results from genome-wide association studies (GWAS) into polygenic risk scores (PRS) to predict disease risk or outcomes is a major aspiration in the field of statistical genetics. While there has been significant progress in this area for many complex diseases, the lack of diversity in GWAS is transferred to PRS research. Discovery of genetic risk factors, especially for cancer traits, are almost exclusively based on individuals with European-ancestry, and it remains unclear if these results can be utilized for PRS applications across non-European ancestries.

Here, we used external European-ancestry based GWAS results to construct breast and prostate cancer PRS and showcase their utility as predictors across African, East Asian, European, and South Asian ancestry groups using data from the UK Biobank. We observed ancestry-specific PRS distributions, that when scaled within each group, could identify individuals at higher risk of prostate and breast cancer in each group. Our study highlights an opportunity to use results from large European GWAS for the construction of PRS in diverse ancestry groups. To realize the full potential of PRS in early detection and prevention of cancer across ethnic groups, we need rapid expanded recruitment of diverse participants in the field of GWAS.

A comparison of five epidemiological models for transmission of SARS-CoV-2 in India

BACKGROUND

Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM).

METHODS

Using COVID-19 case-recovery-death count data reported in India from March 15 to October 15 to train the models, we generate predictions from each of the five models from October 16 to December 31. To compare prediction accuracy with respect to reported cumulative and active case counts and reported cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For reported cumulative cases and deaths, we compute Pearson's and Lin's correlation coefficients to investigate how well the projected and observed reported counts agree. We also present underreporting factors when available, and comment on uncertainty of projections from each model.

RESULTS

For active case counts, SMAPE values are 35.14% (SEIR-fansy) and 37.96% (eSIR). For cumulative case counts, SMAPE values are 6.89% (baseline), 6.59% (eSIR), 2.25% (SAPHIRE) and 2.29% (SEIR-fansy). For cumulative death counts, the SMAPE values are 4.74% (SEIR-fansy), 8.94% (eSIR) and 0.77% (ICM). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) cumulative case counts as well. We compute underreporting factors as of October 31 and note that for cumulative cases, the SEIR-fansy model yields an underreporting factor of 7.25 and ICM model yields 4.54 for the same quantity. For total (sum of reported and unreported) cumulative deaths the SEIR-fansy model reports an underreporting factor of 2.97. On October 31, we observe 8.18 million cumulative reported cases, while the projections (in millions) from the baseline model are 8.71 (95% credible interval: 8.63-8.80), while eSIR yields 8.35 (7.19-9.60), SAPHIRE returns 8.17 (7.90-8.52) and SEIR-fansy projects 8.51 (8.18-8.85) million cases. Cumulative case projections from the eSIR model have the highest uncertainty in terms of width of 95% credible intervals, followed by those from SAPHIRE, the baseline model and finally SEIR-fansy.

CONCLUSIONS

In this comparative paper, we describe five different models used to study the transmission dynamics of the SARS-Cov-2 virus in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. The largest variability across models is observed in predicting the "total" number of infections including reported and unreported cases (on which we have no validation data). The degree of under-reporting has been a major concern in India and is characterized in this report. Overall, the SEIR-fansy model appeared to be a good choice with publicly available R-package and desired flexibility plus accuracy.

Incorporating false negative tests in epidemiological models for SARS-CoV-2 transmission and reconciling with seroprevalence estimates

Susceptible-Exposed-Infected-Removed (SEIR)-type epidemiologic models, modeling unascertained infections latently, can predict unreported cases and deaths assuming perfect testing. We apply a method we developed to account for the high false negative rates of diagnostic RT-PCR tests for detecting an active SARS-CoV-2 infection in a classic SEIR model. The number of unascertained cases and false negatives being unobservable in a real study, population-based serosurveys can help validate model projections. Applying our method to training data from Delhi, India, during March 15-June 30, 2020, we estimate the underreporting factor for cases at 34-53 (deaths: 8-13) on July 10, 2020, largely consistent with the findings of the first round of serosurveys for Delhi (done during June 27-July 10, 2020) with an estimated 22.86% IgG antibody prevalence, yielding estimated underreporting factors of 30-42 for cases. Together, these imply approximately 96-98% cases in Delhi remained unreported (July 10, 2020). Updated calculations using training data during March 15-December 31, 2020 yield estimated underreporting factor for cases at 13-22 (deaths: 3-7) on January 23, 2021, which are again consistent with the latest (fifth) round of serosurveys for Delhi (done during January 15-23, 2021) with an estimated 56.13% IgG antibody prevalence, yielding an estimated range for the underreporting factor for cases at 17-21. Together, these updated estimates imply approximately 92-96% cases in Delhi remained unreported (January 23, 2021). Such model-based estimates, updated with latest data, provide a viable alternative to repeated resource-intensive serosurveys for tracking unreported cases and deaths and gauging the true extent of the pandemic.

Patterns of repeated diagnostic testing for COVID-19 in relation to patient characteristics and outcomes

BACKGROUND

Whilst the COVID-19 diagnostic test has a high false-negative rate, not everyone initially negative is re-tested. Michigan Medicine, a primary regional centre, provided an ideal setting for studying testing patterns during the first wave of the pandemic.

OBJECTIVES

To identify the characteristics of patients who underwent repeated testing for COVID-19 and determine if repeated testing was associated with downstream outcomes amongst positive cases.

METHODS

Characteristics, test results, and health outcomes for patients presenting for a COVID-19 diagnostic test were collected. We examined whether patient characteristics differed with repeated testing and estimated a false-negative rate for the test. We then studied repeated testing patterns in patients with severe COVID-19-related outcomes.

RESULTS

Patient age, sex, body mass index, neighbourhood poverty levels, pre-existing type 2 diabetes, circulatory, kidney, and liver diseases, and cough, fever/chills, and pain symptoms 14 days prior to a first test were associated with repeated testing. Amongst patients with a positive result, age (OR: 1.17; 95% CI: (1.05, 1.34)) and pre-existing kidney diseases (OR: 2.26; 95% CI: (1.41, 3.68)) remained significant. Hospitalization (OR: 7.88; 95% CI: (5.15, 12.26)) and ICU-level care (OR: 6.93; 95% CI: (4.44, 10.92)) were associated with repeated testing. The estimated false-negative rate was 23.8% (95% CI: (19.5%, 28.5%)).

