Longitudinal Clinical Information Research Articles

The clinical relevance of a polygenic risk score for type 2 diabetes mellitus in the Korean population

The clinical utility of a type 2 diabetes mellitus (T2DM) polygenic risk score (PRS) in the East Asian population remains underexplored. We aimed to examine the potential prognostic value of a T2DM PRS and assess its viability as a clinical instrument. We first established a T2DM PRS for 5490 Korean individuals using East Asian Biobank data (269,487 samples). Subsequently, we assessed the predictive capability of this T2DM PRS in a prospective longitudinal study with baseline data and data from seven additional follow-ups. Our analysis showed that the T2DM PRS could predict the transition of glucose tolerance stages from normal glucose tolerance to prediabetes and from prediabetes to T2DM. Moreover, T2DM patients in the top-decile PRS group were more likely to be treated with insulin (hazard ratio = 1.69, p value = 2.31E−02) than were those in the remaining PRS groups. T2DM PRS values were significantly high in the severe diabetes subgroup, characterized by insulin resistance and β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-cell dysfunction (p value = 0.0012). The prediction models with the T2DM PRS had significantly greater Harrel’s C-indices than did corresponding models without it. By utilizing prospective longitudinal study data and extensive clinical risk factor information, our analysis provides valuable insights into the multifaceted clinical utility of the T2DM PRS.

Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study

Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This studyaimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals. In this prospective, multicentre, observational cohort study done in nine centres in six countries (Australia, Indonesia, Singapore, Italy, Czechia, and France) blood samples and RNA sequencing were used to develop a robust model of predicting secondary bacterial infections in the respiratory tract of patients with COVID-19. Eligible participants were older than 18 years, had known or suspected COVID-19, and symptoms of a recent respiratory infection. A control cohort of participants without COVID-19 who were older than 18 years and with no infection symptoms was also recruited from one Australian centre. In the pre-analysis phase, data were filtered to include only individuals with complete blood transcriptomics and patient data (ie, age, sex, location, and WHO severity score at the time of sample collection). The dataset was then divided randomly (4:1) into a training set (80%) and a test set (20%). Gene expression data in the training set and control cohort were used for differential expression analysis. Differentially expressed genes, along with WHO severity score, location, age, and sex, were used for feature selection with least absolute shrinkage and selection operator (LASSO) in the training set. For LASSO analysis, samples were excluded if gene expression data were not obtained at study admission, no longitudinal clinical information was available, a bacterial infection at the time of study admission was present, or a fungal infection in the absence of a bacterial infection was detected. LASSO regression was performed using three subsets of predictor variables: patient data alone, gene expression data alone, or a combination of patient data and gene expression data. The accuracy of the resultant models was tested on data from the test set. Between March, 2020, and October, 2021, we recruited 536 SARS-CoV-2-positive individuals and between June, 2013, and January, 2020, we recruited 74 participants into the control cohort. After prefiltering analysis and other exclusions, samples from 158 individuals were analysed in the training set and 47 in the test set. The expression of seven host genes (DAPP1, CST3, FGL2, GCH1, CIITA, UPP1, and RN7SL1) in the blood at the time of study admission was identified by LASSO as predictive of the risk of developing a secondary bacterial infection of the respiratory tract more than 24 h after study admission. Specifically, the expression of these genes in combination with a patient's WHO severity score at the time of study enrolment resulted in an area under the curve of 0·98 (95% CI 0·89-1·00), a true positive rate (sensitivity) of 1·00 (95% CI 1·00-1·00), and a true negative rate (specificity) of 0·94 (95% CI 0·89-1·00) in the test cohort. The combination of patient data and host transcriptomics at hospital admission identified all seven individuals in the training and test sets who developed a bacterial infection of the respiratory tract 5-9 days after hospital admission. These data raise the possibility that host transcriptomics at the time of clinical presentation, together with machine learning, can forward predict the risk of secondary bacterial infections and allow for the more targeted use of antibiotics in viral infection. Snow Medical Research Foundation, the National Health and Medical Research Council, the Jack Ma Foundation, the Helmholtz-Association, the A2 Milk Company, National Institute of Allergy and Infectious Disease, and the Fondazione AIRC Associazione Italiana per la Ricerca contro il Cancro.

Development of an individualized risk calculator of treatment resistance in patients with first-episode psychosis (TRipCal) using automated machine learning: a 12-year follow-up study with clozapine prescription as a proxy indicator.

