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Personal History of Gastrointestinal Conditions Protocol PhenX Toolkit

- 9/20/21 - 1 form, 1 itemgroup, 10 items, 1 language
Itemgroup: PhenX - personal history of gastrointestinal conditions protocol
This self-administered questionnaire asks respondents about their personal history of several gastrointestinal conditions including colon or rectal polyps, gallbladder conditions, gastric ulcers, Barrett’s esophagus, ulcerative colitis/Crohn’s disease, colon or rectal cancer, pancreatic cancer, and gastrointestinal bleeding. The questionnaire also asks about a history of gastrointestinal procedures such as a colonoscopy, a sigmoidoscopy, and an upper endoscopy. ODM derived from: https://cde.nlm.nih.gov Primary source: https://www.phenxtoolkit.org/ Recent publication: Hendershot, T., Pan, H., Haines, J., Harlan, W.R., Marazita, M.L., McCarty, C.A., Ramos, E.M., and Hamilton, C.M. (2015) Using the PhenX toolkit to add standard measures to a study. Curr. Protoc. Hum. Genet. 86:1.21.1-1.21.17. doi: 10.1002/0471142905.hg0121s86 Permission to publish granted by Carol M. Hamilton.

Acute Myeloid Leukemia - Lab: Biobanking

- 9/20/21 - 13 forms, 1 itemgroup, 3 items, 1 language
Itemgroup: Cancer Biobank
Acute Myeloid Leukemia Common Data Elements Holz

Lung cancer (clinical) DSS Metadata Online Registry (METeOR)

- 9/7/16 - 1 form, 6 itemgroups, 47 items, 1 language
Itemgroups: Cancer treatment, Health service event, Patient, Person (address), Person with cancer, Person
Health sector data set specifications from METeOR, Australia's repository for national metadata standards, developed by the Australian Institute of Health and Welfare (http://meteor.aihw.gov.au/content/index.phtml/itemId/345165) Lung cancer (clinical) DSS: The purpose of the Lung cancer (clinical) data set specification (LCCDSS) is to define data standards for the national collection of lung cancer clinical data so that data collected is consistent and reliable. Collection of this data set specification is not mandated but it is recommended as best practice if clinical cancer data are to be collected. It will facilitate more consistent data collection while enabling individual treatment centres or health service areas to develop data extraction and collection processes and policies that are appropriate for their service settings. The Lung cancer (clinical) data set specification is used in conjunction with the Cancer (clinical) data set specification (CCDSS). Mandatory reporting regulations have enabled population-based cancer registries in Australia to collect standard information on all incident cases of cancer apart from non-melanoma skin cancers, from which incidence, mortality and overall survival have been determined and trends monitored. The CCDSS provides a framework for the collection of more detailed and comprehensive clinical data such as stage of cancer at diagnosis, other prognostic characteristics, cancer treatment and patient outcomes. The Lung cancer (clinical) data set specification will support prospective data collection from the time a person with cancer symptoms is referred or first presents to a hospital or specialist through the entire duration of their illness. The definitions used in this data set specification are designed to capture the provision of cancer care on a day-to-day level. They relate to the cancer care pathway and the need to optimise care by correctly diagnosing, evaluating and managing patients with cancer. In addition, end-points and patterns of care can be monitored to understand both the appropriateness and effectiveness of cancer care. The data elements specified provide a framework for: • promoting the delivery of evidence-based care to patients with cancer • facilitating the ongoing improvement in the quality and safety of cancer management in treatment settings • improving the epidemiological and public health understanding of cancer • informing treatment guidelines and professional education • guiding resource planning and the evaluation of cancer control activities They will facilitate the aggregation of data across different treatment centres. The underlying long-term goal is to provide data support to improve outcomes for patients by increasing the quality and length of life. For example, a comparison of the actual management of patients with best practice guidelines may identify shortfalls in treatment and limitations in access to treatment modalities for some patients. Metadata and Classifications Unit Australian Institute of Health and Welfare GPO Box 570 Canberra ACT 2601

Top 300 Laboratory Value Dataset of the Medical Informatics Initiative Germany (MII) (Extended Version) - CHEM