CONCLUSIONS

Whilst most patients were tested once and received a negative result, a meaningful subset underwent multiple rounds of testing. These results shed light on testing patterns and have important implications for understanding the variation of repeated testing results within and between patients.

A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine

Background: We performed a phenome-wide association study to identify pre-existing conditions related to Coronavirus disease 2019 (COVID-19) prognosis across the medical phenome and how they vary by race. Methods: The study is comprised of 53,853 patients who were tested/diagnosed for COVID-19 between 10 March and 2 September 2020 at a large academic medical center. Results: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with intensive care unit (ICU) admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks. Conclusions: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine

Background:

We perform a phenome-wide scan to identify pre-existing conditions related to COVID-19 susceptibility and prognosis across the medical phenome and how they vary by race.

Methods:

The study is comprised of 53,853 patients who were tested/positive for COVID-19 between March 10 and September 2, 2020 at a large academic medical center.

Results:

Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with ICU admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks.

Conclusions:

Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks

BACKGROUND

Traditional methods for disease risk prediction and assessment, such as diagnostic tests using serum, urine, blood, saliva or imaging biomarkers, have been important for identifying high-risk individuals for many diseases, leading to early detection and improved survival. For pancreatic cancer, traditional methods for screening have been largely unsuccessful in identifying high-risk individuals in advance of disease progression leading to high mortality and poor survival. Electronic health records (EHR) linked to genetic profiles provide an opportunity to integrate multiple sources of patient information for risk prediction and stratification. We leverage a constellation of temporally associated diagnoses available in the EHR to construct a summary risk score, called a phenotype risk score (PheRS), for identifying individuals at high-risk for having pancreatic cancer. The proposed PheRS approach incorporates the time with respect to disease onset into the prediction framework. We combine and contrast the PheRS with more well-known measures of inherited susceptibility, namely, the polygenic risk scores (PRS) for prediction of pancreatic cancer.

METHODOLOGY

We first calculated pairwise, unadjusted associations between pancreatic cancer diagnosis and all possible other diagnoses across the medical phenome. We call these pairwise associations co-occurrences. After accounting for cross-phenotype correlations, the multivariable association estimates from a subset of relatively independent diagnoses were used to create a weighted sum PheRS. We constructed time-restricted risk scores using data from 38,359 participants in the Michigan Genomics Initiative (MGI) based on the diagnoses contained in the EHR at 0, 1, 2, and 5 years prior to the target pancreatic cancer diagnosis. The PheRS was assessed for predictability in the UK Biobank (UKB). We tested the relative contribution of PheRS when added to a model containing a summary measure of inherited genetic susceptibility (PRS) plus other covariates like age, sex, smoking status, drinking status, and body mass index (BMI).

RESULTS

Our exploration of co-occurrence patterns identified expected associations while also revealing unexpected relationships that may warrant closer attention. Solely using the pancreatic cancer PheRS at 5 years before the target diagnoses yielded an AUC of 0.60 (95% CI = [0.58, 0.62]) in UKB. A larger predictive model including PheRS, PRS, and the covariates at the 5-year threshold achieved an AUC of 0.74 (95% CI = [0.72, 0.76]) in UKB. We note that PheRS does contribute independently in the joint model. Finally, scores at the top percentiles of the PheRS distribution demonstrated promise in terms of risk stratification. Scores in the top 2% were 10.20 (95% CI = [9.34, 12.99]) times more likely to identify cases than those in the bottom 98% in UKB at the 5-year threshold prior to pancreatic cancer diagnosis.

CONCLUSIONS

We developed a framework for creating a time-restricted PheRS from EHR data for pancreatic cancer using the rich information content of a medical phenome. In addition to identifying hypothesis-generating associations for future research, this PheRS demonstrates a potentially important contribution in identifying high-risk individuals, even after adjusting for PRS for pancreatic cancer and other traditional epidemiologic covariates. The methods are generalizable to other phenotypic traits.

PET/MRI for staging patients with Hodgkin lymphoma: equivalent results with PET/CT in a prospective trial

To compare FDG-PET/unenhanced MRI and FDG-PET/diagnostic CT in detecting infiltration in patients with newly diagnosed Hodgkin lymphoma (HL). The endpoint was equivalence between PET/MRI and PET/CT in correctly defining the revised Ann Arbor staging system. Seventy consecutive patients with classical-HL were prospectively investigated for nodal and extra-nodal involvement during pretreatment staging with same-day PET/CT and PET/MRI. Findings indicative of malignancy with the imaging procedures were regarded as lymphoma infiltration; in case of discrepancy, positive-biopsy and/or response to treatment were evidenced as lymphoma. Sixty of the 70 (86%) patients were evaluable having completed the staging program. Disease staging based on either PET/MRI or PET/CT was correct for 54 of the 60 patients (90% vs. 90%), with difference between proportions of 0.0 (95% CI, -9 to 9%; P=0.034 for the equivalence test). As compared with reference standard, invasion of lymph nodes was identified with PET/MRI in 100% and with PET/CT in 100%, of the spleen with PET/MRI in 66% and PET/CT in 55%, of the lung with PET/MRI in 60% and PET/CT in 100%, of the liver with PET/MRI in 67% and PET/CT in 100%, and of the bone with PET/MRI in 100% and PET/CT in 50%. The only statistically significant difference between PET/MRI and PET/CT was observed in bony infiltration detection rates. For PET/CT, iodinate contrast medium infusions' average was 86 mL, and exposure to ionizing radiation was estimated to be 4-fold higher than PET/MRI. PET/MRI is a promising safe new alternative in the care of patients with HL.

Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations

OBJECTIVES

To evaluate the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India and unmask the state-wise variations in terms of multiple public health metrics.

DESIGN

Cohort study (daily time series of case counts).

SETTING

Observational and population based.

PARTICIPANTS

Confirmed COVID-19 cases nationally and across 20 states that accounted for >99% of the current cumulative case counts in India until 31 May 2020.

EXPOSURE

Lockdown (non-medical intervention).

MAIN OUTCOMES AND MEASURES

We illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case fatality rates, doubling times of cases, effective reproduction numbers and the scale of testing.