About 15-40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of development of TR early in the course of illness is important to provide personalized intervention. A total of 1400 patients with FEP enrolled in the early intervention for psychosis service or receiving the standard psychiatric service between July 1, 1998, and June 30, 2003, for the first time were included. Clozapine prescriptions until June 2015, as a proxy of TR, were obtained. Premorbid information, baseline characteristics, and monthly clinical information were retrieved systematically from the electronic clinical management system (CMS). Training and testing samples were established with random subsampling. An automated machine learning (autoML) approach was used to optimize the ML algorithm and hyperparameters selection to establish four probabilistic classification models (baseline, 12-month, 24-month, and 36-month information) of TR development. This study found 191 FEP patients (13.7%) who had ever been prescribed clozapine over the follow-up periods. The ML pipelines identified with autoML had an area under the receiver operating characteristic curve ranging from 0.676 (baseline information) to 0.774 (36-month information) in predicting future TR. Features of baseline information, including schizophrenia diagnosis and age of onset, and longitudinal clinical information including symptoms variability, relapse, and use of antipsychotics and anticholinergic medications were important predictors and were included in the risk calculator. The risk calculator for future TR development in FEP patients (TRipCal) developed in this study could support the continuous development of data-driven clinical tools to assist personalized interventions to prevent or postpone TR development in the early course of illness and reduce delay in clozapine initiation.

An information‐sharing framework of patient label propagation method to reveal distinct blood biomarkers and signatures in Alzheimer’s disease subgroups

AbstractBackgroundAlzheimer’s Disease (AD) is a complex and heterogenous dementia and the early detection remains very challenging. Although progresses have been made to identify serum biomarkers associated with AD, it is still difficult to generalize the findings into clinical setting and trace back to learn how these AD biomarkers are progressed through the regulatory pathways and how they play the roles in the disease mechanisms. Furthermore, distinct patterns or signatures among atypical progressing subgroups has not been investigated.MethodWe defined four subgroups of AD patients based on the trajectories of tauopathy and cognitive decline. Using the blood transcriptomic data from ROSMAP and ADNI, we performed hierarchical clustering for the top 1000 variable genes to obtain the gene signatures in each group. An NMF‐based decomposition and network assisted label propagation method was applied. Finally, we use the inferred label for ADNI patients to assess the distinct progression patterns for each AD subtype and validate and uncovering the genes and their enriched pathways that are associated with flagged progression biomarkers.ResultOur preliminary results show that two gene expression clusters exist for both ROSMAP and ADNI data. Our previous study identified the activation of calcium channel related pathway exhibits a detrimental role in AD, with representative genes such as TGFB1, and S100A12. We observe the same pattern for these two genes in a subset of patients from ADNI gene expression data with an enrichment in neovascularization processes. We are now propagating patient group labels with the prior evidence of the alignment between these two data sets and investigating the primary genes linked to specific biomarker progression patterns through longitudinal analyses.ConclusionWe identified several altered pathways and genes that potentially render atypical AD progression patterns reflected by various biomarkers. Future work is undergoing to further explore the link between the gene signatures in each subgroup and the blood transcriptomic sub‐clusters, as well as the comparisons of the two blood transcriptomic data for potential longitudinal clinical information mapping between the two datasets.

Semi-supervised calibration of noisy event risk (SCANER) with electronic health records

ObjectiveElectronic health records (EHR), containing detailed longitudinal clinical information on a large number of patients and covering broad patient populations, open opportunities for comprehensive predictive modeling of disease progression and treatment response. However, since EHRs were originally constructed for administrative purposes not for research, in the EHR-linked studies, it is often not feasible to capture reliable information for analytical variables, especially in the survival setting, when both accurate event status and event times are needed for model building. For example, progression-free survival (PFS), a commonly used survival outcome for cancer patients, often involves complex information embedded in free-text clinical notes and cannot be extracted reliably. Proxies of PFS time such as time to the first mention of progression in the notes are at best good approximations to the true event time. This leads to difficulty in efficiently estimating event rates for an EHR patient cohort. Estimating survival rates based on error-prone outcome definitions can lead to biased results and hamper the power in the downstream analysis. On the other hand, extracting accurate event time information via manual annotation is time and resource intensive. The objective of this study is to develop a calibrated survival rate estimator using noisy outcomes from EHR data. Materials and MethodsIn this paper, we propose a two-stage semi-supervised calibration of noisy event rate (SCANER) estimator that can effectively overcome censoring induced dependency and attains more robust performance (i.e., not sensitive to misspecification of the imputation model) by fully utilizing both a small-labeled set of gold-standard survival outcomes annotated via manual chart review and a set of proxy features automatically captured via EHR in the unlabeled set. We validate the SCANER estimator by estimating the PFS rates for a virtual cohort of lung cancer patients from one large tertiary care center and the ICU-free survival rates for COVID patients from two large tertiary care centers. ResultsIn terms of survival rate estimates, the SCANER had very similar point estimates compared to the complete-case Kaplan Meier estimator. On the other hand, other benchmark methods for comparison, which fail to account for the induced dependency between event time and the censoring time conditioning on surrogate outcomes, produced biased results across all three case studies. In terms of standard errors, the SCANER estimator was more efficient than the KM estimator, with up to 50% efficiency gain. ConclusionThe SCANER estimator achieves more efficient, robust, and accurate survival rate estimates compared to existing approaches. This promising new approach can also improve the resolution (i.e., granularity of event time) by using labels conditioning on multiple surrogates, particularly among less common or poorly coded conditions.