- 12/6/19 - 11 forms, 139 itemgroups, 447 items, 1 language
Itemgroups: Creatinine, Potassium, Sodium, Glucose, C reactive protein, Alanine aminotransferase, Urea, Aspartate aminotransferase, Gamma glutamyl transferase, Albumin, Chloride, Glomerular filtration rate/1.73 sq M.predicted, Calcium.ionized, Calcium, Lactate dehydrogenase, Bilirubin, Creatine kinase, Alkaline phosphatase, Protein, Thyrotropin, Base excess, Urate, Lactate, Bicarbonate, Glomerular filtration rate/1.73 sq M.predicted, pH, Triacylglycerol lipase, Cholesterol.in LDL, C reactive protein, Phosphate, Thyroxine.free, Triiodothyronine.free, Triglyceride, Creatinine in Urine, Methemoglobin/Hemoglobin.total, Cholesterol, Acid phosphatase, Cholinesterase, pH in Urine, Cholesterol.in HDL, Ferritin, Troponin T.cardiac, Procalcitonin, Creatine kinase.MB, Natriuretic peptide.B prohormone N-Terminal, Iron, Calcidiol, Amylase, Beta 2 globulin/Protein.total, Beta 1 globulin/Protein.total, Albumin/Protein.total, Glutamate dehydrogenase, Alpha 1 globulin/Protein.total, Alpha 2 globulin/Protein.total, Beta globulin/Protein.total, Gamma globulin/Protein.total, Osmolality, Carcinoembryonic Ag, Cortisol, Albumin in Urine, Cystatin C, Cobalamins, IgM, IgG, Troponin I.cardiac, Folate, Transferrin, Lutropin, Myoglobin, IgA, Prostate specific Ag, Cholesterol.in LDL/Cholesterol.in HDL, Parathyrin.intact, Interleukin 6, Estradiol, Follitropin, Lipoprotein (little a), Bilirubin.glucuronidated+Bilirubin.albumin bound, Natriuretic peptide.B, Interpretation, Beta-2-Microglobulin, Alpha-1-Microglobulin in Urine, Angiotensin converting enzyme, Immunoglobulin light chains.kappa.free, Base excess^^standard, Protein/Creatinine in Urine, Testosterone, S100 calcium binding protein B, IgE, Prolactin, Bicarbonate^^standard, Osmolality in Urine, Cancer Ag 19-9, IgG in Urine, Osteocalcin, Protein, Ammonia, Bilirubin.non-glucuronidated, Lactate, Creatinine renal clearance, Homocysteine, Pyridoxal phosphate, Glucose, Calcium in Urine, Phosphate in Urine, Choriogonadotropin.beta subunit, Iron saturation, Sodium in Urine, Tumor necrosis factor.alpha, Protein, Corticotropin, Interleukin 2 receptor, Thyroglobulin, Progesterone, Alpha-2-Macroglobulin/Protein.total, Albumin/Creatinine in Urine, Lactate dehydrogenase, Enolase.neuron specific, Thiamine, Insulin-like growth factor-I, Immunoglobulin light chains.lambda.free, Dehydroepiandrosterone sulfate, Creatinine, Albumin, IgG, Bilirubin.glucuronidated, Choriogonadotropin (pregnancy test) in Urine, Holo-transcobalamin II, IgA, Albumin, IgM, Testosterone.free, Prostate specific Ag.free, Aldosterone, Triacylglycerol lipase, Sex hormone binding globulin, Urea in Urine, IgG subclass 4, Creatine kinase.MB/Creatine kinase.total
The Top 300 Dataset (Kerndatensatz Labor) was developed by the Medical Informatics Initiative Germany (MII) and contains frequency-sorted, LOINC-coded analyses of the university clinics in Göttingen, Gießen, Munich, Greifswald and Erlangen. This form shows a detailed description containing primary and secondary LOINC codes of the 300 most common laboratory parameters. For each of the 300 laboratory parameters an itemgroup was created with all accepted variants of the laboratory parameters as items (secondary LOINC codes), the first item being the preferred LOINC variant of the MII (primary LOINC code). The itemgroup title is based on the primary LOINC code. The list is divided into subforms by the associated classes of laboratory parameters. If more than one class, the parameters can be found in the MIXED subform. An overview of the preferred presentation can also be found in the MDM portal (Laboratory Tests (Overview)). https://www.medizininformatik-initiative.de/de/labordaten-in-der-medizininformatik-initiative Explanation of abbreviations for types of specimen and classes/subforms: Type of specimen: Bld = Blood BldA = arterial blood Ser = Serum Plas = Plasma RBC = Red blood cells PPP = Platelet poor plasma Urine sed = Urine sediment CSF = Cerebral spinal fluid Body fld = Body fluid Classes (Subforms): CHEM = Chemistry HEM/BC = Hematology/Blood Cell Count COAG = Coagulation study UA = Urinalysis CELLMARK = Cell markers CLIN = clinical not elsewhere classified DRUG/TOX = drug levels & toxicology SERO = serology MICRO = Microbiology SPEC = Specimen characteristics

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