RESULTS

The estimated effective reproduction number R for India was 3.36 (95% CI 3.03 to 3.71) on 24 March, whereas the average of estimates from 25 May to 31 May stands at 1.27 (95% CI 1.26 to 1.28). Similarly, the estimated doubling time across India was at 3.56 days on 24 March, and the past 7-day average for the same on 31 May is 14.37 days. The average daily number of tests increased from 1717 (19-25 March) to 113 372 (25-31 May) while the test positivity rate increased from 2.1% to 4.2%, respectively. However, various states exhibit substantial departures from these national patterns.

CONCLUSIONS

Patterns of change over lockdown periods indicate the lockdown has been partly effective in slowing the spread of the virus nationally. However, there exist large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualisation tools that are daily updated at covind19.org.

Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks

To facilitate scientific collaboration on polygenic risk scores (PRSs) research, we created an extensive PRS online repository for 35 common cancer traits integrating freely available genome-wide association studies (GWASs) summary statistics from three sources: published GWASs, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWASs. Our framework condenses these summary statistics into PRSs using various approaches such as linkage disequilibrium pruning/p value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRSs in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRSs. We expect this integrated platform to accelerate PRS-related cancer research.

Integrated analysis of DNA methylation profile in HLA-G gene and imaging in coronary heart disease

Aims

Immune endothelial inflammation, underlie coronary heart disease (CHD) related phenotypes, could provide new insight into the pathobiology of the disease. We investigated DNA methylation level of the unique CpG island of HLA-G gene in CHD patients and evaluated the correlation with cardiac computed tomography angiography (CCTA) features.

Methods

Thirty-two patients that underwent CCTA for suspected CHD were enrolled for this study. Obstructive CHD group included fourteen patients, in which there was a stenosis greater than or equal to 50% in one or more of the major coronary arteries detected; whereas subjects with Calcium (Ca) Score=0, uninjured coronaries and with no obstructive CHD were considered as control subjects (Ctrls) (n=18). For both groups, DNA methylation profile of the whole 5'UTR-CpG island of HLA-G was measured. The plasma soluble HLA-G (sHLA-G) levels were detected in all subjects by specific ELISA assay. Statistical analysis was performed using R software.

Results

For the first time, our study reported that 1) a significant hypomethylation characterized three specific fragments (B, C and F) of the 5'UTR-CpG island (p=0.05) of HLA-G gene in CHD patients compared to Ctrl group; 2) hypomethylation level of one specific fragment positively correlated with coronary Ca score, a relevant parameter of CCTA (p&lt;0.05) between two groups.

Conclusions

Our results showed that reduced levels of circulating HLA-G molecules could derive from epigenetic marks inducing hypomethylation of specific regions into 5'UTR-CpG island of HLA-G gene in CHD patients with obstructive coronary stenosis vs non critical stenosis group.

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): Italian Minister of Health

Characteristics Associated With Racial/Ethnic Disparities in COVID-19 Outcomes in an Academic Health Care System

Importance

Black patients are overrepresented in the number of COVID-19 infections, hospitalizations, and deaths in the US. Reasons for this disparity may be due to underlying comorbidities or sociodemographic factors that require further exploration.

Objective

To systematically determine patient characteristics associated with racial/ethnic disparities in COVID-19 outcomes.

Design, Setting, and Participants

This retrospective cohort study used comparative groups of patients tested or treated for COVID-19 at the University of Michigan from March 10, 2020, to April 22, 2020, with an outcome update through July 28, 2020. A group of randomly selected untested individuals were included for comparison. Examined factors included race/ethnicity, age, smoking, alcohol consumption, comorbidities, body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), and residential-level socioeconomic characteristics.

Exposure

In-house polymerase chain reaction (PCR) tests, commercial antibody tests, nasopharynx or oropharynx PCR deployed by the Michigan Department of Health and Human Services and reverse transcription-PCR tests performed in external labs.

Main Outcomes and Measures

The main outcomes were being tested for COVID-19, having test results positive for COVID-19 or being diagnosed with COVID-19, being hospitalized for COVID-19, requiring intensive care unit (ICU) admission for COVID-19, and COVID-19-related mortality (including inpatient and outpatient). Medical comorbidities were defined from the International Classification of Diseases, Ninth Revision, and International Classification of Diseases, Tenth Revision, codes and were aggregated into a comorbidity score. Associations with COVID-19 outcomes were examined using odds ratios (ORs).

Results

Of 5698 patients tested for COVID-19 (mean [SD] age, 47.4 [20.9] years; 2167 [38.0%] men; mean [SD] BMI, 30.0 [8.0]), most were non-Hispanic White (3740 patients [65.6%]) or non-Hispanic Black (1058 patients [18.6%]). The comparison group included 7168 individuals who were not tested (mean [SD] age, 43.1 [24.1] years; 3257 [45.4%] men; mean [SD] BMI, 28.5 [7.1]). Among 1139 patients diagnosed with COVID-19, 492 (43.2%) were White and 442 (38.8%) were Black; 523 (45.9%) were hospitalized, 283 (24.7%) were admitted to the ICU, and 88 (7.7%) died. Adjusting for age, sex, socioeconomic status, and comorbidity score, Black patients were more likely to be hospitalized compared with White patients (OR, 1.72 [95% CI, 1.15-2.58]; P = .009). In addition to older age, male sex, and obesity, living in densely populated areas was associated with increased risk of hospitalization (OR, 1.10 [95% CI, 1.01-1.19]; P = .02). In the overall population, higher risk of hospitalization was also observed in patients with preexisting type 2 diabetes (OR, 1.82 [95% CI, 1.25-2.64]; P = .02) and kidney disease (OR, 2.87 [95% CI, 1.87-4.42]; P < .001). Compared with White patients, obesity was associated with higher risk of having test results positive for COVID-19 among Black patients (White: OR, 1.37 [95% CI, 1.01-1.84]; P = .04. Black: OR, 3.11 [95% CI, 1.64-5.90]; P < .001; P for interaction = .02). Having any cancer was associated with higher risk of positive COVID-19 test results for Black patients (OR, 1.82 [95% CI, 1.19-2.78]; P = .005) but not White patients (OR, 1.08 [95% CI, 0.84-1.40]; P = .53; P for interaction = .04). Overall comorbidity burden was associated with higher risk of hospitalization in White patients (OR, 1.30 [95% CI, 1.11-1.53]; P = .001) but not in Black patients (OR, 0.99 [95% CI, 0.83-1.17]; P = .88; P for interaction = .02), as was type 2 diabetes (White: OR, 2.59 [95% CI, 1.49-4.48]; P < .001; Black: OR, 1.17 [95% CI, 0.66-2.06]; P = .59; P for interaction = .046). No statistically significant racial differences were found in ICU admission and mortality based on adjusted analysis.