The evolution of cancer genomic medicine in Japan and the role of the National Cancer Center Japan.

The journey to implement cancer genomic medicine (CGM) in oncology practice began in the 1980s, which is considered the dawn of genetic and genomic cancer research. At the time, a variety of activating oncogenic alterations and their functional significance were unveiled in cancer cells, which led to the development of molecular targeted therapies in the 2000s and beyond. Although CGM is still a relatively new discipline and it is difficult to predict to what extent CGM will benefit the diverse pool of cancer patients, the National Cancer Center (NCC) of Japan has already contributed considerably to CGM advancement for the conquest of cancer. Looking back at these past achievements of the NCC, we predict that the future of CGM will involve the following: 1) A biobank of paired cancerous and non-cancerous tissues and cells from various cancer types and stages will be developed. The quantity and quality of these samples will be compatible with omics analyses. All biobank samples will be linked to longitudinal clinical information. 2) New technologies, such as whole-genome sequencing and artificial intelligence, will be introduced and new bioresources for functional and pharmacologic analyses (e.g., a patient-derived xenograft library) will be systematically deployed. 3) Fast and bidirectional translational research (bench-to-bedside and bedside-to-bench) performed by basic researchers and clinical investigators, preferably working alongside each other at the same institution, will be implemented; 4) Close collaborations between academia, industry, regulatory bodies, and funding agencies will be established. 5) There will be an investment in the other branch of CGM, personalized preventive medicine, based on the individual's genetic predisposition to cancer.

The matrix revolutions: How databases and database linkages will transform epidemiologic research.

The matrix revolutions: How databases and database linkages will transform epidemiologic research.

The Rossy Progressive Supranuclear Palsy Centre: Creation and Initial Experience.

To describe the development and initial experience of a clinical research program in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) in Canada: The Rossy PSP Centre, to share the data acquisition tools adopted, and to report preliminary results. Extensive demographic and longitudinal clinical information is collected every 6 months using standardized forms. Biofluids are collected for biobanking and genetic analysis, and many patients are enrolled in neuroimaging research protocols. Brain donation is an important component of the program, and standardized processing protocols have been established, including very short death to autopsy times in patients undergoing medical assistance in dying. Between Oct 2019 and Dec 2021, 132 patients were screened, 91 fulfilling criteria for PSP and 19 for CBS; age 71 years; 41% female; duration 5 years, age-of-onset 66 years. The most common symptoms at onset were postural instability and falls (45%), cognitive-behavioral changes (22%), and Parkinsonism (9%). The predominant clinical phenotype was Richardson syndrome (82%). Levodopa and amantadine resulted in partial and short-lasting benefit. The Rossy PSP Centre has been established to advance clinical and basic research in PSP and related tauopathies. The extent of the clinical data collected permits deep phenotyping of patients and allows for future clinical and basic research. Preliminary results showed expected distribution of phenotypes, demographics, and response to symptomatic treatments in our cohort. Longitudinal data will provide insight into the early diagnosis and management of PSP. Future steps include enrollment of patients in earlier stages, development of biomarkers, and fast-tracking well-characterized patients into clinical trials.

Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer's Disease Conversion.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model’s decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI.

Establishment of Prospective Registry of Active Surveillance for Prostate Cancer: The Korean Urological Oncology Society Database.

