ABSTRACT
Pseudomonas aeruginosa is intrinsically resistant to many antimicrobial drugs, making carbapenems crucial in clinical management. During July-October 2015 in the United States, we piloted laboratory-based surveillance for carbapenem-resistant P. aeruginosa (CRPA) at sentinel facilities in Georgia, New Mexico, Oregon, and Tennessee, and population-based surveillance in Monroe County, NY. An incident case was the first P. aeruginosa isolate resistant to antipseudomonal carbapenems from a patient in a 30-day period from any source except the nares, rectum or perirectal area, or feces. We found 294 incident cases among 274 patients. Cases were most commonly identified from respiratory sites (120/294; 40.8%) and urine (111/294; 37.8%); most (223/280; 79.6%) occurred in patients with healthcare facility inpatient stays in the prior year. Genes encoding carbapenemases were identified in 3 (2.3%) of 129 isolates tested. The burden of CRPA was high at facilities under surveillance, but carbapenemase-producing CRPA were rare.
Subject(s)
Carbapenems/pharmacology , Communicable Diseases, Emerging/epidemiology , Communicable Diseases, Emerging/microbiology , Pseudomonas Infections/epidemiology , Pseudomonas Infections/microbiology , Pseudomonas aeruginosa/drug effects , beta-Lactam Resistance , Adolescent , Adult , Aged , Aged, 80 and over , Carbapenems/therapeutic use , Child , Child, Preschool , Communicable Diseases, Emerging/history , Comorbidity , Female , History, 21st Century , Humans , Infant , Male , Microbial Sensitivity Tests , Middle Aged , Pseudomonas Infections/history , Public Health Surveillance , United States/epidemiology , Young AdultABSTRACT
BACKGROUND: We developed and validated an automated database case definition for diabetes in children and youth to facilitate pharmacoepidemiologic investigations of medications and the risk of diabetes. METHODS: The present study was part of an in-progress retrospective cohort study of antipsychotics and diabetes in Tennessee Medicaid enrollees aged 6-24 years. Diabetes was identified from diabetes-related medical care encounters: hospitalizations, outpatient visits, and filled prescriptions. The definition required either a primary inpatient diagnosis or at least two other encounters of different types, most commonly an outpatient diagnosis with a prescription. Type 1 diabetes was defined by insulin prescriptions with at most one oral hypoglycemic prescription; other cases were considered type 2 diabetes. The definition was validated for cohort members in the 15 county region geographically proximate to the investigators. Medical records were reviewed and adjudicated for cases that met the automated database definition as well as for a sample of persons with other diabetes-related medical care encounters. RESULTS: The study included 64 cases that met the automated database definition. Records were adjudicated for 46 (71.9%), of which 41 (89.1%) met clinical criteria for newly diagnosed diabetes. The positive predictive value for type 1 diabetes was 80.0%. For type 2 and unspecified diabetes combined, the positive predictive value was 83.9%. The estimated sensitivity of the definition, based on adjudication for a sample of 30 cases not meeting the automated database definition, was 64.8%. CONCLUSION: These results suggest that the automated database case definition for diabetes may be useful for pharmacoepidemiologic studies of medications and diabetes.
Subject(s)
Antipsychotic Agents/therapeutic use , Diabetes Mellitus/diagnosis , Hypoglycemic Agents/therapeutic use , Adolescent , Child , Databases as Topic , Diabetes Mellitus/drug therapy , Electronic Prescribing/standards , Female , Humans , Hypoglycemic Agents/administration & dosage , Male , Medicaid , Reproducibility of Results , Tennessee , United States , Young AdultABSTRACT
BACKGROUND: Diabetic ketoacidosis (DKA) is a potentially life-threatening complication of treatment with some atypical antipsychotic drugs in children and youth. Because drug-associated DKA is rare, large automated health outcomes databases may be a valuable data source for conducting pharmacoepidemiologic studies of DKA associated with exposure to individual antipsychotic drugs. However, no validated computer case definition of DKA exists. We sought to assess the positive predictive value (PPV) of a computer case definition to detect incident cases of DKA, using automated records of Tennessee Medicaid as the data source and medical record confirmation as a "gold standard." METHODS: The computer case definition of DKA was developed from a retrospective cohort study of antipsychotic-related type 2 diabetes mellitus (1996-2007) in Tennessee Medicaid enrollees, aged 6-24 years. Thirty potential cases with any DKA diagnosis (ICD-9 250.1, ICD-10 E1x.1) were identified from inpatient encounter claims. Medical records were reviewed to determine if they met the clinical definition of DKA. RESULTS: Of 30 potential cases, 27 (90%) were successfully abstracted and adjudicated. Of these, 24 cases were confirmed by medical record review (PPV 88.9%, 95% CI 71.9 to 96.1%). Three non-confirmed cases presented acutely with severe hyperglycemia, but had no evidence of acidosis. CONCLUSIONS: Diabetic ketoacidosis in children and youth can be identified in a computerized Medicaid database using our case definition, which could be useful for automated database studies in which drug-associated DKA is the outcome of interest.
