The use of automated data to identify complications and comorbidities of diabetes: A validation study
ABSTRACT We evaluated the accuracy of administrative data for identifying complications and comorbidities of diabetes using International Classification of Diseases, 9th edition, Clinical Modification and Current Procedural Terminology codes. The records of 471 randomly selected diabetic patients were reviewed for complications from January 1, 1993 to December 31, 1995; chart data served to validate automated data. The complications with the highest sensitivity determined by a diagnosis in the medical records identified within +/-60 days of the database date were myocardial infarction (95.2%); amputation (94.4%); ischemic heart disease (90.3%); stroke (91.2%); osteomyelitis (79.2%); and retinal detachment, vitreous hemorrhage, and vitrectomy (73.5%). With the exception of amputation (82.9%), positive predictive value was low when based on a diagnosis identified within +/-60 days of the database date but increased with relaxation of the time constraints to include confirmation of the condition at any time during 1993-1995: ulcers (88.5%); amputation (85.4%); and retinal detachment, vitreous hemorrhage and vitrectomy (79.8%). Automated data are useful for ascertaining potential cases of some diabetic complications but require confirmatory evidence when they are to be used for research purposes.
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ABSTRACT: To identify better cells for the treatment of diabetic critical limb ischemia (CLI) and foot ulcer in a pilot trial. Under ordinary treatment, the limbs of 41 type 2 diabetic patients with bilateral CLI and foot ulcer were injected intramuscularly with bone marrow mesenchymal stem cells (BMMSCs), bone marrow-derived mononuclear cells (BMMNCs), or normal saline (NS). The ulcer healing rate of the BMMSC group was significantly higher than that of BMMNCs at 6 weeks after injection (P=0.022), and reached 100% 4 weeks earlier than BMMNC group. After 24 weeks of follow-up, the improvements in limb perfusion induced by the BMMSCs transplantation were more significant than those by BMMNCs in terms of painless walking time (P=0.040), ankle-brachial index (ABI) (P=0.017), transcutaneous oxygen pressure (TcO(2)) (P=0.001), and magnetic resonance angiography (MRA) analysis (P=0.018). There was no significant difference between the groups in terms of pain relief and amputation and there was no serious adverse events related to both cell injections. BMMSCs therapy may be better tolerated and more effective than BMMNCs for increasing lower limb perfusion and promoting foot ulcer healing in diabetic patients with CLI.Diabetes research and clinical practice 04/2011; 92(1):26-36. DOI:10.1016/j.diabres.2010.12.010 · 2.54 Impact Factor
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ABSTRACT: Routine databases containing large amounts of clinical data represent a tremendous opportunity for the evaluation of health care practices and outcomes. However, data collected for administrative purposes has limitations in content, accuracy and completeness. Routine entry of clinical information directly into clinical information systems by care providers is one strategy to address this problem. We developed a structured data entry method, the Clinical Data Framework (CDF), which has been used to support the capture of clinical information by clinicians in the normal process of care delivery. A study of the CDF over a two month period showed that it improved the accuracy of completeness of data collection over a coding method which was based on selection of ICD-9-CM codes.Studies in health technology and informatics 02/2001; 84(Pt 1):609-13.
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ABSTRACT: Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2008. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Includes bibliographical references (p. 119-128). In this thesis, we describe the use of medical insurance claims data in three important areas of medicine. First, we develop expert- trained statistical models of quality of care based on variables derived from insurance claims. Such models can be used to identify patients who are receiving poor care so that interventions can be arranged to improve their care. Second, we develop an algorithm that utilizes claims data to perform post-marketing surveillance of drugs to detect previously unknown side effects. The algorithm performed strongly in several realistic simulation tests, detecting side effects a large fraction of the time while controlling the false detection rate. Lastly, we use insurance claims data to improve our understanding of the costs of care for patients who suffer from depression and a chronic disease. by David Czerwinski. Ph.D.