To establish a prospective registry for the active surveillance (AS) of prostate cancer (PC) using the Korean Urological Oncology Society (KUOS) database and to present interim analysis. The KUOS registry of AS for PC (KUOS-AS-PC) was organized in May 2019 and comprises multiple institutions nationwide. The eligibility criteria were as follows: patients with (1) pathologically proven PC; (2) pre-biopsy prostate-specific antigen (PSA) ≤20 ng/mL; (3) International Society of Urological Pathology (ISUP) grade 1 or 2 (no cribriform pattern 4); (4) clinical T stage ≤T2c; (5) positive core ratio ≤50%; and (6) maximal cancer involvement in the core ≤50%. Detailed longitudinal clinical information, including multi-parametric magnetic resonance imaging and disease-specific outcomes, was recorded. From May 2019 to June 2021, 296 patients were enrolled, and 284 were analyzed. The mean±standard deviation (SD) age at enrollment was 68.7±8.2 years. The median follow-up period was 11.2 months (5.9-16.8 mo). Majority of patients had pre-biopsy PSA ≤10 ng/mL (91.2%), PSA density <0.2 ng/mL² (79.7%), ISUP grade group 1 (94.4%), single positive core (65.7%), maximal cancer involvement in the core ≤20% (78.1%), and clinical T stage of T1c or lower (72.9%). Fifty-two (18.3%) discontinued AS for various reasons. Interventions included radical prostatectomy (80.8%), transurethral prostatectomy (5.8%), primary androgen deprivation therapy (5.8%), radiation (5.8%), and focal therapy (1.9%). The mean±SD time to intervention was 8.9±5.2 months. The reasons for discontinuation included pathologic reclassification (59.6%), patient preference (25.0%), and radiologic reclassification (9.6%). Two (4.8%) patients with pathologic Gleason score upgraded to ISUP grade group 4, no biochemical recurrence. The KUOS established a successful prospective database of PC patients undergoing AS in Korea, named the KUOS-AS-PC registry.

Splenomegaly (SPML) in Polycythemia Vera (PV): Its Clinical Significance and Relation to Myelofibrosis and Survival

▪Introduction: SPML is considered a hallmark of PV, but its frequency, as reported in the literature, ranges from 20% to 75%. It has been assumed, without documentation, that SPML may be accompanied by symptoms and signs and may affect prognosis. Moreover, expensive radiographic tests have become mandatory in some recent phase 2 drug trials in PV (and ET) to carefully document spleen size on trial entry and its change, if any, during therapy. Because systematic studies have not been performed, we studied SPML in patients (pts) with PV at their initial diagnosis (DX) or at first presentation (PRES) at our institution, Weill Cornell Medicine (WCM), and determined its clinical significance.Methods: This single-center retrospective study was approved by the WCM institutional review board. A systematic literature search including PubMed, Embase, and Cochrane School relevant to the specific search questions was unrevealing. We used a research data repository based on an automated query system which had aggregated longitudinal clinical information pertaining to our PV patients for our analysis of spleen size (Abu-Zeinah et al. Leukemia 2021). Standardized PV diagnostic criteria were used for all pts (Silver et al. Blood 2013). As a tertiary referral center within a major metropolitan area, our PV population is composed of those diagnosed at WCM (DX) and those presenting to WCM some time after diagnosis (PRES). Degree of SPML was categorized into 3 subgroups: (1) <1 cm if the spleen was not enlarged, (2) palpable 1-5 cm, or (3) more than 5.0 cm below the left costal margin of the abdomen in the medial clavicular line in the supine or left lateral decubitus position; it was also considered enlarged after splenic ultrasound scan (US) based upon the method and verified formula of Chow KU, et al. Radiology 2016. Spleen size was correlated with age, sex, race, and ELN risk score, and symptoms including pruritus, night sweats, anorexia, abdominal discomfort and pain. Progression to myelofibrosis (MF) with myeloid metaplasia was defined per ELN/IWG-MRT criteria. Spleen measurements after progression to MF were excluded. Peripheral blood smears were routinely examined to view RBC morphology and to exclude leukoerythroblastosis. MF-free survival (MFS) and overall survival (OS) were calculated using the Kaplan-Meier log rank test among the various spleen subgroups. Multivariable survival analysis (MVA) was performed using a Cox proportional hazards model.Results: From our 470 PV dataset, 351 pts had documented spleen size at DX (165) or PRES (186). The median age for all patients was 60 years (yr), for DX 54 yr, for PRES 62 yr. The median DX ages of SPML <1 cm, 1-5 cm, and >5 cm were 56, 50, and 54 yr respectively (p=0.011). The median time between first evaluation for PV and first visit at WCM (PRES) was 2 years (range 0-30). 49% were female and 13% were non-white (Figure 1a). There was no correlation between spleen size and ELN risk scores. The linkage between SPML and symptoms will be reported. Overall survival of the three groups was similar at 12 years (Figure 1b). SPML at presentation, however, was associated with increased risk of MF (1-5cm versus 0: HR 2.56, p=0.026; >=5cm versus 0: HR 5.64, p<0.001), independent of age or disease duration in MVA.Discussion & Conclusion: Patients who had SPML > 5cm at presentation had a worse MFS than those with a lesser degree of SPML or no SPML (1-5 cm or <1 cm). For determining SPML, clinical examination and calculated US length were equally satisfactory. In the absence of clinical SPML, radiographic tests appear unnecessary. However, for SPML > 5cm, and unusual body types, more detailed radiographic studies may still be required. SPML was more common in younger patients, suggesting more aggressive disease and earlier progression to MF. ELN risk category did not correlate with SPML, suggesting an additional reason for its revision. Our PV patients with SPML > 5cm at DX or PRES did not have a decreased OS at 12 years, but did have a reduced MF-free survival. These data support the WHO mandated requirement for marrow biopsy for diagnosis of PV, especially for patients with SPML but also as a baseline requirement to establish the presence of MF less than grade 2. Patients with SPML> 5cm appear to be at high risk of MF progression and must be monitored closely for this event and treated appropriately. [Display omitted] DisclosuresSilver: Abbvie: Consultancy; PharamEssentia: Consultancy, Speakers Bureau. Scandura: CR&T (Foudation): Research Funding; European Leukemia net: Honoraria, Other: travel fees ; MPN-RF (Foundation): Research Funding; Constellation: Research Funding; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Abu-Zeinah: PharmaEssentia: Consultancy.

