ObjectiveWe aimed to test for an association between the amount of circulating fetal cell‐free DNA and trisomy, and whether NIPS failure due to low fetal fraction indicates trisomy risk.MethodMaternal BMI, maternal age, fetal sex, gestational age, fetal cfDNA fraction, and NIPS results was collected on 2374 pregnancies. Additional clinical information was available for 1180 research consented patients. We investigated associations between fetal fraction and available variables and determined the success rate of repeat NIPS testing.ResultsFetal trisomy was marginally associated with decreased fetal fraction (P = .067). However, the proportions of trisomy events were not significantly increased in women who had failed NIPS due to low fetal fraction (<4%) (OR = 1.37 [0.3‐7.4]; P = .714). 66% of repeated NIPS after a second blood draw were successful.ConclusionFailure to meet the clinical cutoff of 4% fetal fraction established for NIPS accuracy did not suggest increased risk for trisomy in our cohort. Because repeat testing was successful in the majority of cases and most failures were explained by high BMI and low gestational age, a redraw may be an appropriate next step before invasive screening due to concerns for trisomic pregnancies.
The DJBL, when used for a period of 6 months, is effective in the control of diabetes, weight loss, improvement of insulin resistance, and decrease of cardiovascular risk among morbidly obese patients with type 2 diabetes mellitus.
BACKGROUND
Delirium is underdiagnosed in clinical practice, and is not routinely coded for billing. Manual chart review can be used to identify the occurrence of delirium, however, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) has the capability to process raw text in electronic health records (EHRs) and determine the meaning of the information. We developed and validated NLP algorithms to automatically identify the occurrence of delirium from EHRs.
METHODS
This study used a randomly selected cohort from the population-based Mayo Clinic Biobank (n=300, age>=65). We adopted the standardized evidence-based framework confusion assessment method (CAM) to develop and evaluate NLP algorithms to identify the occurrence of delirium using clinical notes in EHRs. Two NLP algorithms were developed based on CAM criteria; one based on the original CAM (NLP-CAM; delirium vs. no delirium) and another based on our modified CAM (NLP-mCAM; definite, possible, and no delirium). The sensitivity, specificity, and accuracy were used for concordance in delirium status between NLP algorithms and manual chart review as the gold standard. The prevalence of delirium cases was examined using ICD-9, NLP-CAM, and NLP-mCAM.
RESULTS
NLP-CAM demonstrated a sensitivity, specificity and accuracy of 0.919, 1.000 and 0.967, respectively. NLP-mCAM demonstrated sensitivity, specificity and accuracy of 0.827, 0.913 and 0.827, respectively. The prevalence analysis of delirium showed that the NLP-CAM algorithm identified 12,651 (9.4%) delirium patients, the NLP-mCAM algorithm identified 20,611 (15.3%) definite delirium cases and 10,762 (8.0%) possible cases.
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