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Transversus Abdominis Aircraft Obstruct Versus Intraperitoneal Nearby Anaesthetics within Weight loss surgery: An organized Assessment and Circle Meta-analysis.
Purpose The objectives of our study were to determine drug use, type and incidence of all adverse event associated with drug or drug-related processes (Adverse Drug Events, ADE) among pediatric inpatients in relation to hospital unit and length of stay. Patients and Methods 600 pediatric (0-18 years) admissions at a Swedish university hospital during one year were included and stratified in blocks to 150 neonatal, surgical/orthopedic, medicine and emergency-medicine unit admissions, respectively. Adverse events were identified from medical records using a pediatric trigger tool. All triggers identifying an adverse event related to drugs and drug-related devices were included. Data on drug use were extracted from the hospital drug-data warehouse. Results In total, 17794 daily drug orders were administrated to 486 (81.0% exposed) admissions. Parental nutrition, potassium salts and morphine constituted half of all high-risk drugs prescribed. Two-thirds of intravenous irritating drug doses consisted of vancomycinth of hospital stay. © 2020 Nydert et al.Purpose According to the 2015 American Geriatrics Society (AGS) Beers criteria, most antipsychotics are inappropriate in Parkinson's disease (PD) patients due to the risk of worsening Parkinsonian symptoms. This study examined the incidence and predictors of inappropriate antipsychotic use among long-term care residents with PD and comorbid depression. Patients and Methods This retrospective cohort study utilized 2007-2009 Minimum Data Set (MDS) linked to Chronic Condition Warehouse (CCW) Medicare data files involving patients with PD and comorbid depression. Using a 12-month baseline and a 24-month follow-up, the study examined incidence of inappropriate atypical antipsychotics, namely asenapine, brexpiprazole, iloperidone, lurasidone, olanzapine, paliperidone, risperidone, or ziprasidone as specified in the 2015 AGS Beers criteria. Appropriate atypical antipsychotic included aripiprazole, clozapine, or quetiapine. Multivariable logistic regression was used to examine various sociodemographic and clinical fa-demographics and clinical factors were associated with inappropriate antipsychotic use in older patients with PD. Concerted efforts are needed to reduce inappropriate atypical antipsychotic use among PD patients. © 2020 Chekani et al.Objective Electronic health records (EHR) data-discontinuity, i.e. receiving care outside of a particular EHR system, may cause misclassification of study variables. SY-5609 clinical trial We aimed to validate an algorithm to identify patients with high EHR data-continuity to reduce such bias. Materials and Methods We analyzed data from two EHR systems linked with Medicare claims data from 2007 through 2014, one in Massachusetts (MA, n=80,588) and the other in North Carolina (NC, n=33,207). We quantified EHR data-continuity by Mean Proportion of Encounters Captured (MPEC) by the EHR system when compared to complete recording in claims data. The prediction model for MPEC was developed in MA and validated in NC. Stratified by predicted EHR data-continuity, we quantified misclassification of 40 key variables by Mean Standardized Differences (MSD) between the proportions of these variables based on EHR alone vs the linked claims-EHR data. Results The mean MPEC was 27% in the MA and 26% in the NC system. The predicted and observed EHR data-continuity was highly correlated (Spearman correlation=0.78 and 0.73, respectively). The misclassification (MSD) of 40 variables in patients of the predicted EHR data-continuity cohort was significantly smaller (44%, 95% CI 40-48%) than that in the remaining population. Discussion The comorbidity profiles were similar in patients with high vs low EHR data-continuity. Therefore, restricting an analysis to patients with high EHR data-continuity may reduce information bias while preserving the representativeness of the study cohort. Conclusion We have successfully validated an algorithm that can identify a high EHR data-continuity cohort representative of the source population. © 2020 Lin et al.Aim The health-care databases may be a valuable source for epidemiological research in hip fracture surgery, if the diagnoses are valid. We examined the validity of hip fracture diagnoses and surgical procedure codes in the Danish Multidisciplinary Hip Fracture Registry (DMHFR) and the Danish National Patient Registry (DNPR) by calculating the positive predictive value (PPV). Methods We identified a random sample of 750 hip fracture patients registered in the DMHFR between 2014 and 2017. Diagnoses have been coded by the 10th revision of the International Classification of Diseases, while procedures have been coded by the Nordic Medico-Statistical Committee classification in the DNPR and directly transferred to the DMHFR. Using the surgical procedure description from the medical record as gold standard, we estimated the PPV of the hip fracture diagnoses and surgical procedure codes in the DMHFR and the DNPR with 95% confidence interval (CIs). Results The PPV was 90% (95% CI 86%-93%) for fracture of the neck of femur, 92% (95% CI 87%-95%) for trochanteric fracture, and 83% (95% CI 78%-88%) for subtrochanteric fracture. Joining trochanteric and subtrochanteric fracture resulted in a PPV of 97% (95% CI 95%-98%). Procedure codes had a PPV of 100% for primary prosthetic replacement and internal fixation with intramedullary nail, 96% (95% CI 85%-99%) for internal fixation using screws alone, 91% (95% CI 84%-96%) for internal fixation using plates and screws, and 89% (95% CI 83%-94%) for internal fixation with other or combined methods. Stratifying by age group, gender, hospital type and calendar year of surgery showed similar results as the overall PPV estimates. Conclusion Our findings indicate a high quality of the hip fracture diagnoses and corresponding procedure codes in the DMHFR and the DNPR, with a majority of PPVs above 90%. Thus, the DMHFR and the DNPR are a valuable data source on hip fracture for epidemiological research. © 2020 Hjelholt et al.Background Among men and women diagnosed with colorectal cancer (CRC), 20-50% will develop a cancer recurrence. Cancer recurrences are not routinely captured by most population-based registries; however, linkage across Danish registries allows for the development of predictive models to detect recurrence. Successful application of such models in population-based settings requires validation against a gold standard to ensure the accuracy of recurrence identification. Objective We apply a recently developed validation study design for prospectively collected validation data to validate predicted CRC recurrences against gold standard diagnoses from medical records in an actively followed cohort of CRC patients in Denmark. Methods We use a Bayesian monitoring framework, traditionally used in clinical trials, to iteratively update classification parameters (positive and negative predictive values, and sensitivity and specificity) in an adaptive validation substudy design. This design allows determination of the sample size necessary to estimate the corresponding parameters and to identify when validation efforts can cease based on predefined criteria for parameter values and levels of precision.
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