In This Issue

In This Issue

The Clinical and Hematologic Course of Fanconi Anemia Amongst Affected Siblings

Fanconi anemia is the most common inherited bone marrow failure syndrome characterized by impaired DNA repair pathway. Patients with Fanconi anemia often present with congenital anomalies and up to 70% of patients develop bone marrow failure (BMF) during the first decade of life, with high risk of progression to myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). While high penetrance of BMF phenotype is a key characteristic of Fanconi anemia, patients may have variable onset and severity. Despite extensive research, there are no available data whether affected siblings with Fanconi anemia have a similar or different clinical course.First, we reviewed medical records of 9 sets of probands and affected siblings with Fanconi anemia who were seen at the Memorial Sloan Kettering Cancer Center (MSKCC) in the last 30 years (Figure 1). As longitudinal and detailed clinical information was available for these sibling pairs, we collected data by retrospective chart review on the onset of BMF, MDS or AML, time to hematopoietic stem cell transplantation (HSCT), the presence of any congenital anomaly, microcephaly, renal anomaly, low birth weight, short stature, learning disability and frequent infections between older and younger siblings. After observing a signal of positive correlation in BMF onset between siblings, we moved onto reviewing additional sibling pairs in the International Fanconi Anemia Registry (IFAR), where additional 62 sibling pairs with available hematologic onset information were registered. IFAR defines hematologic onset as the onset of BMF, MDS or AML.The Spearman rank correlation coefficient was computed to assess correlation between sibling pair's clinical manifestation for those seen at MSKCC. We observed a positive correlation in the time to bone marrow failure, defined by development of any peripheral blood cytopenias, between sibling pairs, although the p value was borderline due to small sample size (correlation coefficient r=0.75, p=0.05, number of pairs = 7). We excluded two pairs of siblings in which one of the siblings was known to have blood mosaicism. The sample size was too small to compute statistics for the time to MDS/AML or time to HSCT. We also calculated the Cohen's kappa coefficient to determine whether a particular clinical event was likely to occur in both siblings or neither of siblings. We found that kidney anomaly (kappa coefficient = 0.5714; 95% CI 0.0982 - 1) and microcephaly (kappa coefficient = 1; 95% CI 1-1) showed good agreement between siblings, whereas we did not find good agreement between siblings in skeletal anomaly, cardiac anomaly, low birth weight, short stature, growth retardation, learning disability or frequent infections. To assess correlation of hematologic onset in all available sets of probands and affected siblings, the Spearman's rank correlation was calculated on total 69 sibling pairs. We found a positive correlation in the hematologic onset, defined as time to bone marrow failure, MDS or AML, whichever comes first (correlation coefficient r=0.6767, p <0.0001; Figure 2).In summary, affected siblings with Fanconi anemia tend to develop hematologic manifestations at similar ages with a median age difference of 2.18 years (range 0 - 15.36 years; 95% CI 2.61 - 4.17 years). Among other clinical manifestations, kidney anomaly and microcephaly appeared to occur concordantly between siblings. We plan to reach out to other institutions to enhance collaboration and increase number of research participants. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Association of Structural Magnetic Resonance Imaging Measures With Psychosis Onset in Individuals at Clinical High Risk for Developing Psychosis