Conclusions and Relevance

These findings suggest that preexisting type 2 diabetes or kidney diseases and living in high-population density areas were associated with higher risk for COVID-19 hospitalization. Associations of risk factors with COVID-19 outcomes differed by race.

Understanding the patterns of repeated testing for COVID-19: Association with patient characteristics and outcomes

Importance The diagnostic tests for COVID-19 have a high false negative rate, but not everyone with an initial negative result is re-tested. Michigan Medicine, being one of the primary regional centers accepting COVID-19 cases, provided an ideal setting for studying COVID-19 repeated testing patterns during the first wave of the pandemic. Objective To identify the characteristics of patients who underwent repeated testing for COVID-19 and determine if repeated testing was associated with patient characteristics and with downstream outcomes among positive cases. Design This cross-sectional study described the pattern of testing for COVID-19 at Michigan Medicine. The main hypothesis under consideration is whether patient characteristics differed between those tested once and those who underwent multiple tests. We then restrict our attention to those that had at least one positive test and study repeated testing patterns in patients with severe COVID-19 related outcomes (testing positive, hospitalization and ICU care). Setting Demographic and clinical characteristics, test results, and health outcomes for 15,920 patients presenting to Michigan Medicine between March 10 and June 4, 2020 for a diagnostic test for COVID-19 were collected from their electronic medical records on June 24, 2020. Data on the number and types of tests administered to a given patient, as well as the sequences of patient-specific test results were derived from records of patient laboratory results. Participants Anyone tested between March 10 and June 4, 2020 at Michigan Medicine with a diagnostic test for COVID-19 in their Electronic Health Records were included in our analysis. Exposures Comparison of repeated testing across patient demographics, clinical characteristics, and patient outcomes Main Outcomes and Measures Whether patients underwent repeated diagnostic testing for SARS CoV-2 in Michigan Medicine Results Between March 10th and June 4th, 19,540 tests were ordered for 15,920 patients, with most patients only tested once (13596, 85.4%) and never testing positive (14753, 92.7%). There were 5 patients who got tested 10 or more times and there were substantial variations in test results within a patient. After fully adjusting for patient and neighborhood socioeconomic status (NSES) and demographic characteristics, patients with circulatory diseases (OR: 1.42; 95% CI: (1.18, 1.72)), any cancer (OR: 1.14; 95% CI: (1.01, 1.29)), Type 2 diabetes (OR: 1.22; 95% CI: (1.06, 1.39)), kidney diseases (OR: 1.95; 95% CI: (1.71, 2.23)), and liver diseases (OR: 1.30; 95% CI: (1.11, 1.50)) were found to have higher odds of undergoing repeated testing when compared to those without. Additionally, as compared to non-Hispanic whites, non-Hispanic blacks were found to have higher odds (OR: 1.21; 95% CI: (1.03, 1.43)) of receiving additional testing. Females were found to have lower odds (OR: 0.86; 95% CI: (0.76, 0.96)) of receiving additional testing than males. Neighborhood poverty level also affected whether to receive additional testing. For 1% increase in proportion of population with annual income below the federal poverty level, the odds ratio of receiving repeated testing is 1.01 (OR: 1.01; 95% CI: (1.00, 1.01)). Focusing on only those 1167 patients with at least one positive result in their full testing history, patient age in years (OR: 1.01; 95% CI: (1.00, 1.03)), prior history of kidney diseases (OR: 2.15; 95% CI: (1.36, 3.41)) remained significantly different between patients who underwent repeated testing and those who did not. After adjusting for both patient demographic factors and NSES, hospitalization (OR: 7.44; 95% CI: (4.92, 11.41)) and ICU-level care (OR: 6.97; 95% CI: (4.48, 10.98)) were significantly associated with repeated testing. Of these 1167 patients, 306 got repeated testing and 1118 tests were done on these 306 patients, of which 810 (72.5%) were done during inpatient stays, substantiating that most repeated tests for test positive patients were done during hospitalization or ICU care. Additionally, using repeated testing data we estimate the "real world" false negative rate of the RT-PCR diagnostic test was 23.8% (95% CI: (19.5%, 28.5%)). Conclusions and Relevance This study sought to quantify the pattern of repeated testing for COVID-19 at Michigan Medicine. While most patients were tested once and received a negative result, a meaningful subset of patients (2324, 14.6% of the population who got tested) underwent multiple rounds of testing (5,944 tests were done in total on these 2324 patients, with an average of 2.6 tests per person), with 10 or more tests for five patients. Both hospitalizations and ICU care differed significantly between patients who underwent repeated testing versus those only tested once as expected. These results shed light on testing patterns and have important implications for understanding the variation of repeated testing results within and between patients.

COVID-19 outcomes, risk factors and associations by race: a comprehensive analysis using electronic health records data in Michigan Medicine

IMPORTANCE

Blacks/African-Americans are overrepresented in the number of COVID-19 infections, hospitalizations and deaths. Reasons for this disparity have not been well-characterized but may be due to underlying comorbidities or sociodemographic factors.

OBJECTIVE

To systematically determine patient characteristics associated with racial/ethnic disparities in COVID-19 outcomes.

DESIGN

A retrospective cohort study with comparative control groups.

SETTING

Patients tested for COVID-19 at University of Michigan Medicine from March 10, 2020 to April 22, 2020.

PARTICIPANTS

5,698 tested patients and two sets of comparison groups who were not tested for COVID-19: randomly selected unmatched controls (n = 7,211) and frequency-matched controls by race, age, and sex (n = 13,351). Main Outcomes and Measures: We identified factors associated with testing and testing positive for COVID-19, being hospitalized, requiring intensive care unit (ICU) admission, and mortality (in/out-patient during the time frame). Factors included race/ethnicity, age, smoking, alcohol consumption, healthcare utilization, and residential-level socioeconomic characteristics (SES; i.e., education, unemployment, population density, and poverty rate). Medical comorbidities were defined from the International Classification of Diseases (ICD) codes, and were aggregated into a comorbidity score.

