A Systematic Review of Crohn's Disease Case Definitions in Administrative or Claims Databases.

BACKGROUND: We sought to review Crohn's disease (CD) case definitions that use diagnosis, procedure, and medication claims. METHODS: We searched PubMed and Embase from inception through January 31, 2022, using terms related to CD, inflammatory bowel disease, administrative claims, or validity. Each article was scrutinized by 2 authors independently screening and abstracting data. Collected data included participant characteristics, case definition characteristics, and case definition validity. When diagnostic accuracy was provided for multiple case definitions, we extracted the case definition selected by the authors. All diagnostic accuracy characteristics were captured. RESULTS: We identified 30 studies that evaluated a case definition using claims data to identify CD patients. The most common case definition included counts of diagnosis codes (57%) followed by a combination of diagnosis codes and medications (20%). All but 1 study validated the case definition with a medical chart review. In 2 studies, the patient's primary care provider completed a survey to confirm disease status. The positive predictive value of the case definitions ranged from 18% (≥1 code at a single U.S. health plan) to 100% (≥1 code plus a relevant prescription at a U.S. hospital). More complex case definitions (eg, ≥1 code + prescription or ≥2 codes) had lower variability in positive predictive value (≥80%) and specificity (≥85%) than the ≥1 code requirement. CONCLUSIONS: Health services researchers should validate case definitions in their research cohorts. When such validation cannot be performed, we recommend using a more complex case definition. Studies without a validated CD case definition should use sensitivity analyses to confirm the robustness of their results.

This systematic review of Crohn's disease (CD) case definitions identified that complex case definitions such as ≥1 diagnosis code + ≥1 prescription had desirable diagnostic accuracy properties.

Exome sequencing and analysis of 454,787 UK Biobank participants.

A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study2. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P ≤ 2.18 × 10-11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene-trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.

Implementation of low-dose CT screening in two different health care systems: Mount Sinai Healthcare System and Phoenix VA Health Care System.

Implementation of lung screening (LS) programs is challenging even among health care organizations that have the motivation, the resources, and more importantly, the goal of providing for life-saving early detection, diagnosis, and treatment of lung cancer. We provide a case study of LS implementation in different healthcare systems, at the Mount Sinai Healthcare System (MSHS) in New York City, and at the Phoenix Veterans Affairs Health Care System (PVAHCS) in Phoenix, Arizona. This will illustrate the commonalities and differences of the LS implementation process in two very different health care systems in very different parts of the United States. Underlying the successful implementation of these LS programs was the use of a comprehensive management system, the Early Lung Cancer Action Program (ELCAP) Management SystemTM. The collaboration between MSHS and PVAHCS over the past decade led to the ELCAP Management SystemTM being gifted by the Early Diagnosis and Treatment Research Foundation to the PVAHCS, to develop a "VA-ELCAP" version. While there remain challenges and opportunities to continue improving LS and its implementation, there is an increasing realization that most patients who are diagnosed with lung cancer as a result of annual LS can be cured, and that of all the possible risks associated with LS, the greater risk of all is for heavy cigarette smokers not to be screened. We identified 10 critical components in implementing a LS program. We provided the details of each of these components for the two healthcare systems. Most importantly, is that continual re-evaluation of the screening program is needed based on the ongoing quality assurance program and database of the actual screenings. At minimum, there should be an annual review and updating. As early diagnosis of lung cancer must be followed by optimal treatment to be effective, treatment advances for small, early lung cancers diagnosed as a result of screening also need to be assessed and incorporated into the entire screening and treatment program.

Computer-Aided Theragnosis Based on Tumor Volumetric Information in Breast Cancer.

OBJECTIVE: A computer-assisted technology has recently been proposed for the assessment of therapeutic responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer (LABC). The system, however, extracted features from individual scans in a tumor irrespective of its relation to the other scans of the same patient, ignoring the volumetric information. This study addresses this problem by introducing a novel engineered texton-based method in order to account for volumetric information in the design of textural descriptors to represent tumor scans. METHODS: A noninvasive computer-aided-theragnosis (CAT) system was developed by employing multiparametric QUS spectral and backscatter coefficient maps. The proceeding was composed of two subdictionaries: one built on the "pretreatment" and another on "week " scans, where was 1, 4, or 8. The learned dictionary of each patient was subsequently used to compute the model (histogram of textons) for each scan of the patient. Advanced machine learning techniques including a kernel-based dissimilarity measure to estimate the distances between "pretreatment" and "mid-treatment" scans as an indication of treatment effectiveness, learning from imbalanced data, and supervised learning were subsequently employed on the texton-based features. RESULTS: The performance of the CAT system was tested using statistical tests of significance and leave-one-subject-out (LOSO) classification on 56 LABC patients. The proposed texton-based CAT system indicated significant differences in changes between the responding and nonresponding patient populations and achieved high accuracy, sensitivity, and specificity in discriminating between the two patient groups early after the start of treatment, i.e., on weeks 1 and 4 of several months of treatment. Specifically, the CAT system achieved the area under curve of 0.81, 0.83, and 0.85 on weeks 1, 4, and 8, respectively. CONCLUSION: The proposed texton-based CAT system accounted for the volumetric information in "pretreatment" and "mid-treatment" scans of each patient. It was demonstrated that this attribute of the CAT system could boost its performance compared to the cases that the features were extracted from solely individual scans.

A method to identify serine kinase substrates. Akt phosphorylates a novel adipocyte protein with a Rab GTPase-activating protein (GAP) domain.

This study describes a method for the identification of the substrates of specific serine kinases. An antibody specific for the phosphomotif generated by the kinase is used to isolate phosphorylated substrates by immunoprecipitation, and the isolated proteins are identified by tandem mass spectrometry of peptides. This method was applied to the identification of substrates for the protein kinase Akt, which specifically phosphorylates the RXRXXS/T motif. 3T3-L1 adipocytes were treated with insulin to activate Akt, and the putative Akt substrate proteins were isolated by immunoprecipitation with an antibody against the phospho form of this motif. This led to the identification of a novel 160-kDa substrate for Akt. The 160-kDa substrate for Akt, which was designated AS160, has a Rab GAP domain. Recombinant AS160 was shown to be a substrate for Akt, and two sites of phosphorylation, both in RXRXXS/T motifs, were identified by mass spectrometry and mutation. Insulin treatment of adipocytes caused AS160 to redistribute from the low density microsomes to the cytosol.