The ENIGMA clinical high risk (CHR) for psychosis initiative, the largest pooled neuroimaging sample of individuals at CHR to date, aims to discover robust neurobiological markers of psychosis risk. To investigate baseline structural neuroimaging differences between individuals at CHR and healthy controls as well as between participants at CHR who later developed a psychotic disorder (CHR-PS+) and those who did not (CHR-PS-). In this case-control study, baseline T1-weighted magnetic resonance imaging (MRI) data were pooled from 31 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. CHR status was assessed using the Comprehensive Assessment of At-Risk Mental States or Structured Interview for Prodromal Syndromes. MRI scans were processed using harmonized protocols and analyzed within a mega-analysis and meta-analysis framework from January to October 2020. Measures of regional cortical thickness (CT), surface area, and subcortical volumes were extracted from T1-weighted MRI scans. Independent variables were group (CHR group vs control group) and conversion status (CHR-PS+ group vs CHR-PS- group vs control group). Of the 3169 included participants, 1428 (45.1%) were female, and the mean (SD; range) age was 21.1 (4.9; 9.5-39.9) years. This study included 1792 individuals at CHR and 1377 healthy controls. Using longitudinal clinical information, 253 in the CHR-PS+ group, 1234 in the CHR-PS- group, and 305 at CHR without follow-up data were identified. Compared with healthy controls, individuals at CHR exhibited widespread lower CT measures (mean [range] Cohen d = -0.13 [-0.17 to -0.09]), but not surface area or subcortical volume. Lower CT measures in the fusiform, superior temporal, and paracentral regions were associated with psychosis conversion (mean Cohen d = -0.22; 95% CI, -0.35 to 0.10). Among healthy controls, compared with those in the CHR-PS+ group, age showed a stronger negative association with left fusiform CT measures (F = 9.8; P < .001; q < .001) and left paracentral CT measures (F = 5.9; P = .005; q = .02). Effect sizes representing lower CT associated with psychosis conversion resembled patterns of CT differences observed in ENIGMA studies of schizophrenia (ρ = 0.35; 95% CI, 0.12 to 0.55; P = .004) and individuals with 22q11.2 microdeletion syndrome and a psychotic disorder diagnosis (ρ = 0.43; 95% CI, 0.20 to 0.61; P = .001). This study provides evidence for widespread subtle, lower CT measures in individuals at CHR. The pattern of CT measure differences in those in the CHR-PS+ group was similar to those reported in other large-scale investigations of psychosis. Additionally, a subset of these regions displayed abnormal age associations. Widespread disruptions in CT coupled with abnormal age associations in those at CHR may point to disruptions in postnatal brain developmental processes.

A longitudinal footprint of genetic epilepsies using automated electronic medical record interpretation

PurposeChildhood epilepsies have a strong genetic contribution, but the disease trajectory for many genetic etiologies remains unknown. Electronic medical record (EMR) data potentially allow for the analysis of longitudinal clinical information but this has not yet been explored. MethodsWe analyzed provider-entered neurological diagnoses made at 62,104 patient encounters from 658 individuals with known or presumed genetic epilepsies. To harmonize clinical terminology, we mapped clinical descriptors to Human Phenotype Ontology (HPO) terms and inferred higher-level phenotypic concepts. We then binned the resulting 286,085 HPO terms to 100 3-month time intervals and assessed gene–phenotype associations at each interval. ResultsWe analyzed a median follow-up of 6.9 years per patient and a cumulative 3251 patient years. Correcting for multiple testing, we identified significant associations between “Status epilepticus” with SCN1A at 1.0 years, “Severe intellectual disability” with PURA at 9.75 years, and “Infantile spasms” and “Epileptic spasms” with STXBP1 at 0.5 years. The identified associations reflect known clinical features of these conditions, and manual chart review excluded provider bias. ConclusionSome aspects of the longitudinal disease histories can be reconstructed through EMR data and reveal significant gene–phenotype associations, even within closely related conditions. Gene-specific EMR footprints may enable outcome studies and clinical decision support.

Using electronic health records for population health sciences: a case study to evaluate the associations between changes in left ventricular ejection fraction and the built environment.