RESULTS

Of 5,698 patients, (median age, 47 years; 38% male; mean BMI, 30.1), the majority were non-Hispanic Whites (NHW, 59.2%) and non-Hispanic Black/African-Americans (NHAA, 17.2%). Among 1,119 diagnosed, there were 41.2% NHW and 37.4% NHAA; 44.8% hospitalized, 20.6% admitted to ICU, and 3.8% died. Adjusting for age, sex, and SES, NHAA were 1.66 times more likely to be hospitalized (95% CI, 1.09-2.52; P=.02), 1.52 times more likely to enter ICU (95% CI, 0.92-2.52; P=.10). In addition to older age, male sex and obesity, high population density neighborhood (OR, 1.27 associated with one SD change [95% CI, 1.20-1.76]; P=.02) was associated with hospitalization. Pre-existing kidney disease led to 2.55 times higher risk of hospitalization (95% CI, 1.62-4.02; P<.001) in the overall population and 11.9 times higher mortality risk in NHAA (95% CI, 2.2-64.7, P=.004).

CONCLUSIONS AND RELEVANCE

Pre-existing type II diabetes/kidney diseases and living in high population density areas were associated with high risk for COVID-19 susceptibility and poor prognosis. Association of risk factors with COVID-19 outcomes differed by race. NHAA patients were disproportionately affected by obesity and kidney disease.

A Comprehensive Public Health Evaluation of Lockdown as a Non-pharmaceutical Intervention on COVID-19 Spread in India: National Trends Masking State Level Variations

Introduction

India has been under four phases of a national lockdown from March 25 to May 31 in response to the COVID-19 pandemic. Unmasking the state-wise variation in the effect of the nationwide lockdown on the progression of the pandemic could inform dynamic policy interventions towards containment and mitigation.

Methods

Using data on confirmed COVID-19 cases across 20 states that accounted for more than 99% of the cumulative case counts in India till May 31, 2020, we illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case-fatality rates, doubling times of cases, effective reproduction numbers, and the scale of testing.

Results

The estimated effective reproduction number R for India was 3.36 (95% confidence interval (CI): [3.03, 3.71]) on March 24, whereas the average of estimates from May 25 - May 31 stands at 1.27 (95% CI: [1.26, 1.28]). Similarly, the estimated doubling time across India was at 3.56 days on March 24, and the past 7-day average for the same on May 31 is 14.37 days. The average daily number of tests have increased from 1,717 (March 19-25) to 131,772 (May 25-31) with an estimated testing shortfall of 4.58 million tests nationally by May 31. However, various states exhibit substantial departures from these national patterns.

Conclusions

Patterns of change over lockdown periods indicate the lockdown has been effective in slowing the spread of the virus nationally. The COVID-19 outbreak in India displays large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualization tools that are daily updated at covind19.org.

Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms

With only 536 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25. The lockdown was first extended to May 3 soon after the analysis of this paper was completed, and then to May 18 while this paper was being revised. In this paper, we use a Bayesian extension of the Susceptible-Infected-Removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other less severe non-pharmaceutical interventions. We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, can reduce the total number of cases in the short term, and buy India invaluable time to prepare its healthcare and disease-monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured by reduction in the number of cases). A longer lockdown between 42-56 days is preferable to substantially "flatten the curve" when compared to 21-28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and, thus, inform policymakers about one aspect of this multi-faceted decision-making problem. We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods and tools that can enable data-driven policymaking during a pandemic. Our software products are available at covind19.org.

Are women leaders significantly better at controlling the contagion during the COVID-19 pandemic?

Recent media articles have suggested that women-led countries are doing better in terms of their responses to the COVID-19 pandemic. We examine an ensemble of public health metrics to assess the control of COVID-19 epidemic in women-versus men-led countries worldwide based on data available up to June 3. The median of the distribution of median time-varying effective reproduction number for women- and men-led countries were 0.89 and 1.14 respectively with the 95% two-sample bootstrap-based confidence interval for the difference (women - men) being [-0.34, 0.02]. In terms of scale of testing, the median percentage of population tested were 3.28% (women), 1.59% (men) [95% CI: (-1.29%, 3.60%)] with test positive rates of 2.69% (women) and 4.94% (men) respectively. It appears that though statistically not significant, countries led by women have an edge over countries led by men in terms of public health metrics for controlling the spread of the COVID-19 pandemic worldwide.

In vivo imaging of CNS microglial activation/macrophage infiltration with combined [18F]DPA-714-PET and SPIO-MRI in a mouse model of relapsing remitting experimental autoimmune encephalomyelitis

Purpose

To evaluate the feasibility and sensitivity of multimodality PET/CT and MRI imaging for non-invasive characterization of brain microglial/macrophage activation occurring during the acute phase in a mouse model of relapsing remitting multiple sclerosis (RR-MS) using [¹⁸F]DPA-714, a selective radioligand for the 18-kDa translocator protein (TSPO), superparamagnetic iron oxide particles (SPIO), and ex vivo immunohistochemistry.

Methods

Experimental autoimmune encephalomyelitis (EAE) was induced in female SJL/J mice by immunization with PLP_139–151. Seven symptomatic EAE mice and five controls underwent both PET/CT and MRI studies between 11 and 14 days post-immunization. SPIO was injected i.v. in the same animals immediately after [¹⁸F]DPA-714 and MRI acquisition was performed after 24 h. Regional brain volumes were defined according to a mouse brain atlas on co-registered PET and SPIO-MRI images. [¹⁸F]DPA-714 standardized uptake value (SUV) ratios (SUVR), with unaffected neocortex as reference, and SPIO fractional volumes (SPIO-Vol) were generated. Both SUVR and SPIO-Vol values were correlated with the clinical score (CS) and among them. Five EAE and four control mice underwent immunohistochemical analysis with the aim of identifying activated microglia/macrophage and TSPO expressions.