ObjectiveElectronic health record (EHR) data linked with address-based metrics using geographic information systems (GIS) are emerging data sources in population health studies. This study examined this approach through a case study on the associations between changes in ejection fraction (EF) and the built environment among heart failure (HF) patients.Materials and MethodsWe identified 1287 HF patients with at least 2 left ventricular EF measurements that are minimally 1 year apart. EHR data were obtained at an academic medical center in New York for patients who visited between 2012 and 2017. Longitudinal clinical information was linked with address-based built environment metrics related to transportation, air quality, land use, and accessibility by GIS. The primary outcome is the increase in the severity of EF categories. Statistical analyses were performed using mixed-effects models, including a subgroup analysis of patients who initially had normal EF measurements.ResultsPreviously reported effects from the built environment among HF patients were identified. Increased daily nitrogen dioxide concentration was associated with the outcome while controlling for known HF risk factors including sex, comorbidities, and medication usage. In the subgroup analysis, the outcome was significantly associated with decreased distance to subway stops and increased distance to parks.ConclusionsPopulation health studies using EHR data may drive efficient hypothesis generation and enable novel information technology-based interventions. The availability of more precise outcome measurements and home locations, and frequent collection of individual-level social determinants of health may further drive the use of EHR data in population health studies.

The Pulmonary Fibrosis Foundation Patient Registry. Rationale, Design, and Methods.

Detailed understanding of longitudinal behavior, response to therapy, and applicable biomarkers for interstitial lung diseases (ILDs) is lacking. There is a need for a large multicenter registry that provides researchers and clinicians access to well-characterized data not limited to patients with idiopathic pulmonary fibrosis. The Pulmonary Fibrosis Foundation Patient Registry (PFF-PR) is a database that collects baseline and longitudinal demographic and clinical information about patients with ILDs in the United States. The objective of this study is to describe the patient population, data collection process, and opportunities for retrospective and prospective research with the PFF-PR. Individuals 18 years or older who had ILD diagnosed and who were seen at PFF-PR centers who provided informed consent were eligible to participate. Baseline and longitudinal demographic, spirometric, radiographic, morbidity, and mortality data are recorded into a secure electronic data capture system. Starting in 2016, the PFF-PR has collected data on 2,003 patients at 42 clinical sites in the United States. At the time of enrollment, the mean age of participants was 68 years old. Most (62%) of participants were male, and 58% had a positive smoking history. The mean forced vital capacity was 69% predicted, and the mean diffusing capacity of the lung for carbon monoxide was 43% predicted. Forty-one percent of patients were using supplemental oxygen, and 39% were on antifibrotic therapy. Reasons for attrition were mostly death or transplant, with low rates of loss to follow-up or withdrawal. The PFF-PR is a large multicenter United States-based registry that provides researchers and clinicians access to well-characterized ILD patient data.

349. Implementation of a Multidisciplinary HIV-Pulmonary Clinic

BackgroundAmong people with HIV (PWH), pulmonary comorbiditiees are a leading cause of morbidity and mortality. As PWH live a near-normal lifespan with ART, the focus has shifted from opportunistic infections to chronic disease. This includes chronic obstructive pulmonary disease (COPD) and lung cancer, for which PWH with and without concomitant tobacco use are at high risk. We sought to improve access to and quality of pulmonary care for PWH by instituting a pulmonary clinic co-located within a Ryan White-funded HIV clinic in the Southeastern United States.MethodsA pulmonologist with expertise in lung disease in PWH began seeing patients one half-day twice monthly beginning in 2017. Longitudinal demographic, clinical, and appointment information was collected on each patient.ResultsFifty patients were referred to the HIV-Pulmonary clinic. Of the 32 patients seen for an initial visit, the mean age was 55, 63% were male, and all were on ART. The majority were current (52%) or prior smokers (31%) with a mean pack-year history of 42. Over 40% of the patients had a COPD diagnosis and 25% had no prior pulmonary diagnosis. The majority of patients had not engaged in pulmonary care within the past year, as 63% had never seen a pulmonologist and another 28% did not follow-up with a prior provider. After the first visit, 69% either followed up or had a pending follow-up. Of the 17 current smokers, all were offered assistance with smoking cessation and 59% engaged. Of the 10 patients who were eligible for lung cancer screening (LCS) by current guidelines, all engaged in shared decision making and 40% pursued annual screening CT scans.ConclusionChronic pulmonary diseases are increasing relevant comorbidity in PWH on ART. Our HIV-pulmonary clinic demonstrates the utility and feasibility of integration of sub-specialty consultative care in comprehensive care of PWH. By introducing a general pulmonary clinic within the existing infrastructure of the HIV clinic at our institution, we were able to engage PWH with and at risk for lung disease in longitudinal care to prevent, detect, and treat pulmonary disease. Future goals of this interdisciplinary design include increased compliance with current COPD treatment guidelines and LCS, improved rates of smoking cessation, and continued collaboration between the ID and pulmonary providers.Disclosures All authors: No reported disclosures.