Results

SUVR and SPIO-Vol values were significantly increased in EAE compared with controls in the hippocampus (p < 0.01; p < 0.02, respectively), thalamus (p < 0.02; p < 0.05, respectively), and cerebellum and brainstem (p < 0.02), while only SPIO-Vol was significantly increased in the caudate/putamen (p < 0.05). Both SUVR and SPIO-Vol values were positively significantly correlated with CS and among them in the same regions. TSPO/Iba1 and F4/80/Prussian blue staining immunohistochemistry suggests that increased activated microglia/macrophages underlay TSPO expression and SPIO uptake in symptomatic EAE mice.

Conclusions

These preliminary results suggest that both activated microglia and infiltrated macrophages are present in vulnerable brain regions during the acute phase of PLP-EAE and contribute to disease severity. Both [¹⁸F]DPA-714-PET and SPIO-MRI appear suitable modalities for preclinical study of neuroinflammation in MS mice models.

2-deoxy-2[F-18] fluoro-D-glucose positron emission tomography Deauville scale and core-needle biopsy to determine successful management after six doxorubicin, bleomycin, vinblastine and dacarbazine cycles in advanced-stage Hodgkin lymphoma

BACKGROUND

The clinical impact of the positivity of the Deauville scale (DS) of positron emission tomography (PET) performed at the end of doxorubicin, bleomycin, vinblastine and dacarbazine (ABVD) in patients with advanced Hodgkin lymphoma (HL), in terms of providing rationale to shift poor responders onto a more intensive regimen, remain to be validated by histopathology.

PATIENTS AND METHODS

This prospective trial involved patients with stage IIB/IV HL who after six ABVD cycles underwent PET (PET6) and core-needle cutting biopsy (CNCB) of 2-deoxy-2[F-18] fluoro-d-glucose (FDG)-avid lymph nodes. Patients received high-dose chemotherapy/autologous haematopoietic stem cell rescue (HDCT/AHSCR) if CNCB was positive for HL, alternatively, if CNCB or PET was negative, received observation or consolidation radiotherapy (cRT) on residual nodal masses, as initially planned. The end-point was 5-year progression-free survival (PFS).

RESULTS

In all, 43 of the 169 (25%) evaluable patients were PET6 positive (DS 4, 32; DS 5, 11). Among them, histology showed malignancy (HL) in 100% of DS 5 scores and in 12.5% of DS 4 scores. Fifteen patients with positive biopsy received HDCT/AHSCR, whereas 28 patients with negative biopsy, as well as 126 patients with negative PET6, continued the original plan (cRT, 78 patients; observation, 76 patients). The 5-year PFS in the negative PET6 group, negative biopsy group and positive biopsy group was 95.4%, 100% and 52.5%, respectively.

CONCLUSION

DS positivity of end-of-ABVD PET in advanced HL carried a certain number of CNCB-proven non-malignant FDG-uptakes. The DS 4 scores which were found to have negative histology appeared to benefit from continuing the original non-intensive therapeutic plane as indicated by the successful outcome in more than 95% of them by obtaining similar 5-year PFS to the PET6-negative group. By contrast, the DS 5 score had consistently positive histology and was associated with unsuccessful conventional therapy, promptly requiring treatment intensification or innovative therapeutic approaches.

The Undiagnosed Diseases Network International: Five years and more!

Undiagnosed rare diseases (URDs) account for a significant portion of the overall rare disease burden, depending upon the country. Hence, URDs represent an unmet medical need. A specific challenge posed by the ensemble of the URD patient cohort is the heterogeneity of its composition; the group, indeed, includes very rare, still unidentified conditions as well as clinical variants of recognized rare diseases. Exact disease recognition requires new approaches that cut across national and institutional boundaries, may need the implementation of methods new to diagnostics, and embrace clinical care and research. To address these issues, the Undiagnosed Diseases Network International (UDNI) was established in 2014, with the major aims of providing diagnoses to patients, implementing additional diagnostic tools, and fostering research on novel diseases, their mechanisms, and their pathways. The UDNI involves centres with internationally recognized expertise, and its scientific resources and know-how aim to fill the knowledge gaps that impede diagnosis, in particularly for ultra-rare diseases. Consequently, the UDNI fosters the translation of research into medical practice, aided by active patient involvement. The goals of the UDNI are to work collaboratively and at an international scale to: 1) provide diagnoses for individuals who have conditions that have eluded diagnosis by clinical experts; 2) gain insights into the etiology and pathogenesis of novel diseases; 3) contribute to standards of diagnosing unsolved patients; and 4) share the results of UDNI research in a timely manner and as broadly as possible.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Biobanks linked to electronic health records provide rich resources for health-related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis-generating studies of disease-treatment, disease-exposure, and disease-gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank-based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank-based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.

Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions

The statistical practice of modeling interaction with two linear main effects and a product term is ubiquitous in the statistical and epidemiological literature. Most data modelers are aware that the misspecification of main effects can potentially cause severe type I error inflation in tests for interactions, leading to spurious detection of interactions. However, modeling practice has not changed. In this article, we focus on the specific situation where the main effects in the model are misspecified as linear terms and characterize its impact on common tests for statistical interaction. We then propose some simple alternatives that fix the issue of potential type I error inflation in testing interaction due to main effect misspecification. We show that when using the sandwich variance estimator for a linear regression model with a quantitative outcome and two independent factors, both the Wald and score tests asymptotically maintain the correct type I error rate. However, if the independence assumption does not hold or the outcome is binary, using the sandwich estimator does not fix the problem. We further demonstrate that flexibly modeling the main effect under a generalized additive model can largely reduce or often remove bias in the estimates and maintain the correct type I error rate for both quantitative and binary outcomes regardless of the independence assumption. We show, under the independence assumption and for a continuous outcome, overfitting and flexibly modeling the main effects does not lead to power loss asymptotically relative to a correctly specified main effect model. Our simulation study further demonstrates the empirical fact that using flexible models for the main effects does not result in a significant loss of power for testing interaction in general. Our results provide an improved understanding of the strengths and limitations for tests of interaction in the presence of main effect misspecification. Using data from a large biobank study "The Michigan Genomics Initiative", we present two examples of interaction analysis in support of our results.

Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms

With only 536 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25. The lockdown was first extended to May 3 soon after the analysis of this paper was completed, and then to May 18 while this paper was being revised. In this paper, we use a Bayesian extension of the Susceptible-Infected-Removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other less severe non-pharmaceutical interventions. We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, can reduce the total number of cases in the short term, and buy India invaluable time to prepare its healthcare and disease-monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured by reduction in the number of cases). A longer lockdown between 42-56 days is preferable to substantially "flatten the curve" when compared to 21-28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and, thus, inform policymakers about one aspect of this multi-faceted decision-making problem. We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods and tools that can enable data-driven policymaking during a pandemic. Our software products are available at covind19.org.

Non-Gaussian models of diffusion weighted imaging for detection and characterization of prostate cancer: a systematic review and meta-analysis

The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. In the last decade, due to the mono-exponential model limitations, several studies investigated non-Gaussian DWI models and their utility in PCa diagnosis. Since their results were often inconsistent and conflicting, we performed a systematic review of studies from 2012 examining the most commonly used Non-Gaussian DWI models for PCa detection and characterization. A meta-analysis was conducted to assess the ability of each Non-Gaussian model to detect PCa lesions and distinguish between low and intermediate/high grade lesions. Weighted mean differences and 95% confidence intervals were calculated and the heterogeneity was estimated using the I² statistic. 29 studies were selected for the systematic review, whose results showed inconsistence and an unclear idea about the actual usefulness and the added value of the Non-Gaussian model parameters. 12 studies were considered in the meta-analyses, which showed statistical significance for several non-Gaussian parameters for PCa detection, and to a lesser extent for PCa characterization. Our findings showed that Non-Gaussian model parameters may potentially play a role in the detection and characterization of PCa but further studies are required to identify a standardized DWI acquisition protocol for PCa diagnosis.

P6431Association of circulating miR-765, miR-93-5p and miR-433-3p with obstructive coronary heart disease evaluated by cardiac computed tomography

Background

Although advances in diagnosis, treatment and prognosis, coronary heart disease (CHD) is still the most prevalent cause of mortality and morbidity worldwide. Thus, there is still the need to identify both novel diagnostic and prognostic biomarkers to improve the clinical decision-making and help to stratify patients for early preventive treatment. Epigenetic-sensitive mechanisms may be related both to pathogenesis and prognosis of CHD. Among the epigenetic hallmarks, microRNAs (miRNAs), acting as flexible modulators of gene expression, could represent attractive candidate biomarkers useful in clinical practice.

Purpose

We prospectively investigated the expression pattern of circulating miRNAs in patients undergoing Cardiac Computed Tomography (CCT) for suspected CHD (n=95) with the aim to integrate molecular findings with morphological and clinical parameters derived by CCT.

Methods

CCT was performed with a third-generation dual source multidetector computed tomography scanner. Peripheral venous blood samples were collected into EDTA after a 12 h fasting in the same day of CCT, before imaging execution and the levels of 42 selected plasmatic miRNAs were analyzed by qRT-PCR.

Results

Let-7c-5p, miR-765, miR-483-5p, miR-31-5p and miR-206 were upregulated in CHD patients (n=66) vs healthy subjects HS (n=29) as well as let-7c-5p, miR-765, miR- 483-5p showed higher expression in obstructive CHD (n=36) compared to no obstructive CHD patients (n=66). In addition, miR-93-5p and miR-433-3p showed an upregulation in patients with critical coronary stenosis. Multivariate regression analysis demonstrated that miR-765, miR-31-5p and miR-206 were independently associated with CHD also in combination with Framingham risk score. Relevantly, miR-765, miR-93-5p and miR-433-3p were obstructive CHD predictors. ROC curve analysis also revealed a good performance for miR-765, miR-93-5p and miR-433-3p on predicting CHD severity.

Circulating microRNA expression

Conclusions

Our study represents a combined epigenetic/imaging approach useful to support the diagnosis and prediction of CHD.

Acknowledgement/Funding

Italian Ministry of Health grants: “Giovani Ricercatori 2011-12” (project code GR-2011-02349436) and “Ricerca Corrente 2018”

Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb

Polygenic risk scores (PRS) are designed to serve as single summary measures that are easy to construct, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The primary focus of this paper is to demonstrate how we can combine PRS and electronic health records data to better understand the shared and unique genetic architecture and etiology of disease subtypes that may be both related and heterogeneous. PRS construction strategies often depend on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. We consider several choices for constructing a PRS using data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict not just the primary phenotype but also secondary phenotypes derived from electronic health records (EHR). This study was conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. We examine the three most common skin cancer subtypes in the USA: basal cell carcinoma, cutaneous squamous cell carcinoma, and melanoma. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate PRS associations with secondary traits. PheWAS results are then replicated using population-based UK Biobank data and compared across various PRS construction methods. We develop an accompanying visual catalog called PRSweb that provides detailed PheWAS results and allows users to directly compare different PRS construction methods.

A comprehensive gene-environment interaction analysis in Ovarian Cancer using genome-wide significant common variants

As a follow-up to genome-wide association analysis of common variants associated with ovarian carcinoma (cancer), our study considers seven well-known ovarian cancer risk factors and their interactions with 28 genome-wide significant common genetic variants. The interaction analyses were based on data from 9971 ovarian cancer cases and 15,566 controls from 17 case-control studies. Likelihood ratio and Wald tests for multiplicative interaction and for relative excess risk due to additive interaction were used. The top multiplicative interaction was noted between oral contraceptive pill (OCP) use (ever vs. never) and rs13255292 (p value = 3.48 × 10-4 ). Among women with the TT genotype for this variant, the odds ratio for OCP use was 0.53 (95% CI = 0.46-0.60) compared to 0.71 (95%CI = 0.66-0.77) for women with the CC genotype. When stratified by duration of OCP use, women with 1-5 years of OCP use exhibited differential protective benefit across genotypes. However, no interaction on either the multiplicative or additive scale was found to be statistically significant after multiple testing correction. The results suggest that OCP use may offer increased benefit for women who are carriers of the T allele in rs13255292. On the other hand, for women carrying the C allele in this variant, longer (5+ years) use of OCP may reduce the impact of carrying the risk allele of this SNP. Replication of this finding is needed. The study presents a comprehensive analytic framework for conducting gene-environment analysis in ovarian cancer.

Sentinel Lymph Node Detection by Lymphoscintigraphy in Malignant Melanoma

Aims and Background

Sentinel lymph node (SLN) detection is currently employed in patients with malignant melanoma (MM) to spare them unnecessary lymph node dissection.