Abstract 4945: Project Survival: Engineering a phenomic and artificial intelligence driven precision medicine biomarker pipeline for pancreatic adenocarcinomas

Abstract Pancreatic cancer is a complex and dynamic disorder necessitating a comprehensive clinical design integrated with robust OMICS technologies and AI analytics to identify potential molecular and clinical signatures of diagnosis, progression, and treatment outcomes. Project Survival is a multisite prospective longitudinal study currently in the 4th year of a 6 year initiative of sampling and analysis of subjects in 6 categories: healthy volunteers with a first degree relative with pancreatic cancer (N=39), pancreatitis (N=34), pancreatic cystic neoplasm (N=52), suspicious pancreatic masses with pathology other than pancreatic cancer (N=22), early stage (N=66), locally advanced (N=123), and metastatic pancreatic cancer (N=99). All diseased patients are longitudinally sampled multiple times per year for sera, plasma, buffy coat, saliva, urine, and tumor/adjacent normal tissue. The BERG Interrogative Biology® platform is employed for multi-omic mass spectrometry analysis (metabolomics, lipidomics and proteomics) and applies artificial intelligence (bAIcis®, BERG Artificial Intelligence Clinical Information System) technologies. bAIcis® is harnessed to align the multi-omic profiles with longitudinal clinical information to infer probabilistic cause-and-effect relationships among molecular and clinical variables in a network-based model. Multiple longitudinal time points continue to be collected during the course of the six-year timeline enabling dynamic modeling. The value of this longitudinal study is in the epidemiological assessment of patient type progression to more advanced stages and identification of biomarkers and clinical features that align with the shifts observed in the patient populations. Collectively, we are incorporating patient progression with longitudinal sampling to investigate predictive signatures of disease advancement. Biomarker panels with AUC &amp;gt; 0.7 will be pursued in a further prospective clinical study with a larger subject number. The integration of multi-omic analysis with artificial intelligence has identified several biomarker panels that meet numerous unmet needs for the identification and clinical stratification of pancreatic adenocarcinoma. Citation Format: Eric Michael Grund, Michael A. Kiebish, Viatcheslav R. Akmaev, Rangaprasad Sarangarajan, John J. Crowley, Amy Stoll-D'Astice, Tori Singer, Corinne Decicco, Wendy Hori, Abena Darkwah, Lixia Zhang, Valerie Bussberg, Leonardo O. Rodrigues, Emily Y. Chen, Tomislav Dragovich, Manuel Hidalgo, Niven R. Narain, A James Moser. Project Survival: Engineering a phenomic and artificial intelligence driven precision medicine biomarker pipeline for pancreatic adenocarcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4945.

Establishment of the Seoul National University Prospectively Enrolled Registry for Genitourinary Cancer (SUPER-GUC): A prospective, multidisciplinary, bio-bank linked cohort and research platform.

PurposeTo establish a prospective, comprehensive, multidisciplinary, bio-bank linked genitourinary cancer cohort based on standard real practice.Materials and MethodsWe established the Seoul National University Prospectively Enrolled Registry for Genitourinary Cancer (SUPER-GUC), a prospective cohort clinical database and bio-specimen repository system for prostate cancer (SUPER-PC), renal cell carcinoma (SUPER-RCC), and urothelial cancer (SUPER-UC) at a high-volume, tertiary institution. Each cohort consists of several sub-cohorts based on treatment or disease status. Detailed longitudinal clinical information, and general and disease specific patient-reported outcomes are captured. We use the same evaluation format and questionnaires for all participating departments. Patients' blood, urine, tumor, and normal tissues are collected. The number of registered patients and their basic characteristics are summarized. For the surgical sub-cohort, study participation, bio-specimen, and tissue banking rates are analyzed.ResultsSince March 2016, 11 sub-cohorts for all disease statuses have been opened, ranging from low-risk localized to metastatic disease. SUPER-PC, SUPER-RCC, and SUPER-UC enrolled 929, 796, and 1,221 patients, respectively. Study participation, bio-sampling, and fresh frozen tumor banking rates of surgical sub-cohorts were 89.0% to 93.1%, 91.2% to 99.1%, and 56.9% to 79.1%, respectively.ConclusionsSUPER-GUC is a study platform for comparative outcome, quality-of-life, and translational (genetics, biomarkers) research for genitourinary cancer.