Methods and Study Design

We investigated 241 patients (130 men and 111 women, median age, 50 years (range, 14-92)) with MM (192 before and 51 after surgical biopsy); two of them had more than one melanoma lesion. In each patient approx. 10 MBq of 99mTc Nanocoll in 0.1 mL (Nycomed Amersham Sorin; particle size range, 3-80 nm) was injected intradermally around the MM lesion or surgical scar. Dynamic acquisition was performed for 20 minutes (20 frames/min) and the study was concluded within four hours of injection. Using an external radioactive marker, the skin over the SLN was marked with China ink.

Results

294 SLNs were scintigraphically identified: 117 in the inguinal region, 147 in the axillae, four in the submandibular region, three in the laterocervical region and 23 at other sites. In two patients no drainage was detected. In 43 patients more than one sentinel node was identified. In 13 patients with lesions located in the trunk the tracer drained towards multiple lymph node stations or unexpected lymph nodes (nine cases). Histology and immunohistochemistry diagnosed MM in 25 SLNs; 19 were positive for metastasis with hematoxylin-eosin staining, five with Hmb45 and one with CD68 immunostaining. All 25 detected lymphatic basins were excised. In nine of these basins there was metastatic involvement of at least one other lymph node besides the SLN. During follow-up, which ranged from six to 86 months, metastatic disease was found in only one patient with a histologically negative SLN six months after surgery.

Conclusions

This study confirms the utility of scintigraphic SLN detection in patients with MM. In most of the cases the procedure led the surgeon to evaluate the drainage area, which is unpredictable for lesions in the trunk and may be difficult to delineate using only patent blue dye. Furthermore, in approximately 10% of cases we observed dual drainage from individual lesions, mainly those located on the trunk. We will proceed to compare the results obtained during follow-up with those of an investigational group of patients with melanoma who were not subjected to lymphoscintigraphy for SLN detection in order to obtain well-founded information on the prognostic value of this technique.

Epirubicin in Combination Chemotherapy in the Treatment of Advanced Stage Non-Hodgkin's Lymphomas

From April 1981 to May 1984, 23 patients with advanced non-Hodgkin's lymphomas were treated with CEOP (cyclophosphamide, epirubicin, vincristine, and prednisone) or OEPP (vincristine, epirubicin, procarbazine, and prednisone) combination chemotherapy. CR was achieved in 58 % and PR in 31 % of the patients, giving an overall response rate of 89 %. Nine of 15 (60 %) previously untreated patients with unfavorable histology obtained a CR and 5 a PR. Median relapse-free survival was 33 months; median overall survival has not yet been reached, and the probability of survival for CRs was 91 % after 54 months of follow-up. Acute toxicity was quite acceptable, and chronic cardiac toxicity was detected in 6 patients only. In conclusion, epirubicin used in combination chemotherapies induced durable remissions and prolonged survivals in advanced non-Hodgkin's lymphomas.

Malignant Melanoma of the Nipple: A Case Studied with Radiolabeled Monoclonal Antibody

We describe a case of histologically confirmed malignant melanoma of the nipple. The rare occurrence of these lesions accounts for the relative lack of criteria for standard surgical treatment. After a conventional workup including mammography, chest X ray, bone scan, liver ultrasonography and cytologic smear of the lesion, we used specific radiolabeled monoclonal antibody and external photoscanning to differentiate melanoma from Paget's disease. The patient underwent wide local excision of the lesion and axillary node dissection, and tumor control is optimal since she has no evidence of disease after 5 years of follow-up.

A multiparametric and multiscale approach to automated segmentation of brain veins

Cerebral vein analysis provides a fundamental tool to study brain diseases such as neurodegenerative disorders or traumatic brain injuries. In order to assess the vascular anatomy, manual segmentation approaches can be used but are observer-dependent and time-consuming. In the present work, a fully automated cerebral vein segmentation method is proposed, based on a multiscale and multiparametric approach. The combined investigation of the R2(*)- and a Vesselness probability-map was used to obtain a fast and highly reliable classification of venous voxels. A semiquantitative analysis showed that our approach outperformed the previous state-of-the-art algorithm both in sensitivity and specificity. Inclusion of this tool within a parametric brain framework may therefore pave the way for a quantitative study of the intracranial venous system.

Anti-CEA and Other Antibodies in the Study of Gastrointestinal Tumors

Localization of gastrointestinal tumors by means of labeled monoclonal antibodies is a new, sensitive and suitable technique currently used in several centers. Encouraging results have been documented with several monoclonal antibodies by different authors. This article reviews our experience with radioimmunoscintigraphy in 59 patients with colorectal cancer in follow-up, using 131I and 111In labeled B72.3, and in 16 patients with primary gastrointestinal tumors using 99mTc anti-CEA monoclonal antibody (type F023C5). The sensitivity of both B72.3 and anti-CEA was greater than 70% either for primary tumors and abdominal recurrences or distant metastases except hepatic ones. A significant gradient in antibody uptake was measured on surgical biopsies between tumors and normal tissues allowing a good in vivo contrast for gamma detection. We have defined the impact of some factors affecting in vivo tumor targeting. In fact, pharmacodynamics of MAbs, percentage of injected dose bound to tissues were measured, and in particular antigenic content in tumor nodules was quantified. Furthermore, the results of RIS were compared to those obtained by CT and other imaging modalities.

The SubCons webserver: A user friendly web interface for state-of-the-art subcellular localization prediction

SubCons is a recently developed method that predicts the subcellular localization of a protein. It combines predictions from four predictors using a Random Forest classifier. Here, we present the user-friendly web-interface implementation of SubCons. Starting from a protein sequence, the server rapidly predicts the subcellular localizations of an individual protein. In addition, the server accepts the submission of sets of proteins either by uploading the files or programmatically by using command line WSDL API scripts. This makes SubCons ideal for proteome wide analyses allowing the user to scan a whole proteome in few days. From the web page, it is also possible to download precalculated predictions for several eukaryotic organisms. To evaluate the performance of SubCons we present a benchmark of LocTree3 and SubCons using two recent mass-spectrometry based datasets of mouse and drosophila proteins. The server is available at http://subcons.bioinfo.se/.